[DISCUSS] FLIP-44: Support Local Aggregation in Flink

[Kurt Young]

Hi all,

As vino said in previous emails, I think we should first discuss and decide
what kind of use cases this FLIP want to
resolve, and what the API should look like. From my side, I think this is
probably the root cause of current divergence.

My understand is (from the FLIP title and motivation section of the
document), we want to have a proper support of
local aggregation, or pre aggregation. This is not a very new idea, most
SQL engine already did this improvement. And
the core concept about this is, there should be an AggregateFunction, no
matter it's a Flink runtime's AggregateFunction or
SQL's UserDefinedAggregateFunction. Both aggregation have concept of
intermediate data type, sometimes we call it ACC.
I quickly went through the POC piotr did before [1], it also directly uses
AggregateFunction.

But the thing is, after reading the design of this FLIP, I can't help
myself feeling that this FLIP is not targeting to have a proper
local aggregation support. It actually want to introduce another concept:
LocalKeyBy, and how to split and merge local key groups,
and how to properly support state on local key. Local aggregation just
happened to be one possible use case of LocalKeyBy.
But it lacks supporting the essential concept of local aggregation, which
is intermediate data type. Without this, I really don't thing
it is a good fit of local aggregation.

Here I want to make sure of the scope or the goal about this FLIP, do we
want to have a proper local aggregation engine, or we
just want to introduce a new concept called LocalKeyBy?

[1]: https://github.com/apache/flink/pull/4626

Best,
Kurt


On Wed, Jun 19, 2019 at 5:13 PM vino yang <[hidden email]> wrote:

> Hi Hequn,
>
> Thanks for your comments!
>
> I agree that allowing local aggregation reusing window API and refining
> window operator to make it match both requirements (come from our and Kurt)
> is a good decision!
>
> Concerning your questions:
>
> 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1) may be
> meaningless.
>
> Yes, it does not make sense in most cases. However, I also want to note
> users should know the right semantics of localKeyBy and use it correctly.
> Because this issue also exists for the global keyBy, consider this example:
> input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also meaningless.
>
> 2. About the semantics of
> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
>
> Good catch! I agree with you that it's not good to enable all
> functionalities for localKeyBy from KeyedStream.
> Currently, We do not support some APIs such as
> connect/join/intervalJoin/coGroup. This is due to that we force the
> operators on LocalKeyedStreams chained with the inputs.
>
> Best,
> Vino
>
>
> Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
>
> > Hi,
> >
> > Thanks a lot for your great discussion and great to see that some
> agreement
> > has been reached on the "local aggregate engine"!
> >
> > ===> Considering the abstract engine,
> > I'm thinking is it valuable for us to extend the current window to meet
> > both demands raised by Kurt and Vino? There are some benefits we can get:
> >
> > 1. The interfaces of the window are complete and clear. With windows, we
> > can define a lot of ways to split the data and perform different
> > computations.
> > 2. We can also leverage the window to do miniBatch for the global
> > aggregation, i.e, we can use the window to bundle data belong to the same
> > key, for every bundle we only need to read and write once state. This can
> > greatly reduce state IO and improve performance.
> > 3. A lot of other use cases can also benefit from the window base on
> memory
> > or stateless.
> >
> > ===> As for the API,
> > I think it is good to make our API more flexible. However, we may need to
> > make our API meaningful.
> >
> > Take my previous reply as an example,
> > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may be
> meaningless.
> > Another example I find is the intervalJoin, e.g.,
> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In this case, it
> > will bring problems if input1 and input2 share different parallelism. We
> > don't know which input should the join chained with? Even if they share
> the
> > same parallelism, it's hard to tell what the join is doing. There are
> maybe
> > some other problems.
> >
> > From this point of view, it's at least not good to enable all
> > functionalities for localKeyBy from KeyedStream?
> >
> > Great to also have your opinions.
> >
> > Best, Hequn
> >
> >
> >
> >
> > On Wed, Jun 19, 2019 at 10:24 AM vino yang <[hidden email]>
> wrote:
> >
> > > Hi Kurt and Piotrek,
> > >
> > > Thanks for your comments.
> > >
> > > I agree that we can provide a better abstraction to be compatible with
> > two
> > > different implementations.
> > >
> > > First of all, I think we should consider what kind of scenarios we need
> > to
> > > support in *API* level?
> > >
> > > We have some use cases which need to a customized aggregation through
> > > KeyedProcessFunction, (in the usage of our localKeyBy.window they can
> use
> > > ProcessWindowFunction).
> > >
> > > Shall we support these flexible use scenarios?
> > >
> > > Best,
> > > Vino
> > >
> > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
> > >
> > > > Hi Piotr,
> > > >
> > > > Thanks for joining the discussion. Make “local aggregation" abstract
> > > enough
> > > > sounds good to me, we could
> > > > implement and verify alternative solutions for use cases of local
> > > > aggregation. Maybe we will find both solutions
> > > > are appropriate for different scenarios.
> > > >
> > > > Starting from a simple one sounds a practical way to go. What do you
> > > think,
> > > > vino?
> > > >
> > > > Best,
> > > > Kurt
> > > >
> > > >
> > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <[hidden email]>
> > > > wrote:
> > > >
> > > > > Hi Kurt and Vino,
> > > > >
> > > > > I think there is a trade of hat we need to consider for the local
> > > > > aggregation.
> > > > >
> > > > > Generally speaking I would agree with Kurt about local
> > aggregation/pre
> > > > > aggregation not using Flink's state flush the operator on a
> > checkpoint.
> > > > > Network IO is usually cheaper compared to Disks IO. This has
> however
> > > > couple
> > > > > of issues:
> > > > > 1. It can explode number of in-flight records during checkpoint
> > barrier
> > > > > alignment, making checkpointing slower and decrease the actual
> > > > throughput.
> > > > > 2. This trades Disks IO on the local aggregation machine with CPU
> > (and
> > > > > Disks IO in case of RocksDB) on the final aggregation machine. This
> > is
> > > > > fine, as long there is no huge data skew. If there is only a
> handful
> > > (or
> > > > > even one single) hot keys, it might be better to keep the
> persistent
> > > > state
> > > > > in the LocalAggregationOperator to offload final aggregation as
> much
> > as
> > > > > possible.
> > > > > 3. With frequent checkpointing local aggregation effectiveness
> would
> > > > > degrade.
> > > > >
> > > > > I assume Kurt is correct, that in your use cases stateless operator
> > was
> > > > > behaving better, but I could easily see other use cases as well.
> For
> > > > > example someone is already using RocksDB, and his job is
> bottlenecked
> > > on
> > > > a
> > > > > single window operator instance because of the data skew. In that
> > case
> > > > > stateful local aggregation would be probably a better choice.
> > > > >
> > > > > Because of that, I think we should eventually provide both versions
> > and
> > > > in
> > > > > the initial version we should at least make the “local aggregation
> > > > engine”
> > > > > abstract enough, that one could easily provide different
> > implementation
> > > > > strategy.
> > > > >
> > > > > Piotrek
> > > > >
> > > > > > On 18 Jun 2019, at 11:46, Kurt Young <[hidden email]> wrote:
> > > > > >
> > > > > > Hi,
> > > > > >
> > > > > > For the trigger, it depends on what operator we want to use under
> > the
> > > > > API.
> > > > > > If we choose to use window operator,
> > > > > > we should also use window's trigger. However, I also think reuse
> > > window
> > > > > > operator for this scenario may not be
> > > > > > the best choice. The reasons are the following:
> > > > > >
> > > > > > 1. As a lot of people already pointed out, window relies heavily
> on
> > > > state
> > > > > > and it will definitely effect performance. You can
> > > > > > argue that one can use heap based statebackend, but this will
> > > introduce
> > > > > > extra coupling. Especially we have a chance to
> > > > > > design a pure stateless operator.
> > > > > > 2. The window operator is *the most* complicated operator Flink
> > > > currently
> > > > > > have. Maybe we only need to pick a subset of
> > > > > > window operator to achieve the goal, but once the user wants to
> > have
> > > a
> > > > > deep
> > > > > > look at the localAggregation operator, it's still
> > > > > > hard to find out what's going on under the window operator. For
> > > > > simplicity,
> > > > > > I would also recommend we introduce a dedicated
> > > > > > lightweight operator, which also much easier for a user to learn
> > and
> > > > use.
> > > > > >
> > > > > > For your question about increasing the burden in
> > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the only thing
> this
> > > > > function
> > > > > > need
> > > > > > to do is output all the partial results, it's purely cpu
> workload,
> > > not
> > > > > > introducing any IO. I want to point out that even if we have this
> > > > > > cost, we reduced another barrier align cost of the operator,
> which
> > is
> > > > the
> > > > > > sync flush stage of the state, if you introduced state. This
> > > > > > flush actually will introduce disk IO, and I think it's worthy to
> > > > > exchange
> > > > > > this cost with purely CPU workload. And we do have some
> > > > > > observations about these two behavior (as i said before, we
> > actually
> > > > > > implemented both solutions), the stateless one actually performs
> > > > > > better both in performance and barrier align time.
> > > > > >
> > > > > > Best,
> > > > > > Kurt
> > > > > >
> > > > > >
> > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <[hidden email]
> >
> > > > wrote:
> > > > > >
> > > > > >> Hi Kurt,
> > > > > >>
> > > > > >> Thanks for your example. Now, it looks more clearly for me.
> > > > > >>
> > > > > >> From your example code snippet, I saw the localAggregate API has
> > > three
> > > > > >> parameters:
> > > > > >>
> > > > > >>   1. key field
> > > > > >>   2. PartitionAvg
> > > > > >>   3. CountTrigger: Does this trigger comes from window package?
> > > > > >>
> > > > > >> I will compare our and your design from API and operator level:
> > > > > >>
> > > > > >> *From the API level:*
> > > > > >>
> > > > > >> As I replied to @dianfu in the old email thread,[1] the Window
> API
> > > can
> > > > > >> provide the second and the third parameter right now.
> > > > > >>
> > > > > >> If you reuse specified interface or class, such as *Trigger* or
> > > > > >> *CounterTrigger* provided by window package, but do not use
> window
> > > > API,
> > > > > >> it's not reasonable.
> > > > > >> And if you do not reuse these interface or class, you would need
> > to
> > > > > >> introduce more things however they are looked similar to the
> > things
> > > > > >> provided by window package.
> > > > > >>
> > > > > >> The window package has provided several types of the window and
> > many
> > > > > >> triggers and let users customize it. What's more, the user is
> more
> > > > > familiar
> > > > > >> with Window API.
> > > > > >>
> > > > > >> This is the reason why we just provide localKeyBy API and reuse
> > the
> > > > > window
> > > > > >> API. It reduces unnecessary components such as triggers and the
> > > > > mechanism
> > > > > >> of buffer (based on count num or time).
> > > > > >> And it has a clear and easy to understand semantics.
> > > > > >>
> > > > > >> *From the operator level:*
> > > > > >>
> > > > > >> We reused window operator, so we can get all the benefits from
> > state
> > > > and
> > > > > >> checkpoint.
> > > > > >>
> > > > > >> From your design, you named the operator under localAggregate
> API
> > > is a
> > > > > >> *stateless* operator. IMO, it is still a state, it is just not
> > Flink
> > > > > >> managed state.
> > > > > >> About the memory buffer (I think it's still not very clear, if
> you
> > > > have
> > > > > >> time, can you give more detail information or answer my
> > questions),
> > > I
> > > > > have
> > > > > >> some questions:
> > > > > >>
> > > > > >>   - if it just a raw JVM heap memory buffer, how to support
> fault
> > > > > >>   tolerance, if the job is configured EXACTLY-ONCE semantic
> > > guarantee?
> > > > > >>   - if you thought the memory buffer(non-Flink state), has
> better
> > > > > >>   performance. In our design, users can also config HEAP state
> > > backend
> > > > > to
> > > > > >>   provide the performance close to your mechanism.
> > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()` related to the
> > > > timing
> > > > > of
> > > > > >>   snapshot. IMO, the flush action should be a synchronized
> action?
> > > (if
> > > > > >> not,
> > > > > >>   please point out my mistake) I still think we should not
> depend
> > on
> > > > the
> > > > > >>   timing of checkpoint. Checkpoint related operations are
> inherent
> > > > > >>   performance sensitive, we should not increase its burden
> > anymore.
> > > > Our
> > > > > >>   implementation based on the mechanism of Flink's checkpoint,
> > which
> > > > can
> > > > > >>   benefit from the asnyc snapshot and incremental checkpoint.
> IMO,
> > > the
> > > > > >>   performance is not a problem, and we also do not find the
> > > > performance
> > > > > >> issue
> > > > > >>   in our production.
> > > > > >>
> > > > > >> [1]:
> > > > > >>
> > > > > >>
> > > > >
> > > >
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > > > >>
> > > > > >> Best,
> > > > > >> Vino
> > > > > >>
> > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二 下午2:27写道：
> > > > > >>
> > > > > >>> Yeah, sorry for not expressing myself clearly. I will try to
> > > provide
> > > > > more
> > > > > >>> details to make sure we are on the same page.
> > > > > >>>
> > > > > >>> For DataStream API, it shouldn't be optimized automatically.
> You
> > > have
> > > > > to
> > > > > >>> explicitly call API to do local aggregation
> > > > > >>> as well as the trigger policy of the local aggregation. Take
> > > average
> > > > > for
> > > > > >>> example, the user program may look like this (just a draft):
> > > > > >>>
> > > > > >>> assuming the input type is DataStream<Tupl2<String, Int>>
> > > > > >>>
> > > > > >>> ds.localAggregate(
> > > > > >>>        0,                                       // The local
> key,
> > > > which
> > > > > >> is
> > > > > >>> the String from Tuple2
> > > > > >>>        PartitionAvg(1),                 // The partial
> > aggregation
> > > > > >>> function, produces Tuple2<Long, Int>, indicating sum and count
> > > > > >>>        CountTrigger.of(1000L)    // Trigger policy, note this
> > > should
> > > > be
> > > > > >>> best effort, and also be composited with time based or memory
> > size
> > > > > based
> > > > > >>> trigger
> > > > > >>>    )                                           // The return
> type
> > > is
> > > > > >> local
> > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> > > > > >>>    .keyBy(0)                             // Further keyby it
> with
> > > > > >> required
> > > > > >>> key
> > > > > >>>    .aggregate(1)                      // This will merge all
> the
> > > > > partial
> > > > > >>> results and get the final average.
> > > > > >>>
> > > > > >>> (This is only a draft, only trying to explain what it looks
> > like. )
> > > > > >>>
> > > > > >>> The local aggregate operator can be stateless, we can keep a
> > memory
> > > > > >> buffer
> > > > > >>> or other efficient data structure to improve the aggregate
> > > > performance.
> > > > > >>>
> > > > > >>> Let me know if you have any other questions.
> > > > > >>>
> > > > > >>> Best,
> > > > > >>> Kurt
> > > > > >>>
> > > > > >>>
> > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> [hidden email]
> > >
> > > > > wrote:
> > > > > >>>
> > > > > >>>> Hi Kurt,
> > > > > >>>>
> > > > > >>>> Thanks for your reply.
> > > > > >>>>
> > > > > >>>> Actually, I am not against you to raise your design.
> > > > > >>>>
> > > > > >>>> From your description before, I just can imagine your
> high-level
> > > > > >>>> implementation is about SQL and the optimization is inner of
> the
> > > > API.
> > > > > >> Is
> > > > > >>> it
> > > > > >>>> automatically? how to give the configuration option about
> > trigger
> > > > > >>>> pre-aggregation?
> > > > > >>>>
> > > > > >>>> Maybe after I get more information, it sounds more reasonable.
> > > > > >>>>
> > > > > >>>> IMO, first of all, it would be better to make your user
> > interface
> > > > > >>> concrete,
> > > > > >>>> it's the basis of the discussion.
> > > > > >>>>
> > > > > >>>> For example, can you give an example code snippet to introduce
> > how
> > > > to
> > > > > >>> help
> > > > > >>>> users to process data skew caused by the jobs which built with
> > > > > >> DataStream
> > > > > >>>> API?
> > > > > >>>>
> > > > > >>>> If you give more details we can discuss further more. I think
> if
> > > one
> > > > > >>> design
> > > > > >>>> introduces an exact interface and another does not.
> > > > > >>>>
> > > > > >>>> The implementation has an obvious difference. For example, we
> > > > > introduce
> > > > > >>> an
> > > > > >>>> exact API in DataStream named localKeyBy, about the
> > > pre-aggregation
> > > > we
> > > > > >>> need
> > > > > >>>> to define the trigger mechanism of local aggregation, so we
> find
> > > > > reused
> > > > > >>>> window API and operator is a good choice. This is a reasoning
> > link
> > > > > from
> > > > > >>>> design to implementation.
> > > > > >>>>
> > > > > >>>> What do you think?
> > > > > >>>>
> > > > > >>>> Best,
> > > > > >>>> Vino
> > > > > >>>>
> > > > > >>>>
> > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二 上午11:58写道：
> > > > > >>>>
> > > > > >>>>> Hi Vino,
> > > > > >>>>>
> > > > > >>>>> Now I feel that we may have different understandings about
> what
> > > > kind
> > > > > >> of
> > > > > >>>>> problems or improvements you want to
> > > > > >>>>> resolve. Currently, most of the feedback are focusing on *how
> > to
> > > > do a
> > > > > >>>>> proper local aggregation to improve performance
> > > > > >>>>> and maybe solving the data skew issue*. And my gut feeling is
> > > this
> > > > is
> > > > > >>>>> exactly what users want at the first place,
> > > > > >>>>> especially those +1s. (Sorry to try to summarize here, please
> > > > correct
> > > > > >>> me
> > > > > >>>> if
> > > > > >>>>> i'm wrong).
> > > > > >>>>>
> > > > > >>>>> But I still think the design is somehow diverged from the
> goal.
> > > If
> > > > we
> > > > > >>>> want
> > > > > >>>>> to have an efficient and powerful way to
> > > > > >>>>> have local aggregation, supporting intermedia result type is
> > > > > >> essential
> > > > > >>>> IMO.
> > > > > >>>>> Both runtime's `AggregateFunction` and
> > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a proper support of
> > > > > >>>> intermediate
> > > > > >>>>> result type and can do `merge` operation
> > > > > >>>>> on them.
> > > > > >>>>>
> > > > > >>>>> Now, we have a lightweight alternatives which performs well,
> > and
> > > > > >> have a
> > > > > >>>>> nice fit with the local aggregate requirements.
> > > > > >>>>> Mostly importantly,  it's much less complex because it's
> > > stateless.
> > > > > >> And
> > > > > >>>> it
> > > > > >>>>> can also achieve the similar multiple-aggregation
> > > > > >>>>> scenario.
> > > > > >>>>>
> > > > > >>>>> I still not convinced why we shouldn't consider it as a first
> > > step.
> > > > > >>>>>
> > > > > >>>>> Best,
> > > > > >>>>> Kurt
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> > > [hidden email]>
> > > > > >>>> wrote:
> > > > > >>>>>
> > > > > >>>>>> Hi Kurt,
> > > > > >>>>>>
> > > > > >>>>>> Thanks for your comments.
> > > > > >>>>>>
> > > > > >>>>>> It seems we both implemented local aggregation feature to
> > > optimize
> > > > > >>> the
> > > > > >>>>>> issue of data skew.
> > > > > >>>>>> However, IMHO, the API level of optimizing revenue is
> > different.
> > > > > >>>>>>
> > > > > >>>>>> *Your optimization benefits from Flink SQL and it's not
> user's
> > > > > >>>> faces.(If
> > > > > >>>>> I
> > > > > >>>>>> understand it incorrectly, please correct this.)*
> > > > > >>>>>> *Our implementation employs it as an optimization tool API
> for
> > > > > >>>>> DataStream,
> > > > > >>>>>> it just like a local version of the keyBy API.*
> > > > > >>>>>>
> > > > > >>>>>> Based on this, I want to say support it as a DataStream API
> > can
> > > > > >>> provide
> > > > > >>>>>> these advantages:
> > > > > >>>>>>
> > > > > >>>>>>
> > > > > >>>>>>   - The localKeyBy API has a clear semantic and it's
> flexible
> > > not
> > > > > >>> only
> > > > > >>>>> for
> > > > > >>>>>>   processing data skew but also for implementing some user
> > > cases,
> > > > > >>> for
> > > > > >>>>>>   example, if we want to calculate the multiple-level
> > > aggregation,
> > > > > >>> we
> > > > > >>>>> can
> > > > > >>>>>> do
> > > > > >>>>>>   multiple-level aggregation in the local aggregation:
> > > > > >>>>>>   input.localKeyBy("a").sum(1).localKeyBy("b").window(); //
> > here
> > > > > >> "a"
> > > > > >>>> is
> > > > > >>>>> a
> > > > > >>>>>>   sub-category, while "b" is a category, here we do not need
> > to
> > > > > >>>> shuffle
> > > > > >>>>>> data
> > > > > >>>>>>   in the network.
> > > > > >>>>>>   - The users of DataStream API will benefit from this.
> > > Actually,
> > > > > >> we
> > > > > >>>>> have
> > > > > >>>>>>   a lot of scenes need to use DataStream API. Currently,
> > > > > >> DataStream
> > > > > >>>> API
> > > > > >>>>> is
> > > > > >>>>>>   the cornerstone of the physical plan of Flink SQL. With a
> > > > > >>> localKeyBy
> > > > > >>>>>> API,
> > > > > >>>>>>   the optimization of SQL at least may use this optimized
> API,
> > > > > >> this
> > > > > >>>> is a
> > > > > >>>>>>   further topic.
> > > > > >>>>>>   - Based on the window operator, our state would benefit
> from
> > > > > >> Flink
> > > > > >>>>> State
> > > > > >>>>>>   and checkpoint, we do not need to worry about OOM and job
> > > > > >> failed.
> > > > > >>>>>>
> > > > > >>>>>> Now, about your questions:
> > > > > >>>>>>
> > > > > >>>>>> 1. About our design cannot change the data type and about
> the
> > > > > >>>>>> implementation of average:
> > > > > >>>>>>
> > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is an API
> provides
> > > to
> > > > > >> the
> > > > > >>>>> users
> > > > > >>>>>> who use DataStream API to build their jobs.
> > > > > >>>>>> Users should know its semantics and the difference with
> keyBy
> > > API,
> > > > > >> so
> > > > > >>>> if
> > > > > >>>>>> they want to the average aggregation, they should carry
> local
> > > sum
> > > > > >>>> result
> > > > > >>>>>> and local count result.
> > > > > >>>>>> I admit that it will be convenient to use keyBy directly.
> But
> > we
> > > > > >> need
> > > > > >>>> to
> > > > > >>>>>> pay a little price when we get some benefits. I think this
> > price
> > > > is
> > > > > >>>>>> reasonable. Considering that the DataStream API itself is a
> > > > > >> low-level
> > > > > >>>> API
> > > > > >>>>>> (at least for now).
> > > > > >>>>>>
> > > > > >>>>>> 2. About stateless operator and
> > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
> > > > > >>>>>>
> > > > > >>>>>> Actually, I have discussed this opinion with @dianfu in the
> > old
> > > > > >> mail
> > > > > >>>>>> thread. I will copy my opinion from there:
> > > > > >>>>>>
> > > > > >>>>>>   - for your design, you still need somewhere to give the
> > users
> > > > > >>>>> configure
> > > > > >>>>>>   the trigger threshold (maybe memory availability?), this
> > > design
> > > > > >>>> cannot
> > > > > >>>>>>   guarantee a deterministic semantics (it will bring trouble
> > for
> > > > > >>>> testing
> > > > > >>>>>> and
> > > > > >>>>>>   debugging).
> > > > > >>>>>>   - if the implementation depends on the timing of
> checkpoint,
> > > it
> > > > > >>>> would
> > > > > >>>>>>   affect the checkpoint's progress, and the buffered data
> may
> > > > > >> cause
> > > > > >>>> OOM
> > > > > >>>>>>   issue. In addition, if the operator is stateless, it can
> not
> > > > > >>> provide
> > > > > >>>>>> fault
> > > > > >>>>>>   tolerance.
> > > > > >>>>>>
> > > > > >>>>>> Best,
> > > > > >>>>>> Vino
> > > > > >>>>>>
> > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二 上午9:22写道：
> > > > > >>>>>>
> > > > > >>>>>>> Hi Vino,
> > > > > >>>>>>>
> > > > > >>>>>>> Thanks for the proposal, I like the general idea and IMO
> it's
> > > > > >> very
> > > > > >>>>> useful
> > > > > >>>>>>> feature.
> > > > > >>>>>>> But after reading through the document, I feel that we may
> > over
> > > > > >>>> design
> > > > > >>>>>> the
> > > > > >>>>>>> required
> > > > > >>>>>>> operator for proper local aggregation. The main reason is
> we
> > > want
> > > > > >>> to
> > > > > >>>>>> have a
> > > > > >>>>>>> clear definition and behavior about the "local keyed state"
> > > which
> > > > > >>> in
> > > > > >>>> my
> > > > > >>>>>>> opinion is not
> > > > > >>>>>>> necessary for local aggregation, at least for start.
> > > > > >>>>>>>
> > > > > >>>>>>> Another issue I noticed is the local key by operator cannot
> > > > > >> change
> > > > > >>>>>> element
> > > > > >>>>>>> type, it will
> > > > > >>>>>>> also restrict a lot of use cases which can be benefit from
> > > local
> > > > > >>>>>>> aggregation, like "average".
> > > > > >>>>>>>
> > > > > >>>>>>> We also did similar logic in SQL and the only thing need to
> > be
> > > > > >> done
> > > > > >>>> is
> > > > > >>>>>>> introduce
> > > > > >>>>>>> a stateless lightweight operator which is *chained* before
> > > > > >>> `keyby()`.
> > > > > >>>>> The
> > > > > >>>>>>> operator will flush all buffered
> > > > > >>>>>>> elements during
> `StreamOperator::prepareSnapshotPreBarrier()`
> > > and
> > > > > >>>> make
> > > > > >>>>>>> himself stateless.
> > > > > >>>>>>> By the way, in the earlier version we also did the similar
> > > > > >> approach
> > > > > >>>> by
> > > > > >>>>>>> introducing a stateful
> > > > > >>>>>>> local aggregation operator but it's not performed as well
> as
> > > the
> > > > > >>>> later
> > > > > >>>>>> one,
> > > > > >>>>>>> and also effect the barrie
> > > > > >>>>>>> alignment time. The later one is fairly simple and more
> > > > > >> efficient.
> > > > > >>>>>>>
> > > > > >>>>>>> I would highly suggest you to consider to have a stateless
> > > > > >> approach
> > > > > >>>> at
> > > > > >>>>>> the
> > > > > >>>>>>> first step.
> > > > > >>>>>>>
> > > > > >>>>>>> Best,
> > > > > >>>>>>> Kurt
> > > > > >>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <[hidden email]>
> > > > > >> wrote:
> > > > > >>>>>>>
> > > > > >>>>>>>> Hi Vino,
> > > > > >>>>>>>>
> > > > > >>>>>>>> Thanks for the proposal.
> > > > > >>>>>>>>
> > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
> > > > > >>>>>>>>
> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> > > > > >> have
> > > > > >>>> you
> > > > > >>>>>>> done
> > > > > >>>>>>>> some benchmark?
> > > > > >>>>>>>> Because I'm curious about how much performance improvement
> > can
> > > > > >> we
> > > > > >>>> get
> > > > > >>>>>> by
> > > > > >>>>>>>> using count window as the local operator.
> > > > > >>>>>>>>
> > > > > >>>>>>>> Best,
> > > > > >>>>>>>> Jark
> > > > > >>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> > > [hidden email]
> > > > > >>>
> > > > > >>>>> wrote:
> > > > > >>>>>>>>
> > > > > >>>>>>>>> Hi Hequn,
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> Thanks for your reply.
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> The purpose of localKeyBy API is to provide a tool which
> > can
> > > > > >>> let
> > > > > >>>>>> users
> > > > > >>>>>>> do
> > > > > >>>>>>>>> pre-aggregation in the local. The behavior of the
> > > > > >>> pre-aggregation
> > > > > >>>>> is
> > > > > >>>>>>>>> similar to keyBy API.
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> So the three cases are different, I will describe them
> one
> > by
> > > > > >>>> one:
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> *In this case, the result is event-driven, each event can
> > > > > >>> produce
> > > > > >>>>> one
> > > > > >>>>>>> sum
> > > > > >>>>>>>>> aggregation result and it is the latest one from the
> source
> > > > > >>>> start.*
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> *In this case, the semantic may have a problem, it would
> do
> > > > > >> the
> > > > > >>>>> local
> > > > > >>>>>>> sum
> > > > > >>>>>>>>> aggregation and will produce the latest partial result
> from
> > > > > >> the
> > > > > >>>>>> source
> > > > > >>>>>>>>> start for every event. *
> > > > > >>>>>>>>> *These latest partial results from the same key are
> hashed
> > to
> > > > > >>> one
> > > > > >>>>>> node
> > > > > >>>>>>> to
> > > > > >>>>>>>>> do the global sum aggregation.*
> > > > > >>>>>>>>> *In the global aggregation, when it received multiple
> > partial
> > > > > >>>>> results
> > > > > >>>>>>>> (they
> > > > > >>>>>>>>> are all calculated from the source start) and sum them
> will
> > > > > >> get
> > > > > >>>> the
> > > > > >>>>>>> wrong
> > > > > >>>>>>>>> result.*
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> 3.
> > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> *In this case, it would just get a partial aggregation
> > result
> > > > > >>> for
> > > > > >>>>>> the 5
> > > > > >>>>>>>>> records in the count window. The partial aggregation
> > results
> > > > > >>> from
> > > > > >>>>> the
> > > > > >>>>>>>> same
> > > > > >>>>>>>>> key will be aggregated globally.*
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> So the first case and the third case can get the *same*
> > > > > >> result,
> > > > > >>>> the
> > > > > >>>>>>>>> difference is the output-style and the latency.
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> Generally speaking, the local key API is just an
> > optimization
> > > > > >>>> API.
> > > > > >>>>> We
> > > > > >>>>>>> do
> > > > > >>>>>>>>> not limit the user's usage, but the user has to
> understand
> > > > > >> its
> > > > > >>>>>>> semantics
> > > > > >>>>>>>>> and use it correctly.
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> Best,
> > > > > >>>>>>>>> Vino
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> Hequn Cheng <[hidden email]> 于2019年6月17日周一
> 下午4:18写道：
> > > > > >>>>>>>>>
> > > > > >>>>>>>>>> Hi Vino,
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> Thanks for the proposal, I think it is a very good
> > feature!
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> One thing I want to make sure is the semantics for the
> > > > > >>>>>> `localKeyBy`.
> > > > > >>>>>>>> From
> > > > > >>>>>>>>>> the document, the `localKeyBy` API returns an instance
> of
> > > > > >>>>>>> `KeyedStream`
> > > > > >>>>>>>>>> which can also perform sum(), so in this case, what's
> the
> > > > > >>>>> semantics
> > > > > >>>>>>> for
> > > > > >>>>>>>>>> `localKeyBy()`. For example, will the following code
> share
> > > > > >>> the
> > > > > >>>>> same
> > > > > >>>>>>>>> result?
> > > > > >>>>>>>>>> and what're the differences between them?
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> > > > > >>>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > > > >>>>>>>>>> 3.
> > > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> Would also be great if we can add this into the
> document.
> > > > > >>> Thank
> > > > > >>>>> you
> > > > > >>>>>>>> very
> > > > > >>>>>>>>>> much.
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> Best, Hequn
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino yang <
> > > > > >>>>> [hidden email]>
> > > > > >>>>>>>>> wrote:
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>>> Hi Aljoscha,
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> I have looked at the "*Process*" section of FLIP wiki
> > > > > >>>> page.[1]
> > > > > >>>>>> This
> > > > > >>>>>>>>> mail
> > > > > >>>>>>>>>>> thread indicates that it has proceeded to the third
> step.
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> When I looked at the fourth step(vote step), I didn't
> > > > > >> find
> > > > > >>>> the
> > > > > >>>>>>>>>>> prerequisites for starting the voting process.
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> Considering that the discussion of this feature has
> been
> > > > > >>> done
> > > > > >>>>> in
> > > > > >>>>>>> the
> > > > > >>>>>>>>> old
> > > > > >>>>>>>>>>> thread. [2] So can you tell me when should I start
> > > > > >> voting?
> > > > > >>>> Can
> > > > > >>>>> I
> > > > > >>>>>>>> start
> > > > > >>>>>>>>>> now?
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> Best,
> > > > > >>>>>>>>>>> Vino
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> [1]:
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>
> > > > > >>
> > > > >
> > > >
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> > > > > >>>>>>>>>>> [2]:
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>
> > > > > >>
> > > > >
> > > >
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> leesf <[hidden email]> 于2019年6月13日周四 上午9:19写道：
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your efforts.
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>> Best,
> > > > > >>>>>>>>>>>> Leesf
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>> vino yang <[hidden email]> 于2019年6月12日周三
> > > > > >>> 下午5:46写道：
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Hi folks,
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> I would like to start the FLIP discussion thread
> > > > > >> about
> > > > > >>>>>>> supporting
> > > > > >>>>>>>>>> local
> > > > > >>>>>>>>>>>>> aggregation in Flink.
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> In short, this feature can effectively alleviate data
> > > > > >>>> skew.
> > > > > >>>>>>> This
> > > > > >>>>>>>> is
> > > > > >>>>>>>>>> the
> > > > > >>>>>>>>>>>>> FLIP:
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>
> > > > > >>
> > > > >
> > > >
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Currently, keyed streams are widely used to perform
> > > > > >>>>>> aggregating
> > > > > >>>>>>>>>>>> operations
> > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the elements that
> > > > > >>> have
> > > > > >>>>> the
> > > > > >>>>>>> same
> > > > > >>>>>>>>>> key.
> > > > > >>>>>>>>>>>> When
> > > > > >>>>>>>>>>>>> executed at runtime, the elements with the same key
> > > > > >>> will
> > > > > >>>> be
> > > > > >>>>>>> sent
> > > > > >>>>>>>> to
> > > > > >>>>>>>>>> and
> > > > > >>>>>>>>>>>>> aggregated by the same task.
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> The performance of these aggregating operations is
> > > > > >> very
> > > > > >>>>>>> sensitive
> > > > > >>>>>>>>> to
> > > > > >>>>>>>>>>> the
> > > > > >>>>>>>>>>>>> distribution of keys. In the cases where the
> > > > > >>> distribution
> > > > > >>>>> of
> > > > > >>>>>>> keys
> > > > > >>>>>>>>>>>> follows a
> > > > > >>>>>>>>>>>>> powerful law, the performance will be significantly
> > > > > >>>>>> downgraded.
> > > > > >>>>>>>>> More
> > > > > >>>>>>>>>>>>> unluckily, increasing the degree of parallelism does
> > > > > >>> not
> > > > > >>>>> help
> > > > > >>>>>>>> when
> > > > > >>>>>>>>> a
> > > > > >>>>>>>>>>> task
> > > > > >>>>>>>>>>>>> is overloaded by a single key.
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted method to
> > > > > >> reduce
> > > > > >>>> the
> > > > > >>>>>>>>>> performance
> > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose the
> > > > > >> aggregating
> > > > > >>>>>>>> operations
> > > > > >>>>>>>>>> into
> > > > > >>>>>>>>>>>> two
> > > > > >>>>>>>>>>>>> phases. In the first phase, we aggregate the elements
> > > > > >>> of
> > > > > >>>>> the
> > > > > >>>>>>> same
> > > > > >>>>>>>>> key
> > > > > >>>>>>>>>>> at
> > > > > >>>>>>>>>>>>> the sender side to obtain partial results. Then at
> > > > > >> the
> > > > > >>>>> second
> > > > > >>>>>>>>> phase,
> > > > > >>>>>>>>>>>> these
> > > > > >>>>>>>>>>>>> partial results are sent to receivers according to
> > > > > >>> their
> > > > > >>>>> keys
> > > > > >>>>>>> and
> > > > > >>>>>>>>> are
> > > > > >>>>>>>>>>>>> combined to obtain the final result. Since the number
> > > > > >>> of
> > > > > >>>>>>> partial
> > > > > >>>>>>>>>>> results
> > > > > >>>>>>>>>>>>> received by each receiver is limited by the number of
> > > > > >>>>>> senders,
> > > > > >>>>>>>> the
> > > > > >>>>>>>>>>>>> imbalance among receivers can be reduced. Besides, by
> > > > > >>>>>> reducing
> > > > > >>>>>>>> the
> > > > > >>>>>>>>>>> amount
> > > > > >>>>>>>>>>>>> of transferred data the performance can be further
> > > > > >>>>> improved.
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> *More details*:
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Design documentation:
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>
> > > > > >>
> > > > >
> > > >
> > >
> >
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Old discussion thread:
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>
> > > > > >>
> > > > >
> > > >
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> > > > > >>>>>>>>> https://issues.apache.org/jira/browse/FLINK-12786
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> We are looking forwards to your feedback!
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Best,
> > > > > >>>>>>>>>>>>> Vino
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>
> > > > > >>
> > > > >
> > > > >
> > > >
> > >
> >
>

[vino yang]

Hi Kurt,

Thanks for your comments.

It seems we come to a consensus that we should alleviate the performance
degraded by data skew with local aggregation. In this FLIP, our key
solution is to introduce local keyed partition to achieve this goal.

I also agree that we can benefit a lot from the usage of AggregateFunction.
In combination with localKeyBy, We can easily use it to achieve local
aggregation:

   - input.localKeyBy(0).aggregate()
   - input.localKeyBy(0).window().aggregate()


I think the only problem here is the choices between

   - (1) Introducing a new primitive called localKeyBy and implement local
   aggregation with existing operators, or
   - (2) Introducing an operator called localAggregation which is composed
   of a key selector, a window-like operator, and an aggregate function.


There may exist some optimization opportunities by providing a composited
interface for local aggregation. But at the same time, in my opinion, we
lose flexibility (Or we need certain efforts to achieve the same
flexibility).

As said in the previous mails, we have many use cases where the aggregation
is very complicated and cannot be performed with AggregateFunction. For
example, users may perform windowed aggregations according to time, data
values, or even external storage. Typically, they now use
KeyedProcessFunction or customized triggers to implement these
aggregations. It's not easy to address data skew in such cases with a
composited interface for local aggregation.

Given that Data Stream API is exactly targeted at these cases where the
application logic is very complicated and optimization does not matter, I
think it's a better choice to provide a relatively low-level and canonical
interface.

The composited interface, on the other side, may be a good choice in
declarative interfaces, including SQL and Table API, as it allows more
optimization opportunities.

Best,
Vino


Kurt Young <[hidden email]> 于2019年6月20日周四 上午10:15写道：

> Hi all,
>
> As vino said in previous emails, I think we should first discuss and decide
> what kind of use cases this FLIP want to
> resolve, and what the API should look like. From my side, I think this is
> probably the root cause of current divergence.
>
> My understand is (from the FLIP title and motivation section of the
> document), we want to have a proper support of
> local aggregation, or pre aggregation. This is not a very new idea, most
> SQL engine already did this improvement. And
> the core concept about this is, there should be an AggregateFunction, no
> matter it's a Flink runtime's AggregateFunction or
> SQL's UserDefinedAggregateFunction. Both aggregation have concept of
> intermediate data type, sometimes we call it ACC.
> I quickly went through the POC piotr did before [1], it also directly uses
> AggregateFunction.
>
> But the thing is, after reading the design of this FLIP, I can't help
> myself feeling that this FLIP is not targeting to have a proper
> local aggregation support. It actually want to introduce another concept:
> LocalKeyBy, and how to split and merge local key groups,
> and how to properly support state on local key. Local aggregation just
> happened to be one possible use case of LocalKeyBy.
> But it lacks supporting the essential concept of local aggregation, which
> is intermediate data type. Without this, I really don't thing
> it is a good fit of local aggregation.
>
> Here I want to make sure of the scope or the goal about this FLIP, do we
> want to have a proper local aggregation engine, or we
> just want to introduce a new concept called LocalKeyBy?
>
> [1]: https://github.com/apache/flink/pull/4626
>
> Best,
> Kurt
>
>
> On Wed, Jun 19, 2019 at 5:13 PM vino yang <[hidden email]> wrote:
>
> > Hi Hequn,
> >
> > Thanks for your comments!
> >
> > I agree that allowing local aggregation reusing window API and refining
> > window operator to make it match both requirements (come from our and
> Kurt)
> > is a good decision!
> >
> > Concerning your questions:
> >
> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1) may be
> > meaningless.
> >
> > Yes, it does not make sense in most cases. However, I also want to note
> > users should know the right semantics of localKeyBy and use it correctly.
> > Because this issue also exists for the global keyBy, consider this
> example:
> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also meaningless.
> >
> > 2. About the semantics of
> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> >
> > Good catch! I agree with you that it's not good to enable all
> > functionalities for localKeyBy from KeyedStream.
> > Currently, We do not support some APIs such as
> > connect/join/intervalJoin/coGroup. This is due to that we force the
> > operators on LocalKeyedStreams chained with the inputs.
> >
> > Best,
> > Vino
> >
> >
> > Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
> >
> > > Hi,
> > >
> > > Thanks a lot for your great discussion and great to see that some
> > agreement
> > > has been reached on the "local aggregate engine"!
> > >
> > > ===> Considering the abstract engine,
> > > I'm thinking is it valuable for us to extend the current window to meet
> > > both demands raised by Kurt and Vino? There are some benefits we can
> get:
> > >
> > > 1. The interfaces of the window are complete and clear. With windows,
> we
> > > can define a lot of ways to split the data and perform different
> > > computations.
> > > 2. We can also leverage the window to do miniBatch for the global
> > > aggregation, i.e, we can use the window to bundle data belong to the
> same
> > > key, for every bundle we only need to read and write once state. This
> can
> > > greatly reduce state IO and improve performance.
> > > 3. A lot of other use cases can also benefit from the window base on
> > memory
> > > or stateless.
> > >
> > > ===> As for the API,
> > > I think it is good to make our API more flexible. However, we may need
> to
> > > make our API meaningful.
> > >
> > > Take my previous reply as an example,
> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may be
> > meaningless.
> > > Another example I find is the intervalJoin, e.g.,
> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In this case,
> it
> > > will bring problems if input1 and input2 share different parallelism.
> We
> > > don't know which input should the join chained with? Even if they share
> > the
> > > same parallelism, it's hard to tell what the join is doing. There are
> > maybe
> > > some other problems.
> > >
> > > From this point of view, it's at least not good to enable all
> > > functionalities for localKeyBy from KeyedStream?
> > >
> > > Great to also have your opinions.
> > >
> > > Best, Hequn
> > >
> > >
> > >
> > >
> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <[hidden email]>
> > wrote:
> > >
> > > > Hi Kurt and Piotrek,
> > > >
> > > > Thanks for your comments.
> > > >
> > > > I agree that we can provide a better abstraction to be compatible
> with
> > > two
> > > > different implementations.
> > > >
> > > > First of all, I think we should consider what kind of scenarios we
> need
> > > to
> > > > support in *API* level?
> > > >
> > > > We have some use cases which need to a customized aggregation through
> > > > KeyedProcessFunction, (in the usage of our localKeyBy.window they can
> > use
> > > > ProcessWindowFunction).
> > > >
> > > > Shall we support these flexible use scenarios?
> > > >
> > > > Best,
> > > > Vino
> > > >
> > > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
> > > >
> > > > > Hi Piotr,
> > > > >
> > > > > Thanks for joining the discussion. Make “local aggregation"
> abstract
> > > > enough
> > > > > sounds good to me, we could
> > > > > implement and verify alternative solutions for use cases of local
> > > > > aggregation. Maybe we will find both solutions
> > > > > are appropriate for different scenarios.
> > > > >
> > > > > Starting from a simple one sounds a practical way to go. What do
> you
> > > > think,
> > > > > vino?
> > > > >
> > > > > Best,
> > > > > Kurt
> > > > >
> > > > >
> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> [hidden email]>
> > > > > wrote:
> > > > >
> > > > > > Hi Kurt and Vino,
> > > > > >
> > > > > > I think there is a trade of hat we need to consider for the local
> > > > > > aggregation.
> > > > > >
> > > > > > Generally speaking I would agree with Kurt about local
> > > aggregation/pre
> > > > > > aggregation not using Flink's state flush the operator on a
> > > checkpoint.
> > > > > > Network IO is usually cheaper compared to Disks IO. This has
> > however
> > > > > couple
> > > > > > of issues:
> > > > > > 1. It can explode number of in-flight records during checkpoint
> > > barrier
> > > > > > alignment, making checkpointing slower and decrease the actual
> > > > > throughput.
> > > > > > 2. This trades Disks IO on the local aggregation machine with CPU
> > > (and
> > > > > > Disks IO in case of RocksDB) on the final aggregation machine.
> This
> > > is
> > > > > > fine, as long there is no huge data skew. If there is only a
> > handful
> > > > (or
> > > > > > even one single) hot keys, it might be better to keep the
> > persistent
> > > > > state
> > > > > > in the LocalAggregationOperator to offload final aggregation as
> > much
> > > as
> > > > > > possible.
> > > > > > 3. With frequent checkpointing local aggregation effectiveness
> > would
> > > > > > degrade.
> > > > > >
> > > > > > I assume Kurt is correct, that in your use cases stateless
> operator
> > > was
> > > > > > behaving better, but I could easily see other use cases as well.
> > For
> > > > > > example someone is already using RocksDB, and his job is
> > bottlenecked
> > > > on
> > > > > a
> > > > > > single window operator instance because of the data skew. In that
> > > case
> > > > > > stateful local aggregation would be probably a better choice.
> > > > > >
> > > > > > Because of that, I think we should eventually provide both
> versions
> > > and
> > > > > in
> > > > > > the initial version we should at least make the “local
> aggregation
> > > > > engine”
> > > > > > abstract enough, that one could easily provide different
> > > implementation
> > > > > > strategy.
> > > > > >
> > > > > > Piotrek
> > > > > >
> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <[hidden email]> wrote:
> > > > > > >
> > > > > > > Hi,
> > > > > > >
> > > > > > > For the trigger, it depends on what operator we want to use
> under
> > > the
> > > > > > API.
> > > > > > > If we choose to use window operator,
> > > > > > > we should also use window's trigger. However, I also think
> reuse
> > > > window
> > > > > > > operator for this scenario may not be
> > > > > > > the best choice. The reasons are the following:
> > > > > > >
> > > > > > > 1. As a lot of people already pointed out, window relies
> heavily
> > on
> > > > > state
> > > > > > > and it will definitely effect performance. You can
> > > > > > > argue that one can use heap based statebackend, but this will
> > > > introduce
> > > > > > > extra coupling. Especially we have a chance to
> > > > > > > design a pure stateless operator.
> > > > > > > 2. The window operator is *the most* complicated operator Flink
> > > > > currently
> > > > > > > have. Maybe we only need to pick a subset of
> > > > > > > window operator to achieve the goal, but once the user wants to
> > > have
> > > > a
> > > > > > deep
> > > > > > > look at the localAggregation operator, it's still
> > > > > > > hard to find out what's going on under the window operator. For
> > > > > > simplicity,
> > > > > > > I would also recommend we introduce a dedicated
> > > > > > > lightweight operator, which also much easier for a user to
> learn
> > > and
> > > > > use.
> > > > > > >
> > > > > > > For your question about increasing the burden in
> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the only thing
> > this
> > > > > > function
> > > > > > > need
> > > > > > > to do is output all the partial results, it's purely cpu
> > workload,
> > > > not
> > > > > > > introducing any IO. I want to point out that even if we have
> this
> > > > > > > cost, we reduced another barrier align cost of the operator,
> > which
> > > is
> > > > > the
> > > > > > > sync flush stage of the state, if you introduced state. This
> > > > > > > flush actually will introduce disk IO, and I think it's worthy
> to
> > > > > > exchange
> > > > > > > this cost with purely CPU workload. And we do have some
> > > > > > > observations about these two behavior (as i said before, we
> > > actually
> > > > > > > implemented both solutions), the stateless one actually
> performs
> > > > > > > better both in performance and barrier align time.
> > > > > > >
> > > > > > > Best,
> > > > > > > Kurt
> > > > > > >
> > > > > > >
> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> [hidden email]
> > >
> > > > > wrote:
> > > > > > >
> > > > > > >> Hi Kurt,
> > > > > > >>
> > > > > > >> Thanks for your example. Now, it looks more clearly for me.
> > > > > > >>
> > > > > > >> From your example code snippet, I saw the localAggregate API
> has
> > > > three
> > > > > > >> parameters:
> > > > > > >>
> > > > > > >>   1. key field
> > > > > > >>   2. PartitionAvg
> > > > > > >>   3. CountTrigger: Does this trigger comes from window
> package?
> > > > > > >>
> > > > > > >> I will compare our and your design from API and operator
> level:
> > > > > > >>
> > > > > > >> *From the API level:*
> > > > > > >>
> > > > > > >> As I replied to @dianfu in the old email thread,[1] the Window
> > API
> > > > can
> > > > > > >> provide the second and the third parameter right now.
> > > > > > >>
> > > > > > >> If you reuse specified interface or class, such as *Trigger*
> or
> > > > > > >> *CounterTrigger* provided by window package, but do not use
> > window
> > > > > API,
> > > > > > >> it's not reasonable.
> > > > > > >> And if you do not reuse these interface or class, you would
> need
> > > to
> > > > > > >> introduce more things however they are looked similar to the
> > > things
> > > > > > >> provided by window package.
> > > > > > >>
> > > > > > >> The window package has provided several types of the window
> and
> > > many
> > > > > > >> triggers and let users customize it. What's more, the user is
> > more
> > > > > > familiar
> > > > > > >> with Window API.
> > > > > > >>
> > > > > > >> This is the reason why we just provide localKeyBy API and
> reuse
> > > the
> > > > > > window
> > > > > > >> API. It reduces unnecessary components such as triggers and
> the
> > > > > > mechanism
> > > > > > >> of buffer (based on count num or time).
> > > > > > >> And it has a clear and easy to understand semantics.
> > > > > > >>
> > > > > > >> *From the operator level:*
> > > > > > >>
> > > > > > >> We reused window operator, so we can get all the benefits from
> > > state
> > > > > and
> > > > > > >> checkpoint.
> > > > > > >>
> > > > > > >> From your design, you named the operator under localAggregate
> > API
> > > > is a
> > > > > > >> *stateless* operator. IMO, it is still a state, it is just not
> > > Flink
> > > > > > >> managed state.
> > > > > > >> About the memory buffer (I think it's still not very clear, if
> > you
> > > > > have
> > > > > > >> time, can you give more detail information or answer my
> > > questions),
> > > > I
> > > > > > have
> > > > > > >> some questions:
> > > > > > >>
> > > > > > >>   - if it just a raw JVM heap memory buffer, how to support
> > fault
> > > > > > >>   tolerance, if the job is configured EXACTLY-ONCE semantic
> > > > guarantee?
> > > > > > >>   - if you thought the memory buffer(non-Flink state), has
> > better
> > > > > > >>   performance. In our design, users can also config HEAP state
> > > > backend
> > > > > > to
> > > > > > >>   provide the performance close to your mechanism.
> > > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()` related to
> the
> > > > > timing
> > > > > > of
> > > > > > >>   snapshot. IMO, the flush action should be a synchronized
> > action?
> > > > (if
> > > > > > >> not,
> > > > > > >>   please point out my mistake) I still think we should not
> > depend
> > > on
> > > > > the
> > > > > > >>   timing of checkpoint. Checkpoint related operations are
> > inherent
> > > > > > >>   performance sensitive, we should not increase its burden
> > > anymore.
> > > > > Our
> > > > > > >>   implementation based on the mechanism of Flink's checkpoint,
> > > which
> > > > > can
> > > > > > >>   benefit from the asnyc snapshot and incremental checkpoint.
> > IMO,
> > > > the
> > > > > > >>   performance is not a problem, and we also do not find the
> > > > > performance
> > > > > > >> issue
> > > > > > >>   in our production.
> > > > > > >>
> > > > > > >> [1]:
> > > > > > >>
> > > > > > >>
> > > > > >
> > > > >
> > > >
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > > > > >>
> > > > > > >> Best,
> > > > > > >> Vino
> > > > > > >>
> > > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二 下午2:27写道：
> > > > > > >>
> > > > > > >>> Yeah, sorry for not expressing myself clearly. I will try to
> > > > provide
> > > > > > more
> > > > > > >>> details to make sure we are on the same page.
> > > > > > >>>
> > > > > > >>> For DataStream API, it shouldn't be optimized automatically.
> > You
> > > > have
> > > > > > to
> > > > > > >>> explicitly call API to do local aggregation
> > > > > > >>> as well as the trigger policy of the local aggregation. Take
> > > > average
> > > > > > for
> > > > > > >>> example, the user program may look like this (just a draft):
> > > > > > >>>
> > > > > > >>> assuming the input type is DataStream<Tupl2<String, Int>>
> > > > > > >>>
> > > > > > >>> ds.localAggregate(
> > > > > > >>>        0,                                       // The local
> > key,
> > > > > which
> > > > > > >> is
> > > > > > >>> the String from Tuple2
> > > > > > >>>        PartitionAvg(1),                 // The partial
> > > aggregation
> > > > > > >>> function, produces Tuple2<Long, Int>, indicating sum and
> count
> > > > > > >>>        CountTrigger.of(1000L)    // Trigger policy, note this
> > > > should
> > > > > be
> > > > > > >>> best effort, and also be composited with time based or memory
> > > size
> > > > > > based
> > > > > > >>> trigger
> > > > > > >>>    )                                           // The return
> > type
> > > > is
> > > > > > >> local
> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> > > > > > >>>    .keyBy(0)                             // Further keyby it
> > with
> > > > > > >> required
> > > > > > >>> key
> > > > > > >>>    .aggregate(1)                      // This will merge all
> > the
> > > > > > partial
> > > > > > >>> results and get the final average.
> > > > > > >>>
> > > > > > >>> (This is only a draft, only trying to explain what it looks
> > > like. )
> > > > > > >>>
> > > > > > >>> The local aggregate operator can be stateless, we can keep a
> > > memory
> > > > > > >> buffer
> > > > > > >>> or other efficient data structure to improve the aggregate
> > > > > performance.
> > > > > > >>>
> > > > > > >>> Let me know if you have any other questions.
> > > > > > >>>
> > > > > > >>> Best,
> > > > > > >>> Kurt
> > > > > > >>>
> > > > > > >>>
> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> > [hidden email]
> > > >
> > > > > > wrote:
> > > > > > >>>
> > > > > > >>>> Hi Kurt,
> > > > > > >>>>
> > > > > > >>>> Thanks for your reply.
> > > > > > >>>>
> > > > > > >>>> Actually, I am not against you to raise your design.
> > > > > > >>>>
> > > > > > >>>> From your description before, I just can imagine your
> > high-level
> > > > > > >>>> implementation is about SQL and the optimization is inner of
> > the
> > > > > API.
> > > > > > >> Is
> > > > > > >>> it
> > > > > > >>>> automatically? how to give the configuration option about
> > > trigger
> > > > > > >>>> pre-aggregation?
> > > > > > >>>>
> > > > > > >>>> Maybe after I get more information, it sounds more
> reasonable.
> > > > > > >>>>
> > > > > > >>>> IMO, first of all, it would be better to make your user
> > > interface
> > > > > > >>> concrete,
> > > > > > >>>> it's the basis of the discussion.
> > > > > > >>>>
> > > > > > >>>> For example, can you give an example code snippet to
> introduce
> > > how
> > > > > to
> > > > > > >>> help
> > > > > > >>>> users to process data skew caused by the jobs which built
> with
> > > > > > >> DataStream
> > > > > > >>>> API?
> > > > > > >>>>
> > > > > > >>>> If you give more details we can discuss further more. I
> think
> > if
> > > > one
> > > > > > >>> design
> > > > > > >>>> introduces an exact interface and another does not.
> > > > > > >>>>
> > > > > > >>>> The implementation has an obvious difference. For example,
> we
> > > > > > introduce
> > > > > > >>> an
> > > > > > >>>> exact API in DataStream named localKeyBy, about the
> > > > pre-aggregation
> > > > > we
> > > > > > >>> need
> > > > > > >>>> to define the trigger mechanism of local aggregation, so we
> > find
> > > > > > reused
> > > > > > >>>> window API and operator is a good choice. This is a
> reasoning
> > > link
> > > > > > from
> > > > > > >>>> design to implementation.
> > > > > > >>>>
> > > > > > >>>> What do you think?
> > > > > > >>>>
> > > > > > >>>> Best,
> > > > > > >>>> Vino
> > > > > > >>>>
> > > > > > >>>>
> > > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二 上午11:58写道：
> > > > > > >>>>
> > > > > > >>>>> Hi Vino,
> > > > > > >>>>>
> > > > > > >>>>> Now I feel that we may have different understandings about
> > what
> > > > > kind
> > > > > > >> of
> > > > > > >>>>> problems or improvements you want to
> > > > > > >>>>> resolve. Currently, most of the feedback are focusing on
> *how
> > > to
> > > > > do a
> > > > > > >>>>> proper local aggregation to improve performance
> > > > > > >>>>> and maybe solving the data skew issue*. And my gut feeling
> is
> > > > this
> > > > > is
> > > > > > >>>>> exactly what users want at the first place,
> > > > > > >>>>> especially those +1s. (Sorry to try to summarize here,
> please
> > > > > correct
> > > > > > >>> me
> > > > > > >>>> if
> > > > > > >>>>> i'm wrong).
> > > > > > >>>>>
> > > > > > >>>>> But I still think the design is somehow diverged from the
> > goal.
> > > > If
> > > > > we
> > > > > > >>>> want
> > > > > > >>>>> to have an efficient and powerful way to
> > > > > > >>>>> have local aggregation, supporting intermedia result type
> is
> > > > > > >> essential
> > > > > > >>>> IMO.
> > > > > > >>>>> Both runtime's `AggregateFunction` and
> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a proper support
> of
> > > > > > >>>> intermediate
> > > > > > >>>>> result type and can do `merge` operation
> > > > > > >>>>> on them.
> > > > > > >>>>>
> > > > > > >>>>> Now, we have a lightweight alternatives which performs
> well,
> > > and
> > > > > > >> have a
> > > > > > >>>>> nice fit with the local aggregate requirements.
> > > > > > >>>>> Mostly importantly,  it's much less complex because it's
> > > > stateless.
> > > > > > >> And
> > > > > > >>>> it
> > > > > > >>>>> can also achieve the similar multiple-aggregation
> > > > > > >>>>> scenario.
> > > > > > >>>>>
> > > > > > >>>>> I still not convinced why we shouldn't consider it as a
> first
> > > > step.
> > > > > > >>>>>
> > > > > > >>>>> Best,
> > > > > > >>>>> Kurt
> > > > > > >>>>>
> > > > > > >>>>>
> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> > > > [hidden email]>
> > > > > > >>>> wrote:
> > > > > > >>>>>
> > > > > > >>>>>> Hi Kurt,
> > > > > > >>>>>>
> > > > > > >>>>>> Thanks for your comments.
> > > > > > >>>>>>
> > > > > > >>>>>> It seems we both implemented local aggregation feature to
> > > > optimize
> > > > > > >>> the
> > > > > > >>>>>> issue of data skew.
> > > > > > >>>>>> However, IMHO, the API level of optimizing revenue is
> > > different.
> > > > > > >>>>>>
> > > > > > >>>>>> *Your optimization benefits from Flink SQL and it's not
> > user's
> > > > > > >>>> faces.(If
> > > > > > >>>>> I
> > > > > > >>>>>> understand it incorrectly, please correct this.)*
> > > > > > >>>>>> *Our implementation employs it as an optimization tool API
> > for
> > > > > > >>>>> DataStream,
> > > > > > >>>>>> it just like a local version of the keyBy API.*
> > > > > > >>>>>>
> > > > > > >>>>>> Based on this, I want to say support it as a DataStream
> API
> > > can
> > > > > > >>> provide
> > > > > > >>>>>> these advantages:
> > > > > > >>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>>   - The localKeyBy API has a clear semantic and it's
> > flexible
> > > > not
> > > > > > >>> only
> > > > > > >>>>> for
> > > > > > >>>>>>   processing data skew but also for implementing some user
> > > > cases,
> > > > > > >>> for
> > > > > > >>>>>>   example, if we want to calculate the multiple-level
> > > > aggregation,
> > > > > > >>> we
> > > > > > >>>>> can
> > > > > > >>>>>> do
> > > > > > >>>>>>   multiple-level aggregation in the local aggregation:
> > > > > > >>>>>>   input.localKeyBy("a").sum(1).localKeyBy("b").window();
> //
> > > here
> > > > > > >> "a"
> > > > > > >>>> is
> > > > > > >>>>> a
> > > > > > >>>>>>   sub-category, while "b" is a category, here we do not
> need
> > > to
> > > > > > >>>> shuffle
> > > > > > >>>>>> data
> > > > > > >>>>>>   in the network.
> > > > > > >>>>>>   - The users of DataStream API will benefit from this.
> > > > Actually,
> > > > > > >> we
> > > > > > >>>>> have
> > > > > > >>>>>>   a lot of scenes need to use DataStream API. Currently,
> > > > > > >> DataStream
> > > > > > >>>> API
> > > > > > >>>>> is
> > > > > > >>>>>>   the cornerstone of the physical plan of Flink SQL. With
> a
> > > > > > >>> localKeyBy
> > > > > > >>>>>> API,
> > > > > > >>>>>>   the optimization of SQL at least may use this optimized
> > API,
> > > > > > >> this
> > > > > > >>>> is a
> > > > > > >>>>>>   further topic.
> > > > > > >>>>>>   - Based on the window operator, our state would benefit
> > from
> > > > > > >> Flink
> > > > > > >>>>> State
> > > > > > >>>>>>   and checkpoint, we do not need to worry about OOM and
> job
> > > > > > >> failed.
> > > > > > >>>>>>
> > > > > > >>>>>> Now, about your questions:
> > > > > > >>>>>>
> > > > > > >>>>>> 1. About our design cannot change the data type and about
> > the
> > > > > > >>>>>> implementation of average:
> > > > > > >>>>>>
> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is an API
> > provides
> > > > to
> > > > > > >> the
> > > > > > >>>>> users
> > > > > > >>>>>> who use DataStream API to build their jobs.
> > > > > > >>>>>> Users should know its semantics and the difference with
> > keyBy
> > > > API,
> > > > > > >> so
> > > > > > >>>> if
> > > > > > >>>>>> they want to the average aggregation, they should carry
> > local
> > > > sum
> > > > > > >>>> result
> > > > > > >>>>>> and local count result.
> > > > > > >>>>>> I admit that it will be convenient to use keyBy directly.
> > But
> > > we
> > > > > > >> need
> > > > > > >>>> to
> > > > > > >>>>>> pay a little price when we get some benefits. I think this
> > > price
> > > > > is
> > > > > > >>>>>> reasonable. Considering that the DataStream API itself is
> a
> > > > > > >> low-level
> > > > > > >>>> API
> > > > > > >>>>>> (at least for now).
> > > > > > >>>>>>
> > > > > > >>>>>> 2. About stateless operator and
> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
> > > > > > >>>>>>
> > > > > > >>>>>> Actually, I have discussed this opinion with @dianfu in
> the
> > > old
> > > > > > >> mail
> > > > > > >>>>>> thread. I will copy my opinion from there:
> > > > > > >>>>>>
> > > > > > >>>>>>   - for your design, you still need somewhere to give the
> > > users
> > > > > > >>>>> configure
> > > > > > >>>>>>   the trigger threshold (maybe memory availability?), this
> > > > design
> > > > > > >>>> cannot
> > > > > > >>>>>>   guarantee a deterministic semantics (it will bring
> trouble
> > > for
> > > > > > >>>> testing
> > > > > > >>>>>> and
> > > > > > >>>>>>   debugging).
> > > > > > >>>>>>   - if the implementation depends on the timing of
> > checkpoint,
> > > > it
> > > > > > >>>> would
> > > > > > >>>>>>   affect the checkpoint's progress, and the buffered data
> > may
> > > > > > >> cause
> > > > > > >>>> OOM
> > > > > > >>>>>>   issue. In addition, if the operator is stateless, it can
> > not
> > > > > > >>> provide
> > > > > > >>>>>> fault
> > > > > > >>>>>>   tolerance.
> > > > > > >>>>>>
> > > > > > >>>>>> Best,
> > > > > > >>>>>> Vino
> > > > > > >>>>>>
> > > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二 上午9:22写道：
> > > > > > >>>>>>
> > > > > > >>>>>>> Hi Vino,
> > > > > > >>>>>>>
> > > > > > >>>>>>> Thanks for the proposal, I like the general idea and IMO
> > it's
> > > > > > >> very
> > > > > > >>>>> useful
> > > > > > >>>>>>> feature.
> > > > > > >>>>>>> But after reading through the document, I feel that we
> may
> > > over
> > > > > > >>>> design
> > > > > > >>>>>> the
> > > > > > >>>>>>> required
> > > > > > >>>>>>> operator for proper local aggregation. The main reason is
> > we
> > > > want
> > > > > > >>> to
> > > > > > >>>>>> have a
> > > > > > >>>>>>> clear definition and behavior about the "local keyed
> state"
> > > > which
> > > > > > >>> in
> > > > > > >>>> my
> > > > > > >>>>>>> opinion is not
> > > > > > >>>>>>> necessary for local aggregation, at least for start.
> > > > > > >>>>>>>
> > > > > > >>>>>>> Another issue I noticed is the local key by operator
> cannot
> > > > > > >> change
> > > > > > >>>>>> element
> > > > > > >>>>>>> type, it will
> > > > > > >>>>>>> also restrict a lot of use cases which can be benefit
> from
> > > > local
> > > > > > >>>>>>> aggregation, like "average".
> > > > > > >>>>>>>
> > > > > > >>>>>>> We also did similar logic in SQL and the only thing need
> to
> > > be
> > > > > > >> done
> > > > > > >>>> is
> > > > > > >>>>>>> introduce
> > > > > > >>>>>>> a stateless lightweight operator which is *chained*
> before
> > > > > > >>> `keyby()`.
> > > > > > >>>>> The
> > > > > > >>>>>>> operator will flush all buffered
> > > > > > >>>>>>> elements during
> > `StreamOperator::prepareSnapshotPreBarrier()`
> > > > and
> > > > > > >>>> make
> > > > > > >>>>>>> himself stateless.
> > > > > > >>>>>>> By the way, in the earlier version we also did the
> similar
> > > > > > >> approach
> > > > > > >>>> by
> > > > > > >>>>>>> introducing a stateful
> > > > > > >>>>>>> local aggregation operator but it's not performed as well
> > as
> > > > the
> > > > > > >>>> later
> > > > > > >>>>>> one,
> > > > > > >>>>>>> and also effect the barrie
> > > > > > >>>>>>> alignment time. The later one is fairly simple and more
> > > > > > >> efficient.
> > > > > > >>>>>>>
> > > > > > >>>>>>> I would highly suggest you to consider to have a
> stateless
> > > > > > >> approach
> > > > > > >>>> at
> > > > > > >>>>>> the
> > > > > > >>>>>>> first step.
> > > > > > >>>>>>>
> > > > > > >>>>>>> Best,
> > > > > > >>>>>>> Kurt
> > > > > > >>>>>>>
> > > > > > >>>>>>>
> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> [hidden email]>
> > > > > > >> wrote:
> > > > > > >>>>>>>
> > > > > > >>>>>>>> Hi Vino,
> > > > > > >>>>>>>>
> > > > > > >>>>>>>> Thanks for the proposal.
> > > > > > >>>>>>>>
> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
> > > > > > >>>>>>>>
> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> > > > > > >> have
> > > > > > >>>> you
> > > > > > >>>>>>> done
> > > > > > >>>>>>>> some benchmark?
> > > > > > >>>>>>>> Because I'm curious about how much performance
> improvement
> > > can
> > > > > > >> we
> > > > > > >>>> get
> > > > > > >>>>>> by
> > > > > > >>>>>>>> using count window as the local operator.
> > > > > > >>>>>>>>
> > > > > > >>>>>>>> Best,
> > > > > > >>>>>>>> Jark
> > > > > > >>>>>>>>
> > > > > > >>>>>>>>
> > > > > > >>>>>>>>
> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> > > > [hidden email]
> > > > > > >>>
> > > > > > >>>>> wrote:
> > > > > > >>>>>>>>
> > > > > > >>>>>>>>> Hi Hequn,
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> Thanks for your reply.
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> The purpose of localKeyBy API is to provide a tool
> which
> > > can
> > > > > > >>> let
> > > > > > >>>>>> users
> > > > > > >>>>>>> do
> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior of the
> > > > > > >>> pre-aggregation
> > > > > > >>>>> is
> > > > > > >>>>>>>>> similar to keyBy API.
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> So the three cases are different, I will describe them
> > one
> > > by
> > > > > > >>>> one:
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> *In this case, the result is event-driven, each event
> can
> > > > > > >>> produce
> > > > > > >>>>> one
> > > > > > >>>>>>> sum
> > > > > > >>>>>>>>> aggregation result and it is the latest one from the
> > source
> > > > > > >>>> start.*
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> *In this case, the semantic may have a problem, it
> would
> > do
> > > > > > >> the
> > > > > > >>>>> local
> > > > > > >>>>>>> sum
> > > > > > >>>>>>>>> aggregation and will produce the latest partial result
> > from
> > > > > > >> the
> > > > > > >>>>>> source
> > > > > > >>>>>>>>> start for every event. *
> > > > > > >>>>>>>>> *These latest partial results from the same key are
> > hashed
> > > to
> > > > > > >>> one
> > > > > > >>>>>> node
> > > > > > >>>>>>> to
> > > > > > >>>>>>>>> do the global sum aggregation.*
> > > > > > >>>>>>>>> *In the global aggregation, when it received multiple
> > > partial
> > > > > > >>>>> results
> > > > > > >>>>>>>> (they
> > > > > > >>>>>>>>> are all calculated from the source start) and sum them
> > will
> > > > > > >> get
> > > > > > >>>> the
> > > > > > >>>>>>> wrong
> > > > > > >>>>>>>>> result.*
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> 3.
> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> *In this case, it would just get a partial aggregation
> > > result
> > > > > > >>> for
> > > > > > >>>>>> the 5
> > > > > > >>>>>>>>> records in the count window. The partial aggregation
> > > results
> > > > > > >>> from
> > > > > > >>>>> the
> > > > > > >>>>>>>> same
> > > > > > >>>>>>>>> key will be aggregated globally.*
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> So the first case and the third case can get the *same*
> > > > > > >> result,
> > > > > > >>>> the
> > > > > > >>>>>>>>> difference is the output-style and the latency.
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> Generally speaking, the local key API is just an
> > > optimization
> > > > > > >>>> API.
> > > > > > >>>>> We
> > > > > > >>>>>>> do
> > > > > > >>>>>>>>> not limit the user's usage, but the user has to
> > understand
> > > > > > >> its
> > > > > > >>>>>>> semantics
> > > > > > >>>>>>>>> and use it correctly.
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> Best,
> > > > > > >>>>>>>>> Vino
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]> 于2019年6月17日周一
> > 下午4:18写道：
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>>> Hi Vino,
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a very good
> > > feature!
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>> One thing I want to make sure is the semantics for the
> > > > > > >>>>>> `localKeyBy`.
> > > > > > >>>>>>>> From
> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns an instance
> > of
> > > > > > >>>>>>> `KeyedStream`
> > > > > > >>>>>>>>>> which can also perform sum(), so in this case, what's
> > the
> > > > > > >>>>> semantics
> > > > > > >>>>>>> for
> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the following code
> > share
> > > > > > >>> the
> > > > > > >>>>> same
> > > > > > >>>>>>>>> result?
> > > > > > >>>>>>>>>> and what're the differences between them?
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> > > > > > >>>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > > > > >>>>>>>>>> 3.
> > > > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>> Would also be great if we can add this into the
> > document.
> > > > > > >>> Thank
> > > > > > >>>>> you
> > > > > > >>>>>>>> very
> > > > > > >>>>>>>>>> much.
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>> Best, Hequn
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino yang <
> > > > > > >>>>> [hidden email]>
> > > > > > >>>>>>>>> wrote:
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>>> Hi Aljoscha,
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section of FLIP wiki
> > > > > > >>>> page.[1]
> > > > > > >>>>>> This
> > > > > > >>>>>>>>> mail
> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to the third
> > step.
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote step), I didn't
> > > > > > >> find
> > > > > > >>>> the
> > > > > > >>>>>>>>>>> prerequisites for starting the voting process.
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>> Considering that the discussion of this feature has
> > been
> > > > > > >>> done
> > > > > > >>>>> in
> > > > > > >>>>>>> the
> > > > > > >>>>>>>>> old
> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when should I start
> > > > > > >> voting?
> > > > > > >>>> Can
> > > > > > >>>>> I
> > > > > > >>>>>>>> start
> > > > > > >>>>>>>>>> now?
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>> Best,
> > > > > > >>>>>>>>>>> Vino
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>> [1]:
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>
> > > > > > >>>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>
> > > > > > >>>>
> > > > > > >>>
> > > > > > >>
> > > > > >
> > > > >
> > > >
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> > > > > > >>>>>>>>>>> [2]:
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>
> > > > > > >>>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>
> > > > > > >>>>
> > > > > > >>>
> > > > > > >>
> > > > > >
> > > > >
> > > >
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>> leesf <[hidden email]> 于2019年6月13日周四 上午9:19写道：
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your efforts.
> > > > > > >>>>>>>>>>>>
> > > > > > >>>>>>>>>>>> Best,
> > > > > > >>>>>>>>>>>> Leesf
> > > > > > >>>>>>>>>>>>
> > > > > > >>>>>>>>>>>> vino yang <[hidden email]> 于2019年6月12日周三
> > > > > > >>> 下午5:46写道：
> > > > > > >>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> Hi folks,
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> I would like to start the FLIP discussion thread
> > > > > > >> about
> > > > > > >>>>>>> supporting
> > > > > > >>>>>>>>>> local
> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> In short, this feature can effectively alleviate
> data
> > > > > > >>>> skew.
> > > > > > >>>>>>> This
> > > > > > >>>>>>>> is
> > > > > > >>>>>>>>>> the
> > > > > > >>>>>>>>>>>>> FLIP:
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>
> > > > > > >>>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>
> > > > > > >>>>
> > > > > > >>>
> > > > > > >>
> > > > > >
> > > > >
> > > >
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely used to perform
> > > > > > >>>>>> aggregating
> > > > > > >>>>>>>>>>>> operations
> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the elements that
> > > > > > >>> have
> > > > > > >>>>> the
> > > > > > >>>>>>> same
> > > > > > >>>>>>>>>> key.
> > > > > > >>>>>>>>>>>> When
> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with the same key
> > > > > > >>> will
> > > > > > >>>> be
> > > > > > >>>>>>> sent
> > > > > > >>>>>>>> to
> > > > > > >>>>>>>>>> and
> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> The performance of these aggregating operations is
> > > > > > >> very
> > > > > > >>>>>>> sensitive
> > > > > > >>>>>>>>> to
> > > > > > >>>>>>>>>>> the
> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases where the
> > > > > > >>> distribution
> > > > > > >>>>> of
> > > > > > >>>>>>> keys
> > > > > > >>>>>>>>>>>> follows a
> > > > > > >>>>>>>>>>>>> powerful law, the performance will be significantly
> > > > > > >>>>>> downgraded.
> > > > > > >>>>>>>>> More
> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of parallelism
> does
> > > > > > >>> not
> > > > > > >>>>> help
> > > > > > >>>>>>>> when
> > > > > > >>>>>>>>> a
> > > > > > >>>>>>>>>>> task
> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted method to
> > > > > > >> reduce
> > > > > > >>>> the
> > > > > > >>>>>>>>>> performance
> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose the
> > > > > > >> aggregating
> > > > > > >>>>>>>> operations
> > > > > > >>>>>>>>>> into
> > > > > > >>>>>>>>>>>> two
> > > > > > >>>>>>>>>>>>> phases. In the first phase, we aggregate the
> elements
> > > > > > >>> of
> > > > > > >>>>> the
> > > > > > >>>>>>> same
> > > > > > >>>>>>>>> key
> > > > > > >>>>>>>>>>> at
> > > > > > >>>>>>>>>>>>> the sender side to obtain partial results. Then at
> > > > > > >> the
> > > > > > >>>>> second
> > > > > > >>>>>>>>> phase,
> > > > > > >>>>>>>>>>>> these
> > > > > > >>>>>>>>>>>>> partial results are sent to receivers according to
> > > > > > >>> their
> > > > > > >>>>> keys
> > > > > > >>>>>>> and
> > > > > > >>>>>>>>> are
> > > > > > >>>>>>>>>>>>> combined to obtain the final result. Since the
> number
> > > > > > >>> of
> > > > > > >>>>>>> partial
> > > > > > >>>>>>>>>>> results
> > > > > > >>>>>>>>>>>>> received by each receiver is limited by the number
> of
> > > > > > >>>>>> senders,
> > > > > > >>>>>>>> the
> > > > > > >>>>>>>>>>>>> imbalance among receivers can be reduced. Besides,
> by
> > > > > > >>>>>> reducing
> > > > > > >>>>>>>> the
> > > > > > >>>>>>>>>>> amount
> > > > > > >>>>>>>>>>>>> of transferred data the performance can be further
> > > > > > >>>>> improved.
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> *More details*:
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> Design documentation:
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>
> > > > > > >>>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>
> > > > > > >>>>
> > > > > > >>>
> > > > > > >>
> > > > > >
> > > > >
> > > >
> > >
> >
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> Old discussion thread:
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>
> > > > > > >>>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>
> > > > > > >>>>
> > > > > > >>>
> > > > > > >>
> > > > > >
> > > > >
> > > >
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> > > > > > >>>>>>>>> https://issues.apache.org/jira/browse/FLINK-12786
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> We are looking forwards to your feedback!
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>> Best,
> > > > > > >>>>>>>>>>>>> Vino
> > > > > > >>>>>>>>>>>>>
> > > > > > >>>>>>>>>>>>
> > > > > > >>>>>>>>>>>
> > > > > > >>>>>>>>>>
> > > > > > >>>>>>>>>
> > > > > > >>>>>>>>
> > > > > > >>>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>
> > > > > > >>>>
> > > > > > >>>
> > > > > > >>
> > > > > >
> > > > > >
> > > > >
> > > >
> > >
> >
>

[vino yang]

Hi all,

I am happy we have a wonderful discussion and received many valuable
opinions in the last few days.

Now, let me try to summarize what we have reached consensus about the
changes in the design.

   - provide a unified abstraction to support two kinds of implementation;
   - reuse WindowOperator and try to enhance it so that we can make the
   intermediate result of the local aggregation can be buffered and flushed to
   support two kinds of implementation;
   - keep the API design of localKeyBy, but declare the disabled some APIs
   we cannot support currently, and provide a configurable API for users to
   choose how to handle intermediate result;

The above three points have been updated in the design doc. Any
questions, please let me know.

@Aljoscha Krettek <[hidden email]> What do you think? Any further
comments?

Best,
Vino

vino yang <[hidden email]> 于2019年6月20日周四 下午2:02写道：

> Hi Kurt,
>
> Thanks for your comments.
>
> It seems we come to a consensus that we should alleviate the performance
> degraded by data skew with local aggregation. In this FLIP, our key
> solution is to introduce local keyed partition to achieve this goal.
>
> I also agree that we can benefit a lot from the usage of
> AggregateFunction. In combination with localKeyBy, We can easily use it to
> achieve local aggregation:
>
>    - input.localKeyBy(0).aggregate()
>    - input.localKeyBy(0).window().aggregate()
>
>
> I think the only problem here is the choices between
>
>    - (1) Introducing a new primitive called localKeyBy and implement
>    local aggregation with existing operators, or
>    - (2) Introducing an operator called localAggregation which is
>    composed of a key selector, a window-like operator, and an aggregate
>    function.
>
>
> There may exist some optimization opportunities by providing a composited
> interface for local aggregation. But at the same time, in my opinion, we
> lose flexibility (Or we need certain efforts to achieve the same
> flexibility).
>
> As said in the previous mails, we have many use cases where the
> aggregation is very complicated and cannot be performed with
> AggregateFunction. For example, users may perform windowed aggregations
> according to time, data values, or even external storage. Typically, they
> now use KeyedProcessFunction or customized triggers to implement these
> aggregations. It's not easy to address data skew in such cases with a
> composited interface for local aggregation.
>
> Given that Data Stream API is exactly targeted at these cases where the
> application logic is very complicated and optimization does not matter, I
> think it's a better choice to provide a relatively low-level and canonical
> interface.
>
> The composited interface, on the other side, may be a good choice in
> declarative interfaces, including SQL and Table API, as it allows more
> optimization opportunities.
>
> Best,
> Vino
>
>
> Kurt Young <[hidden email]> 于2019年6月20日周四 上午10:15写道：
>
>> Hi all,
>>
>> As vino said in previous emails, I think we should first discuss and
>> decide
>> what kind of use cases this FLIP want to
>> resolve, and what the API should look like. From my side, I think this is
>> probably the root cause of current divergence.
>>
>> My understand is (from the FLIP title and motivation section of the
>> document), we want to have a proper support of
>> local aggregation, or pre aggregation. This is not a very new idea, most
>> SQL engine already did this improvement. And
>> the core concept about this is, there should be an AggregateFunction, no
>> matter it's a Flink runtime's AggregateFunction or
>> SQL's UserDefinedAggregateFunction. Both aggregation have concept of
>> intermediate data type, sometimes we call it ACC.
>> I quickly went through the POC piotr did before [1], it also directly uses
>> AggregateFunction.
>>
>> But the thing is, after reading the design of this FLIP, I can't help
>> myself feeling that this FLIP is not targeting to have a proper
>> local aggregation support. It actually want to introduce another concept:
>> LocalKeyBy, and how to split and merge local key groups,
>> and how to properly support state on local key. Local aggregation just
>> happened to be one possible use case of LocalKeyBy.
>> But it lacks supporting the essential concept of local aggregation, which
>> is intermediate data type. Without this, I really don't thing
>> it is a good fit of local aggregation.
>>
>> Here I want to make sure of the scope or the goal about this FLIP, do we
>> want to have a proper local aggregation engine, or we
>> just want to introduce a new concept called LocalKeyBy?
>>
>> [1]: https://github.com/apache/flink/pull/4626
>>
>> Best,
>> Kurt
>>
>>
>> On Wed, Jun 19, 2019 at 5:13 PM vino yang <[hidden email]> wrote:
>>
>> > Hi Hequn,
>> >
>> > Thanks for your comments!
>> >
>> > I agree that allowing local aggregation reusing window API and refining
>> > window operator to make it match both requirements (come from our and
>> Kurt)
>> > is a good decision!
>> >
>> > Concerning your questions:
>> >
>> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1) may be
>> > meaningless.
>> >
>> > Yes, it does not make sense in most cases. However, I also want to note
>> > users should know the right semantics of localKeyBy and use it
>> correctly.
>> > Because this issue also exists for the global keyBy, consider this
>> example:
>> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also meaningless.
>> >
>> > 2. About the semantics of
>> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
>> >
>> > Good catch! I agree with you that it's not good to enable all
>> > functionalities for localKeyBy from KeyedStream.
>> > Currently, We do not support some APIs such as
>> > connect/join/intervalJoin/coGroup. This is due to that we force the
>> > operators on LocalKeyedStreams chained with the inputs.
>> >
>> > Best,
>> > Vino
>> >
>> >
>> > Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
>> >
>> > > Hi,
>> > >
>> > > Thanks a lot for your great discussion and great to see that some
>> > agreement
>> > > has been reached on the "local aggregate engine"!
>> > >
>> > > ===> Considering the abstract engine,
>> > > I'm thinking is it valuable for us to extend the current window to
>> meet
>> > > both demands raised by Kurt and Vino? There are some benefits we can
>> get:
>> > >
>> > > 1. The interfaces of the window are complete and clear. With windows,
>> we
>> > > can define a lot of ways to split the data and perform different
>> > > computations.
>> > > 2. We can also leverage the window to do miniBatch for the global
>> > > aggregation, i.e, we can use the window to bundle data belong to the
>> same
>> > > key, for every bundle we only need to read and write once state. This
>> can
>> > > greatly reduce state IO and improve performance.
>> > > 3. A lot of other use cases can also benefit from the window base on
>> > memory
>> > > or stateless.
>> > >
>> > > ===> As for the API,
>> > > I think it is good to make our API more flexible. However, we may
>> need to
>> > > make our API meaningful.
>> > >
>> > > Take my previous reply as an example,
>> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may be
>> > meaningless.
>> > > Another example I find is the intervalJoin, e.g.,
>> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In this
>> case, it
>> > > will bring problems if input1 and input2 share different parallelism.
>> We
>> > > don't know which input should the join chained with? Even if they
>> share
>> > the
>> > > same parallelism, it's hard to tell what the join is doing. There are
>> > maybe
>> > > some other problems.
>> > >
>> > > From this point of view, it's at least not good to enable all
>> > > functionalities for localKeyBy from KeyedStream?
>> > >
>> > > Great to also have your opinions.
>> > >
>> > > Best, Hequn
>> > >
>> > >
>> > >
>> > >
>> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <[hidden email]>
>> > wrote:
>> > >
>> > > > Hi Kurt and Piotrek,
>> > > >
>> > > > Thanks for your comments.
>> > > >
>> > > > I agree that we can provide a better abstraction to be compatible
>> with
>> > > two
>> > > > different implementations.
>> > > >
>> > > > First of all, I think we should consider what kind of scenarios we
>> need
>> > > to
>> > > > support in *API* level?
>> > > >
>> > > > We have some use cases which need to a customized aggregation
>> through
>> > > > KeyedProcessFunction, (in the usage of our localKeyBy.window they
>> can
>> > use
>> > > > ProcessWindowFunction).
>> > > >
>> > > > Shall we support these flexible use scenarios?
>> > > >
>> > > > Best,
>> > > > Vino
>> > > >
>> > > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
>> > > >
>> > > > > Hi Piotr,
>> > > > >
>> > > > > Thanks for joining the discussion. Make “local aggregation"
>> abstract
>> > > > enough
>> > > > > sounds good to me, we could
>> > > > > implement and verify alternative solutions for use cases of local
>> > > > > aggregation. Maybe we will find both solutions
>> > > > > are appropriate for different scenarios.
>> > > > >
>> > > > > Starting from a simple one sounds a practical way to go. What do
>> you
>> > > > think,
>> > > > > vino?
>> > > > >
>> > > > > Best,
>> > > > > Kurt
>> > > > >
>> > > > >
>> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
>> [hidden email]>
>> > > > > wrote:
>> > > > >
>> > > > > > Hi Kurt and Vino,
>> > > > > >
>> > > > > > I think there is a trade of hat we need to consider for the
>> local
>> > > > > > aggregation.
>> > > > > >
>> > > > > > Generally speaking I would agree with Kurt about local
>> > > aggregation/pre
>> > > > > > aggregation not using Flink's state flush the operator on a
>> > > checkpoint.
>> > > > > > Network IO is usually cheaper compared to Disks IO. This has
>> > however
>> > > > > couple
>> > > > > > of issues:
>> > > > > > 1. It can explode number of in-flight records during checkpoint
>> > > barrier
>> > > > > > alignment, making checkpointing slower and decrease the actual
>> > > > > throughput.
>> > > > > > 2. This trades Disks IO on the local aggregation machine with
>> CPU
>> > > (and
>> > > > > > Disks IO in case of RocksDB) on the final aggregation machine.
>> This
>> > > is
>> > > > > > fine, as long there is no huge data skew. If there is only a
>> > handful
>> > > > (or
>> > > > > > even one single) hot keys, it might be better to keep the
>> > persistent
>> > > > > state
>> > > > > > in the LocalAggregationOperator to offload final aggregation as
>> > much
>> > > as
>> > > > > > possible.
>> > > > > > 3. With frequent checkpointing local aggregation effectiveness
>> > would
>> > > > > > degrade.
>> > > > > >
>> > > > > > I assume Kurt is correct, that in your use cases stateless
>> operator
>> > > was
>> > > > > > behaving better, but I could easily see other use cases as well.
>> > For
>> > > > > > example someone is already using RocksDB, and his job is
>> > bottlenecked
>> > > > on
>> > > > > a
>> > > > > > single window operator instance because of the data skew. In
>> that
>> > > case
>> > > > > > stateful local aggregation would be probably a better choice.
>> > > > > >
>> > > > > > Because of that, I think we should eventually provide both
>> versions
>> > > and
>> > > > > in
>> > > > > > the initial version we should at least make the “local
>> aggregation
>> > > > > engine”
>> > > > > > abstract enough, that one could easily provide different
>> > > implementation
>> > > > > > strategy.
>> > > > > >
>> > > > > > Piotrek
>> > > > > >
>> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <[hidden email]>
>> wrote:
>> > > > > > >
>> > > > > > > Hi,
>> > > > > > >
>> > > > > > > For the trigger, it depends on what operator we want to use
>> under
>> > > the
>> > > > > > API.
>> > > > > > > If we choose to use window operator,
>> > > > > > > we should also use window's trigger. However, I also think
>> reuse
>> > > > window
>> > > > > > > operator for this scenario may not be
>> > > > > > > the best choice. The reasons are the following:
>> > > > > > >
>> > > > > > > 1. As a lot of people already pointed out, window relies
>> heavily
>> > on
>> > > > > state
>> > > > > > > and it will definitely effect performance. You can
>> > > > > > > argue that one can use heap based statebackend, but this will
>> > > > introduce
>> > > > > > > extra coupling. Especially we have a chance to
>> > > > > > > design a pure stateless operator.
>> > > > > > > 2. The window operator is *the most* complicated operator
>> Flink
>> > > > > currently
>> > > > > > > have. Maybe we only need to pick a subset of
>> > > > > > > window operator to achieve the goal, but once the user wants
>> to
>> > > have
>> > > > a
>> > > > > > deep
>> > > > > > > look at the localAggregation operator, it's still
>> > > > > > > hard to find out what's going on under the window operator.
>> For
>> > > > > > simplicity,
>> > > > > > > I would also recommend we introduce a dedicated
>> > > > > > > lightweight operator, which also much easier for a user to
>> learn
>> > > and
>> > > > > use.
>> > > > > > >
>> > > > > > > For your question about increasing the burden in
>> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the only thing
>> > this
>> > > > > > function
>> > > > > > > need
>> > > > > > > to do is output all the partial results, it's purely cpu
>> > workload,
>> > > > not
>> > > > > > > introducing any IO. I want to point out that even if we have
>> this
>> > > > > > > cost, we reduced another barrier align cost of the operator,
>> > which
>> > > is
>> > > > > the
>> > > > > > > sync flush stage of the state, if you introduced state. This
>> > > > > > > flush actually will introduce disk IO, and I think it's
>> worthy to
>> > > > > > exchange
>> > > > > > > this cost with purely CPU workload. And we do have some
>> > > > > > > observations about these two behavior (as i said before, we
>> > > actually
>> > > > > > > implemented both solutions), the stateless one actually
>> performs
>> > > > > > > better both in performance and barrier align time.
>> > > > > > >
>> > > > > > > Best,
>> > > > > > > Kurt
>> > > > > > >
>> > > > > > >
>> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
>> [hidden email]
>> > >
>> > > > > wrote:
>> > > > > > >
>> > > > > > >> Hi Kurt,
>> > > > > > >>
>> > > > > > >> Thanks for your example. Now, it looks more clearly for me.
>> > > > > > >>
>> > > > > > >> From your example code snippet, I saw the localAggregate API
>> has
>> > > > three
>> > > > > > >> parameters:
>> > > > > > >>
>> > > > > > >>   1. key field
>> > > > > > >>   2. PartitionAvg
>> > > > > > >>   3. CountTrigger: Does this trigger comes from window
>> package?
>> > > > > > >>
>> > > > > > >> I will compare our and your design from API and operator
>> level:
>> > > > > > >>
>> > > > > > >> *From the API level:*
>> > > > > > >>
>> > > > > > >> As I replied to @dianfu in the old email thread,[1] the
>> Window
>> > API
>> > > > can
>> > > > > > >> provide the second and the third parameter right now.
>> > > > > > >>
>> > > > > > >> If you reuse specified interface or class, such as *Trigger*
>> or
>> > > > > > >> *CounterTrigger* provided by window package, but do not use
>> > window
>> > > > > API,
>> > > > > > >> it's not reasonable.
>> > > > > > >> And if you do not reuse these interface or class, you would
>> need
>> > > to
>> > > > > > >> introduce more things however they are looked similar to the
>> > > things
>> > > > > > >> provided by window package.
>> > > > > > >>
>> > > > > > >> The window package has provided several types of the window
>> and
>> > > many
>> > > > > > >> triggers and let users customize it. What's more, the user is
>> > more
>> > > > > > familiar
>> > > > > > >> with Window API.
>> > > > > > >>
>> > > > > > >> This is the reason why we just provide localKeyBy API and
>> reuse
>> > > the
>> > > > > > window
>> > > > > > >> API. It reduces unnecessary components such as triggers and
>> the
>> > > > > > mechanism
>> > > > > > >> of buffer (based on count num or time).
>> > > > > > >> And it has a clear and easy to understand semantics.
>> > > > > > >>
>> > > > > > >> *From the operator level:*
>> > > > > > >>
>> > > > > > >> We reused window operator, so we can get all the benefits
>> from
>> > > state
>> > > > > and
>> > > > > > >> checkpoint.
>> > > > > > >>
>> > > > > > >> From your design, you named the operator under localAggregate
>> > API
>> > > > is a
>> > > > > > >> *stateless* operator. IMO, it is still a state, it is just
>> not
>> > > Flink
>> > > > > > >> managed state.
>> > > > > > >> About the memory buffer (I think it's still not very clear,
>> if
>> > you
>> > > > > have
>> > > > > > >> time, can you give more detail information or answer my
>> > > questions),
>> > > > I
>> > > > > > have
>> > > > > > >> some questions:
>> > > > > > >>
>> > > > > > >>   - if it just a raw JVM heap memory buffer, how to support
>> > fault
>> > > > > > >>   tolerance, if the job is configured EXACTLY-ONCE semantic
>> > > > guarantee?
>> > > > > > >>   - if you thought the memory buffer(non-Flink state), has
>> > better
>> > > > > > >>   performance. In our design, users can also config HEAP
>> state
>> > > > backend
>> > > > > > to
>> > > > > > >>   provide the performance close to your mechanism.
>> > > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()` related to
>> the
>> > > > > timing
>> > > > > > of
>> > > > > > >>   snapshot. IMO, the flush action should be a synchronized
>> > action?
>> > > > (if
>> > > > > > >> not,
>> > > > > > >>   please point out my mistake) I still think we should not
>> > depend
>> > > on
>> > > > > the
>> > > > > > >>   timing of checkpoint. Checkpoint related operations are
>> > inherent
>> > > > > > >>   performance sensitive, we should not increase its burden
>> > > anymore.
>> > > > > Our
>> > > > > > >>   implementation based on the mechanism of Flink's
>> checkpoint,
>> > > which
>> > > > > can
>> > > > > > >>   benefit from the asnyc snapshot and incremental checkpoint.
>> > IMO,
>> > > > the
>> > > > > > >>   performance is not a problem, and we also do not find the
>> > > > > performance
>> > > > > > >> issue
>> > > > > > >>   in our production.
>> > > > > > >>
>> > > > > > >> [1]:
>> > > > > > >>
>> > > > > > >>
>> > > > > >
>> > > > >
>> > > >
>> > >
>> >
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> > > > > > >>
>> > > > > > >> Best,
>> > > > > > >> Vino
>> > > > > > >>
>> > > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二 下午2:27写道：
>> > > > > > >>
>> > > > > > >>> Yeah, sorry for not expressing myself clearly. I will try to
>> > > > provide
>> > > > > > more
>> > > > > > >>> details to make sure we are on the same page.
>> > > > > > >>>
>> > > > > > >>> For DataStream API, it shouldn't be optimized automatically.
>> > You
>> > > > have
>> > > > > > to
>> > > > > > >>> explicitly call API to do local aggregation
>> > > > > > >>> as well as the trigger policy of the local aggregation. Take
>> > > > average
>> > > > > > for
>> > > > > > >>> example, the user program may look like this (just a draft):
>> > > > > > >>>
>> > > > > > >>> assuming the input type is DataStream<Tupl2<String, Int>>
>> > > > > > >>>
>> > > > > > >>> ds.localAggregate(
>> > > > > > >>>        0,                                       // The local
>> > key,
>> > > > > which
>> > > > > > >> is
>> > > > > > >>> the String from Tuple2
>> > > > > > >>>        PartitionAvg(1),                 // The partial
>> > > aggregation
>> > > > > > >>> function, produces Tuple2<Long, Int>, indicating sum and
>> count
>> > > > > > >>>        CountTrigger.of(1000L)    // Trigger policy, note
>> this
>> > > > should
>> > > > > be
>> > > > > > >>> best effort, and also be composited with time based or
>> memory
>> > > size
>> > > > > > based
>> > > > > > >>> trigger
>> > > > > > >>>    )                                           // The return
>> > type
>> > > > is
>> > > > > > >> local
>> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
>> > > > > > >>>    .keyBy(0)                             // Further keyby it
>> > with
>> > > > > > >> required
>> > > > > > >>> key
>> > > > > > >>>    .aggregate(1)                      // This will merge all
>> > the
>> > > > > > partial
>> > > > > > >>> results and get the final average.
>> > > > > > >>>
>> > > > > > >>> (This is only a draft, only trying to explain what it looks
>> > > like. )
>> > > > > > >>>
>> > > > > > >>> The local aggregate operator can be stateless, we can keep a
>> > > memory
>> > > > > > >> buffer
>> > > > > > >>> or other efficient data structure to improve the aggregate
>> > > > > performance.
>> > > > > > >>>
>> > > > > > >>> Let me know if you have any other questions.
>> > > > > > >>>
>> > > > > > >>> Best,
>> > > > > > >>> Kurt
>> > > > > > >>>
>> > > > > > >>>
>> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
>> > [hidden email]
>> > > >
>> > > > > > wrote:
>> > > > > > >>>
>> > > > > > >>>> Hi Kurt,
>> > > > > > >>>>
>> > > > > > >>>> Thanks for your reply.
>> > > > > > >>>>
>> > > > > > >>>> Actually, I am not against you to raise your design.
>> > > > > > >>>>
>> > > > > > >>>> From your description before, I just can imagine your
>> > high-level
>> > > > > > >>>> implementation is about SQL and the optimization is inner
>> of
>> > the
>> > > > > API.
>> > > > > > >> Is
>> > > > > > >>> it
>> > > > > > >>>> automatically? how to give the configuration option about
>> > > trigger
>> > > > > > >>>> pre-aggregation?
>> > > > > > >>>>
>> > > > > > >>>> Maybe after I get more information, it sounds more
>> reasonable.
>> > > > > > >>>>
>> > > > > > >>>> IMO, first of all, it would be better to make your user
>> > > interface
>> > > > > > >>> concrete,
>> > > > > > >>>> it's the basis of the discussion.
>> > > > > > >>>>
>> > > > > > >>>> For example, can you give an example code snippet to
>> introduce
>> > > how
>> > > > > to
>> > > > > > >>> help
>> > > > > > >>>> users to process data skew caused by the jobs which built
>> with
>> > > > > > >> DataStream
>> > > > > > >>>> API?
>> > > > > > >>>>
>> > > > > > >>>> If you give more details we can discuss further more. I
>> think
>> > if
>> > > > one
>> > > > > > >>> design
>> > > > > > >>>> introduces an exact interface and another does not.
>> > > > > > >>>>
>> > > > > > >>>> The implementation has an obvious difference. For example,
>> we
>> > > > > > introduce
>> > > > > > >>> an
>> > > > > > >>>> exact API in DataStream named localKeyBy, about the
>> > > > pre-aggregation
>> > > > > we
>> > > > > > >>> need
>> > > > > > >>>> to define the trigger mechanism of local aggregation, so we
>> > find
>> > > > > > reused
>> > > > > > >>>> window API and operator is a good choice. This is a
>> reasoning
>> > > link
>> > > > > > from
>> > > > > > >>>> design to implementation.
>> > > > > > >>>>
>> > > > > > >>>> What do you think?
>> > > > > > >>>>
>> > > > > > >>>> Best,
>> > > > > > >>>> Vino
>> > > > > > >>>>
>> > > > > > >>>>
>> > > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二 上午11:58写道：
>> > > > > > >>>>
>> > > > > > >>>>> Hi Vino,
>> > > > > > >>>>>
>> > > > > > >>>>> Now I feel that we may have different understandings about
>> > what
>> > > > > kind
>> > > > > > >> of
>> > > > > > >>>>> problems or improvements you want to
>> > > > > > >>>>> resolve. Currently, most of the feedback are focusing on
>> *how
>> > > to
>> > > > > do a
>> > > > > > >>>>> proper local aggregation to improve performance
>> > > > > > >>>>> and maybe solving the data skew issue*. And my gut
>> feeling is
>> > > > this
>> > > > > is
>> > > > > > >>>>> exactly what users want at the first place,
>> > > > > > >>>>> especially those +1s. (Sorry to try to summarize here,
>> please
>> > > > > correct
>> > > > > > >>> me
>> > > > > > >>>> if
>> > > > > > >>>>> i'm wrong).
>> > > > > > >>>>>
>> > > > > > >>>>> But I still think the design is somehow diverged from the
>> > goal.
>> > > > If
>> > > > > we
>> > > > > > >>>> want
>> > > > > > >>>>> to have an efficient and powerful way to
>> > > > > > >>>>> have local aggregation, supporting intermedia result type
>> is
>> > > > > > >> essential
>> > > > > > >>>> IMO.
>> > > > > > >>>>> Both runtime's `AggregateFunction` and
>> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a proper
>> support of
>> > > > > > >>>> intermediate
>> > > > > > >>>>> result type and can do `merge` operation
>> > > > > > >>>>> on them.
>> > > > > > >>>>>
>> > > > > > >>>>> Now, we have a lightweight alternatives which performs
>> well,
>> > > and
>> > > > > > >> have a
>> > > > > > >>>>> nice fit with the local aggregate requirements.
>> > > > > > >>>>> Mostly importantly,  it's much less complex because it's
>> > > > stateless.
>> > > > > > >> And
>> > > > > > >>>> it
>> > > > > > >>>>> can also achieve the similar multiple-aggregation
>> > > > > > >>>>> scenario.
>> > > > > > >>>>>
>> > > > > > >>>>> I still not convinced why we shouldn't consider it as a
>> first
>> > > > step.
>> > > > > > >>>>>
>> > > > > > >>>>> Best,
>> > > > > > >>>>> Kurt
>> > > > > > >>>>>
>> > > > > > >>>>>
>> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
>> > > > [hidden email]>
>> > > > > > >>>> wrote:
>> > > > > > >>>>>
>> > > > > > >>>>>> Hi Kurt,
>> > > > > > >>>>>>
>> > > > > > >>>>>> Thanks for your comments.
>> > > > > > >>>>>>
>> > > > > > >>>>>> It seems we both implemented local aggregation feature to
>> > > > optimize
>> > > > > > >>> the
>> > > > > > >>>>>> issue of data skew.
>> > > > > > >>>>>> However, IMHO, the API level of optimizing revenue is
>> > > different.
>> > > > > > >>>>>>
>> > > > > > >>>>>> *Your optimization benefits from Flink SQL and it's not
>> > user's
>> > > > > > >>>> faces.(If
>> > > > > > >>>>> I
>> > > > > > >>>>>> understand it incorrectly, please correct this.)*
>> > > > > > >>>>>> *Our implementation employs it as an optimization tool
>> API
>> > for
>> > > > > > >>>>> DataStream,
>> > > > > > >>>>>> it just like a local version of the keyBy API.*
>> > > > > > >>>>>>
>> > > > > > >>>>>> Based on this, I want to say support it as a DataStream
>> API
>> > > can
>> > > > > > >>> provide
>> > > > > > >>>>>> these advantages:
>> > > > > > >>>>>>
>> > > > > > >>>>>>
>> > > > > > >>>>>>   - The localKeyBy API has a clear semantic and it's
>> > flexible
>> > > > not
>> > > > > > >>> only
>> > > > > > >>>>> for
>> > > > > > >>>>>>   processing data skew but also for implementing some
>> user
>> > > > cases,
>> > > > > > >>> for
>> > > > > > >>>>>>   example, if we want to calculate the multiple-level
>> > > > aggregation,
>> > > > > > >>> we
>> > > > > > >>>>> can
>> > > > > > >>>>>> do
>> > > > > > >>>>>>   multiple-level aggregation in the local aggregation:
>> > > > > > >>>>>>   input.localKeyBy("a").sum(1).localKeyBy("b").window();
>> //
>> > > here
>> > > > > > >> "a"
>> > > > > > >>>> is
>> > > > > > >>>>> a
>> > > > > > >>>>>>   sub-category, while "b" is a category, here we do not
>> need
>> > > to
>> > > > > > >>>> shuffle
>> > > > > > >>>>>> data
>> > > > > > >>>>>>   in the network.
>> > > > > > >>>>>>   - The users of DataStream API will benefit from this.
>> > > > Actually,
>> > > > > > >> we
>> > > > > > >>>>> have
>> > > > > > >>>>>>   a lot of scenes need to use DataStream API. Currently,
>> > > > > > >> DataStream
>> > > > > > >>>> API
>> > > > > > >>>>> is
>> > > > > > >>>>>>   the cornerstone of the physical plan of Flink SQL.
>> With a
>> > > > > > >>> localKeyBy
>> > > > > > >>>>>> API,
>> > > > > > >>>>>>   the optimization of SQL at least may use this optimized
>> > API,
>> > > > > > >> this
>> > > > > > >>>> is a
>> > > > > > >>>>>>   further topic.
>> > > > > > >>>>>>   - Based on the window operator, our state would benefit
>> > from
>> > > > > > >> Flink
>> > > > > > >>>>> State
>> > > > > > >>>>>>   and checkpoint, we do not need to worry about OOM and
>> job
>> > > > > > >> failed.
>> > > > > > >>>>>>
>> > > > > > >>>>>> Now, about your questions:
>> > > > > > >>>>>>
>> > > > > > >>>>>> 1. About our design cannot change the data type and about
>> > the
>> > > > > > >>>>>> implementation of average:
>> > > > > > >>>>>>
>> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is an API
>> > provides
>> > > > to
>> > > > > > >> the
>> > > > > > >>>>> users
>> > > > > > >>>>>> who use DataStream API to build their jobs.
>> > > > > > >>>>>> Users should know its semantics and the difference with
>> > keyBy
>> > > > API,
>> > > > > > >> so
>> > > > > > >>>> if
>> > > > > > >>>>>> they want to the average aggregation, they should carry
>> > local
>> > > > sum
>> > > > > > >>>> result
>> > > > > > >>>>>> and local count result.
>> > > > > > >>>>>> I admit that it will be convenient to use keyBy directly.
>> > But
>> > > we
>> > > > > > >> need
>> > > > > > >>>> to
>> > > > > > >>>>>> pay a little price when we get some benefits. I think
>> this
>> > > price
>> > > > > is
>> > > > > > >>>>>> reasonable. Considering that the DataStream API itself
>> is a
>> > > > > > >> low-level
>> > > > > > >>>> API
>> > > > > > >>>>>> (at least for now).
>> > > > > > >>>>>>
>> > > > > > >>>>>> 2. About stateless operator and
>> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
>> > > > > > >>>>>>
>> > > > > > >>>>>> Actually, I have discussed this opinion with @dianfu in
>> the
>> > > old
>> > > > > > >> mail
>> > > > > > >>>>>> thread. I will copy my opinion from there:
>> > > > > > >>>>>>
>> > > > > > >>>>>>   - for your design, you still need somewhere to give the
>> > > users
>> > > > > > >>>>> configure
>> > > > > > >>>>>>   the trigger threshold (maybe memory availability?),
>> this
>> > > > design
>> > > > > > >>>> cannot
>> > > > > > >>>>>>   guarantee a deterministic semantics (it will bring
>> trouble
>> > > for
>> > > > > > >>>> testing
>> > > > > > >>>>>> and
>> > > > > > >>>>>>   debugging).
>> > > > > > >>>>>>   - if the implementation depends on the timing of
>> > checkpoint,
>> > > > it
>> > > > > > >>>> would
>> > > > > > >>>>>>   affect the checkpoint's progress, and the buffered data
>> > may
>> > > > > > >> cause
>> > > > > > >>>> OOM
>> > > > > > >>>>>>   issue. In addition, if the operator is stateless, it
>> can
>> > not
>> > > > > > >>> provide
>> > > > > > >>>>>> fault
>> > > > > > >>>>>>   tolerance.
>> > > > > > >>>>>>
>> > > > > > >>>>>> Best,
>> > > > > > >>>>>> Vino
>> > > > > > >>>>>>
>> > > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二 上午9:22写道：
>> > > > > > >>>>>>
>> > > > > > >>>>>>> Hi Vino,
>> > > > > > >>>>>>>
>> > > > > > >>>>>>> Thanks for the proposal, I like the general idea and IMO
>> > it's
>> > > > > > >> very
>> > > > > > >>>>> useful
>> > > > > > >>>>>>> feature.
>> > > > > > >>>>>>> But after reading through the document, I feel that we
>> may
>> > > over
>> > > > > > >>>> design
>> > > > > > >>>>>> the
>> > > > > > >>>>>>> required
>> > > > > > >>>>>>> operator for proper local aggregation. The main reason
>> is
>> > we
>> > > > want
>> > > > > > >>> to
>> > > > > > >>>>>> have a
>> > > > > > >>>>>>> clear definition and behavior about the "local keyed
>> state"
>> > > > which
>> > > > > > >>> in
>> > > > > > >>>> my
>> > > > > > >>>>>>> opinion is not
>> > > > > > >>>>>>> necessary for local aggregation, at least for start.
>> > > > > > >>>>>>>
>> > > > > > >>>>>>> Another issue I noticed is the local key by operator
>> cannot
>> > > > > > >> change
>> > > > > > >>>>>> element
>> > > > > > >>>>>>> type, it will
>> > > > > > >>>>>>> also restrict a lot of use cases which can be benefit
>> from
>> > > > local
>> > > > > > >>>>>>> aggregation, like "average".
>> > > > > > >>>>>>>
>> > > > > > >>>>>>> We also did similar logic in SQL and the only thing
>> need to
>> > > be
>> > > > > > >> done
>> > > > > > >>>> is
>> > > > > > >>>>>>> introduce
>> > > > > > >>>>>>> a stateless lightweight operator which is *chained*
>> before
>> > > > > > >>> `keyby()`.
>> > > > > > >>>>> The
>> > > > > > >>>>>>> operator will flush all buffered
>> > > > > > >>>>>>> elements during
>> > `StreamOperator::prepareSnapshotPreBarrier()`
>> > > > and
>> > > > > > >>>> make
>> > > > > > >>>>>>> himself stateless.
>> > > > > > >>>>>>> By the way, in the earlier version we also did the
>> similar
>> > > > > > >> approach
>> > > > > > >>>> by
>> > > > > > >>>>>>> introducing a stateful
>> > > > > > >>>>>>> local aggregation operator but it's not performed as
>> well
>> > as
>> > > > the
>> > > > > > >>>> later
>> > > > > > >>>>>> one,
>> > > > > > >>>>>>> and also effect the barrie
>> > > > > > >>>>>>> alignment time. The later one is fairly simple and more
>> > > > > > >> efficient.
>> > > > > > >>>>>>>
>> > > > > > >>>>>>> I would highly suggest you to consider to have a
>> stateless
>> > > > > > >> approach
>> > > > > > >>>> at
>> > > > > > >>>>>> the
>> > > > > > >>>>>>> first step.
>> > > > > > >>>>>>>
>> > > > > > >>>>>>> Best,
>> > > > > > >>>>>>> Kurt
>> > > > > > >>>>>>>
>> > > > > > >>>>>>>
>> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
>> [hidden email]>
>> > > > > > >> wrote:
>> > > > > > >>>>>>>
>> > > > > > >>>>>>>> Hi Vino,
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>> Thanks for the proposal.
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
>> > > > > > >>>>>>>>
>> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
>> > > > > > >> have
>> > > > > > >>>> you
>> > > > > > >>>>>>> done
>> > > > > > >>>>>>>> some benchmark?
>> > > > > > >>>>>>>> Because I'm curious about how much performance
>> improvement
>> > > can
>> > > > > > >> we
>> > > > > > >>>> get
>> > > > > > >>>>>> by
>> > > > > > >>>>>>>> using count window as the local operator.
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>> Best,
>> > > > > > >>>>>>>> Jark
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
>> > > > [hidden email]
>> > > > > > >>>
>> > > > > > >>>>> wrote:
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>>> Hi Hequn,
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> Thanks for your reply.
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> The purpose of localKeyBy API is to provide a tool
>> which
>> > > can
>> > > > > > >>> let
>> > > > > > >>>>>> users
>> > > > > > >>>>>>> do
>> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior of the
>> > > > > > >>> pre-aggregation
>> > > > > > >>>>> is
>> > > > > > >>>>>>>>> similar to keyBy API.
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> So the three cases are different, I will describe them
>> > one
>> > > by
>> > > > > > >>>> one:
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> *In this case, the result is event-driven, each event
>> can
>> > > > > > >>> produce
>> > > > > > >>>>> one
>> > > > > > >>>>>>> sum
>> > > > > > >>>>>>>>> aggregation result and it is the latest one from the
>> > source
>> > > > > > >>>> start.*
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> *In this case, the semantic may have a problem, it
>> would
>> > do
>> > > > > > >> the
>> > > > > > >>>>> local
>> > > > > > >>>>>>> sum
>> > > > > > >>>>>>>>> aggregation and will produce the latest partial result
>> > from
>> > > > > > >> the
>> > > > > > >>>>>> source
>> > > > > > >>>>>>>>> start for every event. *
>> > > > > > >>>>>>>>> *These latest partial results from the same key are
>> > hashed
>> > > to
>> > > > > > >>> one
>> > > > > > >>>>>> node
>> > > > > > >>>>>>> to
>> > > > > > >>>>>>>>> do the global sum aggregation.*
>> > > > > > >>>>>>>>> *In the global aggregation, when it received multiple
>> > > partial
>> > > > > > >>>>> results
>> > > > > > >>>>>>>> (they
>> > > > > > >>>>>>>>> are all calculated from the source start) and sum them
>> > will
>> > > > > > >> get
>> > > > > > >>>> the
>> > > > > > >>>>>>> wrong
>> > > > > > >>>>>>>>> result.*
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> 3.
>> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> *In this case, it would just get a partial aggregation
>> > > result
>> > > > > > >>> for
>> > > > > > >>>>>> the 5
>> > > > > > >>>>>>>>> records in the count window. The partial aggregation
>> > > results
>> > > > > > >>> from
>> > > > > > >>>>> the
>> > > > > > >>>>>>>> same
>> > > > > > >>>>>>>>> key will be aggregated globally.*
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> So the first case and the third case can get the
>> *same*
>> > > > > > >> result,
>> > > > > > >>>> the
>> > > > > > >>>>>>>>> difference is the output-style and the latency.
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> Generally speaking, the local key API is just an
>> > > optimization
>> > > > > > >>>> API.
>> > > > > > >>>>> We
>> > > > > > >>>>>>> do
>> > > > > > >>>>>>>>> not limit the user's usage, but the user has to
>> > understand
>> > > > > > >> its
>> > > > > > >>>>>>> semantics
>> > > > > > >>>>>>>>> and use it correctly.
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> Best,
>> > > > > > >>>>>>>>> Vino
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]> 于2019年6月17日周一
>> > 下午4:18写道：
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>>> Hi Vino,
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a very good
>> > > feature!
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>> One thing I want to make sure is the semantics for
>> the
>> > > > > > >>>>>> `localKeyBy`.
>> > > > > > >>>>>>>> From
>> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns an
>> instance
>> > of
>> > > > > > >>>>>>> `KeyedStream`
>> > > > > > >>>>>>>>>> which can also perform sum(), so in this case, what's
>> > the
>> > > > > > >>>>> semantics
>> > > > > > >>>>>>> for
>> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the following code
>> > share
>> > > > > > >>> the
>> > > > > > >>>>> same
>> > > > > > >>>>>>>>> result?
>> > > > > > >>>>>>>>>> and what're the differences between them?
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
>> > > > > > >>>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>> > > > > > >>>>>>>>>> 3.
>> > > > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>> Would also be great if we can add this into the
>> > document.
>> > > > > > >>> Thank
>> > > > > > >>>>> you
>> > > > > > >>>>>>>> very
>> > > > > > >>>>>>>>>> much.
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>> Best, Hequn
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino yang <
>> > > > > > >>>>> [hidden email]>
>> > > > > > >>>>>>>>> wrote:
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>>> Hi Aljoscha,
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section of FLIP
>> wiki
>> > > > > > >>>> page.[1]
>> > > > > > >>>>>> This
>> > > > > > >>>>>>>>> mail
>> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to the third
>> > step.
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote step), I
>> didn't
>> > > > > > >> find
>> > > > > > >>>> the
>> > > > > > >>>>>>>>>>> prerequisites for starting the voting process.
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>> Considering that the discussion of this feature has
>> > been
>> > > > > > >>> done
>> > > > > > >>>>> in
>> > > > > > >>>>>>> the
>> > > > > > >>>>>>>>> old
>> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when should I start
>> > > > > > >> voting?
>> > > > > > >>>> Can
>> > > > > > >>>>> I
>> > > > > > >>>>>>>> start
>> > > > > > >>>>>>>>>> now?
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>> Best,
>> > > > > > >>>>>>>>>>> Vino
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>> [1]:
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>
>> > > > > > >>>>>>
>> > > > > > >>>>>
>> > > > > > >>>>
>> > > > > > >>>
>> > > > > > >>
>> > > > > >
>> > > > >
>> > > >
>> > >
>> >
>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>> > > > > > >>>>>>>>>>> [2]:
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>
>> > > > > > >>>>>>
>> > > > > > >>>>>
>> > > > > > >>>>
>> > > > > > >>>
>> > > > > > >>
>> > > > > >
>> > > > >
>> > > >
>> > >
>> >
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>> leesf <[hidden email]> 于2019年6月13日周四 上午9:19写道：
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your efforts.
>> > > > > > >>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>> Best,
>> > > > > > >>>>>>>>>>>> Leesf
>> > > > > > >>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>> vino yang <[hidden email]> 于2019年6月12日周三
>> > > > > > >>> 下午5:46写道：
>> > > > > > >>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> Hi folks,
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> I would like to start the FLIP discussion thread
>> > > > > > >> about
>> > > > > > >>>>>>> supporting
>> > > > > > >>>>>>>>>> local
>> > > > > > >>>>>>>>>>>>> aggregation in Flink.
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> In short, this feature can effectively alleviate
>> data
>> > > > > > >>>> skew.
>> > > > > > >>>>>>> This
>> > > > > > >>>>>>>> is
>> > > > > > >>>>>>>>>> the
>> > > > > > >>>>>>>>>>>>> FLIP:
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>
>> > > > > > >>>>>>
>> > > > > > >>>>>
>> > > > > > >>>>
>> > > > > > >>>
>> > > > > > >>
>> > > > > >
>> > > > >
>> > > >
>> > >
>> >
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely used to
>> perform
>> > > > > > >>>>>> aggregating
>> > > > > > >>>>>>>>>>>> operations
>> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the elements
>> that
>> > > > > > >>> have
>> > > > > > >>>>> the
>> > > > > > >>>>>>> same
>> > > > > > >>>>>>>>>> key.
>> > > > > > >>>>>>>>>>>> When
>> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with the same
>> key
>> > > > > > >>> will
>> > > > > > >>>> be
>> > > > > > >>>>>>> sent
>> > > > > > >>>>>>>> to
>> > > > > > >>>>>>>>>> and
>> > > > > > >>>>>>>>>>>>> aggregated by the same task.
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> The performance of these aggregating operations is
>> > > > > > >> very
>> > > > > > >>>>>>> sensitive
>> > > > > > >>>>>>>>> to
>> > > > > > >>>>>>>>>>> the
>> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases where the
>> > > > > > >>> distribution
>> > > > > > >>>>> of
>> > > > > > >>>>>>> keys
>> > > > > > >>>>>>>>>>>> follows a
>> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
>> significantly
>> > > > > > >>>>>> downgraded.
>> > > > > > >>>>>>>>> More
>> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of parallelism
>> does
>> > > > > > >>> not
>> > > > > > >>>>> help
>> > > > > > >>>>>>>> when
>> > > > > > >>>>>>>>> a
>> > > > > > >>>>>>>>>>> task
>> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted method to
>> > > > > > >> reduce
>> > > > > > >>>> the
>> > > > > > >>>>>>>>>> performance
>> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose the
>> > > > > > >> aggregating
>> > > > > > >>>>>>>> operations
>> > > > > > >>>>>>>>>> into
>> > > > > > >>>>>>>>>>>> two
>> > > > > > >>>>>>>>>>>>> phases. In the first phase, we aggregate the
>> elements
>> > > > > > >>> of
>> > > > > > >>>>> the
>> > > > > > >>>>>>> same
>> > > > > > >>>>>>>>> key
>> > > > > > >>>>>>>>>>> at
>> > > > > > >>>>>>>>>>>>> the sender side to obtain partial results. Then at
>> > > > > > >> the
>> > > > > > >>>>> second
>> > > > > > >>>>>>>>> phase,
>> > > > > > >>>>>>>>>>>> these
>> > > > > > >>>>>>>>>>>>> partial results are sent to receivers according to
>> > > > > > >>> their
>> > > > > > >>>>> keys
>> > > > > > >>>>>>> and
>> > > > > > >>>>>>>>> are
>> > > > > > >>>>>>>>>>>>> combined to obtain the final result. Since the
>> number
>> > > > > > >>> of
>> > > > > > >>>>>>> partial
>> > > > > > >>>>>>>>>>> results
>> > > > > > >>>>>>>>>>>>> received by each receiver is limited by the
>> number of
>> > > > > > >>>>>> senders,
>> > > > > > >>>>>>>> the
>> > > > > > >>>>>>>>>>>>> imbalance among receivers can be reduced.
>> Besides, by
>> > > > > > >>>>>> reducing
>> > > > > > >>>>>>>> the
>> > > > > > >>>>>>>>>>> amount
>> > > > > > >>>>>>>>>>>>> of transferred data the performance can be further
>> > > > > > >>>>> improved.
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> *More details*:
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> Design documentation:
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>
>> > > > > > >>>>>>
>> > > > > > >>>>>
>> > > > > > >>>>
>> > > > > > >>>
>> > > > > > >>
>> > > > > >
>> > > > >
>> > > >
>> > >
>> >
>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> Old discussion thread:
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>
>> > > > > > >>>>>>
>> > > > > > >>>>>
>> > > > > > >>>>
>> > > > > > >>>
>> > > > > > >>
>> > > > > >
>> > > > >
>> > > >
>> > >
>> >
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
>> > > > > > >>>>>>>>> https://issues.apache.org/jira/browse/FLINK-12786
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> We are looking forwards to your feedback!
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>> Best,
>> > > > > > >>>>>>>>>>>>> Vino
>> > > > > > >>>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>>
>> > > > > > >>>>>>>>>>>
>> > > > > > >>>>>>>>>>
>> > > > > > >>>>>>>>>
>> > > > > > >>>>>>>>
>> > > > > > >>>>>>>
>> > > > > > >>>>>>
>> > > > > > >>>>>
>> > > > > > >>>>
>> > > > > > >>>
>> > > > > > >>
>> > > > > >
>> > > > > >
>> > > > >
>> > > >
>> > >
>> >
>>
>

[Kurt Young]

Hi vino,

Sorry I don't see the consensus about reusing window operator and keep the
API design of localKeyBy. But I think we should definitely more thoughts
about this topic.

I also try to loop in Stephan for this discussion.

Best,
Kurt


On Mon, Jun 24, 2019 at 3:26 PM vino yang <[hidden email]> wrote:

> Hi all,
>
> I am happy we have a wonderful discussion and received many valuable
> opinions in the last few days.
>
> Now, let me try to summarize what we have reached consensus about the
> changes in the design.
>
>    - provide a unified abstraction to support two kinds of implementation;
>    - reuse WindowOperator and try to enhance it so that we can make the
>    intermediate result of the local aggregation can be buffered and
> flushed to
>    support two kinds of implementation;
>    - keep the API design of localKeyBy, but declare the disabled some APIs
>    we cannot support currently, and provide a configurable API for users to
>    choose how to handle intermediate result;
>
> The above three points have been updated in the design doc. Any
> questions, please let me know.
>
> @Aljoscha Krettek <[hidden email]> What do you think? Any further
> comments?
>
> Best,
> Vino
>
> vino yang <[hidden email]> 于2019年6月20日周四 下午2:02写道：
>
> > Hi Kurt,
> >
> > Thanks for your comments.
> >
> > It seems we come to a consensus that we should alleviate the performance
> > degraded by data skew with local aggregation. In this FLIP, our key
> > solution is to introduce local keyed partition to achieve this goal.
> >
> > I also agree that we can benefit a lot from the usage of
> > AggregateFunction. In combination with localKeyBy, We can easily use it
> to
> > achieve local aggregation:
> >
> >    - input.localKeyBy(0).aggregate()
> >    - input.localKeyBy(0).window().aggregate()
> >
> >
> > I think the only problem here is the choices between
> >
> >    - (1) Introducing a new primitive called localKeyBy and implement
> >    local aggregation with existing operators, or
> >    - (2) Introducing an operator called localAggregation which is
> >    composed of a key selector, a window-like operator, and an aggregate
> >    function.
> >
> >
> > There may exist some optimization opportunities by providing a composited
> > interface for local aggregation. But at the same time, in my opinion, we
> > lose flexibility (Or we need certain efforts to achieve the same
> > flexibility).
> >
> > As said in the previous mails, we have many use cases where the
> > aggregation is very complicated and cannot be performed with
> > AggregateFunction. For example, users may perform windowed aggregations
> > according to time, data values, or even external storage. Typically, they
> > now use KeyedProcessFunction or customized triggers to implement these
> > aggregations. It's not easy to address data skew in such cases with a
> > composited interface for local aggregation.
> >
> > Given that Data Stream API is exactly targeted at these cases where the
> > application logic is very complicated and optimization does not matter, I
> > think it's a better choice to provide a relatively low-level and
> canonical
> > interface.
> >
> > The composited interface, on the other side, may be a good choice in
> > declarative interfaces, including SQL and Table API, as it allows more
> > optimization opportunities.
> >
> > Best,
> > Vino
> >
> >
> > Kurt Young <[hidden email]> 于2019年6月20日周四 上午10:15写道：
> >
> >> Hi all,
> >>
> >> As vino said in previous emails, I think we should first discuss and
> >> decide
> >> what kind of use cases this FLIP want to
> >> resolve, and what the API should look like. From my side, I think this
> is
> >> probably the root cause of current divergence.
> >>
> >> My understand is (from the FLIP title and motivation section of the
> >> document), we want to have a proper support of
> >> local aggregation, or pre aggregation. This is not a very new idea, most
> >> SQL engine already did this improvement. And
> >> the core concept about this is, there should be an AggregateFunction, no
> >> matter it's a Flink runtime's AggregateFunction or
> >> SQL's UserDefinedAggregateFunction. Both aggregation have concept of
> >> intermediate data type, sometimes we call it ACC.
> >> I quickly went through the POC piotr did before [1], it also directly
> uses
> >> AggregateFunction.
> >>
> >> But the thing is, after reading the design of this FLIP, I can't help
> >> myself feeling that this FLIP is not targeting to have a proper
> >> local aggregation support. It actually want to introduce another
> concept:
> >> LocalKeyBy, and how to split and merge local key groups,
> >> and how to properly support state on local key. Local aggregation just
> >> happened to be one possible use case of LocalKeyBy.
> >> But it lacks supporting the essential concept of local aggregation,
> which
> >> is intermediate data type. Without this, I really don't thing
> >> it is a good fit of local aggregation.
> >>
> >> Here I want to make sure of the scope or the goal about this FLIP, do we
> >> want to have a proper local aggregation engine, or we
> >> just want to introduce a new concept called LocalKeyBy?
> >>
> >> [1]: https://github.com/apache/flink/pull/4626
> >>
> >> Best,
> >> Kurt
> >>
> >>
> >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <[hidden email]>
> wrote:
> >>
> >> > Hi Hequn,
> >> >
> >> > Thanks for your comments!
> >> >
> >> > I agree that allowing local aggregation reusing window API and
> refining
> >> > window operator to make it match both requirements (come from our and
> >> Kurt)
> >> > is a good decision!
> >> >
> >> > Concerning your questions:
> >> >
> >> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1) may be
> >> > meaningless.
> >> >
> >> > Yes, it does not make sense in most cases. However, I also want to
> note
> >> > users should know the right semantics of localKeyBy and use it
> >> correctly.
> >> > Because this issue also exists for the global keyBy, consider this
> >> example:
> >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also meaningless.
> >> >
> >> > 2. About the semantics of
> >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> >> >
> >> > Good catch! I agree with you that it's not good to enable all
> >> > functionalities for localKeyBy from KeyedStream.
> >> > Currently, We do not support some APIs such as
> >> > connect/join/intervalJoin/coGroup. This is due to that we force the
> >> > operators on LocalKeyedStreams chained with the inputs.
> >> >
> >> > Best,
> >> > Vino
> >> >
> >> >
> >> > Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
> >> >
> >> > > Hi,
> >> > >
> >> > > Thanks a lot for your great discussion and great to see that some
> >> > agreement
> >> > > has been reached on the "local aggregate engine"!
> >> > >
> >> > > ===> Considering the abstract engine,
> >> > > I'm thinking is it valuable for us to extend the current window to
> >> meet
> >> > > both demands raised by Kurt and Vino? There are some benefits we can
> >> get:
> >> > >
> >> > > 1. The interfaces of the window are complete and clear. With
> windows,
> >> we
> >> > > can define a lot of ways to split the data and perform different
> >> > > computations.
> >> > > 2. We can also leverage the window to do miniBatch for the global
> >> > > aggregation, i.e, we can use the window to bundle data belong to the
> >> same
> >> > > key, for every bundle we only need to read and write once state.
> This
> >> can
> >> > > greatly reduce state IO and improve performance.
> >> > > 3. A lot of other use cases can also benefit from the window base on
> >> > memory
> >> > > or stateless.
> >> > >
> >> > > ===> As for the API,
> >> > > I think it is good to make our API more flexible. However, we may
> >> need to
> >> > > make our API meaningful.
> >> > >
> >> > > Take my previous reply as an example,
> >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may be
> >> > meaningless.
> >> > > Another example I find is the intervalJoin, e.g.,
> >> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In this
> >> case, it
> >> > > will bring problems if input1 and input2 share different
> parallelism.
> >> We
> >> > > don't know which input should the join chained with? Even if they
> >> share
> >> > the
> >> > > same parallelism, it's hard to tell what the join is doing. There
> are
> >> > maybe
> >> > > some other problems.
> >> > >
> >> > > From this point of view, it's at least not good to enable all
> >> > > functionalities for localKeyBy from KeyedStream?
> >> > >
> >> > > Great to also have your opinions.
> >> > >
> >> > > Best, Hequn
> >> > >
> >> > >
> >> > >
> >> > >
> >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <[hidden email]>
> >> > wrote:
> >> > >
> >> > > > Hi Kurt and Piotrek,
> >> > > >
> >> > > > Thanks for your comments.
> >> > > >
> >> > > > I agree that we can provide a better abstraction to be compatible
> >> with
> >> > > two
> >> > > > different implementations.
> >> > > >
> >> > > > First of all, I think we should consider what kind of scenarios we
> >> need
> >> > > to
> >> > > > support in *API* level?
> >> > > >
> >> > > > We have some use cases which need to a customized aggregation
> >> through
> >> > > > KeyedProcessFunction, (in the usage of our localKeyBy.window they
> >> can
> >> > use
> >> > > > ProcessWindowFunction).
> >> > > >
> >> > > > Shall we support these flexible use scenarios?
> >> > > >
> >> > > > Best,
> >> > > > Vino
> >> > > >
> >> > > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
> >> > > >
> >> > > > > Hi Piotr,
> >> > > > >
> >> > > > > Thanks for joining the discussion. Make “local aggregation"
> >> abstract
> >> > > > enough
> >> > > > > sounds good to me, we could
> >> > > > > implement and verify alternative solutions for use cases of
> local
> >> > > > > aggregation. Maybe we will find both solutions
> >> > > > > are appropriate for different scenarios.
> >> > > > >
> >> > > > > Starting from a simple one sounds a practical way to go. What do
> >> you
> >> > > > think,
> >> > > > > vino?
> >> > > > >
> >> > > > > Best,
> >> > > > > Kurt
> >> > > > >
> >> > > > >
> >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> >> [hidden email]>
> >> > > > > wrote:
> >> > > > >
> >> > > > > > Hi Kurt and Vino,
> >> > > > > >
> >> > > > > > I think there is a trade of hat we need to consider for the
> >> local
> >> > > > > > aggregation.
> >> > > > > >
> >> > > > > > Generally speaking I would agree with Kurt about local
> >> > > aggregation/pre
> >> > > > > > aggregation not using Flink's state flush the operator on a
> >> > > checkpoint.
> >> > > > > > Network IO is usually cheaper compared to Disks IO. This has
> >> > however
> >> > > > > couple
> >> > > > > > of issues:
> >> > > > > > 1. It can explode number of in-flight records during
> checkpoint
> >> > > barrier
> >> > > > > > alignment, making checkpointing slower and decrease the actual
> >> > > > > throughput.
> >> > > > > > 2. This trades Disks IO on the local aggregation machine with
> >> CPU
> >> > > (and
> >> > > > > > Disks IO in case of RocksDB) on the final aggregation machine.
> >> This
> >> > > is
> >> > > > > > fine, as long there is no huge data skew. If there is only a
> >> > handful
> >> > > > (or
> >> > > > > > even one single) hot keys, it might be better to keep the
> >> > persistent
> >> > > > > state
> >> > > > > > in the LocalAggregationOperator to offload final aggregation
> as
> >> > much
> >> > > as
> >> > > > > > possible.
> >> > > > > > 3. With frequent checkpointing local aggregation effectiveness
> >> > would
> >> > > > > > degrade.
> >> > > > > >
> >> > > > > > I assume Kurt is correct, that in your use cases stateless
> >> operator
> >> > > was
> >> > > > > > behaving better, but I could easily see other use cases as
> well.
> >> > For
> >> > > > > > example someone is already using RocksDB, and his job is
> >> > bottlenecked
> >> > > > on
> >> > > > > a
> >> > > > > > single window operator instance because of the data skew. In
> >> that
> >> > > case
> >> > > > > > stateful local aggregation would be probably a better choice.
> >> > > > > >
> >> > > > > > Because of that, I think we should eventually provide both
> >> versions
> >> > > and
> >> > > > > in
> >> > > > > > the initial version we should at least make the “local
> >> aggregation
> >> > > > > engine”
> >> > > > > > abstract enough, that one could easily provide different
> >> > > implementation
> >> > > > > > strategy.
> >> > > > > >
> >> > > > > > Piotrek
> >> > > > > >
> >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <[hidden email]>
> >> wrote:
> >> > > > > > >
> >> > > > > > > Hi,
> >> > > > > > >
> >> > > > > > > For the trigger, it depends on what operator we want to use
> >> under
> >> > > the
> >> > > > > > API.
> >> > > > > > > If we choose to use window operator,
> >> > > > > > > we should also use window's trigger. However, I also think
> >> reuse
> >> > > > window
> >> > > > > > > operator for this scenario may not be
> >> > > > > > > the best choice. The reasons are the following:
> >> > > > > > >
> >> > > > > > > 1. As a lot of people already pointed out, window relies
> >> heavily
> >> > on
> >> > > > > state
> >> > > > > > > and it will definitely effect performance. You can
> >> > > > > > > argue that one can use heap based statebackend, but this
> will
> >> > > > introduce
> >> > > > > > > extra coupling. Especially we have a chance to
> >> > > > > > > design a pure stateless operator.
> >> > > > > > > 2. The window operator is *the most* complicated operator
> >> Flink
> >> > > > > currently
> >> > > > > > > have. Maybe we only need to pick a subset of
> >> > > > > > > window operator to achieve the goal, but once the user wants
> >> to
> >> > > have
> >> > > > a
> >> > > > > > deep
> >> > > > > > > look at the localAggregation operator, it's still
> >> > > > > > > hard to find out what's going on under the window operator.
> >> For
> >> > > > > > simplicity,
> >> > > > > > > I would also recommend we introduce a dedicated
> >> > > > > > > lightweight operator, which also much easier for a user to
> >> learn
> >> > > and
> >> > > > > use.
> >> > > > > > >
> >> > > > > > > For your question about increasing the burden in
> >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the only
> thing
> >> > this
> >> > > > > > function
> >> > > > > > > need
> >> > > > > > > to do is output all the partial results, it's purely cpu
> >> > workload,
> >> > > > not
> >> > > > > > > introducing any IO. I want to point out that even if we have
> >> this
> >> > > > > > > cost, we reduced another barrier align cost of the operator,
> >> > which
> >> > > is
> >> > > > > the
> >> > > > > > > sync flush stage of the state, if you introduced state. This
> >> > > > > > > flush actually will introduce disk IO, and I think it's
> >> worthy to
> >> > > > > > exchange
> >> > > > > > > this cost with purely CPU workload. And we do have some
> >> > > > > > > observations about these two behavior (as i said before, we
> >> > > actually
> >> > > > > > > implemented both solutions), the stateless one actually
> >> performs
> >> > > > > > > better both in performance and barrier align time.
> >> > > > > > >
> >> > > > > > > Best,
> >> > > > > > > Kurt
> >> > > > > > >
> >> > > > > > >
> >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> >> [hidden email]
> >> > >
> >> > > > > wrote:
> >> > > > > > >
> >> > > > > > >> Hi Kurt,
> >> > > > > > >>
> >> > > > > > >> Thanks for your example. Now, it looks more clearly for me.
> >> > > > > > >>
> >> > > > > > >> From your example code snippet, I saw the localAggregate
> API
> >> has
> >> > > > three
> >> > > > > > >> parameters:
> >> > > > > > >>
> >> > > > > > >>   1. key field
> >> > > > > > >>   2. PartitionAvg
> >> > > > > > >>   3. CountTrigger: Does this trigger comes from window
> >> package?
> >> > > > > > >>
> >> > > > > > >> I will compare our and your design from API and operator
> >> level:
> >> > > > > > >>
> >> > > > > > >> *From the API level:*
> >> > > > > > >>
> >> > > > > > >> As I replied to @dianfu in the old email thread,[1] the
> >> Window
> >> > API
> >> > > > can
> >> > > > > > >> provide the second and the third parameter right now.
> >> > > > > > >>
> >> > > > > > >> If you reuse specified interface or class, such as
> *Trigger*
> >> or
> >> > > > > > >> *CounterTrigger* provided by window package, but do not use
> >> > window
> >> > > > > API,
> >> > > > > > >> it's not reasonable.
> >> > > > > > >> And if you do not reuse these interface or class, you would
> >> need
> >> > > to
> >> > > > > > >> introduce more things however they are looked similar to
> the
> >> > > things
> >> > > > > > >> provided by window package.
> >> > > > > > >>
> >> > > > > > >> The window package has provided several types of the window
> >> and
> >> > > many
> >> > > > > > >> triggers and let users customize it. What's more, the user
> is
> >> > more
> >> > > > > > familiar
> >> > > > > > >> with Window API.
> >> > > > > > >>
> >> > > > > > >> This is the reason why we just provide localKeyBy API and
> >> reuse
> >> > > the
> >> > > > > > window
> >> > > > > > >> API. It reduces unnecessary components such as triggers and
> >> the
> >> > > > > > mechanism
> >> > > > > > >> of buffer (based on count num or time).
> >> > > > > > >> And it has a clear and easy to understand semantics.
> >> > > > > > >>
> >> > > > > > >> *From the operator level:*
> >> > > > > > >>
> >> > > > > > >> We reused window operator, so we can get all the benefits
> >> from
> >> > > state
> >> > > > > and
> >> > > > > > >> checkpoint.
> >> > > > > > >>
> >> > > > > > >> From your design, you named the operator under
> localAggregate
> >> > API
> >> > > > is a
> >> > > > > > >> *stateless* operator. IMO, it is still a state, it is just
> >> not
> >> > > Flink
> >> > > > > > >> managed state.
> >> > > > > > >> About the memory buffer (I think it's still not very clear,
> >> if
> >> > you
> >> > > > > have
> >> > > > > > >> time, can you give more detail information or answer my
> >> > > questions),
> >> > > > I
> >> > > > > > have
> >> > > > > > >> some questions:
> >> > > > > > >>
> >> > > > > > >>   - if it just a raw JVM heap memory buffer, how to support
> >> > fault
> >> > > > > > >>   tolerance, if the job is configured EXACTLY-ONCE semantic
> >> > > > guarantee?
> >> > > > > > >>   - if you thought the memory buffer(non-Flink state), has
> >> > better
> >> > > > > > >>   performance. In our design, users can also config HEAP
> >> state
> >> > > > backend
> >> > > > > > to
> >> > > > > > >>   provide the performance close to your mechanism.
> >> > > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()` related
> to
> >> the
> >> > > > > timing
> >> > > > > > of
> >> > > > > > >>   snapshot. IMO, the flush action should be a synchronized
> >> > action?
> >> > > > (if
> >> > > > > > >> not,
> >> > > > > > >>   please point out my mistake) I still think we should not
> >> > depend
> >> > > on
> >> > > > > the
> >> > > > > > >>   timing of checkpoint. Checkpoint related operations are
> >> > inherent
> >> > > > > > >>   performance sensitive, we should not increase its burden
> >> > > anymore.
> >> > > > > Our
> >> > > > > > >>   implementation based on the mechanism of Flink's
> >> checkpoint,
> >> > > which
> >> > > > > can
> >> > > > > > >>   benefit from the asnyc snapshot and incremental
> checkpoint.
> >> > IMO,
> >> > > > the
> >> > > > > > >>   performance is not a problem, and we also do not find the
> >> > > > > performance
> >> > > > > > >> issue
> >> > > > > > >>   in our production.
> >> > > > > > >>
> >> > > > > > >> [1]:
> >> > > > > > >>
> >> > > > > > >>
> >> > > > > >
> >> > > > >
> >> > > >
> >> > >
> >> >
> >>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >> > > > > > >>
> >> > > > > > >> Best,
> >> > > > > > >> Vino
> >> > > > > > >>
> >> > > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二 下午2:27写道：
> >> > > > > > >>
> >> > > > > > >>> Yeah, sorry for not expressing myself clearly. I will try
> to
> >> > > > provide
> >> > > > > > more
> >> > > > > > >>> details to make sure we are on the same page.
> >> > > > > > >>>
> >> > > > > > >>> For DataStream API, it shouldn't be optimized
> automatically.
> >> > You
> >> > > > have
> >> > > > > > to
> >> > > > > > >>> explicitly call API to do local aggregation
> >> > > > > > >>> as well as the trigger policy of the local aggregation.
> Take
> >> > > > average
> >> > > > > > for
> >> > > > > > >>> example, the user program may look like this (just a
> draft):
> >> > > > > > >>>
> >> > > > > > >>> assuming the input type is DataStream<Tupl2<String, Int>>
> >> > > > > > >>>
> >> > > > > > >>> ds.localAggregate(
> >> > > > > > >>>        0,                                       // The
> local
> >> > key,
> >> > > > > which
> >> > > > > > >> is
> >> > > > > > >>> the String from Tuple2
> >> > > > > > >>>        PartitionAvg(1),                 // The partial
> >> > > aggregation
> >> > > > > > >>> function, produces Tuple2<Long, Int>, indicating sum and
> >> count
> >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger policy, note
> >> this
> >> > > > should
> >> > > > > be
> >> > > > > > >>> best effort, and also be composited with time based or
> >> memory
> >> > > size
> >> > > > > > based
> >> > > > > > >>> trigger
> >> > > > > > >>>    )                                           // The
> return
> >> > type
> >> > > > is
> >> > > > > > >> local
> >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> >> > > > > > >>>    .keyBy(0)                             // Further keyby
> it
> >> > with
> >> > > > > > >> required
> >> > > > > > >>> key
> >> > > > > > >>>    .aggregate(1)                      // This will merge
> all
> >> > the
> >> > > > > > partial
> >> > > > > > >>> results and get the final average.
> >> > > > > > >>>
> >> > > > > > >>> (This is only a draft, only trying to explain what it
> looks
> >> > > like. )
> >> > > > > > >>>
> >> > > > > > >>> The local aggregate operator can be stateless, we can
> keep a
> >> > > memory
> >> > > > > > >> buffer
> >> > > > > > >>> or other efficient data structure to improve the aggregate
> >> > > > > performance.
> >> > > > > > >>>
> >> > > > > > >>> Let me know if you have any other questions.
> >> > > > > > >>>
> >> > > > > > >>> Best,
> >> > > > > > >>> Kurt
> >> > > > > > >>>
> >> > > > > > >>>
> >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> >> > [hidden email]
> >> > > >
> >> > > > > > wrote:
> >> > > > > > >>>
> >> > > > > > >>>> Hi Kurt,
> >> > > > > > >>>>
> >> > > > > > >>>> Thanks for your reply.
> >> > > > > > >>>>
> >> > > > > > >>>> Actually, I am not against you to raise your design.
> >> > > > > > >>>>
> >> > > > > > >>>> From your description before, I just can imagine your
> >> > high-level
> >> > > > > > >>>> implementation is about SQL and the optimization is inner
> >> of
> >> > the
> >> > > > > API.
> >> > > > > > >> Is
> >> > > > > > >>> it
> >> > > > > > >>>> automatically? how to give the configuration option about
> >> > > trigger
> >> > > > > > >>>> pre-aggregation?
> >> > > > > > >>>>
> >> > > > > > >>>> Maybe after I get more information, it sounds more
> >> reasonable.
> >> > > > > > >>>>
> >> > > > > > >>>> IMO, first of all, it would be better to make your user
> >> > > interface
> >> > > > > > >>> concrete,
> >> > > > > > >>>> it's the basis of the discussion.
> >> > > > > > >>>>
> >> > > > > > >>>> For example, can you give an example code snippet to
> >> introduce
> >> > > how
> >> > > > > to
> >> > > > > > >>> help
> >> > > > > > >>>> users to process data skew caused by the jobs which built
> >> with
> >> > > > > > >> DataStream
> >> > > > > > >>>> API?
> >> > > > > > >>>>
> >> > > > > > >>>> If you give more details we can discuss further more. I
> >> think
> >> > if
> >> > > > one
> >> > > > > > >>> design
> >> > > > > > >>>> introduces an exact interface and another does not.
> >> > > > > > >>>>
> >> > > > > > >>>> The implementation has an obvious difference. For
> example,
> >> we
> >> > > > > > introduce
> >> > > > > > >>> an
> >> > > > > > >>>> exact API in DataStream named localKeyBy, about the
> >> > > > pre-aggregation
> >> > > > > we
> >> > > > > > >>> need
> >> > > > > > >>>> to define the trigger mechanism of local aggregation, so
> we
> >> > find
> >> > > > > > reused
> >> > > > > > >>>> window API and operator is a good choice. This is a
> >> reasoning
> >> > > link
> >> > > > > > from
> >> > > > > > >>>> design to implementation.
> >> > > > > > >>>>
> >> > > > > > >>>> What do you think?
> >> > > > > > >>>>
> >> > > > > > >>>> Best,
> >> > > > > > >>>> Vino
> >> > > > > > >>>>
> >> > > > > > >>>>
> >> > > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二 上午11:58写道：
> >> > > > > > >>>>
> >> > > > > > >>>>> Hi Vino,
> >> > > > > > >>>>>
> >> > > > > > >>>>> Now I feel that we may have different understandings
> about
> >> > what
> >> > > > > kind
> >> > > > > > >> of
> >> > > > > > >>>>> problems or improvements you want to
> >> > > > > > >>>>> resolve. Currently, most of the feedback are focusing on
> >> *how
> >> > > to
> >> > > > > do a
> >> > > > > > >>>>> proper local aggregation to improve performance
> >> > > > > > >>>>> and maybe solving the data skew issue*. And my gut
> >> feeling is
> >> > > > this
> >> > > > > is
> >> > > > > > >>>>> exactly what users want at the first place,
> >> > > > > > >>>>> especially those +1s. (Sorry to try to summarize here,
> >> please
> >> > > > > correct
> >> > > > > > >>> me
> >> > > > > > >>>> if
> >> > > > > > >>>>> i'm wrong).
> >> > > > > > >>>>>
> >> > > > > > >>>>> But I still think the design is somehow diverged from
> the
> >> > goal.
> >> > > > If
> >> > > > > we
> >> > > > > > >>>> want
> >> > > > > > >>>>> to have an efficient and powerful way to
> >> > > > > > >>>>> have local aggregation, supporting intermedia result
> type
> >> is
> >> > > > > > >> essential
> >> > > > > > >>>> IMO.
> >> > > > > > >>>>> Both runtime's `AggregateFunction` and
> >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a proper
> >> support of
> >> > > > > > >>>> intermediate
> >> > > > > > >>>>> result type and can do `merge` operation
> >> > > > > > >>>>> on them.
> >> > > > > > >>>>>
> >> > > > > > >>>>> Now, we have a lightweight alternatives which performs
> >> well,
> >> > > and
> >> > > > > > >> have a
> >> > > > > > >>>>> nice fit with the local aggregate requirements.
> >> > > > > > >>>>> Mostly importantly,  it's much less complex because it's
> >> > > > stateless.
> >> > > > > > >> And
> >> > > > > > >>>> it
> >> > > > > > >>>>> can also achieve the similar multiple-aggregation
> >> > > > > > >>>>> scenario.
> >> > > > > > >>>>>
> >> > > > > > >>>>> I still not convinced why we shouldn't consider it as a
> >> first
> >> > > > step.
> >> > > > > > >>>>>
> >> > > > > > >>>>> Best,
> >> > > > > > >>>>> Kurt
> >> > > > > > >>>>>
> >> > > > > > >>>>>
> >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> >> > > > [hidden email]>
> >> > > > > > >>>> wrote:
> >> > > > > > >>>>>
> >> > > > > > >>>>>> Hi Kurt,
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> Thanks for your comments.
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> It seems we both implemented local aggregation feature
> to
> >> > > > optimize
> >> > > > > > >>> the
> >> > > > > > >>>>>> issue of data skew.
> >> > > > > > >>>>>> However, IMHO, the API level of optimizing revenue is
> >> > > different.
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> *Your optimization benefits from Flink SQL and it's not
> >> > user's
> >> > > > > > >>>> faces.(If
> >> > > > > > >>>>> I
> >> > > > > > >>>>>> understand it incorrectly, please correct this.)*
> >> > > > > > >>>>>> *Our implementation employs it as an optimization tool
> >> API
> >> > for
> >> > > > > > >>>>> DataStream,
> >> > > > > > >>>>>> it just like a local version of the keyBy API.*
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> Based on this, I want to say support it as a DataStream
> >> API
> >> > > can
> >> > > > > > >>> provide
> >> > > > > > >>>>>> these advantages:
> >> > > > > > >>>>>>
> >> > > > > > >>>>>>
> >> > > > > > >>>>>>   - The localKeyBy API has a clear semantic and it's
> >> > flexible
> >> > > > not
> >> > > > > > >>> only
> >> > > > > > >>>>> for
> >> > > > > > >>>>>>   processing data skew but also for implementing some
> >> user
> >> > > > cases,
> >> > > > > > >>> for
> >> > > > > > >>>>>>   example, if we want to calculate the multiple-level
> >> > > > aggregation,
> >> > > > > > >>> we
> >> > > > > > >>>>> can
> >> > > > > > >>>>>> do
> >> > > > > > >>>>>>   multiple-level aggregation in the local aggregation:
> >> > > > > > >>>>>>
>  input.localKeyBy("a").sum(1).localKeyBy("b").window();
> >> //
> >> > > here
> >> > > > > > >> "a"
> >> > > > > > >>>> is
> >> > > > > > >>>>> a
> >> > > > > > >>>>>>   sub-category, while "b" is a category, here we do not
> >> need
> >> > > to
> >> > > > > > >>>> shuffle
> >> > > > > > >>>>>> data
> >> > > > > > >>>>>>   in the network.
> >> > > > > > >>>>>>   - The users of DataStream API will benefit from this.
> >> > > > Actually,
> >> > > > > > >> we
> >> > > > > > >>>>> have
> >> > > > > > >>>>>>   a lot of scenes need to use DataStream API.
> Currently,
> >> > > > > > >> DataStream
> >> > > > > > >>>> API
> >> > > > > > >>>>> is
> >> > > > > > >>>>>>   the cornerstone of the physical plan of Flink SQL.
> >> With a
> >> > > > > > >>> localKeyBy
> >> > > > > > >>>>>> API,
> >> > > > > > >>>>>>   the optimization of SQL at least may use this
> optimized
> >> > API,
> >> > > > > > >> this
> >> > > > > > >>>> is a
> >> > > > > > >>>>>>   further topic.
> >> > > > > > >>>>>>   - Based on the window operator, our state would
> benefit
> >> > from
> >> > > > > > >> Flink
> >> > > > > > >>>>> State
> >> > > > > > >>>>>>   and checkpoint, we do not need to worry about OOM and
> >> job
> >> > > > > > >> failed.
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> Now, about your questions:
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> 1. About our design cannot change the data type and
> about
> >> > the
> >> > > > > > >>>>>> implementation of average:
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is an API
> >> > provides
> >> > > > to
> >> > > > > > >> the
> >> > > > > > >>>>> users
> >> > > > > > >>>>>> who use DataStream API to build their jobs.
> >> > > > > > >>>>>> Users should know its semantics and the difference with
> >> > keyBy
> >> > > > API,
> >> > > > > > >> so
> >> > > > > > >>>> if
> >> > > > > > >>>>>> they want to the average aggregation, they should carry
> >> > local
> >> > > > sum
> >> > > > > > >>>> result
> >> > > > > > >>>>>> and local count result.
> >> > > > > > >>>>>> I admit that it will be convenient to use keyBy
> directly.
> >> > But
> >> > > we
> >> > > > > > >> need
> >> > > > > > >>>> to
> >> > > > > > >>>>>> pay a little price when we get some benefits. I think
> >> this
> >> > > price
> >> > > > > is
> >> > > > > > >>>>>> reasonable. Considering that the DataStream API itself
> >> is a
> >> > > > > > >> low-level
> >> > > > > > >>>> API
> >> > > > > > >>>>>> (at least for now).
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> 2. About stateless operator and
> >> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> Actually, I have discussed this opinion with @dianfu in
> >> the
> >> > > old
> >> > > > > > >> mail
> >> > > > > > >>>>>> thread. I will copy my opinion from there:
> >> > > > > > >>>>>>
> >> > > > > > >>>>>>   - for your design, you still need somewhere to give
> the
> >> > > users
> >> > > > > > >>>>> configure
> >> > > > > > >>>>>>   the trigger threshold (maybe memory availability?),
> >> this
> >> > > > design
> >> > > > > > >>>> cannot
> >> > > > > > >>>>>>   guarantee a deterministic semantics (it will bring
> >> trouble
> >> > > for
> >> > > > > > >>>> testing
> >> > > > > > >>>>>> and
> >> > > > > > >>>>>>   debugging).
> >> > > > > > >>>>>>   - if the implementation depends on the timing of
> >> > checkpoint,
> >> > > > it
> >> > > > > > >>>> would
> >> > > > > > >>>>>>   affect the checkpoint's progress, and the buffered
> data
> >> > may
> >> > > > > > >> cause
> >> > > > > > >>>> OOM
> >> > > > > > >>>>>>   issue. In addition, if the operator is stateless, it
> >> can
> >> > not
> >> > > > > > >>> provide
> >> > > > > > >>>>>> fault
> >> > > > > > >>>>>>   tolerance.
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> Best,
> >> > > > > > >>>>>> Vino
> >> > > > > > >>>>>>
> >> > > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二 上午9:22写道：
> >> > > > > > >>>>>>
> >> > > > > > >>>>>>> Hi Vino,
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>> Thanks for the proposal, I like the general idea and
> IMO
> >> > it's
> >> > > > > > >> very
> >> > > > > > >>>>> useful
> >> > > > > > >>>>>>> feature.
> >> > > > > > >>>>>>> But after reading through the document, I feel that we
> >> may
> >> > > over
> >> > > > > > >>>> design
> >> > > > > > >>>>>> the
> >> > > > > > >>>>>>> required
> >> > > > > > >>>>>>> operator for proper local aggregation. The main reason
> >> is
> >> > we
> >> > > > want
> >> > > > > > >>> to
> >> > > > > > >>>>>> have a
> >> > > > > > >>>>>>> clear definition and behavior about the "local keyed
> >> state"
> >> > > > which
> >> > > > > > >>> in
> >> > > > > > >>>> my
> >> > > > > > >>>>>>> opinion is not
> >> > > > > > >>>>>>> necessary for local aggregation, at least for start.
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>> Another issue I noticed is the local key by operator
> >> cannot
> >> > > > > > >> change
> >> > > > > > >>>>>> element
> >> > > > > > >>>>>>> type, it will
> >> > > > > > >>>>>>> also restrict a lot of use cases which can be benefit
> >> from
> >> > > > local
> >> > > > > > >>>>>>> aggregation, like "average".
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>> We also did similar logic in SQL and the only thing
> >> need to
> >> > > be
> >> > > > > > >> done
> >> > > > > > >>>> is
> >> > > > > > >>>>>>> introduce
> >> > > > > > >>>>>>> a stateless lightweight operator which is *chained*
> >> before
> >> > > > > > >>> `keyby()`.
> >> > > > > > >>>>> The
> >> > > > > > >>>>>>> operator will flush all buffered
> >> > > > > > >>>>>>> elements during
> >> > `StreamOperator::prepareSnapshotPreBarrier()`
> >> > > > and
> >> > > > > > >>>> make
> >> > > > > > >>>>>>> himself stateless.
> >> > > > > > >>>>>>> By the way, in the earlier version we also did the
> >> similar
> >> > > > > > >> approach
> >> > > > > > >>>> by
> >> > > > > > >>>>>>> introducing a stateful
> >> > > > > > >>>>>>> local aggregation operator but it's not performed as
> >> well
> >> > as
> >> > > > the
> >> > > > > > >>>> later
> >> > > > > > >>>>>> one,
> >> > > > > > >>>>>>> and also effect the barrie
> >> > > > > > >>>>>>> alignment time. The later one is fairly simple and
> more
> >> > > > > > >> efficient.
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>> I would highly suggest you to consider to have a
> >> stateless
> >> > > > > > >> approach
> >> > > > > > >>>> at
> >> > > > > > >>>>>> the
> >> > > > > > >>>>>>> first step.
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>> Best,
> >> > > > > > >>>>>>> Kurt
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> >> [hidden email]>
> >> > > > > > >> wrote:
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>>> Hi Vino,
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>> Thanks for the proposal.
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
> >> > > > > > >>>>>>>>
> >> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> >> > > > > > >> have
> >> > > > > > >>>> you
> >> > > > > > >>>>>>> done
> >> > > > > > >>>>>>>> some benchmark?
> >> > > > > > >>>>>>>> Because I'm curious about how much performance
> >> improvement
> >> > > can
> >> > > > > > >> we
> >> > > > > > >>>> get
> >> > > > > > >>>>>> by
> >> > > > > > >>>>>>>> using count window as the local operator.
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>> Best,
> >> > > > > > >>>>>>>> Jark
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> >> > > > [hidden email]
> >> > > > > > >>>
> >> > > > > > >>>>> wrote:
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>>> Hi Hequn,
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> Thanks for your reply.
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to provide a tool
> >> which
> >> > > can
> >> > > > > > >>> let
> >> > > > > > >>>>>> users
> >> > > > > > >>>>>>> do
> >> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior of the
> >> > > > > > >>> pre-aggregation
> >> > > > > > >>>>> is
> >> > > > > > >>>>>>>>> similar to keyBy API.
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> So the three cases are different, I will describe
> them
> >> > one
> >> > > by
> >> > > > > > >>>> one:
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> *In this case, the result is event-driven, each
> event
> >> can
> >> > > > > > >>> produce
> >> > > > > > >>>>> one
> >> > > > > > >>>>>>> sum
> >> > > > > > >>>>>>>>> aggregation result and it is the latest one from the
> >> > source
> >> > > > > > >>>> start.*
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> *In this case, the semantic may have a problem, it
> >> would
> >> > do
> >> > > > > > >> the
> >> > > > > > >>>>> local
> >> > > > > > >>>>>>> sum
> >> > > > > > >>>>>>>>> aggregation and will produce the latest partial
> result
> >> > from
> >> > > > > > >> the
> >> > > > > > >>>>>> source
> >> > > > > > >>>>>>>>> start for every event. *
> >> > > > > > >>>>>>>>> *These latest partial results from the same key are
> >> > hashed
> >> > > to
> >> > > > > > >>> one
> >> > > > > > >>>>>> node
> >> > > > > > >>>>>>> to
> >> > > > > > >>>>>>>>> do the global sum aggregation.*
> >> > > > > > >>>>>>>>> *In the global aggregation, when it received
> multiple
> >> > > partial
> >> > > > > > >>>>> results
> >> > > > > > >>>>>>>> (they
> >> > > > > > >>>>>>>>> are all calculated from the source start) and sum
> them
> >> > will
> >> > > > > > >> get
> >> > > > > > >>>> the
> >> > > > > > >>>>>>> wrong
> >> > > > > > >>>>>>>>> result.*
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> 3.
> >> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> *In this case, it would just get a partial
> aggregation
> >> > > result
> >> > > > > > >>> for
> >> > > > > > >>>>>> the 5
> >> > > > > > >>>>>>>>> records in the count window. The partial aggregation
> >> > > results
> >> > > > > > >>> from
> >> > > > > > >>>>> the
> >> > > > > > >>>>>>>> same
> >> > > > > > >>>>>>>>> key will be aggregated globally.*
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> So the first case and the third case can get the
> >> *same*
> >> > > > > > >> result,
> >> > > > > > >>>> the
> >> > > > > > >>>>>>>>> difference is the output-style and the latency.
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> Generally speaking, the local key API is just an
> >> > > optimization
> >> > > > > > >>>> API.
> >> > > > > > >>>>> We
> >> > > > > > >>>>>>> do
> >> > > > > > >>>>>>>>> not limit the user's usage, but the user has to
> >> > understand
> >> > > > > > >> its
> >> > > > > > >>>>>>> semantics
> >> > > > > > >>>>>>>>> and use it correctly.
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> Best,
> >> > > > > > >>>>>>>>> Vino
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]> 于2019年6月17日周一
> >> > 下午4:18写道：
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>>> Hi Vino,
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a very good
> >> > > feature!
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>> One thing I want to make sure is the semantics for
> >> the
> >> > > > > > >>>>>> `localKeyBy`.
> >> > > > > > >>>>>>>> From
> >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns an
> >> instance
> >> > of
> >> > > > > > >>>>>>> `KeyedStream`
> >> > > > > > >>>>>>>>>> which can also perform sum(), so in this case,
> what's
> >> > the
> >> > > > > > >>>>> semantics
> >> > > > > > >>>>>>> for
> >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the following
> code
> >> > share
> >> > > > > > >>> the
> >> > > > > > >>>>> same
> >> > > > > > >>>>>>>>> result?
> >> > > > > > >>>>>>>>>> and what're the differences between them?
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> >> > > > > > >>>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> >> > > > > > >>>>>>>>>> 3.
> >> > > > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>> Would also be great if we can add this into the
> >> > document.
> >> > > > > > >>> Thank
> >> > > > > > >>>>> you
> >> > > > > > >>>>>>>> very
> >> > > > > > >>>>>>>>>> much.
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>> Best, Hequn
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino yang <
> >> > > > > > >>>>> [hidden email]>
> >> > > > > > >>>>>>>>> wrote:
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>>> Hi Aljoscha,
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section of FLIP
> >> wiki
> >> > > > > > >>>> page.[1]
> >> > > > > > >>>>>> This
> >> > > > > > >>>>>>>>> mail
> >> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to the
> third
> >> > step.
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote step), I
> >> didn't
> >> > > > > > >> find
> >> > > > > > >>>> the
> >> > > > > > >>>>>>>>>>> prerequisites for starting the voting process.
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>> Considering that the discussion of this feature
> has
> >> > been
> >> > > > > > >>> done
> >> > > > > > >>>>> in
> >> > > > > > >>>>>>> the
> >> > > > > > >>>>>>>>> old
> >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when should I start
> >> > > > > > >> voting?
> >> > > > > > >>>> Can
> >> > > > > > >>>>> I
> >> > > > > > >>>>>>>> start
> >> > > > > > >>>>>>>>>> now?
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>> Best,
> >> > > > > > >>>>>>>>>>> Vino
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>> [1]:
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>
> >> > > > > > >>>>>
> >> > > > > > >>>>
> >> > > > > > >>>
> >> > > > > > >>
> >> > > > > >
> >> > > > >
> >> > > >
> >> > >
> >> >
> >>
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> >> > > > > > >>>>>>>>>>> [2]:
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>
> >> > > > > > >>>>>
> >> > > > > > >>>>
> >> > > > > > >>>
> >> > > > > > >>
> >> > > > > >
> >> > > > >
> >> > > >
> >> > >
> >> >
> >>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>> leesf <[hidden email]> 于2019年6月13日周四
> 上午9:19写道：
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your efforts.
> >> > > > > > >>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>> Best,
> >> > > > > > >>>>>>>>>>>> Leesf
> >> > > > > > >>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]> 于2019年6月12日周三
> >> > > > > > >>> 下午5:46写道：
> >> > > > > > >>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> Hi folks,
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP discussion thread
> >> > > > > > >> about
> >> > > > > > >>>>>>> supporting
> >> > > > > > >>>>>>>>>> local
> >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> In short, this feature can effectively alleviate
> >> data
> >> > > > > > >>>> skew.
> >> > > > > > >>>>>>> This
> >> > > > > > >>>>>>>> is
> >> > > > > > >>>>>>>>>> the
> >> > > > > > >>>>>>>>>>>>> FLIP:
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>
> >> > > > > > >>>>>
> >> > > > > > >>>>
> >> > > > > > >>>
> >> > > > > > >>
> >> > > > > >
> >> > > > >
> >> > > >
> >> > >
> >> >
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely used to
> >> perform
> >> > > > > > >>>>>> aggregating
> >> > > > > > >>>>>>>>>>>> operations
> >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the elements
> >> that
> >> > > > > > >>> have
> >> > > > > > >>>>> the
> >> > > > > > >>>>>>> same
> >> > > > > > >>>>>>>>>> key.
> >> > > > > > >>>>>>>>>>>> When
> >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with the same
> >> key
> >> > > > > > >>> will
> >> > > > > > >>>> be
> >> > > > > > >>>>>>> sent
> >> > > > > > >>>>>>>> to
> >> > > > > > >>>>>>>>>> and
> >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> The performance of these aggregating operations
> is
> >> > > > > > >> very
> >> > > > > > >>>>>>> sensitive
> >> > > > > > >>>>>>>>> to
> >> > > > > > >>>>>>>>>>> the
> >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases where the
> >> > > > > > >>> distribution
> >> > > > > > >>>>> of
> >> > > > > > >>>>>>> keys
> >> > > > > > >>>>>>>>>>>> follows a
> >> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
> >> significantly
> >> > > > > > >>>>>> downgraded.
> >> > > > > > >>>>>>>>> More
> >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of parallelism
> >> does
> >> > > > > > >>> not
> >> > > > > > >>>>> help
> >> > > > > > >>>>>>>> when
> >> > > > > > >>>>>>>>> a
> >> > > > > > >>>>>>>>>>> task
> >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted method to
> >> > > > > > >> reduce
> >> > > > > > >>>> the
> >> > > > > > >>>>>>>>>> performance
> >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose the
> >> > > > > > >> aggregating
> >> > > > > > >>>>>>>> operations
> >> > > > > > >>>>>>>>>> into
> >> > > > > > >>>>>>>>>>>> two
> >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we aggregate the
> >> elements
> >> > > > > > >>> of
> >> > > > > > >>>>> the
> >> > > > > > >>>>>>> same
> >> > > > > > >>>>>>>>> key
> >> > > > > > >>>>>>>>>>> at
> >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial results. Then
> at
> >> > > > > > >> the
> >> > > > > > >>>>> second
> >> > > > > > >>>>>>>>> phase,
> >> > > > > > >>>>>>>>>>>> these
> >> > > > > > >>>>>>>>>>>>> partial results are sent to receivers according
> to
> >> > > > > > >>> their
> >> > > > > > >>>>> keys
> >> > > > > > >>>>>>> and
> >> > > > > > >>>>>>>>> are
> >> > > > > > >>>>>>>>>>>>> combined to obtain the final result. Since the
> >> number
> >> > > > > > >>> of
> >> > > > > > >>>>>>> partial
> >> > > > > > >>>>>>>>>>> results
> >> > > > > > >>>>>>>>>>>>> received by each receiver is limited by the
> >> number of
> >> > > > > > >>>>>> senders,
> >> > > > > > >>>>>>>> the
> >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be reduced.
> >> Besides, by
> >> > > > > > >>>>>> reducing
> >> > > > > > >>>>>>>> the
> >> > > > > > >>>>>>>>>>> amount
> >> > > > > > >>>>>>>>>>>>> of transferred data the performance can be
> further
> >> > > > > > >>>>> improved.
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> *More details*:
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> Design documentation:
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>
> >> > > > > > >>>>>
> >> > > > > > >>>>
> >> > > > > > >>>
> >> > > > > > >>
> >> > > > > >
> >> > > > >
> >> > > >
> >> > >
> >> >
> >>
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> Old discussion thread:
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>
> >> > > > > > >>>>>
> >> > > > > > >>>>
> >> > > > > > >>>
> >> > > > > > >>
> >> > > > > >
> >> > > > >
> >> > > >
> >> > >
> >> >
> >>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> >> > > > > > >>>>>>>>> https://issues.apache.org/jira/browse/FLINK-12786
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> We are looking forwards to your feedback!
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>> Best,
> >> > > > > > >>>>>>>>>>>>> Vino
> >> > > > > > >>>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>>
> >> > > > > > >>>>>>>>>>>
> >> > > > > > >>>>>>>>>>
> >> > > > > > >>>>>>>>>
> >> > > > > > >>>>>>>>
> >> > > > > > >>>>>>>
> >> > > > > > >>>>>>
> >> > > > > > >>>>>
> >> > > > > > >>>>
> >> > > > > > >>>
> >> > > > > > >>
> >> > > > > >
> >> > > > > >
> >> > > > >
> >> > > >
> >> > >
> >> >
> >>
> >
>

[vino yang]

Hi Kurt,

You did not give more further different opinions, so I thought you have
agreed with the design after we promised to support two kinds of
implementation.

In API level, we have answered your question about pass an
AggregateFunction to do the aggregation. No matter introduce localKeyBy API
or not, we can support AggregateFunction.

So what's your different opinion now? Can you share it with us?

Best,
Vino

Kurt Young <[hidden email]> 于2019年6月24日周一 下午4:24写道：

> Hi vino,
>
> Sorry I don't see the consensus about reusing window operator and keep the
> API design of localKeyBy. But I think we should definitely more thoughts
> about this topic.
>
> I also try to loop in Stephan for this discussion.
>
> Best,
> Kurt
>
>
> On Mon, Jun 24, 2019 at 3:26 PM vino yang <[hidden email]> wrote:
>
> > Hi all,
> >
> > I am happy we have a wonderful discussion and received many valuable
> > opinions in the last few days.
> >
> > Now, let me try to summarize what we have reached consensus about the
> > changes in the design.
> >
> >    - provide a unified abstraction to support two kinds of
> implementation;
> >    - reuse WindowOperator and try to enhance it so that we can make the
> >    intermediate result of the local aggregation can be buffered and
> > flushed to
> >    support two kinds of implementation;
> >    - keep the API design of localKeyBy, but declare the disabled some
> APIs
> >    we cannot support currently, and provide a configurable API for users
> to
> >    choose how to handle intermediate result;
> >
> > The above three points have been updated in the design doc. Any
> > questions, please let me know.
> >
> > @Aljoscha Krettek <[hidden email]> What do you think? Any further
> > comments?
> >
> > Best,
> > Vino
> >
> > vino yang <[hidden email]> 于2019年6月20日周四 下午2:02写道：
> >
> > > Hi Kurt,
> > >
> > > Thanks for your comments.
> > >
> > > It seems we come to a consensus that we should alleviate the
> performance
> > > degraded by data skew with local aggregation. In this FLIP, our key
> > > solution is to introduce local keyed partition to achieve this goal.
> > >
> > > I also agree that we can benefit a lot from the usage of
> > > AggregateFunction. In combination with localKeyBy, We can easily use it
> > to
> > > achieve local aggregation:
> > >
> > >    - input.localKeyBy(0).aggregate()
> > >    - input.localKeyBy(0).window().aggregate()
> > >
> > >
> > > I think the only problem here is the choices between
> > >
> > >    - (1) Introducing a new primitive called localKeyBy and implement
> > >    local aggregation with existing operators, or
> > >    - (2) Introducing an operator called localAggregation which is
> > >    composed of a key selector, a window-like operator, and an aggregate
> > >    function.
> > >
> > >
> > > There may exist some optimization opportunities by providing a
> composited
> > > interface for local aggregation. But at the same time, in my opinion,
> we
> > > lose flexibility (Or we need certain efforts to achieve the same
> > > flexibility).
> > >
> > > As said in the previous mails, we have many use cases where the
> > > aggregation is very complicated and cannot be performed with
> > > AggregateFunction. For example, users may perform windowed aggregations
> > > according to time, data values, or even external storage. Typically,
> they
> > > now use KeyedProcessFunction or customized triggers to implement these
> > > aggregations. It's not easy to address data skew in such cases with a
> > > composited interface for local aggregation.
> > >
> > > Given that Data Stream API is exactly targeted at these cases where the
> > > application logic is very complicated and optimization does not
> matter, I
> > > think it's a better choice to provide a relatively low-level and
> > canonical
> > > interface.
> > >
> > > The composited interface, on the other side, may be a good choice in
> > > declarative interfaces, including SQL and Table API, as it allows more
> > > optimization opportunities.
> > >
> > > Best,
> > > Vino
> > >
> > >
> > > Kurt Young <[hidden email]> 于2019年6月20日周四 上午10:15写道：
> > >
> > >> Hi all,
> > >>
> > >> As vino said in previous emails, I think we should first discuss and
> > >> decide
> > >> what kind of use cases this FLIP want to
> > >> resolve, and what the API should look like. From my side, I think this
> > is
> > >> probably the root cause of current divergence.
> > >>
> > >> My understand is (from the FLIP title and motivation section of the
> > >> document), we want to have a proper support of
> > >> local aggregation, or pre aggregation. This is not a very new idea,
> most
> > >> SQL engine already did this improvement. And
> > >> the core concept about this is, there should be an AggregateFunction,
> no
> > >> matter it's a Flink runtime's AggregateFunction or
> > >> SQL's UserDefinedAggregateFunction. Both aggregation have concept of
> > >> intermediate data type, sometimes we call it ACC.
> > >> I quickly went through the POC piotr did before [1], it also directly
> > uses
> > >> AggregateFunction.
> > >>
> > >> But the thing is, after reading the design of this FLIP, I can't help
> > >> myself feeling that this FLIP is not targeting to have a proper
> > >> local aggregation support. It actually want to introduce another
> > concept:
> > >> LocalKeyBy, and how to split and merge local key groups,
> > >> and how to properly support state on local key. Local aggregation just
> > >> happened to be one possible use case of LocalKeyBy.
> > >> But it lacks supporting the essential concept of local aggregation,
> > which
> > >> is intermediate data type. Without this, I really don't thing
> > >> it is a good fit of local aggregation.
> > >>
> > >> Here I want to make sure of the scope or the goal about this FLIP, do
> we
> > >> want to have a proper local aggregation engine, or we
> > >> just want to introduce a new concept called LocalKeyBy?
> > >>
> > >> [1]: https://github.com/apache/flink/pull/4626
> > >>
> > >> Best,
> > >> Kurt
> > >>
> > >>
> > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <[hidden email]>
> > wrote:
> > >>
> > >> > Hi Hequn,
> > >> >
> > >> > Thanks for your comments!
> > >> >
> > >> > I agree that allowing local aggregation reusing window API and
> > refining
> > >> > window operator to make it match both requirements (come from our
> and
> > >> Kurt)
> > >> > is a good decision!
> > >> >
> > >> > Concerning your questions:
> > >> >
> > >> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1) may be
> > >> > meaningless.
> > >> >
> > >> > Yes, it does not make sense in most cases. However, I also want to
> > note
> > >> > users should know the right semantics of localKeyBy and use it
> > >> correctly.
> > >> > Because this issue also exists for the global keyBy, consider this
> > >> example:
> > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also
> meaningless.
> > >> >
> > >> > 2. About the semantics of
> > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> > >> >
> > >> > Good catch! I agree with you that it's not good to enable all
> > >> > functionalities for localKeyBy from KeyedStream.
> > >> > Currently, We do not support some APIs such as
> > >> > connect/join/intervalJoin/coGroup. This is due to that we force the
> > >> > operators on LocalKeyedStreams chained with the inputs.
> > >> >
> > >> > Best,
> > >> > Vino
> > >> >
> > >> >
> > >> > Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
> > >> >
> > >> > > Hi,
> > >> > >
> > >> > > Thanks a lot for your great discussion and great to see that some
> > >> > agreement
> > >> > > has been reached on the "local aggregate engine"!
> > >> > >
> > >> > > ===> Considering the abstract engine,
> > >> > > I'm thinking is it valuable for us to extend the current window to
> > >> meet
> > >> > > both demands raised by Kurt and Vino? There are some benefits we
> can
> > >> get:
> > >> > >
> > >> > > 1. The interfaces of the window are complete and clear. With
> > windows,
> > >> we
> > >> > > can define a lot of ways to split the data and perform different
> > >> > > computations.
> > >> > > 2. We can also leverage the window to do miniBatch for the global
> > >> > > aggregation, i.e, we can use the window to bundle data belong to
> the
> > >> same
> > >> > > key, for every bundle we only need to read and write once state.
> > This
> > >> can
> > >> > > greatly reduce state IO and improve performance.
> > >> > > 3. A lot of other use cases can also benefit from the window base
> on
> > >> > memory
> > >> > > or stateless.
> > >> > >
> > >> > > ===> As for the API,
> > >> > > I think it is good to make our API more flexible. However, we may
> > >> need to
> > >> > > make our API meaningful.
> > >> > >
> > >> > > Take my previous reply as an example,
> > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may be
> > >> > meaningless.
> > >> > > Another example I find is the intervalJoin, e.g.,
> > >> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In this
> > >> case, it
> > >> > > will bring problems if input1 and input2 share different
> > parallelism.
> > >> We
> > >> > > don't know which input should the join chained with? Even if they
> > >> share
> > >> > the
> > >> > > same parallelism, it's hard to tell what the join is doing. There
> > are
> > >> > maybe
> > >> > > some other problems.
> > >> > >
> > >> > > From this point of view, it's at least not good to enable all
> > >> > > functionalities for localKeyBy from KeyedStream?
> > >> > >
> > >> > > Great to also have your opinions.
> > >> > >
> > >> > > Best, Hequn
> > >> > >
> > >> > >
> > >> > >
> > >> > >
> > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <[hidden email]
> >
> > >> > wrote:
> > >> > >
> > >> > > > Hi Kurt and Piotrek,
> > >> > > >
> > >> > > > Thanks for your comments.
> > >> > > >
> > >> > > > I agree that we can provide a better abstraction to be
> compatible
> > >> with
> > >> > > two
> > >> > > > different implementations.
> > >> > > >
> > >> > > > First of all, I think we should consider what kind of scenarios
> we
> > >> need
> > >> > > to
> > >> > > > support in *API* level?
> > >> > > >
> > >> > > > We have some use cases which need to a customized aggregation
> > >> through
> > >> > > > KeyedProcessFunction, (in the usage of our localKeyBy.window
> they
> > >> can
> > >> > use
> > >> > > > ProcessWindowFunction).
> > >> > > >
> > >> > > > Shall we support these flexible use scenarios?
> > >> > > >
> > >> > > > Best,
> > >> > > > Vino
> > >> > > >
> > >> > > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
> > >> > > >
> > >> > > > > Hi Piotr,
> > >> > > > >
> > >> > > > > Thanks for joining the discussion. Make “local aggregation"
> > >> abstract
> > >> > > > enough
> > >> > > > > sounds good to me, we could
> > >> > > > > implement and verify alternative solutions for use cases of
> > local
> > >> > > > > aggregation. Maybe we will find both solutions
> > >> > > > > are appropriate for different scenarios.
> > >> > > > >
> > >> > > > > Starting from a simple one sounds a practical way to go. What
> do
> > >> you
> > >> > > > think,
> > >> > > > > vino?
> > >> > > > >
> > >> > > > > Best,
> > >> > > > > Kurt
> > >> > > > >
> > >> > > > >
> > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> > >> [hidden email]>
> > >> > > > > wrote:
> > >> > > > >
> > >> > > > > > Hi Kurt and Vino,
> > >> > > > > >
> > >> > > > > > I think there is a trade of hat we need to consider for the
> > >> local
> > >> > > > > > aggregation.
> > >> > > > > >
> > >> > > > > > Generally speaking I would agree with Kurt about local
> > >> > > aggregation/pre
> > >> > > > > > aggregation not using Flink's state flush the operator on a
> > >> > > checkpoint.
> > >> > > > > > Network IO is usually cheaper compared to Disks IO. This has
> > >> > however
> > >> > > > > couple
> > >> > > > > > of issues:
> > >> > > > > > 1. It can explode number of in-flight records during
> > checkpoint
> > >> > > barrier
> > >> > > > > > alignment, making checkpointing slower and decrease the
> actual
> > >> > > > > throughput.
> > >> > > > > > 2. This trades Disks IO on the local aggregation machine
> with
> > >> CPU
> > >> > > (and
> > >> > > > > > Disks IO in case of RocksDB) on the final aggregation
> machine.
> > >> This
> > >> > > is
> > >> > > > > > fine, as long there is no huge data skew. If there is only a
> > >> > handful
> > >> > > > (or
> > >> > > > > > even one single) hot keys, it might be better to keep the
> > >> > persistent
> > >> > > > > state
> > >> > > > > > in the LocalAggregationOperator to offload final aggregation
> > as
> > >> > much
> > >> > > as
> > >> > > > > > possible.
> > >> > > > > > 3. With frequent checkpointing local aggregation
> effectiveness
> > >> > would
> > >> > > > > > degrade.
> > >> > > > > >
> > >> > > > > > I assume Kurt is correct, that in your use cases stateless
> > >> operator
> > >> > > was
> > >> > > > > > behaving better, but I could easily see other use cases as
> > well.
> > >> > For
> > >> > > > > > example someone is already using RocksDB, and his job is
> > >> > bottlenecked
> > >> > > > on
> > >> > > > > a
> > >> > > > > > single window operator instance because of the data skew. In
> > >> that
> > >> > > case
> > >> > > > > > stateful local aggregation would be probably a better
> choice.
> > >> > > > > >
> > >> > > > > > Because of that, I think we should eventually provide both
> > >> versions
> > >> > > and
> > >> > > > > in
> > >> > > > > > the initial version we should at least make the “local
> > >> aggregation
> > >> > > > > engine”
> > >> > > > > > abstract enough, that one could easily provide different
> > >> > > implementation
> > >> > > > > > strategy.
> > >> > > > > >
> > >> > > > > > Piotrek
> > >> > > > > >
> > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <[hidden email]>
> > >> wrote:
> > >> > > > > > >
> > >> > > > > > > Hi,
> > >> > > > > > >
> > >> > > > > > > For the trigger, it depends on what operator we want to
> use
> > >> under
> > >> > > the
> > >> > > > > > API.
> > >> > > > > > > If we choose to use window operator,
> > >> > > > > > > we should also use window's trigger. However, I also think
> > >> reuse
> > >> > > > window
> > >> > > > > > > operator for this scenario may not be
> > >> > > > > > > the best choice. The reasons are the following:
> > >> > > > > > >
> > >> > > > > > > 1. As a lot of people already pointed out, window relies
> > >> heavily
> > >> > on
> > >> > > > > state
> > >> > > > > > > and it will definitely effect performance. You can
> > >> > > > > > > argue that one can use heap based statebackend, but this
> > will
> > >> > > > introduce
> > >> > > > > > > extra coupling. Especially we have a chance to
> > >> > > > > > > design a pure stateless operator.
> > >> > > > > > > 2. The window operator is *the most* complicated operator
> > >> Flink
> > >> > > > > currently
> > >> > > > > > > have. Maybe we only need to pick a subset of
> > >> > > > > > > window operator to achieve the goal, but once the user
> wants
> > >> to
> > >> > > have
> > >> > > > a
> > >> > > > > > deep
> > >> > > > > > > look at the localAggregation operator, it's still
> > >> > > > > > > hard to find out what's going on under the window
> operator.
> > >> For
> > >> > > > > > simplicity,
> > >> > > > > > > I would also recommend we introduce a dedicated
> > >> > > > > > > lightweight operator, which also much easier for a user to
> > >> learn
> > >> > > and
> > >> > > > > use.
> > >> > > > > > >
> > >> > > > > > > For your question about increasing the burden in
> > >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the only
> > thing
> > >> > this
> > >> > > > > > function
> > >> > > > > > > need
> > >> > > > > > > to do is output all the partial results, it's purely cpu
> > >> > workload,
> > >> > > > not
> > >> > > > > > > introducing any IO. I want to point out that even if we
> have
> > >> this
> > >> > > > > > > cost, we reduced another barrier align cost of the
> operator,
> > >> > which
> > >> > > is
> > >> > > > > the
> > >> > > > > > > sync flush stage of the state, if you introduced state.
> This
> > >> > > > > > > flush actually will introduce disk IO, and I think it's
> > >> worthy to
> > >> > > > > > exchange
> > >> > > > > > > this cost with purely CPU workload. And we do have some
> > >> > > > > > > observations about these two behavior (as i said before,
> we
> > >> > > actually
> > >> > > > > > > implemented both solutions), the stateless one actually
> > >> performs
> > >> > > > > > > better both in performance and barrier align time.
> > >> > > > > > >
> > >> > > > > > > Best,
> > >> > > > > > > Kurt
> > >> > > > > > >
> > >> > > > > > >
> > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> > >> [hidden email]
> > >> > >
> > >> > > > > wrote:
> > >> > > > > > >
> > >> > > > > > >> Hi Kurt,
> > >> > > > > > >>
> > >> > > > > > >> Thanks for your example. Now, it looks more clearly for
> me.
> > >> > > > > > >>
> > >> > > > > > >> From your example code snippet, I saw the localAggregate
> > API
> > >> has
> > >> > > > three
> > >> > > > > > >> parameters:
> > >> > > > > > >>
> > >> > > > > > >>   1. key field
> > >> > > > > > >>   2. PartitionAvg
> > >> > > > > > >>   3. CountTrigger: Does this trigger comes from window
> > >> package?
> > >> > > > > > >>
> > >> > > > > > >> I will compare our and your design from API and operator
> > >> level:
> > >> > > > > > >>
> > >> > > > > > >> *From the API level:*
> > >> > > > > > >>
> > >> > > > > > >> As I replied to @dianfu in the old email thread,[1] the
> > >> Window
> > >> > API
> > >> > > > can
> > >> > > > > > >> provide the second and the third parameter right now.
> > >> > > > > > >>
> > >> > > > > > >> If you reuse specified interface or class, such as
> > *Trigger*
> > >> or
> > >> > > > > > >> *CounterTrigger* provided by window package, but do not
> use
> > >> > window
> > >> > > > > API,
> > >> > > > > > >> it's not reasonable.
> > >> > > > > > >> And if you do not reuse these interface or class, you
> would
> > >> need
> > >> > > to
> > >> > > > > > >> introduce more things however they are looked similar to
> > the
> > >> > > things
> > >> > > > > > >> provided by window package.
> > >> > > > > > >>
> > >> > > > > > >> The window package has provided several types of the
> window
> > >> and
> > >> > > many
> > >> > > > > > >> triggers and let users customize it. What's more, the
> user
> > is
> > >> > more
> > >> > > > > > familiar
> > >> > > > > > >> with Window API.
> > >> > > > > > >>
> > >> > > > > > >> This is the reason why we just provide localKeyBy API and
> > >> reuse
> > >> > > the
> > >> > > > > > window
> > >> > > > > > >> API. It reduces unnecessary components such as triggers
> and
> > >> the
> > >> > > > > > mechanism
> > >> > > > > > >> of buffer (based on count num or time).
> > >> > > > > > >> And it has a clear and easy to understand semantics.
> > >> > > > > > >>
> > >> > > > > > >> *From the operator level:*
> > >> > > > > > >>
> > >> > > > > > >> We reused window operator, so we can get all the benefits
> > >> from
> > >> > > state
> > >> > > > > and
> > >> > > > > > >> checkpoint.
> > >> > > > > > >>
> > >> > > > > > >> From your design, you named the operator under
> > localAggregate
> > >> > API
> > >> > > > is a
> > >> > > > > > >> *stateless* operator. IMO, it is still a state, it is
> just
> > >> not
> > >> > > Flink
> > >> > > > > > >> managed state.
> > >> > > > > > >> About the memory buffer (I think it's still not very
> clear,
> > >> if
> > >> > you
> > >> > > > > have
> > >> > > > > > >> time, can you give more detail information or answer my
> > >> > > questions),
> > >> > > > I
> > >> > > > > > have
> > >> > > > > > >> some questions:
> > >> > > > > > >>
> > >> > > > > > >>   - if it just a raw JVM heap memory buffer, how to
> support
> > >> > fault
> > >> > > > > > >>   tolerance, if the job is configured EXACTLY-ONCE
> semantic
> > >> > > > guarantee?
> > >> > > > > > >>   - if you thought the memory buffer(non-Flink state),
> has
> > >> > better
> > >> > > > > > >>   performance. In our design, users can also config HEAP
> > >> state
> > >> > > > backend
> > >> > > > > > to
> > >> > > > > > >>   provide the performance close to your mechanism.
> > >> > > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()` related
> > to
> > >> the
> > >> > > > > timing
> > >> > > > > > of
> > >> > > > > > >>   snapshot. IMO, the flush action should be a
> synchronized
> > >> > action?
> > >> > > > (if
> > >> > > > > > >> not,
> > >> > > > > > >>   please point out my mistake) I still think we should
> not
> > >> > depend
> > >> > > on
> > >> > > > > the
> > >> > > > > > >>   timing of checkpoint. Checkpoint related operations are
> > >> > inherent
> > >> > > > > > >>   performance sensitive, we should not increase its
> burden
> > >> > > anymore.
> > >> > > > > Our
> > >> > > > > > >>   implementation based on the mechanism of Flink's
> > >> checkpoint,
> > >> > > which
> > >> > > > > can
> > >> > > > > > >>   benefit from the asnyc snapshot and incremental
> > checkpoint.
> > >> > IMO,
> > >> > > > the
> > >> > > > > > >>   performance is not a problem, and we also do not find
> the
> > >> > > > > performance
> > >> > > > > > >> issue
> > >> > > > > > >>   in our production.
> > >> > > > > > >>
> > >> > > > > > >> [1]:
> > >> > > > > > >>
> > >> > > > > > >>
> > >> > > > > >
> > >> > > > >
> > >> > > >
> > >> > >
> > >> >
> > >>
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > >> > > > > > >>
> > >> > > > > > >> Best,
> > >> > > > > > >> Vino
> > >> > > > > > >>
> > >> > > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二 下午2:27写道：
> > >> > > > > > >>
> > >> > > > > > >>> Yeah, sorry for not expressing myself clearly. I will
> try
> > to
> > >> > > > provide
> > >> > > > > > more
> > >> > > > > > >>> details to make sure we are on the same page.
> > >> > > > > > >>>
> > >> > > > > > >>> For DataStream API, it shouldn't be optimized
> > automatically.
> > >> > You
> > >> > > > have
> > >> > > > > > to
> > >> > > > > > >>> explicitly call API to do local aggregation
> > >> > > > > > >>> as well as the trigger policy of the local aggregation.
> > Take
> > >> > > > average
> > >> > > > > > for
> > >> > > > > > >>> example, the user program may look like this (just a
> > draft):
> > >> > > > > > >>>
> > >> > > > > > >>> assuming the input type is DataStream<Tupl2<String,
> Int>>
> > >> > > > > > >>>
> > >> > > > > > >>> ds.localAggregate(
> > >> > > > > > >>>        0,                                       // The
> > local
> > >> > key,
> > >> > > > > which
> > >> > > > > > >> is
> > >> > > > > > >>> the String from Tuple2
> > >> > > > > > >>>        PartitionAvg(1),                 // The partial
> > >> > > aggregation
> > >> > > > > > >>> function, produces Tuple2<Long, Int>, indicating sum and
> > >> count
> > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger policy, note
> > >> this
> > >> > > > should
> > >> > > > > be
> > >> > > > > > >>> best effort, and also be composited with time based or
> > >> memory
> > >> > > size
> > >> > > > > > based
> > >> > > > > > >>> trigger
> > >> > > > > > >>>    )                                           // The
> > return
> > >> > type
> > >> > > > is
> > >> > > > > > >> local
> > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> > >> > > > > > >>>    .keyBy(0)                             // Further
> keyby
> > it
> > >> > with
> > >> > > > > > >> required
> > >> > > > > > >>> key
> > >> > > > > > >>>    .aggregate(1)                      // This will merge
> > all
> > >> > the
> > >> > > > > > partial
> > >> > > > > > >>> results and get the final average.
> > >> > > > > > >>>
> > >> > > > > > >>> (This is only a draft, only trying to explain what it
> > looks
> > >> > > like. )
> > >> > > > > > >>>
> > >> > > > > > >>> The local aggregate operator can be stateless, we can
> > keep a
> > >> > > memory
> > >> > > > > > >> buffer
> > >> > > > > > >>> or other efficient data structure to improve the
> aggregate
> > >> > > > > performance.
> > >> > > > > > >>>
> > >> > > > > > >>> Let me know if you have any other questions.
> > >> > > > > > >>>
> > >> > > > > > >>> Best,
> > >> > > > > > >>> Kurt
> > >> > > > > > >>>
> > >> > > > > > >>>
> > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> > >> > [hidden email]
> > >> > > >
> > >> > > > > > wrote:
> > >> > > > > > >>>
> > >> > > > > > >>>> Hi Kurt,
> > >> > > > > > >>>>
> > >> > > > > > >>>> Thanks for your reply.
> > >> > > > > > >>>>
> > >> > > > > > >>>> Actually, I am not against you to raise your design.
> > >> > > > > > >>>>
> > >> > > > > > >>>> From your description before, I just can imagine your
> > >> > high-level
> > >> > > > > > >>>> implementation is about SQL and the optimization is
> inner
> > >> of
> > >> > the
> > >> > > > > API.
> > >> > > > > > >> Is
> > >> > > > > > >>> it
> > >> > > > > > >>>> automatically? how to give the configuration option
> about
> > >> > > trigger
> > >> > > > > > >>>> pre-aggregation?
> > >> > > > > > >>>>
> > >> > > > > > >>>> Maybe after I get more information, it sounds more
> > >> reasonable.
> > >> > > > > > >>>>
> > >> > > > > > >>>> IMO, first of all, it would be better to make your user
> > >> > > interface
> > >> > > > > > >>> concrete,
> > >> > > > > > >>>> it's the basis of the discussion.
> > >> > > > > > >>>>
> > >> > > > > > >>>> For example, can you give an example code snippet to
> > >> introduce
> > >> > > how
> > >> > > > > to
> > >> > > > > > >>> help
> > >> > > > > > >>>> users to process data skew caused by the jobs which
> built
> > >> with
> > >> > > > > > >> DataStream
> > >> > > > > > >>>> API?
> > >> > > > > > >>>>
> > >> > > > > > >>>> If you give more details we can discuss further more. I
> > >> think
> > >> > if
> > >> > > > one
> > >> > > > > > >>> design
> > >> > > > > > >>>> introduces an exact interface and another does not.
> > >> > > > > > >>>>
> > >> > > > > > >>>> The implementation has an obvious difference. For
> > example,
> > >> we
> > >> > > > > > introduce
> > >> > > > > > >>> an
> > >> > > > > > >>>> exact API in DataStream named localKeyBy, about the
> > >> > > > pre-aggregation
> > >> > > > > we
> > >> > > > > > >>> need
> > >> > > > > > >>>> to define the trigger mechanism of local aggregation,
> so
> > we
> > >> > find
> > >> > > > > > reused
> > >> > > > > > >>>> window API and operator is a good choice. This is a
> > >> reasoning
> > >> > > link
> > >> > > > > > from
> > >> > > > > > >>>> design to implementation.
> > >> > > > > > >>>>
> > >> > > > > > >>>> What do you think?
> > >> > > > > > >>>>
> > >> > > > > > >>>> Best,
> > >> > > > > > >>>> Vino
> > >> > > > > > >>>>
> > >> > > > > > >>>>
> > >> > > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二 上午11:58写道：
> > >> > > > > > >>>>
> > >> > > > > > >>>>> Hi Vino,
> > >> > > > > > >>>>>
> > >> > > > > > >>>>> Now I feel that we may have different understandings
> > about
> > >> > what
> > >> > > > > kind
> > >> > > > > > >> of
> > >> > > > > > >>>>> problems or improvements you want to
> > >> > > > > > >>>>> resolve. Currently, most of the feedback are focusing
> on
> > >> *how
> > >> > > to
> > >> > > > > do a
> > >> > > > > > >>>>> proper local aggregation to improve performance
> > >> > > > > > >>>>> and maybe solving the data skew issue*. And my gut
> > >> feeling is
> > >> > > > this
> > >> > > > > is
> > >> > > > > > >>>>> exactly what users want at the first place,
> > >> > > > > > >>>>> especially those +1s. (Sorry to try to summarize here,
> > >> please
> > >> > > > > correct
> > >> > > > > > >>> me
> > >> > > > > > >>>> if
> > >> > > > > > >>>>> i'm wrong).
> > >> > > > > > >>>>>
> > >> > > > > > >>>>> But I still think the design is somehow diverged from
> > the
> > >> > goal.
> > >> > > > If
> > >> > > > > we
> > >> > > > > > >>>> want
> > >> > > > > > >>>>> to have an efficient and powerful way to
> > >> > > > > > >>>>> have local aggregation, supporting intermedia result
> > type
> > >> is
> > >> > > > > > >> essential
> > >> > > > > > >>>> IMO.
> > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
> > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a proper
> > >> support of
> > >> > > > > > >>>> intermediate
> > >> > > > > > >>>>> result type and can do `merge` operation
> > >> > > > > > >>>>> on them.
> > >> > > > > > >>>>>
> > >> > > > > > >>>>> Now, we have a lightweight alternatives which performs
> > >> well,
> > >> > > and
> > >> > > > > > >> have a
> > >> > > > > > >>>>> nice fit with the local aggregate requirements.
> > >> > > > > > >>>>> Mostly importantly,  it's much less complex because
> it's
> > >> > > > stateless.
> > >> > > > > > >> And
> > >> > > > > > >>>> it
> > >> > > > > > >>>>> can also achieve the similar multiple-aggregation
> > >> > > > > > >>>>> scenario.
> > >> > > > > > >>>>>
> > >> > > > > > >>>>> I still not convinced why we shouldn't consider it as
> a
> > >> first
> > >> > > > step.
> > >> > > > > > >>>>>
> > >> > > > > > >>>>> Best,
> > >> > > > > > >>>>> Kurt
> > >> > > > > > >>>>>
> > >> > > > > > >>>>>
> > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> > >> > > > [hidden email]>
> > >> > > > > > >>>> wrote:
> > >> > > > > > >>>>>
> > >> > > > > > >>>>>> Hi Kurt,
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> Thanks for your comments.
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> It seems we both implemented local aggregation
> feature
> > to
> > >> > > > optimize
> > >> > > > > > >>> the
> > >> > > > > > >>>>>> issue of data skew.
> > >> > > > > > >>>>>> However, IMHO, the API level of optimizing revenue is
> > >> > > different.
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> *Your optimization benefits from Flink SQL and it's
> not
> > >> > user's
> > >> > > > > > >>>> faces.(If
> > >> > > > > > >>>>> I
> > >> > > > > > >>>>>> understand it incorrectly, please correct this.)*
> > >> > > > > > >>>>>> *Our implementation employs it as an optimization
> tool
> > >> API
> > >> > for
> > >> > > > > > >>>>> DataStream,
> > >> > > > > > >>>>>> it just like a local version of the keyBy API.*
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> Based on this, I want to say support it as a
> DataStream
> > >> API
> > >> > > can
> > >> > > > > > >>> provide
> > >> > > > > > >>>>>> these advantages:
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>>   - The localKeyBy API has a clear semantic and it's
> > >> > flexible
> > >> > > > not
> > >> > > > > > >>> only
> > >> > > > > > >>>>> for
> > >> > > > > > >>>>>>   processing data skew but also for implementing some
> > >> user
> > >> > > > cases,
> > >> > > > > > >>> for
> > >> > > > > > >>>>>>   example, if we want to calculate the multiple-level
> > >> > > > aggregation,
> > >> > > > > > >>> we
> > >> > > > > > >>>>> can
> > >> > > > > > >>>>>> do
> > >> > > > > > >>>>>>   multiple-level aggregation in the local
> aggregation:
> > >> > > > > > >>>>>>
> >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
> > >> //
> > >> > > here
> > >> > > > > > >> "a"
> > >> > > > > > >>>> is
> > >> > > > > > >>>>> a
> > >> > > > > > >>>>>>   sub-category, while "b" is a category, here we do
> not
> > >> need
> > >> > > to
> > >> > > > > > >>>> shuffle
> > >> > > > > > >>>>>> data
> > >> > > > > > >>>>>>   in the network.
> > >> > > > > > >>>>>>   - The users of DataStream API will benefit from
> this.
> > >> > > > Actually,
> > >> > > > > > >> we
> > >> > > > > > >>>>> have
> > >> > > > > > >>>>>>   a lot of scenes need to use DataStream API.
> > Currently,
> > >> > > > > > >> DataStream
> > >> > > > > > >>>> API
> > >> > > > > > >>>>> is
> > >> > > > > > >>>>>>   the cornerstone of the physical plan of Flink SQL.
> > >> With a
> > >> > > > > > >>> localKeyBy
> > >> > > > > > >>>>>> API,
> > >> > > > > > >>>>>>   the optimization of SQL at least may use this
> > optimized
> > >> > API,
> > >> > > > > > >> this
> > >> > > > > > >>>> is a
> > >> > > > > > >>>>>>   further topic.
> > >> > > > > > >>>>>>   - Based on the window operator, our state would
> > benefit
> > >> > from
> > >> > > > > > >> Flink
> > >> > > > > > >>>>> State
> > >> > > > > > >>>>>>   and checkpoint, we do not need to worry about OOM
> and
> > >> job
> > >> > > > > > >> failed.
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> Now, about your questions:
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> 1. About our design cannot change the data type and
> > about
> > >> > the
> > >> > > > > > >>>>>> implementation of average:
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is an API
> > >> > provides
> > >> > > > to
> > >> > > > > > >> the
> > >> > > > > > >>>>> users
> > >> > > > > > >>>>>> who use DataStream API to build their jobs.
> > >> > > > > > >>>>>> Users should know its semantics and the difference
> with
> > >> > keyBy
> > >> > > > API,
> > >> > > > > > >> so
> > >> > > > > > >>>> if
> > >> > > > > > >>>>>> they want to the average aggregation, they should
> carry
> > >> > local
> > >> > > > sum
> > >> > > > > > >>>> result
> > >> > > > > > >>>>>> and local count result.
> > >> > > > > > >>>>>> I admit that it will be convenient to use keyBy
> > directly.
> > >> > But
> > >> > > we
> > >> > > > > > >> need
> > >> > > > > > >>>> to
> > >> > > > > > >>>>>> pay a little price when we get some benefits. I think
> > >> this
> > >> > > price
> > >> > > > > is
> > >> > > > > > >>>>>> reasonable. Considering that the DataStream API
> itself
> > >> is a
> > >> > > > > > >> low-level
> > >> > > > > > >>>> API
> > >> > > > > > >>>>>> (at least for now).
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> 2. About stateless operator and
> > >> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> Actually, I have discussed this opinion with @dianfu
> in
> > >> the
> > >> > > old
> > >> > > > > > >> mail
> > >> > > > > > >>>>>> thread. I will copy my opinion from there:
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>>   - for your design, you still need somewhere to give
> > the
> > >> > > users
> > >> > > > > > >>>>> configure
> > >> > > > > > >>>>>>   the trigger threshold (maybe memory availability?),
> > >> this
> > >> > > > design
> > >> > > > > > >>>> cannot
> > >> > > > > > >>>>>>   guarantee a deterministic semantics (it will bring
> > >> trouble
> > >> > > for
> > >> > > > > > >>>> testing
> > >> > > > > > >>>>>> and
> > >> > > > > > >>>>>>   debugging).
> > >> > > > > > >>>>>>   - if the implementation depends on the timing of
> > >> > checkpoint,
> > >> > > > it
> > >> > > > > > >>>> would
> > >> > > > > > >>>>>>   affect the checkpoint's progress, and the buffered
> > data
> > >> > may
> > >> > > > > > >> cause
> > >> > > > > > >>>> OOM
> > >> > > > > > >>>>>>   issue. In addition, if the operator is stateless,
> it
> > >> can
> > >> > not
> > >> > > > > > >>> provide
> > >> > > > > > >>>>>> fault
> > >> > > > > > >>>>>>   tolerance.
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> Best,
> > >> > > > > > >>>>>> Vino
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二
> 上午9:22写道：
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>>> Hi Vino,
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>> Thanks for the proposal, I like the general idea and
> > IMO
> > >> > it's
> > >> > > > > > >> very
> > >> > > > > > >>>>> useful
> > >> > > > > > >>>>>>> feature.
> > >> > > > > > >>>>>>> But after reading through the document, I feel that
> we
> > >> may
> > >> > > over
> > >> > > > > > >>>> design
> > >> > > > > > >>>>>> the
> > >> > > > > > >>>>>>> required
> > >> > > > > > >>>>>>> operator for proper local aggregation. The main
> reason
> > >> is
> > >> > we
> > >> > > > want
> > >> > > > > > >>> to
> > >> > > > > > >>>>>> have a
> > >> > > > > > >>>>>>> clear definition and behavior about the "local keyed
> > >> state"
> > >> > > > which
> > >> > > > > > >>> in
> > >> > > > > > >>>> my
> > >> > > > > > >>>>>>> opinion is not
> > >> > > > > > >>>>>>> necessary for local aggregation, at least for start.
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>> Another issue I noticed is the local key by operator
> > >> cannot
> > >> > > > > > >> change
> > >> > > > > > >>>>>> element
> > >> > > > > > >>>>>>> type, it will
> > >> > > > > > >>>>>>> also restrict a lot of use cases which can be
> benefit
> > >> from
> > >> > > > local
> > >> > > > > > >>>>>>> aggregation, like "average".
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>> We also did similar logic in SQL and the only thing
> > >> need to
> > >> > > be
> > >> > > > > > >> done
> > >> > > > > > >>>> is
> > >> > > > > > >>>>>>> introduce
> > >> > > > > > >>>>>>> a stateless lightweight operator which is *chained*
> > >> before
> > >> > > > > > >>> `keyby()`.
> > >> > > > > > >>>>> The
> > >> > > > > > >>>>>>> operator will flush all buffered
> > >> > > > > > >>>>>>> elements during
> > >> > `StreamOperator::prepareSnapshotPreBarrier()`
> > >> > > > and
> > >> > > > > > >>>> make
> > >> > > > > > >>>>>>> himself stateless.
> > >> > > > > > >>>>>>> By the way, in the earlier version we also did the
> > >> similar
> > >> > > > > > >> approach
> > >> > > > > > >>>> by
> > >> > > > > > >>>>>>> introducing a stateful
> > >> > > > > > >>>>>>> local aggregation operator but it's not performed as
> > >> well
> > >> > as
> > >> > > > the
> > >> > > > > > >>>> later
> > >> > > > > > >>>>>> one,
> > >> > > > > > >>>>>>> and also effect the barrie
> > >> > > > > > >>>>>>> alignment time. The later one is fairly simple and
> > more
> > >> > > > > > >> efficient.
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>> I would highly suggest you to consider to have a
> > >> stateless
> > >> > > > > > >> approach
> > >> > > > > > >>>> at
> > >> > > > > > >>>>>> the
> > >> > > > > > >>>>>>> first step.
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>> Best,
> > >> > > > > > >>>>>>> Kurt
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> > >> [hidden email]>
> > >> > > > > > >> wrote:
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>>> Hi Vino,
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>> Thanks for the proposal.
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
> > >> > > > > > >>>>>>>>
> > >> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> > >> > > > > > >> have
> > >> > > > > > >>>> you
> > >> > > > > > >>>>>>> done
> > >> > > > > > >>>>>>>> some benchmark?
> > >> > > > > > >>>>>>>> Because I'm curious about how much performance
> > >> improvement
> > >> > > can
> > >> > > > > > >> we
> > >> > > > > > >>>> get
> > >> > > > > > >>>>>> by
> > >> > > > > > >>>>>>>> using count window as the local operator.
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>> Best,
> > >> > > > > > >>>>>>>> Jark
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> > >> > > > [hidden email]
> > >> > > > > > >>>
> > >> > > > > > >>>>> wrote:
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>>> Hi Hequn,
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> Thanks for your reply.
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to provide a tool
> > >> which
> > >> > > can
> > >> > > > > > >>> let
> > >> > > > > > >>>>>> users
> > >> > > > > > >>>>>>> do
> > >> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior of the
> > >> > > > > > >>> pre-aggregation
> > >> > > > > > >>>>> is
> > >> > > > > > >>>>>>>>> similar to keyBy API.
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> So the three cases are different, I will describe
> > them
> > >> > one
> > >> > > by
> > >> > > > > > >>>> one:
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> *In this case, the result is event-driven, each
> > event
> > >> can
> > >> > > > > > >>> produce
> > >> > > > > > >>>>> one
> > >> > > > > > >>>>>>> sum
> > >> > > > > > >>>>>>>>> aggregation result and it is the latest one from
> the
> > >> > source
> > >> > > > > > >>>> start.*
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> *In this case, the semantic may have a problem, it
> > >> would
> > >> > do
> > >> > > > > > >> the
> > >> > > > > > >>>>> local
> > >> > > > > > >>>>>>> sum
> > >> > > > > > >>>>>>>>> aggregation and will produce the latest partial
> > result
> > >> > from
> > >> > > > > > >> the
> > >> > > > > > >>>>>> source
> > >> > > > > > >>>>>>>>> start for every event. *
> > >> > > > > > >>>>>>>>> *These latest partial results from the same key
> are
> > >> > hashed
> > >> > > to
> > >> > > > > > >>> one
> > >> > > > > > >>>>>> node
> > >> > > > > > >>>>>>> to
> > >> > > > > > >>>>>>>>> do the global sum aggregation.*
> > >> > > > > > >>>>>>>>> *In the global aggregation, when it received
> > multiple
> > >> > > partial
> > >> > > > > > >>>>> results
> > >> > > > > > >>>>>>>> (they
> > >> > > > > > >>>>>>>>> are all calculated from the source start) and sum
> > them
> > >> > will
> > >> > > > > > >> get
> > >> > > > > > >>>> the
> > >> > > > > > >>>>>>> wrong
> > >> > > > > > >>>>>>>>> result.*
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> 3.
> > >> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> *In this case, it would just get a partial
> > aggregation
> > >> > > result
> > >> > > > > > >>> for
> > >> > > > > > >>>>>> the 5
> > >> > > > > > >>>>>>>>> records in the count window. The partial
> aggregation
> > >> > > results
> > >> > > > > > >>> from
> > >> > > > > > >>>>> the
> > >> > > > > > >>>>>>>> same
> > >> > > > > > >>>>>>>>> key will be aggregated globally.*
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> So the first case and the third case can get the
> > >> *same*
> > >> > > > > > >> result,
> > >> > > > > > >>>> the
> > >> > > > > > >>>>>>>>> difference is the output-style and the latency.
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> Generally speaking, the local key API is just an
> > >> > > optimization
> > >> > > > > > >>>> API.
> > >> > > > > > >>>>> We
> > >> > > > > > >>>>>>> do
> > >> > > > > > >>>>>>>>> not limit the user's usage, but the user has to
> > >> > understand
> > >> > > > > > >> its
> > >> > > > > > >>>>>>> semantics
> > >> > > > > > >>>>>>>>> and use it correctly.
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> Best,
> > >> > > > > > >>>>>>>>> Vino
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]> 于2019年6月17日周一
> > >> > 下午4:18写道：
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>>> Hi Vino,
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a very
> good
> > >> > > feature!
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>> One thing I want to make sure is the semantics
> for
> > >> the
> > >> > > > > > >>>>>> `localKeyBy`.
> > >> > > > > > >>>>>>>> From
> > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns an
> > >> instance
> > >> > of
> > >> > > > > > >>>>>>> `KeyedStream`
> > >> > > > > > >>>>>>>>>> which can also perform sum(), so in this case,
> > what's
> > >> > the
> > >> > > > > > >>>>> semantics
> > >> > > > > > >>>>>>> for
> > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the following
> > code
> > >> > share
> > >> > > > > > >>> the
> > >> > > > > > >>>>> same
> > >> > > > > > >>>>>>>>> result?
> > >> > > > > > >>>>>>>>>> and what're the differences between them?
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> > >> > > > > > >>>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > >> > > > > > >>>>>>>>>> 3.
> > >> > > > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>> Would also be great if we can add this into the
> > >> > document.
> > >> > > > > > >>> Thank
> > >> > > > > > >>>>> you
> > >> > > > > > >>>>>>>> very
> > >> > > > > > >>>>>>>>>> much.
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>> Best, Hequn
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino yang <
> > >> > > > > > >>>>> [hidden email]>
> > >> > > > > > >>>>>>>>> wrote:
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section of FLIP
> > >> wiki
> > >> > > > > > >>>> page.[1]
> > >> > > > > > >>>>>> This
> > >> > > > > > >>>>>>>>> mail
> > >> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to the
> > third
> > >> > step.
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote step), I
> > >> didn't
> > >> > > > > > >> find
> > >> > > > > > >>>> the
> > >> > > > > > >>>>>>>>>>> prerequisites for starting the voting process.
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>> Considering that the discussion of this feature
> > has
> > >> > been
> > >> > > > > > >>> done
> > >> > > > > > >>>>> in
> > >> > > > > > >>>>>>> the
> > >> > > > > > >>>>>>>>> old
> > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when should I
> start
> > >> > > > > > >> voting?
> > >> > > > > > >>>> Can
> > >> > > > > > >>>>> I
> > >> > > > > > >>>>>>>> start
> > >> > > > > > >>>>>>>>>> now?
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>> Best,
> > >> > > > > > >>>>>>>>>>> Vino
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>> [1]:
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>
> > >> > > > > > >>>>
> > >> > > > > > >>>
> > >> > > > > > >>
> > >> > > > > >
> > >> > > > >
> > >> > > >
> > >> > >
> > >> >
> > >>
> >
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> > >> > > > > > >>>>>>>>>>> [2]:
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>
> > >> > > > > > >>>>
> > >> > > > > > >>>
> > >> > > > > > >>
> > >> > > > > >
> > >> > > > >
> > >> > > >
> > >> > >
> > >> >
> > >>
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>> leesf <[hidden email]> 于2019年6月13日周四
> > 上午9:19写道：
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your efforts.
> > >> > > > > > >>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>> Best,
> > >> > > > > > >>>>>>>>>>>> Leesf
> > >> > > > > > >>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]>
> 于2019年6月12日周三
> > >> > > > > > >>> 下午5:46写道：
> > >> > > > > > >>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> Hi folks,
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP discussion
> thread
> > >> > > > > > >> about
> > >> > > > > > >>>>>>> supporting
> > >> > > > > > >>>>>>>>>> local
> > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> In short, this feature can effectively
> alleviate
> > >> data
> > >> > > > > > >>>> skew.
> > >> > > > > > >>>>>>> This
> > >> > > > > > >>>>>>>> is
> > >> > > > > > >>>>>>>>>> the
> > >> > > > > > >>>>>>>>>>>>> FLIP:
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>
> > >> > > > > > >>>>
> > >> > > > > > >>>
> > >> > > > > > >>
> > >> > > > > >
> > >> > > > >
> > >> > > >
> > >> > >
> > >> >
> > >>
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely used to
> > >> perform
> > >> > > > > > >>>>>> aggregating
> > >> > > > > > >>>>>>>>>>>> operations
> > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the elements
> > >> that
> > >> > > > > > >>> have
> > >> > > > > > >>>>> the
> > >> > > > > > >>>>>>> same
> > >> > > > > > >>>>>>>>>> key.
> > >> > > > > > >>>>>>>>>>>> When
> > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with the
> same
> > >> key
> > >> > > > > > >>> will
> > >> > > > > > >>>> be
> > >> > > > > > >>>>>>> sent
> > >> > > > > > >>>>>>>> to
> > >> > > > > > >>>>>>>>>> and
> > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> The performance of these aggregating
> operations
> > is
> > >> > > > > > >> very
> > >> > > > > > >>>>>>> sensitive
> > >> > > > > > >>>>>>>>> to
> > >> > > > > > >>>>>>>>>>> the
> > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases where the
> > >> > > > > > >>> distribution
> > >> > > > > > >>>>> of
> > >> > > > > > >>>>>>> keys
> > >> > > > > > >>>>>>>>>>>> follows a
> > >> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
> > >> significantly
> > >> > > > > > >>>>>> downgraded.
> > >> > > > > > >>>>>>>>> More
> > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of
> parallelism
> > >> does
> > >> > > > > > >>> not
> > >> > > > > > >>>>> help
> > >> > > > > > >>>>>>>> when
> > >> > > > > > >>>>>>>>> a
> > >> > > > > > >>>>>>>>>>> task
> > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted method
> to
> > >> > > > > > >> reduce
> > >> > > > > > >>>> the
> > >> > > > > > >>>>>>>>>> performance
> > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose the
> > >> > > > > > >> aggregating
> > >> > > > > > >>>>>>>> operations
> > >> > > > > > >>>>>>>>>> into
> > >> > > > > > >>>>>>>>>>>> two
> > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we aggregate the
> > >> elements
> > >> > > > > > >>> of
> > >> > > > > > >>>>> the
> > >> > > > > > >>>>>>> same
> > >> > > > > > >>>>>>>>> key
> > >> > > > > > >>>>>>>>>>> at
> > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial results.
> Then
> > at
> > >> > > > > > >> the
> > >> > > > > > >>>>> second
> > >> > > > > > >>>>>>>>> phase,
> > >> > > > > > >>>>>>>>>>>> these
> > >> > > > > > >>>>>>>>>>>>> partial results are sent to receivers
> according
> > to
> > >> > > > > > >>> their
> > >> > > > > > >>>>> keys
> > >> > > > > > >>>>>>> and
> > >> > > > > > >>>>>>>>> are
> > >> > > > > > >>>>>>>>>>>>> combined to obtain the final result. Since the
> > >> number
> > >> > > > > > >>> of
> > >> > > > > > >>>>>>> partial
> > >> > > > > > >>>>>>>>>>> results
> > >> > > > > > >>>>>>>>>>>>> received by each receiver is limited by the
> > >> number of
> > >> > > > > > >>>>>> senders,
> > >> > > > > > >>>>>>>> the
> > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be reduced.
> > >> Besides, by
> > >> > > > > > >>>>>> reducing
> > >> > > > > > >>>>>>>> the
> > >> > > > > > >>>>>>>>>>> amount
> > >> > > > > > >>>>>>>>>>>>> of transferred data the performance can be
> > further
> > >> > > > > > >>>>> improved.
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> *More details*:
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> Design documentation:
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>
> > >> > > > > > >>>>
> > >> > > > > > >>>
> > >> > > > > > >>
> > >> > > > > >
> > >> > > > >
> > >> > > >
> > >> > >
> > >> >
> > >>
> >
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>
> > >> > > > > > >>>>
> > >> > > > > > >>>
> > >> > > > > > >>
> > >> > > > > >
> > >> > > > >
> > >> > > >
> > >> > >
> > >> >
> > >>
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> > >> > > > > > >>>>>>>>> https://issues.apache.org/jira/browse/FLINK-12786
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your feedback!
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>> Best,
> > >> > > > > > >>>>>>>>>>>>> Vino
> > >> > > > > > >>>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>>
> > >> > > > > > >>>>>>>>>>
> > >> > > > > > >>>>>>>>>
> > >> > > > > > >>>>>>>>
> > >> > > > > > >>>>>>>
> > >> > > > > > >>>>>>
> > >> > > > > > >>>>>
> > >> > > > > > >>>>
> > >> > > > > > >>>
> > >> > > > > > >>
> > >> > > > > >
> > >> > > > > >
> > >> > > > >
> > >> > > >
> > >> > >
> > >> >
> > >>
> > >
> >
>

[Kurt Young]

Hi vino,

I think there are several things still need discussion.

a) We all agree that we should first go with a unified abstraction, but the
abstraction is not reflected by the FLIP.
If your answer is "locakKeyBy" API, then I would ask how do we combine with
`AggregateFunction`, and how do
we do proper local aggregation for those have different intermediate result
type, like AVG. Could you add these
to the document?

b) From implementation side, reusing window operator is one of the possible
solutions, but not we base on window
operator to have two different implementations. What I understanding is,
one of the possible implementations should
not touch window operator.

c) 80% of your FLIP content is actually describing how do we support local
keyed state. I don't know if this is necessary
to introduce at the first step and we should also involve committers work
on state backend to share their thoughts.

Best,
Kurt


On Mon, Jun 24, 2019 at 5:17 PM vino yang <[hidden email]> wrote:

> Hi Kurt,
>
> You did not give more further different opinions, so I thought you have
> agreed with the design after we promised to support two kinds of
> implementation.
>
> In API level, we have answered your question about pass an
> AggregateFunction to do the aggregation. No matter introduce localKeyBy API
> or not, we can support AggregateFunction.
>
> So what's your different opinion now? Can you share it with us?
>
> Best,
> Vino
>
> Kurt Young <[hidden email]> 于2019年6月24日周一 下午4:24写道：
>
> > Hi vino,
> >
> > Sorry I don't see the consensus about reusing window operator and keep
> the
> > API design of localKeyBy. But I think we should definitely more thoughts
> > about this topic.
> >
> > I also try to loop in Stephan for this discussion.
> >
> > Best,
> > Kurt
> >
> >
> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <[hidden email]> wrote:
> >
> > > Hi all,
> > >
> > > I am happy we have a wonderful discussion and received many valuable
> > > opinions in the last few days.
> > >
> > > Now, let me try to summarize what we have reached consensus about the
> > > changes in the design.
> > >
> > >    - provide a unified abstraction to support two kinds of
> > implementation;
> > >    - reuse WindowOperator and try to enhance it so that we can make the
> > >    intermediate result of the local aggregation can be buffered and
> > > flushed to
> > >    support two kinds of implementation;
> > >    - keep the API design of localKeyBy, but declare the disabled some
> > APIs
> > >    we cannot support currently, and provide a configurable API for
> users
> > to
> > >    choose how to handle intermediate result;
> > >
> > > The above three points have been updated in the design doc. Any
> > > questions, please let me know.
> > >
> > > @Aljoscha Krettek <[hidden email]> What do you think? Any further
> > > comments?
> > >
> > > Best,
> > > Vino
> > >
> > > vino yang <[hidden email]> 于2019年6月20日周四 下午2:02写道：
> > >
> > > > Hi Kurt,
> > > >
> > > > Thanks for your comments.
> > > >
> > > > It seems we come to a consensus that we should alleviate the
> > performance
> > > > degraded by data skew with local aggregation. In this FLIP, our key
> > > > solution is to introduce local keyed partition to achieve this goal.
> > > >
> > > > I also agree that we can benefit a lot from the usage of
> > > > AggregateFunction. In combination with localKeyBy, We can easily use
> it
> > > to
> > > > achieve local aggregation:
> > > >
> > > >    - input.localKeyBy(0).aggregate()
> > > >    - input.localKeyBy(0).window().aggregate()
> > > >
> > > >
> > > > I think the only problem here is the choices between
> > > >
> > > >    - (1) Introducing a new primitive called localKeyBy and implement
> > > >    local aggregation with existing operators, or
> > > >    - (2) Introducing an operator called localAggregation which is
> > > >    composed of a key selector, a window-like operator, and an
> aggregate
> > > >    function.
> > > >
> > > >
> > > > There may exist some optimization opportunities by providing a
> > composited
> > > > interface for local aggregation. But at the same time, in my opinion,
> > we
> > > > lose flexibility (Or we need certain efforts to achieve the same
> > > > flexibility).
> > > >
> > > > As said in the previous mails, we have many use cases where the
> > > > aggregation is very complicated and cannot be performed with
> > > > AggregateFunction. For example, users may perform windowed
> aggregations
> > > > according to time, data values, or even external storage. Typically,
> > they
> > > > now use KeyedProcessFunction or customized triggers to implement
> these
> > > > aggregations. It's not easy to address data skew in such cases with a
> > > > composited interface for local aggregation.
> > > >
> > > > Given that Data Stream API is exactly targeted at these cases where
> the
> > > > application logic is very complicated and optimization does not
> > matter, I
> > > > think it's a better choice to provide a relatively low-level and
> > > canonical
> > > > interface.
> > > >
> > > > The composited interface, on the other side, may be a good choice in
> > > > declarative interfaces, including SQL and Table API, as it allows
> more
> > > > optimization opportunities.
> > > >
> > > > Best,
> > > > Vino
> > > >
> > > >
> > > > Kurt Young <[hidden email]> 于2019年6月20日周四 上午10:15写道：
> > > >
> > > >> Hi all,
> > > >>
> > > >> As vino said in previous emails, I think we should first discuss and
> > > >> decide
> > > >> what kind of use cases this FLIP want to
> > > >> resolve, and what the API should look like. From my side, I think
> this
> > > is
> > > >> probably the root cause of current divergence.
> > > >>
> > > >> My understand is (from the FLIP title and motivation section of the
> > > >> document), we want to have a proper support of
> > > >> local aggregation, or pre aggregation. This is not a very new idea,
> > most
> > > >> SQL engine already did this improvement. And
> > > >> the core concept about this is, there should be an
> AggregateFunction,
> > no
> > > >> matter it's a Flink runtime's AggregateFunction or
> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have concept of
> > > >> intermediate data type, sometimes we call it ACC.
> > > >> I quickly went through the POC piotr did before [1], it also
> directly
> > > uses
> > > >> AggregateFunction.
> > > >>
> > > >> But the thing is, after reading the design of this FLIP, I can't
> help
> > > >> myself feeling that this FLIP is not targeting to have a proper
> > > >> local aggregation support. It actually want to introduce another
> > > concept:
> > > >> LocalKeyBy, and how to split and merge local key groups,
> > > >> and how to properly support state on local key. Local aggregation
> just
> > > >> happened to be one possible use case of LocalKeyBy.
> > > >> But it lacks supporting the essential concept of local aggregation,
> > > which
> > > >> is intermediate data type. Without this, I really don't thing
> > > >> it is a good fit of local aggregation.
> > > >>
> > > >> Here I want to make sure of the scope or the goal about this FLIP,
> do
> > we
> > > >> want to have a proper local aggregation engine, or we
> > > >> just want to introduce a new concept called LocalKeyBy?
> > > >>
> > > >> [1]: https://github.com/apache/flink/pull/4626
> > > >>
> > > >> Best,
> > > >> Kurt
> > > >>
> > > >>
> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <[hidden email]>
> > > wrote:
> > > >>
> > > >> > Hi Hequn,
> > > >> >
> > > >> > Thanks for your comments!
> > > >> >
> > > >> > I agree that allowing local aggregation reusing window API and
> > > refining
> > > >> > window operator to make it match both requirements (come from our
> > and
> > > >> Kurt)
> > > >> > is a good decision!
> > > >> >
> > > >> > Concerning your questions:
> > > >> >
> > > >> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1) may be
> > > >> > meaningless.
> > > >> >
> > > >> > Yes, it does not make sense in most cases. However, I also want to
> > > note
> > > >> > users should know the right semantics of localKeyBy and use it
> > > >> correctly.
> > > >> > Because this issue also exists for the global keyBy, consider this
> > > >> example:
> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also
> > meaningless.
> > > >> >
> > > >> > 2. About the semantics of
> > > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> > > >> >
> > > >> > Good catch! I agree with you that it's not good to enable all
> > > >> > functionalities for localKeyBy from KeyedStream.
> > > >> > Currently, We do not support some APIs such as
> > > >> > connect/join/intervalJoin/coGroup. This is due to that we force
> the
> > > >> > operators on LocalKeyedStreams chained with the inputs.
> > > >> >
> > > >> > Best,
> > > >> > Vino
> > > >> >
> > > >> >
> > > >> > Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
> > > >> >
> > > >> > > Hi,
> > > >> > >
> > > >> > > Thanks a lot for your great discussion and great to see that
> some
> > > >> > agreement
> > > >> > > has been reached on the "local aggregate engine"!
> > > >> > >
> > > >> > > ===> Considering the abstract engine,
> > > >> > > I'm thinking is it valuable for us to extend the current window
> to
> > > >> meet
> > > >> > > both demands raised by Kurt and Vino? There are some benefits we
> > can
> > > >> get:
> > > >> > >
> > > >> > > 1. The interfaces of the window are complete and clear. With
> > > windows,
> > > >> we
> > > >> > > can define a lot of ways to split the data and perform different
> > > >> > > computations.
> > > >> > > 2. We can also leverage the window to do miniBatch for the
> global
> > > >> > > aggregation, i.e, we can use the window to bundle data belong to
> > the
> > > >> same
> > > >> > > key, for every bundle we only need to read and write once state.
> > > This
> > > >> can
> > > >> > > greatly reduce state IO and improve performance.
> > > >> > > 3. A lot of other use cases can also benefit from the window
> base
> > on
> > > >> > memory
> > > >> > > or stateless.
> > > >> > >
> > > >> > > ===> As for the API,
> > > >> > > I think it is good to make our API more flexible. However, we
> may
> > > >> need to
> > > >> > > make our API meaningful.
> > > >> > >
> > > >> > > Take my previous reply as an example,
> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may be
> > > >> > meaningless.
> > > >> > > Another example I find is the intervalJoin, e.g.,
> > > >> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In this
> > > >> case, it
> > > >> > > will bring problems if input1 and input2 share different
> > > parallelism.
> > > >> We
> > > >> > > don't know which input should the join chained with? Even if
> they
> > > >> share
> > > >> > the
> > > >> > > same parallelism, it's hard to tell what the join is doing.
> There
> > > are
> > > >> > maybe
> > > >> > > some other problems.
> > > >> > >
> > > >> > > From this point of view, it's at least not good to enable all
> > > >> > > functionalities for localKeyBy from KeyedStream?
> > > >> > >
> > > >> > > Great to also have your opinions.
> > > >> > >
> > > >> > > Best, Hequn
> > > >> > >
> > > >> > >
> > > >> > >
> > > >> > >
> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
> [hidden email]
> > >
> > > >> > wrote:
> > > >> > >
> > > >> > > > Hi Kurt and Piotrek,
> > > >> > > >
> > > >> > > > Thanks for your comments.
> > > >> > > >
> > > >> > > > I agree that we can provide a better abstraction to be
> > compatible
> > > >> with
> > > >> > > two
> > > >> > > > different implementations.
> > > >> > > >
> > > >> > > > First of all, I think we should consider what kind of
> scenarios
> > we
> > > >> need
> > > >> > > to
> > > >> > > > support in *API* level?
> > > >> > > >
> > > >> > > > We have some use cases which need to a customized aggregation
> > > >> through
> > > >> > > > KeyedProcessFunction, (in the usage of our localKeyBy.window
> > they
> > > >> can
> > > >> > use
> > > >> > > > ProcessWindowFunction).
> > > >> > > >
> > > >> > > > Shall we support these flexible use scenarios?
> > > >> > > >
> > > >> > > > Best,
> > > >> > > > Vino
> > > >> > > >
> > > >> > > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
> > > >> > > >
> > > >> > > > > Hi Piotr,
> > > >> > > > >
> > > >> > > > > Thanks for joining the discussion. Make “local aggregation"
> > > >> abstract
> > > >> > > > enough
> > > >> > > > > sounds good to me, we could
> > > >> > > > > implement and verify alternative solutions for use cases of
> > > local
> > > >> > > > > aggregation. Maybe we will find both solutions
> > > >> > > > > are appropriate for different scenarios.
> > > >> > > > >
> > > >> > > > > Starting from a simple one sounds a practical way to go.
> What
> > do
> > > >> you
> > > >> > > > think,
> > > >> > > > > vino?
> > > >> > > > >
> > > >> > > > > Best,
> > > >> > > > > Kurt
> > > >> > > > >
> > > >> > > > >
> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> > > >> [hidden email]>
> > > >> > > > > wrote:
> > > >> > > > >
> > > >> > > > > > Hi Kurt and Vino,
> > > >> > > > > >
> > > >> > > > > > I think there is a trade of hat we need to consider for
> the
> > > >> local
> > > >> > > > > > aggregation.
> > > >> > > > > >
> > > >> > > > > > Generally speaking I would agree with Kurt about local
> > > >> > > aggregation/pre
> > > >> > > > > > aggregation not using Flink's state flush the operator on
> a
> > > >> > > checkpoint.
> > > >> > > > > > Network IO is usually cheaper compared to Disks IO. This
> has
> > > >> > however
> > > >> > > > > couple
> > > >> > > > > > of issues:
> > > >> > > > > > 1. It can explode number of in-flight records during
> > > checkpoint
> > > >> > > barrier
> > > >> > > > > > alignment, making checkpointing slower and decrease the
> > actual
> > > >> > > > > throughput.
> > > >> > > > > > 2. This trades Disks IO on the local aggregation machine
> > with
> > > >> CPU
> > > >> > > (and
> > > >> > > > > > Disks IO in case of RocksDB) on the final aggregation
> > machine.
> > > >> This
> > > >> > > is
> > > >> > > > > > fine, as long there is no huge data skew. If there is
> only a
> > > >> > handful
> > > >> > > > (or
> > > >> > > > > > even one single) hot keys, it might be better to keep the
> > > >> > persistent
> > > >> > > > > state
> > > >> > > > > > in the LocalAggregationOperator to offload final
> aggregation
> > > as
> > > >> > much
> > > >> > > as
> > > >> > > > > > possible.
> > > >> > > > > > 3. With frequent checkpointing local aggregation
> > effectiveness
> > > >> > would
> > > >> > > > > > degrade.
> > > >> > > > > >
> > > >> > > > > > I assume Kurt is correct, that in your use cases stateless
> > > >> operator
> > > >> > > was
> > > >> > > > > > behaving better, but I could easily see other use cases as
> > > well.
> > > >> > For
> > > >> > > > > > example someone is already using RocksDB, and his job is
> > > >> > bottlenecked
> > > >> > > > on
> > > >> > > > > a
> > > >> > > > > > single window operator instance because of the data skew.
> In
> > > >> that
> > > >> > > case
> > > >> > > > > > stateful local aggregation would be probably a better
> > choice.
> > > >> > > > > >
> > > >> > > > > > Because of that, I think we should eventually provide both
> > > >> versions
> > > >> > > and
> > > >> > > > > in
> > > >> > > > > > the initial version we should at least make the “local
> > > >> aggregation
> > > >> > > > > engine”
> > > >> > > > > > abstract enough, that one could easily provide different
> > > >> > > implementation
> > > >> > > > > > strategy.
> > > >> > > > > >
> > > >> > > > > > Piotrek
> > > >> > > > > >
> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <[hidden email]>
> > > >> wrote:
> > > >> > > > > > >
> > > >> > > > > > > Hi,
> > > >> > > > > > >
> > > >> > > > > > > For the trigger, it depends on what operator we want to
> > use
> > > >> under
> > > >> > > the
> > > >> > > > > > API.
> > > >> > > > > > > If we choose to use window operator,
> > > >> > > > > > > we should also use window's trigger. However, I also
> think
> > > >> reuse
> > > >> > > > window
> > > >> > > > > > > operator for this scenario may not be
> > > >> > > > > > > the best choice. The reasons are the following:
> > > >> > > > > > >
> > > >> > > > > > > 1. As a lot of people already pointed out, window relies
> > > >> heavily
> > > >> > on
> > > >> > > > > state
> > > >> > > > > > > and it will definitely effect performance. You can
> > > >> > > > > > > argue that one can use heap based statebackend, but this
> > > will
> > > >> > > > introduce
> > > >> > > > > > > extra coupling. Especially we have a chance to
> > > >> > > > > > > design a pure stateless operator.
> > > >> > > > > > > 2. The window operator is *the most* complicated
> operator
> > > >> Flink
> > > >> > > > > currently
> > > >> > > > > > > have. Maybe we only need to pick a subset of
> > > >> > > > > > > window operator to achieve the goal, but once the user
> > wants
> > > >> to
> > > >> > > have
> > > >> > > > a
> > > >> > > > > > deep
> > > >> > > > > > > look at the localAggregation operator, it's still
> > > >> > > > > > > hard to find out what's going on under the window
> > operator.
> > > >> For
> > > >> > > > > > simplicity,
> > > >> > > > > > > I would also recommend we introduce a dedicated
> > > >> > > > > > > lightweight operator, which also much easier for a user
> to
> > > >> learn
> > > >> > > and
> > > >> > > > > use.
> > > >> > > > > > >
> > > >> > > > > > > For your question about increasing the burden in
> > > >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the only
> > > thing
> > > >> > this
> > > >> > > > > > function
> > > >> > > > > > > need
> > > >> > > > > > > to do is output all the partial results, it's purely cpu
> > > >> > workload,
> > > >> > > > not
> > > >> > > > > > > introducing any IO. I want to point out that even if we
> > have
> > > >> this
> > > >> > > > > > > cost, we reduced another barrier align cost of the
> > operator,
> > > >> > which
> > > >> > > is
> > > >> > > > > the
> > > >> > > > > > > sync flush stage of the state, if you introduced state.
> > This
> > > >> > > > > > > flush actually will introduce disk IO, and I think it's
> > > >> worthy to
> > > >> > > > > > exchange
> > > >> > > > > > > this cost with purely CPU workload. And we do have some
> > > >> > > > > > > observations about these two behavior (as i said before,
> > we
> > > >> > > actually
> > > >> > > > > > > implemented both solutions), the stateless one actually
> > > >> performs
> > > >> > > > > > > better both in performance and barrier align time.
> > > >> > > > > > >
> > > >> > > > > > > Best,
> > > >> > > > > > > Kurt
> > > >> > > > > > >
> > > >> > > > > > >
> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> > > >> [hidden email]
> > > >> > >
> > > >> > > > > wrote:
> > > >> > > > > > >
> > > >> > > > > > >> Hi Kurt,
> > > >> > > > > > >>
> > > >> > > > > > >> Thanks for your example. Now, it looks more clearly for
> > me.
> > > >> > > > > > >>
> > > >> > > > > > >> From your example code snippet, I saw the
> localAggregate
> > > API
> > > >> has
> > > >> > > > three
> > > >> > > > > > >> parameters:
> > > >> > > > > > >>
> > > >> > > > > > >>   1. key field
> > > >> > > > > > >>   2. PartitionAvg
> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes from window
> > > >> package?
> > > >> > > > > > >>
> > > >> > > > > > >> I will compare our and your design from API and
> operator
> > > >> level:
> > > >> > > > > > >>
> > > >> > > > > > >> *From the API level:*
> > > >> > > > > > >>
> > > >> > > > > > >> As I replied to @dianfu in the old email thread,[1] the
> > > >> Window
> > > >> > API
> > > >> > > > can
> > > >> > > > > > >> provide the second and the third parameter right now.
> > > >> > > > > > >>
> > > >> > > > > > >> If you reuse specified interface or class, such as
> > > *Trigger*
> > > >> or
> > > >> > > > > > >> *CounterTrigger* provided by window package, but do not
> > use
> > > >> > window
> > > >> > > > > API,
> > > >> > > > > > >> it's not reasonable.
> > > >> > > > > > >> And if you do not reuse these interface or class, you
> > would
> > > >> need
> > > >> > > to
> > > >> > > > > > >> introduce more things however they are looked similar
> to
> > > the
> > > >> > > things
> > > >> > > > > > >> provided by window package.
> > > >> > > > > > >>
> > > >> > > > > > >> The window package has provided several types of the
> > window
> > > >> and
> > > >> > > many
> > > >> > > > > > >> triggers and let users customize it. What's more, the
> > user
> > > is
> > > >> > more
> > > >> > > > > > familiar
> > > >> > > > > > >> with Window API.
> > > >> > > > > > >>
> > > >> > > > > > >> This is the reason why we just provide localKeyBy API
> and
> > > >> reuse
> > > >> > > the
> > > >> > > > > > window
> > > >> > > > > > >> API. It reduces unnecessary components such as triggers
> > and
> > > >> the
> > > >> > > > > > mechanism
> > > >> > > > > > >> of buffer (based on count num or time).
> > > >> > > > > > >> And it has a clear and easy to understand semantics.
> > > >> > > > > > >>
> > > >> > > > > > >> *From the operator level:*
> > > >> > > > > > >>
> > > >> > > > > > >> We reused window operator, so we can get all the
> benefits
> > > >> from
> > > >> > > state
> > > >> > > > > and
> > > >> > > > > > >> checkpoint.
> > > >> > > > > > >>
> > > >> > > > > > >> From your design, you named the operator under
> > > localAggregate
> > > >> > API
> > > >> > > > is a
> > > >> > > > > > >> *stateless* operator. IMO, it is still a state, it is
> > just
> > > >> not
> > > >> > > Flink
> > > >> > > > > > >> managed state.
> > > >> > > > > > >> About the memory buffer (I think it's still not very
> > clear,
> > > >> if
> > > >> > you
> > > >> > > > > have
> > > >> > > > > > >> time, can you give more detail information or answer my
> > > >> > > questions),
> > > >> > > > I
> > > >> > > > > > have
> > > >> > > > > > >> some questions:
> > > >> > > > > > >>
> > > >> > > > > > >>   - if it just a raw JVM heap memory buffer, how to
> > support
> > > >> > fault
> > > >> > > > > > >>   tolerance, if the job is configured EXACTLY-ONCE
> > semantic
> > > >> > > > guarantee?
> > > >> > > > > > >>   - if you thought the memory buffer(non-Flink state),
> > has
> > > >> > better
> > > >> > > > > > >>   performance. In our design, users can also config
> HEAP
> > > >> state
> > > >> > > > backend
> > > >> > > > > > to
> > > >> > > > > > >>   provide the performance close to your mechanism.
> > > >> > > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()`
> related
> > > to
> > > >> the
> > > >> > > > > timing
> > > >> > > > > > of
> > > >> > > > > > >>   snapshot. IMO, the flush action should be a
> > synchronized
> > > >> > action?
> > > >> > > > (if
> > > >> > > > > > >> not,
> > > >> > > > > > >>   please point out my mistake) I still think we should
> > not
> > > >> > depend
> > > >> > > on
> > > >> > > > > the
> > > >> > > > > > >>   timing of checkpoint. Checkpoint related operations
> are
> > > >> > inherent
> > > >> > > > > > >>   performance sensitive, we should not increase its
> > burden
> > > >> > > anymore.
> > > >> > > > > Our
> > > >> > > > > > >>   implementation based on the mechanism of Flink's
> > > >> checkpoint,
> > > >> > > which
> > > >> > > > > can
> > > >> > > > > > >>   benefit from the asnyc snapshot and incremental
> > > checkpoint.
> > > >> > IMO,
> > > >> > > > the
> > > >> > > > > > >>   performance is not a problem, and we also do not find
> > the
> > > >> > > > > performance
> > > >> > > > > > >> issue
> > > >> > > > > > >>   in our production.
> > > >> > > > > > >>
> > > >> > > > > > >> [1]:
> > > >> > > > > > >>
> > > >> > > > > > >>
> > > >> > > > > >
> > > >> > > > >
> > > >> > > >
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > >> > > > > > >>
> > > >> > > > > > >> Best,
> > > >> > > > > > >> Vino
> > > >> > > > > > >>
> > > >> > > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二 下午2:27写道：
> > > >> > > > > > >>
> > > >> > > > > > >>> Yeah, sorry for not expressing myself clearly. I will
> > try
> > > to
> > > >> > > > provide
> > > >> > > > > > more
> > > >> > > > > > >>> details to make sure we are on the same page.
> > > >> > > > > > >>>
> > > >> > > > > > >>> For DataStream API, it shouldn't be optimized
> > > automatically.
> > > >> > You
> > > >> > > > have
> > > >> > > > > > to
> > > >> > > > > > >>> explicitly call API to do local aggregation
> > > >> > > > > > >>> as well as the trigger policy of the local
> aggregation.
> > > Take
> > > >> > > > average
> > > >> > > > > > for
> > > >> > > > > > >>> example, the user program may look like this (just a
> > > draft):
> > > >> > > > > > >>>
> > > >> > > > > > >>> assuming the input type is DataStream<Tupl2<String,
> > Int>>
> > > >> > > > > > >>>
> > > >> > > > > > >>> ds.localAggregate(
> > > >> > > > > > >>>        0,                                       // The
> > > local
> > > >> > key,
> > > >> > > > > which
> > > >> > > > > > >> is
> > > >> > > > > > >>> the String from Tuple2
> > > >> > > > > > >>>        PartitionAvg(1),                 // The partial
> > > >> > > aggregation
> > > >> > > > > > >>> function, produces Tuple2<Long, Int>, indicating sum
> and
> > > >> count
> > > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger policy,
> note
> > > >> this
> > > >> > > > should
> > > >> > > > > be
> > > >> > > > > > >>> best effort, and also be composited with time based or
> > > >> memory
> > > >> > > size
> > > >> > > > > > based
> > > >> > > > > > >>> trigger
> > > >> > > > > > >>>    )                                           // The
> > > return
> > > >> > type
> > > >> > > > is
> > > >> > > > > > >> local
> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> > > >> > > > > > >>>    .keyBy(0)                             // Further
> > keyby
> > > it
> > > >> > with
> > > >> > > > > > >> required
> > > >> > > > > > >>> key
> > > >> > > > > > >>>    .aggregate(1)                      // This will
> merge
> > > all
> > > >> > the
> > > >> > > > > > partial
> > > >> > > > > > >>> results and get the final average.
> > > >> > > > > > >>>
> > > >> > > > > > >>> (This is only a draft, only trying to explain what it
> > > looks
> > > >> > > like. )
> > > >> > > > > > >>>
> > > >> > > > > > >>> The local aggregate operator can be stateless, we can
> > > keep a
> > > >> > > memory
> > > >> > > > > > >> buffer
> > > >> > > > > > >>> or other efficient data structure to improve the
> > aggregate
> > > >> > > > > performance.
> > > >> > > > > > >>>
> > > >> > > > > > >>> Let me know if you have any other questions.
> > > >> > > > > > >>>
> > > >> > > > > > >>> Best,
> > > >> > > > > > >>> Kurt
> > > >> > > > > > >>>
> > > >> > > > > > >>>
> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> > > >> > [hidden email]
> > > >> > > >
> > > >> > > > > > wrote:
> > > >> > > > > > >>>
> > > >> > > > > > >>>> Hi Kurt,
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> Thanks for your reply.
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> Actually, I am not against you to raise your design.
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> From your description before, I just can imagine your
> > > >> > high-level
> > > >> > > > > > >>>> implementation is about SQL and the optimization is
> > inner
> > > >> of
> > > >> > the
> > > >> > > > > API.
> > > >> > > > > > >> Is
> > > >> > > > > > >>> it
> > > >> > > > > > >>>> automatically? how to give the configuration option
> > about
> > > >> > > trigger
> > > >> > > > > > >>>> pre-aggregation?
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> Maybe after I get more information, it sounds more
> > > >> reasonable.
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> IMO, first of all, it would be better to make your
> user
> > > >> > > interface
> > > >> > > > > > >>> concrete,
> > > >> > > > > > >>>> it's the basis of the discussion.
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> For example, can you give an example code snippet to
> > > >> introduce
> > > >> > > how
> > > >> > > > > to
> > > >> > > > > > >>> help
> > > >> > > > > > >>>> users to process data skew caused by the jobs which
> > built
> > > >> with
> > > >> > > > > > >> DataStream
> > > >> > > > > > >>>> API?
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> If you give more details we can discuss further
> more. I
> > > >> think
> > > >> > if
> > > >> > > > one
> > > >> > > > > > >>> design
> > > >> > > > > > >>>> introduces an exact interface and another does not.
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> The implementation has an obvious difference. For
> > > example,
> > > >> we
> > > >> > > > > > introduce
> > > >> > > > > > >>> an
> > > >> > > > > > >>>> exact API in DataStream named localKeyBy, about the
> > > >> > > > pre-aggregation
> > > >> > > > > we
> > > >> > > > > > >>> need
> > > >> > > > > > >>>> to define the trigger mechanism of local aggregation,
> > so
> > > we
> > > >> > find
> > > >> > > > > > reused
> > > >> > > > > > >>>> window API and operator is a good choice. This is a
> > > >> reasoning
> > > >> > > link
> > > >> > > > > > from
> > > >> > > > > > >>>> design to implementation.
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> What do you think?
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> Best,
> > > >> > > > > > >>>> Vino
> > > >> > > > > > >>>>
> > > >> > > > > > >>>>
> > > >> > > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二
> 上午11:58写道：
> > > >> > > > > > >>>>
> > > >> > > > > > >>>>> Hi Vino,
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>> Now I feel that we may have different understandings
> > > about
> > > >> > what
> > > >> > > > > kind
> > > >> > > > > > >> of
> > > >> > > > > > >>>>> problems or improvements you want to
> > > >> > > > > > >>>>> resolve. Currently, most of the feedback are
> focusing
> > on
> > > >> *how
> > > >> > > to
> > > >> > > > > do a
> > > >> > > > > > >>>>> proper local aggregation to improve performance
> > > >> > > > > > >>>>> and maybe solving the data skew issue*. And my gut
> > > >> feeling is
> > > >> > > > this
> > > >> > > > > is
> > > >> > > > > > >>>>> exactly what users want at the first place,
> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to summarize
> here,
> > > >> please
> > > >> > > > > correct
> > > >> > > > > > >>> me
> > > >> > > > > > >>>> if
> > > >> > > > > > >>>>> i'm wrong).
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>> But I still think the design is somehow diverged
> from
> > > the
> > > >> > goal.
> > > >> > > > If
> > > >> > > > > we
> > > >> > > > > > >>>> want
> > > >> > > > > > >>>>> to have an efficient and powerful way to
> > > >> > > > > > >>>>> have local aggregation, supporting intermedia result
> > > type
> > > >> is
> > > >> > > > > > >> essential
> > > >> > > > > > >>>> IMO.
> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a proper
> > > >> support of
> > > >> > > > > > >>>> intermediate
> > > >> > > > > > >>>>> result type and can do `merge` operation
> > > >> > > > > > >>>>> on them.
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>> Now, we have a lightweight alternatives which
> performs
> > > >> well,
> > > >> > > and
> > > >> > > > > > >> have a
> > > >> > > > > > >>>>> nice fit with the local aggregate requirements.
> > > >> > > > > > >>>>> Mostly importantly,  it's much less complex because
> > it's
> > > >> > > > stateless.
> > > >> > > > > > >> And
> > > >> > > > > > >>>> it
> > > >> > > > > > >>>>> can also achieve the similar multiple-aggregation
> > > >> > > > > > >>>>> scenario.
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>> I still not convinced why we shouldn't consider it
> as
> > a
> > > >> first
> > > >> > > > step.
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>> Best,
> > > >> > > > > > >>>>> Kurt
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> > > >> > > > [hidden email]>
> > > >> > > > > > >>>> wrote:
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>>> Hi Kurt,
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> Thanks for your comments.
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> It seems we both implemented local aggregation
> > feature
> > > to
> > > >> > > > optimize
> > > >> > > > > > >>> the
> > > >> > > > > > >>>>>> issue of data skew.
> > > >> > > > > > >>>>>> However, IMHO, the API level of optimizing revenue
> is
> > > >> > > different.
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> *Your optimization benefits from Flink SQL and it's
> > not
> > > >> > user's
> > > >> > > > > > >>>> faces.(If
> > > >> > > > > > >>>>> I
> > > >> > > > > > >>>>>> understand it incorrectly, please correct this.)*
> > > >> > > > > > >>>>>> *Our implementation employs it as an optimization
> > tool
> > > >> API
> > > >> > for
> > > >> > > > > > >>>>> DataStream,
> > > >> > > > > > >>>>>> it just like a local version of the keyBy API.*
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> Based on this, I want to say support it as a
> > DataStream
> > > >> API
> > > >> > > can
> > > >> > > > > > >>> provide
> > > >> > > > > > >>>>>> these advantages:
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear semantic and
> it's
> > > >> > flexible
> > > >> > > > not
> > > >> > > > > > >>> only
> > > >> > > > > > >>>>> for
> > > >> > > > > > >>>>>>   processing data skew but also for implementing
> some
> > > >> user
> > > >> > > > cases,
> > > >> > > > > > >>> for
> > > >> > > > > > >>>>>>   example, if we want to calculate the
> multiple-level
> > > >> > > > aggregation,
> > > >> > > > > > >>> we
> > > >> > > > > > >>>>> can
> > > >> > > > > > >>>>>> do
> > > >> > > > > > >>>>>>   multiple-level aggregation in the local
> > aggregation:
> > > >> > > > > > >>>>>>
> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
> > > >> //
> > > >> > > here
> > > >> > > > > > >> "a"
> > > >> > > > > > >>>> is
> > > >> > > > > > >>>>> a
> > > >> > > > > > >>>>>>   sub-category, while "b" is a category, here we do
> > not
> > > >> need
> > > >> > > to
> > > >> > > > > > >>>> shuffle
> > > >> > > > > > >>>>>> data
> > > >> > > > > > >>>>>>   in the network.
> > > >> > > > > > >>>>>>   - The users of DataStream API will benefit from
> > this.
> > > >> > > > Actually,
> > > >> > > > > > >> we
> > > >> > > > > > >>>>> have
> > > >> > > > > > >>>>>>   a lot of scenes need to use DataStream API.
> > > Currently,
> > > >> > > > > > >> DataStream
> > > >> > > > > > >>>> API
> > > >> > > > > > >>>>> is
> > > >> > > > > > >>>>>>   the cornerstone of the physical plan of Flink
> SQL.
> > > >> With a
> > > >> > > > > > >>> localKeyBy
> > > >> > > > > > >>>>>> API,
> > > >> > > > > > >>>>>>   the optimization of SQL at least may use this
> > > optimized
> > > >> > API,
> > > >> > > > > > >> this
> > > >> > > > > > >>>> is a
> > > >> > > > > > >>>>>>   further topic.
> > > >> > > > > > >>>>>>   - Based on the window operator, our state would
> > > benefit
> > > >> > from
> > > >> > > > > > >> Flink
> > > >> > > > > > >>>>> State
> > > >> > > > > > >>>>>>   and checkpoint, we do not need to worry about OOM
> > and
> > > >> job
> > > >> > > > > > >> failed.
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> Now, about your questions:
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> 1. About our design cannot change the data type and
> > > about
> > > >> > the
> > > >> > > > > > >>>>>> implementation of average:
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is an
> API
> > > >> > provides
> > > >> > > > to
> > > >> > > > > > >> the
> > > >> > > > > > >>>>> users
> > > >> > > > > > >>>>>> who use DataStream API to build their jobs.
> > > >> > > > > > >>>>>> Users should know its semantics and the difference
> > with
> > > >> > keyBy
> > > >> > > > API,
> > > >> > > > > > >> so
> > > >> > > > > > >>>> if
> > > >> > > > > > >>>>>> they want to the average aggregation, they should
> > carry
> > > >> > local
> > > >> > > > sum
> > > >> > > > > > >>>> result
> > > >> > > > > > >>>>>> and local count result.
> > > >> > > > > > >>>>>> I admit that it will be convenient to use keyBy
> > > directly.
> > > >> > But
> > > >> > > we
> > > >> > > > > > >> need
> > > >> > > > > > >>>> to
> > > >> > > > > > >>>>>> pay a little price when we get some benefits. I
> think
> > > >> this
> > > >> > > price
> > > >> > > > > is
> > > >> > > > > > >>>>>> reasonable. Considering that the DataStream API
> > itself
> > > >> is a
> > > >> > > > > > >> low-level
> > > >> > > > > > >>>> API
> > > >> > > > > > >>>>>> (at least for now).
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> 2. About stateless operator and
> > > >> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> Actually, I have discussed this opinion with
> @dianfu
> > in
> > > >> the
> > > >> > > old
> > > >> > > > > > >> mail
> > > >> > > > > > >>>>>> thread. I will copy my opinion from there:
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>>   - for your design, you still need somewhere to
> give
> > > the
> > > >> > > users
> > > >> > > > > > >>>>> configure
> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
> availability?),
> > > >> this
> > > >> > > > design
> > > >> > > > > > >>>> cannot
> > > >> > > > > > >>>>>>   guarantee a deterministic semantics (it will
> bring
> > > >> trouble
> > > >> > > for
> > > >> > > > > > >>>> testing
> > > >> > > > > > >>>>>> and
> > > >> > > > > > >>>>>>   debugging).
> > > >> > > > > > >>>>>>   - if the implementation depends on the timing of
> > > >> > checkpoint,
> > > >> > > > it
> > > >> > > > > > >>>> would
> > > >> > > > > > >>>>>>   affect the checkpoint's progress, and the
> buffered
> > > data
> > > >> > may
> > > >> > > > > > >> cause
> > > >> > > > > > >>>> OOM
> > > >> > > > > > >>>>>>   issue. In addition, if the operator is stateless,
> > it
> > > >> can
> > > >> > not
> > > >> > > > > > >>> provide
> > > >> > > > > > >>>>>> fault
> > > >> > > > > > >>>>>>   tolerance.
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> Best,
> > > >> > > > > > >>>>>> Vino
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二
> > 上午9:22写道：
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>>> Hi Vino,
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the general idea
> and
> > > IMO
> > > >> > it's
> > > >> > > > > > >> very
> > > >> > > > > > >>>>> useful
> > > >> > > > > > >>>>>>> feature.
> > > >> > > > > > >>>>>>> But after reading through the document, I feel
> that
> > we
> > > >> may
> > > >> > > over
> > > >> > > > > > >>>> design
> > > >> > > > > > >>>>>> the
> > > >> > > > > > >>>>>>> required
> > > >> > > > > > >>>>>>> operator for proper local aggregation. The main
> > reason
> > > >> is
> > > >> > we
> > > >> > > > want
> > > >> > > > > > >>> to
> > > >> > > > > > >>>>>> have a
> > > >> > > > > > >>>>>>> clear definition and behavior about the "local
> keyed
> > > >> state"
> > > >> > > > which
> > > >> > > > > > >>> in
> > > >> > > > > > >>>> my
> > > >> > > > > > >>>>>>> opinion is not
> > > >> > > > > > >>>>>>> necessary for local aggregation, at least for
> start.
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>> Another issue I noticed is the local key by
> operator
> > > >> cannot
> > > >> > > > > > >> change
> > > >> > > > > > >>>>>> element
> > > >> > > > > > >>>>>>> type, it will
> > > >> > > > > > >>>>>>> also restrict a lot of use cases which can be
> > benefit
> > > >> from
> > > >> > > > local
> > > >> > > > > > >>>>>>> aggregation, like "average".
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>> We also did similar logic in SQL and the only
> thing
> > > >> need to
> > > >> > > be
> > > >> > > > > > >> done
> > > >> > > > > > >>>> is
> > > >> > > > > > >>>>>>> introduce
> > > >> > > > > > >>>>>>> a stateless lightweight operator which is
> *chained*
> > > >> before
> > > >> > > > > > >>> `keyby()`.
> > > >> > > > > > >>>>> The
> > > >> > > > > > >>>>>>> operator will flush all buffered
> > > >> > > > > > >>>>>>> elements during
> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
> > > >> > > > and
> > > >> > > > > > >>>> make
> > > >> > > > > > >>>>>>> himself stateless.
> > > >> > > > > > >>>>>>> By the way, in the earlier version we also did the
> > > >> similar
> > > >> > > > > > >> approach
> > > >> > > > > > >>>> by
> > > >> > > > > > >>>>>>> introducing a stateful
> > > >> > > > > > >>>>>>> local aggregation operator but it's not performed
> as
> > > >> well
> > > >> > as
> > > >> > > > the
> > > >> > > > > > >>>> later
> > > >> > > > > > >>>>>> one,
> > > >> > > > > > >>>>>>> and also effect the barrie
> > > >> > > > > > >>>>>>> alignment time. The later one is fairly simple and
> > > more
> > > >> > > > > > >> efficient.
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>> I would highly suggest you to consider to have a
> > > >> stateless
> > > >> > > > > > >> approach
> > > >> > > > > > >>>> at
> > > >> > > > > > >>>>>> the
> > > >> > > > > > >>>>>>> first step.
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>> Best,
> > > >> > > > > > >>>>>>> Kurt
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> > > >> [hidden email]>
> > > >> > > > > > >> wrote:
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>>> Hi Vino,
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>> Thanks for the proposal.
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
> > > >> > > > > > >>>>>>>>
> > > >> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> > > >> > > > > > >> have
> > > >> > > > > > >>>> you
> > > >> > > > > > >>>>>>> done
> > > >> > > > > > >>>>>>>> some benchmark?
> > > >> > > > > > >>>>>>>> Because I'm curious about how much performance
> > > >> improvement
> > > >> > > can
> > > >> > > > > > >> we
> > > >> > > > > > >>>> get
> > > >> > > > > > >>>>>> by
> > > >> > > > > > >>>>>>>> using count window as the local operator.
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>> Best,
> > > >> > > > > > >>>>>>>> Jark
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> > > >> > > > [hidden email]
> > > >> > > > > > >>>
> > > >> > > > > > >>>>> wrote:
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>>> Hi Hequn,
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> Thanks for your reply.
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to provide a
> tool
> > > >> which
> > > >> > > can
> > > >> > > > > > >>> let
> > > >> > > > > > >>>>>> users
> > > >> > > > > > >>>>>>> do
> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior of
> the
> > > >> > > > > > >>> pre-aggregation
> > > >> > > > > > >>>>> is
> > > >> > > > > > >>>>>>>>> similar to keyBy API.
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> So the three cases are different, I will
> describe
> > > them
> > > >> > one
> > > >> > > by
> > > >> > > > > > >>>> one:
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> *In this case, the result is event-driven, each
> > > event
> > > >> can
> > > >> > > > > > >>> produce
> > > >> > > > > > >>>>> one
> > > >> > > > > > >>>>>>> sum
> > > >> > > > > > >>>>>>>>> aggregation result and it is the latest one from
> > the
> > > >> > source
> > > >> > > > > > >>>> start.*
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have a problem,
> it
> > > >> would
> > > >> > do
> > > >> > > > > > >> the
> > > >> > > > > > >>>>> local
> > > >> > > > > > >>>>>>> sum
> > > >> > > > > > >>>>>>>>> aggregation and will produce the latest partial
> > > result
> > > >> > from
> > > >> > > > > > >> the
> > > >> > > > > > >>>>>> source
> > > >> > > > > > >>>>>>>>> start for every event. *
> > > >> > > > > > >>>>>>>>> *These latest partial results from the same key
> > are
> > > >> > hashed
> > > >> > > to
> > > >> > > > > > >>> one
> > > >> > > > > > >>>>>> node
> > > >> > > > > > >>>>>>> to
> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it received
> > > multiple
> > > >> > > partial
> > > >> > > > > > >>>>> results
> > > >> > > > > > >>>>>>>> (they
> > > >> > > > > > >>>>>>>>> are all calculated from the source start) and
> sum
> > > them
> > > >> > will
> > > >> > > > > > >> get
> > > >> > > > > > >>>> the
> > > >> > > > > > >>>>>>> wrong
> > > >> > > > > > >>>>>>>>> result.*
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> 3.
> > > >> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> *In this case, it would just get a partial
> > > aggregation
> > > >> > > result
> > > >> > > > > > >>> for
> > > >> > > > > > >>>>>> the 5
> > > >> > > > > > >>>>>>>>> records in the count window. The partial
> > aggregation
> > > >> > > results
> > > >> > > > > > >>> from
> > > >> > > > > > >>>>> the
> > > >> > > > > > >>>>>>>> same
> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> So the first case and the third case can get the
> > > >> *same*
> > > >> > > > > > >> result,
> > > >> > > > > > >>>> the
> > > >> > > > > > >>>>>>>>> difference is the output-style and the latency.
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> Generally speaking, the local key API is just an
> > > >> > > optimization
> > > >> > > > > > >>>> API.
> > > >> > > > > > >>>>> We
> > > >> > > > > > >>>>>>> do
> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the user has to
> > > >> > understand
> > > >> > > > > > >> its
> > > >> > > > > > >>>>>>> semantics
> > > >> > > > > > >>>>>>>>> and use it correctly.
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> Best,
> > > >> > > > > > >>>>>>>>> Vino
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]>
> 于2019年6月17日周一
> > > >> > 下午4:18写道：
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>>> Hi Vino,
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a very
> > good
> > > >> > > feature!
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the semantics
> > for
> > > >> the
> > > >> > > > > > >>>>>> `localKeyBy`.
> > > >> > > > > > >>>>>>>> From
> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns an
> > > >> instance
> > > >> > of
> > > >> > > > > > >>>>>>> `KeyedStream`
> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in this case,
> > > what's
> > > >> > the
> > > >> > > > > > >>>>> semantics
> > > >> > > > > > >>>>>>> for
> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the following
> > > code
> > > >> > share
> > > >> > > > > > >>> the
> > > >> > > > > > >>>>> same
> > > >> > > > > > >>>>>>>>> result?
> > > >> > > > > > >>>>>>>>>> and what're the differences between them?
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> > > >> > > > > > >>>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > >> > > > > > >>>>>>>>>> 3.
> > > >> > > > > > >>
> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>> Would also be great if we can add this into the
> > > >> > document.
> > > >> > > > > > >>> Thank
> > > >> > > > > > >>>>> you
> > > >> > > > > > >>>>>>>> very
> > > >> > > > > > >>>>>>>>>> much.
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>> Best, Hequn
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino yang <
> > > >> > > > > > >>>>> [hidden email]>
> > > >> > > > > > >>>>>>>>> wrote:
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section of
> FLIP
> > > >> wiki
> > > >> > > > > > >>>> page.[1]
> > > >> > > > > > >>>>>> This
> > > >> > > > > > >>>>>>>>> mail
> > > >> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to the
> > > third
> > > >> > step.
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote step), I
> > > >> didn't
> > > >> > > > > > >> find
> > > >> > > > > > >>>> the
> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the voting process.
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of this
> feature
> > > has
> > > >> > been
> > > >> > > > > > >>> done
> > > >> > > > > > >>>>> in
> > > >> > > > > > >>>>>>> the
> > > >> > > > > > >>>>>>>>> old
> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when should I
> > start
> > > >> > > > > > >> voting?
> > > >> > > > > > >>>> Can
> > > >> > > > > > >>>>> I
> > > >> > > > > > >>>>>>>> start
> > > >> > > > > > >>>>>>>>>> now?
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>> Best,
> > > >> > > > > > >>>>>>>>>>> Vino
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>> [1]:
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>
> > > >> > > > > > >>>
> > > >> > > > > > >>
> > > >> > > > > >
> > > >> > > > >
> > > >> > > >
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> > > >> > > > > > >>>>>>>>>>> [2]:
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>
> > > >> > > > > > >>>
> > > >> > > > > > >>
> > > >> > > > > >
> > > >> > > > >
> > > >> > > >
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email]> 于2019年6月13日周四
> > > 上午9:19写道：
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your efforts.
> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>> Best,
> > > >> > > > > > >>>>>>>>>>>> Leesf
> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]>
> > 于2019年6月12日周三
> > > >> > > > > > >>> 下午5:46写道：
> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP discussion
> > thread
> > > >> > > > > > >> about
> > > >> > > > > > >>>>>>> supporting
> > > >> > > > > > >>>>>>>>>> local
> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can effectively
> > alleviate
> > > >> data
> > > >> > > > > > >>>> skew.
> > > >> > > > > > >>>>>>> This
> > > >> > > > > > >>>>>>>> is
> > > >> > > > > > >>>>>>>>>> the
> > > >> > > > > > >>>>>>>>>>>>> FLIP:
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>
> > > >> > > > > > >>>
> > > >> > > > > > >>
> > > >> > > > > >
> > > >> > > > >
> > > >> > > >
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely used to
> > > >> perform
> > > >> > > > > > >>>>>> aggregating
> > > >> > > > > > >>>>>>>>>>>> operations
> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the
> elements
> > > >> that
> > > >> > > > > > >>> have
> > > >> > > > > > >>>>> the
> > > >> > > > > > >>>>>>> same
> > > >> > > > > > >>>>>>>>>> key.
> > > >> > > > > > >>>>>>>>>>>> When
> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with the
> > same
> > > >> key
> > > >> > > > > > >>> will
> > > >> > > > > > >>>> be
> > > >> > > > > > >>>>>>> sent
> > > >> > > > > > >>>>>>>> to
> > > >> > > > > > >>>>>>>>>> and
> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> The performance of these aggregating
> > operations
> > > is
> > > >> > > > > > >> very
> > > >> > > > > > >>>>>>> sensitive
> > > >> > > > > > >>>>>>>>> to
> > > >> > > > > > >>>>>>>>>>> the
> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases where the
> > > >> > > > > > >>> distribution
> > > >> > > > > > >>>>> of
> > > >> > > > > > >>>>>>> keys
> > > >> > > > > > >>>>>>>>>>>> follows a
> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
> > > >> significantly
> > > >> > > > > > >>>>>> downgraded.
> > > >> > > > > > >>>>>>>>> More
> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of
> > parallelism
> > > >> does
> > > >> > > > > > >>> not
> > > >> > > > > > >>>>> help
> > > >> > > > > > >>>>>>>> when
> > > >> > > > > > >>>>>>>>> a
> > > >> > > > > > >>>>>>>>>>> task
> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted method
> > to
> > > >> > > > > > >> reduce
> > > >> > > > > > >>>> the
> > > >> > > > > > >>>>>>>>>> performance
> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose the
> > > >> > > > > > >> aggregating
> > > >> > > > > > >>>>>>>> operations
> > > >> > > > > > >>>>>>>>>> into
> > > >> > > > > > >>>>>>>>>>>> two
> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we aggregate the
> > > >> elements
> > > >> > > > > > >>> of
> > > >> > > > > > >>>>> the
> > > >> > > > > > >>>>>>> same
> > > >> > > > > > >>>>>>>>> key
> > > >> > > > > > >>>>>>>>>>> at
> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial results.
> > Then
> > > at
> > > >> > > > > > >> the
> > > >> > > > > > >>>>> second
> > > >> > > > > > >>>>>>>>> phase,
> > > >> > > > > > >>>>>>>>>>>> these
> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to receivers
> > according
> > > to
> > > >> > > > > > >>> their
> > > >> > > > > > >>>>> keys
> > > >> > > > > > >>>>>>> and
> > > >> > > > > > >>>>>>>>> are
> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final result. Since
> the
> > > >> number
> > > >> > > > > > >>> of
> > > >> > > > > > >>>>>>> partial
> > > >> > > > > > >>>>>>>>>>> results
> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is limited by the
> > > >> number of
> > > >> > > > > > >>>>>> senders,
> > > >> > > > > > >>>>>>>> the
> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be reduced.
> > > >> Besides, by
> > > >> > > > > > >>>>>> reducing
> > > >> > > > > > >>>>>>>> the
> > > >> > > > > > >>>>>>>>>>> amount
> > > >> > > > > > >>>>>>>>>>>>> of transferred data the performance can be
> > > further
> > > >> > > > > > >>>>> improved.
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> *More details*:
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>
> > > >> > > > > > >>>
> > > >> > > > > > >>
> > > >> > > > > >
> > > >> > > > >
> > > >> > > >
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>
> > > >> > > > > > >>>
> > > >> > > > > > >>
> > > >> > > > > >
> > > >> > > > >
> > > >> > > >
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> > > >> > > > > > >>>>>>>>>
> https://issues.apache.org/jira/browse/FLINK-12786
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your feedback!
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>> Best,
> > > >> > > > > > >>>>>>>>>>>>> Vino
> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>>
> > > >> > > > > > >>>>>>>>>>
> > > >> > > > > > >>>>>>>>>
> > > >> > > > > > >>>>>>>>
> > > >> > > > > > >>>>>>>
> > > >> > > > > > >>>>>>
> > > >> > > > > > >>>>>
> > > >> > > > > > >>>>
> > > >> > > > > > >>>
> > > >> > > > > > >>
> > > >> > > > > >
> > > >> > > > > >
> > > >> > > > >
> > > >> > > >
> > > >> > >
> > > >> >
> > > >>
> > > >
> > >
> >
>

[Kurt Young]

Hi vino,

One thing to add,  for a), I think use one or two examples like how to do
local aggregation on a sliding window,
and how do we do local aggregation on an unbounded aggregate, will do a lot
help.

Best,
Kurt


On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email]> wrote:

> Hi vino,
>
> I think there are several things still need discussion.
>
> a) We all agree that we should first go with a unified abstraction, but
> the abstraction is not reflected by the FLIP.
> If your answer is "locakKeyBy" API, then I would ask how do we combine
> with `AggregateFunction`, and how do
> we do proper local aggregation for those have different intermediate
> result type, like AVG. Could you add these
> to the document?
>
> b) From implementation side, reusing window operator is one of the
> possible solutions, but not we base on window
> operator to have two different implementations. What I understanding is,
> one of the possible implementations should
> not touch window operator.
>
> c) 80% of your FLIP content is actually describing how do we support local
> keyed state. I don't know if this is necessary
> to introduce at the first step and we should also involve committers work
> on state backend to share their thoughts.
>
> Best,
> Kurt
>
>
> On Mon, Jun 24, 2019 at 5:17 PM vino yang <[hidden email]> wrote:
>
>> Hi Kurt,
>>
>> You did not give more further different opinions, so I thought you have
>> agreed with the design after we promised to support two kinds of
>> implementation.
>>
>> In API level, we have answered your question about pass an
>> AggregateFunction to do the aggregation. No matter introduce localKeyBy
>> API
>> or not, we can support AggregateFunction.
>>
>> So what's your different opinion now? Can you share it with us?
>>
>> Best,
>> Vino
>>
>> Kurt Young <[hidden email]> 于2019年6月24日周一 下午4:24写道：
>>
>> > Hi vino,
>> >
>> > Sorry I don't see the consensus about reusing window operator and keep
>> the
>> > API design of localKeyBy. But I think we should definitely more thoughts
>> > about this topic.
>> >
>> > I also try to loop in Stephan for this discussion.
>> >
>> > Best,
>> > Kurt
>> >
>> >
>> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <[hidden email]>
>> wrote:
>> >
>> > > Hi all,
>> > >
>> > > I am happy we have a wonderful discussion and received many valuable
>> > > opinions in the last few days.
>> > >
>> > > Now, let me try to summarize what we have reached consensus about the
>> > > changes in the design.
>> > >
>> > >    - provide a unified abstraction to support two kinds of
>> > implementation;
>> > >    - reuse WindowOperator and try to enhance it so that we can make
>> the
>> > >    intermediate result of the local aggregation can be buffered and
>> > > flushed to
>> > >    support two kinds of implementation;
>> > >    - keep the API design of localKeyBy, but declare the disabled some
>> > APIs
>> > >    we cannot support currently, and provide a configurable API for
>> users
>> > to
>> > >    choose how to handle intermediate result;
>> > >
>> > > The above three points have been updated in the design doc. Any
>> > > questions, please let me know.
>> > >
>> > > @Aljoscha Krettek <[hidden email]> What do you think? Any
>> further
>> > > comments?
>> > >
>> > > Best,
>> > > Vino
>> > >
>> > > vino yang <[hidden email]> 于2019年6月20日周四 下午2:02写道：
>> > >
>> > > > Hi Kurt,
>> > > >
>> > > > Thanks for your comments.
>> > > >
>> > > > It seems we come to a consensus that we should alleviate the
>> > performance
>> > > > degraded by data skew with local aggregation. In this FLIP, our key
>> > > > solution is to introduce local keyed partition to achieve this goal.
>> > > >
>> > > > I also agree that we can benefit a lot from the usage of
>> > > > AggregateFunction. In combination with localKeyBy, We can easily
>> use it
>> > > to
>> > > > achieve local aggregation:
>> > > >
>> > > >    - input.localKeyBy(0).aggregate()
>> > > >    - input.localKeyBy(0).window().aggregate()
>> > > >
>> > > >
>> > > > I think the only problem here is the choices between
>> > > >
>> > > >    - (1) Introducing a new primitive called localKeyBy and implement
>> > > >    local aggregation with existing operators, or
>> > > >    - (2) Introducing an operator called localAggregation which is
>> > > >    composed of a key selector, a window-like operator, and an
>> aggregate
>> > > >    function.
>> > > >
>> > > >
>> > > > There may exist some optimization opportunities by providing a
>> > composited
>> > > > interface for local aggregation. But at the same time, in my
>> opinion,
>> > we
>> > > > lose flexibility (Or we need certain efforts to achieve the same
>> > > > flexibility).
>> > > >
>> > > > As said in the previous mails, we have many use cases where the
>> > > > aggregation is very complicated and cannot be performed with
>> > > > AggregateFunction. For example, users may perform windowed
>> aggregations
>> > > > according to time, data values, or even external storage. Typically,
>> > they
>> > > > now use KeyedProcessFunction or customized triggers to implement
>> these
>> > > > aggregations. It's not easy to address data skew in such cases with
>> a
>> > > > composited interface for local aggregation.
>> > > >
>> > > > Given that Data Stream API is exactly targeted at these cases where
>> the
>> > > > application logic is very complicated and optimization does not
>> > matter, I
>> > > > think it's a better choice to provide a relatively low-level and
>> > > canonical
>> > > > interface.
>> > > >
>> > > > The composited interface, on the other side, may be a good choice in
>> > > > declarative interfaces, including SQL and Table API, as it allows
>> more
>> > > > optimization opportunities.
>> > > >
>> > > > Best,
>> > > > Vino
>> > > >
>> > > >
>> > > > Kurt Young <[hidden email]> 于2019年6月20日周四 上午10:15写道：
>> > > >
>> > > >> Hi all,
>> > > >>
>> > > >> As vino said in previous emails, I think we should first discuss
>> and
>> > > >> decide
>> > > >> what kind of use cases this FLIP want to
>> > > >> resolve, and what the API should look like. From my side, I think
>> this
>> > > is
>> > > >> probably the root cause of current divergence.
>> > > >>
>> > > >> My understand is (from the FLIP title and motivation section of the
>> > > >> document), we want to have a proper support of
>> > > >> local aggregation, or pre aggregation. This is not a very new idea,
>> > most
>> > > >> SQL engine already did this improvement. And
>> > > >> the core concept about this is, there should be an
>> AggregateFunction,
>> > no
>> > > >> matter it's a Flink runtime's AggregateFunction or
>> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have concept
>> of
>> > > >> intermediate data type, sometimes we call it ACC.
>> > > >> I quickly went through the POC piotr did before [1], it also
>> directly
>> > > uses
>> > > >> AggregateFunction.
>> > > >>
>> > > >> But the thing is, after reading the design of this FLIP, I can't
>> help
>> > > >> myself feeling that this FLIP is not targeting to have a proper
>> > > >> local aggregation support. It actually want to introduce another
>> > > concept:
>> > > >> LocalKeyBy, and how to split and merge local key groups,
>> > > >> and how to properly support state on local key. Local aggregation
>> just
>> > > >> happened to be one possible use case of LocalKeyBy.
>> > > >> But it lacks supporting the essential concept of local aggregation,
>> > > which
>> > > >> is intermediate data type. Without this, I really don't thing
>> > > >> it is a good fit of local aggregation.
>> > > >>
>> > > >> Here I want to make sure of the scope or the goal about this FLIP,
>> do
>> > we
>> > > >> want to have a proper local aggregation engine, or we
>> > > >> just want to introduce a new concept called LocalKeyBy?
>> > > >>
>> > > >> [1]: https://github.com/apache/flink/pull/4626
>> > > >>
>> > > >> Best,
>> > > >> Kurt
>> > > >>
>> > > >>
>> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <[hidden email]>
>> > > wrote:
>> > > >>
>> > > >> > Hi Hequn,
>> > > >> >
>> > > >> > Thanks for your comments!
>> > > >> >
>> > > >> > I agree that allowing local aggregation reusing window API and
>> > > refining
>> > > >> > window operator to make it match both requirements (come from our
>> > and
>> > > >> Kurt)
>> > > >> > is a good decision!
>> > > >> >
>> > > >> > Concerning your questions:
>> > > >> >
>> > > >> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1) may
>> be
>> > > >> > meaningless.
>> > > >> >
>> > > >> > Yes, it does not make sense in most cases. However, I also want
>> to
>> > > note
>> > > >> > users should know the right semantics of localKeyBy and use it
>> > > >> correctly.
>> > > >> > Because this issue also exists for the global keyBy, consider
>> this
>> > > >> example:
>> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also
>> > meaningless.
>> > > >> >
>> > > >> > 2. About the semantics of
>> > > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
>> > > >> >
>> > > >> > Good catch! I agree with you that it's not good to enable all
>> > > >> > functionalities for localKeyBy from KeyedStream.
>> > > >> > Currently, We do not support some APIs such as
>> > > >> > connect/join/intervalJoin/coGroup. This is due to that we force
>> the
>> > > >> > operators on LocalKeyedStreams chained with the inputs.
>> > > >> >
>> > > >> > Best,
>> > > >> > Vino
>> > > >> >
>> > > >> >
>> > > >> > Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
>> > > >> >
>> > > >> > > Hi,
>> > > >> > >
>> > > >> > > Thanks a lot for your great discussion and great to see that
>> some
>> > > >> > agreement
>> > > >> > > has been reached on the "local aggregate engine"!
>> > > >> > >
>> > > >> > > ===> Considering the abstract engine,
>> > > >> > > I'm thinking is it valuable for us to extend the current
>> window to
>> > > >> meet
>> > > >> > > both demands raised by Kurt and Vino? There are some benefits
>> we
>> > can
>> > > >> get:
>> > > >> > >
>> > > >> > > 1. The interfaces of the window are complete and clear. With
>> > > windows,
>> > > >> we
>> > > >> > > can define a lot of ways to split the data and perform
>> different
>> > > >> > > computations.
>> > > >> > > 2. We can also leverage the window to do miniBatch for the
>> global
>> > > >> > > aggregation, i.e, we can use the window to bundle data belong
>> to
>> > the
>> > > >> same
>> > > >> > > key, for every bundle we only need to read and write once
>> state.
>> > > This
>> > > >> can
>> > > >> > > greatly reduce state IO and improve performance.
>> > > >> > > 3. A lot of other use cases can also benefit from the window
>> base
>> > on
>> > > >> > memory
>> > > >> > > or stateless.
>> > > >> > >
>> > > >> > > ===> As for the API,
>> > > >> > > I think it is good to make our API more flexible. However, we
>> may
>> > > >> need to
>> > > >> > > make our API meaningful.
>> > > >> > >
>> > > >> > > Take my previous reply as an example,
>> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may be
>> > > >> > meaningless.
>> > > >> > > Another example I find is the intervalJoin, e.g.,
>> > > >> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
>> this
>> > > >> case, it
>> > > >> > > will bring problems if input1 and input2 share different
>> > > parallelism.
>> > > >> We
>> > > >> > > don't know which input should the join chained with? Even if
>> they
>> > > >> share
>> > > >> > the
>> > > >> > > same parallelism, it's hard to tell what the join is doing.
>> There
>> > > are
>> > > >> > maybe
>> > > >> > > some other problems.
>> > > >> > >
>> > > >> > > From this point of view, it's at least not good to enable all
>> > > >> > > functionalities for localKeyBy from KeyedStream?
>> > > >> > >
>> > > >> > > Great to also have your opinions.
>> > > >> > >
>> > > >> > > Best, Hequn
>> > > >> > >
>> > > >> > >
>> > > >> > >
>> > > >> > >
>> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
>> [hidden email]
>> > >
>> > > >> > wrote:
>> > > >> > >
>> > > >> > > > Hi Kurt and Piotrek,
>> > > >> > > >
>> > > >> > > > Thanks for your comments.
>> > > >> > > >
>> > > >> > > > I agree that we can provide a better abstraction to be
>> > compatible
>> > > >> with
>> > > >> > > two
>> > > >> > > > different implementations.
>> > > >> > > >
>> > > >> > > > First of all, I think we should consider what kind of
>> scenarios
>> > we
>> > > >> need
>> > > >> > > to
>> > > >> > > > support in *API* level?
>> > > >> > > >
>> > > >> > > > We have some use cases which need to a customized aggregation
>> > > >> through
>> > > >> > > > KeyedProcessFunction, (in the usage of our localKeyBy.window
>> > they
>> > > >> can
>> > > >> > use
>> > > >> > > > ProcessWindowFunction).
>> > > >> > > >
>> > > >> > > > Shall we support these flexible use scenarios?
>> > > >> > > >
>> > > >> > > > Best,
>> > > >> > > > Vino
>> > > >> > > >
>> > > >> > > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
>> > > >> > > >
>> > > >> > > > > Hi Piotr,
>> > > >> > > > >
>> > > >> > > > > Thanks for joining the discussion. Make “local aggregation"
>> > > >> abstract
>> > > >> > > > enough
>> > > >> > > > > sounds good to me, we could
>> > > >> > > > > implement and verify alternative solutions for use cases of
>> > > local
>> > > >> > > > > aggregation. Maybe we will find both solutions
>> > > >> > > > > are appropriate for different scenarios.
>> > > >> > > > >
>> > > >> > > > > Starting from a simple one sounds a practical way to go.
>> What
>> > do
>> > > >> you
>> > > >> > > > think,
>> > > >> > > > > vino?
>> > > >> > > > >
>> > > >> > > > > Best,
>> > > >> > > > > Kurt
>> > > >> > > > >
>> > > >> > > > >
>> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
>> > > >> [hidden email]>
>> > > >> > > > > wrote:
>> > > >> > > > >
>> > > >> > > > > > Hi Kurt and Vino,
>> > > >> > > > > >
>> > > >> > > > > > I think there is a trade of hat we need to consider for
>> the
>> > > >> local
>> > > >> > > > > > aggregation.
>> > > >> > > > > >
>> > > >> > > > > > Generally speaking I would agree with Kurt about local
>> > > >> > > aggregation/pre
>> > > >> > > > > > aggregation not using Flink's state flush the operator
>> on a
>> > > >> > > checkpoint.
>> > > >> > > > > > Network IO is usually cheaper compared to Disks IO. This
>> has
>> > > >> > however
>> > > >> > > > > couple
>> > > >> > > > > > of issues:
>> > > >> > > > > > 1. It can explode number of in-flight records during
>> > > checkpoint
>> > > >> > > barrier
>> > > >> > > > > > alignment, making checkpointing slower and decrease the
>> > actual
>> > > >> > > > > throughput.
>> > > >> > > > > > 2. This trades Disks IO on the local aggregation machine
>> > with
>> > > >> CPU
>> > > >> > > (and
>> > > >> > > > > > Disks IO in case of RocksDB) on the final aggregation
>> > machine.
>> > > >> This
>> > > >> > > is
>> > > >> > > > > > fine, as long there is no huge data skew. If there is
>> only a
>> > > >> > handful
>> > > >> > > > (or
>> > > >> > > > > > even one single) hot keys, it might be better to keep the
>> > > >> > persistent
>> > > >> > > > > state
>> > > >> > > > > > in the LocalAggregationOperator to offload final
>> aggregation
>> > > as
>> > > >> > much
>> > > >> > > as
>> > > >> > > > > > possible.
>> > > >> > > > > > 3. With frequent checkpointing local aggregation
>> > effectiveness
>> > > >> > would
>> > > >> > > > > > degrade.
>> > > >> > > > > >
>> > > >> > > > > > I assume Kurt is correct, that in your use cases
>> stateless
>> > > >> operator
>> > > >> > > was
>> > > >> > > > > > behaving better, but I could easily see other use cases
>> as
>> > > well.
>> > > >> > For
>> > > >> > > > > > example someone is already using RocksDB, and his job is
>> > > >> > bottlenecked
>> > > >> > > > on
>> > > >> > > > > a
>> > > >> > > > > > single window operator instance because of the data
>> skew. In
>> > > >> that
>> > > >> > > case
>> > > >> > > > > > stateful local aggregation would be probably a better
>> > choice.
>> > > >> > > > > >
>> > > >> > > > > > Because of that, I think we should eventually provide
>> both
>> > > >> versions
>> > > >> > > and
>> > > >> > > > > in
>> > > >> > > > > > the initial version we should at least make the “local
>> > > >> aggregation
>> > > >> > > > > engine”
>> > > >> > > > > > abstract enough, that one could easily provide different
>> > > >> > > implementation
>> > > >> > > > > > strategy.
>> > > >> > > > > >
>> > > >> > > > > > Piotrek
>> > > >> > > > > >
>> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <[hidden email]
>> >
>> > > >> wrote:
>> > > >> > > > > > >
>> > > >> > > > > > > Hi,
>> > > >> > > > > > >
>> > > >> > > > > > > For the trigger, it depends on what operator we want to
>> > use
>> > > >> under
>> > > >> > > the
>> > > >> > > > > > API.
>> > > >> > > > > > > If we choose to use window operator,
>> > > >> > > > > > > we should also use window's trigger. However, I also
>> think
>> > > >> reuse
>> > > >> > > > window
>> > > >> > > > > > > operator for this scenario may not be
>> > > >> > > > > > > the best choice. The reasons are the following:
>> > > >> > > > > > >
>> > > >> > > > > > > 1. As a lot of people already pointed out, window
>> relies
>> > > >> heavily
>> > > >> > on
>> > > >> > > > > state
>> > > >> > > > > > > and it will definitely effect performance. You can
>> > > >> > > > > > > argue that one can use heap based statebackend, but
>> this
>> > > will
>> > > >> > > > introduce
>> > > >> > > > > > > extra coupling. Especially we have a chance to
>> > > >> > > > > > > design a pure stateless operator.
>> > > >> > > > > > > 2. The window operator is *the most* complicated
>> operator
>> > > >> Flink
>> > > >> > > > > currently
>> > > >> > > > > > > have. Maybe we only need to pick a subset of
>> > > >> > > > > > > window operator to achieve the goal, but once the user
>> > wants
>> > > >> to
>> > > >> > > have
>> > > >> > > > a
>> > > >> > > > > > deep
>> > > >> > > > > > > look at the localAggregation operator, it's still
>> > > >> > > > > > > hard to find out what's going on under the window
>> > operator.
>> > > >> For
>> > > >> > > > > > simplicity,
>> > > >> > > > > > > I would also recommend we introduce a dedicated
>> > > >> > > > > > > lightweight operator, which also much easier for a
>> user to
>> > > >> learn
>> > > >> > > and
>> > > >> > > > > use.
>> > > >> > > > > > >
>> > > >> > > > > > > For your question about increasing the burden in
>> > > >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the only
>> > > thing
>> > > >> > this
>> > > >> > > > > > function
>> > > >> > > > > > > need
>> > > >> > > > > > > to do is output all the partial results, it's purely
>> cpu
>> > > >> > workload,
>> > > >> > > > not
>> > > >> > > > > > > introducing any IO. I want to point out that even if we
>> > have
>> > > >> this
>> > > >> > > > > > > cost, we reduced another barrier align cost of the
>> > operator,
>> > > >> > which
>> > > >> > > is
>> > > >> > > > > the
>> > > >> > > > > > > sync flush stage of the state, if you introduced state.
>> > This
>> > > >> > > > > > > flush actually will introduce disk IO, and I think it's
>> > > >> worthy to
>> > > >> > > > > > exchange
>> > > >> > > > > > > this cost with purely CPU workload. And we do have some
>> > > >> > > > > > > observations about these two behavior (as i said
>> before,
>> > we
>> > > >> > > actually
>> > > >> > > > > > > implemented both solutions), the stateless one actually
>> > > >> performs
>> > > >> > > > > > > better both in performance and barrier align time.
>> > > >> > > > > > >
>> > > >> > > > > > > Best,
>> > > >> > > > > > > Kurt
>> > > >> > > > > > >
>> > > >> > > > > > >
>> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
>> > > >> [hidden email]
>> > > >> > >
>> > > >> > > > > wrote:
>> > > >> > > > > > >
>> > > >> > > > > > >> Hi Kurt,
>> > > >> > > > > > >>
>> > > >> > > > > > >> Thanks for your example. Now, it looks more clearly
>> for
>> > me.
>> > > >> > > > > > >>
>> > > >> > > > > > >> From your example code snippet, I saw the
>> localAggregate
>> > > API
>> > > >> has
>> > > >> > > > three
>> > > >> > > > > > >> parameters:
>> > > >> > > > > > >>
>> > > >> > > > > > >>   1. key field
>> > > >> > > > > > >>   2. PartitionAvg
>> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes from window
>> > > >> package?
>> > > >> > > > > > >>
>> > > >> > > > > > >> I will compare our and your design from API and
>> operator
>> > > >> level:
>> > > >> > > > > > >>
>> > > >> > > > > > >> *From the API level:*
>> > > >> > > > > > >>
>> > > >> > > > > > >> As I replied to @dianfu in the old email thread,[1]
>> the
>> > > >> Window
>> > > >> > API
>> > > >> > > > can
>> > > >> > > > > > >> provide the second and the third parameter right now.
>> > > >> > > > > > >>
>> > > >> > > > > > >> If you reuse specified interface or class, such as
>> > > *Trigger*
>> > > >> or
>> > > >> > > > > > >> *CounterTrigger* provided by window package, but do
>> not
>> > use
>> > > >> > window
>> > > >> > > > > API,
>> > > >> > > > > > >> it's not reasonable.
>> > > >> > > > > > >> And if you do not reuse these interface or class, you
>> > would
>> > > >> need
>> > > >> > > to
>> > > >> > > > > > >> introduce more things however they are looked similar
>> to
>> > > the
>> > > >> > > things
>> > > >> > > > > > >> provided by window package.
>> > > >> > > > > > >>
>> > > >> > > > > > >> The window package has provided several types of the
>> > window
>> > > >> and
>> > > >> > > many
>> > > >> > > > > > >> triggers and let users customize it. What's more, the
>> > user
>> > > is
>> > > >> > more
>> > > >> > > > > > familiar
>> > > >> > > > > > >> with Window API.
>> > > >> > > > > > >>
>> > > >> > > > > > >> This is the reason why we just provide localKeyBy API
>> and
>> > > >> reuse
>> > > >> > > the
>> > > >> > > > > > window
>> > > >> > > > > > >> API. It reduces unnecessary components such as
>> triggers
>> > and
>> > > >> the
>> > > >> > > > > > mechanism
>> > > >> > > > > > >> of buffer (based on count num or time).
>> > > >> > > > > > >> And it has a clear and easy to understand semantics.
>> > > >> > > > > > >>
>> > > >> > > > > > >> *From the operator level:*
>> > > >> > > > > > >>
>> > > >> > > > > > >> We reused window operator, so we can get all the
>> benefits
>> > > >> from
>> > > >> > > state
>> > > >> > > > > and
>> > > >> > > > > > >> checkpoint.
>> > > >> > > > > > >>
>> > > >> > > > > > >> From your design, you named the operator under
>> > > localAggregate
>> > > >> > API
>> > > >> > > > is a
>> > > >> > > > > > >> *stateless* operator. IMO, it is still a state, it is
>> > just
>> > > >> not
>> > > >> > > Flink
>> > > >> > > > > > >> managed state.
>> > > >> > > > > > >> About the memory buffer (I think it's still not very
>> > clear,
>> > > >> if
>> > > >> > you
>> > > >> > > > > have
>> > > >> > > > > > >> time, can you give more detail information or answer
>> my
>> > > >> > > questions),
>> > > >> > > > I
>> > > >> > > > > > have
>> > > >> > > > > > >> some questions:
>> > > >> > > > > > >>
>> > > >> > > > > > >>   - if it just a raw JVM heap memory buffer, how to
>> > support
>> > > >> > fault
>> > > >> > > > > > >>   tolerance, if the job is configured EXACTLY-ONCE
>> > semantic
>> > > >> > > > guarantee?
>> > > >> > > > > > >>   - if you thought the memory buffer(non-Flink state),
>> > has
>> > > >> > better
>> > > >> > > > > > >>   performance. In our design, users can also config
>> HEAP
>> > > >> state
>> > > >> > > > backend
>> > > >> > > > > > to
>> > > >> > > > > > >>   provide the performance close to your mechanism.
>> > > >> > > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()`
>> related
>> > > to
>> > > >> the
>> > > >> > > > > timing
>> > > >> > > > > > of
>> > > >> > > > > > >>   snapshot. IMO, the flush action should be a
>> > synchronized
>> > > >> > action?
>> > > >> > > > (if
>> > > >> > > > > > >> not,
>> > > >> > > > > > >>   please point out my mistake) I still think we should
>> > not
>> > > >> > depend
>> > > >> > > on
>> > > >> > > > > the
>> > > >> > > > > > >>   timing of checkpoint. Checkpoint related operations
>> are
>> > > >> > inherent
>> > > >> > > > > > >>   performance sensitive, we should not increase its
>> > burden
>> > > >> > > anymore.
>> > > >> > > > > Our
>> > > >> > > > > > >>   implementation based on the mechanism of Flink's
>> > > >> checkpoint,
>> > > >> > > which
>> > > >> > > > > can
>> > > >> > > > > > >>   benefit from the asnyc snapshot and incremental
>> > > checkpoint.
>> > > >> > IMO,
>> > > >> > > > the
>> > > >> > > > > > >>   performance is not a problem, and we also do not
>> find
>> > the
>> > > >> > > > > performance
>> > > >> > > > > > >> issue
>> > > >> > > > > > >>   in our production.
>> > > >> > > > > > >>
>> > > >> > > > > > >> [1]:
>> > > >> > > > > > >>
>> > > >> > > > > > >>
>> > > >> > > > > >
>> > > >> > > > >
>> > > >> > > >
>> > > >> > >
>> > > >> >
>> > > >>
>> > >
>> >
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> > > >> > > > > > >>
>> > > >> > > > > > >> Best,
>> > > >> > > > > > >> Vino
>> > > >> > > > > > >>
>> > > >> > > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二 下午2:27写道：
>> > > >> > > > > > >>
>> > > >> > > > > > >>> Yeah, sorry for not expressing myself clearly. I will
>> > try
>> > > to
>> > > >> > > > provide
>> > > >> > > > > > more
>> > > >> > > > > > >>> details to make sure we are on the same page.
>> > > >> > > > > > >>>
>> > > >> > > > > > >>> For DataStream API, it shouldn't be optimized
>> > > automatically.
>> > > >> > You
>> > > >> > > > have
>> > > >> > > > > > to
>> > > >> > > > > > >>> explicitly call API to do local aggregation
>> > > >> > > > > > >>> as well as the trigger policy of the local
>> aggregation.
>> > > Take
>> > > >> > > > average
>> > > >> > > > > > for
>> > > >> > > > > > >>> example, the user program may look like this (just a
>> > > draft):
>> > > >> > > > > > >>>
>> > > >> > > > > > >>> assuming the input type is DataStream<Tupl2<String,
>> > Int>>
>> > > >> > > > > > >>>
>> > > >> > > > > > >>> ds.localAggregate(
>> > > >> > > > > > >>>        0,                                       //
>> The
>> > > local
>> > > >> > key,
>> > > >> > > > > which
>> > > >> > > > > > >> is
>> > > >> > > > > > >>> the String from Tuple2
>> > > >> > > > > > >>>        PartitionAvg(1),                 // The
>> partial
>> > > >> > > aggregation
>> > > >> > > > > > >>> function, produces Tuple2<Long, Int>, indicating sum
>> and
>> > > >> count
>> > > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger policy,
>> note
>> > > >> this
>> > > >> > > > should
>> > > >> > > > > be
>> > > >> > > > > > >>> best effort, and also be composited with time based
>> or
>> > > >> memory
>> > > >> > > size
>> > > >> > > > > > based
>> > > >> > > > > > >>> trigger
>> > > >> > > > > > >>>    )                                           // The
>> > > return
>> > > >> > type
>> > > >> > > > is
>> > > >> > > > > > >> local
>> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
>> > > >> > > > > > >>>    .keyBy(0)                             // Further
>> > keyby
>> > > it
>> > > >> > with
>> > > >> > > > > > >> required
>> > > >> > > > > > >>> key
>> > > >> > > > > > >>>    .aggregate(1)                      // This will
>> merge
>> > > all
>> > > >> > the
>> > > >> > > > > > partial
>> > > >> > > > > > >>> results and get the final average.
>> > > >> > > > > > >>>
>> > > >> > > > > > >>> (This is only a draft, only trying to explain what it
>> > > looks
>> > > >> > > like. )
>> > > >> > > > > > >>>
>> > > >> > > > > > >>> The local aggregate operator can be stateless, we can
>> > > keep a
>> > > >> > > memory
>> > > >> > > > > > >> buffer
>> > > >> > > > > > >>> or other efficient data structure to improve the
>> > aggregate
>> > > >> > > > > performance.
>> > > >> > > > > > >>>
>> > > >> > > > > > >>> Let me know if you have any other questions.
>> > > >> > > > > > >>>
>> > > >> > > > > > >>> Best,
>> > > >> > > > > > >>> Kurt
>> > > >> > > > > > >>>
>> > > >> > > > > > >>>
>> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
>> > > >> > [hidden email]
>> > > >> > > >
>> > > >> > > > > > wrote:
>> > > >> > > > > > >>>
>> > > >> > > > > > >>>> Hi Kurt,
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> Thanks for your reply.
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> Actually, I am not against you to raise your design.
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> From your description before, I just can imagine
>> your
>> > > >> > high-level
>> > > >> > > > > > >>>> implementation is about SQL and the optimization is
>> > inner
>> > > >> of
>> > > >> > the
>> > > >> > > > > API.
>> > > >> > > > > > >> Is
>> > > >> > > > > > >>> it
>> > > >> > > > > > >>>> automatically? how to give the configuration option
>> > about
>> > > >> > > trigger
>> > > >> > > > > > >>>> pre-aggregation?
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> Maybe after I get more information, it sounds more
>> > > >> reasonable.
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> IMO, first of all, it would be better to make your
>> user
>> > > >> > > interface
>> > > >> > > > > > >>> concrete,
>> > > >> > > > > > >>>> it's the basis of the discussion.
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> For example, can you give an example code snippet to
>> > > >> introduce
>> > > >> > > how
>> > > >> > > > > to
>> > > >> > > > > > >>> help
>> > > >> > > > > > >>>> users to process data skew caused by the jobs which
>> > built
>> > > >> with
>> > > >> > > > > > >> DataStream
>> > > >> > > > > > >>>> API?
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> If you give more details we can discuss further
>> more. I
>> > > >> think
>> > > >> > if
>> > > >> > > > one
>> > > >> > > > > > >>> design
>> > > >> > > > > > >>>> introduces an exact interface and another does not.
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> The implementation has an obvious difference. For
>> > > example,
>> > > >> we
>> > > >> > > > > > introduce
>> > > >> > > > > > >>> an
>> > > >> > > > > > >>>> exact API in DataStream named localKeyBy, about the
>> > > >> > > > pre-aggregation
>> > > >> > > > > we
>> > > >> > > > > > >>> need
>> > > >> > > > > > >>>> to define the trigger mechanism of local
>> aggregation,
>> > so
>> > > we
>> > > >> > find
>> > > >> > > > > > reused
>> > > >> > > > > > >>>> window API and operator is a good choice. This is a
>> > > >> reasoning
>> > > >> > > link
>> > > >> > > > > > from
>> > > >> > > > > > >>>> design to implementation.
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> What do you think?
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> Best,
>> > > >> > > > > > >>>> Vino
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二
>> 上午11:58写道：
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>>> Hi Vino,
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>> Now I feel that we may have different
>> understandings
>> > > about
>> > > >> > what
>> > > >> > > > > kind
>> > > >> > > > > > >> of
>> > > >> > > > > > >>>>> problems or improvements you want to
>> > > >> > > > > > >>>>> resolve. Currently, most of the feedback are
>> focusing
>> > on
>> > > >> *how
>> > > >> > > to
>> > > >> > > > > do a
>> > > >> > > > > > >>>>> proper local aggregation to improve performance
>> > > >> > > > > > >>>>> and maybe solving the data skew issue*. And my gut
>> > > >> feeling is
>> > > >> > > > this
>> > > >> > > > > is
>> > > >> > > > > > >>>>> exactly what users want at the first place,
>> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to summarize
>> here,
>> > > >> please
>> > > >> > > > > correct
>> > > >> > > > > > >>> me
>> > > >> > > > > > >>>> if
>> > > >> > > > > > >>>>> i'm wrong).
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>> But I still think the design is somehow diverged
>> from
>> > > the
>> > > >> > goal.
>> > > >> > > > If
>> > > >> > > > > we
>> > > >> > > > > > >>>> want
>> > > >> > > > > > >>>>> to have an efficient and powerful way to
>> > > >> > > > > > >>>>> have local aggregation, supporting intermedia
>> result
>> > > type
>> > > >> is
>> > > >> > > > > > >> essential
>> > > >> > > > > > >>>> IMO.
>> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
>> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a proper
>> > > >> support of
>> > > >> > > > > > >>>> intermediate
>> > > >> > > > > > >>>>> result type and can do `merge` operation
>> > > >> > > > > > >>>>> on them.
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>> Now, we have a lightweight alternatives which
>> performs
>> > > >> well,
>> > > >> > > and
>> > > >> > > > > > >> have a
>> > > >> > > > > > >>>>> nice fit with the local aggregate requirements.
>> > > >> > > > > > >>>>> Mostly importantly,  it's much less complex because
>> > it's
>> > > >> > > > stateless.
>> > > >> > > > > > >> And
>> > > >> > > > > > >>>> it
>> > > >> > > > > > >>>>> can also achieve the similar multiple-aggregation
>> > > >> > > > > > >>>>> scenario.
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>> I still not convinced why we shouldn't consider it
>> as
>> > a
>> > > >> first
>> > > >> > > > step.
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>> Best,
>> > > >> > > > > > >>>>> Kurt
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
>> > > >> > > > [hidden email]>
>> > > >> > > > > > >>>> wrote:
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>>> Hi Kurt,
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> Thanks for your comments.
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> It seems we both implemented local aggregation
>> > feature
>> > > to
>> > > >> > > > optimize
>> > > >> > > > > > >>> the
>> > > >> > > > > > >>>>>> issue of data skew.
>> > > >> > > > > > >>>>>> However, IMHO, the API level of optimizing
>> revenue is
>> > > >> > > different.
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> *Your optimization benefits from Flink SQL and
>> it's
>> > not
>> > > >> > user's
>> > > >> > > > > > >>>> faces.(If
>> > > >> > > > > > >>>>> I
>> > > >> > > > > > >>>>>> understand it incorrectly, please correct this.)*
>> > > >> > > > > > >>>>>> *Our implementation employs it as an optimization
>> > tool
>> > > >> API
>> > > >> > for
>> > > >> > > > > > >>>>> DataStream,
>> > > >> > > > > > >>>>>> it just like a local version of the keyBy API.*
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> Based on this, I want to say support it as a
>> > DataStream
>> > > >> API
>> > > >> > > can
>> > > >> > > > > > >>> provide
>> > > >> > > > > > >>>>>> these advantages:
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear semantic and
>> it's
>> > > >> > flexible
>> > > >> > > > not
>> > > >> > > > > > >>> only
>> > > >> > > > > > >>>>> for
>> > > >> > > > > > >>>>>>   processing data skew but also for implementing
>> some
>> > > >> user
>> > > >> > > > cases,
>> > > >> > > > > > >>> for
>> > > >> > > > > > >>>>>>   example, if we want to calculate the
>> multiple-level
>> > > >> > > > aggregation,
>> > > >> > > > > > >>> we
>> > > >> > > > > > >>>>> can
>> > > >> > > > > > >>>>>> do
>> > > >> > > > > > >>>>>>   multiple-level aggregation in the local
>> > aggregation:
>> > > >> > > > > > >>>>>>
>> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
>> > > >> //
>> > > >> > > here
>> > > >> > > > > > >> "a"
>> > > >> > > > > > >>>> is
>> > > >> > > > > > >>>>> a
>> > > >> > > > > > >>>>>>   sub-category, while "b" is a category, here we
>> do
>> > not
>> > > >> need
>> > > >> > > to
>> > > >> > > > > > >>>> shuffle
>> > > >> > > > > > >>>>>> data
>> > > >> > > > > > >>>>>>   in the network.
>> > > >> > > > > > >>>>>>   - The users of DataStream API will benefit from
>> > this.
>> > > >> > > > Actually,
>> > > >> > > > > > >> we
>> > > >> > > > > > >>>>> have
>> > > >> > > > > > >>>>>>   a lot of scenes need to use DataStream API.
>> > > Currently,
>> > > >> > > > > > >> DataStream
>> > > >> > > > > > >>>> API
>> > > >> > > > > > >>>>> is
>> > > >> > > > > > >>>>>>   the cornerstone of the physical plan of Flink
>> SQL.
>> > > >> With a
>> > > >> > > > > > >>> localKeyBy
>> > > >> > > > > > >>>>>> API,
>> > > >> > > > > > >>>>>>   the optimization of SQL at least may use this
>> > > optimized
>> > > >> > API,
>> > > >> > > > > > >> this
>> > > >> > > > > > >>>> is a
>> > > >> > > > > > >>>>>>   further topic.
>> > > >> > > > > > >>>>>>   - Based on the window operator, our state would
>> > > benefit
>> > > >> > from
>> > > >> > > > > > >> Flink
>> > > >> > > > > > >>>>> State
>> > > >> > > > > > >>>>>>   and checkpoint, we do not need to worry about
>> OOM
>> > and
>> > > >> job
>> > > >> > > > > > >> failed.
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> Now, about your questions:
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> 1. About our design cannot change the data type
>> and
>> > > about
>> > > >> > the
>> > > >> > > > > > >>>>>> implementation of average:
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is an
>> API
>> > > >> > provides
>> > > >> > > > to
>> > > >> > > > > > >> the
>> > > >> > > > > > >>>>> users
>> > > >> > > > > > >>>>>> who use DataStream API to build their jobs.
>> > > >> > > > > > >>>>>> Users should know its semantics and the difference
>> > with
>> > > >> > keyBy
>> > > >> > > > API,
>> > > >> > > > > > >> so
>> > > >> > > > > > >>>> if
>> > > >> > > > > > >>>>>> they want to the average aggregation, they should
>> > carry
>> > > >> > local
>> > > >> > > > sum
>> > > >> > > > > > >>>> result
>> > > >> > > > > > >>>>>> and local count result.
>> > > >> > > > > > >>>>>> I admit that it will be convenient to use keyBy
>> > > directly.
>> > > >> > But
>> > > >> > > we
>> > > >> > > > > > >> need
>> > > >> > > > > > >>>> to
>> > > >> > > > > > >>>>>> pay a little price when we get some benefits. I
>> think
>> > > >> this
>> > > >> > > price
>> > > >> > > > > is
>> > > >> > > > > > >>>>>> reasonable. Considering that the DataStream API
>> > itself
>> > > >> is a
>> > > >> > > > > > >> low-level
>> > > >> > > > > > >>>> API
>> > > >> > > > > > >>>>>> (at least for now).
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> 2. About stateless operator and
>> > > >> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> Actually, I have discussed this opinion with
>> @dianfu
>> > in
>> > > >> the
>> > > >> > > old
>> > > >> > > > > > >> mail
>> > > >> > > > > > >>>>>> thread. I will copy my opinion from there:
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>>   - for your design, you still need somewhere to
>> give
>> > > the
>> > > >> > > users
>> > > >> > > > > > >>>>> configure
>> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
>> availability?),
>> > > >> this
>> > > >> > > > design
>> > > >> > > > > > >>>> cannot
>> > > >> > > > > > >>>>>>   guarantee a deterministic semantics (it will
>> bring
>> > > >> trouble
>> > > >> > > for
>> > > >> > > > > > >>>> testing
>> > > >> > > > > > >>>>>> and
>> > > >> > > > > > >>>>>>   debugging).
>> > > >> > > > > > >>>>>>   - if the implementation depends on the timing of
>> > > >> > checkpoint,
>> > > >> > > > it
>> > > >> > > > > > >>>> would
>> > > >> > > > > > >>>>>>   affect the checkpoint's progress, and the
>> buffered
>> > > data
>> > > >> > may
>> > > >> > > > > > >> cause
>> > > >> > > > > > >>>> OOM
>> > > >> > > > > > >>>>>>   issue. In addition, if the operator is
>> stateless,
>> > it
>> > > >> can
>> > > >> > not
>> > > >> > > > > > >>> provide
>> > > >> > > > > > >>>>>> fault
>> > > >> > > > > > >>>>>>   tolerance.
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> Best,
>> > > >> > > > > > >>>>>> Vino
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二
>> > 上午9:22写道：
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>>> Hi Vino,
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the general idea
>> and
>> > > IMO
>> > > >> > it's
>> > > >> > > > > > >> very
>> > > >> > > > > > >>>>> useful
>> > > >> > > > > > >>>>>>> feature.
>> > > >> > > > > > >>>>>>> But after reading through the document, I feel
>> that
>> > we
>> > > >> may
>> > > >> > > over
>> > > >> > > > > > >>>> design
>> > > >> > > > > > >>>>>> the
>> > > >> > > > > > >>>>>>> required
>> > > >> > > > > > >>>>>>> operator for proper local aggregation. The main
>> > reason
>> > > >> is
>> > > >> > we
>> > > >> > > > want
>> > > >> > > > > > >>> to
>> > > >> > > > > > >>>>>> have a
>> > > >> > > > > > >>>>>>> clear definition and behavior about the "local
>> keyed
>> > > >> state"
>> > > >> > > > which
>> > > >> > > > > > >>> in
>> > > >> > > > > > >>>> my
>> > > >> > > > > > >>>>>>> opinion is not
>> > > >> > > > > > >>>>>>> necessary for local aggregation, at least for
>> start.
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>> Another issue I noticed is the local key by
>> operator
>> > > >> cannot
>> > > >> > > > > > >> change
>> > > >> > > > > > >>>>>> element
>> > > >> > > > > > >>>>>>> type, it will
>> > > >> > > > > > >>>>>>> also restrict a lot of use cases which can be
>> > benefit
>> > > >> from
>> > > >> > > > local
>> > > >> > > > > > >>>>>>> aggregation, like "average".
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>> We also did similar logic in SQL and the only
>> thing
>> > > >> need to
>> > > >> > > be
>> > > >> > > > > > >> done
>> > > >> > > > > > >>>> is
>> > > >> > > > > > >>>>>>> introduce
>> > > >> > > > > > >>>>>>> a stateless lightweight operator which is
>> *chained*
>> > > >> before
>> > > >> > > > > > >>> `keyby()`.
>> > > >> > > > > > >>>>> The
>> > > >> > > > > > >>>>>>> operator will flush all buffered
>> > > >> > > > > > >>>>>>> elements during
>> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
>> > > >> > > > and
>> > > >> > > > > > >>>> make
>> > > >> > > > > > >>>>>>> himself stateless.
>> > > >> > > > > > >>>>>>> By the way, in the earlier version we also did
>> the
>> > > >> similar
>> > > >> > > > > > >> approach
>> > > >> > > > > > >>>> by
>> > > >> > > > > > >>>>>>> introducing a stateful
>> > > >> > > > > > >>>>>>> local aggregation operator but it's not
>> performed as
>> > > >> well
>> > > >> > as
>> > > >> > > > the
>> > > >> > > > > > >>>> later
>> > > >> > > > > > >>>>>> one,
>> > > >> > > > > > >>>>>>> and also effect the barrie
>> > > >> > > > > > >>>>>>> alignment time. The later one is fairly simple
>> and
>> > > more
>> > > >> > > > > > >> efficient.
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>> I would highly suggest you to consider to have a
>> > > >> stateless
>> > > >> > > > > > >> approach
>> > > >> > > > > > >>>> at
>> > > >> > > > > > >>>>>> the
>> > > >> > > > > > >>>>>>> first step.
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>> Best,
>> > > >> > > > > > >>>>>>> Kurt
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
>> > > >> [hidden email]>
>> > > >> > > > > > >> wrote:
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>>> Hi Vino,
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>> Thanks for the proposal.
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
>> > > >> > > > > > >>>>>>>>
>> > > >> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
>> > > >> > > > > > >> have
>> > > >> > > > > > >>>> you
>> > > >> > > > > > >>>>>>> done
>> > > >> > > > > > >>>>>>>> some benchmark?
>> > > >> > > > > > >>>>>>>> Because I'm curious about how much performance
>> > > >> improvement
>> > > >> > > can
>> > > >> > > > > > >> we
>> > > >> > > > > > >>>> get
>> > > >> > > > > > >>>>>> by
>> > > >> > > > > > >>>>>>>> using count window as the local operator.
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>> Best,
>> > > >> > > > > > >>>>>>>> Jark
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
>> > > >> > > > [hidden email]
>> > > >> > > > > > >>>
>> > > >> > > > > > >>>>> wrote:
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>>> Hi Hequn,
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> Thanks for your reply.
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to provide a
>> tool
>> > > >> which
>> > > >> > > can
>> > > >> > > > > > >>> let
>> > > >> > > > > > >>>>>> users
>> > > >> > > > > > >>>>>>> do
>> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior of
>> the
>> > > >> > > > > > >>> pre-aggregation
>> > > >> > > > > > >>>>> is
>> > > >> > > > > > >>>>>>>>> similar to keyBy API.
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> So the three cases are different, I will
>> describe
>> > > them
>> > > >> > one
>> > > >> > > by
>> > > >> > > > > > >>>> one:
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> *In this case, the result is event-driven, each
>> > > event
>> > > >> can
>> > > >> > > > > > >>> produce
>> > > >> > > > > > >>>>> one
>> > > >> > > > > > >>>>>>> sum
>> > > >> > > > > > >>>>>>>>> aggregation result and it is the latest one
>> from
>> > the
>> > > >> > source
>> > > >> > > > > > >>>> start.*
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have a
>> problem, it
>> > > >> would
>> > > >> > do
>> > > >> > > > > > >> the
>> > > >> > > > > > >>>>> local
>> > > >> > > > > > >>>>>>> sum
>> > > >> > > > > > >>>>>>>>> aggregation and will produce the latest partial
>> > > result
>> > > >> > from
>> > > >> > > > > > >> the
>> > > >> > > > > > >>>>>> source
>> > > >> > > > > > >>>>>>>>> start for every event. *
>> > > >> > > > > > >>>>>>>>> *These latest partial results from the same key
>> > are
>> > > >> > hashed
>> > > >> > > to
>> > > >> > > > > > >>> one
>> > > >> > > > > > >>>>>> node
>> > > >> > > > > > >>>>>>> to
>> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
>> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it received
>> > > multiple
>> > > >> > > partial
>> > > >> > > > > > >>>>> results
>> > > >> > > > > > >>>>>>>> (they
>> > > >> > > > > > >>>>>>>>> are all calculated from the source start) and
>> sum
>> > > them
>> > > >> > will
>> > > >> > > > > > >> get
>> > > >> > > > > > >>>> the
>> > > >> > > > > > >>>>>>> wrong
>> > > >> > > > > > >>>>>>>>> result.*
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> 3.
>> > > >> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> *In this case, it would just get a partial
>> > > aggregation
>> > > >> > > result
>> > > >> > > > > > >>> for
>> > > >> > > > > > >>>>>> the 5
>> > > >> > > > > > >>>>>>>>> records in the count window. The partial
>> > aggregation
>> > > >> > > results
>> > > >> > > > > > >>> from
>> > > >> > > > > > >>>>> the
>> > > >> > > > > > >>>>>>>> same
>> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> So the first case and the third case can get
>> the
>> > > >> *same*
>> > > >> > > > > > >> result,
>> > > >> > > > > > >>>> the
>> > > >> > > > > > >>>>>>>>> difference is the output-style and the latency.
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> Generally speaking, the local key API is just
>> an
>> > > >> > > optimization
>> > > >> > > > > > >>>> API.
>> > > >> > > > > > >>>>> We
>> > > >> > > > > > >>>>>>> do
>> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the user has to
>> > > >> > understand
>> > > >> > > > > > >> its
>> > > >> > > > > > >>>>>>> semantics
>> > > >> > > > > > >>>>>>>>> and use it correctly.
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> Best,
>> > > >> > > > > > >>>>>>>>> Vino
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]>
>> 于2019年6月17日周一
>> > > >> > 下午4:18写道：
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>>> Hi Vino,
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a very
>> > good
>> > > >> > > feature!
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the semantics
>> > for
>> > > >> the
>> > > >> > > > > > >>>>>> `localKeyBy`.
>> > > >> > > > > > >>>>>>>> From
>> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns an
>> > > >> instance
>> > > >> > of
>> > > >> > > > > > >>>>>>> `KeyedStream`
>> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in this case,
>> > > what's
>> > > >> > the
>> > > >> > > > > > >>>>> semantics
>> > > >> > > > > > >>>>>>> for
>> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the
>> following
>> > > code
>> > > >> > share
>> > > >> > > > > > >>> the
>> > > >> > > > > > >>>>> same
>> > > >> > > > > > >>>>>>>>> result?
>> > > >> > > > > > >>>>>>>>>> and what're the differences between them?
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
>> > > >> > > > > > >>>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>> > > >> > > > > > >>>>>>>>>> 3.
>> > > >> > > > > > >>
>> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>> Would also be great if we can add this into
>> the
>> > > >> > document.
>> > > >> > > > > > >>> Thank
>> > > >> > > > > > >>>>> you
>> > > >> > > > > > >>>>>>>> very
>> > > >> > > > > > >>>>>>>>>> much.
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>> Best, Hequn
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino yang <
>> > > >> > > > > > >>>>> [hidden email]>
>> > > >> > > > > > >>>>>>>>> wrote:
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section of
>> FLIP
>> > > >> wiki
>> > > >> > > > > > >>>> page.[1]
>> > > >> > > > > > >>>>>> This
>> > > >> > > > > > >>>>>>>>> mail
>> > > >> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to the
>> > > third
>> > > >> > step.
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote step),
>> I
>> > > >> didn't
>> > > >> > > > > > >> find
>> > > >> > > > > > >>>> the
>> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the voting
>> process.
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of this
>> feature
>> > > has
>> > > >> > been
>> > > >> > > > > > >>> done
>> > > >> > > > > > >>>>> in
>> > > >> > > > > > >>>>>>> the
>> > > >> > > > > > >>>>>>>>> old
>> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when should I
>> > start
>> > > >> > > > > > >> voting?
>> > > >> > > > > > >>>> Can
>> > > >> > > > > > >>>>> I
>> > > >> > > > > > >>>>>>>> start
>> > > >> > > > > > >>>>>>>>>> now?
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>> Best,
>> > > >> > > > > > >>>>>>>>>>> Vino
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>> [1]:
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>
>> > > >> > > > > > >>
>> > > >> > > > > >
>> > > >> > > > >
>> > > >> > > >
>> > > >> > >
>> > > >> >
>> > > >>
>> > >
>> >
>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>> > > >> > > > > > >>>>>>>>>>> [2]:
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>
>> > > >> > > > > > >>
>> > > >> > > > > >
>> > > >> > > > >
>> > > >> > > >
>> > > >> > >
>> > > >> >
>> > > >>
>> > >
>> >
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email]> 于2019年6月13日周四
>> > > 上午9:19写道：
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your
>> efforts.
>> > > >> > > > > > >>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>> Best,
>> > > >> > > > > > >>>>>>>>>>>> Leesf
>> > > >> > > > > > >>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]>
>> > 于2019年6月12日周三
>> > > >> > > > > > >>> 下午5:46写道：
>> > > >> > > > > > >>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP discussion
>> > thread
>> > > >> > > > > > >> about
>> > > >> > > > > > >>>>>>> supporting
>> > > >> > > > > > >>>>>>>>>> local
>> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can effectively
>> > alleviate
>> > > >> data
>> > > >> > > > > > >>>> skew.
>> > > >> > > > > > >>>>>>> This
>> > > >> > > > > > >>>>>>>> is
>> > > >> > > > > > >>>>>>>>>> the
>> > > >> > > > > > >>>>>>>>>>>>> FLIP:
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>
>> > > >> > > > > > >>
>> > > >> > > > > >
>> > > >> > > > >
>> > > >> > > >
>> > > >> > >
>> > > >> >
>> > > >>
>> > >
>> >
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely used to
>> > > >> perform
>> > > >> > > > > > >>>>>> aggregating
>> > > >> > > > > > >>>>>>>>>>>> operations
>> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the
>> elements
>> > > >> that
>> > > >> > > > > > >>> have
>> > > >> > > > > > >>>>> the
>> > > >> > > > > > >>>>>>> same
>> > > >> > > > > > >>>>>>>>>> key.
>> > > >> > > > > > >>>>>>>>>>>> When
>> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with the
>> > same
>> > > >> key
>> > > >> > > > > > >>> will
>> > > >> > > > > > >>>> be
>> > > >> > > > > > >>>>>>> sent
>> > > >> > > > > > >>>>>>>> to
>> > > >> > > > > > >>>>>>>>>> and
>> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> The performance of these aggregating
>> > operations
>> > > is
>> > > >> > > > > > >> very
>> > > >> > > > > > >>>>>>> sensitive
>> > > >> > > > > > >>>>>>>>> to
>> > > >> > > > > > >>>>>>>>>>> the
>> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases where
>> the
>> > > >> > > > > > >>> distribution
>> > > >> > > > > > >>>>> of
>> > > >> > > > > > >>>>>>> keys
>> > > >> > > > > > >>>>>>>>>>>> follows a
>> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
>> > > >> significantly
>> > > >> > > > > > >>>>>> downgraded.
>> > > >> > > > > > >>>>>>>>> More
>> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of
>> > parallelism
>> > > >> does
>> > > >> > > > > > >>> not
>> > > >> > > > > > >>>>> help
>> > > >> > > > > > >>>>>>>> when
>> > > >> > > > > > >>>>>>>>> a
>> > > >> > > > > > >>>>>>>>>>> task
>> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted
>> method
>> > to
>> > > >> > > > > > >> reduce
>> > > >> > > > > > >>>> the
>> > > >> > > > > > >>>>>>>>>> performance
>> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose the
>> > > >> > > > > > >> aggregating
>> > > >> > > > > > >>>>>>>> operations
>> > > >> > > > > > >>>>>>>>>> into
>> > > >> > > > > > >>>>>>>>>>>> two
>> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we aggregate
>> the
>> > > >> elements
>> > > >> > > > > > >>> of
>> > > >> > > > > > >>>>> the
>> > > >> > > > > > >>>>>>> same
>> > > >> > > > > > >>>>>>>>> key
>> > > >> > > > > > >>>>>>>>>>> at
>> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial results.
>> > Then
>> > > at
>> > > >> > > > > > >> the
>> > > >> > > > > > >>>>> second
>> > > >> > > > > > >>>>>>>>> phase,
>> > > >> > > > > > >>>>>>>>>>>> these
>> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to receivers
>> > according
>> > > to
>> > > >> > > > > > >>> their
>> > > >> > > > > > >>>>> keys
>> > > >> > > > > > >>>>>>> and
>> > > >> > > > > > >>>>>>>>> are
>> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final result. Since
>> the
>> > > >> number
>> > > >> > > > > > >>> of
>> > > >> > > > > > >>>>>>> partial
>> > > >> > > > > > >>>>>>>>>>> results
>> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is limited by the
>> > > >> number of
>> > > >> > > > > > >>>>>> senders,
>> > > >> > > > > > >>>>>>>> the
>> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be reduced.
>> > > >> Besides, by
>> > > >> > > > > > >>>>>> reducing
>> > > >> > > > > > >>>>>>>> the
>> > > >> > > > > > >>>>>>>>>>> amount
>> > > >> > > > > > >>>>>>>>>>>>> of transferred data the performance can be
>> > > further
>> > > >> > > > > > >>>>> improved.
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> *More details*:
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>
>> > > >> > > > > > >>
>> > > >> > > > > >
>> > > >> > > > >
>> > > >> > > >
>> > > >> > >
>> > > >> >
>> > > >>
>> > >
>> >
>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>
>> > > >> > > > > > >>
>> > > >> > > > > >
>> > > >> > > > >
>> > > >> > > >
>> > > >> > >
>> > > >> >
>> > > >>
>> > >
>> >
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
>> > > >> > > > > > >>>>>>>>>
>> https://issues.apache.org/jira/browse/FLINK-12786
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your feedback!
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>> Best,
>> > > >> > > > > > >>>>>>>>>>>>> Vino
>> > > >> > > > > > >>>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>>
>> > > >> > > > > > >>>>>>>>>
>> > > >> > > > > > >>>>>>>>
>> > > >> > > > > > >>>>>>>
>> > > >> > > > > > >>>>>>
>> > > >> > > > > > >>>>>
>> > > >> > > > > > >>>>
>> > > >> > > > > > >>>
>> > > >> > > > > > >>
>> > > >> > > > > >
>> > > >> > > > > >
>> > > >> > > > >
>> > > >> > > >
>> > > >> > >
>> > > >> >
>> > > >>
>> > > >
>> > >
>> >
>>
>

[Simon Su]

Hi vino


Thanks for proposal.
For Local Aggregation I have a question about doing this in window aggregation. As we know , window aggregation like sliding window should based on
Time trigger, and there may exists a problem in event time if we do local aggregation. For example if I want to do a 5s sliding window with count agg:


1. I have input with 4 parallelism and data are firstly randomly pass in 4 partitions.
2. We do LocalAggregation in each of them and we get a partial count result.
3. Forward partial result to a node with same key then do the final aggregation.


It seems no problem but what will happen if data skew in event time ? If we have a continuous time sequence in 3 of 4 input partitions, for example , we have a continuous time sequence in partition 1, 2, 3 but data to partition 4 was delay for some reason, and we just get 3 partial result for the moment, does final aggregation need to wait for the 4th partial result because of data delay ? If so , how long we need to wait for ? If not, does it mean that
The final aggregation will wait forever ?


Thanks,
Simon


On 06/18/2019 10:06，vino yang<[hidden email]> wrote：
Hi Jark,

We have done a comparative test. The effect is obvious.

From our observation, the optimized effect mainly depends on two factors:


- the degree of the skew: this factor depends on users business ;
- the size of the window: localKeyBy support all the type of window
which provided by Flink. Obviously, the larger the size of the window, the
more obvious the effect.

In production, we can not decide the first factor. About the second factor,
it's the result of a trade-off. The size of the window affects the latency
of the pre-aggregation. That's to say:


- the larger the size of the window, the more obvious the effect;
- the larger the size of the window, the larger latency of the result

Best,
Vino

Jark Wu <[hidden email]> 于2019年6月17日周一 下午7:32写道：

Hi Vino,

Thanks for the proposal.

Regarding to the "input.keyBy(0).sum(1)" vs
"input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)", have you done
some benchmark?
Because I'm curious about how much performance improvement can we get by
using count window as the local operator.

Best,
Jark



On Mon, 17 Jun 2019 at 17:48, vino yang <[hidden email]> wrote:

Hi Hequn,

Thanks for your reply.

The purpose of localKeyBy API is to provide a tool which can let users do
pre-aggregation in the local. The behavior of the pre-aggregation is
similar to keyBy API.

So the three cases are different, I will describe them one by one:

1. input.keyBy(0).sum(1)

*In this case, the result is event-driven, each event can produce one sum
aggregation result and it is the latest one from the source start.*

2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)

*In this case, the semantic may have a problem, it would do the local sum
aggregation and will produce the latest partial result from the source
start for every event. *
*These latest partial results from the same key are hashed to one node to
do the global sum aggregation.*
*In the global aggregation, when it received multiple partial results
(they
are all calculated from the source start) and sum them will get the wrong
result.*

3. input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)

*In this case, it would just get a partial aggregation result for the 5
records in the count window. The partial aggregation results from the
same
key will be aggregated globally.*

So the first case and the third case can get the *same* result, the
difference is the output-style and the latency.

Generally speaking, the local key API is just an optimization API. We do
not limit the user's usage, but the user has to understand its semantics
and use it correctly.

Best,
Vino

Hequn Cheng <[hidden email]> 于2019年6月17日周一 下午4:18写道：

Hi Vino,

Thanks for the proposal, I think it is a very good feature!

One thing I want to make sure is the semantics for the `localKeyBy`.
From
the document, the `localKeyBy` API returns an instance of `KeyedStream`
which can also perform sum(), so in this case, what's the semantics for
`localKeyBy()`. For example, will the following code share the same
result?
and what're the differences between them?

1. input.keyBy(0).sum(1)
2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
3. input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)

Would also be great if we can add this into the document. Thank you
very
much.

Best, Hequn


On Fri, Jun 14, 2019 at 11:34 AM vino yang <[hidden email]>
wrote:

Hi Aljoscha,

I have looked at the "*Process*" section of FLIP wiki page.[1] This
mail
thread indicates that it has proceeded to the third step.

When I looked at the fourth step(vote step), I didn't find the
prerequisites for starting the voting process.

Considering that the discussion of this feature has been done in the
old
thread. [2] So can you tell me when should I start voting? Can I
start
now?

Best,
Vino

[1]:




https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
[2]:




http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308

leesf <[hidden email]> 于2019年6月13日周四 上午9:19写道：

+1 for the FLIP, thank vino for your efforts.

Best,
Leesf

vino yang <[hidden email]> 于2019年6月12日周三 下午5:46写道：

Hi folks,

I would like to start the FLIP discussion thread about supporting
local
aggregation in Flink.

In short, this feature can effectively alleviate data skew. This
is
the
FLIP:







https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink


*Motivation* (copied from FLIP)

Currently, keyed streams are widely used to perform aggregating
operations
(e.g., reduce, sum and window) on the elements that have the same
key.
When
executed at runtime, the elements with the same key will be sent
to
and
aggregated by the same task.

The performance of these aggregating operations is very sensitive
to
the
distribution of keys. In the cases where the distribution of keys
follows a
powerful law, the performance will be significantly downgraded.
More
unluckily, increasing the degree of parallelism does not help
when
a
task
is overloaded by a single key.

Local aggregation is a widely-adopted method to reduce the
performance
degraded by data skew. We can decompose the aggregating
operations
into
two
phases. In the first phase, we aggregate the elements of the same
key
at
the sender side to obtain partial results. Then at the second
phase,
these
partial results are sent to receivers according to their keys and
are
combined to obtain the final result. Since the number of partial
results
received by each receiver is limited by the number of senders,
the
imbalance among receivers can be reduced. Besides, by reducing
the
amount
of transferred data the performance can be further improved.

*More details*:

Design documentation:






https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing

Old discussion thread:






http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308

JIRA: FLINK-12786 <
https://issues.apache.org/jira/browse/FLINK-12786


We are looking forwards to your feedback!

Best,
Vino

[vino yang]

Hi Simon,

Good question!

For event time semantics, we reuse the window operator can keep the correct
behavior which is the same as the current window operator. The window
operator will trigger based on the watermark.

About your example, the window of three partitions will trigger normally.
For the delayed partition, it should not trigger if there is no correct
watermark. The behavior is the same as input.keyBy(0).window in event time
semantics.

For processing idle partition scenarios, currently, Flink allows
calling markAsTemporarilyIdle to send StreamStatus.IDLE to the downstream.

Best,
Vino

Shu Su <[hidden email]> 于2019年6月24日周一 下午9:13写道：

> Hi vino
>
>
> Thanks for proposal.
> For Local Aggregation I have a question about doing this in window
> aggregation. As we know , window aggregation like sliding window should
> based on
> Time trigger, and there may exists a problem in event time if we do local
> aggregation. For example if I want to do a 5s sliding window with count agg:
>
>
> 1. I have input with 4 parallelism and data are firstly randomly pass in 4
> partitions.
> 2. We do LocalAggregation in each of them and we get a partial count
> result.
> 3. Forward partial result to a node with same key then do the final
> aggregation.
>
>
> It seems no problem but what will happen if data skew in event time ? If
> we have a continuous time sequence in 3 of 4 input partitions, for example
> , we have a continuous time sequence in partition 1, 2, 3 but data to
> partition 4 was delay for some reason, and we just get 3 partial result for
> the moment, does final aggregation need to wait for the 4th partial result
> because of data delay ? If so , how long we need to wait for ? If not, does
> it mean that
> The final aggregation will wait forever ?
>
>
> Thanks,
> Simon
>
>
> On 06/18/2019 10:06，vino yang<[hidden email]> wrote：
> Hi Jark,
>
> We have done a comparative test. The effect is obvious.
>
> From our observation, the optimized effect mainly depends on two factors:
>
>
> - the degree of the skew: this factor depends on users business ;
> - the size of the window: localKeyBy support all the type of window
> which provided by Flink. Obviously, the larger the size of the window, the
> more obvious the effect.
>
> In production, we can not decide the first factor. About the second factor,
> it's the result of a trade-off. The size of the window affects the latency
> of the pre-aggregation. That's to say:
>
>
> - the larger the size of the window, the more obvious the effect;
> - the larger the size of the window, the larger latency of the result
>
> Best,
> Vino
>
> Jark Wu <[hidden email]> 于2019年6月17日周一 下午7:32写道：
>
> Hi Vino,
>
> Thanks for the proposal.
>
> Regarding to the "input.keyBy(0).sum(1)" vs
> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)", have you done
> some benchmark?
> Because I'm curious about how much performance improvement can we get by
> using count window as the local operator.
>
> Best,
> Jark
>
>
>
> On Mon, 17 Jun 2019 at 17:48, vino yang <[hidden email]> wrote:
>
> Hi Hequn,
>
> Thanks for your reply.
>
> The purpose of localKeyBy API is to provide a tool which can let users do
> pre-aggregation in the local. The behavior of the pre-aggregation is
> similar to keyBy API.
>
> So the three cases are different, I will describe them one by one:
>
> 1. input.keyBy(0).sum(1)
>
> *In this case, the result is event-driven, each event can produce one sum
> aggregation result and it is the latest one from the source start.*
>
> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>
> *In this case, the semantic may have a problem, it would do the local sum
> aggregation and will produce the latest partial result from the source
> start for every event. *
> *These latest partial results from the same key are hashed to one node to
> do the global sum aggregation.*
> *In the global aggregation, when it received multiple partial results
> (they
> are all calculated from the source start) and sum them will get the wrong
> result.*
>
> 3. input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>
> *In this case, it would just get a partial aggregation result for the 5
> records in the count window. The partial aggregation results from the
> same
> key will be aggregated globally.*
>
> So the first case and the third case can get the *same* result, the
> difference is the output-style and the latency.
>
> Generally speaking, the local key API is just an optimization API. We do
> not limit the user's usage, but the user has to understand its semantics
> and use it correctly.
>
> Best,
> Vino
>
> Hequn Cheng <[hidden email]> 于2019年6月17日周一 下午4:18写道：
>
> Hi Vino,
>
> Thanks for the proposal, I think it is a very good feature!
>
> One thing I want to make sure is the semantics for the `localKeyBy`.
> From
> the document, the `localKeyBy` API returns an instance of `KeyedStream`
> which can also perform sum(), so in this case, what's the semantics for
> `localKeyBy()`. For example, will the following code share the same
> result?
> and what're the differences between them?
>
> 1. input.keyBy(0).sum(1)
> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> 3. input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>
> Would also be great if we can add this into the document. Thank you
> very
> much.
>
> Best, Hequn
>
>
> On Fri, Jun 14, 2019 at 11:34 AM vino yang <[hidden email]>
> wrote:
>
> Hi Aljoscha,
>
> I have looked at the "*Process*" section of FLIP wiki page.[1] This
> mail
> thread indicates that it has proceeded to the third step.
>
> When I looked at the fourth step(vote step), I didn't find the
> prerequisites for starting the voting process.
>
> Considering that the discussion of this feature has been done in the
> old
> thread. [2] So can you tell me when should I start voting? Can I
> start
> now?
>
> Best,
> Vino
>
> [1]:
>
>
>
>
>
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> [2]:
>
>
>
>
>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>
> leesf <[hidden email]> 于2019年6月13日周四 上午9:19写道：
>
> +1 for the FLIP, thank vino for your efforts.
>
> Best,
> Leesf
>
> vino yang <[hidden email]> 于2019年6月12日周三 下午5:46写道：
>
> Hi folks,
>
> I would like to start the FLIP discussion thread about supporting
> local
> aggregation in Flink.
>
> In short, this feature can effectively alleviate data skew. This
> is
> the
> FLIP:
>
>
>
>
>
>
>
>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>
>
> *Motivation* (copied from FLIP)
>
> Currently, keyed streams are widely used to perform aggregating
> operations
> (e.g., reduce, sum and window) on the elements that have the same
> key.
> When
> executed at runtime, the elements with the same key will be sent
> to
> and
> aggregated by the same task.
>
> The performance of these aggregating operations is very sensitive
> to
> the
> distribution of keys. In the cases where the distribution of keys
> follows a
> powerful law, the performance will be significantly downgraded.
> More
> unluckily, increasing the degree of parallelism does not help
> when
> a
> task
> is overloaded by a single key.
>
> Local aggregation is a widely-adopted method to reduce the
> performance
> degraded by data skew. We can decompose the aggregating
> operations
> into
> two
> phases. In the first phase, we aggregate the elements of the same
> key
> at
> the sender side to obtain partial results. Then at the second
> phase,
> these
> partial results are sent to receivers according to their keys and
> are
> combined to obtain the final result. Since the number of partial
> results
> received by each receiver is limited by the number of senders,
> the
> imbalance among receivers can be reduced. Besides, by reducing
> the
> amount
> of transferred data the performance can be further improved.
>
> *More details*:
>
> Design documentation:
>
>
>
>
>
>
>
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>
> Old discussion thread:
>
>
>
>
>
>
>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>
> JIRA: FLINK-12786 <
> https://issues.apache.org/jira/browse/FLINK-12786
>
>
> We are looking forwards to your feedback!
>
> Best,
> Vino
>
>
>
>
>
>
>

[Simon Su]

Hi Vino


Thanks for your reply.


It seems feasible if a StreamStatus.IDLE was send to downstream, Still two questions.
 1. Do we need to add a method to allow users control when to send StreamStatus.IDLE to downsteram in this case?
 2. If a partial data comes after your IDLE status to downstream, does this means final side will get rid of the partial data ? Or we have a mechanism to handle this ?


Thanks,
Simon
On 06/25/2019 10:56，vino yang<[hidden email]> wrote：
Hi Simon,

Good question!

For event time semantics, we reuse the window operator can keep the correct
behavior which is the same as the current window operator. The window
operator will trigger based on the watermark.

About your example, the window of three partitions will trigger normally.
For the delayed partition, it should not trigger if there is no correct
watermark. The behavior is the same as input.keyBy(0).window in event time
semantics.

For processing idle partition scenarios, currently, Flink allows
calling markAsTemporarilyIdle to send StreamStatus.IDLE to the downstream.

Best,
Vino

Shu Su <[hidden email]> 于2019年6月24日周一 下午9:13写道：

Hi vino


Thanks for proposal.
For Local Aggregation I have a question about doing this in window
aggregation. As we know , window aggregation like sliding window should
based on
Time trigger, and there may exists a problem in event time if we do local
aggregation. For example if I want to do a 5s sliding window with count agg:


1. I have input with 4 parallelism and data are firstly randomly pass in 4
partitions.
2. We do LocalAggregation in each of them and we get a partial count
result.
3. Forward partial result to a node with same key then do the final
aggregation.


It seems no problem but what will happen if data skew in event time ? If
we have a continuous time sequence in 3 of 4 input partitions, for example
, we have a continuous time sequence in partition 1, 2, 3 but data to
partition 4 was delay for some reason, and we just get 3 partial result for
the moment, does final aggregation need to wait for the 4th partial result
because of data delay ? If so , how long we need to wait for ? If not, does
it mean that
The final aggregation will wait forever ?


Thanks,
Simon


On 06/18/2019 10:06，vino yang<[hidden email]> wrote：
Hi Jark,

We have done a comparative test. The effect is obvious.

From our observation, the optimized effect mainly depends on two factors:


- the degree of the skew: this factor depends on users business ;
- the size of the window: localKeyBy support all the type of window
which provided by Flink. Obviously, the larger the size of the window, the
more obvious the effect.

In production, we can not decide the first factor. About the second factor,
it's the result of a trade-off. The size of the window affects the latency
of the pre-aggregation. That's to say:


- the larger the size of the window, the more obvious the effect;
- the larger the size of the window, the larger latency of the result

Best,
Vino

Jark Wu <[hidden email]> 于2019年6月17日周一 下午7:32写道：

Hi Vino,

Thanks for the proposal.

Regarding to the "input.keyBy(0).sum(1)" vs
"input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)", have you done
some benchmark?
Because I'm curious about how much performance improvement can we get by
using count window as the local operator.

Best,
Jark



On Mon, 17 Jun 2019 at 17:48, vino yang <[hidden email]> wrote:

Hi Hequn,

Thanks for your reply.

The purpose of localKeyBy API is to provide a tool which can let users do
pre-aggregation in the local. The behavior of the pre-aggregation is
similar to keyBy API.

So the three cases are different, I will describe them one by one:

1. input.keyBy(0).sum(1)

*In this case, the result is event-driven, each event can produce one sum
aggregation result and it is the latest one from the source start.*

2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)

*In this case, the semantic may have a problem, it would do the local sum
aggregation and will produce the latest partial result from the source
start for every event. *
*These latest partial results from the same key are hashed to one node to
do the global sum aggregation.*
*In the global aggregation, when it received multiple partial results
(they
are all calculated from the source start) and sum them will get the wrong
result.*

3. input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)

*In this case, it would just get a partial aggregation result for the 5
records in the count window. The partial aggregation results from the
same
key will be aggregated globally.*

So the first case and the third case can get the *same* result, the
difference is the output-style and the latency.

Generally speaking, the local key API is just an optimization API. We do
not limit the user's usage, but the user has to understand its semantics
and use it correctly.

Best,
Vino

Hequn Cheng <[hidden email]> 于2019年6月17日周一 下午4:18写道：

Hi Vino,

Thanks for the proposal, I think it is a very good feature!

One thing I want to make sure is the semantics for the `localKeyBy`.
From
the document, the `localKeyBy` API returns an instance of `KeyedStream`
which can also perform sum(), so in this case, what's the semantics for
`localKeyBy()`. For example, will the following code share the same
result?
and what're the differences between them?

1. input.keyBy(0).sum(1)
2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
3. input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)

Would also be great if we can add this into the document. Thank you
very
much.

Best, Hequn


On Fri, Jun 14, 2019 at 11:34 AM vino yang <[hidden email]>
wrote:

Hi Aljoscha,

I have looked at the "*Process*" section of FLIP wiki page.[1] This
mail
thread indicates that it has proceeded to the third step.

When I looked at the fourth step(vote step), I didn't find the
prerequisites for starting the voting process.

Considering that the discussion of this feature has been done in the
old
thread. [2] So can you tell me when should I start voting? Can I
start
now?

Best,
Vino

[1]:





https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
[2]:





http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308

leesf <[hidden email]> 于2019年6月13日周四 上午9:19写道：

+1 for the FLIP, thank vino for your efforts.

Best,
Leesf

vino yang <[hidden email]> 于2019年6月12日周三 下午5:46写道：

Hi folks,

I would like to start the FLIP discussion thread about supporting
local
aggregation in Flink.

In short, this feature can effectively alleviate data skew. This
is
the
FLIP:








https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink


*Motivation* (copied from FLIP)

Currently, keyed streams are widely used to perform aggregating
operations
(e.g., reduce, sum and window) on the elements that have the same
key.
When
executed at runtime, the elements with the same key will be sent
to
and
aggregated by the same task.

The performance of these aggregating operations is very sensitive
to
the
distribution of keys. In the cases where the distribution of keys
follows a
powerful law, the performance will be significantly downgraded.
More
unluckily, increasing the degree of parallelism does not help
when
a
task
is overloaded by a single key.

Local aggregation is a widely-adopted method to reduce the
performance
degraded by data skew. We can decompose the aggregating
operations
into
two
phases. In the first phase, we aggregate the elements of the same
key
at
the sender side to obtain partial results. Then at the second
phase,
these
partial results are sent to receivers according to their keys and
are
combined to obtain the final result. Since the number of partial
results
received by each receiver is limited by the number of senders,
the
imbalance among receivers can be reduced. Besides, by reducing
the
amount
of transferred data the performance can be further improved.

*More details*:

Design documentation:







https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing

Old discussion thread:







http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308

JIRA: FLINK-12786 <
https://issues.apache.org/jira/browse/FLINK-12786


We are looking forwards to your feedback!

Best,
Vino

[vino yang]

Hi Simon,

IMO, we do not need special processing for your example scenarios, Flink
suggests users extracting watermarks in source function.

Generally, the IDLE is temporary status, when the data coming, it will send
ACTIVE status to the downstream and the processing will continue.

Keep in mind, all the cases are the same as window operator, there is no
difference. So, I think we do not need to worry about these things.

Best,
Vino

Shu Su <[hidden email]> 于2019年6月25日周二 上午11:36写道：

>
>
> Hi Vino
>
>
> Thanks for your reply.
>
>
> It seems feasible if a StreamStatus.IDLE was send to downstream, Still two
> questions.
>  1. Do we need to add a method to allow users control when to send
> StreamStatus.IDLE to downsteram in this case?
>  2. If a partial data comes after your IDLE status to downstream, does
> this means final side will get rid of the partial data ? Or we have a
> mechanism to handle this ?
>
>
> Thanks,
> Simon
> On 06/25/2019 10:56，vino yang<[hidden email]> wrote：
> Hi Simon,
>
> Good question!
>
> For event time semantics, we reuse the window operator can keep the correct
> behavior which is the same as the current window operator. The window
> operator will trigger based on the watermark.
>
> About your example, the window of three partitions will trigger normally.
> For the delayed partition, it should not trigger if there is no correct
> watermark. The behavior is the same as input.keyBy(0).window in event time
> semantics.
>
> For processing idle partition scenarios, currently, Flink allows
> calling markAsTemporarilyIdle to send StreamStatus.IDLE to the downstream.
>
> Best,
> Vino
>
> Shu Su <[hidden email]> 于2019年6月24日周一 下午9:13写道：
>
> Hi vino
>
>
> Thanks for proposal.
> For Local Aggregation I have a question about doing this in window
> aggregation. As we know , window aggregation like sliding window should
> based on
> Time trigger, and there may exists a problem in event time if we do local
> aggregation. For example if I want to do a 5s sliding window with count
> agg:
>
>
> 1. I have input with 4 parallelism and data are firstly randomly pass in 4
> partitions.
> 2. We do LocalAggregation in each of them and we get a partial count
> result.
> 3. Forward partial result to a node with same key then do the final
> aggregation.
>
>
> It seems no problem but what will happen if data skew in event time ? If
> we have a continuous time sequence in 3 of 4 input partitions, for example
> , we have a continuous time sequence in partition 1, 2, 3 but data to
> partition 4 was delay for some reason, and we just get 3 partial result for
> the moment, does final aggregation need to wait for the 4th partial result
> because of data delay ? If so , how long we need to wait for ? If not, does
> it mean that
> The final aggregation will wait forever ?
>
>
> Thanks,
> Simon
>
>
> On 06/18/2019 10:06，vino yang<[hidden email]> wrote：
> Hi Jark,
>
> We have done a comparative test. The effect is obvious.
>
> From our observation, the optimized effect mainly depends on two factors:
>
>
> - the degree of the skew: this factor depends on users business ;
> - the size of the window: localKeyBy support all the type of window
> which provided by Flink. Obviously, the larger the size of the window, the
> more obvious the effect.
>
> In production, we can not decide the first factor. About the second factor,
> it's the result of a trade-off. The size of the window affects the latency
> of the pre-aggregation. That's to say:
>
>
> - the larger the size of the window, the more obvious the effect;
> - the larger the size of the window, the larger latency of the result
>
> Best,
> Vino
>
> Jark Wu <[hidden email]> 于2019年6月17日周一 下午7:32写道：
>
> Hi Vino,
>
> Thanks for the proposal.
>
> Regarding to the "input.keyBy(0).sum(1)" vs
> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)", have you done
> some benchmark?
> Because I'm curious about how much performance improvement can we get by
> using count window as the local operator.
>
> Best,
> Jark
>
>
>
> On Mon, 17 Jun 2019 at 17:48, vino yang <[hidden email]> wrote:
>
> Hi Hequn,
>
> Thanks for your reply.
>
> The purpose of localKeyBy API is to provide a tool which can let users do
> pre-aggregation in the local. The behavior of the pre-aggregation is
> similar to keyBy API.
>
> So the three cases are different, I will describe them one by one:
>
> 1. input.keyBy(0).sum(1)
>
> *In this case, the result is event-driven, each event can produce one sum
> aggregation result and it is the latest one from the source start.*
>
> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>
> *In this case, the semantic may have a problem, it would do the local sum
> aggregation and will produce the latest partial result from the source
> start for every event. *
> *These latest partial results from the same key are hashed to one node to
> do the global sum aggregation.*
> *In the global aggregation, when it received multiple partial results
> (they
> are all calculated from the source start) and sum them will get the wrong
> result.*
>
> 3. input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>
> *In this case, it would just get a partial aggregation result for the 5
> records in the count window. The partial aggregation results from the
> same
> key will be aggregated globally.*
>
> So the first case and the third case can get the *same* result, the
> difference is the output-style and the latency.
>
> Generally speaking, the local key API is just an optimization API. We do
> not limit the user's usage, but the user has to understand its semantics
> and use it correctly.
>
> Best,
> Vino
>
> Hequn Cheng <[hidden email]> 于2019年6月17日周一 下午4:18写道：
>
> Hi Vino,
>
> Thanks for the proposal, I think it is a very good feature!
>
> One thing I want to make sure is the semantics for the `localKeyBy`.
> From
> the document, the `localKeyBy` API returns an instance of `KeyedStream`
> which can also perform sum(), so in this case, what's the semantics for
> `localKeyBy()`. For example, will the following code share the same
> result?
> and what're the differences between them?
>
> 1. input.keyBy(0).sum(1)
> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> 3. input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>
> Would also be great if we can add this into the document. Thank you
> very
> much.
>
> Best, Hequn
>
>
> On Fri, Jun 14, 2019 at 11:34 AM vino yang <[hidden email]>
> wrote:
>
> Hi Aljoscha,
>
> I have looked at the "*Process*" section of FLIP wiki page.[1] This
> mail
> thread indicates that it has proceeded to the third step.
>
> When I looked at the fourth step(vote step), I didn't find the
> prerequisites for starting the voting process.
>
> Considering that the discussion of this feature has been done in the
> old
> thread. [2] So can you tell me when should I start voting? Can I
> start
> now?
>
> Best,
> Vino
>
> [1]:
>
>
>
>
>
>
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> [2]:
>
>
>
>
>
>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>
> leesf <[hidden email]> 于2019年6月13日周四 上午9:19写道：
>
> +1 for the FLIP, thank vino for your efforts.
>
> Best,
> Leesf
>
> vino yang <[hidden email]> 于2019年6月12日周三 下午5:46写道：
>
> Hi folks,
>
> I would like to start the FLIP discussion thread about supporting
> local
> aggregation in Flink.
>
> In short, this feature can effectively alleviate data skew. This
> is
> the
> FLIP:
>
>
>
>
>
>
>
>
>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>
>
> *Motivation* (copied from FLIP)
>
> Currently, keyed streams are widely used to perform aggregating
> operations
> (e.g., reduce, sum and window) on the elements that have the same
> key.
> When
> executed at runtime, the elements with the same key will be sent
> to
> and
> aggregated by the same task.
>
> The performance of these aggregating operations is very sensitive
> to
> the
> distribution of keys. In the cases where the distribution of keys
> follows a
> powerful law, the performance will be significantly downgraded.
> More
> unluckily, increasing the degree of parallelism does not help
> when
> a
> task
> is overloaded by a single key.
>
> Local aggregation is a widely-adopted method to reduce the
> performance
> degraded by data skew. We can decompose the aggregating
> operations
> into
> two
> phases. In the first phase, we aggregate the elements of the same
> key
> at
> the sender side to obtain partial results. Then at the second
> phase,
> these
> partial results are sent to receivers according to their keys and
> are
> combined to obtain the final result. Since the number of partial
> results
> received by each receiver is limited by the number of senders,
> the
> imbalance among receivers can be reduced. Besides, by reducing
> the
> amount
> of transferred data the performance can be further improved.
>
> *More details*:
>
> Design documentation:
>
>
>
>
>
>
>
>
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>
> Old discussion thread:
>
>
>
>
>
>
>
>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>
> JIRA: FLINK-12786 <
> https://issues.apache.org/jira/browse/FLINK-12786
>
>
> We are looking forwards to your feedback!
>
> Best,
> Vino
>
>
>
>
>
>
>
>

[vino yang]

Hi Kurt,

Answer your questions:

a) Sorry, I just updated the Google doc, still have no time update the
FLIP, will update FLIP as soon as possible.
About your description at this point, I have a question, what does it mean:
how do we combine with
`AggregateFunction`?

I have shown you the examples which Flink has supported:

   - input.localKeyBy(0).aggregate()
   - input.localKeyBy(0).window().aggregate()

You can show me a example about how do we combine with `AggregateFuncion`
through your localAggregate API.

About the example, how to do the local aggregation for AVG, consider this
code:









*DataStream<Tuple2<String, Long>> source = null; source .localKeyBy(0)
.timeWindow(Time.seconds(60)) .aggregate(agg1, new
WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>, String,
TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60)) .aggregate(agg2,
new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, String,
TimeWindow>());*

*agg1:*
*signature : new AggregateFunction<Tuple2<String, Long>, Tuple2<Long,
Long>, Tuple2<Long, Long>>() {}*
*input param type: Tuple2<String, Long> f0: key, f1: value*
*intermediate result type: Tuple2<Long, Long>, f0: local aggregated sum;
f1: local aggregated count*
*output param type:  Tuple2<Long, Long>, f0: local aggregated sum; f1:
local aggregated count*

*agg2:*
*signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
Tuple2<String, Long>>() {},*
*input param type: Tuple3<String, Long, Long>, f0: key, f1:  local
aggregated sum; f2: local aggregated count*

*intermediate result type: Long  avg result*
*output param type:  Tuple2<String, Long> f0: key, f1 avg result*

For sliding window, we just need to change the window type if users want to
do.
Again, we try to give the design and implementation in the DataStream
level. So I believe we can match all the requirements(It's just that the
implementation may be different) comes from the SQL level.

b) Yes, Theoretically, your thought is right. But in reality, it cannot
bring many benefits.
If we want to get the benefits from the window API, while we do not reuse
the window operator? And just copy some many duplicated code to another
operator?

c) OK, I agree to let the state backend committers join this discussion.

Best,
Vino


Kurt Young <[hidden email]> 于2019年6月24日周一 下午6:53写道：

> Hi vino,
>
> One thing to add,  for a), I think use one or two examples like how to do
> local aggregation on a sliding window,
> and how do we do local aggregation on an unbounded aggregate, will do a lot
> help.
>
> Best,
> Kurt
>
>
> On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email]> wrote:
>
> > Hi vino,
> >
> > I think there are several things still need discussion.
> >
> > a) We all agree that we should first go with a unified abstraction, but
> > the abstraction is not reflected by the FLIP.
> > If your answer is "locakKeyBy" API, then I would ask how do we combine
> > with `AggregateFunction`, and how do
> > we do proper local aggregation for those have different intermediate
> > result type, like AVG. Could you add these
> > to the document?
> >
> > b) From implementation side, reusing window operator is one of the
> > possible solutions, but not we base on window
> > operator to have two different implementations. What I understanding is,
> > one of the possible implementations should
> > not touch window operator.
> >
> > c) 80% of your FLIP content is actually describing how do we support
> local
> > keyed state. I don't know if this is necessary
> > to introduce at the first step and we should also involve committers work
> > on state backend to share their thoughts.
> >
> > Best,
> > Kurt
> >
> >
> > On Mon, Jun 24, 2019 at 5:17 PM vino yang <[hidden email]> wrote:
> >
> >> Hi Kurt,
> >>
> >> You did not give more further different opinions, so I thought you have
> >> agreed with the design after we promised to support two kinds of
> >> implementation.
> >>
> >> In API level, we have answered your question about pass an
> >> AggregateFunction to do the aggregation. No matter introduce localKeyBy
> >> API
> >> or not, we can support AggregateFunction.
> >>
> >> So what's your different opinion now? Can you share it with us?
> >>
> >> Best,
> >> Vino
> >>
> >> Kurt Young <[hidden email]> 于2019年6月24日周一 下午4:24写道：
> >>
> >> > Hi vino,
> >> >
> >> > Sorry I don't see the consensus about reusing window operator and keep
> >> the
> >> > API design of localKeyBy. But I think we should definitely more
> thoughts
> >> > about this topic.
> >> >
> >> > I also try to loop in Stephan for this discussion.
> >> >
> >> > Best,
> >> > Kurt
> >> >
> >> >
> >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <[hidden email]>
> >> wrote:
> >> >
> >> > > Hi all,
> >> > >
> >> > > I am happy we have a wonderful discussion and received many valuable
> >> > > opinions in the last few days.
> >> > >
> >> > > Now, let me try to summarize what we have reached consensus about
> the
> >> > > changes in the design.
> >> > >
> >> > >    - provide a unified abstraction to support two kinds of
> >> > implementation;
> >> > >    - reuse WindowOperator and try to enhance it so that we can make
> >> the
> >> > >    intermediate result of the local aggregation can be buffered and
> >> > > flushed to
> >> > >    support two kinds of implementation;
> >> > >    - keep the API design of localKeyBy, but declare the disabled
> some
> >> > APIs
> >> > >    we cannot support currently, and provide a configurable API for
> >> users
> >> > to
> >> > >    choose how to handle intermediate result;
> >> > >
> >> > > The above three points have been updated in the design doc. Any
> >> > > questions, please let me know.
> >> > >
> >> > > @Aljoscha Krettek <[hidden email]> What do you think? Any
> >> further
> >> > > comments?
> >> > >
> >> > > Best,
> >> > > Vino
> >> > >
> >> > > vino yang <[hidden email]> 于2019年6月20日周四 下午2:02写道：
> >> > >
> >> > > > Hi Kurt,
> >> > > >
> >> > > > Thanks for your comments.
> >> > > >
> >> > > > It seems we come to a consensus that we should alleviate the
> >> > performance
> >> > > > degraded by data skew with local aggregation. In this FLIP, our
> key
> >> > > > solution is to introduce local keyed partition to achieve this
> goal.
> >> > > >
> >> > > > I also agree that we can benefit a lot from the usage of
> >> > > > AggregateFunction. In combination with localKeyBy, We can easily
> >> use it
> >> > > to
> >> > > > achieve local aggregation:
> >> > > >
> >> > > >    - input.localKeyBy(0).aggregate()
> >> > > >    - input.localKeyBy(0).window().aggregate()
> >> > > >
> >> > > >
> >> > > > I think the only problem here is the choices between
> >> > > >
> >> > > >    - (1) Introducing a new primitive called localKeyBy and
> implement
> >> > > >    local aggregation with existing operators, or
> >> > > >    - (2) Introducing an operator called localAggregation which is
> >> > > >    composed of a key selector, a window-like operator, and an
> >> aggregate
> >> > > >    function.
> >> > > >
> >> > > >
> >> > > > There may exist some optimization opportunities by providing a
> >> > composited
> >> > > > interface for local aggregation. But at the same time, in my
> >> opinion,
> >> > we
> >> > > > lose flexibility (Or we need certain efforts to achieve the same
> >> > > > flexibility).
> >> > > >
> >> > > > As said in the previous mails, we have many use cases where the
> >> > > > aggregation is very complicated and cannot be performed with
> >> > > > AggregateFunction. For example, users may perform windowed
> >> aggregations
> >> > > > according to time, data values, or even external storage.
> Typically,
> >> > they
> >> > > > now use KeyedProcessFunction or customized triggers to implement
> >> these
> >> > > > aggregations. It's not easy to address data skew in such cases
> with
> >> a
> >> > > > composited interface for local aggregation.
> >> > > >
> >> > > > Given that Data Stream API is exactly targeted at these cases
> where
> >> the
> >> > > > application logic is very complicated and optimization does not
> >> > matter, I
> >> > > > think it's a better choice to provide a relatively low-level and
> >> > > canonical
> >> > > > interface.
> >> > > >
> >> > > > The composited interface, on the other side, may be a good choice
> in
> >> > > > declarative interfaces, including SQL and Table API, as it allows
> >> more
> >> > > > optimization opportunities.
> >> > > >
> >> > > > Best,
> >> > > > Vino
> >> > > >
> >> > > >
> >> > > > Kurt Young <[hidden email]> 于2019年6月20日周四 上午10:15写道：
> >> > > >
> >> > > >> Hi all,
> >> > > >>
> >> > > >> As vino said in previous emails, I think we should first discuss
> >> and
> >> > > >> decide
> >> > > >> what kind of use cases this FLIP want to
> >> > > >> resolve, and what the API should look like. From my side, I think
> >> this
> >> > > is
> >> > > >> probably the root cause of current divergence.
> >> > > >>
> >> > > >> My understand is (from the FLIP title and motivation section of
> the
> >> > > >> document), we want to have a proper support of
> >> > > >> local aggregation, or pre aggregation. This is not a very new
> idea,
> >> > most
> >> > > >> SQL engine already did this improvement. And
> >> > > >> the core concept about this is, there should be an
> >> AggregateFunction,
> >> > no
> >> > > >> matter it's a Flink runtime's AggregateFunction or
> >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have concept
> >> of
> >> > > >> intermediate data type, sometimes we call it ACC.
> >> > > >> I quickly went through the POC piotr did before [1], it also
> >> directly
> >> > > uses
> >> > > >> AggregateFunction.
> >> > > >>
> >> > > >> But the thing is, after reading the design of this FLIP, I can't
> >> help
> >> > > >> myself feeling that this FLIP is not targeting to have a proper
> >> > > >> local aggregation support. It actually want to introduce another
> >> > > concept:
> >> > > >> LocalKeyBy, and how to split and merge local key groups,
> >> > > >> and how to properly support state on local key. Local aggregation
> >> just
> >> > > >> happened to be one possible use case of LocalKeyBy.
> >> > > >> But it lacks supporting the essential concept of local
> aggregation,
> >> > > which
> >> > > >> is intermediate data type. Without this, I really don't thing
> >> > > >> it is a good fit of local aggregation.
> >> > > >>
> >> > > >> Here I want to make sure of the scope or the goal about this
> FLIP,
> >> do
> >> > we
> >> > > >> want to have a proper local aggregation engine, or we
> >> > > >> just want to introduce a new concept called LocalKeyBy?
> >> > > >>
> >> > > >> [1]: https://github.com/apache/flink/pull/4626
> >> > > >>
> >> > > >> Best,
> >> > > >> Kurt
> >> > > >>
> >> > > >>
> >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <[hidden email]
> >
> >> > > wrote:
> >> > > >>
> >> > > >> > Hi Hequn,
> >> > > >> >
> >> > > >> > Thanks for your comments!
> >> > > >> >
> >> > > >> > I agree that allowing local aggregation reusing window API and
> >> > > refining
> >> > > >> > window operator to make it match both requirements (come from
> our
> >> > and
> >> > > >> Kurt)
> >> > > >> > is a good decision!
> >> > > >> >
> >> > > >> > Concerning your questions:
> >> > > >> >
> >> > > >> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1) may
> >> be
> >> > > >> > meaningless.
> >> > > >> >
> >> > > >> > Yes, it does not make sense in most cases. However, I also want
> >> to
> >> > > note
> >> > > >> > users should know the right semantics of localKeyBy and use it
> >> > > >> correctly.
> >> > > >> > Because this issue also exists for the global keyBy, consider
> >> this
> >> > > >> example:
> >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also
> >> > meaningless.
> >> > > >> >
> >> > > >> > 2. About the semantics of
> >> > > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> >> > > >> >
> >> > > >> > Good catch! I agree with you that it's not good to enable all
> >> > > >> > functionalities for localKeyBy from KeyedStream.
> >> > > >> > Currently, We do not support some APIs such as
> >> > > >> > connect/join/intervalJoin/coGroup. This is due to that we force
> >> the
> >> > > >> > operators on LocalKeyedStreams chained with the inputs.
> >> > > >> >
> >> > > >> > Best,
> >> > > >> > Vino
> >> > > >> >
> >> > > >> >
> >> > > >> > Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
> >> > > >> >
> >> > > >> > > Hi,
> >> > > >> > >
> >> > > >> > > Thanks a lot for your great discussion and great to see that
> >> some
> >> > > >> > agreement
> >> > > >> > > has been reached on the "local aggregate engine"!
> >> > > >> > >
> >> > > >> > > ===> Considering the abstract engine,
> >> > > >> > > I'm thinking is it valuable for us to extend the current
> >> window to
> >> > > >> meet
> >> > > >> > > both demands raised by Kurt and Vino? There are some benefits
> >> we
> >> > can
> >> > > >> get:
> >> > > >> > >
> >> > > >> > > 1. The interfaces of the window are complete and clear. With
> >> > > windows,
> >> > > >> we
> >> > > >> > > can define a lot of ways to split the data and perform
> >> different
> >> > > >> > > computations.
> >> > > >> > > 2. We can also leverage the window to do miniBatch for the
> >> global
> >> > > >> > > aggregation, i.e, we can use the window to bundle data belong
> >> to
> >> > the
> >> > > >> same
> >> > > >> > > key, for every bundle we only need to read and write once
> >> state.
> >> > > This
> >> > > >> can
> >> > > >> > > greatly reduce state IO and improve performance.
> >> > > >> > > 3. A lot of other use cases can also benefit from the window
> >> base
> >> > on
> >> > > >> > memory
> >> > > >> > > or stateless.
> >> > > >> > >
> >> > > >> > > ===> As for the API,
> >> > > >> > > I think it is good to make our API more flexible. However, we
> >> may
> >> > > >> need to
> >> > > >> > > make our API meaningful.
> >> > > >> > >
> >> > > >> > > Take my previous reply as an example,
> >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may be
> >> > > >> > meaningless.
> >> > > >> > > Another example I find is the intervalJoin, e.g.,
> >> > > >> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
> >> this
> >> > > >> case, it
> >> > > >> > > will bring problems if input1 and input2 share different
> >> > > parallelism.
> >> > > >> We
> >> > > >> > > don't know which input should the join chained with? Even if
> >> they
> >> > > >> share
> >> > > >> > the
> >> > > >> > > same parallelism, it's hard to tell what the join is doing.
> >> There
> >> > > are
> >> > > >> > maybe
> >> > > >> > > some other problems.
> >> > > >> > >
> >> > > >> > > From this point of view, it's at least not good to enable all
> >> > > >> > > functionalities for localKeyBy from KeyedStream?
> >> > > >> > >
> >> > > >> > > Great to also have your opinions.
> >> > > >> > >
> >> > > >> > > Best, Hequn
> >> > > >> > >
> >> > > >> > >
> >> > > >> > >
> >> > > >> > >
> >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
> >> [hidden email]
> >> > >
> >> > > >> > wrote:
> >> > > >> > >
> >> > > >> > > > Hi Kurt and Piotrek,
> >> > > >> > > >
> >> > > >> > > > Thanks for your comments.
> >> > > >> > > >
> >> > > >> > > > I agree that we can provide a better abstraction to be
> >> > compatible
> >> > > >> with
> >> > > >> > > two
> >> > > >> > > > different implementations.
> >> > > >> > > >
> >> > > >> > > > First of all, I think we should consider what kind of
> >> scenarios
> >> > we
> >> > > >> need
> >> > > >> > > to
> >> > > >> > > > support in *API* level?
> >> > > >> > > >
> >> > > >> > > > We have some use cases which need to a customized
> aggregation
> >> > > >> through
> >> > > >> > > > KeyedProcessFunction, (in the usage of our
> localKeyBy.window
> >> > they
> >> > > >> can
> >> > > >> > use
> >> > > >> > > > ProcessWindowFunction).
> >> > > >> > > >
> >> > > >> > > > Shall we support these flexible use scenarios?
> >> > > >> > > >
> >> > > >> > > > Best,
> >> > > >> > > > Vino
> >> > > >> > > >
> >> > > >> > > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
> >> > > >> > > >
> >> > > >> > > > > Hi Piotr,
> >> > > >> > > > >
> >> > > >> > > > > Thanks for joining the discussion. Make “local
> aggregation"
> >> > > >> abstract
> >> > > >> > > > enough
> >> > > >> > > > > sounds good to me, we could
> >> > > >> > > > > implement and verify alternative solutions for use cases
> of
> >> > > local
> >> > > >> > > > > aggregation. Maybe we will find both solutions
> >> > > >> > > > > are appropriate for different scenarios.
> >> > > >> > > > >
> >> > > >> > > > > Starting from a simple one sounds a practical way to go.
> >> What
> >> > do
> >> > > >> you
> >> > > >> > > > think,
> >> > > >> > > > > vino?
> >> > > >> > > > >
> >> > > >> > > > > Best,
> >> > > >> > > > > Kurt
> >> > > >> > > > >
> >> > > >> > > > >
> >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> >> > > >> [hidden email]>
> >> > > >> > > > > wrote:
> >> > > >> > > > >
> >> > > >> > > > > > Hi Kurt and Vino,
> >> > > >> > > > > >
> >> > > >> > > > > > I think there is a trade of hat we need to consider for
> >> the
> >> > > >> local
> >> > > >> > > > > > aggregation.
> >> > > >> > > > > >
> >> > > >> > > > > > Generally speaking I would agree with Kurt about local
> >> > > >> > > aggregation/pre
> >> > > >> > > > > > aggregation not using Flink's state flush the operator
> >> on a
> >> > > >> > > checkpoint.
> >> > > >> > > > > > Network IO is usually cheaper compared to Disks IO.
> This
> >> has
> >> > > >> > however
> >> > > >> > > > > couple
> >> > > >> > > > > > of issues:
> >> > > >> > > > > > 1. It can explode number of in-flight records during
> >> > > checkpoint
> >> > > >> > > barrier
> >> > > >> > > > > > alignment, making checkpointing slower and decrease the
> >> > actual
> >> > > >> > > > > throughput.
> >> > > >> > > > > > 2. This trades Disks IO on the local aggregation
> machine
> >> > with
> >> > > >> CPU
> >> > > >> > > (and
> >> > > >> > > > > > Disks IO in case of RocksDB) on the final aggregation
> >> > machine.
> >> > > >> This
> >> > > >> > > is
> >> > > >> > > > > > fine, as long there is no huge data skew. If there is
> >> only a
> >> > > >> > handful
> >> > > >> > > > (or
> >> > > >> > > > > > even one single) hot keys, it might be better to keep
> the
> >> > > >> > persistent
> >> > > >> > > > > state
> >> > > >> > > > > > in the LocalAggregationOperator to offload final
> >> aggregation
> >> > > as
> >> > > >> > much
> >> > > >> > > as
> >> > > >> > > > > > possible.
> >> > > >> > > > > > 3. With frequent checkpointing local aggregation
> >> > effectiveness
> >> > > >> > would
> >> > > >> > > > > > degrade.
> >> > > >> > > > > >
> >> > > >> > > > > > I assume Kurt is correct, that in your use cases
> >> stateless
> >> > > >> operator
> >> > > >> > > was
> >> > > >> > > > > > behaving better, but I could easily see other use cases
> >> as
> >> > > well.
> >> > > >> > For
> >> > > >> > > > > > example someone is already using RocksDB, and his job
> is
> >> > > >> > bottlenecked
> >> > > >> > > > on
> >> > > >> > > > > a
> >> > > >> > > > > > single window operator instance because of the data
> >> skew. In
> >> > > >> that
> >> > > >> > > case
> >> > > >> > > > > > stateful local aggregation would be probably a better
> >> > choice.
> >> > > >> > > > > >
> >> > > >> > > > > > Because of that, I think we should eventually provide
> >> both
> >> > > >> versions
> >> > > >> > > and
> >> > > >> > > > > in
> >> > > >> > > > > > the initial version we should at least make the “local
> >> > > >> aggregation
> >> > > >> > > > > engine”
> >> > > >> > > > > > abstract enough, that one could easily provide
> different
> >> > > >> > > implementation
> >> > > >> > > > > > strategy.
> >> > > >> > > > > >
> >> > > >> > > > > > Piotrek
> >> > > >> > > > > >
> >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
> [hidden email]
> >> >
> >> > > >> wrote:
> >> > > >> > > > > > >
> >> > > >> > > > > > > Hi,
> >> > > >> > > > > > >
> >> > > >> > > > > > > For the trigger, it depends on what operator we want
> to
> >> > use
> >> > > >> under
> >> > > >> > > the
> >> > > >> > > > > > API.
> >> > > >> > > > > > > If we choose to use window operator,
> >> > > >> > > > > > > we should also use window's trigger. However, I also
> >> think
> >> > > >> reuse
> >> > > >> > > > window
> >> > > >> > > > > > > operator for this scenario may not be
> >> > > >> > > > > > > the best choice. The reasons are the following:
> >> > > >> > > > > > >
> >> > > >> > > > > > > 1. As a lot of people already pointed out, window
> >> relies
> >> > > >> heavily
> >> > > >> > on
> >> > > >> > > > > state
> >> > > >> > > > > > > and it will definitely effect performance. You can
> >> > > >> > > > > > > argue that one can use heap based statebackend, but
> >> this
> >> > > will
> >> > > >> > > > introduce
> >> > > >> > > > > > > extra coupling. Especially we have a chance to
> >> > > >> > > > > > > design a pure stateless operator.
> >> > > >> > > > > > > 2. The window operator is *the most* complicated
> >> operator
> >> > > >> Flink
> >> > > >> > > > > currently
> >> > > >> > > > > > > have. Maybe we only need to pick a subset of
> >> > > >> > > > > > > window operator to achieve the goal, but once the
> user
> >> > wants
> >> > > >> to
> >> > > >> > > have
> >> > > >> > > > a
> >> > > >> > > > > > deep
> >> > > >> > > > > > > look at the localAggregation operator, it's still
> >> > > >> > > > > > > hard to find out what's going on under the window
> >> > operator.
> >> > > >> For
> >> > > >> > > > > > simplicity,
> >> > > >> > > > > > > I would also recommend we introduce a dedicated
> >> > > >> > > > > > > lightweight operator, which also much easier for a
> >> user to
> >> > > >> learn
> >> > > >> > > and
> >> > > >> > > > > use.
> >> > > >> > > > > > >
> >> > > >> > > > > > > For your question about increasing the burden in
> >> > > >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the
> only
> >> > > thing
> >> > > >> > this
> >> > > >> > > > > > function
> >> > > >> > > > > > > need
> >> > > >> > > > > > > to do is output all the partial results, it's purely
> >> cpu
> >> > > >> > workload,
> >> > > >> > > > not
> >> > > >> > > > > > > introducing any IO. I want to point out that even if
> we
> >> > have
> >> > > >> this
> >> > > >> > > > > > > cost, we reduced another barrier align cost of the
> >> > operator,
> >> > > >> > which
> >> > > >> > > is
> >> > > >> > > > > the
> >> > > >> > > > > > > sync flush stage of the state, if you introduced
> state.
> >> > This
> >> > > >> > > > > > > flush actually will introduce disk IO, and I think
> it's
> >> > > >> worthy to
> >> > > >> > > > > > exchange
> >> > > >> > > > > > > this cost with purely CPU workload. And we do have
> some
> >> > > >> > > > > > > observations about these two behavior (as i said
> >> before,
> >> > we
> >> > > >> > > actually
> >> > > >> > > > > > > implemented both solutions), the stateless one
> actually
> >> > > >> performs
> >> > > >> > > > > > > better both in performance and barrier align time.
> >> > > >> > > > > > >
> >> > > >> > > > > > > Best,
> >> > > >> > > > > > > Kurt
> >> > > >> > > > > > >
> >> > > >> > > > > > >
> >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> >> > > >> [hidden email]
> >> > > >> > >
> >> > > >> > > > > wrote:
> >> > > >> > > > > > >
> >> > > >> > > > > > >> Hi Kurt,
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> Thanks for your example. Now, it looks more clearly
> >> for
> >> > me.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> From your example code snippet, I saw the
> >> localAggregate
> >> > > API
> >> > > >> has
> >> > > >> > > > three
> >> > > >> > > > > > >> parameters:
> >> > > >> > > > > > >>
> >> > > >> > > > > > >>   1. key field
> >> > > >> > > > > > >>   2. PartitionAvg
> >> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes from
> window
> >> > > >> package?
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> I will compare our and your design from API and
> >> operator
> >> > > >> level:
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> *From the API level:*
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> As I replied to @dianfu in the old email thread,[1]
> >> the
> >> > > >> Window
> >> > > >> > API
> >> > > >> > > > can
> >> > > >> > > > > > >> provide the second and the third parameter right
> now.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> If you reuse specified interface or class, such as
> >> > > *Trigger*
> >> > > >> or
> >> > > >> > > > > > >> *CounterTrigger* provided by window package, but do
> >> not
> >> > use
> >> > > >> > window
> >> > > >> > > > > API,
> >> > > >> > > > > > >> it's not reasonable.
> >> > > >> > > > > > >> And if you do not reuse these interface or class,
> you
> >> > would
> >> > > >> need
> >> > > >> > > to
> >> > > >> > > > > > >> introduce more things however they are looked
> similar
> >> to
> >> > > the
> >> > > >> > > things
> >> > > >> > > > > > >> provided by window package.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> The window package has provided several types of the
> >> > window
> >> > > >> and
> >> > > >> > > many
> >> > > >> > > > > > >> triggers and let users customize it. What's more,
> the
> >> > user
> >> > > is
> >> > > >> > more
> >> > > >> > > > > > familiar
> >> > > >> > > > > > >> with Window API.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> This is the reason why we just provide localKeyBy
> API
> >> and
> >> > > >> reuse
> >> > > >> > > the
> >> > > >> > > > > > window
> >> > > >> > > > > > >> API. It reduces unnecessary components such as
> >> triggers
> >> > and
> >> > > >> the
> >> > > >> > > > > > mechanism
> >> > > >> > > > > > >> of buffer (based on count num or time).
> >> > > >> > > > > > >> And it has a clear and easy to understand semantics.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> *From the operator level:*
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> We reused window operator, so we can get all the
> >> benefits
> >> > > >> from
> >> > > >> > > state
> >> > > >> > > > > and
> >> > > >> > > > > > >> checkpoint.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> From your design, you named the operator under
> >> > > localAggregate
> >> > > >> > API
> >> > > >> > > > is a
> >> > > >> > > > > > >> *stateless* operator. IMO, it is still a state, it
> is
> >> > just
> >> > > >> not
> >> > > >> > > Flink
> >> > > >> > > > > > >> managed state.
> >> > > >> > > > > > >> About the memory buffer (I think it's still not very
> >> > clear,
> >> > > >> if
> >> > > >> > you
> >> > > >> > > > > have
> >> > > >> > > > > > >> time, can you give more detail information or answer
> >> my
> >> > > >> > > questions),
> >> > > >> > > > I
> >> > > >> > > > > > have
> >> > > >> > > > > > >> some questions:
> >> > > >> > > > > > >>
> >> > > >> > > > > > >>   - if it just a raw JVM heap memory buffer, how to
> >> > support
> >> > > >> > fault
> >> > > >> > > > > > >>   tolerance, if the job is configured EXACTLY-ONCE
> >> > semantic
> >> > > >> > > > guarantee?
> >> > > >> > > > > > >>   - if you thought the memory buffer(non-Flink
> state),
> >> > has
> >> > > >> > better
> >> > > >> > > > > > >>   performance. In our design, users can also config
> >> HEAP
> >> > > >> state
> >> > > >> > > > backend
> >> > > >> > > > > > to
> >> > > >> > > > > > >>   provide the performance close to your mechanism.
> >> > > >> > > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()`
> >> related
> >> > > to
> >> > > >> the
> >> > > >> > > > > timing
> >> > > >> > > > > > of
> >> > > >> > > > > > >>   snapshot. IMO, the flush action should be a
> >> > synchronized
> >> > > >> > action?
> >> > > >> > > > (if
> >> > > >> > > > > > >> not,
> >> > > >> > > > > > >>   please point out my mistake) I still think we
> should
> >> > not
> >> > > >> > depend
> >> > > >> > > on
> >> > > >> > > > > the
> >> > > >> > > > > > >>   timing of checkpoint. Checkpoint related
> operations
> >> are
> >> > > >> > inherent
> >> > > >> > > > > > >>   performance sensitive, we should not increase its
> >> > burden
> >> > > >> > > anymore.
> >> > > >> > > > > Our
> >> > > >> > > > > > >>   implementation based on the mechanism of Flink's
> >> > > >> checkpoint,
> >> > > >> > > which
> >> > > >> > > > > can
> >> > > >> > > > > > >>   benefit from the asnyc snapshot and incremental
> >> > > checkpoint.
> >> > > >> > IMO,
> >> > > >> > > > the
> >> > > >> > > > > > >>   performance is not a problem, and we also do not
> >> find
> >> > the
> >> > > >> > > > > performance
> >> > > >> > > > > > >> issue
> >> > > >> > > > > > >>   in our production.
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> [1]:
> >> > > >> > > > > > >>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> Best,
> >> > > >> > > > > > >> Vino
> >> > > >> > > > > > >>
> >> > > >> > > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二
> 下午2:27写道：
> >> > > >> > > > > > >>
> >> > > >> > > > > > >>> Yeah, sorry for not expressing myself clearly. I
> will
> >> > try
> >> > > to
> >> > > >> > > > provide
> >> > > >> > > > > > more
> >> > > >> > > > > > >>> details to make sure we are on the same page.
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> For DataStream API, it shouldn't be optimized
> >> > > automatically.
> >> > > >> > You
> >> > > >> > > > have
> >> > > >> > > > > > to
> >> > > >> > > > > > >>> explicitly call API to do local aggregation
> >> > > >> > > > > > >>> as well as the trigger policy of the local
> >> aggregation.
> >> > > Take
> >> > > >> > > > average
> >> > > >> > > > > > for
> >> > > >> > > > > > >>> example, the user program may look like this (just
> a
> >> > > draft):
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> assuming the input type is DataStream<Tupl2<String,
> >> > Int>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> ds.localAggregate(
> >> > > >> > > > > > >>>        0,                                       //
> >> The
> >> > > local
> >> > > >> > key,
> >> > > >> > > > > which
> >> > > >> > > > > > >> is
> >> > > >> > > > > > >>> the String from Tuple2
> >> > > >> > > > > > >>>        PartitionAvg(1),                 // The
> >> partial
> >> > > >> > > aggregation
> >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>, indicating
> sum
> >> and
> >> > > >> count
> >> > > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger policy,
> >> note
> >> > > >> this
> >> > > >> > > > should
> >> > > >> > > > > be
> >> > > >> > > > > > >>> best effort, and also be composited with time based
> >> or
> >> > > >> memory
> >> > > >> > > size
> >> > > >> > > > > > based
> >> > > >> > > > > > >>> trigger
> >> > > >> > > > > > >>>    )                                           //
> The
> >> > > return
> >> > > >> > type
> >> > > >> > > > is
> >> > > >> > > > > > >> local
> >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> >> > > >> > > > > > >>>    .keyBy(0)                             // Further
> >> > keyby
> >> > > it
> >> > > >> > with
> >> > > >> > > > > > >> required
> >> > > >> > > > > > >>> key
> >> > > >> > > > > > >>>    .aggregate(1)                      // This will
> >> merge
> >> > > all
> >> > > >> > the
> >> > > >> > > > > > partial
> >> > > >> > > > > > >>> results and get the final average.
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> (This is only a draft, only trying to explain what
> it
> >> > > looks
> >> > > >> > > like. )
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> The local aggregate operator can be stateless, we
> can
> >> > > keep a
> >> > > >> > > memory
> >> > > >> > > > > > >> buffer
> >> > > >> > > > > > >>> or other efficient data structure to improve the
> >> > aggregate
> >> > > >> > > > > performance.
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> Let me know if you have any other questions.
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> Best,
> >> > > >> > > > > > >>> Kurt
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> >> > > >> > [hidden email]
> >> > > >> > > >
> >> > > >> > > > > > wrote:
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>>> Hi Kurt,
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Thanks for your reply.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Actually, I am not against you to raise your
> design.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> From your description before, I just can imagine
> >> your
> >> > > >> > high-level
> >> > > >> > > > > > >>>> implementation is about SQL and the optimization
> is
> >> > inner
> >> > > >> of
> >> > > >> > the
> >> > > >> > > > > API.
> >> > > >> > > > > > >> Is
> >> > > >> > > > > > >>> it
> >> > > >> > > > > > >>>> automatically? how to give the configuration
> option
> >> > about
> >> > > >> > > trigger
> >> > > >> > > > > > >>>> pre-aggregation?
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Maybe after I get more information, it sounds more
> >> > > >> reasonable.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> IMO, first of all, it would be better to make your
> >> user
> >> > > >> > > interface
> >> > > >> > > > > > >>> concrete,
> >> > > >> > > > > > >>>> it's the basis of the discussion.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> For example, can you give an example code snippet
> to
> >> > > >> introduce
> >> > > >> > > how
> >> > > >> > > > > to
> >> > > >> > > > > > >>> help
> >> > > >> > > > > > >>>> users to process data skew caused by the jobs
> which
> >> > built
> >> > > >> with
> >> > > >> > > > > > >> DataStream
> >> > > >> > > > > > >>>> API?
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> If you give more details we can discuss further
> >> more. I
> >> > > >> think
> >> > > >> > if
> >> > > >> > > > one
> >> > > >> > > > > > >>> design
> >> > > >> > > > > > >>>> introduces an exact interface and another does
> not.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> The implementation has an obvious difference. For
> >> > > example,
> >> > > >> we
> >> > > >> > > > > > introduce
> >> > > >> > > > > > >>> an
> >> > > >> > > > > > >>>> exact API in DataStream named localKeyBy, about
> the
> >> > > >> > > > pre-aggregation
> >> > > >> > > > > we
> >> > > >> > > > > > >>> need
> >> > > >> > > > > > >>>> to define the trigger mechanism of local
> >> aggregation,
> >> > so
> >> > > we
> >> > > >> > find
> >> > > >> > > > > > reused
> >> > > >> > > > > > >>>> window API and operator is a good choice. This is
> a
> >> > > >> reasoning
> >> > > >> > > link
> >> > > >> > > > > > from
> >> > > >> > > > > > >>>> design to implementation.
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> What do you think?
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Best,
> >> > > >> > > > > > >>>> Vino
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二
> >> 上午11:58写道：
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>>> Hi Vino,
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> Now I feel that we may have different
> >> understandings
> >> > > about
> >> > > >> > what
> >> > > >> > > > > kind
> >> > > >> > > > > > >> of
> >> > > >> > > > > > >>>>> problems or improvements you want to
> >> > > >> > > > > > >>>>> resolve. Currently, most of the feedback are
> >> focusing
> >> > on
> >> > > >> *how
> >> > > >> > > to
> >> > > >> > > > > do a
> >> > > >> > > > > > >>>>> proper local aggregation to improve performance
> >> > > >> > > > > > >>>>> and maybe solving the data skew issue*. And my
> gut
> >> > > >> feeling is
> >> > > >> > > > this
> >> > > >> > > > > is
> >> > > >> > > > > > >>>>> exactly what users want at the first place,
> >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to summarize
> >> here,
> >> > > >> please
> >> > > >> > > > > correct
> >> > > >> > > > > > >>> me
> >> > > >> > > > > > >>>> if
> >> > > >> > > > > > >>>>> i'm wrong).
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> But I still think the design is somehow diverged
> >> from
> >> > > the
> >> > > >> > goal.
> >> > > >> > > > If
> >> > > >> > > > > we
> >> > > >> > > > > > >>>> want
> >> > > >> > > > > > >>>>> to have an efficient and powerful way to
> >> > > >> > > > > > >>>>> have local aggregation, supporting intermedia
> >> result
> >> > > type
> >> > > >> is
> >> > > >> > > > > > >> essential
> >> > > >> > > > > > >>>> IMO.
> >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
> >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a
> proper
> >> > > >> support of
> >> > > >> > > > > > >>>> intermediate
> >> > > >> > > > > > >>>>> result type and can do `merge` operation
> >> > > >> > > > > > >>>>> on them.
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> Now, we have a lightweight alternatives which
> >> performs
> >> > > >> well,
> >> > > >> > > and
> >> > > >> > > > > > >> have a
> >> > > >> > > > > > >>>>> nice fit with the local aggregate requirements.
> >> > > >> > > > > > >>>>> Mostly importantly,  it's much less complex
> because
> >> > it's
> >> > > >> > > > stateless.
> >> > > >> > > > > > >> And
> >> > > >> > > > > > >>>> it
> >> > > >> > > > > > >>>>> can also achieve the similar multiple-aggregation
> >> > > >> > > > > > >>>>> scenario.
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> I still not convinced why we shouldn't consider
> it
> >> as
> >> > a
> >> > > >> first
> >> > > >> > > > step.
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> Best,
> >> > > >> > > > > > >>>>> Kurt
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> >> > > >> > > > [hidden email]>
> >> > > >> > > > > > >>>> wrote:
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>>> Hi Kurt,
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Thanks for your comments.
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> It seems we both implemented local aggregation
> >> > feature
> >> > > to
> >> > > >> > > > optimize
> >> > > >> > > > > > >>> the
> >> > > >> > > > > > >>>>>> issue of data skew.
> >> > > >> > > > > > >>>>>> However, IMHO, the API level of optimizing
> >> revenue is
> >> > > >> > > different.
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> *Your optimization benefits from Flink SQL and
> >> it's
> >> > not
> >> > > >> > user's
> >> > > >> > > > > > >>>> faces.(If
> >> > > >> > > > > > >>>>> I
> >> > > >> > > > > > >>>>>> understand it incorrectly, please correct
> this.)*
> >> > > >> > > > > > >>>>>> *Our implementation employs it as an
> optimization
> >> > tool
> >> > > >> API
> >> > > >> > for
> >> > > >> > > > > > >>>>> DataStream,
> >> > > >> > > > > > >>>>>> it just like a local version of the keyBy API.*
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Based on this, I want to say support it as a
> >> > DataStream
> >> > > >> API
> >> > > >> > > can
> >> > > >> > > > > > >>> provide
> >> > > >> > > > > > >>>>>> these advantages:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear semantic and
> >> it's
> >> > > >> > flexible
> >> > > >> > > > not
> >> > > >> > > > > > >>> only
> >> > > >> > > > > > >>>>> for
> >> > > >> > > > > > >>>>>>   processing data skew but also for implementing
> >> some
> >> > > >> user
> >> > > >> > > > cases,
> >> > > >> > > > > > >>> for
> >> > > >> > > > > > >>>>>>   example, if we want to calculate the
> >> multiple-level
> >> > > >> > > > aggregation,
> >> > > >> > > > > > >>> we
> >> > > >> > > > > > >>>>> can
> >> > > >> > > > > > >>>>>> do
> >> > > >> > > > > > >>>>>>   multiple-level aggregation in the local
> >> > aggregation:
> >> > > >> > > > > > >>>>>>
> >> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
> >> > > >> //
> >> > > >> > > here
> >> > > >> > > > > > >> "a"
> >> > > >> > > > > > >>>> is
> >> > > >> > > > > > >>>>> a
> >> > > >> > > > > > >>>>>>   sub-category, while "b" is a category, here we
> >> do
> >> > not
> >> > > >> need
> >> > > >> > > to
> >> > > >> > > > > > >>>> shuffle
> >> > > >> > > > > > >>>>>> data
> >> > > >> > > > > > >>>>>>   in the network.
> >> > > >> > > > > > >>>>>>   - The users of DataStream API will benefit
> from
> >> > this.
> >> > > >> > > > Actually,
> >> > > >> > > > > > >> we
> >> > > >> > > > > > >>>>> have
> >> > > >> > > > > > >>>>>>   a lot of scenes need to use DataStream API.
> >> > > Currently,
> >> > > >> > > > > > >> DataStream
> >> > > >> > > > > > >>>> API
> >> > > >> > > > > > >>>>> is
> >> > > >> > > > > > >>>>>>   the cornerstone of the physical plan of Flink
> >> SQL.
> >> > > >> With a
> >> > > >> > > > > > >>> localKeyBy
> >> > > >> > > > > > >>>>>> API,
> >> > > >> > > > > > >>>>>>   the optimization of SQL at least may use this
> >> > > optimized
> >> > > >> > API,
> >> > > >> > > > > > >> this
> >> > > >> > > > > > >>>> is a
> >> > > >> > > > > > >>>>>>   further topic.
> >> > > >> > > > > > >>>>>>   - Based on the window operator, our state
> would
> >> > > benefit
> >> > > >> > from
> >> > > >> > > > > > >> Flink
> >> > > >> > > > > > >>>>> State
> >> > > >> > > > > > >>>>>>   and checkpoint, we do not need to worry about
> >> OOM
> >> > and
> >> > > >> job
> >> > > >> > > > > > >> failed.
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Now, about your questions:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> 1. About our design cannot change the data type
> >> and
> >> > > about
> >> > > >> > the
> >> > > >> > > > > > >>>>>> implementation of average:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is
> an
> >> API
> >> > > >> > provides
> >> > > >> > > > to
> >> > > >> > > > > > >> the
> >> > > >> > > > > > >>>>> users
> >> > > >> > > > > > >>>>>> who use DataStream API to build their jobs.
> >> > > >> > > > > > >>>>>> Users should know its semantics and the
> difference
> >> > with
> >> > > >> > keyBy
> >> > > >> > > > API,
> >> > > >> > > > > > >> so
> >> > > >> > > > > > >>>> if
> >> > > >> > > > > > >>>>>> they want to the average aggregation, they
> should
> >> > carry
> >> > > >> > local
> >> > > >> > > > sum
> >> > > >> > > > > > >>>> result
> >> > > >> > > > > > >>>>>> and local count result.
> >> > > >> > > > > > >>>>>> I admit that it will be convenient to use keyBy
> >> > > directly.
> >> > > >> > But
> >> > > >> > > we
> >> > > >> > > > > > >> need
> >> > > >> > > > > > >>>> to
> >> > > >> > > > > > >>>>>> pay a little price when we get some benefits. I
> >> think
> >> > > >> this
> >> > > >> > > price
> >> > > >> > > > > is
> >> > > >> > > > > > >>>>>> reasonable. Considering that the DataStream API
> >> > itself
> >> > > >> is a
> >> > > >> > > > > > >> low-level
> >> > > >> > > > > > >>>> API
> >> > > >> > > > > > >>>>>> (at least for now).
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> 2. About stateless operator and
> >> > > >> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Actually, I have discussed this opinion with
> >> @dianfu
> >> > in
> >> > > >> the
> >> > > >> > > old
> >> > > >> > > > > > >> mail
> >> > > >> > > > > > >>>>>> thread. I will copy my opinion from there:
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>>   - for your design, you still need somewhere to
> >> give
> >> > > the
> >> > > >> > > users
> >> > > >> > > > > > >>>>> configure
> >> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
> >> availability?),
> >> > > >> this
> >> > > >> > > > design
> >> > > >> > > > > > >>>> cannot
> >> > > >> > > > > > >>>>>>   guarantee a deterministic semantics (it will
> >> bring
> >> > > >> trouble
> >> > > >> > > for
> >> > > >> > > > > > >>>> testing
> >> > > >> > > > > > >>>>>> and
> >> > > >> > > > > > >>>>>>   debugging).
> >> > > >> > > > > > >>>>>>   - if the implementation depends on the timing
> of
> >> > > >> > checkpoint,
> >> > > >> > > > it
> >> > > >> > > > > > >>>> would
> >> > > >> > > > > > >>>>>>   affect the checkpoint's progress, and the
> >> buffered
> >> > > data
> >> > > >> > may
> >> > > >> > > > > > >> cause
> >> > > >> > > > > > >>>> OOM
> >> > > >> > > > > > >>>>>>   issue. In addition, if the operator is
> >> stateless,
> >> > it
> >> > > >> can
> >> > > >> > not
> >> > > >> > > > > > >>> provide
> >> > > >> > > > > > >>>>>> fault
> >> > > >> > > > > > >>>>>>   tolerance.
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Best,
> >> > > >> > > > > > >>>>>> Vino
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二
> >> > 上午9:22写道：
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>>> Hi Vino,
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the general
> idea
> >> and
> >> > > IMO
> >> > > >> > it's
> >> > > >> > > > > > >> very
> >> > > >> > > > > > >>>>> useful
> >> > > >> > > > > > >>>>>>> feature.
> >> > > >> > > > > > >>>>>>> But after reading through the document, I feel
> >> that
> >> > we
> >> > > >> may
> >> > > >> > > over
> >> > > >> > > > > > >>>> design
> >> > > >> > > > > > >>>>>> the
> >> > > >> > > > > > >>>>>>> required
> >> > > >> > > > > > >>>>>>> operator for proper local aggregation. The main
> >> > reason
> >> > > >> is
> >> > > >> > we
> >> > > >> > > > want
> >> > > >> > > > > > >>> to
> >> > > >> > > > > > >>>>>> have a
> >> > > >> > > > > > >>>>>>> clear definition and behavior about the "local
> >> keyed
> >> > > >> state"
> >> > > >> > > > which
> >> > > >> > > > > > >>> in
> >> > > >> > > > > > >>>> my
> >> > > >> > > > > > >>>>>>> opinion is not
> >> > > >> > > > > > >>>>>>> necessary for local aggregation, at least for
> >> start.
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> Another issue I noticed is the local key by
> >> operator
> >> > > >> cannot
> >> > > >> > > > > > >> change
> >> > > >> > > > > > >>>>>> element
> >> > > >> > > > > > >>>>>>> type, it will
> >> > > >> > > > > > >>>>>>> also restrict a lot of use cases which can be
> >> > benefit
> >> > > >> from
> >> > > >> > > > local
> >> > > >> > > > > > >>>>>>> aggregation, like "average".
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and the only
> >> thing
> >> > > >> need to
> >> > > >> > > be
> >> > > >> > > > > > >> done
> >> > > >> > > > > > >>>> is
> >> > > >> > > > > > >>>>>>> introduce
> >> > > >> > > > > > >>>>>>> a stateless lightweight operator which is
> >> *chained*
> >> > > >> before
> >> > > >> > > > > > >>> `keyby()`.
> >> > > >> > > > > > >>>>> The
> >> > > >> > > > > > >>>>>>> operator will flush all buffered
> >> > > >> > > > > > >>>>>>> elements during
> >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
> >> > > >> > > > and
> >> > > >> > > > > > >>>> make
> >> > > >> > > > > > >>>>>>> himself stateless.
> >> > > >> > > > > > >>>>>>> By the way, in the earlier version we also did
> >> the
> >> > > >> similar
> >> > > >> > > > > > >> approach
> >> > > >> > > > > > >>>> by
> >> > > >> > > > > > >>>>>>> introducing a stateful
> >> > > >> > > > > > >>>>>>> local aggregation operator but it's not
> >> performed as
> >> > > >> well
> >> > > >> > as
> >> > > >> > > > the
> >> > > >> > > > > > >>>> later
> >> > > >> > > > > > >>>>>> one,
> >> > > >> > > > > > >>>>>>> and also effect the barrie
> >> > > >> > > > > > >>>>>>> alignment time. The later one is fairly simple
> >> and
> >> > > more
> >> > > >> > > > > > >> efficient.
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> I would highly suggest you to consider to have
> a
> >> > > >> stateless
> >> > > >> > > > > > >> approach
> >> > > >> > > > > > >>>> at
> >> > > >> > > > > > >>>>>> the
> >> > > >> > > > > > >>>>>>> first step.
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> Best,
> >> > > >> > > > > > >>>>>>> Kurt
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> >> > > >> [hidden email]>
> >> > > >> > > > > > >> wrote:
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>>> Hi Vino,
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> >> > > >> > > > > > >> have
> >> > > >> > > > > > >>>> you
> >> > > >> > > > > > >>>>>>> done
> >> > > >> > > > > > >>>>>>>> some benchmark?
> >> > > >> > > > > > >>>>>>>> Because I'm curious about how much performance
> >> > > >> improvement
> >> > > >> > > can
> >> > > >> > > > > > >> we
> >> > > >> > > > > > >>>> get
> >> > > >> > > > > > >>>>>> by
> >> > > >> > > > > > >>>>>>>> using count window as the local operator.
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>> Jark
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> >> > > >> > > > [hidden email]
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>>>> wrote:
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Hi Hequn,
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to provide a
> >> tool
> >> > > >> which
> >> > > >> > > can
> >> > > >> > > > > > >>> let
> >> > > >> > > > > > >>>>>> users
> >> > > >> > > > > > >>>>>>> do
> >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior of
> >> the
> >> > > >> > > > > > >>> pre-aggregation
> >> > > >> > > > > > >>>>> is
> >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> So the three cases are different, I will
> >> describe
> >> > > them
> >> > > >> > one
> >> > > >> > > by
> >> > > >> > > > > > >>>> one:
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> *In this case, the result is event-driven,
> each
> >> > > event
> >> > > >> can
> >> > > >> > > > > > >>> produce
> >> > > >> > > > > > >>>>> one
> >> > > >> > > > > > >>>>>>> sum
> >> > > >> > > > > > >>>>>>>>> aggregation result and it is the latest one
> >> from
> >> > the
> >> > > >> > source
> >> > > >> > > > > > >>>> start.*
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> 2. input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have a
> >> problem, it
> >> > > >> would
> >> > > >> > do
> >> > > >> > > > > > >> the
> >> > > >> > > > > > >>>>> local
> >> > > >> > > > > > >>>>>>> sum
> >> > > >> > > > > > >>>>>>>>> aggregation and will produce the latest
> partial
> >> > > result
> >> > > >> > from
> >> > > >> > > > > > >> the
> >> > > >> > > > > > >>>>>> source
> >> > > >> > > > > > >>>>>>>>> start for every event. *
> >> > > >> > > > > > >>>>>>>>> *These latest partial results from the same
> key
> >> > are
> >> > > >> > hashed
> >> > > >> > > to
> >> > > >> > > > > > >>> one
> >> > > >> > > > > > >>>>>> node
> >> > > >> > > > > > >>>>>>> to
> >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
> >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it received
> >> > > multiple
> >> > > >> > > partial
> >> > > >> > > > > > >>>>> results
> >> > > >> > > > > > >>>>>>>> (they
> >> > > >> > > > > > >>>>>>>>> are all calculated from the source start) and
> >> sum
> >> > > them
> >> > > >> > will
> >> > > >> > > > > > >> get
> >> > > >> > > > > > >>>> the
> >> > > >> > > > > > >>>>>>> wrong
> >> > > >> > > > > > >>>>>>>>> result.*
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> 3.
> >> > > >> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a partial
> >> > > aggregation
> >> > > >> > > result
> >> > > >> > > > > > >>> for
> >> > > >> > > > > > >>>>>> the 5
> >> > > >> > > > > > >>>>>>>>> records in the count window. The partial
> >> > aggregation
> >> > > >> > > results
> >> > > >> > > > > > >>> from
> >> > > >> > > > > > >>>>> the
> >> > > >> > > > > > >>>>>>>> same
> >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> So the first case and the third case can get
> >> the
> >> > > >> *same*
> >> > > >> > > > > > >> result,
> >> > > >> > > > > > >>>> the
> >> > > >> > > > > > >>>>>>>>> difference is the output-style and the
> latency.
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key API is just
> >> an
> >> > > >> > > optimization
> >> > > >> > > > > > >>>> API.
> >> > > >> > > > > > >>>>> We
> >> > > >> > > > > > >>>>>>> do
> >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the user has
> to
> >> > > >> > understand
> >> > > >> > > > > > >> its
> >> > > >> > > > > > >>>>>>> semantics
> >> > > >> > > > > > >>>>>>>>> and use it correctly.
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>>> Vino
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]>
> >> 于2019年6月17日周一
> >> > > >> > 下午4:18写道：
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> Hi Vino,
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a
> very
> >> > good
> >> > > >> > > feature!
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the
> semantics
> >> > for
> >> > > >> the
> >> > > >> > > > > > >>>>>> `localKeyBy`.
> >> > > >> > > > > > >>>>>>>> From
> >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns
> an
> >> > > >> instance
> >> > > >> > of
> >> > > >> > > > > > >>>>>>> `KeyedStream`
> >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in this
> case,
> >> > > what's
> >> > > >> > the
> >> > > >> > > > > > >>>>> semantics
> >> > > >> > > > > > >>>>>>> for
> >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the
> >> following
> >> > > code
> >> > > >> > share
> >> > > >> > > > > > >>> the
> >> > > >> > > > > > >>>>> same
> >> > > >> > > > > > >>>>>>>>> result?
> >> > > >> > > > > > >>>>>>>>>> and what're the differences between them?
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>> 2.
> input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>> 3.
> >> > > >> > > > > > >>
> >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add this into
> >> the
> >> > > >> > document.
> >> > > >> > > > > > >>> Thank
> >> > > >> > > > > > >>>>> you
> >> > > >> > > > > > >>>>>>>> very
> >> > > >> > > > > > >>>>>>>>>> much.
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> Best, Hequn
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino yang <
> >> > > >> > > > > > >>>>> [hidden email]>
> >> > > >> > > > > > >>>>>>>>> wrote:
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section of
> >> FLIP
> >> > > >> wiki
> >> > > >> > > > > > >>>> page.[1]
> >> > > >> > > > > > >>>>>> This
> >> > > >> > > > > > >>>>>>>>> mail
> >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to
> the
> >> > > third
> >> > > >> > step.
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote
> step),
> >> I
> >> > > >> didn't
> >> > > >> > > > > > >> find
> >> > > >> > > > > > >>>> the
> >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the voting
> >> process.
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of this
> >> feature
> >> > > has
> >> > > >> > been
> >> > > >> > > > > > >>> done
> >> > > >> > > > > > >>>>> in
> >> > > >> > > > > > >>>>>>> the
> >> > > >> > > > > > >>>>>>>>> old
> >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when should
> I
> >> > start
> >> > > >> > > > > > >> voting?
> >> > > >> > > > > > >>>> Can
> >> > > >> > > > > > >>>>> I
> >> > > >> > > > > > >>>>>>>> start
> >> > > >> > > > > > >>>>>>>>>> now?
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>>>>> Vino
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> [1]:
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> >> > > >> > > > > > >>>>>>>>>>> [2]:
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email]> 于2019年6月13日周四
> >> > > 上午9:19写道：
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your
> >> efforts.
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>>>>>> Leesf
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]>
> >> > 于2019年6月12日周三
> >> > > >> > > > > > >>> 下午5:46写道：
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP discussion
> >> > thread
> >> > > >> > > > > > >> about
> >> > > >> > > > > > >>>>>>> supporting
> >> > > >> > > > > > >>>>>>>>>> local
> >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can effectively
> >> > alleviate
> >> > > >> data
> >> > > >> > > > > > >>>> skew.
> >> > > >> > > > > > >>>>>>> This
> >> > > >> > > > > > >>>>>>>> is
> >> > > >> > > > > > >>>>>>>>>> the
> >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely used
> to
> >> > > >> perform
> >> > > >> > > > > > >>>>>> aggregating
> >> > > >> > > > > > >>>>>>>>>>>> operations
> >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the
> >> elements
> >> > > >> that
> >> > > >> > > > > > >>> have
> >> > > >> > > > > > >>>>> the
> >> > > >> > > > > > >>>>>>> same
> >> > > >> > > > > > >>>>>>>>>> key.
> >> > > >> > > > > > >>>>>>>>>>>> When
> >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with
> the
> >> > same
> >> > > >> key
> >> > > >> > > > > > >>> will
> >> > > >> > > > > > >>>> be
> >> > > >> > > > > > >>>>>>> sent
> >> > > >> > > > > > >>>>>>>> to
> >> > > >> > > > > > >>>>>>>>>> and
> >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> The performance of these aggregating
> >> > operations
> >> > > is
> >> > > >> > > > > > >> very
> >> > > >> > > > > > >>>>>>> sensitive
> >> > > >> > > > > > >>>>>>>>> to
> >> > > >> > > > > > >>>>>>>>>>> the
> >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases where
> >> the
> >> > > >> > > > > > >>> distribution
> >> > > >> > > > > > >>>>> of
> >> > > >> > > > > > >>>>>>> keys
> >> > > >> > > > > > >>>>>>>>>>>> follows a
> >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
> >> > > >> significantly
> >> > > >> > > > > > >>>>>> downgraded.
> >> > > >> > > > > > >>>>>>>>> More
> >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of
> >> > parallelism
> >> > > >> does
> >> > > >> > > > > > >>> not
> >> > > >> > > > > > >>>>> help
> >> > > >> > > > > > >>>>>>>> when
> >> > > >> > > > > > >>>>>>>>> a
> >> > > >> > > > > > >>>>>>>>>>> task
> >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted
> >> method
> >> > to
> >> > > >> > > > > > >> reduce
> >> > > >> > > > > > >>>> the
> >> > > >> > > > > > >>>>>>>>>> performance
> >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose
> the
> >> > > >> > > > > > >> aggregating
> >> > > >> > > > > > >>>>>>>> operations
> >> > > >> > > > > > >>>>>>>>>> into
> >> > > >> > > > > > >>>>>>>>>>>> two
> >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we aggregate
> >> the
> >> > > >> elements
> >> > > >> > > > > > >>> of
> >> > > >> > > > > > >>>>> the
> >> > > >> > > > > > >>>>>>> same
> >> > > >> > > > > > >>>>>>>>> key
> >> > > >> > > > > > >>>>>>>>>>> at
> >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial
> results.
> >> > Then
> >> > > at
> >> > > >> > > > > > >> the
> >> > > >> > > > > > >>>>> second
> >> > > >> > > > > > >>>>>>>>> phase,
> >> > > >> > > > > > >>>>>>>>>>>> these
> >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to receivers
> >> > according
> >> > > to
> >> > > >> > > > > > >>> their
> >> > > >> > > > > > >>>>> keys
> >> > > >> > > > > > >>>>>>> and
> >> > > >> > > > > > >>>>>>>>> are
> >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final result.
> Since
> >> the
> >> > > >> number
> >> > > >> > > > > > >>> of
> >> > > >> > > > > > >>>>>>> partial
> >> > > >> > > > > > >>>>>>>>>>> results
> >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is limited by
> the
> >> > > >> number of
> >> > > >> > > > > > >>>>>> senders,
> >> > > >> > > > > > >>>>>>>> the
> >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be reduced.
> >> > > >> Besides, by
> >> > > >> > > > > > >>>>>> reducing
> >> > > >> > > > > > >>>>>>>> the
> >> > > >> > > > > > >>>>>>>>>>> amount
> >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the performance can
> be
> >> > > further
> >> > > >> > > > > > >>>>> improved.
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > >
> >> >
> >>
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> >> > > >> > > > > > >>>>>>>>>
> >> https://issues.apache.org/jira/browse/FLINK-12786
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your feedback!
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>> Best,
> >> > > >> > > > > > >>>>>>>>>>>>> Vino
> >> > > >> > > > > > >>>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>>
> >> > > >> > > > > > >>>>>>>>>
> >> > > >> > > > > > >>>>>>>>
> >> > > >> > > > > > >>>>>>>
> >> > > >> > > > > > >>>>>>
> >> > > >> > > > > > >>>>>
> >> > > >> > > > > > >>>>
> >> > > >> > > > > > >>>
> >> > > >> > > > > > >>
> >> > > >> > > > > >
> >> > > >> > > > > >
> >> > > >> > > > >
> >> > > >> > > >
> >> > > >> > >
> >> > > >> >
> >> > > >>
> >> > > >
> >> > >
> >> >
> >>
> >
>

[Jark Wu-2]

Thanks for the long discussion Vino, Kurt, Hequn, Piotr and others,

It seems that we still have some different ideas about the API
(localKeyBy()?) and implementation details (reuse window operator? local
keyed state?).
And the discussion is stalled and mixed with motivation and API and
implementation discussion.

In order to make some progress in this topic, I want to summarize the
points (pls correct me if I'm wrong or missing sth) and would suggest to
split
 the topic into following aspects and discuss them one by one.

1) What's the main purpose of this FLIP?
 - From the title of this FLIP, it is to support local aggregate. However
from the content of the FLIP, 80% are introducing a new state called local
keyed state.
 - If we mainly want to introduce local keyed state, then we should
re-title the FLIP and involve in more people who works on state.
 - If we mainly want to support local aggregate, then we can jump to step 2
to discuss the API design.

2) What does the API look like?
 - Vino proposed to use "localKeyBy()" to do local process, the output of
local process is the result type of aggregate function.
  a) For non-windowed aggregate:
input.localKeyBy(..).aggregate(agg1).keyBy(..).aggregate(agg2)    **NOT
SUPPORT**
  b) For windowed aggregate:
input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)

3) What's the implementation detail?
 - may reuse window operator or not.
 - may introduce a new state concepts or not.
 - may not have state in local operator by flushing buffers in
prepareSnapshotPreBarrier
 - and so on...
 - we can discuss these later when we reach a consensus on API

--------------------

Here are my thoughts:

1) Purpose of this FLIP
 - From the motivation section in the FLIP, I think the purpose is to
support local aggregation to solve the data skew issue.
   Then I think we should focus on how to provide a easy to use and clear
API to support **local aggregation**.
 - Vino's point is centered around the local keyed state API (or
localKeyBy()), and how to leverage the local keyed state API to support
local aggregation.
   But I'm afraid it's not a good way to design API for local aggregation.

2) local aggregation API
 - IMO, the method call chain
"input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)"
is not such easy to use.
   Because we have to provide two implementation for an aggregation (one
for partial agg, another for final agg). And we have to take care of
   the first window call, an inappropriate window call will break the
sematics.
 - From my point of view, local aggregation is a mature concept which
should output the intermediate accumulator (ACC) in the past period of time
(a trigger).
   And the downstream final aggregation will merge ACCs received from local
side, and output the current final result.
 - The current "AggregateFunction" API in DataStream already has the
accumulator type and "merge" method. So the only thing user need to do is
how to enable
   local aggregation opimization and set a trigger.
 - One idea comes to my head is that, assume we have a windowed aggregation
stream: "val stream = input.keyBy().window(w).aggregate(agg)". We can
provide an API on the stream.
   For exmaple, "stream.enableLocalAggregation(Trigger)", the trigger can
be "ContinuousEventTimeTrigger.of(Time.of(Time.minutes(1)))". Then it will
be optmized into
   local operator + final operator, and local operator will combine records
every minute on event time.
 - In this way, there is only one line added, and the output is the same
with before, because it is just an opimization.


Regards,
Jark



On Tue, 25 Jun 2019 at 14:34, vino yang <[hidden email]> wrote:

> Hi Kurt,
>
> Answer your questions:
>
> a) Sorry, I just updated the Google doc, still have no time update the
> FLIP, will update FLIP as soon as possible.
> About your description at this point, I have a question, what does it mean:
> how do we combine with
> `AggregateFunction`?
>
> I have shown you the examples which Flink has supported:
>
>    - input.localKeyBy(0).aggregate()
>    - input.localKeyBy(0).window().aggregate()
>
> You can show me a example about how do we combine with `AggregateFuncion`
> through your localAggregate API.
>
> About the example, how to do the local aggregation for AVG, consider this
> code:
>
>
>
>
>
>
>
>
>
> *DataStream<Tuple2<String, Long>> source = null; source .localKeyBy(0)
> .timeWindow(Time.seconds(60)) .aggregate(agg1, new
> WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>, String,
> TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60)) .aggregate(agg2,
> new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, String,
> TimeWindow>());*
>
> *agg1:*
> *signature : new AggregateFunction<Tuple2<String, Long>, Tuple2<Long,
> Long>, Tuple2<Long, Long>>() {}*
> *input param type: Tuple2<String, Long> f0: key, f1: value*
> *intermediate result type: Tuple2<Long, Long>, f0: local aggregated sum;
> f1: local aggregated count*
> *output param type:  Tuple2<Long, Long>, f0: local aggregated sum; f1:
> local aggregated count*
>
> *agg2:*
> *signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
> Tuple2<String, Long>>() {},*
> *input param type: Tuple3<String, Long, Long>, f0: key, f1:  local
> aggregated sum; f2: local aggregated count*
>
> *intermediate result type: Long  avg result*
> *output param type:  Tuple2<String, Long> f0: key, f1 avg result*
>
> For sliding window, we just need to change the window type if users want to
> do.
> Again, we try to give the design and implementation in the DataStream
> level. So I believe we can match all the requirements(It's just that the
> implementation may be different) comes from the SQL level.
>
> b) Yes, Theoretically, your thought is right. But in reality, it cannot
> bring many benefits.
> If we want to get the benefits from the window API, while we do not reuse
> the window operator? And just copy some many duplicated code to another
> operator?
>
> c) OK, I agree to let the state backend committers join this discussion.
>
> Best,
> Vino
>
>
> Kurt Young <[hidden email]> 于2019年6月24日周一 下午6:53写道：
>
> > Hi vino,
> >
> > One thing to add,  for a), I think use one or two examples like how to do
> > local aggregation on a sliding window,
> > and how do we do local aggregation on an unbounded aggregate, will do a
> lot
> > help.
> >
> > Best,
> > Kurt
> >
> >
> > On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email]> wrote:
> >
> > > Hi vino,
> > >
> > > I think there are several things still need discussion.
> > >
> > > a) We all agree that we should first go with a unified abstraction, but
> > > the abstraction is not reflected by the FLIP.
> > > If your answer is "locakKeyBy" API, then I would ask how do we combine
> > > with `AggregateFunction`, and how do
> > > we do proper local aggregation for those have different intermediate
> > > result type, like AVG. Could you add these
> > > to the document?
> > >
> > > b) From implementation side, reusing window operator is one of the
> > > possible solutions, but not we base on window
> > > operator to have two different implementations. What I understanding
> is,
> > > one of the possible implementations should
> > > not touch window operator.
> > >
> > > c) 80% of your FLIP content is actually describing how do we support
> > local
> > > keyed state. I don't know if this is necessary
> > > to introduce at the first step and we should also involve committers
> work
> > > on state backend to share their thoughts.
> > >
> > > Best,
> > > Kurt
> > >
> > >
> > > On Mon, Jun 24, 2019 at 5:17 PM vino yang <[hidden email]>
> wrote:
> > >
> > >> Hi Kurt,
> > >>
> > >> You did not give more further different opinions, so I thought you
> have
> > >> agreed with the design after we promised to support two kinds of
> > >> implementation.
> > >>
> > >> In API level, we have answered your question about pass an
> > >> AggregateFunction to do the aggregation. No matter introduce
> localKeyBy
> > >> API
> > >> or not, we can support AggregateFunction.
> > >>
> > >> So what's your different opinion now? Can you share it with us?
> > >>
> > >> Best,
> > >> Vino
> > >>
> > >> Kurt Young <[hidden email]> 于2019年6月24日周一 下午4:24写道：
> > >>
> > >> > Hi vino,
> > >> >
> > >> > Sorry I don't see the consensus about reusing window operator and
> keep
> > >> the
> > >> > API design of localKeyBy. But I think we should definitely more
> > thoughts
> > >> > about this topic.
> > >> >
> > >> > I also try to loop in Stephan for this discussion.
> > >> >
> > >> > Best,
> > >> > Kurt
> > >> >
> > >> >
> > >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <[hidden email]>
> > >> wrote:
> > >> >
> > >> > > Hi all,
> > >> > >
> > >> > > I am happy we have a wonderful discussion and received many
> valuable
> > >> > > opinions in the last few days.
> > >> > >
> > >> > > Now, let me try to summarize what we have reached consensus about
> > the
> > >> > > changes in the design.
> > >> > >
> > >> > >    - provide a unified abstraction to support two kinds of
> > >> > implementation;
> > >> > >    - reuse WindowOperator and try to enhance it so that we can
> make
> > >> the
> > >> > >    intermediate result of the local aggregation can be buffered
> and
> > >> > > flushed to
> > >> > >    support two kinds of implementation;
> > >> > >    - keep the API design of localKeyBy, but declare the disabled
> > some
> > >> > APIs
> > >> > >    we cannot support currently, and provide a configurable API for
> > >> users
> > >> > to
> > >> > >    choose how to handle intermediate result;
> > >> > >
> > >> > > The above three points have been updated in the design doc. Any
> > >> > > questions, please let me know.
> > >> > >
> > >> > > @Aljoscha Krettek <[hidden email]> What do you think? Any
> > >> further
> > >> > > comments?
> > >> > >
> > >> > > Best,
> > >> > > Vino
> > >> > >
> > >> > > vino yang <[hidden email]> 于2019年6月20日周四 下午2:02写道：
> > >> > >
> > >> > > > Hi Kurt,
> > >> > > >
> > >> > > > Thanks for your comments.
> > >> > > >
> > >> > > > It seems we come to a consensus that we should alleviate the
> > >> > performance
> > >> > > > degraded by data skew with local aggregation. In this FLIP, our
> > key
> > >> > > > solution is to introduce local keyed partition to achieve this
> > goal.
> > >> > > >
> > >> > > > I also agree that we can benefit a lot from the usage of
> > >> > > > AggregateFunction. In combination with localKeyBy, We can easily
> > >> use it
> > >> > > to
> > >> > > > achieve local aggregation:
> > >> > > >
> > >> > > >    - input.localKeyBy(0).aggregate()
> > >> > > >    - input.localKeyBy(0).window().aggregate()
> > >> > > >
> > >> > > >
> > >> > > > I think the only problem here is the choices between
> > >> > > >
> > >> > > >    - (1) Introducing a new primitive called localKeyBy and
> > implement
> > >> > > >    local aggregation with existing operators, or
> > >> > > >    - (2) Introducing an operator called localAggregation which
> is
> > >> > > >    composed of a key selector, a window-like operator, and an
> > >> aggregate
> > >> > > >    function.
> > >> > > >
> > >> > > >
> > >> > > > There may exist some optimization opportunities by providing a
> > >> > composited
> > >> > > > interface for local aggregation. But at the same time, in my
> > >> opinion,
> > >> > we
> > >> > > > lose flexibility (Or we need certain efforts to achieve the same
> > >> > > > flexibility).
> > >> > > >
> > >> > > > As said in the previous mails, we have many use cases where the
> > >> > > > aggregation is very complicated and cannot be performed with
> > >> > > > AggregateFunction. For example, users may perform windowed
> > >> aggregations
> > >> > > > according to time, data values, or even external storage.
> > Typically,
> > >> > they
> > >> > > > now use KeyedProcessFunction or customized triggers to implement
> > >> these
> > >> > > > aggregations. It's not easy to address data skew in such cases
> > with
> > >> a
> > >> > > > composited interface for local aggregation.
> > >> > > >
> > >> > > > Given that Data Stream API is exactly targeted at these cases
> > where
> > >> the
> > >> > > > application logic is very complicated and optimization does not
> > >> > matter, I
> > >> > > > think it's a better choice to provide a relatively low-level and
> > >> > > canonical
> > >> > > > interface.
> > >> > > >
> > >> > > > The composited interface, on the other side, may be a good
> choice
> > in
> > >> > > > declarative interfaces, including SQL and Table API, as it
> allows
> > >> more
> > >> > > > optimization opportunities.
> > >> > > >
> > >> > > > Best,
> > >> > > > Vino
> > >> > > >
> > >> > > >
> > >> > > > Kurt Young <[hidden email]> 于2019年6月20日周四 上午10:15写道：
> > >> > > >
> > >> > > >> Hi all,
> > >> > > >>
> > >> > > >> As vino said in previous emails, I think we should first
> discuss
> > >> and
> > >> > > >> decide
> > >> > > >> what kind of use cases this FLIP want to
> > >> > > >> resolve, and what the API should look like. From my side, I
> think
> > >> this
> > >> > > is
> > >> > > >> probably the root cause of current divergence.
> > >> > > >>
> > >> > > >> My understand is (from the FLIP title and motivation section of
> > the
> > >> > > >> document), we want to have a proper support of
> > >> > > >> local aggregation, or pre aggregation. This is not a very new
> > idea,
> > >> > most
> > >> > > >> SQL engine already did this improvement. And
> > >> > > >> the core concept about this is, there should be an
> > >> AggregateFunction,
> > >> > no
> > >> > > >> matter it's a Flink runtime's AggregateFunction or
> > >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have
> concept
> > >> of
> > >> > > >> intermediate data type, sometimes we call it ACC.
> > >> > > >> I quickly went through the POC piotr did before [1], it also
> > >> directly
> > >> > > uses
> > >> > > >> AggregateFunction.
> > >> > > >>
> > >> > > >> But the thing is, after reading the design of this FLIP, I
> can't
> > >> help
> > >> > > >> myself feeling that this FLIP is not targeting to have a proper
> > >> > > >> local aggregation support. It actually want to introduce
> another
> > >> > > concept:
> > >> > > >> LocalKeyBy, and how to split and merge local key groups,
> > >> > > >> and how to properly support state on local key. Local
> aggregation
> > >> just
> > >> > > >> happened to be one possible use case of LocalKeyBy.
> > >> > > >> But it lacks supporting the essential concept of local
> > aggregation,
> > >> > > which
> > >> > > >> is intermediate data type. Without this, I really don't thing
> > >> > > >> it is a good fit of local aggregation.
> > >> > > >>
> > >> > > >> Here I want to make sure of the scope or the goal about this
> > FLIP,
> > >> do
> > >> > we
> > >> > > >> want to have a proper local aggregation engine, or we
> > >> > > >> just want to introduce a new concept called LocalKeyBy?
> > >> > > >>
> > >> > > >> [1]: https://github.com/apache/flink/pull/4626
> > >> > > >>
> > >> > > >> Best,
> > >> > > >> Kurt
> > >> > > >>
> > >> > > >>
> > >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <
> [hidden email]
> > >
> > >> > > wrote:
> > >> > > >>
> > >> > > >> > Hi Hequn,
> > >> > > >> >
> > >> > > >> > Thanks for your comments!
> > >> > > >> >
> > >> > > >> > I agree that allowing local aggregation reusing window API
> and
> > >> > > refining
> > >> > > >> > window operator to make it match both requirements (come from
> > our
> > >> > and
> > >> > > >> Kurt)
> > >> > > >> > is a good decision!
> > >> > > >> >
> > >> > > >> > Concerning your questions:
> > >> > > >> >
> > >> > > >> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> may
> > >> be
> > >> > > >> > meaningless.
> > >> > > >> >
> > >> > > >> > Yes, it does not make sense in most cases. However, I also
> want
> > >> to
> > >> > > note
> > >> > > >> > users should know the right semantics of localKeyBy and use
> it
> > >> > > >> correctly.
> > >> > > >> > Because this issue also exists for the global keyBy, consider
> > >> this
> > >> > > >> example:
> > >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also
> > >> > meaningless.
> > >> > > >> >
> > >> > > >> > 2. About the semantics of
> > >> > > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> > >> > > >> >
> > >> > > >> > Good catch! I agree with you that it's not good to enable all
> > >> > > >> > functionalities for localKeyBy from KeyedStream.
> > >> > > >> > Currently, We do not support some APIs such as
> > >> > > >> > connect/join/intervalJoin/coGroup. This is due to that we
> force
> > >> the
> > >> > > >> > operators on LocalKeyedStreams chained with the inputs.
> > >> > > >> >
> > >> > > >> > Best,
> > >> > > >> > Vino
> > >> > > >> >
> > >> > > >> >
> > >> > > >> > Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
> > >> > > >> >
> > >> > > >> > > Hi,
> > >> > > >> > >
> > >> > > >> > > Thanks a lot for your great discussion and great to see
> that
> > >> some
> > >> > > >> > agreement
> > >> > > >> > > has been reached on the "local aggregate engine"!
> > >> > > >> > >
> > >> > > >> > > ===> Considering the abstract engine,
> > >> > > >> > > I'm thinking is it valuable for us to extend the current
> > >> window to
> > >> > > >> meet
> > >> > > >> > > both demands raised by Kurt and Vino? There are some
> benefits
> > >> we
> > >> > can
> > >> > > >> get:
> > >> > > >> > >
> > >> > > >> > > 1. The interfaces of the window are complete and clear.
> With
> > >> > > windows,
> > >> > > >> we
> > >> > > >> > > can define a lot of ways to split the data and perform
> > >> different
> > >> > > >> > > computations.
> > >> > > >> > > 2. We can also leverage the window to do miniBatch for the
> > >> global
> > >> > > >> > > aggregation, i.e, we can use the window to bundle data
> belong
> > >> to
> > >> > the
> > >> > > >> same
> > >> > > >> > > key, for every bundle we only need to read and write once
> > >> state.
> > >> > > This
> > >> > > >> can
> > >> > > >> > > greatly reduce state IO and improve performance.
> > >> > > >> > > 3. A lot of other use cases can also benefit from the
> window
> > >> base
> > >> > on
> > >> > > >> > memory
> > >> > > >> > > or stateless.
> > >> > > >> > >
> > >> > > >> > > ===> As for the API,
> > >> > > >> > > I think it is good to make our API more flexible. However,
> we
> > >> may
> > >> > > >> need to
> > >> > > >> > > make our API meaningful.
> > >> > > >> > >
> > >> > > >> > > Take my previous reply as an example,
> > >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may
> be
> > >> > > >> > meaningless.
> > >> > > >> > > Another example I find is the intervalJoin, e.g.,
> > >> > > >> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
> > >> this
> > >> > > >> case, it
> > >> > > >> > > will bring problems if input1 and input2 share different
> > >> > > parallelism.
> > >> > > >> We
> > >> > > >> > > don't know which input should the join chained with? Even
> if
> > >> they
> > >> > > >> share
> > >> > > >> > the
> > >> > > >> > > same parallelism, it's hard to tell what the join is doing.
> > >> There
> > >> > > are
> > >> > > >> > maybe
> > >> > > >> > > some other problems.
> > >> > > >> > >
> > >> > > >> > > From this point of view, it's at least not good to enable
> all
> > >> > > >> > > functionalities for localKeyBy from KeyedStream?
> > >> > > >> > >
> > >> > > >> > > Great to also have your opinions.
> > >> > > >> > >
> > >> > > >> > > Best, Hequn
> > >> > > >> > >
> > >> > > >> > >
> > >> > > >> > >
> > >> > > >> > >
> > >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
> > >> [hidden email]
> > >> > >
> > >> > > >> > wrote:
> > >> > > >> > >
> > >> > > >> > > > Hi Kurt and Piotrek,
> > >> > > >> > > >
> > >> > > >> > > > Thanks for your comments.
> > >> > > >> > > >
> > >> > > >> > > > I agree that we can provide a better abstraction to be
> > >> > compatible
> > >> > > >> with
> > >> > > >> > > two
> > >> > > >> > > > different implementations.
> > >> > > >> > > >
> > >> > > >> > > > First of all, I think we should consider what kind of
> > >> scenarios
> > >> > we
> > >> > > >> need
> > >> > > >> > > to
> > >> > > >> > > > support in *API* level?
> > >> > > >> > > >
> > >> > > >> > > > We have some use cases which need to a customized
> > aggregation
> > >> > > >> through
> > >> > > >> > > > KeyedProcessFunction, (in the usage of our
> > localKeyBy.window
> > >> > they
> > >> > > >> can
> > >> > > >> > use
> > >> > > >> > > > ProcessWindowFunction).
> > >> > > >> > > >
> > >> > > >> > > > Shall we support these flexible use scenarios?
> > >> > > >> > > >
> > >> > > >> > > > Best,
> > >> > > >> > > > Vino
> > >> > > >> > > >
> > >> > > >> > > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
> > >> > > >> > > >
> > >> > > >> > > > > Hi Piotr,
> > >> > > >> > > > >
> > >> > > >> > > > > Thanks for joining the discussion. Make “local
> > aggregation"
> > >> > > >> abstract
> > >> > > >> > > > enough
> > >> > > >> > > > > sounds good to me, we could
> > >> > > >> > > > > implement and verify alternative solutions for use
> cases
> > of
> > >> > > local
> > >> > > >> > > > > aggregation. Maybe we will find both solutions
> > >> > > >> > > > > are appropriate for different scenarios.
> > >> > > >> > > > >
> > >> > > >> > > > > Starting from a simple one sounds a practical way to
> go.
> > >> What
> > >> > do
> > >> > > >> you
> > >> > > >> > > > think,
> > >> > > >> > > > > vino?
> > >> > > >> > > > >
> > >> > > >> > > > > Best,
> > >> > > >> > > > > Kurt
> > >> > > >> > > > >
> > >> > > >> > > > >
> > >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> > >> > > >> [hidden email]>
> > >> > > >> > > > > wrote:
> > >> > > >> > > > >
> > >> > > >> > > > > > Hi Kurt and Vino,
> > >> > > >> > > > > >
> > >> > > >> > > > > > I think there is a trade of hat we need to consider
> for
> > >> the
> > >> > > >> local
> > >> > > >> > > > > > aggregation.
> > >> > > >> > > > > >
> > >> > > >> > > > > > Generally speaking I would agree with Kurt about
> local
> > >> > > >> > > aggregation/pre
> > >> > > >> > > > > > aggregation not using Flink's state flush the
> operator
> > >> on a
> > >> > > >> > > checkpoint.
> > >> > > >> > > > > > Network IO is usually cheaper compared to Disks IO.
> > This
> > >> has
> > >> > > >> > however
> > >> > > >> > > > > couple
> > >> > > >> > > > > > of issues:
> > >> > > >> > > > > > 1. It can explode number of in-flight records during
> > >> > > checkpoint
> > >> > > >> > > barrier
> > >> > > >> > > > > > alignment, making checkpointing slower and decrease
> the
> > >> > actual
> > >> > > >> > > > > throughput.
> > >> > > >> > > > > > 2. This trades Disks IO on the local aggregation
> > machine
> > >> > with
> > >> > > >> CPU
> > >> > > >> > > (and
> > >> > > >> > > > > > Disks IO in case of RocksDB) on the final aggregation
> > >> > machine.
> > >> > > >> This
> > >> > > >> > > is
> > >> > > >> > > > > > fine, as long there is no huge data skew. If there is
> > >> only a
> > >> > > >> > handful
> > >> > > >> > > > (or
> > >> > > >> > > > > > even one single) hot keys, it might be better to keep
> > the
> > >> > > >> > persistent
> > >> > > >> > > > > state
> > >> > > >> > > > > > in the LocalAggregationOperator to offload final
> > >> aggregation
> > >> > > as
> > >> > > >> > much
> > >> > > >> > > as
> > >> > > >> > > > > > possible.
> > >> > > >> > > > > > 3. With frequent checkpointing local aggregation
> > >> > effectiveness
> > >> > > >> > would
> > >> > > >> > > > > > degrade.
> > >> > > >> > > > > >
> > >> > > >> > > > > > I assume Kurt is correct, that in your use cases
> > >> stateless
> > >> > > >> operator
> > >> > > >> > > was
> > >> > > >> > > > > > behaving better, but I could easily see other use
> cases
> > >> as
> > >> > > well.
> > >> > > >> > For
> > >> > > >> > > > > > example someone is already using RocksDB, and his job
> > is
> > >> > > >> > bottlenecked
> > >> > > >> > > > on
> > >> > > >> > > > > a
> > >> > > >> > > > > > single window operator instance because of the data
> > >> skew. In
> > >> > > >> that
> > >> > > >> > > case
> > >> > > >> > > > > > stateful local aggregation would be probably a better
> > >> > choice.
> > >> > > >> > > > > >
> > >> > > >> > > > > > Because of that, I think we should eventually provide
> > >> both
> > >> > > >> versions
> > >> > > >> > > and
> > >> > > >> > > > > in
> > >> > > >> > > > > > the initial version we should at least make the
> “local
> > >> > > >> aggregation
> > >> > > >> > > > > engine”
> > >> > > >> > > > > > abstract enough, that one could easily provide
> > different
> > >> > > >> > > implementation
> > >> > > >> > > > > > strategy.
> > >> > > >> > > > > >
> > >> > > >> > > > > > Piotrek
> > >> > > >> > > > > >
> > >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
> > [hidden email]
> > >> >
> > >> > > >> wrote:
> > >> > > >> > > > > > >
> > >> > > >> > > > > > > Hi,
> > >> > > >> > > > > > >
> > >> > > >> > > > > > > For the trigger, it depends on what operator we
> want
> > to
> > >> > use
> > >> > > >> under
> > >> > > >> > > the
> > >> > > >> > > > > > API.
> > >> > > >> > > > > > > If we choose to use window operator,
> > >> > > >> > > > > > > we should also use window's trigger. However, I
> also
> > >> think
> > >> > > >> reuse
> > >> > > >> > > > window
> > >> > > >> > > > > > > operator for this scenario may not be
> > >> > > >> > > > > > > the best choice. The reasons are the following:
> > >> > > >> > > > > > >
> > >> > > >> > > > > > > 1. As a lot of people already pointed out, window
> > >> relies
> > >> > > >> heavily
> > >> > > >> > on
> > >> > > >> > > > > state
> > >> > > >> > > > > > > and it will definitely effect performance. You can
> > >> > > >> > > > > > > argue that one can use heap based statebackend, but
> > >> this
> > >> > > will
> > >> > > >> > > > introduce
> > >> > > >> > > > > > > extra coupling. Especially we have a chance to
> > >> > > >> > > > > > > design a pure stateless operator.
> > >> > > >> > > > > > > 2. The window operator is *the most* complicated
> > >> operator
> > >> > > >> Flink
> > >> > > >> > > > > currently
> > >> > > >> > > > > > > have. Maybe we only need to pick a subset of
> > >> > > >> > > > > > > window operator to achieve the goal, but once the
> > user
> > >> > wants
> > >> > > >> to
> > >> > > >> > > have
> > >> > > >> > > > a
> > >> > > >> > > > > > deep
> > >> > > >> > > > > > > look at the localAggregation operator, it's still
> > >> > > >> > > > > > > hard to find out what's going on under the window
> > >> > operator.
> > >> > > >> For
> > >> > > >> > > > > > simplicity,
> > >> > > >> > > > > > > I would also recommend we introduce a dedicated
> > >> > > >> > > > > > > lightweight operator, which also much easier for a
> > >> user to
> > >> > > >> learn
> > >> > > >> > > and
> > >> > > >> > > > > use.
> > >> > > >> > > > > > >
> > >> > > >> > > > > > > For your question about increasing the burden in
> > >> > > >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the
> > only
> > >> > > thing
> > >> > > >> > this
> > >> > > >> > > > > > function
> > >> > > >> > > > > > > need
> > >> > > >> > > > > > > to do is output all the partial results, it's
> purely
> > >> cpu
> > >> > > >> > workload,
> > >> > > >> > > > not
> > >> > > >> > > > > > > introducing any IO. I want to point out that even
> if
> > we
> > >> > have
> > >> > > >> this
> > >> > > >> > > > > > > cost, we reduced another barrier align cost of the
> > >> > operator,
> > >> > > >> > which
> > >> > > >> > > is
> > >> > > >> > > > > the
> > >> > > >> > > > > > > sync flush stage of the state, if you introduced
> > state.
> > >> > This
> > >> > > >> > > > > > > flush actually will introduce disk IO, and I think
> > it's
> > >> > > >> worthy to
> > >> > > >> > > > > > exchange
> > >> > > >> > > > > > > this cost with purely CPU workload. And we do have
> > some
> > >> > > >> > > > > > > observations about these two behavior (as i said
> > >> before,
> > >> > we
> > >> > > >> > > actually
> > >> > > >> > > > > > > implemented both solutions), the stateless one
> > actually
> > >> > > >> performs
> > >> > > >> > > > > > > better both in performance and barrier align time.
> > >> > > >> > > > > > >
> > >> > > >> > > > > > > Best,
> > >> > > >> > > > > > > Kurt
> > >> > > >> > > > > > >
> > >> > > >> > > > > > >
> > >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> > >> > > >> [hidden email]
> > >> > > >> > >
> > >> > > >> > > > > wrote:
> > >> > > >> > > > > > >
> > >> > > >> > > > > > >> Hi Kurt,
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> Thanks for your example. Now, it looks more
> clearly
> > >> for
> > >> > me.
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> From your example code snippet, I saw the
> > >> localAggregate
> > >> > > API
> > >> > > >> has
> > >> > > >> > > > three
> > >> > > >> > > > > > >> parameters:
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >>   1. key field
> > >> > > >> > > > > > >>   2. PartitionAvg
> > >> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes from
> > window
> > >> > > >> package?
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> I will compare our and your design from API and
> > >> operator
> > >> > > >> level:
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> *From the API level:*
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> As I replied to @dianfu in the old email
> thread,[1]
> > >> the
> > >> > > >> Window
> > >> > > >> > API
> > >> > > >> > > > can
> > >> > > >> > > > > > >> provide the second and the third parameter right
> > now.
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> If you reuse specified interface or class, such as
> > >> > > *Trigger*
> > >> > > >> or
> > >> > > >> > > > > > >> *CounterTrigger* provided by window package, but
> do
> > >> not
> > >> > use
> > >> > > >> > window
> > >> > > >> > > > > API,
> > >> > > >> > > > > > >> it's not reasonable.
> > >> > > >> > > > > > >> And if you do not reuse these interface or class,
> > you
> > >> > would
> > >> > > >> need
> > >> > > >> > > to
> > >> > > >> > > > > > >> introduce more things however they are looked
> > similar
> > >> to
> > >> > > the
> > >> > > >> > > things
> > >> > > >> > > > > > >> provided by window package.
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> The window package has provided several types of
> the
> > >> > window
> > >> > > >> and
> > >> > > >> > > many
> > >> > > >> > > > > > >> triggers and let users customize it. What's more,
> > the
> > >> > user
> > >> > > is
> > >> > > >> > more
> > >> > > >> > > > > > familiar
> > >> > > >> > > > > > >> with Window API.
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> This is the reason why we just provide localKeyBy
> > API
> > >> and
> > >> > > >> reuse
> > >> > > >> > > the
> > >> > > >> > > > > > window
> > >> > > >> > > > > > >> API. It reduces unnecessary components such as
> > >> triggers
> > >> > and
> > >> > > >> the
> > >> > > >> > > > > > mechanism
> > >> > > >> > > > > > >> of buffer (based on count num or time).
> > >> > > >> > > > > > >> And it has a clear and easy to understand
> semantics.
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> *From the operator level:*
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> We reused window operator, so we can get all the
> > >> benefits
> > >> > > >> from
> > >> > > >> > > state
> > >> > > >> > > > > and
> > >> > > >> > > > > > >> checkpoint.
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> From your design, you named the operator under
> > >> > > localAggregate
> > >> > > >> > API
> > >> > > >> > > > is a
> > >> > > >> > > > > > >> *stateless* operator. IMO, it is still a state, it
> > is
> > >> > just
> > >> > > >> not
> > >> > > >> > > Flink
> > >> > > >> > > > > > >> managed state.
> > >> > > >> > > > > > >> About the memory buffer (I think it's still not
> very
> > >> > clear,
> > >> > > >> if
> > >> > > >> > you
> > >> > > >> > > > > have
> > >> > > >> > > > > > >> time, can you give more detail information or
> answer
> > >> my
> > >> > > >> > > questions),
> > >> > > >> > > > I
> > >> > > >> > > > > > have
> > >> > > >> > > > > > >> some questions:
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >>   - if it just a raw JVM heap memory buffer, how
> to
> > >> > support
> > >> > > >> > fault
> > >> > > >> > > > > > >>   tolerance, if the job is configured EXACTLY-ONCE
> > >> > semantic
> > >> > > >> > > > guarantee?
> > >> > > >> > > > > > >>   - if you thought the memory buffer(non-Flink
> > state),
> > >> > has
> > >> > > >> > better
> > >> > > >> > > > > > >>   performance. In our design, users can also
> config
> > >> HEAP
> > >> > > >> state
> > >> > > >> > > > backend
> > >> > > >> > > > > > to
> > >> > > >> > > > > > >>   provide the performance close to your mechanism.
> > >> > > >> > > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()`
> > >> related
> > >> > > to
> > >> > > >> the
> > >> > > >> > > > > timing
> > >> > > >> > > > > > of
> > >> > > >> > > > > > >>   snapshot. IMO, the flush action should be a
> > >> > synchronized
> > >> > > >> > action?
> > >> > > >> > > > (if
> > >> > > >> > > > > > >> not,
> > >> > > >> > > > > > >>   please point out my mistake) I still think we
> > should
> > >> > not
> > >> > > >> > depend
> > >> > > >> > > on
> > >> > > >> > > > > the
> > >> > > >> > > > > > >>   timing of checkpoint. Checkpoint related
> > operations
> > >> are
> > >> > > >> > inherent
> > >> > > >> > > > > > >>   performance sensitive, we should not increase
> its
> > >> > burden
> > >> > > >> > > anymore.
> > >> > > >> > > > > Our
> > >> > > >> > > > > > >>   implementation based on the mechanism of Flink's
> > >> > > >> checkpoint,
> > >> > > >> > > which
> > >> > > >> > > > > can
> > >> > > >> > > > > > >>   benefit from the asnyc snapshot and incremental
> > >> > > checkpoint.
> > >> > > >> > IMO,
> > >> > > >> > > > the
> > >> > > >> > > > > > >>   performance is not a problem, and we also do not
> > >> find
> > >> > the
> > >> > > >> > > > > performance
> > >> > > >> > > > > > >> issue
> > >> > > >> > > > > > >>   in our production.
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> [1]:
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >>
> > >> > > >> > > > > >
> > >> > > >> > > > >
> > >> > > >> > > >
> > >> > > >> > >
> > >> > > >> >
> > >> > > >>
> > >> > >
> > >> >
> > >>
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> Best,
> > >> > > >> > > > > > >> Vino
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二
> > 下午2:27写道：
> > >> > > >> > > > > > >>
> > >> > > >> > > > > > >>> Yeah, sorry for not expressing myself clearly. I
> > will
> > >> > try
> > >> > > to
> > >> > > >> > > > provide
> > >> > > >> > > > > > more
> > >> > > >> > > > > > >>> details to make sure we are on the same page.
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>> For DataStream API, it shouldn't be optimized
> > >> > > automatically.
> > >> > > >> > You
> > >> > > >> > > > have
> > >> > > >> > > > > > to
> > >> > > >> > > > > > >>> explicitly call API to do local aggregation
> > >> > > >> > > > > > >>> as well as the trigger policy of the local
> > >> aggregation.
> > >> > > Take
> > >> > > >> > > > average
> > >> > > >> > > > > > for
> > >> > > >> > > > > > >>> example, the user program may look like this
> (just
> > a
> > >> > > draft):
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>> assuming the input type is
> DataStream<Tupl2<String,
> > >> > Int>>
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>> ds.localAggregate(
> > >> > > >> > > > > > >>>        0,
>  //
> > >> The
> > >> > > local
> > >> > > >> > key,
> > >> > > >> > > > > which
> > >> > > >> > > > > > >> is
> > >> > > >> > > > > > >>> the String from Tuple2
> > >> > > >> > > > > > >>>        PartitionAvg(1),                 // The
> > >> partial
> > >> > > >> > > aggregation
> > >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>, indicating
> > sum
> > >> and
> > >> > > >> count
> > >> > > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger
> policy,
> > >> note
> > >> > > >> this
> > >> > > >> > > > should
> > >> > > >> > > > > be
> > >> > > >> > > > > > >>> best effort, and also be composited with time
> based
> > >> or
> > >> > > >> memory
> > >> > > >> > > size
> > >> > > >> > > > > > based
> > >> > > >> > > > > > >>> trigger
> > >> > > >> > > > > > >>>    )                                           //
> > The
> > >> > > return
> > >> > > >> > type
> > >> > > >> > > > is
> > >> > > >> > > > > > >> local
> > >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> > >> > > >> > > > > > >>>    .keyBy(0)                             //
> Further
> > >> > keyby
> > >> > > it
> > >> > > >> > with
> > >> > > >> > > > > > >> required
> > >> > > >> > > > > > >>> key
> > >> > > >> > > > > > >>>    .aggregate(1)                      // This
> will
> > >> merge
> > >> > > all
> > >> > > >> > the
> > >> > > >> > > > > > partial
> > >> > > >> > > > > > >>> results and get the final average.
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>> (This is only a draft, only trying to explain
> what
> > it
> > >> > > looks
> > >> > > >> > > like. )
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>> The local aggregate operator can be stateless, we
> > can
> > >> > > keep a
> > >> > > >> > > memory
> > >> > > >> > > > > > >> buffer
> > >> > > >> > > > > > >>> or other efficient data structure to improve the
> > >> > aggregate
> > >> > > >> > > > > performance.
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>> Let me know if you have any other questions.
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>> Best,
> > >> > > >> > > > > > >>> Kurt
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> > >> > > >> > [hidden email]
> > >> > > >> > > >
> > >> > > >> > > > > > wrote:
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>>> Hi Kurt,
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> Thanks for your reply.
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> Actually, I am not against you to raise your
> > design.
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> From your description before, I just can imagine
> > >> your
> > >> > > >> > high-level
> > >> > > >> > > > > > >>>> implementation is about SQL and the optimization
> > is
> > >> > inner
> > >> > > >> of
> > >> > > >> > the
> > >> > > >> > > > > API.
> > >> > > >> > > > > > >> Is
> > >> > > >> > > > > > >>> it
> > >> > > >> > > > > > >>>> automatically? how to give the configuration
> > option
> > >> > about
> > >> > > >> > > trigger
> > >> > > >> > > > > > >>>> pre-aggregation?
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> Maybe after I get more information, it sounds
> more
> > >> > > >> reasonable.
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> IMO, first of all, it would be better to make
> your
> > >> user
> > >> > > >> > > interface
> > >> > > >> > > > > > >>> concrete,
> > >> > > >> > > > > > >>>> it's the basis of the discussion.
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> For example, can you give an example code
> snippet
> > to
> > >> > > >> introduce
> > >> > > >> > > how
> > >> > > >> > > > > to
> > >> > > >> > > > > > >>> help
> > >> > > >> > > > > > >>>> users to process data skew caused by the jobs
> > which
> > >> > built
> > >> > > >> with
> > >> > > >> > > > > > >> DataStream
> > >> > > >> > > > > > >>>> API?
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> If you give more details we can discuss further
> > >> more. I
> > >> > > >> think
> > >> > > >> > if
> > >> > > >> > > > one
> > >> > > >> > > > > > >>> design
> > >> > > >> > > > > > >>>> introduces an exact interface and another does
> > not.
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> The implementation has an obvious difference.
> For
> > >> > > example,
> > >> > > >> we
> > >> > > >> > > > > > introduce
> > >> > > >> > > > > > >>> an
> > >> > > >> > > > > > >>>> exact API in DataStream named localKeyBy, about
> > the
> > >> > > >> > > > pre-aggregation
> > >> > > >> > > > > we
> > >> > > >> > > > > > >>> need
> > >> > > >> > > > > > >>>> to define the trigger mechanism of local
> > >> aggregation,
> > >> > so
> > >> > > we
> > >> > > >> > find
> > >> > > >> > > > > > reused
> > >> > > >> > > > > > >>>> window API and operator is a good choice. This
> is
> > a
> > >> > > >> reasoning
> > >> > > >> > > link
> > >> > > >> > > > > > from
> > >> > > >> > > > > > >>>> design to implementation.
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> What do you think?
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> Best,
> > >> > > >> > > > > > >>>> Vino
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二
> > >> 上午11:58写道：
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>>> Hi Vino,
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>> Now I feel that we may have different
> > >> understandings
> > >> > > about
> > >> > > >> > what
> > >> > > >> > > > > kind
> > >> > > >> > > > > > >> of
> > >> > > >> > > > > > >>>>> problems or improvements you want to
> > >> > > >> > > > > > >>>>> resolve. Currently, most of the feedback are
> > >> focusing
> > >> > on
> > >> > > >> *how
> > >> > > >> > > to
> > >> > > >> > > > > do a
> > >> > > >> > > > > > >>>>> proper local aggregation to improve performance
> > >> > > >> > > > > > >>>>> and maybe solving the data skew issue*. And my
> > gut
> > >> > > >> feeling is
> > >> > > >> > > > this
> > >> > > >> > > > > is
> > >> > > >> > > > > > >>>>> exactly what users want at the first place,
> > >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to
> summarize
> > >> here,
> > >> > > >> please
> > >> > > >> > > > > correct
> > >> > > >> > > > > > >>> me
> > >> > > >> > > > > > >>>> if
> > >> > > >> > > > > > >>>>> i'm wrong).
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>> But I still think the design is somehow
> diverged
> > >> from
> > >> > > the
> > >> > > >> > goal.
> > >> > > >> > > > If
> > >> > > >> > > > > we
> > >> > > >> > > > > > >>>> want
> > >> > > >> > > > > > >>>>> to have an efficient and powerful way to
> > >> > > >> > > > > > >>>>> have local aggregation, supporting intermedia
> > >> result
> > >> > > type
> > >> > > >> is
> > >> > > >> > > > > > >> essential
> > >> > > >> > > > > > >>>> IMO.
> > >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
> > >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a
> > proper
> > >> > > >> support of
> > >> > > >> > > > > > >>>> intermediate
> > >> > > >> > > > > > >>>>> result type and can do `merge` operation
> > >> > > >> > > > > > >>>>> on them.
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>> Now, we have a lightweight alternatives which
> > >> performs
> > >> > > >> well,
> > >> > > >> > > and
> > >> > > >> > > > > > >> have a
> > >> > > >> > > > > > >>>>> nice fit with the local aggregate requirements.
> > >> > > >> > > > > > >>>>> Mostly importantly,  it's much less complex
> > because
> > >> > it's
> > >> > > >> > > > stateless.
> > >> > > >> > > > > > >> And
> > >> > > >> > > > > > >>>> it
> > >> > > >> > > > > > >>>>> can also achieve the similar
> multiple-aggregation
> > >> > > >> > > > > > >>>>> scenario.
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>> I still not convinced why we shouldn't consider
> > it
> > >> as
> > >> > a
> > >> > > >> first
> > >> > > >> > > > step.
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>> Best,
> > >> > > >> > > > > > >>>>> Kurt
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> > >> > > >> > > > [hidden email]>
> > >> > > >> > > > > > >>>> wrote:
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>>> Hi Kurt,
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> Thanks for your comments.
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> It seems we both implemented local aggregation
> > >> > feature
> > >> > > to
> > >> > > >> > > > optimize
> > >> > > >> > > > > > >>> the
> > >> > > >> > > > > > >>>>>> issue of data skew.
> > >> > > >> > > > > > >>>>>> However, IMHO, the API level of optimizing
> > >> revenue is
> > >> > > >> > > different.
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> *Your optimization benefits from Flink SQL and
> > >> it's
> > >> > not
> > >> > > >> > user's
> > >> > > >> > > > > > >>>> faces.(If
> > >> > > >> > > > > > >>>>> I
> > >> > > >> > > > > > >>>>>> understand it incorrectly, please correct
> > this.)*
> > >> > > >> > > > > > >>>>>> *Our implementation employs it as an
> > optimization
> > >> > tool
> > >> > > >> API
> > >> > > >> > for
> > >> > > >> > > > > > >>>>> DataStream,
> > >> > > >> > > > > > >>>>>> it just like a local version of the keyBy
> API.*
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> Based on this, I want to say support it as a
> > >> > DataStream
> > >> > > >> API
> > >> > > >> > > can
> > >> > > >> > > > > > >>> provide
> > >> > > >> > > > > > >>>>>> these advantages:
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear semantic
> and
> > >> it's
> > >> > > >> > flexible
> > >> > > >> > > > not
> > >> > > >> > > > > > >>> only
> > >> > > >> > > > > > >>>>> for
> > >> > > >> > > > > > >>>>>>   processing data skew but also for
> implementing
> > >> some
> > >> > > >> user
> > >> > > >> > > > cases,
> > >> > > >> > > > > > >>> for
> > >> > > >> > > > > > >>>>>>   example, if we want to calculate the
> > >> multiple-level
> > >> > > >> > > > aggregation,
> > >> > > >> > > > > > >>> we
> > >> > > >> > > > > > >>>>> can
> > >> > > >> > > > > > >>>>>> do
> > >> > > >> > > > > > >>>>>>   multiple-level aggregation in the local
> > >> > aggregation:
> > >> > > >> > > > > > >>>>>>
> > >> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
> > >> > > >> //
> > >> > > >> > > here
> > >> > > >> > > > > > >> "a"
> > >> > > >> > > > > > >>>> is
> > >> > > >> > > > > > >>>>> a
> > >> > > >> > > > > > >>>>>>   sub-category, while "b" is a category, here
> we
> > >> do
> > >> > not
> > >> > > >> need
> > >> > > >> > > to
> > >> > > >> > > > > > >>>> shuffle
> > >> > > >> > > > > > >>>>>> data
> > >> > > >> > > > > > >>>>>>   in the network.
> > >> > > >> > > > > > >>>>>>   - The users of DataStream API will benefit
> > from
> > >> > this.
> > >> > > >> > > > Actually,
> > >> > > >> > > > > > >> we
> > >> > > >> > > > > > >>>>> have
> > >> > > >> > > > > > >>>>>>   a lot of scenes need to use DataStream API.
> > >> > > Currently,
> > >> > > >> > > > > > >> DataStream
> > >> > > >> > > > > > >>>> API
> > >> > > >> > > > > > >>>>> is
> > >> > > >> > > > > > >>>>>>   the cornerstone of the physical plan of
> Flink
> > >> SQL.
> > >> > > >> With a
> > >> > > >> > > > > > >>> localKeyBy
> > >> > > >> > > > > > >>>>>> API,
> > >> > > >> > > > > > >>>>>>   the optimization of SQL at least may use
> this
> > >> > > optimized
> > >> > > >> > API,
> > >> > > >> > > > > > >> this
> > >> > > >> > > > > > >>>> is a
> > >> > > >> > > > > > >>>>>>   further topic.
> > >> > > >> > > > > > >>>>>>   - Based on the window operator, our state
> > would
> > >> > > benefit
> > >> > > >> > from
> > >> > > >> > > > > > >> Flink
> > >> > > >> > > > > > >>>>> State
> > >> > > >> > > > > > >>>>>>   and checkpoint, we do not need to worry
> about
> > >> OOM
> > >> > and
> > >> > > >> job
> > >> > > >> > > > > > >> failed.
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> Now, about your questions:
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> 1. About our design cannot change the data
> type
> > >> and
> > >> > > about
> > >> > > >> > the
> > >> > > >> > > > > > >>>>>> implementation of average:
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is
> > an
> > >> API
> > >> > > >> > provides
> > >> > > >> > > > to
> > >> > > >> > > > > > >> the
> > >> > > >> > > > > > >>>>> users
> > >> > > >> > > > > > >>>>>> who use DataStream API to build their jobs.
> > >> > > >> > > > > > >>>>>> Users should know its semantics and the
> > difference
> > >> > with
> > >> > > >> > keyBy
> > >> > > >> > > > API,
> > >> > > >> > > > > > >> so
> > >> > > >> > > > > > >>>> if
> > >> > > >> > > > > > >>>>>> they want to the average aggregation, they
> > should
> > >> > carry
> > >> > > >> > local
> > >> > > >> > > > sum
> > >> > > >> > > > > > >>>> result
> > >> > > >> > > > > > >>>>>> and local count result.
> > >> > > >> > > > > > >>>>>> I admit that it will be convenient to use
> keyBy
> > >> > > directly.
> > >> > > >> > But
> > >> > > >> > > we
> > >> > > >> > > > > > >> need
> > >> > > >> > > > > > >>>> to
> > >> > > >> > > > > > >>>>>> pay a little price when we get some benefits.
> I
> > >> think
> > >> > > >> this
> > >> > > >> > > price
> > >> > > >> > > > > is
> > >> > > >> > > > > > >>>>>> reasonable. Considering that the DataStream
> API
> > >> > itself
> > >> > > >> is a
> > >> > > >> > > > > > >> low-level
> > >> > > >> > > > > > >>>> API
> > >> > > >> > > > > > >>>>>> (at least for now).
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> 2. About stateless operator and
> > >> > > >> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> Actually, I have discussed this opinion with
> > >> @dianfu
> > >> > in
> > >> > > >> the
> > >> > > >> > > old
> > >> > > >> > > > > > >> mail
> > >> > > >> > > > > > >>>>>> thread. I will copy my opinion from there:
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>>   - for your design, you still need somewhere
> to
> > >> give
> > >> > > the
> > >> > > >> > > users
> > >> > > >> > > > > > >>>>> configure
> > >> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
> > >> availability?),
> > >> > > >> this
> > >> > > >> > > > design
> > >> > > >> > > > > > >>>> cannot
> > >> > > >> > > > > > >>>>>>   guarantee a deterministic semantics (it will
> > >> bring
> > >> > > >> trouble
> > >> > > >> > > for
> > >> > > >> > > > > > >>>> testing
> > >> > > >> > > > > > >>>>>> and
> > >> > > >> > > > > > >>>>>>   debugging).
> > >> > > >> > > > > > >>>>>>   - if the implementation depends on the
> timing
> > of
> > >> > > >> > checkpoint,
> > >> > > >> > > > it
> > >> > > >> > > > > > >>>> would
> > >> > > >> > > > > > >>>>>>   affect the checkpoint's progress, and the
> > >> buffered
> > >> > > data
> > >> > > >> > may
> > >> > > >> > > > > > >> cause
> > >> > > >> > > > > > >>>> OOM
> > >> > > >> > > > > > >>>>>>   issue. In addition, if the operator is
> > >> stateless,
> > >> > it
> > >> > > >> can
> > >> > > >> > not
> > >> > > >> > > > > > >>> provide
> > >> > > >> > > > > > >>>>>> fault
> > >> > > >> > > > > > >>>>>>   tolerance.
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> Best,
> > >> > > >> > > > > > >>>>>> Vino
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二
> > >> > 上午9:22写道：
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>>> Hi Vino,
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the general
> > idea
> > >> and
> > >> > > IMO
> > >> > > >> > it's
> > >> > > >> > > > > > >> very
> > >> > > >> > > > > > >>>>> useful
> > >> > > >> > > > > > >>>>>>> feature.
> > >> > > >> > > > > > >>>>>>> But after reading through the document, I
> feel
> > >> that
> > >> > we
> > >> > > >> may
> > >> > > >> > > over
> > >> > > >> > > > > > >>>> design
> > >> > > >> > > > > > >>>>>> the
> > >> > > >> > > > > > >>>>>>> required
> > >> > > >> > > > > > >>>>>>> operator for proper local aggregation. The
> main
> > >> > reason
> > >> > > >> is
> > >> > > >> > we
> > >> > > >> > > > want
> > >> > > >> > > > > > >>> to
> > >> > > >> > > > > > >>>>>> have a
> > >> > > >> > > > > > >>>>>>> clear definition and behavior about the
> "local
> > >> keyed
> > >> > > >> state"
> > >> > > >> > > > which
> > >> > > >> > > > > > >>> in
> > >> > > >> > > > > > >>>> my
> > >> > > >> > > > > > >>>>>>> opinion is not
> > >> > > >> > > > > > >>>>>>> necessary for local aggregation, at least for
> > >> start.
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>> Another issue I noticed is the local key by
> > >> operator
> > >> > > >> cannot
> > >> > > >> > > > > > >> change
> > >> > > >> > > > > > >>>>>> element
> > >> > > >> > > > > > >>>>>>> type, it will
> > >> > > >> > > > > > >>>>>>> also restrict a lot of use cases which can be
> > >> > benefit
> > >> > > >> from
> > >> > > >> > > > local
> > >> > > >> > > > > > >>>>>>> aggregation, like "average".
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and the only
> > >> thing
> > >> > > >> need to
> > >> > > >> > > be
> > >> > > >> > > > > > >> done
> > >> > > >> > > > > > >>>> is
> > >> > > >> > > > > > >>>>>>> introduce
> > >> > > >> > > > > > >>>>>>> a stateless lightweight operator which is
> > >> *chained*
> > >> > > >> before
> > >> > > >> > > > > > >>> `keyby()`.
> > >> > > >> > > > > > >>>>> The
> > >> > > >> > > > > > >>>>>>> operator will flush all buffered
> > >> > > >> > > > > > >>>>>>> elements during
> > >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
> > >> > > >> > > > and
> > >> > > >> > > > > > >>>> make
> > >> > > >> > > > > > >>>>>>> himself stateless.
> > >> > > >> > > > > > >>>>>>> By the way, in the earlier version we also
> did
> > >> the
> > >> > > >> similar
> > >> > > >> > > > > > >> approach
> > >> > > >> > > > > > >>>> by
> > >> > > >> > > > > > >>>>>>> introducing a stateful
> > >> > > >> > > > > > >>>>>>> local aggregation operator but it's not
> > >> performed as
> > >> > > >> well
> > >> > > >> > as
> > >> > > >> > > > the
> > >> > > >> > > > > > >>>> later
> > >> > > >> > > > > > >>>>>> one,
> > >> > > >> > > > > > >>>>>>> and also effect the barrie
> > >> > > >> > > > > > >>>>>>> alignment time. The later one is fairly
> simple
> > >> and
> > >> > > more
> > >> > > >> > > > > > >> efficient.
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>> I would highly suggest you to consider to
> have
> > a
> > >> > > >> stateless
> > >> > > >> > > > > > >> approach
> > >> > > >> > > > > > >>>> at
> > >> > > >> > > > > > >>>>>> the
> > >> > > >> > > > > > >>>>>>> first step.
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>> Best,
> > >> > > >> > > > > > >>>>>>> Kurt
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> > >> > > >> [hidden email]>
> > >> > > >> > > > > > >> wrote:
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>>> Hi Vino,
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> > >> > > >> > > > > > >> have
> > >> > > >> > > > > > >>>> you
> > >> > > >> > > > > > >>>>>>> done
> > >> > > >> > > > > > >>>>>>>> some benchmark?
> > >> > > >> > > > > > >>>>>>>> Because I'm curious about how much
> performance
> > >> > > >> improvement
> > >> > > >> > > can
> > >> > > >> > > > > > >> we
> > >> > > >> > > > > > >>>> get
> > >> > > >> > > > > > >>>>>> by
> > >> > > >> > > > > > >>>>>>>> using count window as the local operator.
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>> Best,
> > >> > > >> > > > > > >>>>>>>> Jark
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> > >> > > >> > > > [hidden email]
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>>>> wrote:
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> Hi Hequn,
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to
> provide a
> > >> tool
> > >> > > >> which
> > >> > > >> > > can
> > >> > > >> > > > > > >>> let
> > >> > > >> > > > > > >>>>>> users
> > >> > > >> > > > > > >>>>>>> do
> > >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior
> of
> > >> the
> > >> > > >> > > > > > >>> pre-aggregation
> > >> > > >> > > > > > >>>>> is
> > >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> So the three cases are different, I will
> > >> describe
> > >> > > them
> > >> > > >> > one
> > >> > > >> > > by
> > >> > > >> > > > > > >>>> one:
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> *In this case, the result is event-driven,
> > each
> > >> > > event
> > >> > > >> can
> > >> > > >> > > > > > >>> produce
> > >> > > >> > > > > > >>>>> one
> > >> > > >> > > > > > >>>>>>> sum
> > >> > > >> > > > > > >>>>>>>>> aggregation result and it is the latest one
> > >> from
> > >> > the
> > >> > > >> > source
> > >> > > >> > > > > > >>>> start.*
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> 2.
> input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have a
> > >> problem, it
> > >> > > >> would
> > >> > > >> > do
> > >> > > >> > > > > > >> the
> > >> > > >> > > > > > >>>>> local
> > >> > > >> > > > > > >>>>>>> sum
> > >> > > >> > > > > > >>>>>>>>> aggregation and will produce the latest
> > partial
> > >> > > result
> > >> > > >> > from
> > >> > > >> > > > > > >> the
> > >> > > >> > > > > > >>>>>> source
> > >> > > >> > > > > > >>>>>>>>> start for every event. *
> > >> > > >> > > > > > >>>>>>>>> *These latest partial results from the same
> > key
> > >> > are
> > >> > > >> > hashed
> > >> > > >> > > to
> > >> > > >> > > > > > >>> one
> > >> > > >> > > > > > >>>>>> node
> > >> > > >> > > > > > >>>>>>> to
> > >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
> > >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it
> received
> > >> > > multiple
> > >> > > >> > > partial
> > >> > > >> > > > > > >>>>> results
> > >> > > >> > > > > > >>>>>>>> (they
> > >> > > >> > > > > > >>>>>>>>> are all calculated from the source start)
> and
> > >> sum
> > >> > > them
> > >> > > >> > will
> > >> > > >> > > > > > >> get
> > >> > > >> > > > > > >>>> the
> > >> > > >> > > > > > >>>>>>> wrong
> > >> > > >> > > > > > >>>>>>>>> result.*
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> 3.
> > >> > > >> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a partial
> > >> > > aggregation
> > >> > > >> > > result
> > >> > > >> > > > > > >>> for
> > >> > > >> > > > > > >>>>>> the 5
> > >> > > >> > > > > > >>>>>>>>> records in the count window. The partial
> > >> > aggregation
> > >> > > >> > > results
> > >> > > >> > > > > > >>> from
> > >> > > >> > > > > > >>>>> the
> > >> > > >> > > > > > >>>>>>>> same
> > >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> So the first case and the third case can
> get
> > >> the
> > >> > > >> *same*
> > >> > > >> > > > > > >> result,
> > >> > > >> > > > > > >>>> the
> > >> > > >> > > > > > >>>>>>>>> difference is the output-style and the
> > latency.
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key API is
> just
> > >> an
> > >> > > >> > > optimization
> > >> > > >> > > > > > >>>> API.
> > >> > > >> > > > > > >>>>> We
> > >> > > >> > > > > > >>>>>>> do
> > >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the user
> has
> > to
> > >> > > >> > understand
> > >> > > >> > > > > > >> its
> > >> > > >> > > > > > >>>>>>> semantics
> > >> > > >> > > > > > >>>>>>>>> and use it correctly.
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> Best,
> > >> > > >> > > > > > >>>>>>>>> Vino
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]>
> > >> 于2019年6月17日周一
> > >> > > >> > 下午4:18写道：
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>> Hi Vino,
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a
> > very
> > >> > good
> > >> > > >> > > feature!
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the
> > semantics
> > >> > for
> > >> > > >> the
> > >> > > >> > > > > > >>>>>> `localKeyBy`.
> > >> > > >> > > > > > >>>>>>>> From
> > >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns
> > an
> > >> > > >> instance
> > >> > > >> > of
> > >> > > >> > > > > > >>>>>>> `KeyedStream`
> > >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in this
> > case,
> > >> > > what's
> > >> > > >> > the
> > >> > > >> > > > > > >>>>> semantics
> > >> > > >> > > > > > >>>>>>> for
> > >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the
> > >> following
> > >> > > code
> > >> > > >> > share
> > >> > > >> > > > > > >>> the
> > >> > > >> > > > > > >>>>> same
> > >> > > >> > > > > > >>>>>>>>> result?
> > >> > > >> > > > > > >>>>>>>>>> and what're the differences between them?
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> > >> > > >> > > > > > >>>>>>>>>> 2.
> > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > >> > > >> > > > > > >>>>>>>>>> 3.
> > >> > > >> > > > > > >>
> > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add this
> into
> > >> the
> > >> > > >> > document.
> > >> > > >> > > > > > >>> Thank
> > >> > > >> > > > > > >>>>> you
> > >> > > >> > > > > > >>>>>>>> very
> > >> > > >> > > > > > >>>>>>>>>> much.
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>> Best, Hequn
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino
> yang <
> > >> > > >> > > > > > >>>>> [hidden email]>
> > >> > > >> > > > > > >>>>>>>>> wrote:
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section
> of
> > >> FLIP
> > >> > > >> wiki
> > >> > > >> > > > > > >>>> page.[1]
> > >> > > >> > > > > > >>>>>> This
> > >> > > >> > > > > > >>>>>>>>> mail
> > >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to
> > the
> > >> > > third
> > >> > > >> > step.
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote
> > step),
> > >> I
> > >> > > >> didn't
> > >> > > >> > > > > > >> find
> > >> > > >> > > > > > >>>> the
> > >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the voting
> > >> process.
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of this
> > >> feature
> > >> > > has
> > >> > > >> > been
> > >> > > >> > > > > > >>> done
> > >> > > >> > > > > > >>>>> in
> > >> > > >> > > > > > >>>>>>> the
> > >> > > >> > > > > > >>>>>>>>> old
> > >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when
> should
> > I
> > >> > start
> > >> > > >> > > > > > >> voting?
> > >> > > >> > > > > > >>>> Can
> > >> > > >> > > > > > >>>>> I
> > >> > > >> > > > > > >>>>>>>> start
> > >> > > >> > > > > > >>>>>>>>>> now?
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>> Best,
> > >> > > >> > > > > > >>>>>>>>>>> Vino
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>> [1]:
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>
> > >> > > >> > > > > >
> > >> > > >> > > > >
> > >> > > >> > > >
> > >> > > >> > >
> > >> > > >> >
> > >> > > >>
> > >> > >
> > >> >
> > >>
> >
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> > >> > > >> > > > > > >>>>>>>>>>> [2]:
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>
> > >> > > >> > > > > >
> > >> > > >> > > > >
> > >> > > >> > > >
> > >> > > >> > >
> > >> > > >> >
> > >> > > >>
> > >> > >
> > >> >
> > >>
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email]>
> 于2019年6月13日周四
> > >> > > 上午9:19写道：
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your
> > >> efforts.
> > >> > > >> > > > > > >>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>> Best,
> > >> > > >> > > > > > >>>>>>>>>>>> Leesf
> > >> > > >> > > > > > >>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]>
> > >> > 于2019年6月12日周三
> > >> > > >> > > > > > >>> 下午5:46写道：
> > >> > > >> > > > > > >>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP
> discussion
> > >> > thread
> > >> > > >> > > > > > >> about
> > >> > > >> > > > > > >>>>>>> supporting
> > >> > > >> > > > > > >>>>>>>>>> local
> > >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can effectively
> > >> > alleviate
> > >> > > >> data
> > >> > > >> > > > > > >>>> skew.
> > >> > > >> > > > > > >>>>>>> This
> > >> > > >> > > > > > >>>>>>>> is
> > >> > > >> > > > > > >>>>>>>>>> the
> > >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>
> > >> > > >> > > > > >
> > >> > > >> > > > >
> > >> > > >> > > >
> > >> > > >> > >
> > >> > > >> >
> > >> > > >>
> > >> > >
> > >> >
> > >>
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely
> used
> > to
> > >> > > >> perform
> > >> > > >> > > > > > >>>>>> aggregating
> > >> > > >> > > > > > >>>>>>>>>>>> operations
> > >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the
> > >> elements
> > >> > > >> that
> > >> > > >> > > > > > >>> have
> > >> > > >> > > > > > >>>>> the
> > >> > > >> > > > > > >>>>>>> same
> > >> > > >> > > > > > >>>>>>>>>> key.
> > >> > > >> > > > > > >>>>>>>>>>>> When
> > >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with
> > the
> > >> > same
> > >> > > >> key
> > >> > > >> > > > > > >>> will
> > >> > > >> > > > > > >>>> be
> > >> > > >> > > > > > >>>>>>> sent
> > >> > > >> > > > > > >>>>>>>> to
> > >> > > >> > > > > > >>>>>>>>>> and
> > >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> The performance of these aggregating
> > >> > operations
> > >> > > is
> > >> > > >> > > > > > >> very
> > >> > > >> > > > > > >>>>>>> sensitive
> > >> > > >> > > > > > >>>>>>>>> to
> > >> > > >> > > > > > >>>>>>>>>>> the
> > >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases
> where
> > >> the
> > >> > > >> > > > > > >>> distribution
> > >> > > >> > > > > > >>>>> of
> > >> > > >> > > > > > >>>>>>> keys
> > >> > > >> > > > > > >>>>>>>>>>>> follows a
> > >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
> > >> > > >> significantly
> > >> > > >> > > > > > >>>>>> downgraded.
> > >> > > >> > > > > > >>>>>>>>> More
> > >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of
> > >> > parallelism
> > >> > > >> does
> > >> > > >> > > > > > >>> not
> > >> > > >> > > > > > >>>>> help
> > >> > > >> > > > > > >>>>>>>> when
> > >> > > >> > > > > > >>>>>>>>> a
> > >> > > >> > > > > > >>>>>>>>>>> task
> > >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted
> > >> method
> > >> > to
> > >> > > >> > > > > > >> reduce
> > >> > > >> > > > > > >>>> the
> > >> > > >> > > > > > >>>>>>>>>> performance
> > >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose
> > the
> > >> > > >> > > > > > >> aggregating
> > >> > > >> > > > > > >>>>>>>> operations
> > >> > > >> > > > > > >>>>>>>>>> into
> > >> > > >> > > > > > >>>>>>>>>>>> two
> > >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we
> aggregate
> > >> the
> > >> > > >> elements
> > >> > > >> > > > > > >>> of
> > >> > > >> > > > > > >>>>> the
> > >> > > >> > > > > > >>>>>>> same
> > >> > > >> > > > > > >>>>>>>>> key
> > >> > > >> > > > > > >>>>>>>>>>> at
> > >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial
> > results.
> > >> > Then
> > >> > > at
> > >> > > >> > > > > > >> the
> > >> > > >> > > > > > >>>>> second
> > >> > > >> > > > > > >>>>>>>>> phase,
> > >> > > >> > > > > > >>>>>>>>>>>> these
> > >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to receivers
> > >> > according
> > >> > > to
> > >> > > >> > > > > > >>> their
> > >> > > >> > > > > > >>>>> keys
> > >> > > >> > > > > > >>>>>>> and
> > >> > > >> > > > > > >>>>>>>>> are
> > >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final result.
> > Since
> > >> the
> > >> > > >> number
> > >> > > >> > > > > > >>> of
> > >> > > >> > > > > > >>>>>>> partial
> > >> > > >> > > > > > >>>>>>>>>>> results
> > >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is limited by
> > the
> > >> > > >> number of
> > >> > > >> > > > > > >>>>>> senders,
> > >> > > >> > > > > > >>>>>>>> the
> > >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be
> reduced.
> > >> > > >> Besides, by
> > >> > > >> > > > > > >>>>>> reducing
> > >> > > >> > > > > > >>>>>>>> the
> > >> > > >> > > > > > >>>>>>>>>>> amount
> > >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the performance can
> > be
> > >> > > further
> > >> > > >> > > > > > >>>>> improved.
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>
> > >> > > >> > > > > >
> > >> > > >> > > > >
> > >> > > >> > > >
> > >> > > >> > >
> > >> > > >> >
> > >> > > >>
> > >> > >
> > >> >
> > >>
> >
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>
> > >> > > >> > > > > >
> > >> > > >> > > > >
> > >> > > >> > > >
> > >> > > >> > >
> > >> > > >> >
> > >> > > >>
> > >> > >
> > >> >
> > >>
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> > >> > > >> > > > > > >>>>>>>>>
> > >> https://issues.apache.org/jira/browse/FLINK-12786
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your
> feedback!
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>> Best,
> > >> > > >> > > > > > >>>>>>>>>>>>> Vino
> > >> > > >> > > > > > >>>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>>
> > >> > > >> > > > > > >>>>>>>>
> > >> > > >> > > > > > >>>>>>>
> > >> > > >> > > > > > >>>>>>
> > >> > > >> > > > > > >>>>>
> > >> > > >> > > > > > >>>>
> > >> > > >> > > > > > >>>
> > >> > > >> > > > > > >>
> > >> > > >> > > > > >
> > >> > > >> > > > > >
> > >> > > >> > > > >
> > >> > > >> > > >
> > >> > > >> > >
> > >> > > >> >
> > >> > > >>
> > >> > > >
> > >> > >
> > >> >
> > >>
> > >
> >
>

[vino yang]

Hi Jark,

Similar questions and responses have been repeated many times.

Why didn't we spend more sections discussing the API?

Because we try to reuse the ability of KeyedStream. The localKeyBy API just
returns the KeyedStream, that's our design, we can get all the benefit from
the KeyedStream and get further benefit from WindowedStream. The APIs come
from KeyedStream and WindowedStream is long-tested and flexible. Yes, we
spend much space discussing the local keyed state, that's not the goal and
motivation, that's the way to implement local aggregation. It is much more
complicated than the API we introduced, so we spent more section. Of
course, this is the implementation level of the Operator. We also agreed to
support the implementation of buffer+flush and added related instructions
to the documentation. This needs to wait for the community to recognize,
and if the community agrees, we will give more instructions. What's more, I
have indicated before that we welcome state-related commenters to
participate in the discussion, but it is not wise to modify the FLIP title.

About the API of local aggregation:

I don't object to ease of use is very important. But IMHO flexibility is
the most important at the DataStream API level. Otherwise, what does
DataStream mean? The significance of the DataStream API is that it is more
flexible than Table/SQL, if it cannot provide this point then everyone
would just use Table/SQL.

The DataStream API should focus more on flexibility than on automatic
optimization, which allows users to have more possibilities to implement
complex programs and meet specific scenarios. There are a lot of programs
written using the DataStream API that are far more complex than we think.
It is very difficult to optimize at the API level and the benefit is very
low.

I want to say that we support a more generalized local aggregation. I
mentioned in the previous reply that not only the UDF that implements
AggregateFunction is called aggregation. In some complex scenarios, we have
to support local aggregation through ProcessFunction and
ProcessWindowFunction to solve the data skew problem. How do you support
them in the API implementation and optimization you mentioned?

Flexible APIs are arbitrarily combined to result in erroneous semantics,
which does not prove that flexibility is meaningless because the user is
the decision maker. I have been exemplified many times, for many APIs in
DataStream, if we arbitrarily combined them, they also do not have much
practical significance. So, users who use flexible APIs need to understand
what they are doing and what is the right choice.

I think that if we discuss this, there will be no result.

@Stephan Ewen <[hidden email]> , @Aljoscha Krettek <[hidden email]>
 and @Piotr Nowojski <[hidden email]> Do you have further comments?


Jark Wu <[hidden email]> 于2019年6月26日周三 上午11:46写道：

> Thanks for the long discussion Vino, Kurt, Hequn, Piotr and others,
>
> It seems that we still have some different ideas about the API
> (localKeyBy()?) and implementation details (reuse window operator? local
> keyed state?).
> And the discussion is stalled and mixed with motivation and API and
> implementation discussion.
>
> In order to make some progress in this topic, I want to summarize the
> points (pls correct me if I'm wrong or missing sth) and would suggest to
> split
>  the topic into following aspects and discuss them one by one.
>
> 1) What's the main purpose of this FLIP?
>  - From the title of this FLIP, it is to support local aggregate. However
> from the content of the FLIP, 80% are introducing a new state called local
> keyed state.
>  - If we mainly want to introduce local keyed state, then we should
> re-title the FLIP and involve in more people who works on state.
>  - If we mainly want to support local aggregate, then we can jump to step 2
> to discuss the API design.
>
> 2) What does the API look like?
>  - Vino proposed to use "localKeyBy()" to do local process, the output of
> local process is the result type of aggregate function.
>   a) For non-windowed aggregate:
> input.localKeyBy(..).aggregate(agg1).keyBy(..).aggregate(agg2)    **NOT
> SUPPORT**
>   b) For windowed aggregate:
>
> input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)
>
> 3) What's the implementation detail?
>  - may reuse window operator or not.
>  - may introduce a new state concepts or not.
>  - may not have state in local operator by flushing buffers in
> prepareSnapshotPreBarrier
>  - and so on...
>  - we can discuss these later when we reach a consensus on API
>
> --------------------
>
> Here are my thoughts:
>
> 1) Purpose of this FLIP
>  - From the motivation section in the FLIP, I think the purpose is to
> support local aggregation to solve the data skew issue.
>    Then I think we should focus on how to provide a easy to use and clear
> API to support **local aggregation**.
>  - Vino's point is centered around the local keyed state API (or
> localKeyBy()), and how to leverage the local keyed state API to support
> local aggregation.
>    But I'm afraid it's not a good way to design API for local aggregation.
>
> 2) local aggregation API
>  - IMO, the method call chain
>
> "input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)"
> is not such easy to use.
>    Because we have to provide two implementation for an aggregation (one
> for partial agg, another for final agg). And we have to take care of
>    the first window call, an inappropriate window call will break the
> sematics.
>  - From my point of view, local aggregation is a mature concept which
> should output the intermediate accumulator (ACC) in the past period of time
> (a trigger).
>    And the downstream final aggregation will merge ACCs received from local
> side, and output the current final result.
>  - The current "AggregateFunction" API in DataStream already has the
> accumulator type and "merge" method. So the only thing user need to do is
> how to enable
>    local aggregation opimization and set a trigger.
>  - One idea comes to my head is that, assume we have a windowed aggregation
> stream: "val stream = input.keyBy().window(w).aggregate(agg)". We can
> provide an API on the stream.
>    For exmaple, "stream.enableLocalAggregation(Trigger)", the trigger can
> be "ContinuousEventTimeTrigger.of(Time.of(Time.minutes(1)))". Then it will
> be optmized into
>    local operator + final operator, and local operator will combine records
> every minute on event time.
>  - In this way, there is only one line added, and the output is the same
> with before, because it is just an opimization.
>
>
> Regards,
> Jark
>
>
>
> On Tue, 25 Jun 2019 at 14:34, vino yang <[hidden email]> wrote:
>
> > Hi Kurt,
> >
> > Answer your questions:
> >
> > a) Sorry, I just updated the Google doc, still have no time update the
> > FLIP, will update FLIP as soon as possible.
> > About your description at this point, I have a question, what does it
> mean:
> > how do we combine with
> > `AggregateFunction`?
> >
> > I have shown you the examples which Flink has supported:
> >
> >    - input.localKeyBy(0).aggregate()
> >    - input.localKeyBy(0).window().aggregate()
> >
> > You can show me a example about how do we combine with `AggregateFuncion`
> > through your localAggregate API.
> >
> > About the example, how to do the local aggregation for AVG, consider this
> > code:
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > *DataStream<Tuple2<String, Long>> source = null; source .localKeyBy(0)
> > .timeWindow(Time.seconds(60)) .aggregate(agg1, new
> > WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>, String,
> > TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60))
> .aggregate(agg2,
> > new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, String,
> > TimeWindow>());*
> >
> > *agg1:*
> > *signature : new AggregateFunction<Tuple2<String, Long>, Tuple2<Long,
> > Long>, Tuple2<Long, Long>>() {}*
> > *input param type: Tuple2<String, Long> f0: key, f1: value*
> > *intermediate result type: Tuple2<Long, Long>, f0: local aggregated sum;
> > f1: local aggregated count*
> > *output param type:  Tuple2<Long, Long>, f0: local aggregated sum; f1:
> > local aggregated count*
> >
> > *agg2:*
> > *signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
> > Tuple2<String, Long>>() {},*
> > *input param type: Tuple3<String, Long, Long>, f0: key, f1:  local
> > aggregated sum; f2: local aggregated count*
> >
> > *intermediate result type: Long  avg result*
> > *output param type:  Tuple2<String, Long> f0: key, f1 avg result*
> >
> > For sliding window, we just need to change the window type if users want
> to
> > do.
> > Again, we try to give the design and implementation in the DataStream
> > level. So I believe we can match all the requirements(It's just that the
> > implementation may be different) comes from the SQL level.
> >
> > b) Yes, Theoretically, your thought is right. But in reality, it cannot
> > bring many benefits.
> > If we want to get the benefits from the window API, while we do not reuse
> > the window operator? And just copy some many duplicated code to another
> > operator?
> >
> > c) OK, I agree to let the state backend committers join this discussion.
> >
> > Best,
> > Vino
> >
> >
> > Kurt Young <[hidden email]> 于2019年6月24日周一 下午6:53写道：
> >
> > > Hi vino,
> > >
> > > One thing to add,  for a), I think use one or two examples like how to
> do
> > > local aggregation on a sliding window,
> > > and how do we do local aggregation on an unbounded aggregate, will do a
> > lot
> > > help.
> > >
> > > Best,
> > > Kurt
> > >
> > >
> > > On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email]> wrote:
> > >
> > > > Hi vino,
> > > >
> > > > I think there are several things still need discussion.
> > > >
> > > > a) We all agree that we should first go with a unified abstraction,
> but
> > > > the abstraction is not reflected by the FLIP.
> > > > If your answer is "locakKeyBy" API, then I would ask how do we
> combine
> > > > with `AggregateFunction`, and how do
> > > > we do proper local aggregation for those have different intermediate
> > > > result type, like AVG. Could you add these
> > > > to the document?
> > > >
> > > > b) From implementation side, reusing window operator is one of the
> > > > possible solutions, but not we base on window
> > > > operator to have two different implementations. What I understanding
> > is,
> > > > one of the possible implementations should
> > > > not touch window operator.
> > > >
> > > > c) 80% of your FLIP content is actually describing how do we support
> > > local
> > > > keyed state. I don't know if this is necessary
> > > > to introduce at the first step and we should also involve committers
> > work
> > > > on state backend to share their thoughts.
> > > >
> > > > Best,
> > > > Kurt
> > > >
> > > >
> > > > On Mon, Jun 24, 2019 at 5:17 PM vino yang <[hidden email]>
> > wrote:
> > > >
> > > >> Hi Kurt,
> > > >>
> > > >> You did not give more further different opinions, so I thought you
> > have
> > > >> agreed with the design after we promised to support two kinds of
> > > >> implementation.
> > > >>
> > > >> In API level, we have answered your question about pass an
> > > >> AggregateFunction to do the aggregation. No matter introduce
> > localKeyBy
> > > >> API
> > > >> or not, we can support AggregateFunction.
> > > >>
> > > >> So what's your different opinion now? Can you share it with us?
> > > >>
> > > >> Best,
> > > >> Vino
> > > >>
> > > >> Kurt Young <[hidden email]> 于2019年6月24日周一 下午4:24写道：
> > > >>
> > > >> > Hi vino,
> > > >> >
> > > >> > Sorry I don't see the consensus about reusing window operator and
> > keep
> > > >> the
> > > >> > API design of localKeyBy. But I think we should definitely more
> > > thoughts
> > > >> > about this topic.
> > > >> >
> > > >> > I also try to loop in Stephan for this discussion.
> > > >> >
> > > >> > Best,
> > > >> > Kurt
> > > >> >
> > > >> >
> > > >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <[hidden email]>
> > > >> wrote:
> > > >> >
> > > >> > > Hi all,
> > > >> > >
> > > >> > > I am happy we have a wonderful discussion and received many
> > valuable
> > > >> > > opinions in the last few days.
> > > >> > >
> > > >> > > Now, let me try to summarize what we have reached consensus
> about
> > > the
> > > >> > > changes in the design.
> > > >> > >
> > > >> > >    - provide a unified abstraction to support two kinds of
> > > >> > implementation;
> > > >> > >    - reuse WindowOperator and try to enhance it so that we can
> > make
> > > >> the
> > > >> > >    intermediate result of the local aggregation can be buffered
> > and
> > > >> > > flushed to
> > > >> > >    support two kinds of implementation;
> > > >> > >    - keep the API design of localKeyBy, but declare the disabled
> > > some
> > > >> > APIs
> > > >> > >    we cannot support currently, and provide a configurable API
> for
> > > >> users
> > > >> > to
> > > >> > >    choose how to handle intermediate result;
> > > >> > >
> > > >> > > The above three points have been updated in the design doc. Any
> > > >> > > questions, please let me know.
> > > >> > >
> > > >> > > @Aljoscha Krettek <[hidden email]> What do you think? Any
> > > >> further
> > > >> > > comments?
> > > >> > >
> > > >> > > Best,
> > > >> > > Vino
> > > >> > >
> > > >> > > vino yang <[hidden email]> 于2019年6月20日周四 下午2:02写道：
> > > >> > >
> > > >> > > > Hi Kurt,
> > > >> > > >
> > > >> > > > Thanks for your comments.
> > > >> > > >
> > > >> > > > It seems we come to a consensus that we should alleviate the
> > > >> > performance
> > > >> > > > degraded by data skew with local aggregation. In this FLIP,
> our
> > > key
> > > >> > > > solution is to introduce local keyed partition to achieve this
> > > goal.
> > > >> > > >
> > > >> > > > I also agree that we can benefit a lot from the usage of
> > > >> > > > AggregateFunction. In combination with localKeyBy, We can
> easily
> > > >> use it
> > > >> > > to
> > > >> > > > achieve local aggregation:
> > > >> > > >
> > > >> > > >    - input.localKeyBy(0).aggregate()
> > > >> > > >    - input.localKeyBy(0).window().aggregate()
> > > >> > > >
> > > >> > > >
> > > >> > > > I think the only problem here is the choices between
> > > >> > > >
> > > >> > > >    - (1) Introducing a new primitive called localKeyBy and
> > > implement
> > > >> > > >    local aggregation with existing operators, or
> > > >> > > >    - (2) Introducing an operator called localAggregation which
> > is
> > > >> > > >    composed of a key selector, a window-like operator, and an
> > > >> aggregate
> > > >> > > >    function.
> > > >> > > >
> > > >> > > >
> > > >> > > > There may exist some optimization opportunities by providing a
> > > >> > composited
> > > >> > > > interface for local aggregation. But at the same time, in my
> > > >> opinion,
> > > >> > we
> > > >> > > > lose flexibility (Or we need certain efforts to achieve the
> same
> > > >> > > > flexibility).
> > > >> > > >
> > > >> > > > As said in the previous mails, we have many use cases where
> the
> > > >> > > > aggregation is very complicated and cannot be performed with
> > > >> > > > AggregateFunction. For example, users may perform windowed
> > > >> aggregations
> > > >> > > > according to time, data values, or even external storage.
> > > Typically,
> > > >> > they
> > > >> > > > now use KeyedProcessFunction or customized triggers to
> implement
> > > >> these
> > > >> > > > aggregations. It's not easy to address data skew in such cases
> > > with
> > > >> a
> > > >> > > > composited interface for local aggregation.
> > > >> > > >
> > > >> > > > Given that Data Stream API is exactly targeted at these cases
> > > where
> > > >> the
> > > >> > > > application logic is very complicated and optimization does
> not
> > > >> > matter, I
> > > >> > > > think it's a better choice to provide a relatively low-level
> and
> > > >> > > canonical
> > > >> > > > interface.
> > > >> > > >
> > > >> > > > The composited interface, on the other side, may be a good
> > choice
> > > in
> > > >> > > > declarative interfaces, including SQL and Table API, as it
> > allows
> > > >> more
> > > >> > > > optimization opportunities.
> > > >> > > >
> > > >> > > > Best,
> > > >> > > > Vino
> > > >> > > >
> > > >> > > >
> > > >> > > > Kurt Young <[hidden email]> 于2019年6月20日周四 上午10:15写道：
> > > >> > > >
> > > >> > > >> Hi all,
> > > >> > > >>
> > > >> > > >> As vino said in previous emails, I think we should first
> > discuss
> > > >> and
> > > >> > > >> decide
> > > >> > > >> what kind of use cases this FLIP want to
> > > >> > > >> resolve, and what the API should look like. From my side, I
> > think
> > > >> this
> > > >> > > is
> > > >> > > >> probably the root cause of current divergence.
> > > >> > > >>
> > > >> > > >> My understand is (from the FLIP title and motivation section
> of
> > > the
> > > >> > > >> document), we want to have a proper support of
> > > >> > > >> local aggregation, or pre aggregation. This is not a very new
> > > idea,
> > > >> > most
> > > >> > > >> SQL engine already did this improvement. And
> > > >> > > >> the core concept about this is, there should be an
> > > >> AggregateFunction,
> > > >> > no
> > > >> > > >> matter it's a Flink runtime's AggregateFunction or
> > > >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have
> > concept
> > > >> of
> > > >> > > >> intermediate data type, sometimes we call it ACC.
> > > >> > > >> I quickly went through the POC piotr did before [1], it also
> > > >> directly
> > > >> > > uses
> > > >> > > >> AggregateFunction.
> > > >> > > >>
> > > >> > > >> But the thing is, after reading the design of this FLIP, I
> > can't
> > > >> help
> > > >> > > >> myself feeling that this FLIP is not targeting to have a
> proper
> > > >> > > >> local aggregation support. It actually want to introduce
> > another
> > > >> > > concept:
> > > >> > > >> LocalKeyBy, and how to split and merge local key groups,
> > > >> > > >> and how to properly support state on local key. Local
> > aggregation
> > > >> just
> > > >> > > >> happened to be one possible use case of LocalKeyBy.
> > > >> > > >> But it lacks supporting the essential concept of local
> > > aggregation,
> > > >> > > which
> > > >> > > >> is intermediate data type. Without this, I really don't thing
> > > >> > > >> it is a good fit of local aggregation.
> > > >> > > >>
> > > >> > > >> Here I want to make sure of the scope or the goal about this
> > > FLIP,
> > > >> do
> > > >> > we
> > > >> > > >> want to have a proper local aggregation engine, or we
> > > >> > > >> just want to introduce a new concept called LocalKeyBy?
> > > >> > > >>
> > > >> > > >> [1]: https://github.com/apache/flink/pull/4626
> > > >> > > >>
> > > >> > > >> Best,
> > > >> > > >> Kurt
> > > >> > > >>
> > > >> > > >>
> > > >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <
> > [hidden email]
> > > >
> > > >> > > wrote:
> > > >> > > >>
> > > >> > > >> > Hi Hequn,
> > > >> > > >> >
> > > >> > > >> > Thanks for your comments!
> > > >> > > >> >
> > > >> > > >> > I agree that allowing local aggregation reusing window API
> > and
> > > >> > > refining
> > > >> > > >> > window operator to make it match both requirements (come
> from
> > > our
> > > >> > and
> > > >> > > >> Kurt)
> > > >> > > >> > is a good decision!
> > > >> > > >> >
> > > >> > > >> > Concerning your questions:
> > > >> > > >> >
> > > >> > > >> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > may
> > > >> be
> > > >> > > >> > meaningless.
> > > >> > > >> >
> > > >> > > >> > Yes, it does not make sense in most cases. However, I also
> > want
> > > >> to
> > > >> > > note
> > > >> > > >> > users should know the right semantics of localKeyBy and use
> > it
> > > >> > > >> correctly.
> > > >> > > >> > Because this issue also exists for the global keyBy,
> consider
> > > >> this
> > > >> > > >> example:
> > > >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also
> > > >> > meaningless.
> > > >> > > >> >
> > > >> > > >> > 2. About the semantics of
> > > >> > > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> > > >> > > >> >
> > > >> > > >> > Good catch! I agree with you that it's not good to enable
> all
> > > >> > > >> > functionalities for localKeyBy from KeyedStream.
> > > >> > > >> > Currently, We do not support some APIs such as
> > > >> > > >> > connect/join/intervalJoin/coGroup. This is due to that we
> > force
> > > >> the
> > > >> > > >> > operators on LocalKeyedStreams chained with the inputs.
> > > >> > > >> >
> > > >> > > >> > Best,
> > > >> > > >> > Vino
> > > >> > > >> >
> > > >> > > >> >
> > > >> > > >> > Hequn Cheng <[hidden email]> 于2019年6月19日周三 下午3:42写道：
> > > >> > > >> >
> > > >> > > >> > > Hi,
> > > >> > > >> > >
> > > >> > > >> > > Thanks a lot for your great discussion and great to see
> > that
> > > >> some
> > > >> > > >> > agreement
> > > >> > > >> > > has been reached on the "local aggregate engine"!
> > > >> > > >> > >
> > > >> > > >> > > ===> Considering the abstract engine,
> > > >> > > >> > > I'm thinking is it valuable for us to extend the current
> > > >> window to
> > > >> > > >> meet
> > > >> > > >> > > both demands raised by Kurt and Vino? There are some
> > benefits
> > > >> we
> > > >> > can
> > > >> > > >> get:
> > > >> > > >> > >
> > > >> > > >> > > 1. The interfaces of the window are complete and clear.
> > With
> > > >> > > windows,
> > > >> > > >> we
> > > >> > > >> > > can define a lot of ways to split the data and perform
> > > >> different
> > > >> > > >> > > computations.
> > > >> > > >> > > 2. We can also leverage the window to do miniBatch for
> the
> > > >> global
> > > >> > > >> > > aggregation, i.e, we can use the window to bundle data
> > belong
> > > >> to
> > > >> > the
> > > >> > > >> same
> > > >> > > >> > > key, for every bundle we only need to read and write once
> > > >> state.
> > > >> > > This
> > > >> > > >> can
> > > >> > > >> > > greatly reduce state IO and improve performance.
> > > >> > > >> > > 3. A lot of other use cases can also benefit from the
> > window
> > > >> base
> > > >> > on
> > > >> > > >> > memory
> > > >> > > >> > > or stateless.
> > > >> > > >> > >
> > > >> > > >> > > ===> As for the API,
> > > >> > > >> > > I think it is good to make our API more flexible.
> However,
> > we
> > > >> may
> > > >> > > >> need to
> > > >> > > >> > > make our API meaningful.
> > > >> > > >> > >
> > > >> > > >> > > Take my previous reply as an example,
> > > >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result
> may
> > be
> > > >> > > >> > meaningless.
> > > >> > > >> > > Another example I find is the intervalJoin, e.g.,
> > > >> > > >> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> In
> > > >> this
> > > >> > > >> case, it
> > > >> > > >> > > will bring problems if input1 and input2 share different
> > > >> > > parallelism.
> > > >> > > >> We
> > > >> > > >> > > don't know which input should the join chained with? Even
> > if
> > > >> they
> > > >> > > >> share
> > > >> > > >> > the
> > > >> > > >> > > same parallelism, it's hard to tell what the join is
> doing.
> > > >> There
> > > >> > > are
> > > >> > > >> > maybe
> > > >> > > >> > > some other problems.
> > > >> > > >> > >
> > > >> > > >> > > From this point of view, it's at least not good to enable
> > all
> > > >> > > >> > > functionalities for localKeyBy from KeyedStream?
> > > >> > > >> > >
> > > >> > > >> > > Great to also have your opinions.
> > > >> > > >> > >
> > > >> > > >> > > Best, Hequn
> > > >> > > >> > >
> > > >> > > >> > >
> > > >> > > >> > >
> > > >> > > >> > >
> > > >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
> > > >> [hidden email]
> > > >> > >
> > > >> > > >> > wrote:
> > > >> > > >> > >
> > > >> > > >> > > > Hi Kurt and Piotrek,
> > > >> > > >> > > >
> > > >> > > >> > > > Thanks for your comments.
> > > >> > > >> > > >
> > > >> > > >> > > > I agree that we can provide a better abstraction to be
> > > >> > compatible
> > > >> > > >> with
> > > >> > > >> > > two
> > > >> > > >> > > > different implementations.
> > > >> > > >> > > >
> > > >> > > >> > > > First of all, I think we should consider what kind of
> > > >> scenarios
> > > >> > we
> > > >> > > >> need
> > > >> > > >> > > to
> > > >> > > >> > > > support in *API* level?
> > > >> > > >> > > >
> > > >> > > >> > > > We have some use cases which need to a customized
> > > aggregation
> > > >> > > >> through
> > > >> > > >> > > > KeyedProcessFunction, (in the usage of our
> > > localKeyBy.window
> > > >> > they
> > > >> > > >> can
> > > >> > > >> > use
> > > >> > > >> > > > ProcessWindowFunction).
> > > >> > > >> > > >
> > > >> > > >> > > > Shall we support these flexible use scenarios?
> > > >> > > >> > > >
> > > >> > > >> > > > Best,
> > > >> > > >> > > > Vino
> > > >> > > >> > > >
> > > >> > > >> > > > Kurt Young <[hidden email]> 于2019年6月18日周二 下午8:37写道：
> > > >> > > >> > > >
> > > >> > > >> > > > > Hi Piotr,
> > > >> > > >> > > > >
> > > >> > > >> > > > > Thanks for joining the discussion. Make “local
> > > aggregation"
> > > >> > > >> abstract
> > > >> > > >> > > > enough
> > > >> > > >> > > > > sounds good to me, we could
> > > >> > > >> > > > > implement and verify alternative solutions for use
> > cases
> > > of
> > > >> > > local
> > > >> > > >> > > > > aggregation. Maybe we will find both solutions
> > > >> > > >> > > > > are appropriate for different scenarios.
> > > >> > > >> > > > >
> > > >> > > >> > > > > Starting from a simple one sounds a practical way to
> > go.
> > > >> What
> > > >> > do
> > > >> > > >> you
> > > >> > > >> > > > think,
> > > >> > > >> > > > > vino?
> > > >> > > >> > > > >
> > > >> > > >> > > > > Best,
> > > >> > > >> > > > > Kurt
> > > >> > > >> > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> > > >> > > >> [hidden email]>
> > > >> > > >> > > > > wrote:
> > > >> > > >> > > > >
> > > >> > > >> > > > > > Hi Kurt and Vino,
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > I think there is a trade of hat we need to consider
> > for
> > > >> the
> > > >> > > >> local
> > > >> > > >> > > > > > aggregation.
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > Generally speaking I would agree with Kurt about
> > local
> > > >> > > >> > > aggregation/pre
> > > >> > > >> > > > > > aggregation not using Flink's state flush the
> > operator
> > > >> on a
> > > >> > > >> > > checkpoint.
> > > >> > > >> > > > > > Network IO is usually cheaper compared to Disks IO.
> > > This
> > > >> has
> > > >> > > >> > however
> > > >> > > >> > > > > couple
> > > >> > > >> > > > > > of issues:
> > > >> > > >> > > > > > 1. It can explode number of in-flight records
> during
> > > >> > > checkpoint
> > > >> > > >> > > barrier
> > > >> > > >> > > > > > alignment, making checkpointing slower and decrease
> > the
> > > >> > actual
> > > >> > > >> > > > > throughput.
> > > >> > > >> > > > > > 2. This trades Disks IO on the local aggregation
> > > machine
> > > >> > with
> > > >> > > >> CPU
> > > >> > > >> > > (and
> > > >> > > >> > > > > > Disks IO in case of RocksDB) on the final
> aggregation
> > > >> > machine.
> > > >> > > >> This
> > > >> > > >> > > is
> > > >> > > >> > > > > > fine, as long there is no huge data skew. If there
> is
> > > >> only a
> > > >> > > >> > handful
> > > >> > > >> > > > (or
> > > >> > > >> > > > > > even one single) hot keys, it might be better to
> keep
> > > the
> > > >> > > >> > persistent
> > > >> > > >> > > > > state
> > > >> > > >> > > > > > in the LocalAggregationOperator to offload final
> > > >> aggregation
> > > >> > > as
> > > >> > > >> > much
> > > >> > > >> > > as
> > > >> > > >> > > > > > possible.
> > > >> > > >> > > > > > 3. With frequent checkpointing local aggregation
> > > >> > effectiveness
> > > >> > > >> > would
> > > >> > > >> > > > > > degrade.
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > I assume Kurt is correct, that in your use cases
> > > >> stateless
> > > >> > > >> operator
> > > >> > > >> > > was
> > > >> > > >> > > > > > behaving better, but I could easily see other use
> > cases
> > > >> as
> > > >> > > well.
> > > >> > > >> > For
> > > >> > > >> > > > > > example someone is already using RocksDB, and his
> job
> > > is
> > > >> > > >> > bottlenecked
> > > >> > > >> > > > on
> > > >> > > >> > > > > a
> > > >> > > >> > > > > > single window operator instance because of the data
> > > >> skew. In
> > > >> > > >> that
> > > >> > > >> > > case
> > > >> > > >> > > > > > stateful local aggregation would be probably a
> better
> > > >> > choice.
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > Because of that, I think we should eventually
> provide
> > > >> both
> > > >> > > >> versions
> > > >> > > >> > > and
> > > >> > > >> > > > > in
> > > >> > > >> > > > > > the initial version we should at least make the
> > “local
> > > >> > > >> aggregation
> > > >> > > >> > > > > engine”
> > > >> > > >> > > > > > abstract enough, that one could easily provide
> > > different
> > > >> > > >> > > implementation
> > > >> > > >> > > > > > strategy.
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > Piotrek
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
> > > [hidden email]
> > > >> >
> > > >> > > >> wrote:
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > Hi,
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > For the trigger, it depends on what operator we
> > want
> > > to
> > > >> > use
> > > >> > > >> under
> > > >> > > >> > > the
> > > >> > > >> > > > > > API.
> > > >> > > >> > > > > > > If we choose to use window operator,
> > > >> > > >> > > > > > > we should also use window's trigger. However, I
> > also
> > > >> think
> > > >> > > >> reuse
> > > >> > > >> > > > window
> > > >> > > >> > > > > > > operator for this scenario may not be
> > > >> > > >> > > > > > > the best choice. The reasons are the following:
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > 1. As a lot of people already pointed out, window
> > > >> relies
> > > >> > > >> heavily
> > > >> > > >> > on
> > > >> > > >> > > > > state
> > > >> > > >> > > > > > > and it will definitely effect performance. You
> can
> > > >> > > >> > > > > > > argue that one can use heap based statebackend,
> but
> > > >> this
> > > >> > > will
> > > >> > > >> > > > introduce
> > > >> > > >> > > > > > > extra coupling. Especially we have a chance to
> > > >> > > >> > > > > > > design a pure stateless operator.
> > > >> > > >> > > > > > > 2. The window operator is *the most* complicated
> > > >> operator
> > > >> > > >> Flink
> > > >> > > >> > > > > currently
> > > >> > > >> > > > > > > have. Maybe we only need to pick a subset of
> > > >> > > >> > > > > > > window operator to achieve the goal, but once the
> > > user
> > > >> > wants
> > > >> > > >> to
> > > >> > > >> > > have
> > > >> > > >> > > > a
> > > >> > > >> > > > > > deep
> > > >> > > >> > > > > > > look at the localAggregation operator, it's still
> > > >> > > >> > > > > > > hard to find out what's going on under the window
> > > >> > operator.
> > > >> > > >> For
> > > >> > > >> > > > > > simplicity,
> > > >> > > >> > > > > > > I would also recommend we introduce a dedicated
> > > >> > > >> > > > > > > lightweight operator, which also much easier for
> a
> > > >> user to
> > > >> > > >> learn
> > > >> > > >> > > and
> > > >> > > >> > > > > use.
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > For your question about increasing the burden in
> > > >> > > >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`,
> the
> > > only
> > > >> > > thing
> > > >> > > >> > this
> > > >> > > >> > > > > > function
> > > >> > > >> > > > > > > need
> > > >> > > >> > > > > > > to do is output all the partial results, it's
> > purely
> > > >> cpu
> > > >> > > >> > workload,
> > > >> > > >> > > > not
> > > >> > > >> > > > > > > introducing any IO. I want to point out that even
> > if
> > > we
> > > >> > have
> > > >> > > >> this
> > > >> > > >> > > > > > > cost, we reduced another barrier align cost of
> the
> > > >> > operator,
> > > >> > > >> > which
> > > >> > > >> > > is
> > > >> > > >> > > > > the
> > > >> > > >> > > > > > > sync flush stage of the state, if you introduced
> > > state.
> > > >> > This
> > > >> > > >> > > > > > > flush actually will introduce disk IO, and I
> think
> > > it's
> > > >> > > >> worthy to
> > > >> > > >> > > > > > exchange
> > > >> > > >> > > > > > > this cost with purely CPU workload. And we do
> have
> > > some
> > > >> > > >> > > > > > > observations about these two behavior (as i said
> > > >> before,
> > > >> > we
> > > >> > > >> > > actually
> > > >> > > >> > > > > > > implemented both solutions), the stateless one
> > > actually
> > > >> > > >> performs
> > > >> > > >> > > > > > > better both in performance and barrier align
> time.
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > Best,
> > > >> > > >> > > > > > > Kurt
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> > > >> > > >> [hidden email]
> > > >> > > >> > >
> > > >> > > >> > > > > wrote:
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > >> Hi Kurt,
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> Thanks for your example. Now, it looks more
> > clearly
> > > >> for
> > > >> > me.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> From your example code snippet, I saw the
> > > >> localAggregate
> > > >> > > API
> > > >> > > >> has
> > > >> > > >> > > > three
> > > >> > > >> > > > > > >> parameters:
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >>   1. key field
> > > >> > > >> > > > > > >>   2. PartitionAvg
> > > >> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes from
> > > window
> > > >> > > >> package?
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> I will compare our and your design from API and
> > > >> operator
> > > >> > > >> level:
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> *From the API level:*
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> As I replied to @dianfu in the old email
> > thread,[1]
> > > >> the
> > > >> > > >> Window
> > > >> > > >> > API
> > > >> > > >> > > > can
> > > >> > > >> > > > > > >> provide the second and the third parameter right
> > > now.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> If you reuse specified interface or class, such
> as
> > > >> > > *Trigger*
> > > >> > > >> or
> > > >> > > >> > > > > > >> *CounterTrigger* provided by window package, but
> > do
> > > >> not
> > > >> > use
> > > >> > > >> > window
> > > >> > > >> > > > > API,
> > > >> > > >> > > > > > >> it's not reasonable.
> > > >> > > >> > > > > > >> And if you do not reuse these interface or
> class,
> > > you
> > > >> > would
> > > >> > > >> need
> > > >> > > >> > > to
> > > >> > > >> > > > > > >> introduce more things however they are looked
> > > similar
> > > >> to
> > > >> > > the
> > > >> > > >> > > things
> > > >> > > >> > > > > > >> provided by window package.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> The window package has provided several types of
> > the
> > > >> > window
> > > >> > > >> and
> > > >> > > >> > > many
> > > >> > > >> > > > > > >> triggers and let users customize it. What's
> more,
> > > the
> > > >> > user
> > > >> > > is
> > > >> > > >> > more
> > > >> > > >> > > > > > familiar
> > > >> > > >> > > > > > >> with Window API.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> This is the reason why we just provide
> localKeyBy
> > > API
> > > >> and
> > > >> > > >> reuse
> > > >> > > >> > > the
> > > >> > > >> > > > > > window
> > > >> > > >> > > > > > >> API. It reduces unnecessary components such as
> > > >> triggers
> > > >> > and
> > > >> > > >> the
> > > >> > > >> > > > > > mechanism
> > > >> > > >> > > > > > >> of buffer (based on count num or time).
> > > >> > > >> > > > > > >> And it has a clear and easy to understand
> > semantics.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> *From the operator level:*
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> We reused window operator, so we can get all the
> > > >> benefits
> > > >> > > >> from
> > > >> > > >> > > state
> > > >> > > >> > > > > and
> > > >> > > >> > > > > > >> checkpoint.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> From your design, you named the operator under
> > > >> > > localAggregate
> > > >> > > >> > API
> > > >> > > >> > > > is a
> > > >> > > >> > > > > > >> *stateless* operator. IMO, it is still a state,
> it
> > > is
> > > >> > just
> > > >> > > >> not
> > > >> > > >> > > Flink
> > > >> > > >> > > > > > >> managed state.
> > > >> > > >> > > > > > >> About the memory buffer (I think it's still not
> > very
> > > >> > clear,
> > > >> > > >> if
> > > >> > > >> > you
> > > >> > > >> > > > > have
> > > >> > > >> > > > > > >> time, can you give more detail information or
> > answer
> > > >> my
> > > >> > > >> > > questions),
> > > >> > > >> > > > I
> > > >> > > >> > > > > > have
> > > >> > > >> > > > > > >> some questions:
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >>   - if it just a raw JVM heap memory buffer, how
> > to
> > > >> > support
> > > >> > > >> > fault
> > > >> > > >> > > > > > >>   tolerance, if the job is configured
> EXACTLY-ONCE
> > > >> > semantic
> > > >> > > >> > > > guarantee?
> > > >> > > >> > > > > > >>   - if you thought the memory buffer(non-Flink
> > > state),
> > > >> > has
> > > >> > > >> > better
> > > >> > > >> > > > > > >>   performance. In our design, users can also
> > config
> > > >> HEAP
> > > >> > > >> state
> > > >> > > >> > > > backend
> > > >> > > >> > > > > > to
> > > >> > > >> > > > > > >>   provide the performance close to your
> mechanism.
> > > >> > > >> > > > > > >>   -
> `StreamOperator::prepareSnapshotPreBarrier()`
> > > >> related
> > > >> > > to
> > > >> > > >> the
> > > >> > > >> > > > > timing
> > > >> > > >> > > > > > of
> > > >> > > >> > > > > > >>   snapshot. IMO, the flush action should be a
> > > >> > synchronized
> > > >> > > >> > action?
> > > >> > > >> > > > (if
> > > >> > > >> > > > > > >> not,
> > > >> > > >> > > > > > >>   please point out my mistake) I still think we
> > > should
> > > >> > not
> > > >> > > >> > depend
> > > >> > > >> > > on
> > > >> > > >> > > > > the
> > > >> > > >> > > > > > >>   timing of checkpoint. Checkpoint related
> > > operations
> > > >> are
> > > >> > > >> > inherent
> > > >> > > >> > > > > > >>   performance sensitive, we should not increase
> > its
> > > >> > burden
> > > >> > > >> > > anymore.
> > > >> > > >> > > > > Our
> > > >> > > >> > > > > > >>   implementation based on the mechanism of
> Flink's
> > > >> > > >> checkpoint,
> > > >> > > >> > > which
> > > >> > > >> > > > > can
> > > >> > > >> > > > > > >>   benefit from the asnyc snapshot and
> incremental
> > > >> > > checkpoint.
> > > >> > > >> > IMO,
> > > >> > > >> > > > the
> > > >> > > >> > > > > > >>   performance is not a problem, and we also do
> not
> > > >> find
> > > >> > the
> > > >> > > >> > > > > performance
> > > >> > > >> > > > > > >> issue
> > > >> > > >> > > > > > >>   in our production.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> [1]:
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> Best,
> > > >> > > >> > > > > > >> Vino
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> Kurt Young <[hidden email]> 于2019年6月18日周二
> > > 下午2:27写道：
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >>> Yeah, sorry for not expressing myself clearly.
> I
> > > will
> > > >> > try
> > > >> > > to
> > > >> > > >> > > > provide
> > > >> > > >> > > > > > more
> > > >> > > >> > > > > > >>> details to make sure we are on the same page.
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> For DataStream API, it shouldn't be optimized
> > > >> > > automatically.
> > > >> > > >> > You
> > > >> > > >> > > > have
> > > >> > > >> > > > > > to
> > > >> > > >> > > > > > >>> explicitly call API to do local aggregation
> > > >> > > >> > > > > > >>> as well as the trigger policy of the local
> > > >> aggregation.
> > > >> > > Take
> > > >> > > >> > > > average
> > > >> > > >> > > > > > for
> > > >> > > >> > > > > > >>> example, the user program may look like this
> > (just
> > > a
> > > >> > > draft):
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> assuming the input type is
> > DataStream<Tupl2<String,
> > > >> > Int>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> ds.localAggregate(
> > > >> > > >> > > > > > >>>        0,
> >  //
> > > >> The
> > > >> > > local
> > > >> > > >> > key,
> > > >> > > >> > > > > which
> > > >> > > >> > > > > > >> is
> > > >> > > >> > > > > > >>> the String from Tuple2
> > > >> > > >> > > > > > >>>        PartitionAvg(1),                 // The
> > > >> partial
> > > >> > > >> > > aggregation
> > > >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>,
> indicating
> > > sum
> > > >> and
> > > >> > > >> count
> > > >> > > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger
> > policy,
> > > >> note
> > > >> > > >> this
> > > >> > > >> > > > should
> > > >> > > >> > > > > be
> > > >> > > >> > > > > > >>> best effort, and also be composited with time
> > based
> > > >> or
> > > >> > > >> memory
> > > >> > > >> > > size
> > > >> > > >> > > > > > based
> > > >> > > >> > > > > > >>> trigger
> > > >> > > >> > > > > > >>>    )
>  //
> > > The
> > > >> > > return
> > > >> > > >> > type
> > > >> > > >> > > > is
> > > >> > > >> > > > > > >> local
> > > >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> > > >> > > >> > > > > > >>>    .keyBy(0)                             //
> > Further
> > > >> > keyby
> > > >> > > it
> > > >> > > >> > with
> > > >> > > >> > > > > > >> required
> > > >> > > >> > > > > > >>> key
> > > >> > > >> > > > > > >>>    .aggregate(1)                      // This
> > will
> > > >> merge
> > > >> > > all
> > > >> > > >> > the
> > > >> > > >> > > > > > partial
> > > >> > > >> > > > > > >>> results and get the final average.
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> (This is only a draft, only trying to explain
> > what
> > > it
> > > >> > > looks
> > > >> > > >> > > like. )
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> The local aggregate operator can be stateless,
> we
> > > can
> > > >> > > keep a
> > > >> > > >> > > memory
> > > >> > > >> > > > > > >> buffer
> > > >> > > >> > > > > > >>> or other efficient data structure to improve
> the
> > > >> > aggregate
> > > >> > > >> > > > > performance.
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> Let me know if you have any other questions.
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> Best,
> > > >> > > >> > > > > > >>> Kurt
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> > > >> > > >> > [hidden email]
> > > >> > > >> > > >
> > > >> > > >> > > > > > wrote:
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>>> Hi Kurt,
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Thanks for your reply.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Actually, I am not against you to raise your
> > > design.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> From your description before, I just can
> imagine
> > > >> your
> > > >> > > >> > high-level
> > > >> > > >> > > > > > >>>> implementation is about SQL and the
> optimization
> > > is
> > > >> > inner
> > > >> > > >> of
> > > >> > > >> > the
> > > >> > > >> > > > > API.
> > > >> > > >> > > > > > >> Is
> > > >> > > >> > > > > > >>> it
> > > >> > > >> > > > > > >>>> automatically? how to give the configuration
> > > option
> > > >> > about
> > > >> > > >> > > trigger
> > > >> > > >> > > > > > >>>> pre-aggregation?
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Maybe after I get more information, it sounds
> > more
> > > >> > > >> reasonable.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> IMO, first of all, it would be better to make
> > your
> > > >> user
> > > >> > > >> > > interface
> > > >> > > >> > > > > > >>> concrete,
> > > >> > > >> > > > > > >>>> it's the basis of the discussion.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> For example, can you give an example code
> > snippet
> > > to
> > > >> > > >> introduce
> > > >> > > >> > > how
> > > >> > > >> > > > > to
> > > >> > > >> > > > > > >>> help
> > > >> > > >> > > > > > >>>> users to process data skew caused by the jobs
> > > which
> > > >> > built
> > > >> > > >> with
> > > >> > > >> > > > > > >> DataStream
> > > >> > > >> > > > > > >>>> API?
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> If you give more details we can discuss
> further
> > > >> more. I
> > > >> > > >> think
> > > >> > > >> > if
> > > >> > > >> > > > one
> > > >> > > >> > > > > > >>> design
> > > >> > > >> > > > > > >>>> introduces an exact interface and another does
> > > not.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> The implementation has an obvious difference.
> > For
> > > >> > > example,
> > > >> > > >> we
> > > >> > > >> > > > > > introduce
> > > >> > > >> > > > > > >>> an
> > > >> > > >> > > > > > >>>> exact API in DataStream named localKeyBy,
> about
> > > the
> > > >> > > >> > > > pre-aggregation
> > > >> > > >> > > > > we
> > > >> > > >> > > > > > >>> need
> > > >> > > >> > > > > > >>>> to define the trigger mechanism of local
> > > >> aggregation,
> > > >> > so
> > > >> > > we
> > > >> > > >> > find
> > > >> > > >> > > > > > reused
> > > >> > > >> > > > > > >>>> window API and operator is a good choice. This
> > is
> > > a
> > > >> > > >> reasoning
> > > >> > > >> > > link
> > > >> > > >> > > > > > from
> > > >> > > >> > > > > > >>>> design to implementation.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> What do you think?
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Best,
> > > >> > > >> > > > > > >>>> Vino
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Kurt Young <[hidden email]> 于2019年6月18日周二
> > > >> 上午11:58写道：
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>>> Hi Vino,
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> Now I feel that we may have different
> > > >> understandings
> > > >> > > about
> > > >> > > >> > what
> > > >> > > >> > > > > kind
> > > >> > > >> > > > > > >> of
> > > >> > > >> > > > > > >>>>> problems or improvements you want to
> > > >> > > >> > > > > > >>>>> resolve. Currently, most of the feedback are
> > > >> focusing
> > > >> > on
> > > >> > > >> *how
> > > >> > > >> > > to
> > > >> > > >> > > > > do a
> > > >> > > >> > > > > > >>>>> proper local aggregation to improve
> performance
> > > >> > > >> > > > > > >>>>> and maybe solving the data skew issue*. And
> my
> > > gut
> > > >> > > >> feeling is
> > > >> > > >> > > > this
> > > >> > > >> > > > > is
> > > >> > > >> > > > > > >>>>> exactly what users want at the first place,
> > > >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to
> > summarize
> > > >> here,
> > > >> > > >> please
> > > >> > > >> > > > > correct
> > > >> > > >> > > > > > >>> me
> > > >> > > >> > > > > > >>>> if
> > > >> > > >> > > > > > >>>>> i'm wrong).
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> But I still think the design is somehow
> > diverged
> > > >> from
> > > >> > > the
> > > >> > > >> > goal.
> > > >> > > >> > > > If
> > > >> > > >> > > > > we
> > > >> > > >> > > > > > >>>> want
> > > >> > > >> > > > > > >>>>> to have an efficient and powerful way to
> > > >> > > >> > > > > > >>>>> have local aggregation, supporting intermedia
> > > >> result
> > > >> > > type
> > > >> > > >> is
> > > >> > > >> > > > > > >> essential
> > > >> > > >> > > > > > >>>> IMO.
> > > >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
> > > >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a
> > > proper
> > > >> > > >> support of
> > > >> > > >> > > > > > >>>> intermediate
> > > >> > > >> > > > > > >>>>> result type and can do `merge` operation
> > > >> > > >> > > > > > >>>>> on them.
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> Now, we have a lightweight alternatives which
> > > >> performs
> > > >> > > >> well,
> > > >> > > >> > > and
> > > >> > > >> > > > > > >> have a
> > > >> > > >> > > > > > >>>>> nice fit with the local aggregate
> requirements.
> > > >> > > >> > > > > > >>>>> Mostly importantly,  it's much less complex
> > > because
> > > >> > it's
> > > >> > > >> > > > stateless.
> > > >> > > >> > > > > > >> And
> > > >> > > >> > > > > > >>>> it
> > > >> > > >> > > > > > >>>>> can also achieve the similar
> > multiple-aggregation
> > > >> > > >> > > > > > >>>>> scenario.
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> I still not convinced why we shouldn't
> consider
> > > it
> > > >> as
> > > >> > a
> > > >> > > >> first
> > > >> > > >> > > > step.
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> Best,
> > > >> > > >> > > > > > >>>>> Kurt
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> > > >> > > >> > > > [hidden email]>
> > > >> > > >> > > > > > >>>> wrote:
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>>> Hi Kurt,
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Thanks for your comments.
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> It seems we both implemented local
> aggregation
> > > >> > feature
> > > >> > > to
> > > >> > > >> > > > optimize
> > > >> > > >> > > > > > >>> the
> > > >> > > >> > > > > > >>>>>> issue of data skew.
> > > >> > > >> > > > > > >>>>>> However, IMHO, the API level of optimizing
> > > >> revenue is
> > > >> > > >> > > different.
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> *Your optimization benefits from Flink SQL
> and
> > > >> it's
> > > >> > not
> > > >> > > >> > user's
> > > >> > > >> > > > > > >>>> faces.(If
> > > >> > > >> > > > > > >>>>> I
> > > >> > > >> > > > > > >>>>>> understand it incorrectly, please correct
> > > this.)*
> > > >> > > >> > > > > > >>>>>> *Our implementation employs it as an
> > > optimization
> > > >> > tool
> > > >> > > >> API
> > > >> > > >> > for
> > > >> > > >> > > > > > >>>>> DataStream,
> > > >> > > >> > > > > > >>>>>> it just like a local version of the keyBy
> > API.*
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Based on this, I want to say support it as a
> > > >> > DataStream
> > > >> > > >> API
> > > >> > > >> > > can
> > > >> > > >> > > > > > >>> provide
> > > >> > > >> > > > > > >>>>>> these advantages:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear semantic
> > and
> > > >> it's
> > > >> > > >> > flexible
> > > >> > > >> > > > not
> > > >> > > >> > > > > > >>> only
> > > >> > > >> > > > > > >>>>> for
> > > >> > > >> > > > > > >>>>>>   processing data skew but also for
> > implementing
> > > >> some
> > > >> > > >> user
> > > >> > > >> > > > cases,
> > > >> > > >> > > > > > >>> for
> > > >> > > >> > > > > > >>>>>>   example, if we want to calculate the
> > > >> multiple-level
> > > >> > > >> > > > aggregation,
> > > >> > > >> > > > > > >>> we
> > > >> > > >> > > > > > >>>>> can
> > > >> > > >> > > > > > >>>>>> do
> > > >> > > >> > > > > > >>>>>>   multiple-level aggregation in the local
> > > >> > aggregation:
> > > >> > > >> > > > > > >>>>>>
> > > >> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
> > > >> > > >> //
> > > >> > > >> > > here
> > > >> > > >> > > > > > >> "a"
> > > >> > > >> > > > > > >>>> is
> > > >> > > >> > > > > > >>>>> a
> > > >> > > >> > > > > > >>>>>>   sub-category, while "b" is a category,
> here
> > we
> > > >> do
> > > >> > not
> > > >> > > >> need
> > > >> > > >> > > to
> > > >> > > >> > > > > > >>>> shuffle
> > > >> > > >> > > > > > >>>>>> data
> > > >> > > >> > > > > > >>>>>>   in the network.
> > > >> > > >> > > > > > >>>>>>   - The users of DataStream API will benefit
> > > from
> > > >> > this.
> > > >> > > >> > > > Actually,
> > > >> > > >> > > > > > >> we
> > > >> > > >> > > > > > >>>>> have
> > > >> > > >> > > > > > >>>>>>   a lot of scenes need to use DataStream
> API.
> > > >> > > Currently,
> > > >> > > >> > > > > > >> DataStream
> > > >> > > >> > > > > > >>>> API
> > > >> > > >> > > > > > >>>>> is
> > > >> > > >> > > > > > >>>>>>   the cornerstone of the physical plan of
> > Flink
> > > >> SQL.
> > > >> > > >> With a
> > > >> > > >> > > > > > >>> localKeyBy
> > > >> > > >> > > > > > >>>>>> API,
> > > >> > > >> > > > > > >>>>>>   the optimization of SQL at least may use
> > this
> > > >> > > optimized
> > > >> > > >> > API,
> > > >> > > >> > > > > > >> this
> > > >> > > >> > > > > > >>>> is a
> > > >> > > >> > > > > > >>>>>>   further topic.
> > > >> > > >> > > > > > >>>>>>   - Based on the window operator, our state
> > > would
> > > >> > > benefit
> > > >> > > >> > from
> > > >> > > >> > > > > > >> Flink
> > > >> > > >> > > > > > >>>>> State
> > > >> > > >> > > > > > >>>>>>   and checkpoint, we do not need to worry
> > about
> > > >> OOM
> > > >> > and
> > > >> > > >> job
> > > >> > > >> > > > > > >> failed.
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Now, about your questions:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> 1. About our design cannot change the data
> > type
> > > >> and
> > > >> > > about
> > > >> > > >> > the
> > > >> > > >> > > > > > >>>>>> implementation of average:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy
> is
> > > an
> > > >> API
> > > >> > > >> > provides
> > > >> > > >> > > > to
> > > >> > > >> > > > > > >> the
> > > >> > > >> > > > > > >>>>> users
> > > >> > > >> > > > > > >>>>>> who use DataStream API to build their jobs.
> > > >> > > >> > > > > > >>>>>> Users should know its semantics and the
> > > difference
> > > >> > with
> > > >> > > >> > keyBy
> > > >> > > >> > > > API,
> > > >> > > >> > > > > > >> so
> > > >> > > >> > > > > > >>>> if
> > > >> > > >> > > > > > >>>>>> they want to the average aggregation, they
> > > should
> > > >> > carry
> > > >> > > >> > local
> > > >> > > >> > > > sum
> > > >> > > >> > > > > > >>>> result
> > > >> > > >> > > > > > >>>>>> and local count result.
> > > >> > > >> > > > > > >>>>>> I admit that it will be convenient to use
> > keyBy
> > > >> > > directly.
> > > >> > > >> > But
> > > >> > > >> > > we
> > > >> > > >> > > > > > >> need
> > > >> > > >> > > > > > >>>> to
> > > >> > > >> > > > > > >>>>>> pay a little price when we get some
> benefits.
> > I
> > > >> think
> > > >> > > >> this
> > > >> > > >> > > price
> > > >> > > >> > > > > is
> > > >> > > >> > > > > > >>>>>> reasonable. Considering that the DataStream
> > API
> > > >> > itself
> > > >> > > >> is a
> > > >> > > >> > > > > > >> low-level
> > > >> > > >> > > > > > >>>> API
> > > >> > > >> > > > > > >>>>>> (at least for now).
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> 2. About stateless operator and
> > > >> > > >> > > > > > >>>>>>
> `StreamOperator::prepareSnapshotPreBarrier()`:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Actually, I have discussed this opinion with
> > > >> @dianfu
> > > >> > in
> > > >> > > >> the
> > > >> > > >> > > old
> > > >> > > >> > > > > > >> mail
> > > >> > > >> > > > > > >>>>>> thread. I will copy my opinion from there:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>>   - for your design, you still need
> somewhere
> > to
> > > >> give
> > > >> > > the
> > > >> > > >> > > users
> > > >> > > >> > > > > > >>>>> configure
> > > >> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
> > > >> availability?),
> > > >> > > >> this
> > > >> > > >> > > > design
> > > >> > > >> > > > > > >>>> cannot
> > > >> > > >> > > > > > >>>>>>   guarantee a deterministic semantics (it
> will
> > > >> bring
> > > >> > > >> trouble
> > > >> > > >> > > for
> > > >> > > >> > > > > > >>>> testing
> > > >> > > >> > > > > > >>>>>> and
> > > >> > > >> > > > > > >>>>>>   debugging).
> > > >> > > >> > > > > > >>>>>>   - if the implementation depends on the
> > timing
> > > of
> > > >> > > >> > checkpoint,
> > > >> > > >> > > > it
> > > >> > > >> > > > > > >>>> would
> > > >> > > >> > > > > > >>>>>>   affect the checkpoint's progress, and the
> > > >> buffered
> > > >> > > data
> > > >> > > >> > may
> > > >> > > >> > > > > > >> cause
> > > >> > > >> > > > > > >>>> OOM
> > > >> > > >> > > > > > >>>>>>   issue. In addition, if the operator is
> > > >> stateless,
> > > >> > it
> > > >> > > >> can
> > > >> > > >> > not
> > > >> > > >> > > > > > >>> provide
> > > >> > > >> > > > > > >>>>>> fault
> > > >> > > >> > > > > > >>>>>>   tolerance.
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Best,
> > > >> > > >> > > > > > >>>>>> Vino
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Kurt Young <[hidden email]> 于2019年6月18日周二
> > > >> > 上午9:22写道：
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>>> Hi Vino,
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the general
> > > idea
> > > >> and
> > > >> > > IMO
> > > >> > > >> > it's
> > > >> > > >> > > > > > >> very
> > > >> > > >> > > > > > >>>>> useful
> > > >> > > >> > > > > > >>>>>>> feature.
> > > >> > > >> > > > > > >>>>>>> But after reading through the document, I
> > feel
> > > >> that
> > > >> > we
> > > >> > > >> may
> > > >> > > >> > > over
> > > >> > > >> > > > > > >>>> design
> > > >> > > >> > > > > > >>>>>> the
> > > >> > > >> > > > > > >>>>>>> required
> > > >> > > >> > > > > > >>>>>>> operator for proper local aggregation. The
> > main
> > > >> > reason
> > > >> > > >> is
> > > >> > > >> > we
> > > >> > > >> > > > want
> > > >> > > >> > > > > > >>> to
> > > >> > > >> > > > > > >>>>>> have a
> > > >> > > >> > > > > > >>>>>>> clear definition and behavior about the
> > "local
> > > >> keyed
> > > >> > > >> state"
> > > >> > > >> > > > which
> > > >> > > >> > > > > > >>> in
> > > >> > > >> > > > > > >>>> my
> > > >> > > >> > > > > > >>>>>>> opinion is not
> > > >> > > >> > > > > > >>>>>>> necessary for local aggregation, at least
> for
> > > >> start.
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> Another issue I noticed is the local key by
> > > >> operator
> > > >> > > >> cannot
> > > >> > > >> > > > > > >> change
> > > >> > > >> > > > > > >>>>>> element
> > > >> > > >> > > > > > >>>>>>> type, it will
> > > >> > > >> > > > > > >>>>>>> also restrict a lot of use cases which can
> be
> > > >> > benefit
> > > >> > > >> from
> > > >> > > >> > > > local
> > > >> > > >> > > > > > >>>>>>> aggregation, like "average".
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and the
> only
> > > >> thing
> > > >> > > >> need to
> > > >> > > >> > > be
> > > >> > > >> > > > > > >> done
> > > >> > > >> > > > > > >>>> is
> > > >> > > >> > > > > > >>>>>>> introduce
> > > >> > > >> > > > > > >>>>>>> a stateless lightweight operator which is
> > > >> *chained*
> > > >> > > >> before
> > > >> > > >> > > > > > >>> `keyby()`.
> > > >> > > >> > > > > > >>>>> The
> > > >> > > >> > > > > > >>>>>>> operator will flush all buffered
> > > >> > > >> > > > > > >>>>>>> elements during
> > > >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
> > > >> > > >> > > > and
> > > >> > > >> > > > > > >>>> make
> > > >> > > >> > > > > > >>>>>>> himself stateless.
> > > >> > > >> > > > > > >>>>>>> By the way, in the earlier version we also
> > did
> > > >> the
> > > >> > > >> similar
> > > >> > > >> > > > > > >> approach
> > > >> > > >> > > > > > >>>> by
> > > >> > > >> > > > > > >>>>>>> introducing a stateful
> > > >> > > >> > > > > > >>>>>>> local aggregation operator but it's not
> > > >> performed as
> > > >> > > >> well
> > > >> > > >> > as
> > > >> > > >> > > > the
> > > >> > > >> > > > > > >>>> later
> > > >> > > >> > > > > > >>>>>> one,
> > > >> > > >> > > > > > >>>>>>> and also effect the barrie
> > > >> > > >> > > > > > >>>>>>> alignment time. The later one is fairly
> > simple
> > > >> and
> > > >> > > more
> > > >> > > >> > > > > > >> efficient.
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> I would highly suggest you to consider to
> > have
> > > a
> > > >> > > >> stateless
> > > >> > > >> > > > > > >> approach
> > > >> > > >> > > > > > >>>> at
> > > >> > > >> > > > > > >>>>>> the
> > > >> > > >> > > > > > >>>>>>> first step.
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>> Kurt
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> > > >> > > >> [hidden email]>
> > > >> > > >> > > > > > >> wrote:
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>>> Hi Vino,
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)"
> vs
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> >
> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> > > >> > > >> > > > > > >> have
> > > >> > > >> > > > > > >>>> you
> > > >> > > >> > > > > > >>>>>>> done
> > > >> > > >> > > > > > >>>>>>>> some benchmark?
> > > >> > > >> > > > > > >>>>>>>> Because I'm curious about how much
> > performance
> > > >> > > >> improvement
> > > >> > > >> > > can
> > > >> > > >> > > > > > >> we
> > > >> > > >> > > > > > >>>> get
> > > >> > > >> > > > > > >>>>>> by
> > > >> > > >> > > > > > >>>>>>>> using count window as the local operator.
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>> Jark
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> > > >> > > >> > > > [hidden email]
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>>>> wrote:
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Hi Hequn,
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to
> > provide a
> > > >> tool
> > > >> > > >> which
> > > >> > > >> > > can
> > > >> > > >> > > > > > >>> let
> > > >> > > >> > > > > > >>>>>> users
> > > >> > > >> > > > > > >>>>>>> do
> > > >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The
> behavior
> > of
> > > >> the
> > > >> > > >> > > > > > >>> pre-aggregation
> > > >> > > >> > > > > > >>>>> is
> > > >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> So the three cases are different, I will
> > > >> describe
> > > >> > > them
> > > >> > > >> > one
> > > >> > > >> > > by
> > > >> > > >> > > > > > >>>> one:
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> *In this case, the result is
> event-driven,
> > > each
> > > >> > > event
> > > >> > > >> can
> > > >> > > >> > > > > > >>> produce
> > > >> > > >> > > > > > >>>>> one
> > > >> > > >> > > > > > >>>>>>> sum
> > > >> > > >> > > > > > >>>>>>>>> aggregation result and it is the latest
> one
> > > >> from
> > > >> > the
> > > >> > > >> > source
> > > >> > > >> > > > > > >>>> start.*
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> 2.
> > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have a
> > > >> problem, it
> > > >> > > >> would
> > > >> > > >> > do
> > > >> > > >> > > > > > >> the
> > > >> > > >> > > > > > >>>>> local
> > > >> > > >> > > > > > >>>>>>> sum
> > > >> > > >> > > > > > >>>>>>>>> aggregation and will produce the latest
> > > partial
> > > >> > > result
> > > >> > > >> > from
> > > >> > > >> > > > > > >> the
> > > >> > > >> > > > > > >>>>>> source
> > > >> > > >> > > > > > >>>>>>>>> start for every event. *
> > > >> > > >> > > > > > >>>>>>>>> *These latest partial results from the
> same
> > > key
> > > >> > are
> > > >> > > >> > hashed
> > > >> > > >> > > to
> > > >> > > >> > > > > > >>> one
> > > >> > > >> > > > > > >>>>>> node
> > > >> > > >> > > > > > >>>>>>> to
> > > >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
> > > >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it
> > received
> > > >> > > multiple
> > > >> > > >> > > partial
> > > >> > > >> > > > > > >>>>> results
> > > >> > > >> > > > > > >>>>>>>> (they
> > > >> > > >> > > > > > >>>>>>>>> are all calculated from the source start)
> > and
> > > >> sum
> > > >> > > them
> > > >> > > >> > will
> > > >> > > >> > > > > > >> get
> > > >> > > >> > > > > > >>>> the
> > > >> > > >> > > > > > >>>>>>> wrong
> > > >> > > >> > > > > > >>>>>>>>> result.*
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> 3.
> > > >> > > >> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a
> partial
> > > >> > > aggregation
> > > >> > > >> > > result
> > > >> > > >> > > > > > >>> for
> > > >> > > >> > > > > > >>>>>> the 5
> > > >> > > >> > > > > > >>>>>>>>> records in the count window. The partial
> > > >> > aggregation
> > > >> > > >> > > results
> > > >> > > >> > > > > > >>> from
> > > >> > > >> > > > > > >>>>> the
> > > >> > > >> > > > > > >>>>>>>> same
> > > >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> So the first case and the third case can
> > get
> > > >> the
> > > >> > > >> *same*
> > > >> > > >> > > > > > >> result,
> > > >> > > >> > > > > > >>>> the
> > > >> > > >> > > > > > >>>>>>>>> difference is the output-style and the
> > > latency.
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key API is
> > just
> > > >> an
> > > >> > > >> > > optimization
> > > >> > > >> > > > > > >>>> API.
> > > >> > > >> > > > > > >>>>> We
> > > >> > > >> > > > > > >>>>>>> do
> > > >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the user
> > has
> > > to
> > > >> > > >> > understand
> > > >> > > >> > > > > > >> its
> > > >> > > >> > > > > > >>>>>>> semantics
> > > >> > > >> > > > > > >>>>>>>>> and use it correctly.
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>>> Vino
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]>
> > > >> 于2019年6月17日周一
> > > >> > > >> > 下午4:18写道：
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> Hi Vino,
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a
> > > very
> > > >> > good
> > > >> > > >> > > feature!
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the
> > > semantics
> > > >> > for
> > > >> > > >> the
> > > >> > > >> > > > > > >>>>>> `localKeyBy`.
> > > >> > > >> > > > > > >>>>>>>> From
> > > >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API
> returns
> > > an
> > > >> > > >> instance
> > > >> > > >> > of
> > > >> > > >> > > > > > >>>>>>> `KeyedStream`
> > > >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in this
> > > case,
> > > >> > > what's
> > > >> > > >> > the
> > > >> > > >> > > > > > >>>>> semantics
> > > >> > > >> > > > > > >>>>>>> for
> > > >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the
> > > >> following
> > > >> > > code
> > > >> > > >> > share
> > > >> > > >> > > > > > >>> the
> > > >> > > >> > > > > > >>>>> same
> > > >> > > >> > > > > > >>>>>>>>> result?
> > > >> > > >> > > > > > >>>>>>>>>> and what're the differences between
> them?
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>> 2.
> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>> 3.
> > > >> > > >> > > > > > >>
> > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add this
> > into
> > > >> the
> > > >> > > >> > document.
> > > >> > > >> > > > > > >>> Thank
> > > >> > > >> > > > > > >>>>> you
> > > >> > > >> > > > > > >>>>>>>> very
> > > >> > > >> > > > > > >>>>>>>>>> much.
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> Best, Hequn
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino
> > yang <
> > > >> > > >> > > > > > >>>>> [hidden email]>
> > > >> > > >> > > > > > >>>>>>>>> wrote:
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*"
> section
> > of
> > > >> FLIP
> > > >> > > >> wiki
> > > >> > > >> > > > > > >>>> page.[1]
> > > >> > > >> > > > > > >>>>>> This
> > > >> > > >> > > > > > >>>>>>>>> mail
> > > >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has proceeded
> to
> > > the
> > > >> > > third
> > > >> > > >> > step.
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote
> > > step),
> > > >> I
> > > >> > > >> didn't
> > > >> > > >> > > > > > >> find
> > > >> > > >> > > > > > >>>> the
> > > >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the voting
> > > >> process.
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of this
> > > >> feature
> > > >> > > has
> > > >> > > >> > been
> > > >> > > >> > > > > > >>> done
> > > >> > > >> > > > > > >>>>> in
> > > >> > > >> > > > > > >>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>> old
> > > >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when
> > should
> > > I
> > > >> > start
> > > >> > > >> > > > > > >> voting?
> > > >> > > >> > > > > > >>>> Can
> > > >> > > >> > > > > > >>>>> I
> > > >> > > >> > > > > > >>>>>>>> start
> > > >> > > >> > > > > > >>>>>>>>>> now?
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>>>>> Vino
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> [1]:
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> > > >> > > >> > > > > > >>>>>>>>>>> [2]:
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email]>
> > 于2019年6月13日周四
> > > >> > > 上午9:19写道：
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your
> > > >> efforts.
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>>>>>> Leesf
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]>
> > > >> > 于2019年6月12日周三
> > > >> > > >> > > > > > >>> 下午5:46写道：
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP
> > discussion
> > > >> > thread
> > > >> > > >> > > > > > >> about
> > > >> > > >> > > > > > >>>>>>> supporting
> > > >> > > >> > > > > > >>>>>>>>>> local
> > > >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can
> effectively
> > > >> > alleviate
> > > >> > > >> data
> > > >> > > >> > > > > > >>>> skew.
> > > >> > > >> > > > > > >>>>>>> This
> > > >> > > >> > > > > > >>>>>>>> is
> > > >> > > >> > > > > > >>>>>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely
> > used
> > > to
> > > >> > > >> perform
> > > >> > > >> > > > > > >>>>>> aggregating
> > > >> > > >> > > > > > >>>>>>>>>>>> operations
> > > >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the
> > > >> elements
> > > >> > > >> that
> > > >> > > >> > > > > > >>> have
> > > >> > > >> > > > > > >>>>> the
> > > >> > > >> > > > > > >>>>>>> same
> > > >> > > >> > > > > > >>>>>>>>>> key.
> > > >> > > >> > > > > > >>>>>>>>>>>> When
> > > >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements
> with
> > > the
> > > >> > same
> > > >> > > >> key
> > > >> > > >> > > > > > >>> will
> > > >> > > >> > > > > > >>>> be
> > > >> > > >> > > > > > >>>>>>> sent
> > > >> > > >> > > > > > >>>>>>>> to
> > > >> > > >> > > > > > >>>>>>>>>> and
> > > >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> The performance of these aggregating
> > > >> > operations
> > > >> > > is
> > > >> > > >> > > > > > >> very
> > > >> > > >> > > > > > >>>>>>> sensitive
> > > >> > > >> > > > > > >>>>>>>>> to
> > > >> > > >> > > > > > >>>>>>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases
> > where
> > > >> the
> > > >> > > >> > > > > > >>> distribution
> > > >> > > >> > > > > > >>>>> of
> > > >> > > >> > > > > > >>>>>>> keys
> > > >> > > >> > > > > > >>>>>>>>>>>> follows a
> > > >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
> > > >> > > >> significantly
> > > >> > > >> > > > > > >>>>>> downgraded.
> > > >> > > >> > > > > > >>>>>>>>> More
> > > >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of
> > > >> > parallelism
> > > >> > > >> does
> > > >> > > >> > > > > > >>> not
> > > >> > > >> > > > > > >>>>> help
> > > >> > > >> > > > > > >>>>>>>> when
> > > >> > > >> > > > > > >>>>>>>>> a
> > > >> > > >> > > > > > >>>>>>>>>>> task
> > > >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted
> > > >> method
> > > >> > to
> > > >> > > >> > > > > > >> reduce
> > > >> > > >> > > > > > >>>> the
> > > >> > > >> > > > > > >>>>>>>>>> performance
> > > >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can
> decompose
> > > the
> > > >> > > >> > > > > > >> aggregating
> > > >> > > >> > > > > > >>>>>>>> operations
> > > >> > > >> > > > > > >>>>>>>>>> into
> > > >> > > >> > > > > > >>>>>>>>>>>> two
> > > >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we
> > aggregate
> > > >> the
> > > >> > > >> elements
> > > >> > > >> > > > > > >>> of
> > > >> > > >> > > > > > >>>>> the
> > > >> > > >> > > > > > >>>>>>> same
> > > >> > > >> > > > > > >>>>>>>>> key
> > > >> > > >> > > > > > >>>>>>>>>>> at
> > > >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial
> > > results.
> > > >> > Then
> > > >> > > at
> > > >> > > >> > > > > > >> the
> > > >> > > >> > > > > > >>>>> second
> > > >> > > >> > > > > > >>>>>>>>> phase,
> > > >> > > >> > > > > > >>>>>>>>>>>> these
> > > >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to receivers
> > > >> > according
> > > >> > > to
> > > >> > > >> > > > > > >>> their
> > > >> > > >> > > > > > >>>>> keys
> > > >> > > >> > > > > > >>>>>>> and
> > > >> > > >> > > > > > >>>>>>>>> are
> > > >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final result.
> > > Since
> > > >> the
> > > >> > > >> number
> > > >> > > >> > > > > > >>> of
> > > >> > > >> > > > > > >>>>>>> partial
> > > >> > > >> > > > > > >>>>>>>>>>> results
> > > >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is limited
> by
> > > the
> > > >> > > >> number of
> > > >> > > >> > > > > > >>>>>> senders,
> > > >> > > >> > > > > > >>>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be
> > reduced.
> > > >> > > >> Besides, by
> > > >> > > >> > > > > > >>>>>> reducing
> > > >> > > >> > > > > > >>>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>>>> amount
> > > >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the performance
> can
> > > be
> > > >> > > further
> > > >> > > >> > > > > > >>>>> improved.
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> https://issues.apache.org/jira/browse/FLINK-12786
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your
> > feedback!
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>>>>>>> Vino
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > > >
> > > >> > >
> > > >> >
> > > >>
> > > >
> > >
> >
>

[Piotr Nowojski-3]

Hi Jark and Vino,

I agree fully with Jark, that in order to have the discussion focused and to limit the number of parallel topics, we should first focus on one topic. We can first decide on the API and later we can discuss the runtime details. At least as long as we keep the potential requirements of the runtime part in mind while designing the API.

Regarding the automatic optimisation and proposed by Jark:

"stream.enableLocalAggregation(Trigger)”

I would be against that in the DataStream API for the reasons that Vino presented. There was a discussion thread about future directions of Table API vs DataStream API and the consensus was that the automatic optimisations are one of the dividing lines between those two, for at least a couple of reasons. Flexibility and full control over the program was one of them. Another is state migration. Having "stream.enableLocalAggregation(Trigger)” that might add some implicit operators in the job graph can cause problems with savepoint/checkpoint compatibility.

However I haven’t thought about/looked into the details of the Vino’s API proposal, so I can not fully judge it.

Piotrek

> On 26 Jun 2019, at 09:17, vino yang <[hidden email]> wrote:
>
> Hi Jark,
>
> Similar questions and responses have been repeated many times.
>
> Why didn't we spend more sections discussing the API?
>
> Because we try to reuse the ability of KeyedStream. The localKeyBy API just returns the KeyedStream, that's our design, we can get all the benefit from the KeyedStream and get further benefit from WindowedStream. The APIs come from KeyedStream and WindowedStream is long-tested and flexible. Yes, we spend much space discussing the local keyed state, that's not the goal and motivation, that's the way to implement local aggregation. It is much more complicated than the API we introduced, so we spent more section. Of course, this is the implementation level of the Operator. We also agreed to support the implementation of buffer+flush and added related instructions to the documentation. This needs to wait for the community to recognize, and if the community agrees, we will give more instructions. What's more, I have indicated before that we welcome state-related commenters to participate in the discussion, but it is not wise to modify the FLIP title.
>
> About the API of local aggregation:
>
> I don't object to ease of use is very important. But IMHO flexibility is the most important at the DataStream API level. Otherwise, what does DataStream mean? The significance of the DataStream API is that it is more flexible than Table/SQL, if it cannot provide this point then everyone would just use Table/SQL.
>
> The DataStream API should focus more on flexibility than on automatic optimization, which allows users to have more possibilities to implement complex programs and meet specific scenarios. There are a lot of programs written using the DataStream API that are far more complex than we think. It is very difficult to optimize at the API level and the benefit is very low.
>
> I want to say that we support a more generalized local aggregation. I mentioned in the previous reply that not only the UDF that implements AggregateFunction is called aggregation. In some complex scenarios, we have to support local aggregation through ProcessFunction and ProcessWindowFunction to solve the data skew problem. How do you support them in the API implementation and optimization you mentioned?
>
> Flexible APIs are arbitrarily combined to result in erroneous semantics, which does not prove that flexibility is meaningless because the user is the decision maker. I have been exemplified many times, for many APIs in DataStream, if we arbitrarily combined them, they also do not have much practical significance. So, users who use flexible APIs need to understand what they are doing and what is the right choice.
>
> I think that if we discuss this, there will be no result.
>
> @Stephan Ewen <mailto:[hidden email]> , @Aljoscha Krettek <mailto:[hidden email]> and @Piotr Nowojski <mailto:[hidden email]> Do you have further comments?
>
>
> Jark Wu <[hidden email] <mailto:[hidden email]>> 于2019年6月26日周三 上午11:46写道：
> Thanks for the long discussion Vino, Kurt, Hequn, Piotr and others,
>
> It seems that we still have some different ideas about the API
> (localKeyBy()?) and implementation details (reuse window operator? local
> keyed state?).
> And the discussion is stalled and mixed with motivation and API and
> implementation discussion.
>
> In order to make some progress in this topic, I want to summarize the
> points (pls correct me if I'm wrong or missing sth) and would suggest to
> split
>  the topic into following aspects and discuss them one by one.
>
> 1) What's the main purpose of this FLIP?
>  - From the title of this FLIP, it is to support local aggregate. However
> from the content of the FLIP, 80% are introducing a new state called local
> keyed state.
>  - If we mainly want to introduce local keyed state, then we should
> re-title the FLIP and involve in more people who works on state.
>  - If we mainly want to support local aggregate, then we can jump to step 2
> to discuss the API design.
>
> 2) What does the API look like?
>  - Vino proposed to use "localKeyBy()" to do local process, the output of
> local process is the result type of aggregate function.
>   a) For non-windowed aggregate:
> input.localKeyBy(..).aggregate(agg1).keyBy(..).aggregate(agg2)    **NOT
> SUPPORT**
>   b) For windowed aggregate:
> input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)
>
> 3) What's the implementation detail?
>  - may reuse window operator or not.
>  - may introduce a new state concepts or not.
>  - may not have state in local operator by flushing buffers in
> prepareSnapshotPreBarrier
>  - and so on...
>  - we can discuss these later when we reach a consensus on API
>
> --------------------
>
> Here are my thoughts:
>
> 1) Purpose of this FLIP
>  - From the motivation section in the FLIP, I think the purpose is to
> support local aggregation to solve the data skew issue.
>    Then I think we should focus on how to provide a easy to use and clear
> API to support **local aggregation**.
>  - Vino's point is centered around the local keyed state API (or
> localKeyBy()), and how to leverage the local keyed state API to support
> local aggregation.
>    But I'm afraid it's not a good way to design API for local aggregation.
>
> 2) local aggregation API
>  - IMO, the method call chain
> "input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)"
> is not such easy to use.
>    Because we have to provide two implementation for an aggregation (one
> for partial agg, another for final agg). And we have to take care of
>    the first window call, an inappropriate window call will break the
> sematics.
>  - From my point of view, local aggregation is a mature concept which
> should output the intermediate accumulator (ACC) in the past period of time
> (a trigger).
>    And the downstream final aggregation will merge ACCs received from local
> side, and output the current final result.
>  - The current "AggregateFunction" API in DataStream already has the
> accumulator type and "merge" method. So the only thing user need to do is
> how to enable
>    local aggregation opimization and set a trigger.
>  - One idea comes to my head is that, assume we have a windowed aggregation
> stream: "val stream = input.keyBy().window(w).aggregate(agg)". We can
> provide an API on the stream.
>    For exmaple, "stream.enableLocalAggregation(Trigger)", the trigger can
> be "ContinuousEventTimeTrigger.of(Time.of(Time.minutes(1)))". Then it will
> be optmized into
>    local operator + final operator, and local operator will combine records
> every minute on event time.
>  - In this way, there is only one line added, and the output is the same
> with before, because it is just an opimization.
>
>
> Regards,
> Jark
>
>
>
> On Tue, 25 Jun 2019 at 14:34, vino yang <[hidden email] <mailto:[hidden email]>> wrote:
>
> > Hi Kurt,
> >
> > Answer your questions:
> >
> > a) Sorry, I just updated the Google doc, still have no time update the
> > FLIP, will update FLIP as soon as possible.
> > About your description at this point, I have a question, what does it mean:
> > how do we combine with
> > `AggregateFunction`?
> >
> > I have shown you the examples which Flink has supported:
> >
> >    - input.localKeyBy(0).aggregate()
> >    - input.localKeyBy(0).window().aggregate()
> >
> > You can show me a example about how do we combine with `AggregateFuncion`
> > through your localAggregate API.
> >
> > About the example, how to do the local aggregation for AVG, consider this
> > code:
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > *DataStream<Tuple2<String, Long>> source = null; source .localKeyBy(0)
> > .timeWindow(Time.seconds(60)) .aggregate(agg1, new
> > WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>, String,
> > TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60)) .aggregate(agg2,
> > new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, String,
> > TimeWindow>());*
> >
> > *agg1:*
> > *signature : new AggregateFunction<Tuple2<String, Long>, Tuple2<Long,
> > Long>, Tuple2<Long, Long>>() {}*
> > *input param type: Tuple2<String, Long> f0: key, f1: value*
> > *intermediate result type: Tuple2<Long, Long>, f0: local aggregated sum;
> > f1: local aggregated count*
> > *output param type:  Tuple2<Long, Long>, f0: local aggregated sum; f1:
> > local aggregated count*
> >
> > *agg2:*
> > *signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
> > Tuple2<String, Long>>() {},*
> > *input param type: Tuple3<String, Long, Long>, f0: key, f1:  local
> > aggregated sum; f2: local aggregated count*
> >
> > *intermediate result type: Long  avg result*
> > *output param type:  Tuple2<String, Long> f0: key, f1 avg result*
> >
> > For sliding window, we just need to change the window type if users want to
> > do.
> > Again, we try to give the design and implementation in the DataStream
> > level. So I believe we can match all the requirements(It's just that the
> > implementation may be different) comes from the SQL level.
> >
> > b) Yes, Theoretically, your thought is right. But in reality, it cannot
> > bring many benefits.
> > If we want to get the benefits from the window API, while we do not reuse
> > the window operator? And just copy some many duplicated code to another
> > operator?
> >
> > c) OK, I agree to let the state backend committers join this discussion.
> >
> > Best,
> > Vino
> >
> >
> > Kurt Young <[hidden email] <mailto:[hidden email]>> 于2019年6月24日周一 下午6:53写道：
> >
> > > Hi vino,
> > >
> > > One thing to add,  for a), I think use one or two examples like how to do
> > > local aggregation on a sliding window,
> > > and how do we do local aggregation on an unbounded aggregate, will do a
> > lot
> > > help.
> > >
> > > Best,
> > > Kurt
> > >
> > >
> > > On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email] <mailto:[hidden email]>> wrote:
> > >
> > > > Hi vino,
> > > >
> > > > I think there are several things still need discussion.
> > > >
> > > > a) We all agree that we should first go with a unified abstraction, but
> > > > the abstraction is not reflected by the FLIP.
> > > > If your answer is "locakKeyBy" API, then I would ask how do we combine
> > > > with `AggregateFunction`, and how do
> > > > we do proper local aggregation for those have different intermediate
> > > > result type, like AVG. Could you add these
> > > > to the document?
> > > >
> > > > b) From implementation side, reusing window operator is one of the
> > > > possible solutions, but not we base on window
> > > > operator to have two different implementations. What I understanding
> > is,
> > > > one of the possible implementations should
> > > > not touch window operator.
> > > >
> > > > c) 80% of your FLIP content is actually describing how do we support
> > > local
> > > > keyed state. I don't know if this is necessary
> > > > to introduce at the first step and we should also involve committers
> > work
> > > > on state backend to share their thoughts.
> > > >
> > > > Best,
> > > > Kurt
> > > >
> > > >
> > > > On Mon, Jun 24, 2019 at 5:17 PM vino yang <[hidden email] <mailto:[hidden email]>>
> > wrote:
> > > >
> > > >> Hi Kurt,
> > > >>
> > > >> You did not give more further different opinions, so I thought you
> > have
> > > >> agreed with the design after we promised to support two kinds of
> > > >> implementation.
> > > >>
> > > >> In API level, we have answered your question about pass an
> > > >> AggregateFunction to do the aggregation. No matter introduce
> > localKeyBy
> > > >> API
> > > >> or not, we can support AggregateFunction.
> > > >>
> > > >> So what's your different opinion now? Can you share it with us?
> > > >>
> > > >> Best,
> > > >> Vino
> > > >>
> > > >> Kurt Young <[hidden email] <mailto:[hidden email]>> 于2019年6月24日周一 下午4:24写道：
> > > >>
> > > >> > Hi vino,
> > > >> >
> > > >> > Sorry I don't see the consensus about reusing window operator and
> > keep
> > > >> the
> > > >> > API design of localKeyBy. But I think we should definitely more
> > > thoughts
> > > >> > about this topic.
> > > >> >
> > > >> > I also try to loop in Stephan for this discussion.
> > > >> >
> > > >> > Best,
> > > >> > Kurt
> > > >> >
> > > >> >
> > > >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <[hidden email] <mailto:[hidden email]>>
> > > >> wrote:
> > > >> >
> > > >> > > Hi all,
> > > >> > >
> > > >> > > I am happy we have a wonderful discussion and received many
> > valuable
> > > >> > > opinions in the last few days.
> > > >> > >
> > > >> > > Now, let me try to summarize what we have reached consensus about
> > > the
> > > >> > > changes in the design.
> > > >> > >
> > > >> > >    - provide a unified abstraction to support two kinds of
> > > >> > implementation;
> > > >> > >    - reuse WindowOperator and try to enhance it so that we can
> > make
> > > >> the
> > > >> > >    intermediate result of the local aggregation can be buffered
> > and
> > > >> > > flushed to
> > > >> > >    support two kinds of implementation;
> > > >> > >    - keep the API design of localKeyBy, but declare the disabled
> > > some
> > > >> > APIs
> > > >> > >    we cannot support currently, and provide a configurable API for
> > > >> users
> > > >> > to
> > > >> > >    choose how to handle intermediate result;
> > > >> > >
> > > >> > > The above three points have been updated in the design doc. Any
> > > >> > > questions, please let me know.
> > > >> > >
> > > >> > > @Aljoscha Krettek <[hidden email] <mailto:[hidden email]>> What do you think? Any
> > > >> further
> > > >> > > comments?
> > > >> > >
> > > >> > > Best,
> > > >> > > Vino
> > > >> > >
> > > >> > > vino yang <[hidden email] <mailto:[hidden email]>> 于2019年6月20日周四 下午2:02写道：
> > > >> > >
> > > >> > > > Hi Kurt,
> > > >> > > >
> > > >> > > > Thanks for your comments.
> > > >> > > >
> > > >> > > > It seems we come to a consensus that we should alleviate the
> > > >> > performance
> > > >> > > > degraded by data skew with local aggregation. In this FLIP, our
> > > key
> > > >> > > > solution is to introduce local keyed partition to achieve this
> > > goal.
> > > >> > > >
> > > >> > > > I also agree that we can benefit a lot from the usage of
> > > >> > > > AggregateFunction. In combination with localKeyBy, We can easily
> > > >> use it
> > > >> > > to
> > > >> > > > achieve local aggregation:
> > > >> > > >
> > > >> > > >    - input.localKeyBy(0).aggregate()
> > > >> > > >    - input.localKeyBy(0).window().aggregate()
> > > >> > > >
> > > >> > > >
> > > >> > > > I think the only problem here is the choices between
> > > >> > > >
> > > >> > > >    - (1) Introducing a new primitive called localKeyBy and
> > > implement
> > > >> > > >    local aggregation with existing operators, or
> > > >> > > >    - (2) Introducing an operator called localAggregation which
> > is
> > > >> > > >    composed of a key selector, a window-like operator, and an
> > > >> aggregate
> > > >> > > >    function.
> > > >> > > >
> > > >> > > >
> > > >> > > > There may exist some optimization opportunities by providing a
> > > >> > composited
> > > >> > > > interface for local aggregation. But at the same time, in my
> > > >> opinion,
> > > >> > we
> > > >> > > > lose flexibility (Or we need certain efforts to achieve the same
> > > >> > > > flexibility).
> > > >> > > >
> > > >> > > > As said in the previous mails, we have many use cases where the
> > > >> > > > aggregation is very complicated and cannot be performed with
> > > >> > > > AggregateFunction. For example, users may perform windowed
> > > >> aggregations
> > > >> > > > according to time, data values, or even external storage.
> > > Typically,
> > > >> > they
> > > >> > > > now use KeyedProcessFunction or customized triggers to implement
> > > >> these
> > > >> > > > aggregations. It's not easy to address data skew in such cases
> > > with
> > > >> a
> > > >> > > > composited interface for local aggregation.
> > > >> > > >
> > > >> > > > Given that Data Stream API is exactly targeted at these cases
> > > where
> > > >> the
> > > >> > > > application logic is very complicated and optimization does not
> > > >> > matter, I
> > > >> > > > think it's a better choice to provide a relatively low-level and
> > > >> > > canonical
> > > >> > > > interface.
> > > >> > > >
> > > >> > > > The composited interface, on the other side, may be a good
> > choice
> > > in
> > > >> > > > declarative interfaces, including SQL and Table API, as it
> > allows
> > > >> more
> > > >> > > > optimization opportunities.
> > > >> > > >
> > > >> > > > Best,
> > > >> > > > Vino
> > > >> > > >
> > > >> > > >
> > > >> > > > Kurt Young <[hidden email] <mailto:[hidden email]>> 于2019年6月20日周四 上午10:15写道：
> > > >> > > >
> > > >> > > >> Hi all,
> > > >> > > >>
> > > >> > > >> As vino said in previous emails, I think we should first
> > discuss
> > > >> and
> > > >> > > >> decide
> > > >> > > >> what kind of use cases this FLIP want to
> > > >> > > >> resolve, and what the API should look like. From my side, I
> > think
> > > >> this
> > > >> > > is
> > > >> > > >> probably the root cause of current divergence.
> > > >> > > >>
> > > >> > > >> My understand is (from the FLIP title and motivation section of
> > > the
> > > >> > > >> document), we want to have a proper support of
> > > >> > > >> local aggregation, or pre aggregation. This is not a very new
> > > idea,
> > > >> > most
> > > >> > > >> SQL engine already did this improvement. And
> > > >> > > >> the core concept about this is, there should be an
> > > >> AggregateFunction,
> > > >> > no
> > > >> > > >> matter it's a Flink runtime's AggregateFunction or
> > > >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have
> > concept
> > > >> of
> > > >> > > >> intermediate data type, sometimes we call it ACC.
> > > >> > > >> I quickly went through the POC piotr did before [1], it also
> > > >> directly
> > > >> > > uses
> > > >> > > >> AggregateFunction.
> > > >> > > >>
> > > >> > > >> But the thing is, after reading the design of this FLIP, I
> > can't
> > > >> help
> > > >> > > >> myself feeling that this FLIP is not targeting to have a proper
> > > >> > > >> local aggregation support. It actually want to introduce
> > another
> > > >> > > concept:
> > > >> > > >> LocalKeyBy, and how to split and merge local key groups,
> > > >> > > >> and how to properly support state on local key. Local
> > aggregation
> > > >> just
> > > >> > > >> happened to be one possible use case of LocalKeyBy.
> > > >> > > >> But it lacks supporting the essential concept of local
> > > aggregation,
> > > >> > > which
> > > >> > > >> is intermediate data type. Without this, I really don't thing
> > > >> > > >> it is a good fit of local aggregation.
> > > >> > > >>
> > > >> > > >> Here I want to make sure of the scope or the goal about this
> > > FLIP,
> > > >> do
> > > >> > we
> > > >> > > >> want to have a proper local aggregation engine, or we
> > > >> > > >> just want to introduce a new concept called LocalKeyBy?
> > > >> > > >>
> > > >> > > >> [1]: https://github.com/apache/flink/pull/4626 <https://github.com/apache/flink/pull/4626>
> > > >> > > >>
> > > >> > > >> Best,
> > > >> > > >> Kurt
> > > >> > > >>
> > > >> > > >>
> > > >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <
> > [hidden email] <mailto:[hidden email]>
> > > >
> > > >> > > wrote:
> > > >> > > >>
> > > >> > > >> > Hi Hequn,
> > > >> > > >> >
> > > >> > > >> > Thanks for your comments!
> > > >> > > >> >
> > > >> > > >> > I agree that allowing local aggregation reusing window API
> > and
> > > >> > > refining
> > > >> > > >> > window operator to make it match both requirements (come from
> > > our
> > > >> > and
> > > >> > > >> Kurt)
> > > >> > > >> > is a good decision!
> > > >> > > >> >
> > > >> > > >> > Concerning your questions:
> > > >> > > >> >
> > > >> > > >> > 1. The result of input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > may
> > > >> be
> > > >> > > >> > meaningless.
> > > >> > > >> >
> > > >> > > >> > Yes, it does not make sense in most cases. However, I also
> > want
> > > >> to
> > > >> > > note
> > > >> > > >> > users should know the right semantics of localKeyBy and use
> > it
> > > >> > > >> correctly.
> > > >> > > >> > Because this issue also exists for the global keyBy, consider
> > > >> this
> > > >> > > >> example:
> > > >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also
> > > >> > meaningless.
> > > >> > > >> >
> > > >> > > >> > 2. About the semantics of
> > > >> > > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> > > >> > > >> >
> > > >> > > >> > Good catch! I agree with you that it's not good to enable all
> > > >> > > >> > functionalities for localKeyBy from KeyedStream.
> > > >> > > >> > Currently, We do not support some APIs such as
> > > >> > > >> > connect/join/intervalJoin/coGroup. This is due to that we
> > force
> > > >> the
> > > >> > > >> > operators on LocalKeyedStreams chained with the inputs.
> > > >> > > >> >
> > > >> > > >> > Best,
> > > >> > > >> > Vino
> > > >> > > >> >
> > > >> > > >> >
> > > >> > > >> > Hequn Cheng <[hidden email] <mailto:[hidden email]>> 于2019年6月19日周三 下午3:42写道：
> > > >> > > >> >
> > > >> > > >> > > Hi,
> > > >> > > >> > >
> > > >> > > >> > > Thanks a lot for your great discussion and great to see
> > that
> > > >> some
> > > >> > > >> > agreement
> > > >> > > >> > > has been reached on the "local aggregate engine"!
> > > >> > > >> > >
> > > >> > > >> > > ===> Considering the abstract engine,
> > > >> > > >> > > I'm thinking is it valuable for us to extend the current
> > > >> window to
> > > >> > > >> meet
> > > >> > > >> > > both demands raised by Kurt and Vino? There are some
> > benefits
> > > >> we
> > > >> > can
> > > >> > > >> get:
> > > >> > > >> > >
> > > >> > > >> > > 1. The interfaces of the window are complete and clear.
> > With
> > > >> > > windows,
> > > >> > > >> we
> > > >> > > >> > > can define a lot of ways to split the data and perform
> > > >> different
> > > >> > > >> > > computations.
> > > >> > > >> > > 2. We can also leverage the window to do miniBatch for the
> > > >> global
> > > >> > > >> > > aggregation, i.e, we can use the window to bundle data
> > belong
> > > >> to
> > > >> > the
> > > >> > > >> same
> > > >> > > >> > > key, for every bundle we only need to read and write once
> > > >> state.
> > > >> > > This
> > > >> > > >> can
> > > >> > > >> > > greatly reduce state IO and improve performance.
> > > >> > > >> > > 3. A lot of other use cases can also benefit from the
> > window
> > > >> base
> > > >> > on
> > > >> > > >> > memory
> > > >> > > >> > > or stateless.
> > > >> > > >> > >
> > > >> > > >> > > ===> As for the API,
> > > >> > > >> > > I think it is good to make our API more flexible. However,
> > we
> > > >> may
> > > >> > > >> need to
> > > >> > > >> > > make our API meaningful.
> > > >> > > >> > >
> > > >> > > >> > > Take my previous reply as an example,
> > > >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result may
> > be
> > > >> > > >> > meaningless.
> > > >> > > >> > > Another example I find is the intervalJoin, e.g.,
> > > >> > > >> > > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
> > > >> this
> > > >> > > >> case, it
> > > >> > > >> > > will bring problems if input1 and input2 share different
> > > >> > > parallelism.
> > > >> > > >> We
> > > >> > > >> > > don't know which input should the join chained with? Even
> > if
> > > >> they
> > > >> > > >> share
> > > >> > > >> > the
> > > >> > > >> > > same parallelism, it's hard to tell what the join is doing.
> > > >> There
> > > >> > > are
> > > >> > > >> > maybe
> > > >> > > >> > > some other problems.
> > > >> > > >> > >
> > > >> > > >> > > From this point of view, it's at least not good to enable
> > all
> > > >> > > >> > > functionalities for localKeyBy from KeyedStream?
> > > >> > > >> > >
> > > >> > > >> > > Great to also have your opinions.
> > > >> > > >> > >
> > > >> > > >> > > Best, Hequn
> > > >> > > >> > >
> > > >> > > >> > >
> > > >> > > >> > >
> > > >> > > >> > >
> > > >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
> > > >> [hidden email] <mailto:[hidden email]>
> > > >> > >
> > > >> > > >> > wrote:
> > > >> > > >> > >
> > > >> > > >> > > > Hi Kurt and Piotrek,
> > > >> > > >> > > >
> > > >> > > >> > > > Thanks for your comments.
> > > >> > > >> > > >
> > > >> > > >> > > > I agree that we can provide a better abstraction to be
> > > >> > compatible
> > > >> > > >> with
> > > >> > > >> > > two
> > > >> > > >> > > > different implementations.
> > > >> > > >> > > >
> > > >> > > >> > > > First of all, I think we should consider what kind of
> > > >> scenarios
> > > >> > we
> > > >> > > >> need
> > > >> > > >> > > to
> > > >> > > >> > > > support in *API* level?
> > > >> > > >> > > >
> > > >> > > >> > > > We have some use cases which need to a customized
> > > aggregation
> > > >> > > >> through
> > > >> > > >> > > > KeyedProcessFunction, (in the usage of our
> > > localKeyBy.window
> > > >> > they
> > > >> > > >> can
> > > >> > > >> > use
> > > >> > > >> > > > ProcessWindowFunction).
> > > >> > > >> > > >
> > > >> > > >> > > > Shall we support these flexible use scenarios?
> > > >> > > >> > > >
> > > >> > > >> > > > Best,
> > > >> > > >> > > > Vino
> > > >> > > >> > > >
> > > >> > > >> > > > Kurt Young <[hidden email] <mailto:[hidden email]>> 于2019年6月18日周二 下午8:37写道：
> > > >> > > >> > > >
> > > >> > > >> > > > > Hi Piotr,
> > > >> > > >> > > > >
> > > >> > > >> > > > > Thanks for joining the discussion. Make “local
> > > aggregation"
> > > >> > > >> abstract
> > > >> > > >> > > > enough
> > > >> > > >> > > > > sounds good to me, we could
> > > >> > > >> > > > > implement and verify alternative solutions for use
> > cases
> > > of
> > > >> > > local
> > > >> > > >> > > > > aggregation. Maybe we will find both solutions
> > > >> > > >> > > > > are appropriate for different scenarios.
> > > >> > > >> > > > >
> > > >> > > >> > > > > Starting from a simple one sounds a practical way to
> > go.
> > > >> What
> > > >> > do
> > > >> > > >> you
> > > >> > > >> > > > think,
> > > >> > > >> > > > > vino?
> > > >> > > >> > > > >
> > > >> > > >> > > > > Best,
> > > >> > > >> > > > > Kurt
> > > >> > > >> > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> > > >> > > >> [hidden email] <mailto:[hidden email]>>
> > > >> > > >> > > > > wrote:
> > > >> > > >> > > > >
> > > >> > > >> > > > > > Hi Kurt and Vino,
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > I think there is a trade of hat we need to consider
> > for
> > > >> the
> > > >> > > >> local
> > > >> > > >> > > > > > aggregation.
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > Generally speaking I would agree with Kurt about
> > local
> > > >> > > >> > > aggregation/pre
> > > >> > > >> > > > > > aggregation not using Flink's state flush the
> > operator
> > > >> on a
> > > >> > > >> > > checkpoint.
> > > >> > > >> > > > > > Network IO is usually cheaper compared to Disks IO.
> > > This
> > > >> has
> > > >> > > >> > however
> > > >> > > >> > > > > couple
> > > >> > > >> > > > > > of issues:
> > > >> > > >> > > > > > 1. It can explode number of in-flight records during
> > > >> > > checkpoint
> > > >> > > >> > > barrier
> > > >> > > >> > > > > > alignment, making checkpointing slower and decrease
> > the
> > > >> > actual
> > > >> > > >> > > > > throughput.
> > > >> > > >> > > > > > 2. This trades Disks IO on the local aggregation
> > > machine
> > > >> > with
> > > >> > > >> CPU
> > > >> > > >> > > (and
> > > >> > > >> > > > > > Disks IO in case of RocksDB) on the final aggregation
> > > >> > machine.
> > > >> > > >> This
> > > >> > > >> > > is
> > > >> > > >> > > > > > fine, as long there is no huge data skew. If there is
> > > >> only a
> > > >> > > >> > handful
> > > >> > > >> > > > (or
> > > >> > > >> > > > > > even one single) hot keys, it might be better to keep
> > > the
> > > >> > > >> > persistent
> > > >> > > >> > > > > state
> > > >> > > >> > > > > > in the LocalAggregationOperator to offload final
> > > >> aggregation
> > > >> > > as
> > > >> > > >> > much
> > > >> > > >> > > as
> > > >> > > >> > > > > > possible.
> > > >> > > >> > > > > > 3. With frequent checkpointing local aggregation
> > > >> > effectiveness
> > > >> > > >> > would
> > > >> > > >> > > > > > degrade.
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > I assume Kurt is correct, that in your use cases
> > > >> stateless
> > > >> > > >> operator
> > > >> > > >> > > was
> > > >> > > >> > > > > > behaving better, but I could easily see other use
> > cases
> > > >> as
> > > >> > > well.
> > > >> > > >> > For
> > > >> > > >> > > > > > example someone is already using RocksDB, and his job
> > > is
> > > >> > > >> > bottlenecked
> > > >> > > >> > > > on
> > > >> > > >> > > > > a
> > > >> > > >> > > > > > single window operator instance because of the data
> > > >> skew. In
> > > >> > > >> that
> > > >> > > >> > > case
> > > >> > > >> > > > > > stateful local aggregation would be probably a better
> > > >> > choice.
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > Because of that, I think we should eventually provide
> > > >> both
> > > >> > > >> versions
> > > >> > > >> > > and
> > > >> > > >> > > > > in
> > > >> > > >> > > > > > the initial version we should at least make the
> > “local
> > > >> > > >> aggregation
> > > >> > > >> > > > > engine”
> > > >> > > >> > > > > > abstract enough, that one could easily provide
> > > different
> > > >> > > >> > > implementation
> > > >> > > >> > > > > > strategy.
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > Piotrek
> > > >> > > >> > > > > >
> > > >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
> > > [hidden email] <mailto:[hidden email]>
> > > >> >
> > > >> > > >> wrote:
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > Hi,
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > For the trigger, it depends on what operator we
> > want
> > > to
> > > >> > use
> > > >> > > >> under
> > > >> > > >> > > the
> > > >> > > >> > > > > > API.
> > > >> > > >> > > > > > > If we choose to use window operator,
> > > >> > > >> > > > > > > we should also use window's trigger. However, I
> > also
> > > >> think
> > > >> > > >> reuse
> > > >> > > >> > > > window
> > > >> > > >> > > > > > > operator for this scenario may not be
> > > >> > > >> > > > > > > the best choice. The reasons are the following:
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > 1. As a lot of people already pointed out, window
> > > >> relies
> > > >> > > >> heavily
> > > >> > > >> > on
> > > >> > > >> > > > > state
> > > >> > > >> > > > > > > and it will definitely effect performance. You can
> > > >> > > >> > > > > > > argue that one can use heap based statebackend, but
> > > >> this
> > > >> > > will
> > > >> > > >> > > > introduce
> > > >> > > >> > > > > > > extra coupling. Especially we have a chance to
> > > >> > > >> > > > > > > design a pure stateless operator.
> > > >> > > >> > > > > > > 2. The window operator is *the most* complicated
> > > >> operator
> > > >> > > >> Flink
> > > >> > > >> > > > > currently
> > > >> > > >> > > > > > > have. Maybe we only need to pick a subset of
> > > >> > > >> > > > > > > window operator to achieve the goal, but once the
> > > user
> > > >> > wants
> > > >> > > >> to
> > > >> > > >> > > have
> > > >> > > >> > > > a
> > > >> > > >> > > > > > deep
> > > >> > > >> > > > > > > look at the localAggregation operator, it's still
> > > >> > > >> > > > > > > hard to find out what's going on under the window
> > > >> > operator.
> > > >> > > >> For
> > > >> > > >> > > > > > simplicity,
> > > >> > > >> > > > > > > I would also recommend we introduce a dedicated
> > > >> > > >> > > > > > > lightweight operator, which also much easier for a
> > > >> user to
> > > >> > > >> learn
> > > >> > > >> > > and
> > > >> > > >> > > > > use.
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > For your question about increasing the burden in
> > > >> > > >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`, the
> > > only
> > > >> > > thing
> > > >> > > >> > this
> > > >> > > >> > > > > > function
> > > >> > > >> > > > > > > need
> > > >> > > >> > > > > > > to do is output all the partial results, it's
> > purely
> > > >> cpu
> > > >> > > >> > workload,
> > > >> > > >> > > > not
> > > >> > > >> > > > > > > introducing any IO. I want to point out that even
> > if
> > > we
> > > >> > have
> > > >> > > >> this
> > > >> > > >> > > > > > > cost, we reduced another barrier align cost of the
> > > >> > operator,
> > > >> > > >> > which
> > > >> > > >> > > is
> > > >> > > >> > > > > the
> > > >> > > >> > > > > > > sync flush stage of the state, if you introduced
> > > state.
> > > >> > This
> > > >> > > >> > > > > > > flush actually will introduce disk IO, and I think
> > > it's
> > > >> > > >> worthy to
> > > >> > > >> > > > > > exchange
> > > >> > > >> > > > > > > this cost with purely CPU workload. And we do have
> > > some
> > > >> > > >> > > > > > > observations about these two behavior (as i said
> > > >> before,
> > > >> > we
> > > >> > > >> > > actually
> > > >> > > >> > > > > > > implemented both solutions), the stateless one
> > > actually
> > > >> > > >> performs
> > > >> > > >> > > > > > > better both in performance and barrier align time.
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > Best,
> > > >> > > >> > > > > > > Kurt
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> > > >> > > >> [hidden email] <mailto:[hidden email]>
> > > >> > > >> > >
> > > >> > > >> > > > > wrote:
> > > >> > > >> > > > > > >
> > > >> > > >> > > > > > >> Hi Kurt,
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> Thanks for your example. Now, it looks more
> > clearly
> > > >> for
> > > >> > me.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> From your example code snippet, I saw the
> > > >> localAggregate
> > > >> > > API
> > > >> > > >> has
> > > >> > > >> > > > three
> > > >> > > >> > > > > > >> parameters:
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >>   1. key field
> > > >> > > >> > > > > > >>   2. PartitionAvg
> > > >> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes from
> > > window
> > > >> > > >> package?
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> I will compare our and your design from API and
> > > >> operator
> > > >> > > >> level:
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> *From the API level:*
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> As I replied to @dianfu in the old email
> > thread,[1]
> > > >> the
> > > >> > > >> Window
> > > >> > > >> > API
> > > >> > > >> > > > can
> > > >> > > >> > > > > > >> provide the second and the third parameter right
> > > now.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> If you reuse specified interface or class, such as
> > > >> > > *Trigger*
> > > >> > > >> or
> > > >> > > >> > > > > > >> *CounterTrigger* provided by window package, but
> > do
> > > >> not
> > > >> > use
> > > >> > > >> > window
> > > >> > > >> > > > > API,
> > > >> > > >> > > > > > >> it's not reasonable.
> > > >> > > >> > > > > > >> And if you do not reuse these interface or class,
> > > you
> > > >> > would
> > > >> > > >> need
> > > >> > > >> > > to
> > > >> > > >> > > > > > >> introduce more things however they are looked
> > > similar
> > > >> to
> > > >> > > the
> > > >> > > >> > > things
> > > >> > > >> > > > > > >> provided by window package.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> The window package has provided several types of
> > the
> > > >> > window
> > > >> > > >> and
> > > >> > > >> > > many
> > > >> > > >> > > > > > >> triggers and let users customize it. What's more,
> > > the
> > > >> > user
> > > >> > > is
> > > >> > > >> > more
> > > >> > > >> > > > > > familiar
> > > >> > > >> > > > > > >> with Window API.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> This is the reason why we just provide localKeyBy
> > > API
> > > >> and
> > > >> > > >> reuse
> > > >> > > >> > > the
> > > >> > > >> > > > > > window
> > > >> > > >> > > > > > >> API. It reduces unnecessary components such as
> > > >> triggers
> > > >> > and
> > > >> > > >> the
> > > >> > > >> > > > > > mechanism
> > > >> > > >> > > > > > >> of buffer (based on count num or time).
> > > >> > > >> > > > > > >> And it has a clear and easy to understand
> > semantics.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> *From the operator level:*
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> We reused window operator, so we can get all the
> > > >> benefits
> > > >> > > >> from
> > > >> > > >> > > state
> > > >> > > >> > > > > and
> > > >> > > >> > > > > > >> checkpoint.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> From your design, you named the operator under
> > > >> > > localAggregate
> > > >> > > >> > API
> > > >> > > >> > > > is a
> > > >> > > >> > > > > > >> *stateless* operator. IMO, it is still a state, it
> > > is
> > > >> > just
> > > >> > > >> not
> > > >> > > >> > > Flink
> > > >> > > >> > > > > > >> managed state.
> > > >> > > >> > > > > > >> About the memory buffer (I think it's still not
> > very
> > > >> > clear,
> > > >> > > >> if
> > > >> > > >> > you
> > > >> > > >> > > > > have
> > > >> > > >> > > > > > >> time, can you give more detail information or
> > answer
> > > >> my
> > > >> > > >> > > questions),
> > > >> > > >> > > > I
> > > >> > > >> > > > > > have
> > > >> > > >> > > > > > >> some questions:
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >>   - if it just a raw JVM heap memory buffer, how
> > to
> > > >> > support
> > > >> > > >> > fault
> > > >> > > >> > > > > > >>   tolerance, if the job is configured EXACTLY-ONCE
> > > >> > semantic
> > > >> > > >> > > > guarantee?
> > > >> > > >> > > > > > >>   - if you thought the memory buffer(non-Flink
> > > state),
> > > >> > has
> > > >> > > >> > better
> > > >> > > >> > > > > > >>   performance. In our design, users can also
> > config
> > > >> HEAP
> > > >> > > >> state
> > > >> > > >> > > > backend
> > > >> > > >> > > > > > to
> > > >> > > >> > > > > > >>   provide the performance close to your mechanism.
> > > >> > > >> > > > > > >>   - `StreamOperator::prepareSnapshotPreBarrier()`
> > > >> related
> > > >> > > to
> > > >> > > >> the
> > > >> > > >> > > > > timing
> > > >> > > >> > > > > > of
> > > >> > > >> > > > > > >>   snapshot. IMO, the flush action should be a
> > > >> > synchronized
> > > >> > > >> > action?
> > > >> > > >> > > > (if
> > > >> > > >> > > > > > >> not,
> > > >> > > >> > > > > > >>   please point out my mistake) I still think we
> > > should
> > > >> > not
> > > >> > > >> > depend
> > > >> > > >> > > on
> > > >> > > >> > > > > the
> > > >> > > >> > > > > > >>   timing of checkpoint. Checkpoint related
> > > operations
> > > >> are
> > > >> > > >> > inherent
> > > >> > > >> > > > > > >>   performance sensitive, we should not increase
> > its
> > > >> > burden
> > > >> > > >> > > anymore.
> > > >> > > >> > > > > Our
> > > >> > > >> > > > > > >>   implementation based on the mechanism of Flink's
> > > >> > > >> checkpoint,
> > > >> > > >> > > which
> > > >> > > >> > > > > can
> > > >> > > >> > > > > > >>   benefit from the asnyc snapshot and incremental
> > > >> > > checkpoint.
> > > >> > > >> > IMO,
> > > >> > > >> > > > the
> > > >> > > >> > > > > > >>   performance is not a problem, and we also do not
> > > >> find
> > > >> > the
> > > >> > > >> > > > > performance
> > > >> > > >> > > > > > >> issue
> > > >> > > >> > > > > > >>   in our production.
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> [1]:
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> > http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308 <http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> Best,
> > > >> > > >> > > > > > >> Vino
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >> Kurt Young <[hidden email] <mailto:[hidden email]>> 于2019年6月18日周二
> > > 下午2:27写道：
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > > >>> Yeah, sorry for not expressing myself clearly. I
> > > will
> > > >> > try
> > > >> > > to
> > > >> > > >> > > > provide
> > > >> > > >> > > > > > more
> > > >> > > >> > > > > > >>> details to make sure we are on the same page.
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> For DataStream API, it shouldn't be optimized
> > > >> > > automatically.
> > > >> > > >> > You
> > > >> > > >> > > > have
> > > >> > > >> > > > > > to
> > > >> > > >> > > > > > >>> explicitly call API to do local aggregation
> > > >> > > >> > > > > > >>> as well as the trigger policy of the local
> > > >> aggregation.
> > > >> > > Take
> > > >> > > >> > > > average
> > > >> > > >> > > > > > for
> > > >> > > >> > > > > > >>> example, the user program may look like this
> > (just
> > > a
> > > >> > > draft):
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> assuming the input type is
> > DataStream<Tupl2<String,
> > > >> > Int>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> ds.localAggregate(
> > > >> > > >> > > > > > >>>        0,
> >  //
> > > >> The
> > > >> > > local
> > > >> > > >> > key,
> > > >> > > >> > > > > which
> > > >> > > >> > > > > > >> is
> > > >> > > >> > > > > > >>> the String from Tuple2
> > > >> > > >> > > > > > >>>        PartitionAvg(1),                 // The
> > > >> partial
> > > >> > > >> > > aggregation
> > > >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>, indicating
> > > sum
> > > >> and
> > > >> > > >> count
> > > >> > > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger
> > policy,
> > > >> note
> > > >> > > >> this
> > > >> > > >> > > > should
> > > >> > > >> > > > > be
> > > >> > > >> > > > > > >>> best effort, and also be composited with time
> > based
> > > >> or
> > > >> > > >> memory
> > > >> > > >> > > size
> > > >> > > >> > > > > > based
> > > >> > > >> > > > > > >>> trigger
> > > >> > > >> > > > > > >>>    )                                           //
> > > The
> > > >> > > return
> > > >> > > >> > type
> > > >> > > >> > > > is
> > > >> > > >> > > > > > >> local
> > > >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> > > >> > > >> > > > > > >>>    .keyBy(0)                             //
> > Further
> > > >> > keyby
> > > >> > > it
> > > >> > > >> > with
> > > >> > > >> > > > > > >> required
> > > >> > > >> > > > > > >>> key
> > > >> > > >> > > > > > >>>    .aggregate(1)                      // This
> > will
> > > >> merge
> > > >> > > all
> > > >> > > >> > the
> > > >> > > >> > > > > > partial
> > > >> > > >> > > > > > >>> results and get the final average.
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> (This is only a draft, only trying to explain
> > what
> > > it
> > > >> > > looks
> > > >> > > >> > > like. )
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> The local aggregate operator can be stateless, we
> > > can
> > > >> > > keep a
> > > >> > > >> > > memory
> > > >> > > >> > > > > > >> buffer
> > > >> > > >> > > > > > >>> or other efficient data structure to improve the
> > > >> > aggregate
> > > >> > > >> > > > > performance.
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> Let me know if you have any other questions.
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> Best,
> > > >> > > >> > > > > > >>> Kurt
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> > > >> > > >> > [hidden email] <mailto:[hidden email]>
> > > >> > > >> > > >
> > > >> > > >> > > > > > wrote:
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>>> Hi Kurt,
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Thanks for your reply.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Actually, I am not against you to raise your
> > > design.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> From your description before, I just can imagine
> > > >> your
> > > >> > > >> > high-level
> > > >> > > >> > > > > > >>>> implementation is about SQL and the optimization
> > > is
> > > >> > inner
> > > >> > > >> of
> > > >> > > >> > the
> > > >> > > >> > > > > API.
> > > >> > > >> > > > > > >> Is
> > > >> > > >> > > > > > >>> it
> > > >> > > >> > > > > > >>>> automatically? how to give the configuration
> > > option
> > > >> > about
> > > >> > > >> > > trigger
> > > >> > > >> > > > > > >>>> pre-aggregation?
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Maybe after I get more information, it sounds
> > more
> > > >> > > >> reasonable.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> IMO, first of all, it would be better to make
> > your
> > > >> user
> > > >> > > >> > > interface
> > > >> > > >> > > > > > >>> concrete,
> > > >> > > >> > > > > > >>>> it's the basis of the discussion.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> For example, can you give an example code
> > snippet
> > > to
> > > >> > > >> introduce
> > > >> > > >> > > how
> > > >> > > >> > > > > to
> > > >> > > >> > > > > > >>> help
> > > >> > > >> > > > > > >>>> users to process data skew caused by the jobs
> > > which
> > > >> > built
> > > >> > > >> with
> > > >> > > >> > > > > > >> DataStream
> > > >> > > >> > > > > > >>>> API?
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> If you give more details we can discuss further
> > > >> more. I
> > > >> > > >> think
> > > >> > > >> > if
> > > >> > > >> > > > one
> > > >> > > >> > > > > > >>> design
> > > >> > > >> > > > > > >>>> introduces an exact interface and another does
> > > not.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> The implementation has an obvious difference.
> > For
> > > >> > > example,
> > > >> > > >> we
> > > >> > > >> > > > > > introduce
> > > >> > > >> > > > > > >>> an
> > > >> > > >> > > > > > >>>> exact API in DataStream named localKeyBy, about
> > > the
> > > >> > > >> > > > pre-aggregation
> > > >> > > >> > > > > we
> > > >> > > >> > > > > > >>> need
> > > >> > > >> > > > > > >>>> to define the trigger mechanism of local
> > > >> aggregation,
> > > >> > so
> > > >> > > we
> > > >> > > >> > find
> > > >> > > >> > > > > > reused
> > > >> > > >> > > > > > >>>> window API and operator is a good choice. This
> > is
> > > a
> > > >> > > >> reasoning
> > > >> > > >> > > link
> > > >> > > >> > > > > > from
> > > >> > > >> > > > > > >>>> design to implementation.
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> What do you think?
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Best,
> > > >> > > >> > > > > > >>>> Vino
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>> Kurt Young <[hidden email] <mailto:[hidden email]>> 于2019年6月18日周二
> > > >> 上午11:58写道：
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>>> Hi Vino,
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> Now I feel that we may have different
> > > >> understandings
> > > >> > > about
> > > >> > > >> > what
> > > >> > > >> > > > > kind
> > > >> > > >> > > > > > >> of
> > > >> > > >> > > > > > >>>>> problems or improvements you want to
> > > >> > > >> > > > > > >>>>> resolve. Currently, most of the feedback are
> > > >> focusing
> > > >> > on
> > > >> > > >> *how
> > > >> > > >> > > to
> > > >> > > >> > > > > do a
> > > >> > > >> > > > > > >>>>> proper local aggregation to improve performance
> > > >> > > >> > > > > > >>>>> and maybe solving the data skew issue*. And my
> > > gut
> > > >> > > >> feeling is
> > > >> > > >> > > > this
> > > >> > > >> > > > > is
> > > >> > > >> > > > > > >>>>> exactly what users want at the first place,
> > > >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to
> > summarize
> > > >> here,
> > > >> > > >> please
> > > >> > > >> > > > > correct
> > > >> > > >> > > > > > >>> me
> > > >> > > >> > > > > > >>>> if
> > > >> > > >> > > > > > >>>>> i'm wrong).
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> But I still think the design is somehow
> > diverged
> > > >> from
> > > >> > > the
> > > >> > > >> > goal.
> > > >> > > >> > > > If
> > > >> > > >> > > > > we
> > > >> > > >> > > > > > >>>> want
> > > >> > > >> > > > > > >>>>> to have an efficient and powerful way to
> > > >> > > >> > > > > > >>>>> have local aggregation, supporting intermedia
> > > >> result
> > > >> > > type
> > > >> > > >> is
> > > >> > > >> > > > > > >> essential
> > > >> > > >> > > > > > >>>> IMO.
> > > >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
> > > >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a
> > > proper
> > > >> > > >> support of
> > > >> > > >> > > > > > >>>> intermediate
> > > >> > > >> > > > > > >>>>> result type and can do `merge` operation
> > > >> > > >> > > > > > >>>>> on them.
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> Now, we have a lightweight alternatives which
> > > >> performs
> > > >> > > >> well,
> > > >> > > >> > > and
> > > >> > > >> > > > > > >> have a
> > > >> > > >> > > > > > >>>>> nice fit with the local aggregate requirements.
> > > >> > > >> > > > > > >>>>> Mostly importantly,  it's much less complex
> > > because
> > > >> > it's
> > > >> > > >> > > > stateless.
> > > >> > > >> > > > > > >> And
> > > >> > > >> > > > > > >>>> it
> > > >> > > >> > > > > > >>>>> can also achieve the similar
> > multiple-aggregation
> > > >> > > >> > > > > > >>>>> scenario.
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> I still not convinced why we shouldn't consider
> > > it
> > > >> as
> > > >> > a
> > > >> > > >> first
> > > >> > > >> > > > step.
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> Best,
> > > >> > > >> > > > > > >>>>> Kurt
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang <
> > > >> > > >> > > > [hidden email] <mailto:[hidden email]>>
> > > >> > > >> > > > > > >>>> wrote:
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>>> Hi Kurt,
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Thanks for your comments.
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> It seems we both implemented local aggregation
> > > >> > feature
> > > >> > > to
> > > >> > > >> > > > optimize
> > > >> > > >> > > > > > >>> the
> > > >> > > >> > > > > > >>>>>> issue of data skew.
> > > >> > > >> > > > > > >>>>>> However, IMHO, the API level of optimizing
> > > >> revenue is
> > > >> > > >> > > different.
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> *Your optimization benefits from Flink SQL and
> > > >> it's
> > > >> > not
> > > >> > > >> > user's
> > > >> > > >> > > > > > >>>> faces.(If
> > > >> > > >> > > > > > >>>>> I
> > > >> > > >> > > > > > >>>>>> understand it incorrectly, please correct
> > > this.)*
> > > >> > > >> > > > > > >>>>>> *Our implementation employs it as an
> > > optimization
> > > >> > tool
> > > >> > > >> API
> > > >> > > >> > for
> > > >> > > >> > > > > > >>>>> DataStream,
> > > >> > > >> > > > > > >>>>>> it just like a local version of the keyBy
> > API.*
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Based on this, I want to say support it as a
> > > >> > DataStream
> > > >> > > >> API
> > > >> > > >> > > can
> > > >> > > >> > > > > > >>> provide
> > > >> > > >> > > > > > >>>>>> these advantages:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear semantic
> > and
> > > >> it's
> > > >> > > >> > flexible
> > > >> > > >> > > > not
> > > >> > > >> > > > > > >>> only
> > > >> > > >> > > > > > >>>>> for
> > > >> > > >> > > > > > >>>>>>   processing data skew but also for
> > implementing
> > > >> some
> > > >> > > >> user
> > > >> > > >> > > > cases,
> > > >> > > >> > > > > > >>> for
> > > >> > > >> > > > > > >>>>>>   example, if we want to calculate the
> > > >> multiple-level
> > > >> > > >> > > > aggregation,
> > > >> > > >> > > > > > >>> we
> > > >> > > >> > > > > > >>>>> can
> > > >> > > >> > > > > > >>>>>> do
> > > >> > > >> > > > > > >>>>>>   multiple-level aggregation in the local
> > > >> > aggregation:
> > > >> > > >> > > > > > >>>>>>
> > > >> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
> > > >> > > >> //
> > > >> > > >> > > here
> > > >> > > >> > > > > > >> "a"
> > > >> > > >> > > > > > >>>> is
> > > >> > > >> > > > > > >>>>> a
> > > >> > > >> > > > > > >>>>>>   sub-category, while "b" is a category, here
> > we
> > > >> do
> > > >> > not
> > > >> > > >> need
> > > >> > > >> > > to
> > > >> > > >> > > > > > >>>> shuffle
> > > >> > > >> > > > > > >>>>>> data
> > > >> > > >> > > > > > >>>>>>   in the network.
> > > >> > > >> > > > > > >>>>>>   - The users of DataStream API will benefit
> > > from
> > > >> > this.
> > > >> > > >> > > > Actually,
> > > >> > > >> > > > > > >> we
> > > >> > > >> > > > > > >>>>> have
> > > >> > > >> > > > > > >>>>>>   a lot of scenes need to use DataStream API.
> > > >> > > Currently,
> > > >> > > >> > > > > > >> DataStream
> > > >> > > >> > > > > > >>>> API
> > > >> > > >> > > > > > >>>>> is
> > > >> > > >> > > > > > >>>>>>   the cornerstone of the physical plan of
> > Flink
> > > >> SQL.
> > > >> > > >> With a
> > > >> > > >> > > > > > >>> localKeyBy
> > > >> > > >> > > > > > >>>>>> API,
> > > >> > > >> > > > > > >>>>>>   the optimization of SQL at least may use
> > this
> > > >> > > optimized
> > > >> > > >> > API,
> > > >> > > >> > > > > > >> this
> > > >> > > >> > > > > > >>>> is a
> > > >> > > >> > > > > > >>>>>>   further topic.
> > > >> > > >> > > > > > >>>>>>   - Based on the window operator, our state
> > > would
> > > >> > > benefit
> > > >> > > >> > from
> > > >> > > >> > > > > > >> Flink
> > > >> > > >> > > > > > >>>>> State
> > > >> > > >> > > > > > >>>>>>   and checkpoint, we do not need to worry
> > about
> > > >> OOM
> > > >> > and
> > > >> > > >> job
> > > >> > > >> > > > > > >> failed.
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Now, about your questions:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> 1. About our design cannot change the data
> > type
> > > >> and
> > > >> > > about
> > > >> > > >> > the
> > > >> > > >> > > > > > >>>>>> implementation of average:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the localKeyBy is
> > > an
> > > >> API
> > > >> > > >> > provides
> > > >> > > >> > > > to
> > > >> > > >> > > > > > >> the
> > > >> > > >> > > > > > >>>>> users
> > > >> > > >> > > > > > >>>>>> who use DataStream API to build their jobs.
> > > >> > > >> > > > > > >>>>>> Users should know its semantics and the
> > > difference
> > > >> > with
> > > >> > > >> > keyBy
> > > >> > > >> > > > API,
> > > >> > > >> > > > > > >> so
> > > >> > > >> > > > > > >>>> if
> > > >> > > >> > > > > > >>>>>> they want to the average aggregation, they
> > > should
> > > >> > carry
> > > >> > > >> > local
> > > >> > > >> > > > sum
> > > >> > > >> > > > > > >>>> result
> > > >> > > >> > > > > > >>>>>> and local count result.
> > > >> > > >> > > > > > >>>>>> I admit that it will be convenient to use
> > keyBy
> > > >> > > directly.
> > > >> > > >> > But
> > > >> > > >> > > we
> > > >> > > >> > > > > > >> need
> > > >> > > >> > > > > > >>>> to
> > > >> > > >> > > > > > >>>>>> pay a little price when we get some benefits.
> > I
> > > >> think
> > > >> > > >> this
> > > >> > > >> > > price
> > > >> > > >> > > > > is
> > > >> > > >> > > > > > >>>>>> reasonable. Considering that the DataStream
> > API
> > > >> > itself
> > > >> > > >> is a
> > > >> > > >> > > > > > >> low-level
> > > >> > > >> > > > > > >>>> API
> > > >> > > >> > > > > > >>>>>> (at least for now).
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> 2. About stateless operator and
> > > >> > > >> > > > > > >>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Actually, I have discussed this opinion with
> > > >> @dianfu
> > > >> > in
> > > >> > > >> the
> > > >> > > >> > > old
> > > >> > > >> > > > > > >> mail
> > > >> > > >> > > > > > >>>>>> thread. I will copy my opinion from there:
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>>   - for your design, you still need somewhere
> > to
> > > >> give
> > > >> > > the
> > > >> > > >> > > users
> > > >> > > >> > > > > > >>>>> configure
> > > >> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
> > > >> availability?),
> > > >> > > >> this
> > > >> > > >> > > > design
> > > >> > > >> > > > > > >>>> cannot
> > > >> > > >> > > > > > >>>>>>   guarantee a deterministic semantics (it will
> > > >> bring
> > > >> > > >> trouble
> > > >> > > >> > > for
> > > >> > > >> > > > > > >>>> testing
> > > >> > > >> > > > > > >>>>>> and
> > > >> > > >> > > > > > >>>>>>   debugging).
> > > >> > > >> > > > > > >>>>>>   - if the implementation depends on the
> > timing
> > > of
> > > >> > > >> > checkpoint,
> > > >> > > >> > > > it
> > > >> > > >> > > > > > >>>> would
> > > >> > > >> > > > > > >>>>>>   affect the checkpoint's progress, and the
> > > >> buffered
> > > >> > > data
> > > >> > > >> > may
> > > >> > > >> > > > > > >> cause
> > > >> > > >> > > > > > >>>> OOM
> > > >> > > >> > > > > > >>>>>>   issue. In addition, if the operator is
> > > >> stateless,
> > > >> > it
> > > >> > > >> can
> > > >> > > >> > not
> > > >> > > >> > > > > > >>> provide
> > > >> > > >> > > > > > >>>>>> fault
> > > >> > > >> > > > > > >>>>>>   tolerance.
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Best,
> > > >> > > >> > > > > > >>>>>> Vino
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>> Kurt Young <[hidden email] <mailto:[hidden email]>> 于2019年6月18日周二
> > > >> > 上午9:22写道：
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>>> Hi Vino,
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the general
> > > idea
> > > >> and
> > > >> > > IMO
> > > >> > > >> > it's
> > > >> > > >> > > > > > >> very
> > > >> > > >> > > > > > >>>>> useful
> > > >> > > >> > > > > > >>>>>>> feature.
> > > >> > > >> > > > > > >>>>>>> But after reading through the document, I
> > feel
> > > >> that
> > > >> > we
> > > >> > > >> may
> > > >> > > >> > > over
> > > >> > > >> > > > > > >>>> design
> > > >> > > >> > > > > > >>>>>> the
> > > >> > > >> > > > > > >>>>>>> required
> > > >> > > >> > > > > > >>>>>>> operator for proper local aggregation. The
> > main
> > > >> > reason
> > > >> > > >> is
> > > >> > > >> > we
> > > >> > > >> > > > want
> > > >> > > >> > > > > > >>> to
> > > >> > > >> > > > > > >>>>>> have a
> > > >> > > >> > > > > > >>>>>>> clear definition and behavior about the
> > "local
> > > >> keyed
> > > >> > > >> state"
> > > >> > > >> > > > which
> > > >> > > >> > > > > > >>> in
> > > >> > > >> > > > > > >>>> my
> > > >> > > >> > > > > > >>>>>>> opinion is not
> > > >> > > >> > > > > > >>>>>>> necessary for local aggregation, at least for
> > > >> start.
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> Another issue I noticed is the local key by
> > > >> operator
> > > >> > > >> cannot
> > > >> > > >> > > > > > >> change
> > > >> > > >> > > > > > >>>>>> element
> > > >> > > >> > > > > > >>>>>>> type, it will
> > > >> > > >> > > > > > >>>>>>> also restrict a lot of use cases which can be
> > > >> > benefit
> > > >> > > >> from
> > > >> > > >> > > > local
> > > >> > > >> > > > > > >>>>>>> aggregation, like "average".
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and the only
> > > >> thing
> > > >> > > >> need to
> > > >> > > >> > > be
> > > >> > > >> > > > > > >> done
> > > >> > > >> > > > > > >>>> is
> > > >> > > >> > > > > > >>>>>>> introduce
> > > >> > > >> > > > > > >>>>>>> a stateless lightweight operator which is
> > > >> *chained*
> > > >> > > >> before
> > > >> > > >> > > > > > >>> `keyby()`.
> > > >> > > >> > > > > > >>>>> The
> > > >> > > >> > > > > > >>>>>>> operator will flush all buffered
> > > >> > > >> > > > > > >>>>>>> elements during
> > > >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
> > > >> > > >> > > > and
> > > >> > > >> > > > > > >>>> make
> > > >> > > >> > > > > > >>>>>>> himself stateless.
> > > >> > > >> > > > > > >>>>>>> By the way, in the earlier version we also
> > did
> > > >> the
> > > >> > > >> similar
> > > >> > > >> > > > > > >> approach
> > > >> > > >> > > > > > >>>> by
> > > >> > > >> > > > > > >>>>>>> introducing a stateful
> > > >> > > >> > > > > > >>>>>>> local aggregation operator but it's not
> > > >> performed as
> > > >> > > >> well
> > > >> > > >> > as
> > > >> > > >> > > > the
> > > >> > > >> > > > > > >>>> later
> > > >> > > >> > > > > > >>>>>> one,
> > > >> > > >> > > > > > >>>>>>> and also effect the barrie
> > > >> > > >> > > > > > >>>>>>> alignment time. The later one is fairly
> > simple
> > > >> and
> > > >> > > more
> > > >> > > >> > > > > > >> efficient.
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> I would highly suggest you to consider to
> > have
> > > a
> > > >> > > >> stateless
> > > >> > > >> > > > > > >> approach
> > > >> > > >> > > > > > >>>> at
> > > >> > > >> > > > > > >>>>>> the
> > > >> > > >> > > > > > >>>>>>> first step.
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>> Kurt
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> > > >> > > >> [hidden email] <mailto:[hidden email]>>
> > > >> > > >> > > > > > >> wrote:
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>>> Hi Vino,
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>> Regarding to the "input.keyBy(0).sum(1)" vs
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> > > >> > > >> > > > > > >> have
> > > >> > > >> > > > > > >>>> you
> > > >> > > >> > > > > > >>>>>>> done
> > > >> > > >> > > > > > >>>>>>>> some benchmark?
> > > >> > > >> > > > > > >>>>>>>> Because I'm curious about how much
> > performance
> > > >> > > >> improvement
> > > >> > > >> > > can
> > > >> > > >> > > > > > >> we
> > > >> > > >> > > > > > >>>> get
> > > >> > > >> > > > > > >>>>>> by
> > > >> > > >> > > > > > >>>>>>>> using count window as the local operator.
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>> Jark
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang <
> > > >> > > >> > > > [hidden email] <mailto:[hidden email]>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>>>> wrote:
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Hi Hequn,
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to
> > provide a
> > > >> tool
> > > >> > > >> which
> > > >> > > >> > > can
> > > >> > > >> > > > > > >>> let
> > > >> > > >> > > > > > >>>>>> users
> > > >> > > >> > > > > > >>>>>>> do
> > > >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The behavior
> > of
> > > >> the
> > > >> > > >> > > > > > >>> pre-aggregation
> > > >> > > >> > > > > > >>>>> is
> > > >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> So the three cases are different, I will
> > > >> describe
> > > >> > > them
> > > >> > > >> > one
> > > >> > > >> > > by
> > > >> > > >> > > > > > >>>> one:
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> *In this case, the result is event-driven,
> > > each
> > > >> > > event
> > > >> > > >> can
> > > >> > > >> > > > > > >>> produce
> > > >> > > >> > > > > > >>>>> one
> > > >> > > >> > > > > > >>>>>>> sum
> > > >> > > >> > > > > > >>>>>>>>> aggregation result and it is the latest one
> > > >> from
> > > >> > the
> > > >> > > >> > source
> > > >> > > >> > > > > > >>>> start.*
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> 2.
> > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have a
> > > >> problem, it
> > > >> > > >> would
> > > >> > > >> > do
> > > >> > > >> > > > > > >> the
> > > >> > > >> > > > > > >>>>> local
> > > >> > > >> > > > > > >>>>>>> sum
> > > >> > > >> > > > > > >>>>>>>>> aggregation and will produce the latest
> > > partial
> > > >> > > result
> > > >> > > >> > from
> > > >> > > >> > > > > > >> the
> > > >> > > >> > > > > > >>>>>> source
> > > >> > > >> > > > > > >>>>>>>>> start for every event. *
> > > >> > > >> > > > > > >>>>>>>>> *These latest partial results from the same
> > > key
> > > >> > are
> > > >> > > >> > hashed
> > > >> > > >> > > to
> > > >> > > >> > > > > > >>> one
> > > >> > > >> > > > > > >>>>>> node
> > > >> > > >> > > > > > >>>>>>> to
> > > >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
> > > >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it
> > received
> > > >> > > multiple
> > > >> > > >> > > partial
> > > >> > > >> > > > > > >>>>> results
> > > >> > > >> > > > > > >>>>>>>> (they
> > > >> > > >> > > > > > >>>>>>>>> are all calculated from the source start)
> > and
> > > >> sum
> > > >> > > them
> > > >> > > >> > will
> > > >> > > >> > > > > > >> get
> > > >> > > >> > > > > > >>>> the
> > > >> > > >> > > > > > >>>>>>> wrong
> > > >> > > >> > > > > > >>>>>>>>> result.*
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> 3.
> > > >> > > >> > > input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a partial
> > > >> > > aggregation
> > > >> > > >> > > result
> > > >> > > >> > > > > > >>> for
> > > >> > > >> > > > > > >>>>>> the 5
> > > >> > > >> > > > > > >>>>>>>>> records in the count window. The partial
> > > >> > aggregation
> > > >> > > >> > > results
> > > >> > > >> > > > > > >>> from
> > > >> > > >> > > > > > >>>>> the
> > > >> > > >> > > > > > >>>>>>>> same
> > > >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> So the first case and the third case can
> > get
> > > >> the
> > > >> > > >> *same*
> > > >> > > >> > > > > > >> result,
> > > >> > > >> > > > > > >>>> the
> > > >> > > >> > > > > > >>>>>>>>> difference is the output-style and the
> > > latency.
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key API is
> > just
> > > >> an
> > > >> > > >> > > optimization
> > > >> > > >> > > > > > >>>> API.
> > > >> > > >> > > > > > >>>>> We
> > > >> > > >> > > > > > >>>>>>> do
> > > >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the user
> > has
> > > to
> > > >> > > >> > understand
> > > >> > > >> > > > > > >> its
> > > >> > > >> > > > > > >>>>>>> semantics
> > > >> > > >> > > > > > >>>>>>>>> and use it correctly.
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>>> Vino
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email] <mailto:[hidden email]>>
> > > >> 于2019年6月17日周一
> > > >> > > >> > 下午4:18写道：
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> Hi Vino,
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it is a
> > > very
> > > >> > good
> > > >> > > >> > > feature!
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the
> > > semantics
> > > >> > for
> > > >> > > >> the
> > > >> > > >> > > > > > >>>>>> `localKeyBy`.
> > > >> > > >> > > > > > >>>>>>>> From
> > > >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API returns
> > > an
> > > >> > > >> instance
> > > >> > > >> > of
> > > >> > > >> > > > > > >>>>>>> `KeyedStream`
> > > >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in this
> > > case,
> > > >> > > what's
> > > >> > > >> > the
> > > >> > > >> > > > > > >>>>> semantics
> > > >> > > >> > > > > > >>>>>>> for
> > > >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the
> > > >> following
> > > >> > > code
> > > >> > > >> > share
> > > >> > > >> > > > > > >>> the
> > > >> > > >> > > > > > >>>>> same
> > > >> > > >> > > > > > >>>>>>>>> result?
> > > >> > > >> > > > > > >>>>>>>>>> and what're the differences between them?
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>> 2.
> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>> 3.
> > > >> > > >> > > > > > >>
> > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add this
> > into
> > > >> the
> > > >> > > >> > document.
> > > >> > > >> > > > > > >>> Thank
> > > >> > > >> > > > > > >>>>> you
> > > >> > > >> > > > > > >>>>>>>> very
> > > >> > > >> > > > > > >>>>>>>>>> much.
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> Best, Hequn
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino
> > yang <
> > > >> > > >> > > > > > >>>>> [hidden email] <mailto:[hidden email]>>
> > > >> > > >> > > > > > >>>>>>>>> wrote:
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*" section
> > of
> > > >> FLIP
> > > >> > > >> wiki
> > > >> > > >> > > > > > >>>> page.[1]
> > > >> > > >> > > > > > >>>>>> This
> > > >> > > >> > > > > > >>>>>>>>> mail
> > > >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has proceeded to
> > > the
> > > >> > > third
> > > >> > > >> > step.
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote
> > > step),
> > > >> I
> > > >> > > >> didn't
> > > >> > > >> > > > > > >> find
> > > >> > > >> > > > > > >>>> the
> > > >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the voting
> > > >> process.
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of this
> > > >> feature
> > > >> > > has
> > > >> > > >> > been
> > > >> > > >> > > > > > >>> done
> > > >> > > >> > > > > > >>>>> in
> > > >> > > >> > > > > > >>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>> old
> > > >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when
> > should
> > > I
> > > >> > start
> > > >> > > >> > > > > > >> voting?
> > > >> > > >> > > > > > >>>> Can
> > > >> > > >> > > > > > >>>>> I
> > > >> > > >> > > > > > >>>>>>>> start
> > > >> > > >> > > > > > >>>>>>>>>> now?
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>>>>> Vino
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> [1]:
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> > https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up <https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up>
> > > >> > > >> > > > > > >>>>>>>>>>> [2]:
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> > http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308 <http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email] <mailto:[hidden email]>>
> > 于2019年6月13日周四
> > > >> > > 上午9:19写道：
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your
> > > >> efforts.
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>>>>>> Leesf
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email] <mailto:[hidden email]>>
> > > >> > 于2019年6月12日周三
> > > >> > > >> > > > > > >>> 下午5:46写道：
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP
> > discussion
> > > >> > thread
> > > >> > > >> > > > > > >> about
> > > >> > > >> > > > > > >>>>>>> supporting
> > > >> > > >> > > > > > >>>>>>>>>> local
> > > >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can effectively
> > > >> > alleviate
> > > >> > > >> data
> > > >> > > >> > > > > > >>>> skew.
> > > >> > > >> > > > > > >>>>>>> This
> > > >> > > >> > > > > > >>>>>>>> is
> > > >> > > >> > > > > > >>>>>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> > https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink <https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely
> > used
> > > to
> > > >> > > >> perform
> > > >> > > >> > > > > > >>>>>> aggregating
> > > >> > > >> > > > > > >>>>>>>>>>>> operations
> > > >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on the
> > > >> elements
> > > >> > > >> that
> > > >> > > >> > > > > > >>> have
> > > >> > > >> > > > > > >>>>> the
> > > >> > > >> > > > > > >>>>>>> same
> > > >> > > >> > > > > > >>>>>>>>>> key.
> > > >> > > >> > > > > > >>>>>>>>>>>> When
> > > >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements with
> > > the
> > > >> > same
> > > >> > > >> key
> > > >> > > >> > > > > > >>> will
> > > >> > > >> > > > > > >>>> be
> > > >> > > >> > > > > > >>>>>>> sent
> > > >> > > >> > > > > > >>>>>>>> to
> > > >> > > >> > > > > > >>>>>>>>>> and
> > > >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> The performance of these aggregating
> > > >> > operations
> > > >> > > is
> > > >> > > >> > > > > > >> very
> > > >> > > >> > > > > > >>>>>>> sensitive
> > > >> > > >> > > > > > >>>>>>>>> to
> > > >> > > >> > > > > > >>>>>>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases
> > where
> > > >> the
> > > >> > > >> > > > > > >>> distribution
> > > >> > > >> > > > > > >>>>> of
> > > >> > > >> > > > > > >>>>>>> keys
> > > >> > > >> > > > > > >>>>>>>>>>>> follows a
> > > >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance will be
> > > >> > > >> significantly
> > > >> > > >> > > > > > >>>>>> downgraded.
> > > >> > > >> > > > > > >>>>>>>>> More
> > > >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of
> > > >> > parallelism
> > > >> > > >> does
> > > >> > > >> > > > > > >>> not
> > > >> > > >> > > > > > >>>>> help
> > > >> > > >> > > > > > >>>>>>>> when
> > > >> > > >> > > > > > >>>>>>>>> a
> > > >> > > >> > > > > > >>>>>>>>>>> task
> > > >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a widely-adopted
> > > >> method
> > > >> > to
> > > >> > > >> > > > > > >> reduce
> > > >> > > >> > > > > > >>>> the
> > > >> > > >> > > > > > >>>>>>>>>> performance
> > > >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can decompose
> > > the
> > > >> > > >> > > > > > >> aggregating
> > > >> > > >> > > > > > >>>>>>>> operations
> > > >> > > >> > > > > > >>>>>>>>>> into
> > > >> > > >> > > > > > >>>>>>>>>>>> two
> > > >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we
> > aggregate
> > > >> the
> > > >> > > >> elements
> > > >> > > >> > > > > > >>> of
> > > >> > > >> > > > > > >>>>> the
> > > >> > > >> > > > > > >>>>>>> same
> > > >> > > >> > > > > > >>>>>>>>> key
> > > >> > > >> > > > > > >>>>>>>>>>> at
> > > >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial
> > > results.
> > > >> > Then
> > > >> > > at
> > > >> > > >> > > > > > >> the
> > > >> > > >> > > > > > >>>>> second
> > > >> > > >> > > > > > >>>>>>>>> phase,
> > > >> > > >> > > > > > >>>>>>>>>>>> these
> > > >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to receivers
> > > >> > according
> > > >> > > to
> > > >> > > >> > > > > > >>> their
> > > >> > > >> > > > > > >>>>> keys
> > > >> > > >> > > > > > >>>>>>> and
> > > >> > > >> > > > > > >>>>>>>>> are
> > > >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final result.
> > > Since
> > > >> the
> > > >> > > >> number
> > > >> > > >> > > > > > >>> of
> > > >> > > >> > > > > > >>>>>>> partial
> > > >> > > >> > > > > > >>>>>>>>>>> results
> > > >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is limited by
> > > the
> > > >> > > >> number of
> > > >> > > >> > > > > > >>>>>> senders,
> > > >> > > >> > > > > > >>>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be
> > reduced.
> > > >> > > >> Besides, by
> > > >> > > >> > > > > > >>>>>> reducing
> > > >> > > >> > > > > > >>>>>>>> the
> > > >> > > >> > > > > > >>>>>>>>>>> amount
> > > >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the performance can
> > > be
> > > >> > > further
> > > >> > > >> > > > > > >>>>> improved.
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> > https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing <https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > >
> > > >> >
> > > >>
> > >
> > http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308 <http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> https://issues.apache.org/jira/browse/FLINK-12786 <https://issues.apache.org/jira/browse/FLINK-12786>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your
> > feedback!
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>> Best,
> > > >> > > >> > > > > > >>>>>>>>>>>>> Vino
> > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>>
> > > >> > > >> > > > > > >>>>>>>>
> > > >> > > >> > > > > > >>>>>>>
> > > >> > > >> > > > > > >>>>>>
> > > >> > > >> > > > > > >>>>>
> > > >> > > >> > > > > > >>>>
> > > >> > > >> > > > > > >>>
> > > >> > > >> > > > > > >>
> > > >> > > >> > > > > >
> > > >> > > >> > > > > >
> > > >> > > >> > > > >
> > > >> > > >> > > >
> > > >> > > >> > >
> > > >> > > >> >
> > > >> > > >>
> > > >> > > >
> > > >> > >
> > > >> >
> > > >>
> > > >
> > >
> >

[Jark Wu-2]

Hi Piotr,

I think the state migration you raised is a good point. Having
"stream.enableLocalAggregation(Trigger)” might add some implicit operators
which users can't set uid and cause the state compatibility/evolution
problems.
So let's put this in rejected alternatives.

Hi Vino,

You mentioned several times that "DataStream.localKeyBy().process()" can
solve the data skew problem of "DataStream.keyBy().process()".
I'm curious about what's the differences between "DataStream.process()" and
"DataStream.localKeyBy().process()"?
Can't "DataStream.process()" solve the data skew problem?

Best,
Jark


On Wed, 26 Jun 2019 at 18:20, Piotr Nowojski <[hidden email]> wrote:

> Hi Jark and Vino,
>
> I agree fully with Jark, that in order to have the discussion focused and
> to limit the number of parallel topics, we should first focus on one topic.
> We can first decide on the API and later we can discuss the runtime
> details. At least as long as we keep the potential requirements of the
> runtime part in mind while designing the API.
>
> Regarding the automatic optimisation and proposed by Jark:
>
> "stream.enableLocalAggregation(Trigger)”
>
> I would be against that in the DataStream API for the reasons that Vino
> presented. There was a discussion thread about future directions of Table
> API vs DataStream API and the consensus was that the automatic
> optimisations are one of the dividing lines between those two, for at least
> a couple of reasons. Flexibility and full control over the program was one
> of them. Another is state migration. Having
> "stream.enableLocalAggregation(Trigger)” that might add some implicit
> operators in the job graph can cause problems with savepoint/checkpoint
> compatibility.
>
> However I haven’t thought about/looked into the details of the Vino’s API
> proposal, so I can not fully judge it.
>
> Piotrek
>
> > On 26 Jun 2019, at 09:17, vino yang <[hidden email]> wrote:
> >
> > Hi Jark,
> >
> > Similar questions and responses have been repeated many times.
> >
> > Why didn't we spend more sections discussing the API?
> >
> > Because we try to reuse the ability of KeyedStream. The localKeyBy API
> just returns the KeyedStream, that's our design, we can get all the benefit
> from the KeyedStream and get further benefit from WindowedStream. The APIs
> come from KeyedStream and WindowedStream is long-tested and flexible. Yes,
> we spend much space discussing the local keyed state, that's not the goal
> and motivation, that's the way to implement local aggregation. It is much
> more complicated than the API we introduced, so we spent more section. Of
> course, this is the implementation level of the Operator. We also agreed to
> support the implementation of buffer+flush and added related instructions
> to the documentation. This needs to wait for the community to recognize,
> and if the community agrees, we will give more instructions. What's more, I
> have indicated before that we welcome state-related commenters to
> participate in the discussion, but it is not wise to modify the FLIP title.
> >
> > About the API of local aggregation:
> >
> > I don't object to ease of use is very important. But IMHO flexibility is
> the most important at the DataStream API level. Otherwise, what does
> DataStream mean? The significance of the DataStream API is that it is more
> flexible than Table/SQL, if it cannot provide this point then everyone
> would just use Table/SQL.
> >
> > The DataStream API should focus more on flexibility than on automatic
> optimization, which allows users to have more possibilities to implement
> complex programs and meet specific scenarios. There are a lot of programs
> written using the DataStream API that are far more complex than we think.
> It is very difficult to optimize at the API level and the benefit is very
> low.
> >
> > I want to say that we support a more generalized local aggregation. I
> mentioned in the previous reply that not only the UDF that implements
> AggregateFunction is called aggregation. In some complex scenarios, we have
> to support local aggregation through ProcessFunction and
> ProcessWindowFunction to solve the data skew problem. How do you support
> them in the API implementation and optimization you mentioned?
> >
> > Flexible APIs are arbitrarily combined to result in erroneous semantics,
> which does not prove that flexibility is meaningless because the user is
> the decision maker. I have been exemplified many times, for many APIs in
> DataStream, if we arbitrarily combined them, they also do not have much
> practical significance. So, users who use flexible APIs need to understand
> what they are doing and what is the right choice.
> >
> > I think that if we discuss this, there will be no result.
> >
> > @Stephan Ewen <mailto:[hidden email]> , @Aljoscha Krettek <mailto:
> [hidden email]> and @Piotr Nowojski <mailto:[hidden email]> Do
> you have further comments?
> >
> >
> > Jark Wu <[hidden email] <mailto:[hidden email]>> 于2019年6月26日周三
> 上午11:46写道：
> > Thanks for the long discussion Vino, Kurt, Hequn, Piotr and others,
> >
> > It seems that we still have some different ideas about the API
> > (localKeyBy()?) and implementation details (reuse window operator? local
> > keyed state?).
> > And the discussion is stalled and mixed with motivation and API and
> > implementation discussion.
> >
> > In order to make some progress in this topic, I want to summarize the
> > points (pls correct me if I'm wrong or missing sth) and would suggest to
> > split
> >  the topic into following aspects and discuss them one by one.
> >
> > 1) What's the main purpose of this FLIP?
> >  - From the title of this FLIP, it is to support local aggregate. However
> > from the content of the FLIP, 80% are introducing a new state called
> local
> > keyed state.
> >  - If we mainly want to introduce local keyed state, then we should
> > re-title the FLIP and involve in more people who works on state.
> >  - If we mainly want to support local aggregate, then we can jump to
> step 2
> > to discuss the API design.
> >
> > 2) What does the API look like?
> >  - Vino proposed to use "localKeyBy()" to do local process, the output of
> > local process is the result type of aggregate function.
> >   a) For non-windowed aggregate:
> > input.localKeyBy(..).aggregate(agg1).keyBy(..).aggregate(agg2)    **NOT
> > SUPPORT**
> >   b) For windowed aggregate:
> >
> input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)
> >
> > 3) What's the implementation detail?
> >  - may reuse window operator or not.
> >  - may introduce a new state concepts or not.
> >  - may not have state in local operator by flushing buffers in
> > prepareSnapshotPreBarrier
> >  - and so on...
> >  - we can discuss these later when we reach a consensus on API
> >
> > --------------------
> >
> > Here are my thoughts:
> >
> > 1) Purpose of this FLIP
> >  - From the motivation section in the FLIP, I think the purpose is to
> > support local aggregation to solve the data skew issue.
> >    Then I think we should focus on how to provide a easy to use and clear
> > API to support **local aggregation**.
> >  - Vino's point is centered around the local keyed state API (or
> > localKeyBy()), and how to leverage the local keyed state API to support
> > local aggregation.
> >    But I'm afraid it's not a good way to design API for local
> aggregation.
> >
> > 2) local aggregation API
> >  - IMO, the method call chain
> >
> "input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)"
> > is not such easy to use.
> >    Because we have to provide two implementation for an aggregation (one
> > for partial agg, another for final agg). And we have to take care of
> >    the first window call, an inappropriate window call will break the
> > sematics.
> >  - From my point of view, local aggregation is a mature concept which
> > should output the intermediate accumulator (ACC) in the past period of
> time
> > (a trigger).
> >    And the downstream final aggregation will merge ACCs received from
> local
> > side, and output the current final result.
> >  - The current "AggregateFunction" API in DataStream already has the
> > accumulator type and "merge" method. So the only thing user need to do is
> > how to enable
> >    local aggregation opimization and set a trigger.
> >  - One idea comes to my head is that, assume we have a windowed
> aggregation
> > stream: "val stream = input.keyBy().window(w).aggregate(agg)". We can
> > provide an API on the stream.
> >    For exmaple, "stream.enableLocalAggregation(Trigger)", the trigger can
> > be "ContinuousEventTimeTrigger.of(Time.of(Time.minutes(1)))". Then it
> will
> > be optmized into
> >    local operator + final operator, and local operator will combine
> records
> > every minute on event time.
> >  - In this way, there is only one line added, and the output is the same
> > with before, because it is just an opimization.
> >
> >
> > Regards,
> > Jark
> >
> >
> >
> > On Tue, 25 Jun 2019 at 14:34, vino yang <[hidden email] <mailto:
> [hidden email]>> wrote:
> >
> > > Hi Kurt,
> > >
> > > Answer your questions:
> > >
> > > a) Sorry, I just updated the Google doc, still have no time update the
> > > FLIP, will update FLIP as soon as possible.
> > > About your description at this point, I have a question, what does it
> mean:
> > > how do we combine with
> > > `AggregateFunction`?
> > >
> > > I have shown you the examples which Flink has supported:
> > >
> > >    - input.localKeyBy(0).aggregate()
> > >    - input.localKeyBy(0).window().aggregate()
> > >
> > > You can show me a example about how do we combine with
> `AggregateFuncion`
> > > through your localAggregate API.
> > >
> > > About the example, how to do the local aggregation for AVG, consider
> this
> > > code:
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > > *DataStream<Tuple2<String, Long>> source = null; source .localKeyBy(0)
> > > .timeWindow(Time.seconds(60)) .aggregate(agg1, new
> > > WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>, String,
> > > TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60))
> .aggregate(agg2,
> > > new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, String,
> > > TimeWindow>());*
> > >
> > > *agg1:*
> > > *signature : new AggregateFunction<Tuple2<String, Long>, Tuple2<Long,
> > > Long>, Tuple2<Long, Long>>() {}*
> > > *input param type: Tuple2<String, Long> f0: key, f1: value*
> > > *intermediate result type: Tuple2<Long, Long>, f0: local aggregated
> sum;
> > > f1: local aggregated count*
> > > *output param type:  Tuple2<Long, Long>, f0: local aggregated sum; f1:
> > > local aggregated count*
> > >
> > > *agg2:*
> > > *signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
> > > Tuple2<String, Long>>() {},*
> > > *input param type: Tuple3<String, Long, Long>, f0: key, f1:  local
> > > aggregated sum; f2: local aggregated count*
> > >
> > > *intermediate result type: Long  avg result*
> > > *output param type:  Tuple2<String, Long> f0: key, f1 avg result*
> > >
> > > For sliding window, we just need to change the window type if users
> want to
> > > do.
> > > Again, we try to give the design and implementation in the DataStream
> > > level. So I believe we can match all the requirements(It's just that
> the
> > > implementation may be different) comes from the SQL level.
> > >
> > > b) Yes, Theoretically, your thought is right. But in reality, it cannot
> > > bring many benefits.
> > > If we want to get the benefits from the window API, while we do not
> reuse
> > > the window operator? And just copy some many duplicated code to another
> > > operator?
> > >
> > > c) OK, I agree to let the state backend committers join this
> discussion.
> > >
> > > Best,
> > > Vino
> > >
> > >
> > > Kurt Young <[hidden email] <mailto:[hidden email]>> 于2019年6月24日周一
> 下午6:53写道：
> > >
> > > > Hi vino,
> > > >
> > > > One thing to add,  for a), I think use one or two examples like how
> to do
> > > > local aggregation on a sliding window,
> > > > and how do we do local aggregation on an unbounded aggregate, will
> do a
> > > lot
> > > > help.
> > > >
> > > > Best,
> > > > Kurt
> > > >
> > > >
> > > > On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email]
> <mailto:[hidden email]>> wrote:
> > > >
> > > > > Hi vino,
> > > > >
> > > > > I think there are several things still need discussion.
> > > > >
> > > > > a) We all agree that we should first go with a unified
> abstraction, but
> > > > > the abstraction is not reflected by the FLIP.
> > > > > If your answer is "locakKeyBy" API, then I would ask how do we
> combine
> > > > > with `AggregateFunction`, and how do
> > > > > we do proper local aggregation for those have different
> intermediate
> > > > > result type, like AVG. Could you add these
> > > > > to the document?
> > > > >
> > > > > b) From implementation side, reusing window operator is one of the
> > > > > possible solutions, but not we base on window
> > > > > operator to have two different implementations. What I
> understanding
> > > is,
> > > > > one of the possible implementations should
> > > > > not touch window operator.
> > > > >
> > > > > c) 80% of your FLIP content is actually describing how do we
> support
> > > > local
> > > > > keyed state. I don't know if this is necessary
> > > > > to introduce at the first step and we should also involve
> committers
> > > work
> > > > > on state backend to share their thoughts.
> > > > >
> > > > > Best,
> > > > > Kurt
> > > > >
> > > > >
> > > > > On Mon, Jun 24, 2019 at 5:17 PM vino yang <[hidden email]
> <mailto:[hidden email]>>
> > > wrote:
> > > > >
> > > > >> Hi Kurt,
> > > > >>
> > > > >> You did not give more further different opinions, so I thought you
> > > have
> > > > >> agreed with the design after we promised to support two kinds of
> > > > >> implementation.
> > > > >>
> > > > >> In API level, we have answered your question about pass an
> > > > >> AggregateFunction to do the aggregation. No matter introduce
> > > localKeyBy
> > > > >> API
> > > > >> or not, we can support AggregateFunction.
> > > > >>
> > > > >> So what's your different opinion now? Can you share it with us?
> > > > >>
> > > > >> Best,
> > > > >> Vino
> > > > >>
> > > > >> Kurt Young <[hidden email] <mailto:[hidden email]>>
> 于2019年6月24日周一 下午4:24写道：
> > > > >>
> > > > >> > Hi vino,
> > > > >> >
> > > > >> > Sorry I don't see the consensus about reusing window operator
> and
> > > keep
> > > > >> the
> > > > >> > API design of localKeyBy. But I think we should definitely more
> > > > thoughts
> > > > >> > about this topic.
> > > > >> >
> > > > >> > I also try to loop in Stephan for this discussion.
> > > > >> >
> > > > >> > Best,
> > > > >> > Kurt
> > > > >> >
> > > > >> >
> > > > >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <
> [hidden email] <mailto:[hidden email]>>
> > > > >> wrote:
> > > > >> >
> > > > >> > > Hi all,
> > > > >> > >
> > > > >> > > I am happy we have a wonderful discussion and received many
> > > valuable
> > > > >> > > opinions in the last few days.
> > > > >> > >
> > > > >> > > Now, let me try to summarize what we have reached consensus
> about
> > > > the
> > > > >> > > changes in the design.
> > > > >> > >
> > > > >> > >    - provide a unified abstraction to support two kinds of
> > > > >> > implementation;
> > > > >> > >    - reuse WindowOperator and try to enhance it so that we can
> > > make
> > > > >> the
> > > > >> > >    intermediate result of the local aggregation can be
> buffered
> > > and
> > > > >> > > flushed to
> > > > >> > >    support two kinds of implementation;
> > > > >> > >    - keep the API design of localKeyBy, but declare the
> disabled
> > > > some
> > > > >> > APIs
> > > > >> > >    we cannot support currently, and provide a configurable
> API for
> > > > >> users
> > > > >> > to
> > > > >> > >    choose how to handle intermediate result;
> > > > >> > >
> > > > >> > > The above three points have been updated in the design doc.
> Any
> > > > >> > > questions, please let me know.
> > > > >> > >
> > > > >> > > @Aljoscha Krettek <[hidden email] <mailto:
> [hidden email]>> What do you think? Any
> > > > >> further
> > > > >> > > comments?
> > > > >> > >
> > > > >> > > Best,
> > > > >> > > Vino
> > > > >> > >
> > > > >> > > vino yang <[hidden email] <mailto:
> [hidden email]>> 于2019年6月20日周四 下午2:02写道：
> > > > >> > >
> > > > >> > > > Hi Kurt,
> > > > >> > > >
> > > > >> > > > Thanks for your comments.
> > > > >> > > >
> > > > >> > > > It seems we come to a consensus that we should alleviate the
> > > > >> > performance
> > > > >> > > > degraded by data skew with local aggregation. In this FLIP,
> our
> > > > key
> > > > >> > > > solution is to introduce local keyed partition to achieve
> this
> > > > goal.
> > > > >> > > >
> > > > >> > > > I also agree that we can benefit a lot from the usage of
> > > > >> > > > AggregateFunction. In combination with localKeyBy, We can
> easily
> > > > >> use it
> > > > >> > > to
> > > > >> > > > achieve local aggregation:
> > > > >> > > >
> > > > >> > > >    - input.localKeyBy(0).aggregate()
> > > > >> > > >    - input.localKeyBy(0).window().aggregate()
> > > > >> > > >
> > > > >> > > >
> > > > >> > > > I think the only problem here is the choices between
> > > > >> > > >
> > > > >> > > >    - (1) Introducing a new primitive called localKeyBy and
> > > > implement
> > > > >> > > >    local aggregation with existing operators, or
> > > > >> > > >    - (2) Introducing an operator called localAggregation
> which
> > > is
> > > > >> > > >    composed of a key selector, a window-like operator, and
> an
> > > > >> aggregate
> > > > >> > > >    function.
> > > > >> > > >
> > > > >> > > >
> > > > >> > > > There may exist some optimization opportunities by
> providing a
> > > > >> > composited
> > > > >> > > > interface for local aggregation. But at the same time, in my
> > > > >> opinion,
> > > > >> > we
> > > > >> > > > lose flexibility (Or we need certain efforts to achieve the
> same
> > > > >> > > > flexibility).
> > > > >> > > >
> > > > >> > > > As said in the previous mails, we have many use cases where
> the
> > > > >> > > > aggregation is very complicated and cannot be performed with
> > > > >> > > > AggregateFunction. For example, users may perform windowed
> > > > >> aggregations
> > > > >> > > > according to time, data values, or even external storage.
> > > > Typically,
> > > > >> > they
> > > > >> > > > now use KeyedProcessFunction or customized triggers to
> implement
> > > > >> these
> > > > >> > > > aggregations. It's not easy to address data skew in such
> cases
> > > > with
> > > > >> a
> > > > >> > > > composited interface for local aggregation.
> > > > >> > > >
> > > > >> > > > Given that Data Stream API is exactly targeted at these
> cases
> > > > where
> > > > >> the
> > > > >> > > > application logic is very complicated and optimization does
> not
> > > > >> > matter, I
> > > > >> > > > think it's a better choice to provide a relatively
> low-level and
> > > > >> > > canonical
> > > > >> > > > interface.
> > > > >> > > >
> > > > >> > > > The composited interface, on the other side, may be a good
> > > choice
> > > > in
> > > > >> > > > declarative interfaces, including SQL and Table API, as it
> > > allows
> > > > >> more
> > > > >> > > > optimization opportunities.
> > > > >> > > >
> > > > >> > > > Best,
> > > > >> > > > Vino
> > > > >> > > >
> > > > >> > > >
> > > > >> > > > Kurt Young <[hidden email] <mailto:[hidden email]>>
> 于2019年6月20日周四 上午10:15写道：
> > > > >> > > >
> > > > >> > > >> Hi all,
> > > > >> > > >>
> > > > >> > > >> As vino said in previous emails, I think we should first
> > > discuss
> > > > >> and
> > > > >> > > >> decide
> > > > >> > > >> what kind of use cases this FLIP want to
> > > > >> > > >> resolve, and what the API should look like. From my side, I
> > > think
> > > > >> this
> > > > >> > > is
> > > > >> > > >> probably the root cause of current divergence.
> > > > >> > > >>
> > > > >> > > >> My understand is (from the FLIP title and motivation
> section of
> > > > the
> > > > >> > > >> document), we want to have a proper support of
> > > > >> > > >> local aggregation, or pre aggregation. This is not a very
> new
> > > > idea,
> > > > >> > most
> > > > >> > > >> SQL engine already did this improvement. And
> > > > >> > > >> the core concept about this is, there should be an
> > > > >> AggregateFunction,
> > > > >> > no
> > > > >> > > >> matter it's a Flink runtime's AggregateFunction or
> > > > >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have
> > > concept
> > > > >> of
> > > > >> > > >> intermediate data type, sometimes we call it ACC.
> > > > >> > > >> I quickly went through the POC piotr did before [1], it
> also
> > > > >> directly
> > > > >> > > uses
> > > > >> > > >> AggregateFunction.
> > > > >> > > >>
> > > > >> > > >> But the thing is, after reading the design of this FLIP, I
> > > can't
> > > > >> help
> > > > >> > > >> myself feeling that this FLIP is not targeting to have a
> proper
> > > > >> > > >> local aggregation support. It actually want to introduce
> > > another
> > > > >> > > concept:
> > > > >> > > >> LocalKeyBy, and how to split and merge local key groups,
> > > > >> > > >> and how to properly support state on local key. Local
> > > aggregation
> > > > >> just
> > > > >> > > >> happened to be one possible use case of LocalKeyBy.
> > > > >> > > >> But it lacks supporting the essential concept of local
> > > > aggregation,
> > > > >> > > which
> > > > >> > > >> is intermediate data type. Without this, I really don't
> thing
> > > > >> > > >> it is a good fit of local aggregation.
> > > > >> > > >>
> > > > >> > > >> Here I want to make sure of the scope or the goal about
> this
> > > > FLIP,
> > > > >> do
> > > > >> > we
> > > > >> > > >> want to have a proper local aggregation engine, or we
> > > > >> > > >> just want to introduce a new concept called LocalKeyBy?
> > > > >> > > >>
> > > > >> > > >> [1]: https://github.com/apache/flink/pull/4626 <
> https://github.com/apache/flink/pull/4626>
> > > > >> > > >>
> > > > >> > > >> Best,
> > > > >> > > >> Kurt
> > > > >> > > >>
> > > > >> > > >>
> > > > >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <
> > > [hidden email] <mailto:[hidden email]>
> > > > >
> > > > >> > > wrote:
> > > > >> > > >>
> > > > >> > > >> > Hi Hequn,
> > > > >> > > >> >
> > > > >> > > >> > Thanks for your comments!
> > > > >> > > >> >
> > > > >> > > >> > I agree that allowing local aggregation reusing window
> API
> > > and
> > > > >> > > refining
> > > > >> > > >> > window operator to make it match both requirements (come
> from
> > > > our
> > > > >> > and
> > > > >> > > >> Kurt)
> > > > >> > > >> > is a good decision!
> > > > >> > > >> >
> > > > >> > > >> > Concerning your questions:
> > > > >> > > >> >
> > > > >> > > >> > 1. The result of
> input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > may
> > > > >> be
> > > > >> > > >> > meaningless.
> > > > >> > > >> >
> > > > >> > > >> > Yes, it does not make sense in most cases. However, I
> also
> > > want
> > > > >> to
> > > > >> > > note
> > > > >> > > >> > users should know the right semantics of localKeyBy and
> use
> > > it
> > > > >> > > >> correctly.
> > > > >> > > >> > Because this issue also exists for the global keyBy,
> consider
> > > > >> this
> > > > >> > > >> example:
> > > > >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is also
> > > > >> > meaningless.
> > > > >> > > >> >
> > > > >> > > >> > 2. About the semantics of
> > > > >> > > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
> > > > >> > > >> >
> > > > >> > > >> > Good catch! I agree with you that it's not good to
> enable all
> > > > >> > > >> > functionalities for localKeyBy from KeyedStream.
> > > > >> > > >> > Currently, We do not support some APIs such as
> > > > >> > > >> > connect/join/intervalJoin/coGroup. This is due to that we
> > > force
> > > > >> the
> > > > >> > > >> > operators on LocalKeyedStreams chained with the inputs.
> > > > >> > > >> >
> > > > >> > > >> > Best,
> > > > >> > > >> > Vino
> > > > >> > > >> >
> > > > >> > > >> >
> > > > >> > > >> > Hequn Cheng <[hidden email] <mailto:
> [hidden email]>> 于2019年6月19日周三 下午3:42写道：
> > > > >> > > >> >
> > > > >> > > >> > > Hi,
> > > > >> > > >> > >
> > > > >> > > >> > > Thanks a lot for your great discussion and great to see
> > > that
> > > > >> some
> > > > >> > > >> > agreement
> > > > >> > > >> > > has been reached on the "local aggregate engine"!
> > > > >> > > >> > >
> > > > >> > > >> > > ===> Considering the abstract engine,
> > > > >> > > >> > > I'm thinking is it valuable for us to extend the
> current
> > > > >> window to
> > > > >> > > >> meet
> > > > >> > > >> > > both demands raised by Kurt and Vino? There are some
> > > benefits
> > > > >> we
> > > > >> > can
> > > > >> > > >> get:
> > > > >> > > >> > >
> > > > >> > > >> > > 1. The interfaces of the window are complete and clear.
> > > With
> > > > >> > > windows,
> > > > >> > > >> we
> > > > >> > > >> > > can define a lot of ways to split the data and perform
> > > > >> different
> > > > >> > > >> > > computations.
> > > > >> > > >> > > 2. We can also leverage the window to do miniBatch for
> the
> > > > >> global
> > > > >> > > >> > > aggregation, i.e, we can use the window to bundle data
> > > belong
> > > > >> to
> > > > >> > the
> > > > >> > > >> same
> > > > >> > > >> > > key, for every bundle we only need to read and write
> once
> > > > >> state.
> > > > >> > > This
> > > > >> > > >> can
> > > > >> > > >> > > greatly reduce state IO and improve performance.
> > > > >> > > >> > > 3. A lot of other use cases can also benefit from the
> > > window
> > > > >> base
> > > > >> > on
> > > > >> > > >> > memory
> > > > >> > > >> > > or stateless.
> > > > >> > > >> > >
> > > > >> > > >> > > ===> As for the API,
> > > > >> > > >> > > I think it is good to make our API more flexible.
> However,
> > > we
> > > > >> may
> > > > >> > > >> need to
> > > > >> > > >> > > make our API meaningful.
> > > > >> > > >> > >
> > > > >> > > >> > > Take my previous reply as an example,
> > > > >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The result
> may
> > > be
> > > > >> > > >> > meaningless.
> > > > >> > > >> > > Another example I find is the intervalJoin, e.g.,
> > > > >> > > >> > >
> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
> > > > >> this
> > > > >> > > >> case, it
> > > > >> > > >> > > will bring problems if input1 and input2 share
> different
> > > > >> > > parallelism.
> > > > >> > > >> We
> > > > >> > > >> > > don't know which input should the join chained with?
> Even
> > > if
> > > > >> they
> > > > >> > > >> share
> > > > >> > > >> > the
> > > > >> > > >> > > same parallelism, it's hard to tell what the join is
> doing.
> > > > >> There
> > > > >> > > are
> > > > >> > > >> > maybe
> > > > >> > > >> > > some other problems.
> > > > >> > > >> > >
> > > > >> > > >> > > From this point of view, it's at least not good to
> enable
> > > all
> > > > >> > > >> > > functionalities for localKeyBy from KeyedStream?
> > > > >> > > >> > >
> > > > >> > > >> > > Great to also have your opinions.
> > > > >> > > >> > >
> > > > >> > > >> > > Best, Hequn
> > > > >> > > >> > >
> > > > >> > > >> > >
> > > > >> > > >> > >
> > > > >> > > >> > >
> > > > >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
> > > > >> [hidden email] <mailto:[hidden email]>
> > > > >> > >
> > > > >> > > >> > wrote:
> > > > >> > > >> > >
> > > > >> > > >> > > > Hi Kurt and Piotrek,
> > > > >> > > >> > > >
> > > > >> > > >> > > > Thanks for your comments.
> > > > >> > > >> > > >
> > > > >> > > >> > > > I agree that we can provide a better abstraction to
> be
> > > > >> > compatible
> > > > >> > > >> with
> > > > >> > > >> > > two
> > > > >> > > >> > > > different implementations.
> > > > >> > > >> > > >
> > > > >> > > >> > > > First of all, I think we should consider what kind of
> > > > >> scenarios
> > > > >> > we
> > > > >> > > >> need
> > > > >> > > >> > > to
> > > > >> > > >> > > > support in *API* level?
> > > > >> > > >> > > >
> > > > >> > > >> > > > We have some use cases which need to a customized
> > > > aggregation
> > > > >> > > >> through
> > > > >> > > >> > > > KeyedProcessFunction, (in the usage of our
> > > > localKeyBy.window
> > > > >> > they
> > > > >> > > >> can
> > > > >> > > >> > use
> > > > >> > > >> > > > ProcessWindowFunction).
> > > > >> > > >> > > >
> > > > >> > > >> > > > Shall we support these flexible use scenarios?
> > > > >> > > >> > > >
> > > > >> > > >> > > > Best,
> > > > >> > > >> > > > Vino
> > > > >> > > >> > > >
> > > > >> > > >> > > > Kurt Young <[hidden email] <mailto:
> [hidden email]>> 于2019年6月18日周二 下午8:37写道：
> > > > >> > > >> > > >
> > > > >> > > >> > > > > Hi Piotr,
> > > > >> > > >> > > > >
> > > > >> > > >> > > > > Thanks for joining the discussion. Make “local
> > > > aggregation"
> > > > >> > > >> abstract
> > > > >> > > >> > > > enough
> > > > >> > > >> > > > > sounds good to me, we could
> > > > >> > > >> > > > > implement and verify alternative solutions for use
> > > cases
> > > > of
> > > > >> > > local
> > > > >> > > >> > > > > aggregation. Maybe we will find both solutions
> > > > >> > > >> > > > > are appropriate for different scenarios.
> > > > >> > > >> > > > >
> > > > >> > > >> > > > > Starting from a simple one sounds a practical way
> to
> > > go.
> > > > >> What
> > > > >> > do
> > > > >> > > >> you
> > > > >> > > >> > > > think,
> > > > >> > > >> > > > > vino?
> > > > >> > > >> > > > >
> > > > >> > > >> > > > > Best,
> > > > >> > > >> > > > > Kurt
> > > > >> > > >> > > > >
> > > > >> > > >> > > > >
> > > > >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
> > > > >> > > >> > > > > wrote:
> > > > >> > > >> > > > >
> > > > >> > > >> > > > > > Hi Kurt and Vino,
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > > > I think there is a trade of hat we need to
> consider
> > > for
> > > > >> the
> > > > >> > > >> local
> > > > >> > > >> > > > > > aggregation.
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > > > Generally speaking I would agree with Kurt about
> > > local
> > > > >> > > >> > > aggregation/pre
> > > > >> > > >> > > > > > aggregation not using Flink's state flush the
> > > operator
> > > > >> on a
> > > > >> > > >> > > checkpoint.
> > > > >> > > >> > > > > > Network IO is usually cheaper compared to Disks
> IO.
> > > > This
> > > > >> has
> > > > >> > > >> > however
> > > > >> > > >> > > > > couple
> > > > >> > > >> > > > > > of issues:
> > > > >> > > >> > > > > > 1. It can explode number of in-flight records
> during
> > > > >> > > checkpoint
> > > > >> > > >> > > barrier
> > > > >> > > >> > > > > > alignment, making checkpointing slower and
> decrease
> > > the
> > > > >> > actual
> > > > >> > > >> > > > > throughput.
> > > > >> > > >> > > > > > 2. This trades Disks IO on the local aggregation
> > > > machine
> > > > >> > with
> > > > >> > > >> CPU
> > > > >> > > >> > > (and
> > > > >> > > >> > > > > > Disks IO in case of RocksDB) on the final
> aggregation
> > > > >> > machine.
> > > > >> > > >> This
> > > > >> > > >> > > is
> > > > >> > > >> > > > > > fine, as long there is no huge data skew. If
> there is
> > > > >> only a
> > > > >> > > >> > handful
> > > > >> > > >> > > > (or
> > > > >> > > >> > > > > > even one single) hot keys, it might be better to
> keep
> > > > the
> > > > >> > > >> > persistent
> > > > >> > > >> > > > > state
> > > > >> > > >> > > > > > in the LocalAggregationOperator to offload final
> > > > >> aggregation
> > > > >> > > as
> > > > >> > > >> > much
> > > > >> > > >> > > as
> > > > >> > > >> > > > > > possible.
> > > > >> > > >> > > > > > 3. With frequent checkpointing local aggregation
> > > > >> > effectiveness
> > > > >> > > >> > would
> > > > >> > > >> > > > > > degrade.
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > > > I assume Kurt is correct, that in your use cases
> > > > >> stateless
> > > > >> > > >> operator
> > > > >> > > >> > > was
> > > > >> > > >> > > > > > behaving better, but I could easily see other use
> > > cases
> > > > >> as
> > > > >> > > well.
> > > > >> > > >> > For
> > > > >> > > >> > > > > > example someone is already using RocksDB, and
> his job
> > > > is
> > > > >> > > >> > bottlenecked
> > > > >> > > >> > > > on
> > > > >> > > >> > > > > a
> > > > >> > > >> > > > > > single window operator instance because of the
> data
> > > > >> skew. In
> > > > >> > > >> that
> > > > >> > > >> > > case
> > > > >> > > >> > > > > > stateful local aggregation would be probably a
> better
> > > > >> > choice.
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > > > Because of that, I think we should eventually
> provide
> > > > >> both
> > > > >> > > >> versions
> > > > >> > > >> > > and
> > > > >> > > >> > > > > in
> > > > >> > > >> > > > > > the initial version we should at least make the
> > > “local
> > > > >> > > >> aggregation
> > > > >> > > >> > > > > engine”
> > > > >> > > >> > > > > > abstract enough, that one could easily provide
> > > > different
> > > > >> > > >> > > implementation
> > > > >> > > >> > > > > > strategy.
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > > > Piotrek
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
> > > > [hidden email] <mailto:[hidden email]>
> > > > >> >
> > > > >> > > >> wrote:
> > > > >> > > >> > > > > > >
> > > > >> > > >> > > > > > > Hi,
> > > > >> > > >> > > > > > >
> > > > >> > > >> > > > > > > For the trigger, it depends on what operator we
> > > want
> > > > to
> > > > >> > use
> > > > >> > > >> under
> > > > >> > > >> > > the
> > > > >> > > >> > > > > > API.
> > > > >> > > >> > > > > > > If we choose to use window operator,
> > > > >> > > >> > > > > > > we should also use window's trigger. However, I
> > > also
> > > > >> think
> > > > >> > > >> reuse
> > > > >> > > >> > > > window
> > > > >> > > >> > > > > > > operator for this scenario may not be
> > > > >> > > >> > > > > > > the best choice. The reasons are the following:
> > > > >> > > >> > > > > > >
> > > > >> > > >> > > > > > > 1. As a lot of people already pointed out,
> window
> > > > >> relies
> > > > >> > > >> heavily
> > > > >> > > >> > on
> > > > >> > > >> > > > > state
> > > > >> > > >> > > > > > > and it will definitely effect performance. You
> can
> > > > >> > > >> > > > > > > argue that one can use heap based
> statebackend, but
> > > > >> this
> > > > >> > > will
> > > > >> > > >> > > > introduce
> > > > >> > > >> > > > > > > extra coupling. Especially we have a chance to
> > > > >> > > >> > > > > > > design a pure stateless operator.
> > > > >> > > >> > > > > > > 2. The window operator is *the most*
> complicated
> > > > >> operator
> > > > >> > > >> Flink
> > > > >> > > >> > > > > currently
> > > > >> > > >> > > > > > > have. Maybe we only need to pick a subset of
> > > > >> > > >> > > > > > > window operator to achieve the goal, but once
> the
> > > > user
> > > > >> > wants
> > > > >> > > >> to
> > > > >> > > >> > > have
> > > > >> > > >> > > > a
> > > > >> > > >> > > > > > deep
> > > > >> > > >> > > > > > > look at the localAggregation operator, it's
> still
> > > > >> > > >> > > > > > > hard to find out what's going on under the
> window
> > > > >> > operator.
> > > > >> > > >> For
> > > > >> > > >> > > > > > simplicity,
> > > > >> > > >> > > > > > > I would also recommend we introduce a dedicated
> > > > >> > > >> > > > > > > lightweight operator, which also much easier
> for a
> > > > >> user to
> > > > >> > > >> learn
> > > > >> > > >> > > and
> > > > >> > > >> > > > > use.
> > > > >> > > >> > > > > > >
> > > > >> > > >> > > > > > > For your question about increasing the burden
> in
> > > > >> > > >> > > > > > > `StreamOperator::prepareSnapshotPreBarrier()`,
> the
> > > > only
> > > > >> > > thing
> > > > >> > > >> > this
> > > > >> > > >> > > > > > function
> > > > >> > > >> > > > > > > need
> > > > >> > > >> > > > > > > to do is output all the partial results, it's
> > > purely
> > > > >> cpu
> > > > >> > > >> > workload,
> > > > >> > > >> > > > not
> > > > >> > > >> > > > > > > introducing any IO. I want to point out that
> even
> > > if
> > > > we
> > > > >> > have
> > > > >> > > >> this
> > > > >> > > >> > > > > > > cost, we reduced another barrier align cost of
> the
> > > > >> > operator,
> > > > >> > > >> > which
> > > > >> > > >> > > is
> > > > >> > > >> > > > > the
> > > > >> > > >> > > > > > > sync flush stage of the state, if you
> introduced
> > > > state.
> > > > >> > This
> > > > >> > > >> > > > > > > flush actually will introduce disk IO, and I
> think
> > > > it's
> > > > >> > > >> worthy to
> > > > >> > > >> > > > > > exchange
> > > > >> > > >> > > > > > > this cost with purely CPU workload. And we do
> have
> > > > some
> > > > >> > > >> > > > > > > observations about these two behavior (as i
> said
> > > > >> before,
> > > > >> > we
> > > > >> > > >> > > actually
> > > > >> > > >> > > > > > > implemented both solutions), the stateless one
> > > > actually
> > > > >> > > >> performs
> > > > >> > > >> > > > > > > better both in performance and barrier align
> time.
> > > > >> > > >> > > > > > >
> > > > >> > > >> > > > > > > Best,
> > > > >> > > >> > > > > > > Kurt
> > > > >> > > >> > > > > > >
> > > > >> > > >> > > > > > >
> > > > >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
> > > > >> > > >> [hidden email] <mailto:[hidden email]>
> > > > >> > > >> > >
> > > > >> > > >> > > > > wrote:
> > > > >> > > >> > > > > > >
> > > > >> > > >> > > > > > >> Hi Kurt,
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> Thanks for your example. Now, it looks more
> > > clearly
> > > > >> for
> > > > >> > me.
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> From your example code snippet, I saw the
> > > > >> localAggregate
> > > > >> > > API
> > > > >> > > >> has
> > > > >> > > >> > > > three
> > > > >> > > >> > > > > > >> parameters:
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >>   1. key field
> > > > >> > > >> > > > > > >>   2. PartitionAvg
> > > > >> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes
> from
> > > > window
> > > > >> > > >> package?
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> I will compare our and your design from API
> and
> > > > >> operator
> > > > >> > > >> level:
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> *From the API level:*
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> As I replied to @dianfu in the old email
> > > thread,[1]
> > > > >> the
> > > > >> > > >> Window
> > > > >> > > >> > API
> > > > >> > > >> > > > can
> > > > >> > > >> > > > > > >> provide the second and the third parameter
> right
> > > > now.
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> If you reuse specified interface or class,
> such as
> > > > >> > > *Trigger*
> > > > >> > > >> or
> > > > >> > > >> > > > > > >> *CounterTrigger* provided by window package,
> but
> > > do
> > > > >> not
> > > > >> > use
> > > > >> > > >> > window
> > > > >> > > >> > > > > API,
> > > > >> > > >> > > > > > >> it's not reasonable.
> > > > >> > > >> > > > > > >> And if you do not reuse these interface or
> class,
> > > > you
> > > > >> > would
> > > > >> > > >> need
> > > > >> > > >> > > to
> > > > >> > > >> > > > > > >> introduce more things however they are looked
> > > > similar
> > > > >> to
> > > > >> > > the
> > > > >> > > >> > > things
> > > > >> > > >> > > > > > >> provided by window package.
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> The window package has provided several types
> of
> > > the
> > > > >> > window
> > > > >> > > >> and
> > > > >> > > >> > > many
> > > > >> > > >> > > > > > >> triggers and let users customize it. What's
> more,
> > > > the
> > > > >> > user
> > > > >> > > is
> > > > >> > > >> > more
> > > > >> > > >> > > > > > familiar
> > > > >> > > >> > > > > > >> with Window API.
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> This is the reason why we just provide
> localKeyBy
> > > > API
> > > > >> and
> > > > >> > > >> reuse
> > > > >> > > >> > > the
> > > > >> > > >> > > > > > window
> > > > >> > > >> > > > > > >> API. It reduces unnecessary components such as
> > > > >> triggers
> > > > >> > and
> > > > >> > > >> the
> > > > >> > > >> > > > > > mechanism
> > > > >> > > >> > > > > > >> of buffer (based on count num or time).
> > > > >> > > >> > > > > > >> And it has a clear and easy to understand
> > > semantics.
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> *From the operator level:*
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> We reused window operator, so we can get all
> the
> > > > >> benefits
> > > > >> > > >> from
> > > > >> > > >> > > state
> > > > >> > > >> > > > > and
> > > > >> > > >> > > > > > >> checkpoint.
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> From your design, you named the operator under
> > > > >> > > localAggregate
> > > > >> > > >> > API
> > > > >> > > >> > > > is a
> > > > >> > > >> > > > > > >> *stateless* operator. IMO, it is still a
> state, it
> > > > is
> > > > >> > just
> > > > >> > > >> not
> > > > >> > > >> > > Flink
> > > > >> > > >> > > > > > >> managed state.
> > > > >> > > >> > > > > > >> About the memory buffer (I think it's still
> not
> > > very
> > > > >> > clear,
> > > > >> > > >> if
> > > > >> > > >> > you
> > > > >> > > >> > > > > have
> > > > >> > > >> > > > > > >> time, can you give more detail information or
> > > answer
> > > > >> my
> > > > >> > > >> > > questions),
> > > > >> > > >> > > > I
> > > > >> > > >> > > > > > have
> > > > >> > > >> > > > > > >> some questions:
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >>   - if it just a raw JVM heap memory buffer,
> how
> > > to
> > > > >> > support
> > > > >> > > >> > fault
> > > > >> > > >> > > > > > >>   tolerance, if the job is configured
> EXACTLY-ONCE
> > > > >> > semantic
> > > > >> > > >> > > > guarantee?
> > > > >> > > >> > > > > > >>   - if you thought the memory buffer(non-Flink
> > > > state),
> > > > >> > has
> > > > >> > > >> > better
> > > > >> > > >> > > > > > >>   performance. In our design, users can also
> > > config
> > > > >> HEAP
> > > > >> > > >> state
> > > > >> > > >> > > > backend
> > > > >> > > >> > > > > > to
> > > > >> > > >> > > > > > >>   provide the performance close to your
> mechanism.
> > > > >> > > >> > > > > > >>   -
> `StreamOperator::prepareSnapshotPreBarrier()`
> > > > >> related
> > > > >> > > to
> > > > >> > > >> the
> > > > >> > > >> > > > > timing
> > > > >> > > >> > > > > > of
> > > > >> > > >> > > > > > >>   snapshot. IMO, the flush action should be a
> > > > >> > synchronized
> > > > >> > > >> > action?
> > > > >> > > >> > > > (if
> > > > >> > > >> > > > > > >> not,
> > > > >> > > >> > > > > > >>   please point out my mistake) I still think
> we
> > > > should
> > > > >> > not
> > > > >> > > >> > depend
> > > > >> > > >> > > on
> > > > >> > > >> > > > > the
> > > > >> > > >> > > > > > >>   timing of checkpoint. Checkpoint related
> > > > operations
> > > > >> are
> > > > >> > > >> > inherent
> > > > >> > > >> > > > > > >>   performance sensitive, we should not
> increase
> > > its
> > > > >> > burden
> > > > >> > > >> > > anymore.
> > > > >> > > >> > > > > Our
> > > > >> > > >> > > > > > >>   implementation based on the mechanism of
> Flink's
> > > > >> > > >> checkpoint,
> > > > >> > > >> > > which
> > > > >> > > >> > > > > can
> > > > >> > > >> > > > > > >>   benefit from the asnyc snapshot and
> incremental
> > > > >> > > checkpoint.
> > > > >> > > >> > IMO,
> > > > >> > > >> > > > the
> > > > >> > > >> > > > > > >>   performance is not a problem, and we also
> do not
> > > > >> find
> > > > >> > the
> > > > >> > > >> > > > > performance
> > > > >> > > >> > > > > > >> issue
> > > > >> > > >> > > > > > >>   in our production.
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> [1]:
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > >
> > > > >> > > >> > > >
> > > > >> > > >> > >
> > > > >> > > >> >
> > > > >> > > >>
> > > > >> > >
> > > > >> >
> > > > >>
> > > >
> > >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> <
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> Best,
> > > > >> > > >> > > > > > >> Vino
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >> Kurt Young <[hidden email] <mailto:
> [hidden email]>> 于2019年6月18日周二
> > > > 下午2:27写道：
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > > >>> Yeah, sorry for not expressing myself
> clearly. I
> > > > will
> > > > >> > try
> > > > >> > > to
> > > > >> > > >> > > > provide
> > > > >> > > >> > > > > > more
> > > > >> > > >> > > > > > >>> details to make sure we are on the same page.
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>> For DataStream API, it shouldn't be optimized
> > > > >> > > automatically.
> > > > >> > > >> > You
> > > > >> > > >> > > > have
> > > > >> > > >> > > > > > to
> > > > >> > > >> > > > > > >>> explicitly call API to do local aggregation
> > > > >> > > >> > > > > > >>> as well as the trigger policy of the local
> > > > >> aggregation.
> > > > >> > > Take
> > > > >> > > >> > > > average
> > > > >> > > >> > > > > > for
> > > > >> > > >> > > > > > >>> example, the user program may look like this
> > > (just
> > > > a
> > > > >> > > draft):
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>> assuming the input type is
> > > DataStream<Tupl2<String,
> > > > >> > Int>>
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>> ds.localAggregate(
> > > > >> > > >> > > > > > >>>        0,
> > >  //
> > > > >> The
> > > > >> > > local
> > > > >> > > >> > key,
> > > > >> > > >> > > > > which
> > > > >> > > >> > > > > > >> is
> > > > >> > > >> > > > > > >>> the String from Tuple2
> > > > >> > > >> > > > > > >>>        PartitionAvg(1),                 //
> The
> > > > >> partial
> > > > >> > > >> > > aggregation
> > > > >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>,
> indicating
> > > > sum
> > > > >> and
> > > > >> > > >> count
> > > > >> > > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger
> > > policy,
> > > > >> note
> > > > >> > > >> this
> > > > >> > > >> > > > should
> > > > >> > > >> > > > > be
> > > > >> > > >> > > > > > >>> best effort, and also be composited with time
> > > based
> > > > >> or
> > > > >> > > >> memory
> > > > >> > > >> > > size
> > > > >> > > >> > > > > > based
> > > > >> > > >> > > > > > >>> trigger
> > > > >> > > >> > > > > > >>>    )
>    //
> > > > The
> > > > >> > > return
> > > > >> > > >> > type
> > > > >> > > >> > > > is
> > > > >> > > >> > > > > > >> local
> > > > >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
> > > > >> > > >> > > > > > >>>    .keyBy(0)                             //
> > > Further
> > > > >> > keyby
> > > > >> > > it
> > > > >> > > >> > with
> > > > >> > > >> > > > > > >> required
> > > > >> > > >> > > > > > >>> key
> > > > >> > > >> > > > > > >>>    .aggregate(1)                      // This
> > > will
> > > > >> merge
> > > > >> > > all
> > > > >> > > >> > the
> > > > >> > > >> > > > > > partial
> > > > >> > > >> > > > > > >>> results and get the final average.
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>> (This is only a draft, only trying to explain
> > > what
> > > > it
> > > > >> > > looks
> > > > >> > > >> > > like. )
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>> The local aggregate operator can be
> stateless, we
> > > > can
> > > > >> > > keep a
> > > > >> > > >> > > memory
> > > > >> > > >> > > > > > >> buffer
> > > > >> > > >> > > > > > >>> or other efficient data structure to improve
> the
> > > > >> > aggregate
> > > > >> > > >> > > > > performance.
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>> Let me know if you have any other questions.
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>> Best,
> > > > >> > > >> > > > > > >>> Kurt
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
> > > > >> > > >> > [hidden email] <mailto:[hidden email]>
> > > > >> > > >> > > >
> > > > >> > > >> > > > > > wrote:
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>>> Hi Kurt,
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> Thanks for your reply.
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> Actually, I am not against you to raise your
> > > > design.
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> From your description before, I just can
> imagine
> > > > >> your
> > > > >> > > >> > high-level
> > > > >> > > >> > > > > > >>>> implementation is about SQL and the
> optimization
> > > > is
> > > > >> > inner
> > > > >> > > >> of
> > > > >> > > >> > the
> > > > >> > > >> > > > > API.
> > > > >> > > >> > > > > > >> Is
> > > > >> > > >> > > > > > >>> it
> > > > >> > > >> > > > > > >>>> automatically? how to give the configuration
> > > > option
> > > > >> > about
> > > > >> > > >> > > trigger
> > > > >> > > >> > > > > > >>>> pre-aggregation?
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> Maybe after I get more information, it
> sounds
> > > more
> > > > >> > > >> reasonable.
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> IMO, first of all, it would be better to
> make
> > > your
> > > > >> user
> > > > >> > > >> > > interface
> > > > >> > > >> > > > > > >>> concrete,
> > > > >> > > >> > > > > > >>>> it's the basis of the discussion.
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> For example, can you give an example code
> > > snippet
> > > > to
> > > > >> > > >> introduce
> > > > >> > > >> > > how
> > > > >> > > >> > > > > to
> > > > >> > > >> > > > > > >>> help
> > > > >> > > >> > > > > > >>>> users to process data skew caused by the
> jobs
> > > > which
> > > > >> > built
> > > > >> > > >> with
> > > > >> > > >> > > > > > >> DataStream
> > > > >> > > >> > > > > > >>>> API?
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> If you give more details we can discuss
> further
> > > > >> more. I
> > > > >> > > >> think
> > > > >> > > >> > if
> > > > >> > > >> > > > one
> > > > >> > > >> > > > > > >>> design
> > > > >> > > >> > > > > > >>>> introduces an exact interface and another
> does
> > > > not.
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> The implementation has an obvious
> difference.
> > > For
> > > > >> > > example,
> > > > >> > > >> we
> > > > >> > > >> > > > > > introduce
> > > > >> > > >> > > > > > >>> an
> > > > >> > > >> > > > > > >>>> exact API in DataStream named localKeyBy,
> about
> > > > the
> > > > >> > > >> > > > pre-aggregation
> > > > >> > > >> > > > > we
> > > > >> > > >> > > > > > >>> need
> > > > >> > > >> > > > > > >>>> to define the trigger mechanism of local
> > > > >> aggregation,
> > > > >> > so
> > > > >> > > we
> > > > >> > > >> > find
> > > > >> > > >> > > > > > reused
> > > > >> > > >> > > > > > >>>> window API and operator is a good choice.
> This
> > > is
> > > > a
> > > > >> > > >> reasoning
> > > > >> > > >> > > link
> > > > >> > > >> > > > > > from
> > > > >> > > >> > > > > > >>>> design to implementation.
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> What do you think?
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> Best,
> > > > >> > > >> > > > > > >>>> Vino
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>> Kurt Young <[hidden email] <mailto:
> [hidden email]>> 于2019年6月18日周二
> > > > >> 上午11:58写道：
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>>> Hi Vino,
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>> Now I feel that we may have different
> > > > >> understandings
> > > > >> > > about
> > > > >> > > >> > what
> > > > >> > > >> > > > > kind
> > > > >> > > >> > > > > > >> of
> > > > >> > > >> > > > > > >>>>> problems or improvements you want to
> > > > >> > > >> > > > > > >>>>> resolve. Currently, most of the feedback
> are
> > > > >> focusing
> > > > >> > on
> > > > >> > > >> *how
> > > > >> > > >> > > to
> > > > >> > > >> > > > > do a
> > > > >> > > >> > > > > > >>>>> proper local aggregation to improve
> performance
> > > > >> > > >> > > > > > >>>>> and maybe solving the data skew issue*.
> And my
> > > > gut
> > > > >> > > >> feeling is
> > > > >> > > >> > > > this
> > > > >> > > >> > > > > is
> > > > >> > > >> > > > > > >>>>> exactly what users want at the first place,
> > > > >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to
> > > summarize
> > > > >> here,
> > > > >> > > >> please
> > > > >> > > >> > > > > correct
> > > > >> > > >> > > > > > >>> me
> > > > >> > > >> > > > > > >>>> if
> > > > >> > > >> > > > > > >>>>> i'm wrong).
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>> But I still think the design is somehow
> > > diverged
> > > > >> from
> > > > >> > > the
> > > > >> > > >> > goal.
> > > > >> > > >> > > > If
> > > > >> > > >> > > > > we
> > > > >> > > >> > > > > > >>>> want
> > > > >> > > >> > > > > > >>>>> to have an efficient and powerful way to
> > > > >> > > >> > > > > > >>>>> have local aggregation, supporting
> intermedia
> > > > >> result
> > > > >> > > type
> > > > >> > > >> is
> > > > >> > > >> > > > > > >> essential
> > > > >> > > >> > > > > > >>>> IMO.
> > > > >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
> > > > >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have a
> > > > proper
> > > > >> > > >> support of
> > > > >> > > >> > > > > > >>>> intermediate
> > > > >> > > >> > > > > > >>>>> result type and can do `merge` operation
> > > > >> > > >> > > > > > >>>>> on them.
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>> Now, we have a lightweight alternatives
> which
> > > > >> performs
> > > > >> > > >> well,
> > > > >> > > >> > > and
> > > > >> > > >> > > > > > >> have a
> > > > >> > > >> > > > > > >>>>> nice fit with the local aggregate
> requirements.
> > > > >> > > >> > > > > > >>>>> Mostly importantly,  it's much less complex
> > > > because
> > > > >> > it's
> > > > >> > > >> > > > stateless.
> > > > >> > > >> > > > > > >> And
> > > > >> > > >> > > > > > >>>> it
> > > > >> > > >> > > > > > >>>>> can also achieve the similar
> > > multiple-aggregation
> > > > >> > > >> > > > > > >>>>> scenario.
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>> I still not convinced why we shouldn't
> consider
> > > > it
> > > > >> as
> > > > >> > a
> > > > >> > > >> first
> > > > >> > > >> > > > step.
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>> Best,
> > > > >> > > >> > > > > > >>>>> Kurt
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino yang
> <
> > > > >> > > >> > > > [hidden email] <mailto:[hidden email]
> >>
> > > > >> > > >> > > > > > >>>> wrote:
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>>> Hi Kurt,
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> Thanks for your comments.
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> It seems we both implemented local
> aggregation
> > > > >> > feature
> > > > >> > > to
> > > > >> > > >> > > > optimize
> > > > >> > > >> > > > > > >>> the
> > > > >> > > >> > > > > > >>>>>> issue of data skew.
> > > > >> > > >> > > > > > >>>>>> However, IMHO, the API level of optimizing
> > > > >> revenue is
> > > > >> > > >> > > different.
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> *Your optimization benefits from Flink
> SQL and
> > > > >> it's
> > > > >> > not
> > > > >> > > >> > user's
> > > > >> > > >> > > > > > >>>> faces.(If
> > > > >> > > >> > > > > > >>>>> I
> > > > >> > > >> > > > > > >>>>>> understand it incorrectly, please correct
> > > > this.)*
> > > > >> > > >> > > > > > >>>>>> *Our implementation employs it as an
> > > > optimization
> > > > >> > tool
> > > > >> > > >> API
> > > > >> > > >> > for
> > > > >> > > >> > > > > > >>>>> DataStream,
> > > > >> > > >> > > > > > >>>>>> it just like a local version of the keyBy
> > > API.*
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> Based on this, I want to say support it
> as a
> > > > >> > DataStream
> > > > >> > > >> API
> > > > >> > > >> > > can
> > > > >> > > >> > > > > > >>> provide
> > > > >> > > >> > > > > > >>>>>> these advantages:
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear
> semantic
> > > and
> > > > >> it's
> > > > >> > > >> > flexible
> > > > >> > > >> > > > not
> > > > >> > > >> > > > > > >>> only
> > > > >> > > >> > > > > > >>>>> for
> > > > >> > > >> > > > > > >>>>>>   processing data skew but also for
> > > implementing
> > > > >> some
> > > > >> > > >> user
> > > > >> > > >> > > > cases,
> > > > >> > > >> > > > > > >>> for
> > > > >> > > >> > > > > > >>>>>>   example, if we want to calculate the
> > > > >> multiple-level
> > > > >> > > >> > > > aggregation,
> > > > >> > > >> > > > > > >>> we
> > > > >> > > >> > > > > > >>>>> can
> > > > >> > > >> > > > > > >>>>>> do
> > > > >> > > >> > > > > > >>>>>>   multiple-level aggregation in the local
> > > > >> > aggregation:
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
> > > > >> > > >> //
> > > > >> > > >> > > here
> > > > >> > > >> > > > > > >> "a"
> > > > >> > > >> > > > > > >>>> is
> > > > >> > > >> > > > > > >>>>> a
> > > > >> > > >> > > > > > >>>>>>   sub-category, while "b" is a category,
> here
> > > we
> > > > >> do
> > > > >> > not
> > > > >> > > >> need
> > > > >> > > >> > > to
> > > > >> > > >> > > > > > >>>> shuffle
> > > > >> > > >> > > > > > >>>>>> data
> > > > >> > > >> > > > > > >>>>>>   in the network.
> > > > >> > > >> > > > > > >>>>>>   - The users of DataStream API will
> benefit
> > > > from
> > > > >> > this.
> > > > >> > > >> > > > Actually,
> > > > >> > > >> > > > > > >> we
> > > > >> > > >> > > > > > >>>>> have
> > > > >> > > >> > > > > > >>>>>>   a lot of scenes need to use DataStream
> API.
> > > > >> > > Currently,
> > > > >> > > >> > > > > > >> DataStream
> > > > >> > > >> > > > > > >>>> API
> > > > >> > > >> > > > > > >>>>> is
> > > > >> > > >> > > > > > >>>>>>   the cornerstone of the physical plan of
> > > Flink
> > > > >> SQL.
> > > > >> > > >> With a
> > > > >> > > >> > > > > > >>> localKeyBy
> > > > >> > > >> > > > > > >>>>>> API,
> > > > >> > > >> > > > > > >>>>>>   the optimization of SQL at least may use
> > > this
> > > > >> > > optimized
> > > > >> > > >> > API,
> > > > >> > > >> > > > > > >> this
> > > > >> > > >> > > > > > >>>> is a
> > > > >> > > >> > > > > > >>>>>>   further topic.
> > > > >> > > >> > > > > > >>>>>>   - Based on the window operator, our
> state
> > > > would
> > > > >> > > benefit
> > > > >> > > >> > from
> > > > >> > > >> > > > > > >> Flink
> > > > >> > > >> > > > > > >>>>> State
> > > > >> > > >> > > > > > >>>>>>   and checkpoint, we do not need to worry
> > > about
> > > > >> OOM
> > > > >> > and
> > > > >> > > >> job
> > > > >> > > >> > > > > > >> failed.
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> Now, about your questions:
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> 1. About our design cannot change the data
> > > type
> > > > >> and
> > > > >> > > about
> > > > >> > > >> > the
> > > > >> > > >> > > > > > >>>>>> implementation of average:
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the
> localKeyBy is
> > > > an
> > > > >> API
> > > > >> > > >> > provides
> > > > >> > > >> > > > to
> > > > >> > > >> > > > > > >> the
> > > > >> > > >> > > > > > >>>>> users
> > > > >> > > >> > > > > > >>>>>> who use DataStream API to build their
> jobs.
> > > > >> > > >> > > > > > >>>>>> Users should know its semantics and the
> > > > difference
> > > > >> > with
> > > > >> > > >> > keyBy
> > > > >> > > >> > > > API,
> > > > >> > > >> > > > > > >> so
> > > > >> > > >> > > > > > >>>> if
> > > > >> > > >> > > > > > >>>>>> they want to the average aggregation, they
> > > > should
> > > > >> > carry
> > > > >> > > >> > local
> > > > >> > > >> > > > sum
> > > > >> > > >> > > > > > >>>> result
> > > > >> > > >> > > > > > >>>>>> and local count result.
> > > > >> > > >> > > > > > >>>>>> I admit that it will be convenient to use
> > > keyBy
> > > > >> > > directly.
> > > > >> > > >> > But
> > > > >> > > >> > > we
> > > > >> > > >> > > > > > >> need
> > > > >> > > >> > > > > > >>>> to
> > > > >> > > >> > > > > > >>>>>> pay a little price when we get some
> benefits.
> > > I
> > > > >> think
> > > > >> > > >> this
> > > > >> > > >> > > price
> > > > >> > > >> > > > > is
> > > > >> > > >> > > > > > >>>>>> reasonable. Considering that the
> DataStream
> > > API
> > > > >> > itself
> > > > >> > > >> is a
> > > > >> > > >> > > > > > >> low-level
> > > > >> > > >> > > > > > >>>> API
> > > > >> > > >> > > > > > >>>>>> (at least for now).
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> 2. About stateless operator and
> > > > >> > > >> > > > > > >>>>>>
> `StreamOperator::prepareSnapshotPreBarrier()`:
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> Actually, I have discussed this opinion
> with
> > > > >> @dianfu
> > > > >> > in
> > > > >> > > >> the
> > > > >> > > >> > > old
> > > > >> > > >> > > > > > >> mail
> > > > >> > > >> > > > > > >>>>>> thread. I will copy my opinion from there:
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>>   - for your design, you still need
> somewhere
> > > to
> > > > >> give
> > > > >> > > the
> > > > >> > > >> > > users
> > > > >> > > >> > > > > > >>>>> configure
> > > > >> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
> > > > >> availability?),
> > > > >> > > >> this
> > > > >> > > >> > > > design
> > > > >> > > >> > > > > > >>>> cannot
> > > > >> > > >> > > > > > >>>>>>   guarantee a deterministic semantics (it
> will
> > > > >> bring
> > > > >> > > >> trouble
> > > > >> > > >> > > for
> > > > >> > > >> > > > > > >>>> testing
> > > > >> > > >> > > > > > >>>>>> and
> > > > >> > > >> > > > > > >>>>>>   debugging).
> > > > >> > > >> > > > > > >>>>>>   - if the implementation depends on the
> > > timing
> > > > of
> > > > >> > > >> > checkpoint,
> > > > >> > > >> > > > it
> > > > >> > > >> > > > > > >>>> would
> > > > >> > > >> > > > > > >>>>>>   affect the checkpoint's progress, and
> the
> > > > >> buffered
> > > > >> > > data
> > > > >> > > >> > may
> > > > >> > > >> > > > > > >> cause
> > > > >> > > >> > > > > > >>>> OOM
> > > > >> > > >> > > > > > >>>>>>   issue. In addition, if the operator is
> > > > >> stateless,
> > > > >> > it
> > > > >> > > >> can
> > > > >> > > >> > not
> > > > >> > > >> > > > > > >>> provide
> > > > >> > > >> > > > > > >>>>>> fault
> > > > >> > > >> > > > > > >>>>>>   tolerance.
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> Best,
> > > > >> > > >> > > > > > >>>>>> Vino
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>> Kurt Young <[hidden email] <mailto:
> [hidden email]>> 于2019年6月18日周二
> > > > >> > 上午9:22写道：
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>>> Hi Vino,
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the
> general
> > > > idea
> > > > >> and
> > > > >> > > IMO
> > > > >> > > >> > it's
> > > > >> > > >> > > > > > >> very
> > > > >> > > >> > > > > > >>>>> useful
> > > > >> > > >> > > > > > >>>>>>> feature.
> > > > >> > > >> > > > > > >>>>>>> But after reading through the document, I
> > > feel
> > > > >> that
> > > > >> > we
> > > > >> > > >> may
> > > > >> > > >> > > over
> > > > >> > > >> > > > > > >>>> design
> > > > >> > > >> > > > > > >>>>>> the
> > > > >> > > >> > > > > > >>>>>>> required
> > > > >> > > >> > > > > > >>>>>>> operator for proper local aggregation.
> The
> > > main
> > > > >> > reason
> > > > >> > > >> is
> > > > >> > > >> > we
> > > > >> > > >> > > > want
> > > > >> > > >> > > > > > >>> to
> > > > >> > > >> > > > > > >>>>>> have a
> > > > >> > > >> > > > > > >>>>>>> clear definition and behavior about the
> > > "local
> > > > >> keyed
> > > > >> > > >> state"
> > > > >> > > >> > > > which
> > > > >> > > >> > > > > > >>> in
> > > > >> > > >> > > > > > >>>> my
> > > > >> > > >> > > > > > >>>>>>> opinion is not
> > > > >> > > >> > > > > > >>>>>>> necessary for local aggregation, at
> least for
> > > > >> start.
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>> Another issue I noticed is the local key
> by
> > > > >> operator
> > > > >> > > >> cannot
> > > > >> > > >> > > > > > >> change
> > > > >> > > >> > > > > > >>>>>> element
> > > > >> > > >> > > > > > >>>>>>> type, it will
> > > > >> > > >> > > > > > >>>>>>> also restrict a lot of use cases which
> can be
> > > > >> > benefit
> > > > >> > > >> from
> > > > >> > > >> > > > local
> > > > >> > > >> > > > > > >>>>>>> aggregation, like "average".
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and the
> only
> > > > >> thing
> > > > >> > > >> need to
> > > > >> > > >> > > be
> > > > >> > > >> > > > > > >> done
> > > > >> > > >> > > > > > >>>> is
> > > > >> > > >> > > > > > >>>>>>> introduce
> > > > >> > > >> > > > > > >>>>>>> a stateless lightweight operator which is
> > > > >> *chained*
> > > > >> > > >> before
> > > > >> > > >> > > > > > >>> `keyby()`.
> > > > >> > > >> > > > > > >>>>> The
> > > > >> > > >> > > > > > >>>>>>> operator will flush all buffered
> > > > >> > > >> > > > > > >>>>>>> elements during
> > > > >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
> > > > >> > > >> > > > and
> > > > >> > > >> > > > > > >>>> make
> > > > >> > > >> > > > > > >>>>>>> himself stateless.
> > > > >> > > >> > > > > > >>>>>>> By the way, in the earlier version we
> also
> > > did
> > > > >> the
> > > > >> > > >> similar
> > > > >> > > >> > > > > > >> approach
> > > > >> > > >> > > > > > >>>> by
> > > > >> > > >> > > > > > >>>>>>> introducing a stateful
> > > > >> > > >> > > > > > >>>>>>> local aggregation operator but it's not
> > > > >> performed as
> > > > >> > > >> well
> > > > >> > > >> > as
> > > > >> > > >> > > > the
> > > > >> > > >> > > > > > >>>> later
> > > > >> > > >> > > > > > >>>>>> one,
> > > > >> > > >> > > > > > >>>>>>> and also effect the barrie
> > > > >> > > >> > > > > > >>>>>>> alignment time. The later one is fairly
> > > simple
> > > > >> and
> > > > >> > > more
> > > > >> > > >> > > > > > >> efficient.
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>> I would highly suggest you to consider to
> > > have
> > > > a
> > > > >> > > >> stateless
> > > > >> > > >> > > > > > >> approach
> > > > >> > > >> > > > > > >>>> at
> > > > >> > > >> > > > > > >>>>>> the
> > > > >> > > >> > > > > > >>>>>>> first step.
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>> Best,
> > > > >> > > >> > > > > > >>>>>>> Kurt
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu <
> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
> > > > >> > > >> > > > > > >> wrote:
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>>> Hi Vino,
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>> Regarding to the
> "input.keyBy(0).sum(1)" vs
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> >
> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
> > > > >> > > >> > > > > > >> have
> > > > >> > > >> > > > > > >>>> you
> > > > >> > > >> > > > > > >>>>>>> done
> > > > >> > > >> > > > > > >>>>>>>> some benchmark?
> > > > >> > > >> > > > > > >>>>>>>> Because I'm curious about how much
> > > performance
> > > > >> > > >> improvement
> > > > >> > > >> > > can
> > > > >> > > >> > > > > > >> we
> > > > >> > > >> > > > > > >>>> get
> > > > >> > > >> > > > > > >>>>>> by
> > > > >> > > >> > > > > > >>>>>>>> using count window as the local
> operator.
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>> Best,
> > > > >> > > >> > > > > > >>>>>>>> Jark
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino yang
> <
> > > > >> > > >> > > > [hidden email] <mailto:[hidden email]>
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>>>> wrote:
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> Hi Hequn,
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to
> > > provide a
> > > > >> tool
> > > > >> > > >> which
> > > > >> > > >> > > can
> > > > >> > > >> > > > > > >>> let
> > > > >> > > >> > > > > > >>>>>> users
> > > > >> > > >> > > > > > >>>>>>> do
> > > > >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The
> behavior
> > > of
> > > > >> the
> > > > >> > > >> > > > > > >>> pre-aggregation
> > > > >> > > >> > > > > > >>>>> is
> > > > >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> So the three cases are different, I
> will
> > > > >> describe
> > > > >> > > them
> > > > >> > > >> > one
> > > > >> > > >> > > by
> > > > >> > > >> > > > > > >>>> one:
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the result is
> event-driven,
> > > > each
> > > > >> > > event
> > > > >> > > >> can
> > > > >> > > >> > > > > > >>> produce
> > > > >> > > >> > > > > > >>>>> one
> > > > >> > > >> > > > > > >>>>>>> sum
> > > > >> > > >> > > > > > >>>>>>>>> aggregation result and it is the
> latest one
> > > > >> from
> > > > >> > the
> > > > >> > > >> > source
> > > > >> > > >> > > > > > >>>> start.*
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> 2.
> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have a
> > > > >> problem, it
> > > > >> > > >> would
> > > > >> > > >> > do
> > > > >> > > >> > > > > > >> the
> > > > >> > > >> > > > > > >>>>> local
> > > > >> > > >> > > > > > >>>>>>> sum
> > > > >> > > >> > > > > > >>>>>>>>> aggregation and will produce the latest
> > > > partial
> > > > >> > > result
> > > > >> > > >> > from
> > > > >> > > >> > > > > > >> the
> > > > >> > > >> > > > > > >>>>>> source
> > > > >> > > >> > > > > > >>>>>>>>> start for every event. *
> > > > >> > > >> > > > > > >>>>>>>>> *These latest partial results from the
> same
> > > > key
> > > > >> > are
> > > > >> > > >> > hashed
> > > > >> > > >> > > to
> > > > >> > > >> > > > > > >>> one
> > > > >> > > >> > > > > > >>>>>> node
> > > > >> > > >> > > > > > >>>>>>> to
> > > > >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
> > > > >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it
> > > received
> > > > >> > > multiple
> > > > >> > > >> > > partial
> > > > >> > > >> > > > > > >>>>> results
> > > > >> > > >> > > > > > >>>>>>>> (they
> > > > >> > > >> > > > > > >>>>>>>>> are all calculated from the source
> start)
> > > and
> > > > >> sum
> > > > >> > > them
> > > > >> > > >> > will
> > > > >> > > >> > > > > > >> get
> > > > >> > > >> > > > > > >>>> the
> > > > >> > > >> > > > > > >>>>>>> wrong
> > > > >> > > >> > > > > > >>>>>>>>> result.*
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> 3.
> > > > >> > > >> > >
> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a
> partial
> > > > >> > > aggregation
> > > > >> > > >> > > result
> > > > >> > > >> > > > > > >>> for
> > > > >> > > >> > > > > > >>>>>> the 5
> > > > >> > > >> > > > > > >>>>>>>>> records in the count window. The
> partial
> > > > >> > aggregation
> > > > >> > > >> > > results
> > > > >> > > >> > > > > > >>> from
> > > > >> > > >> > > > > > >>>>> the
> > > > >> > > >> > > > > > >>>>>>>> same
> > > > >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> So the first case and the third case
> can
> > > get
> > > > >> the
> > > > >> > > >> *same*
> > > > >> > > >> > > > > > >> result,
> > > > >> > > >> > > > > > >>>> the
> > > > >> > > >> > > > > > >>>>>>>>> difference is the output-style and the
> > > > latency.
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key API
> is
> > > just
> > > > >> an
> > > > >> > > >> > > optimization
> > > > >> > > >> > > > > > >>>> API.
> > > > >> > > >> > > > > > >>>>> We
> > > > >> > > >> > > > > > >>>>>>> do
> > > > >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the
> user
> > > has
> > > > to
> > > > >> > > >> > understand
> > > > >> > > >> > > > > > >> its
> > > > >> > > >> > > > > > >>>>>>> semantics
> > > > >> > > >> > > > > > >>>>>>>>> and use it correctly.
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> Best,
> > > > >> > > >> > > > > > >>>>>>>>> Vino
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]
> <mailto:[hidden email]>>
> > > > >> 于2019年6月17日周一
> > > > >> > > >> > 下午4:18写道：
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>> Hi Vino,
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it
> is a
> > > > very
> > > > >> > good
> > > > >> > > >> > > feature!
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the
> > > > semantics
> > > > >> > for
> > > > >> > > >> the
> > > > >> > > >> > > > > > >>>>>> `localKeyBy`.
> > > > >> > > >> > > > > > >>>>>>>> From
> > > > >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API
> returns
> > > > an
> > > > >> > > >> instance
> > > > >> > > >> > of
> > > > >> > > >> > > > > > >>>>>>> `KeyedStream`
> > > > >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in
> this
> > > > case,
> > > > >> > > what's
> > > > >> > > >> > the
> > > > >> > > >> > > > > > >>>>> semantics
> > > > >> > > >> > > > > > >>>>>>> for
> > > > >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the
> > > > >> following
> > > > >> > > code
> > > > >> > > >> > share
> > > > >> > > >> > > > > > >>> the
> > > > >> > > >> > > > > > >>>>> same
> > > > >> > > >> > > > > > >>>>>>>>> result?
> > > > >> > > >> > > > > > >>>>>>>>>> and what're the differences between
> them?
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
> > > > >> > > >> > > > > > >>>>>>>>>> 2.
> > > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
> > > > >> > > >> > > > > > >>>>>>>>>> 3.
> > > > >> > > >> > > > > > >>
> > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add this
> > > into
> > > > >> the
> > > > >> > > >> > document.
> > > > >> > > >> > > > > > >>> Thank
> > > > >> > > >> > > > > > >>>>> you
> > > > >> > > >> > > > > > >>>>>>>> very
> > > > >> > > >> > > > > > >>>>>>>>>> much.
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>> Best, Hequn
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino
> > > yang <
> > > > >> > > >> > > > > > >>>>> [hidden email] <mailto:
> [hidden email]>>
> > > > >> > > >> > > > > > >>>>>>>>> wrote:
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*"
> section
> > > of
> > > > >> FLIP
> > > > >> > > >> wiki
> > > > >> > > >> > > > > > >>>> page.[1]
> > > > >> > > >> > > > > > >>>>>> This
> > > > >> > > >> > > > > > >>>>>>>>> mail
> > > > >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has
> proceeded to
> > > > the
> > > > >> > > third
> > > > >> > > >> > step.
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth step(vote
> > > > step),
> > > > >> I
> > > > >> > > >> didn't
> > > > >> > > >> > > > > > >> find
> > > > >> > > >> > > > > > >>>> the
> > > > >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the voting
> > > > >> process.
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of
> this
> > > > >> feature
> > > > >> > > has
> > > > >> > > >> > been
> > > > >> > > >> > > > > > >>> done
> > > > >> > > >> > > > > > >>>>> in
> > > > >> > > >> > > > > > >>>>>>> the
> > > > >> > > >> > > > > > >>>>>>>>> old
> > > > >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when
> > > should
> > > > I
> > > > >> > start
> > > > >> > > >> > > > > > >> voting?
> > > > >> > > >> > > > > > >>>> Can
> > > > >> > > >> > > > > > >>>>> I
> > > > >> > > >> > > > > > >>>>>>>> start
> > > > >> > > >> > > > > > >>>>>>>>>> now?
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>> Best,
> > > > >> > > >> > > > > > >>>>>>>>>>> Vino
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>> [1]:
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > >
> > > > >> > > >> > > >
> > > > >> > > >> > >
> > > > >> > > >> >
> > > > >> > > >>
> > > > >> > >
> > > > >> >
> > > > >>
> > > >
> > >
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> <
> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
> >
> > > > >> > > >> > > > > > >>>>>>>>>>> [2]:
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > >
> > > > >> > > >> > > >
> > > > >> > > >> > >
> > > > >> > > >> >
> > > > >> > > >>
> > > > >> > >
> > > > >> >
> > > > >>
> > > >
> > >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> <
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email] <mailto:
> [hidden email]>>
> > > 于2019年6月13日周四
> > > > >> > > 上午9:19写道：
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for your
> > > > >> efforts.
> > > > >> > > >> > > > > > >>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>> Best,
> > > > >> > > >> > > > > > >>>>>>>>>>>> Leesf
> > > > >> > > >> > > > > > >>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]
> <mailto:[hidden email]>>
> > > > >> > 于2019年6月12日周三
> > > > >> > > >> > > > > > >>> 下午5:46写道：
> > > > >> > > >> > > > > > >>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP
> > > discussion
> > > > >> > thread
> > > > >> > > >> > > > > > >> about
> > > > >> > > >> > > > > > >>>>>>> supporting
> > > > >> > > >> > > > > > >>>>>>>>>> local
> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can
> effectively
> > > > >> > alleviate
> > > > >> > > >> data
> > > > >> > > >> > > > > > >>>> skew.
> > > > >> > > >> > > > > > >>>>>>> This
> > > > >> > > >> > > > > > >>>>>>>> is
> > > > >> > > >> > > > > > >>>>>>>>>> the
> > > > >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > >
> > > > >> > > >> > > >
> > > > >> > > >> > >
> > > > >> > > >> >
> > > > >> > > >>
> > > > >> > >
> > > > >> >
> > > > >>
> > > >
> > >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> <
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
> >
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are widely
> > > used
> > > > to
> > > > >> > > >> perform
> > > > >> > > >> > > > > > >>>>>> aggregating
> > > > >> > > >> > > > > > >>>>>>>>>>>> operations
> > > > >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on
> the
> > > > >> elements
> > > > >> > > >> that
> > > > >> > > >> > > > > > >>> have
> > > > >> > > >> > > > > > >>>>> the
> > > > >> > > >> > > > > > >>>>>>> same
> > > > >> > > >> > > > > > >>>>>>>>>> key.
> > > > >> > > >> > > > > > >>>>>>>>>>>> When
> > > > >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements
> with
> > > > the
> > > > >> > same
> > > > >> > > >> key
> > > > >> > > >> > > > > > >>> will
> > > > >> > > >> > > > > > >>>> be
> > > > >> > > >> > > > > > >>>>>>> sent
> > > > >> > > >> > > > > > >>>>>>>> to
> > > > >> > > >> > > > > > >>>>>>>>>> and
> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> The performance of these
> aggregating
> > > > >> > operations
> > > > >> > > is
> > > > >> > > >> > > > > > >> very
> > > > >> > > >> > > > > > >>>>>>> sensitive
> > > > >> > > >> > > > > > >>>>>>>>> to
> > > > >> > > >> > > > > > >>>>>>>>>>> the
> > > > >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases
> > > where
> > > > >> the
> > > > >> > > >> > > > > > >>> distribution
> > > > >> > > >> > > > > > >>>>> of
> > > > >> > > >> > > > > > >>>>>>> keys
> > > > >> > > >> > > > > > >>>>>>>>>>>> follows a
> > > > >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance will
> be
> > > > >> > > >> significantly
> > > > >> > > >> > > > > > >>>>>> downgraded.
> > > > >> > > >> > > > > > >>>>>>>>> More
> > > > >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree of
> > > > >> > parallelism
> > > > >> > > >> does
> > > > >> > > >> > > > > > >>> not
> > > > >> > > >> > > > > > >>>>> help
> > > > >> > > >> > > > > > >>>>>>>> when
> > > > >> > > >> > > > > > >>>>>>>>> a
> > > > >> > > >> > > > > > >>>>>>>>>>> task
> > > > >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a
> widely-adopted
> > > > >> method
> > > > >> > to
> > > > >> > > >> > > > > > >> reduce
> > > > >> > > >> > > > > > >>>> the
> > > > >> > > >> > > > > > >>>>>>>>>> performance
> > > > >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can
> decompose
> > > > the
> > > > >> > > >> > > > > > >> aggregating
> > > > >> > > >> > > > > > >>>>>>>> operations
> > > > >> > > >> > > > > > >>>>>>>>>> into
> > > > >> > > >> > > > > > >>>>>>>>>>>> two
> > > > >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we
> > > aggregate
> > > > >> the
> > > > >> > > >> elements
> > > > >> > > >> > > > > > >>> of
> > > > >> > > >> > > > > > >>>>> the
> > > > >> > > >> > > > > > >>>>>>> same
> > > > >> > > >> > > > > > >>>>>>>>> key
> > > > >> > > >> > > > > > >>>>>>>>>>> at
> > > > >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial
> > > > results.
> > > > >> > Then
> > > > >> > > at
> > > > >> > > >> > > > > > >> the
> > > > >> > > >> > > > > > >>>>> second
> > > > >> > > >> > > > > > >>>>>>>>> phase,
> > > > >> > > >> > > > > > >>>>>>>>>>>> these
> > > > >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to
> receivers
> > > > >> > according
> > > > >> > > to
> > > > >> > > >> > > > > > >>> their
> > > > >> > > >> > > > > > >>>>> keys
> > > > >> > > >> > > > > > >>>>>>> and
> > > > >> > > >> > > > > > >>>>>>>>> are
> > > > >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final
> result.
> > > > Since
> > > > >> the
> > > > >> > > >> number
> > > > >> > > >> > > > > > >>> of
> > > > >> > > >> > > > > > >>>>>>> partial
> > > > >> > > >> > > > > > >>>>>>>>>>> results
> > > > >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is
> limited by
> > > > the
> > > > >> > > >> number of
> > > > >> > > >> > > > > > >>>>>> senders,
> > > > >> > > >> > > > > > >>>>>>>> the
> > > > >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be
> > > reduced.
> > > > >> > > >> Besides, by
> > > > >> > > >> > > > > > >>>>>> reducing
> > > > >> > > >> > > > > > >>>>>>>> the
> > > > >> > > >> > > > > > >>>>>>>>>>> amount
> > > > >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the
> performance can
> > > > be
> > > > >> > > further
> > > > >> > > >> > > > > > >>>>> improved.
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > >
> > > > >> > > >> > > >
> > > > >> > > >> > >
> > > > >> > > >> >
> > > > >> > > >>
> > > > >> > >
> > > > >> >
> > > > >>
> > > >
> > >
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> <
> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
> >
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > >
> > > > >> > > >> > > >
> > > > >> > > >> > >
> > > > >> > > >> >
> > > > >> > > >>
> > > > >> > >
> > > > >> >
> > > > >>
> > > >
> > >
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> <
> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
> >
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> https://issues.apache.org/jira/browse/FLINK-12786 <
> https://issues.apache.org/jira/browse/FLINK-12786>
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your
> > > feedback!
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>> Best,
> > > > >> > > >> > > > > > >>>>>>>>>>>>> Vino
> > > > >> > > >> > > > > > >>>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>>
> > > > >> > > >> > > > > > >>>>>>>
> > > > >> > > >> > > > > > >>>>>>
> > > > >> > > >> > > > > > >>>>>
> > > > >> > > >> > > > > > >>>>
> > > > >> > > >> > > > > > >>>
> > > > >> > > >> > > > > > >>
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > > >
> > > > >> > > >> > > > >
> > > > >> > > >> > > >
> > > > >> > > >> > >
> > > > >> > > >> >
> > > > >> > > >>
> > > > >> > > >
> > > > >> > >
> > > > >> >
> > > > >>
> > > > >
> > > >
> > >
>
>

[vino yang]

Hi Jark,

`DataStream.localKeyBy().process()` has some key difference with
`DataStream.process()`. The former API receive `KeyedProcessFunction`
(sorry my previous reply may let you misunderstood), the latter receive API
receive `ProcessFunction`. When you read the java doc of ProcessFunction,
you can find a "*Note*" statement:

Access to keyed state and timers (which are also scoped to a key) is only
> available if the ProcessFunction is applied on a KeyedStream.


In addition, you can also compare the two implementations(`ProcessOperator`
and `KeyedProcessOperator`) of them to view the difference.

IMO, the "Note" statement means a lot for many use scenarios. For example,
if we cannot access keyed state, we can only use heap memory to buffer data
while it does not guarantee the semantics of correctness! And the timer is
also very important in some scenarios.

That's why we say our API is flexible, it can get most benefits (even
subsequent potential benefits in the future) from KeyedStream.

I have added some instructions on the use of localKeyBy in the FLIP-44
documentation.

Best,
Vino


Jark Wu <[hidden email]> 于2019年6月27日周四 上午10:44写道：

> Hi Piotr,
>
> I think the state migration you raised is a good point. Having
> "stream.enableLocalAggregation(Trigger)” might add some implicit operators
> which users can't set uid and cause the state compatibility/evolution
> problems.
> So let's put this in rejected alternatives.
>
> Hi Vino,
>
> You mentioned several times that "DataStream.localKeyBy().process()" can
> solve the data skew problem of "DataStream.keyBy().process()".
> I'm curious about what's the differences between "DataStream.process()"
> and "DataStream.localKeyBy().process()"?
> Can't "DataStream.process()" solve the data skew problem?
>
> Best,
> Jark
>
>
> On Wed, 26 Jun 2019 at 18:20, Piotr Nowojski <[hidden email]> wrote:
>
>> Hi Jark and Vino,
>>
>> I agree fully with Jark, that in order to have the discussion focused and
>> to limit the number of parallel topics, we should first focus on one topic.
>> We can first decide on the API and later we can discuss the runtime
>> details. At least as long as we keep the potential requirements of the
>> runtime part in mind while designing the API.
>>
>> Regarding the automatic optimisation and proposed by Jark:
>>
>> "stream.enableLocalAggregation(Trigger)”
>>
>> I would be against that in the DataStream API for the reasons that Vino
>> presented. There was a discussion thread about future directions of Table
>> API vs DataStream API and the consensus was that the automatic
>> optimisations are one of the dividing lines between those two, for at least
>> a couple of reasons. Flexibility and full control over the program was one
>> of them. Another is state migration. Having
>> "stream.enableLocalAggregation(Trigger)” that might add some implicit
>> operators in the job graph can cause problems with savepoint/checkpoint
>> compatibility.
>>
>> However I haven’t thought about/looked into the details of the Vino’s API
>> proposal, so I can not fully judge it.
>>
>> Piotrek
>>
>> > On 26 Jun 2019, at 09:17, vino yang <[hidden email]> wrote:
>> >
>> > Hi Jark,
>> >
>> > Similar questions and responses have been repeated many times.
>> >
>> > Why didn't we spend more sections discussing the API?
>> >
>> > Because we try to reuse the ability of KeyedStream. The localKeyBy API
>> just returns the KeyedStream, that's our design, we can get all the benefit
>> from the KeyedStream and get further benefit from WindowedStream. The APIs
>> come from KeyedStream and WindowedStream is long-tested and flexible. Yes,
>> we spend much space discussing the local keyed state, that's not the goal
>> and motivation, that's the way to implement local aggregation. It is much
>> more complicated than the API we introduced, so we spent more section. Of
>> course, this is the implementation level of the Operator. We also agreed to
>> support the implementation of buffer+flush and added related instructions
>> to the documentation. This needs to wait for the community to recognize,
>> and if the community agrees, we will give more instructions. What's more, I
>> have indicated before that we welcome state-related commenters to
>> participate in the discussion, but it is not wise to modify the FLIP title.
>> >
>> > About the API of local aggregation:
>> >
>> > I don't object to ease of use is very important. But IMHO flexibility
>> is the most important at the DataStream API level. Otherwise, what does
>> DataStream mean? The significance of the DataStream API is that it is more
>> flexible than Table/SQL, if it cannot provide this point then everyone
>> would just use Table/SQL.
>> >
>> > The DataStream API should focus more on flexibility than on automatic
>> optimization, which allows users to have more possibilities to implement
>> complex programs and meet specific scenarios. There are a lot of programs
>> written using the DataStream API that are far more complex than we think.
>> It is very difficult to optimize at the API level and the benefit is very
>> low.
>> >
>> > I want to say that we support a more generalized local aggregation. I
>> mentioned in the previous reply that not only the UDF that implements
>> AggregateFunction is called aggregation. In some complex scenarios, we have
>> to support local aggregation through ProcessFunction and
>> ProcessWindowFunction to solve the data skew problem. How do you support
>> them in the API implementation and optimization you mentioned?
>> >
>> > Flexible APIs are arbitrarily combined to result in erroneous
>> semantics, which does not prove that flexibility is meaningless because the
>> user is the decision maker. I have been exemplified many times, for many
>> APIs in DataStream, if we arbitrarily combined them, they also do not have
>> much practical significance. So, users who use flexible APIs need to
>> understand what they are doing and what is the right choice.
>> >
>> > I think that if we discuss this, there will be no result.
>> >
>> > @Stephan Ewen <mailto:[hidden email]> , @Aljoscha Krettek <mailto:
>> [hidden email]> and @Piotr Nowojski <mailto:[hidden email]> Do
>> you have further comments?
>> >
>> >
>> > Jark Wu <[hidden email] <mailto:[hidden email]>> 于2019年6月26日周三
>> 上午11:46写道：
>> > Thanks for the long discussion Vino, Kurt, Hequn, Piotr and others,
>> >
>> > It seems that we still have some different ideas about the API
>> > (localKeyBy()?) and implementation details (reuse window operator? local
>> > keyed state?).
>> > And the discussion is stalled and mixed with motivation and API and
>> > implementation discussion.
>> >
>> > In order to make some progress in this topic, I want to summarize the
>> > points (pls correct me if I'm wrong or missing sth) and would suggest to
>> > split
>> >  the topic into following aspects and discuss them one by one.
>> >
>> > 1) What's the main purpose of this FLIP?
>> >  - From the title of this FLIP, it is to support local aggregate.
>> However
>> > from the content of the FLIP, 80% are introducing a new state called
>> local
>> > keyed state.
>> >  - If we mainly want to introduce local keyed state, then we should
>> > re-title the FLIP and involve in more people who works on state.
>> >  - If we mainly want to support local aggregate, then we can jump to
>> step 2
>> > to discuss the API design.
>> >
>> > 2) What does the API look like?
>> >  - Vino proposed to use "localKeyBy()" to do local process, the output
>> of
>> > local process is the result type of aggregate function.
>> >   a) For non-windowed aggregate:
>> > input.localKeyBy(..).aggregate(agg1).keyBy(..).aggregate(agg2)    **NOT
>> > SUPPORT**
>> >   b) For windowed aggregate:
>> >
>> input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)
>> >
>> > 3) What's the implementation detail?
>> >  - may reuse window operator or not.
>> >  - may introduce a new state concepts or not.
>> >  - may not have state in local operator by flushing buffers in
>> > prepareSnapshotPreBarrier
>> >  - and so on...
>> >  - we can discuss these later when we reach a consensus on API
>> >
>> > --------------------
>> >
>> > Here are my thoughts:
>> >
>> > 1) Purpose of this FLIP
>> >  - From the motivation section in the FLIP, I think the purpose is to
>> > support local aggregation to solve the data skew issue.
>> >    Then I think we should focus on how to provide a easy to use and
>> clear
>> > API to support **local aggregation**.
>> >  - Vino's point is centered around the local keyed state API (or
>> > localKeyBy()), and how to leverage the local keyed state API to support
>> > local aggregation.
>> >    But I'm afraid it's not a good way to design API for local
>> aggregation.
>> >
>> > 2) local aggregation API
>> >  - IMO, the method call chain
>> >
>> "input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)"
>> > is not such easy to use.
>> >    Because we have to provide two implementation for an aggregation (one
>> > for partial agg, another for final agg). And we have to take care of
>> >    the first window call, an inappropriate window call will break the
>> > sematics.
>> >  - From my point of view, local aggregation is a mature concept which
>> > should output the intermediate accumulator (ACC) in the past period of
>> time
>> > (a trigger).
>> >    And the downstream final aggregation will merge ACCs received from
>> local
>> > side, and output the current final result.
>> >  - The current "AggregateFunction" API in DataStream already has the
>> > accumulator type and "merge" method. So the only thing user need to do
>> is
>> > how to enable
>> >    local aggregation opimization and set a trigger.
>> >  - One idea comes to my head is that, assume we have a windowed
>> aggregation
>> > stream: "val stream = input.keyBy().window(w).aggregate(agg)". We can
>> > provide an API on the stream.
>> >    For exmaple, "stream.enableLocalAggregation(Trigger)", the trigger
>> can
>> > be "ContinuousEventTimeTrigger.of(Time.of(Time.minutes(1)))". Then it
>> will
>> > be optmized into
>> >    local operator + final operator, and local operator will combine
>> records
>> > every minute on event time.
>> >  - In this way, there is only one line added, and the output is the same
>> > with before, because it is just an opimization.
>> >
>> >
>> > Regards,
>> > Jark
>> >
>> >
>> >
>> > On Tue, 25 Jun 2019 at 14:34, vino yang <[hidden email] <mailto:
>> [hidden email]>> wrote:
>> >
>> > > Hi Kurt,
>> > >
>> > > Answer your questions:
>> > >
>> > > a) Sorry, I just updated the Google doc, still have no time update the
>> > > FLIP, will update FLIP as soon as possible.
>> > > About your description at this point, I have a question, what does it
>> mean:
>> > > how do we combine with
>> > > `AggregateFunction`?
>> > >
>> > > I have shown you the examples which Flink has supported:
>> > >
>> > >    - input.localKeyBy(0).aggregate()
>> > >    - input.localKeyBy(0).window().aggregate()
>> > >
>> > > You can show me a example about how do we combine with
>> `AggregateFuncion`
>> > > through your localAggregate API.
>> > >
>> > > About the example, how to do the local aggregation for AVG, consider
>> this
>> > > code:
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > >
>> > > *DataStream<Tuple2<String, Long>> source = null; source .localKeyBy(0)
>> > > .timeWindow(Time.seconds(60)) .aggregate(agg1, new
>> > > WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>, String,
>> > > TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60))
>> .aggregate(agg2,
>> > > new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, String,
>> > > TimeWindow>());*
>> > >
>> > > *agg1:*
>> > > *signature : new AggregateFunction<Tuple2<String, Long>, Tuple2<Long,
>> > > Long>, Tuple2<Long, Long>>() {}*
>> > > *input param type: Tuple2<String, Long> f0: key, f1: value*
>> > > *intermediate result type: Tuple2<Long, Long>, f0: local aggregated
>> sum;
>> > > f1: local aggregated count*
>> > > *output param type:  Tuple2<Long, Long>, f0: local aggregated sum; f1:
>> > > local aggregated count*
>> > >
>> > > *agg2:*
>> > > *signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
>> > > Tuple2<String, Long>>() {},*
>> > > *input param type: Tuple3<String, Long, Long>, f0: key, f1:  local
>> > > aggregated sum; f2: local aggregated count*
>> > >
>> > > *intermediate result type: Long  avg result*
>> > > *output param type:  Tuple2<String, Long> f0: key, f1 avg result*
>> > >
>> > > For sliding window, we just need to change the window type if users
>> want to
>> > > do.
>> > > Again, we try to give the design and implementation in the DataStream
>> > > level. So I believe we can match all the requirements(It's just that
>> the
>> > > implementation may be different) comes from the SQL level.
>> > >
>> > > b) Yes, Theoretically, your thought is right. But in reality, it
>> cannot
>> > > bring many benefits.
>> > > If we want to get the benefits from the window API, while we do not
>> reuse
>> > > the window operator? And just copy some many duplicated code to
>> another
>> > > operator?
>> > >
>> > > c) OK, I agree to let the state backend committers join this
>> discussion.
>> > >
>> > > Best,
>> > > Vino
>> > >
>> > >
>> > > Kurt Young <[hidden email] <mailto:[hidden email]>>
>> 于2019年6月24日周一 下午6:53写道：
>> > >
>> > > > Hi vino,
>> > > >
>> > > > One thing to add,  for a), I think use one or two examples like how
>> to do
>> > > > local aggregation on a sliding window,
>> > > > and how do we do local aggregation on an unbounded aggregate, will
>> do a
>> > > lot
>> > > > help.
>> > > >
>> > > > Best,
>> > > > Kurt
>> > > >
>> > > >
>> > > > On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email]
>> <mailto:[hidden email]>> wrote:
>> > > >
>> > > > > Hi vino,
>> > > > >
>> > > > > I think there are several things still need discussion.
>> > > > >
>> > > > > a) We all agree that we should first go with a unified
>> abstraction, but
>> > > > > the abstraction is not reflected by the FLIP.
>> > > > > If your answer is "locakKeyBy" API, then I would ask how do we
>> combine
>> > > > > with `AggregateFunction`, and how do
>> > > > > we do proper local aggregation for those have different
>> intermediate
>> > > > > result type, like AVG. Could you add these
>> > > > > to the document?
>> > > > >
>> > > > > b) From implementation side, reusing window operator is one of the
>> > > > > possible solutions, but not we base on window
>> > > > > operator to have two different implementations. What I
>> understanding
>> > > is,
>> > > > > one of the possible implementations should
>> > > > > not touch window operator.
>> > > > >
>> > > > > c) 80% of your FLIP content is actually describing how do we
>> support
>> > > > local
>> > > > > keyed state. I don't know if this is necessary
>> > > > > to introduce at the first step and we should also involve
>> committers
>> > > work
>> > > > > on state backend to share their thoughts.
>> > > > >
>> > > > > Best,
>> > > > > Kurt
>> > > > >
>> > > > >
>> > > > > On Mon, Jun 24, 2019 at 5:17 PM vino yang <[hidden email]
>> <mailto:[hidden email]>>
>> > > wrote:
>> > > > >
>> > > > >> Hi Kurt,
>> > > > >>
>> > > > >> You did not give more further different opinions, so I thought
>> you
>> > > have
>> > > > >> agreed with the design after we promised to support two kinds of
>> > > > >> implementation.
>> > > > >>
>> > > > >> In API level, we have answered your question about pass an
>> > > > >> AggregateFunction to do the aggregation. No matter introduce
>> > > localKeyBy
>> > > > >> API
>> > > > >> or not, we can support AggregateFunction.
>> > > > >>
>> > > > >> So what's your different opinion now? Can you share it with us?
>> > > > >>
>> > > > >> Best,
>> > > > >> Vino
>> > > > >>
>> > > > >> Kurt Young <[hidden email] <mailto:[hidden email]>>
>> 于2019年6月24日周一 下午4:24写道：
>> > > > >>
>> > > > >> > Hi vino,
>> > > > >> >
>> > > > >> > Sorry I don't see the consensus about reusing window operator
>> and
>> > > keep
>> > > > >> the
>> > > > >> > API design of localKeyBy. But I think we should definitely more
>> > > > thoughts
>> > > > >> > about this topic.
>> > > > >> >
>> > > > >> > I also try to loop in Stephan for this discussion.
>> > > > >> >
>> > > > >> > Best,
>> > > > >> > Kurt
>> > > > >> >
>> > > > >> >
>> > > > >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <
>> [hidden email] <mailto:[hidden email]>>
>> > > > >> wrote:
>> > > > >> >
>> > > > >> > > Hi all,
>> > > > >> > >
>> > > > >> > > I am happy we have a wonderful discussion and received many
>> > > valuable
>> > > > >> > > opinions in the last few days.
>> > > > >> > >
>> > > > >> > > Now, let me try to summarize what we have reached consensus
>> about
>> > > > the
>> > > > >> > > changes in the design.
>> > > > >> > >
>> > > > >> > >    - provide a unified abstraction to support two kinds of
>> > > > >> > implementation;
>> > > > >> > >    - reuse WindowOperator and try to enhance it so that we
>> can
>> > > make
>> > > > >> the
>> > > > >> > >    intermediate result of the local aggregation can be
>> buffered
>> > > and
>> > > > >> > > flushed to
>> > > > >> > >    support two kinds of implementation;
>> > > > >> > >    - keep the API design of localKeyBy, but declare the
>> disabled
>> > > > some
>> > > > >> > APIs
>> > > > >> > >    we cannot support currently, and provide a configurable
>> API for
>> > > > >> users
>> > > > >> > to
>> > > > >> > >    choose how to handle intermediate result;
>> > > > >> > >
>> > > > >> > > The above three points have been updated in the design doc.
>> Any
>> > > > >> > > questions, please let me know.
>> > > > >> > >
>> > > > >> > > @Aljoscha Krettek <[hidden email] <mailto:
>> [hidden email]>> What do you think? Any
>> > > > >> further
>> > > > >> > > comments?
>> > > > >> > >
>> > > > >> > > Best,
>> > > > >> > > Vino
>> > > > >> > >
>> > > > >> > > vino yang <[hidden email] <mailto:
>> [hidden email]>> 于2019年6月20日周四 下午2:02写道：
>> > > > >> > >
>> > > > >> > > > Hi Kurt,
>> > > > >> > > >
>> > > > >> > > > Thanks for your comments.
>> > > > >> > > >
>> > > > >> > > > It seems we come to a consensus that we should alleviate
>> the
>> > > > >> > performance
>> > > > >> > > > degraded by data skew with local aggregation. In this
>> FLIP, our
>> > > > key
>> > > > >> > > > solution is to introduce local keyed partition to achieve
>> this
>> > > > goal.
>> > > > >> > > >
>> > > > >> > > > I also agree that we can benefit a lot from the usage of
>> > > > >> > > > AggregateFunction. In combination with localKeyBy, We can
>> easily
>> > > > >> use it
>> > > > >> > > to
>> > > > >> > > > achieve local aggregation:
>> > > > >> > > >
>> > > > >> > > >    - input.localKeyBy(0).aggregate()
>> > > > >> > > >    - input.localKeyBy(0).window().aggregate()
>> > > > >> > > >
>> > > > >> > > >
>> > > > >> > > > I think the only problem here is the choices between
>> > > > >> > > >
>> > > > >> > > >    - (1) Introducing a new primitive called localKeyBy and
>> > > > implement
>> > > > >> > > >    local aggregation with existing operators, or
>> > > > >> > > >    - (2) Introducing an operator called localAggregation
>> which
>> > > is
>> > > > >> > > >    composed of a key selector, a window-like operator, and
>> an
>> > > > >> aggregate
>> > > > >> > > >    function.
>> > > > >> > > >
>> > > > >> > > >
>> > > > >> > > > There may exist some optimization opportunities by
>> providing a
>> > > > >> > composited
>> > > > >> > > > interface for local aggregation. But at the same time, in
>> my
>> > > > >> opinion,
>> > > > >> > we
>> > > > >> > > > lose flexibility (Or we need certain efforts to achieve
>> the same
>> > > > >> > > > flexibility).
>> > > > >> > > >
>> > > > >> > > > As said in the previous mails, we have many use cases
>> where the
>> > > > >> > > > aggregation is very complicated and cannot be performed
>> with
>> > > > >> > > > AggregateFunction. For example, users may perform windowed
>> > > > >> aggregations
>> > > > >> > > > according to time, data values, or even external storage.
>> > > > Typically,
>> > > > >> > they
>> > > > >> > > > now use KeyedProcessFunction or customized triggers to
>> implement
>> > > > >> these
>> > > > >> > > > aggregations. It's not easy to address data skew in such
>> cases
>> > > > with
>> > > > >> a
>> > > > >> > > > composited interface for local aggregation.
>> > > > >> > > >
>> > > > >> > > > Given that Data Stream API is exactly targeted at these
>> cases
>> > > > where
>> > > > >> the
>> > > > >> > > > application logic is very complicated and optimization
>> does not
>> > > > >> > matter, I
>> > > > >> > > > think it's a better choice to provide a relatively
>> low-level and
>> > > > >> > > canonical
>> > > > >> > > > interface.
>> > > > >> > > >
>> > > > >> > > > The composited interface, on the other side, may be a good
>> > > choice
>> > > > in
>> > > > >> > > > declarative interfaces, including SQL and Table API, as it
>> > > allows
>> > > > >> more
>> > > > >> > > > optimization opportunities.
>> > > > >> > > >
>> > > > >> > > > Best,
>> > > > >> > > > Vino
>> > > > >> > > >
>> > > > >> > > >
>> > > > >> > > > Kurt Young <[hidden email] <mailto:[hidden email]>>
>> 于2019年6月20日周四 上午10:15写道：
>> > > > >> > > >
>> > > > >> > > >> Hi all,
>> > > > >> > > >>
>> > > > >> > > >> As vino said in previous emails, I think we should first
>> > > discuss
>> > > > >> and
>> > > > >> > > >> decide
>> > > > >> > > >> what kind of use cases this FLIP want to
>> > > > >> > > >> resolve, and what the API should look like. From my side,
>> I
>> > > think
>> > > > >> this
>> > > > >> > > is
>> > > > >> > > >> probably the root cause of current divergence.
>> > > > >> > > >>
>> > > > >> > > >> My understand is (from the FLIP title and motivation
>> section of
>> > > > the
>> > > > >> > > >> document), we want to have a proper support of
>> > > > >> > > >> local aggregation, or pre aggregation. This is not a very
>> new
>> > > > idea,
>> > > > >> > most
>> > > > >> > > >> SQL engine already did this improvement. And
>> > > > >> > > >> the core concept about this is, there should be an
>> > > > >> AggregateFunction,
>> > > > >> > no
>> > > > >> > > >> matter it's a Flink runtime's AggregateFunction or
>> > > > >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have
>> > > concept
>> > > > >> of
>> > > > >> > > >> intermediate data type, sometimes we call it ACC.
>> > > > >> > > >> I quickly went through the POC piotr did before [1], it
>> also
>> > > > >> directly
>> > > > >> > > uses
>> > > > >> > > >> AggregateFunction.
>> > > > >> > > >>
>> > > > >> > > >> But the thing is, after reading the design of this FLIP, I
>> > > can't
>> > > > >> help
>> > > > >> > > >> myself feeling that this FLIP is not targeting to have a
>> proper
>> > > > >> > > >> local aggregation support. It actually want to introduce
>> > > another
>> > > > >> > > concept:
>> > > > >> > > >> LocalKeyBy, and how to split and merge local key groups,
>> > > > >> > > >> and how to properly support state on local key. Local
>> > > aggregation
>> > > > >> just
>> > > > >> > > >> happened to be one possible use case of LocalKeyBy.
>> > > > >> > > >> But it lacks supporting the essential concept of local
>> > > > aggregation,
>> > > > >> > > which
>> > > > >> > > >> is intermediate data type. Without this, I really don't
>> thing
>> > > > >> > > >> it is a good fit of local aggregation.
>> > > > >> > > >>
>> > > > >> > > >> Here I want to make sure of the scope or the goal about
>> this
>> > > > FLIP,
>> > > > >> do
>> > > > >> > we
>> > > > >> > > >> want to have a proper local aggregation engine, or we
>> > > > >> > > >> just want to introduce a new concept called LocalKeyBy?
>> > > > >> > > >>
>> > > > >> > > >> [1]: https://github.com/apache/flink/pull/4626 <
>> https://github.com/apache/flink/pull/4626>
>> > > > >> > > >>
>> > > > >> > > >> Best,
>> > > > >> > > >> Kurt
>> > > > >> > > >>
>> > > > >> > > >>
>> > > > >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <
>> > > [hidden email] <mailto:[hidden email]>
>> > > > >
>> > > > >> > > wrote:
>> > > > >> > > >>
>> > > > >> > > >> > Hi Hequn,
>> > > > >> > > >> >
>> > > > >> > > >> > Thanks for your comments!
>> > > > >> > > >> >
>> > > > >> > > >> > I agree that allowing local aggregation reusing window
>> API
>> > > and
>> > > > >> > > refining
>> > > > >> > > >> > window operator to make it match both requirements
>> (come from
>> > > > our
>> > > > >> > and
>> > > > >> > > >> Kurt)
>> > > > >> > > >> > is a good decision!
>> > > > >> > > >> >
>> > > > >> > > >> > Concerning your questions:
>> > > > >> > > >> >
>> > > > >> > > >> > 1. The result of
>> input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>> > > may
>> > > > >> be
>> > > > >> > > >> > meaningless.
>> > > > >> > > >> >
>> > > > >> > > >> > Yes, it does not make sense in most cases. However, I
>> also
>> > > want
>> > > > >> to
>> > > > >> > > note
>> > > > >> > > >> > users should know the right semantics of localKeyBy and
>> use
>> > > it
>> > > > >> > > >> correctly.
>> > > > >> > > >> > Because this issue also exists for the global keyBy,
>> consider
>> > > > >> this
>> > > > >> > > >> example:
>> > > > >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is
>> also
>> > > > >> > meaningless.
>> > > > >> > > >> >
>> > > > >> > > >> > 2. About the semantics of
>> > > > >> > > >> > input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
>> > > > >> > > >> >
>> > > > >> > > >> > Good catch! I agree with you that it's not good to
>> enable all
>> > > > >> > > >> > functionalities for localKeyBy from KeyedStream.
>> > > > >> > > >> > Currently, We do not support some APIs such as
>> > > > >> > > >> > connect/join/intervalJoin/coGroup. This is due to that
>> we
>> > > force
>> > > > >> the
>> > > > >> > > >> > operators on LocalKeyedStreams chained with the inputs.
>> > > > >> > > >> >
>> > > > >> > > >> > Best,
>> > > > >> > > >> > Vino
>> > > > >> > > >> >
>> > > > >> > > >> >
>> > > > >> > > >> > Hequn Cheng <[hidden email] <mailto:
>> [hidden email]>> 于2019年6月19日周三 下午3:42写道：
>> > > > >> > > >> >
>> > > > >> > > >> > > Hi,
>> > > > >> > > >> > >
>> > > > >> > > >> > > Thanks a lot for your great discussion and great to
>> see
>> > > that
>> > > > >> some
>> > > > >> > > >> > agreement
>> > > > >> > > >> > > has been reached on the "local aggregate engine"!
>> > > > >> > > >> > >
>> > > > >> > > >> > > ===> Considering the abstract engine,
>> > > > >> > > >> > > I'm thinking is it valuable for us to extend the
>> current
>> > > > >> window to
>> > > > >> > > >> meet
>> > > > >> > > >> > > both demands raised by Kurt and Vino? There are some
>> > > benefits
>> > > > >> we
>> > > > >> > can
>> > > > >> > > >> get:
>> > > > >> > > >> > >
>> > > > >> > > >> > > 1. The interfaces of the window are complete and
>> clear.
>> > > With
>> > > > >> > > windows,
>> > > > >> > > >> we
>> > > > >> > > >> > > can define a lot of ways to split the data and perform
>> > > > >> different
>> > > > >> > > >> > > computations.
>> > > > >> > > >> > > 2. We can also leverage the window to do miniBatch
>> for the
>> > > > >> global
>> > > > >> > > >> > > aggregation, i.e, we can use the window to bundle data
>> > > belong
>> > > > >> to
>> > > > >> > the
>> > > > >> > > >> same
>> > > > >> > > >> > > key, for every bundle we only need to read and write
>> once
>> > > > >> state.
>> > > > >> > > This
>> > > > >> > > >> can
>> > > > >> > > >> > > greatly reduce state IO and improve performance.
>> > > > >> > > >> > > 3. A lot of other use cases can also benefit from the
>> > > window
>> > > > >> base
>> > > > >> > on
>> > > > >> > > >> > memory
>> > > > >> > > >> > > or stateless.
>> > > > >> > > >> > >
>> > > > >> > > >> > > ===> As for the API,
>> > > > >> > > >> > > I think it is good to make our API more flexible.
>> However,
>> > > we
>> > > > >> may
>> > > > >> > > >> need to
>> > > > >> > > >> > > make our API meaningful.
>> > > > >> > > >> > >
>> > > > >> > > >> > > Take my previous reply as an example,
>> > > > >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The
>> result may
>> > > be
>> > > > >> > > >> > meaningless.
>> > > > >> > > >> > > Another example I find is the intervalJoin, e.g.,
>> > > > >> > > >> > >
>> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
>> > > > >> this
>> > > > >> > > >> case, it
>> > > > >> > > >> > > will bring problems if input1 and input2 share
>> different
>> > > > >> > > parallelism.
>> > > > >> > > >> We
>> > > > >> > > >> > > don't know which input should the join chained with?
>> Even
>> > > if
>> > > > >> they
>> > > > >> > > >> share
>> > > > >> > > >> > the
>> > > > >> > > >> > > same parallelism, it's hard to tell what the join is
>> doing.
>> > > > >> There
>> > > > >> > > are
>> > > > >> > > >> > maybe
>> > > > >> > > >> > > some other problems.
>> > > > >> > > >> > >
>> > > > >> > > >> > > From this point of view, it's at least not good to
>> enable
>> > > all
>> > > > >> > > >> > > functionalities for localKeyBy from KeyedStream?
>> > > > >> > > >> > >
>> > > > >> > > >> > > Great to also have your opinions.
>> > > > >> > > >> > >
>> > > > >> > > >> > > Best, Hequn
>> > > > >> > > >> > >
>> > > > >> > > >> > >
>> > > > >> > > >> > >
>> > > > >> > > >> > >
>> > > > >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
>> > > > >> [hidden email] <mailto:[hidden email]>
>> > > > >> > >
>> > > > >> > > >> > wrote:
>> > > > >> > > >> > >
>> > > > >> > > >> > > > Hi Kurt and Piotrek,
>> > > > >> > > >> > > >
>> > > > >> > > >> > > > Thanks for your comments.
>> > > > >> > > >> > > >
>> > > > >> > > >> > > > I agree that we can provide a better abstraction to
>> be
>> > > > >> > compatible
>> > > > >> > > >> with
>> > > > >> > > >> > > two
>> > > > >> > > >> > > > different implementations.
>> > > > >> > > >> > > >
>> > > > >> > > >> > > > First of all, I think we should consider what kind
>> of
>> > > > >> scenarios
>> > > > >> > we
>> > > > >> > > >> need
>> > > > >> > > >> > > to
>> > > > >> > > >> > > > support in *API* level?
>> > > > >> > > >> > > >
>> > > > >> > > >> > > > We have some use cases which need to a customized
>> > > > aggregation
>> > > > >> > > >> through
>> > > > >> > > >> > > > KeyedProcessFunction, (in the usage of our
>> > > > localKeyBy.window
>> > > > >> > they
>> > > > >> > > >> can
>> > > > >> > > >> > use
>> > > > >> > > >> > > > ProcessWindowFunction).
>> > > > >> > > >> > > >
>> > > > >> > > >> > > > Shall we support these flexible use scenarios?
>> > > > >> > > >> > > >
>> > > > >> > > >> > > > Best,
>> > > > >> > > >> > > > Vino
>> > > > >> > > >> > > >
>> > > > >> > > >> > > > Kurt Young <[hidden email] <mailto:
>> [hidden email]>> 于2019年6月18日周二 下午8:37写道：
>> > > > >> > > >> > > >
>> > > > >> > > >> > > > > Hi Piotr,
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > > > Thanks for joining the discussion. Make “local
>> > > > aggregation"
>> > > > >> > > >> abstract
>> > > > >> > > >> > > > enough
>> > > > >> > > >> > > > > sounds good to me, we could
>> > > > >> > > >> > > > > implement and verify alternative solutions for use
>> > > cases
>> > > > of
>> > > > >> > > local
>> > > > >> > > >> > > > > aggregation. Maybe we will find both solutions
>> > > > >> > > >> > > > > are appropriate for different scenarios.
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > > > Starting from a simple one sounds a practical way
>> to
>> > > go.
>> > > > >> What
>> > > > >> > do
>> > > > >> > > >> you
>> > > > >> > > >> > > > think,
>> > > > >> > > >> > > > > vino?
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > > > Best,
>> > > > >> > > >> > > > > Kurt
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
>> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
>> > > > >> > > >> > > > > wrote:
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > > > > Hi Kurt and Vino,
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > > > I think there is a trade of hat we need to
>> consider
>> > > for
>> > > > >> the
>> > > > >> > > >> local
>> > > > >> > > >> > > > > > aggregation.
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > > > Generally speaking I would agree with Kurt about
>> > > local
>> > > > >> > > >> > > aggregation/pre
>> > > > >> > > >> > > > > > aggregation not using Flink's state flush the
>> > > operator
>> > > > >> on a
>> > > > >> > > >> > > checkpoint.
>> > > > >> > > >> > > > > > Network IO is usually cheaper compared to Disks
>> IO.
>> > > > This
>> > > > >> has
>> > > > >> > > >> > however
>> > > > >> > > >> > > > > couple
>> > > > >> > > >> > > > > > of issues:
>> > > > >> > > >> > > > > > 1. It can explode number of in-flight records
>> during
>> > > > >> > > checkpoint
>> > > > >> > > >> > > barrier
>> > > > >> > > >> > > > > > alignment, making checkpointing slower and
>> decrease
>> > > the
>> > > > >> > actual
>> > > > >> > > >> > > > > throughput.
>> > > > >> > > >> > > > > > 2. This trades Disks IO on the local aggregation
>> > > > machine
>> > > > >> > with
>> > > > >> > > >> CPU
>> > > > >> > > >> > > (and
>> > > > >> > > >> > > > > > Disks IO in case of RocksDB) on the final
>> aggregation
>> > > > >> > machine.
>> > > > >> > > >> This
>> > > > >> > > >> > > is
>> > > > >> > > >> > > > > > fine, as long there is no huge data skew. If
>> there is
>> > > > >> only a
>> > > > >> > > >> > handful
>> > > > >> > > >> > > > (or
>> > > > >> > > >> > > > > > even one single) hot keys, it might be better
>> to keep
>> > > > the
>> > > > >> > > >> > persistent
>> > > > >> > > >> > > > > state
>> > > > >> > > >> > > > > > in the LocalAggregationOperator to offload final
>> > > > >> aggregation
>> > > > >> > > as
>> > > > >> > > >> > much
>> > > > >> > > >> > > as
>> > > > >> > > >> > > > > > possible.
>> > > > >> > > >> > > > > > 3. With frequent checkpointing local aggregation
>> > > > >> > effectiveness
>> > > > >> > > >> > would
>> > > > >> > > >> > > > > > degrade.
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > > > I assume Kurt is correct, that in your use cases
>> > > > >> stateless
>> > > > >> > > >> operator
>> > > > >> > > >> > > was
>> > > > >> > > >> > > > > > behaving better, but I could easily see other
>> use
>> > > cases
>> > > > >> as
>> > > > >> > > well.
>> > > > >> > > >> > For
>> > > > >> > > >> > > > > > example someone is already using RocksDB, and
>> his job
>> > > > is
>> > > > >> > > >> > bottlenecked
>> > > > >> > > >> > > > on
>> > > > >> > > >> > > > > a
>> > > > >> > > >> > > > > > single window operator instance because of the
>> data
>> > > > >> skew. In
>> > > > >> > > >> that
>> > > > >> > > >> > > case
>> > > > >> > > >> > > > > > stateful local aggregation would be probably a
>> better
>> > > > >> > choice.
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > > > Because of that, I think we should eventually
>> provide
>> > > > >> both
>> > > > >> > > >> versions
>> > > > >> > > >> > > and
>> > > > >> > > >> > > > > in
>> > > > >> > > >> > > > > > the initial version we should at least make the
>> > > “local
>> > > > >> > > >> aggregation
>> > > > >> > > >> > > > > engine”
>> > > > >> > > >> > > > > > abstract enough, that one could easily provide
>> > > > different
>> > > > >> > > >> > > implementation
>> > > > >> > > >> > > > > > strategy.
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > > > Piotrek
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
>> > > > [hidden email] <mailto:[hidden email]>
>> > > > >> >
>> > > > >> > > >> wrote:
>> > > > >> > > >> > > > > > >
>> > > > >> > > >> > > > > > > Hi,
>> > > > >> > > >> > > > > > >
>> > > > >> > > >> > > > > > > For the trigger, it depends on what operator
>> we
>> > > want
>> > > > to
>> > > > >> > use
>> > > > >> > > >> under
>> > > > >> > > >> > > the
>> > > > >> > > >> > > > > > API.
>> > > > >> > > >> > > > > > > If we choose to use window operator,
>> > > > >> > > >> > > > > > > we should also use window's trigger. However,
>> I
>> > > also
>> > > > >> think
>> > > > >> > > >> reuse
>> > > > >> > > >> > > > window
>> > > > >> > > >> > > > > > > operator for this scenario may not be
>> > > > >> > > >> > > > > > > the best choice. The reasons are the
>> following:
>> > > > >> > > >> > > > > > >
>> > > > >> > > >> > > > > > > 1. As a lot of people already pointed out,
>> window
>> > > > >> relies
>> > > > >> > > >> heavily
>> > > > >> > > >> > on
>> > > > >> > > >> > > > > state
>> > > > >> > > >> > > > > > > and it will definitely effect performance.
>> You can
>> > > > >> > > >> > > > > > > argue that one can use heap based
>> statebackend, but
>> > > > >> this
>> > > > >> > > will
>> > > > >> > > >> > > > introduce
>> > > > >> > > >> > > > > > > extra coupling. Especially we have a chance to
>> > > > >> > > >> > > > > > > design a pure stateless operator.
>> > > > >> > > >> > > > > > > 2. The window operator is *the most*
>> complicated
>> > > > >> operator
>> > > > >> > > >> Flink
>> > > > >> > > >> > > > > currently
>> > > > >> > > >> > > > > > > have. Maybe we only need to pick a subset of
>> > > > >> > > >> > > > > > > window operator to achieve the goal, but once
>> the
>> > > > user
>> > > > >> > wants
>> > > > >> > > >> to
>> > > > >> > > >> > > have
>> > > > >> > > >> > > > a
>> > > > >> > > >> > > > > > deep
>> > > > >> > > >> > > > > > > look at the localAggregation operator, it's
>> still
>> > > > >> > > >> > > > > > > hard to find out what's going on under the
>> window
>> > > > >> > operator.
>> > > > >> > > >> For
>> > > > >> > > >> > > > > > simplicity,
>> > > > >> > > >> > > > > > > I would also recommend we introduce a
>> dedicated
>> > > > >> > > >> > > > > > > lightweight operator, which also much easier
>> for a
>> > > > >> user to
>> > > > >> > > >> learn
>> > > > >> > > >> > > and
>> > > > >> > > >> > > > > use.
>> > > > >> > > >> > > > > > >
>> > > > >> > > >> > > > > > > For your question about increasing the burden
>> in
>> > > > >> > > >> > > > > > >
>> `StreamOperator::prepareSnapshotPreBarrier()`, the
>> > > > only
>> > > > >> > > thing
>> > > > >> > > >> > this
>> > > > >> > > >> > > > > > function
>> > > > >> > > >> > > > > > > need
>> > > > >> > > >> > > > > > > to do is output all the partial results, it's
>> > > purely
>> > > > >> cpu
>> > > > >> > > >> > workload,
>> > > > >> > > >> > > > not
>> > > > >> > > >> > > > > > > introducing any IO. I want to point out that
>> even
>> > > if
>> > > > we
>> > > > >> > have
>> > > > >> > > >> this
>> > > > >> > > >> > > > > > > cost, we reduced another barrier align cost
>> of the
>> > > > >> > operator,
>> > > > >> > > >> > which
>> > > > >> > > >> > > is
>> > > > >> > > >> > > > > the
>> > > > >> > > >> > > > > > > sync flush stage of the state, if you
>> introduced
>> > > > state.
>> > > > >> > This
>> > > > >> > > >> > > > > > > flush actually will introduce disk IO, and I
>> think
>> > > > it's
>> > > > >> > > >> worthy to
>> > > > >> > > >> > > > > > exchange
>> > > > >> > > >> > > > > > > this cost with purely CPU workload. And we do
>> have
>> > > > some
>> > > > >> > > >> > > > > > > observations about these two behavior (as i
>> said
>> > > > >> before,
>> > > > >> > we
>> > > > >> > > >> > > actually
>> > > > >> > > >> > > > > > > implemented both solutions), the stateless one
>> > > > actually
>> > > > >> > > >> performs
>> > > > >> > > >> > > > > > > better both in performance and barrier align
>> time.
>> > > > >> > > >> > > > > > >
>> > > > >> > > >> > > > > > > Best,
>> > > > >> > > >> > > > > > > Kurt
>> > > > >> > > >> > > > > > >
>> > > > >> > > >> > > > > > >
>> > > > >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
>> > > > >> > > >> [hidden email] <mailto:[hidden email]>
>> > > > >> > > >> > >
>> > > > >> > > >> > > > > wrote:
>> > > > >> > > >> > > > > > >
>> > > > >> > > >> > > > > > >> Hi Kurt,
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> Thanks for your example. Now, it looks more
>> > > clearly
>> > > > >> for
>> > > > >> > me.
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> From your example code snippet, I saw the
>> > > > >> localAggregate
>> > > > >> > > API
>> > > > >> > > >> has
>> > > > >> > > >> > > > three
>> > > > >> > > >> > > > > > >> parameters:
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >>   1. key field
>> > > > >> > > >> > > > > > >>   2. PartitionAvg
>> > > > >> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes
>> from
>> > > > window
>> > > > >> > > >> package?
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> I will compare our and your design from API
>> and
>> > > > >> operator
>> > > > >> > > >> level:
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> *From the API level:*
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> As I replied to @dianfu in the old email
>> > > thread,[1]
>> > > > >> the
>> > > > >> > > >> Window
>> > > > >> > > >> > API
>> > > > >> > > >> > > > can
>> > > > >> > > >> > > > > > >> provide the second and the third parameter
>> right
>> > > > now.
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> If you reuse specified interface or class,
>> such as
>> > > > >> > > *Trigger*
>> > > > >> > > >> or
>> > > > >> > > >> > > > > > >> *CounterTrigger* provided by window package,
>> but
>> > > do
>> > > > >> not
>> > > > >> > use
>> > > > >> > > >> > window
>> > > > >> > > >> > > > > API,
>> > > > >> > > >> > > > > > >> it's not reasonable.
>> > > > >> > > >> > > > > > >> And if you do not reuse these interface or
>> class,
>> > > > you
>> > > > >> > would
>> > > > >> > > >> need
>> > > > >> > > >> > > to
>> > > > >> > > >> > > > > > >> introduce more things however they are looked
>> > > > similar
>> > > > >> to
>> > > > >> > > the
>> > > > >> > > >> > > things
>> > > > >> > > >> > > > > > >> provided by window package.
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> The window package has provided several
>> types of
>> > > the
>> > > > >> > window
>> > > > >> > > >> and
>> > > > >> > > >> > > many
>> > > > >> > > >> > > > > > >> triggers and let users customize it. What's
>> more,
>> > > > the
>> > > > >> > user
>> > > > >> > > is
>> > > > >> > > >> > more
>> > > > >> > > >> > > > > > familiar
>> > > > >> > > >> > > > > > >> with Window API.
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> This is the reason why we just provide
>> localKeyBy
>> > > > API
>> > > > >> and
>> > > > >> > > >> reuse
>> > > > >> > > >> > > the
>> > > > >> > > >> > > > > > window
>> > > > >> > > >> > > > > > >> API. It reduces unnecessary components such
>> as
>> > > > >> triggers
>> > > > >> > and
>> > > > >> > > >> the
>> > > > >> > > >> > > > > > mechanism
>> > > > >> > > >> > > > > > >> of buffer (based on count num or time).
>> > > > >> > > >> > > > > > >> And it has a clear and easy to understand
>> > > semantics.
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> *From the operator level:*
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> We reused window operator, so we can get all
>> the
>> > > > >> benefits
>> > > > >> > > >> from
>> > > > >> > > >> > > state
>> > > > >> > > >> > > > > and
>> > > > >> > > >> > > > > > >> checkpoint.
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> From your design, you named the operator
>> under
>> > > > >> > > localAggregate
>> > > > >> > > >> > API
>> > > > >> > > >> > > > is a
>> > > > >> > > >> > > > > > >> *stateless* operator. IMO, it is still a
>> state, it
>> > > > is
>> > > > >> > just
>> > > > >> > > >> not
>> > > > >> > > >> > > Flink
>> > > > >> > > >> > > > > > >> managed state.
>> > > > >> > > >> > > > > > >> About the memory buffer (I think it's still
>> not
>> > > very
>> > > > >> > clear,
>> > > > >> > > >> if
>> > > > >> > > >> > you
>> > > > >> > > >> > > > > have
>> > > > >> > > >> > > > > > >> time, can you give more detail information or
>> > > answer
>> > > > >> my
>> > > > >> > > >> > > questions),
>> > > > >> > > >> > > > I
>> > > > >> > > >> > > > > > have
>> > > > >> > > >> > > > > > >> some questions:
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >>   - if it just a raw JVM heap memory buffer,
>> how
>> > > to
>> > > > >> > support
>> > > > >> > > >> > fault
>> > > > >> > > >> > > > > > >>   tolerance, if the job is configured
>> EXACTLY-ONCE
>> > > > >> > semantic
>> > > > >> > > >> > > > guarantee?
>> > > > >> > > >> > > > > > >>   - if you thought the memory
>> buffer(non-Flink
>> > > > state),
>> > > > >> > has
>> > > > >> > > >> > better
>> > > > >> > > >> > > > > > >>   performance. In our design, users can also
>> > > config
>> > > > >> HEAP
>> > > > >> > > >> state
>> > > > >> > > >> > > > backend
>> > > > >> > > >> > > > > > to
>> > > > >> > > >> > > > > > >>   provide the performance close to your
>> mechanism.
>> > > > >> > > >> > > > > > >>   -
>> `StreamOperator::prepareSnapshotPreBarrier()`
>> > > > >> related
>> > > > >> > > to
>> > > > >> > > >> the
>> > > > >> > > >> > > > > timing
>> > > > >> > > >> > > > > > of
>> > > > >> > > >> > > > > > >>   snapshot. IMO, the flush action should be a
>> > > > >> > synchronized
>> > > > >> > > >> > action?
>> > > > >> > > >> > > > (if
>> > > > >> > > >> > > > > > >> not,
>> > > > >> > > >> > > > > > >>   please point out my mistake) I still think
>> we
>> > > > should
>> > > > >> > not
>> > > > >> > > >> > depend
>> > > > >> > > >> > > on
>> > > > >> > > >> > > > > the
>> > > > >> > > >> > > > > > >>   timing of checkpoint. Checkpoint related
>> > > > operations
>> > > > >> are
>> > > > >> > > >> > inherent
>> > > > >> > > >> > > > > > >>   performance sensitive, we should not
>> increase
>> > > its
>> > > > >> > burden
>> > > > >> > > >> > > anymore.
>> > > > >> > > >> > > > > Our
>> > > > >> > > >> > > > > > >>   implementation based on the mechanism of
>> Flink's
>> > > > >> > > >> checkpoint,
>> > > > >> > > >> > > which
>> > > > >> > > >> > > > > can
>> > > > >> > > >> > > > > > >>   benefit from the asnyc snapshot and
>> incremental
>> > > > >> > > checkpoint.
>> > > > >> > > >> > IMO,
>> > > > >> > > >> > > > the
>> > > > >> > > >> > > > > > >>   performance is not a problem, and we also
>> do not
>> > > > >> find
>> > > > >> > the
>> > > > >> > > >> > > > > performance
>> > > > >> > > >> > > > > > >> issue
>> > > > >> > > >> > > > > > >>   in our production.
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> [1]:
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > >
>> > > > >> > > >> > >
>> > > > >> > > >> >
>> > > > >> > > >>
>> > > > >> > >
>> > > > >> >
>> > > > >>
>> > > >
>> > >
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> <
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> >
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> Best,
>> > > > >> > > >> > > > > > >> Vino
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >> Kurt Young <[hidden email] <mailto:
>> [hidden email]>> 于2019年6月18日周二
>> > > > 下午2:27写道：
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > > >>> Yeah, sorry for not expressing myself
>> clearly. I
>> > > > will
>> > > > >> > try
>> > > > >> > > to
>> > > > >> > > >> > > > provide
>> > > > >> > > >> > > > > > more
>> > > > >> > > >> > > > > > >>> details to make sure we are on the same
>> page.
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>> For DataStream API, it shouldn't be
>> optimized
>> > > > >> > > automatically.
>> > > > >> > > >> > You
>> > > > >> > > >> > > > have
>> > > > >> > > >> > > > > > to
>> > > > >> > > >> > > > > > >>> explicitly call API to do local aggregation
>> > > > >> > > >> > > > > > >>> as well as the trigger policy of the local
>> > > > >> aggregation.
>> > > > >> > > Take
>> > > > >> > > >> > > > average
>> > > > >> > > >> > > > > > for
>> > > > >> > > >> > > > > > >>> example, the user program may look like this
>> > > (just
>> > > > a
>> > > > >> > > draft):
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>> assuming the input type is
>> > > DataStream<Tupl2<String,
>> > > > >> > Int>>
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>> ds.localAggregate(
>> > > > >> > > >> > > > > > >>>        0,
>> > >  //
>> > > > >> The
>> > > > >> > > local
>> > > > >> > > >> > key,
>> > > > >> > > >> > > > > which
>> > > > >> > > >> > > > > > >> is
>> > > > >> > > >> > > > > > >>> the String from Tuple2
>> > > > >> > > >> > > > > > >>>        PartitionAvg(1),                 //
>> The
>> > > > >> partial
>> > > > >> > > >> > > aggregation
>> > > > >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>,
>> indicating
>> > > > sum
>> > > > >> and
>> > > > >> > > >> count
>> > > > >> > > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger
>> > > policy,
>> > > > >> note
>> > > > >> > > >> this
>> > > > >> > > >> > > > should
>> > > > >> > > >> > > > > be
>> > > > >> > > >> > > > > > >>> best effort, and also be composited with
>> time
>> > > based
>> > > > >> or
>> > > > >> > > >> memory
>> > > > >> > > >> > > size
>> > > > >> > > >> > > > > > based
>> > > > >> > > >> > > > > > >>> trigger
>> > > > >> > > >> > > > > > >>>    )
>>    //
>> > > > The
>> > > > >> > > return
>> > > > >> > > >> > type
>> > > > >> > > >> > > > is
>> > > > >> > > >> > > > > > >> local
>> > > > >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
>> > > > >> > > >> > > > > > >>>    .keyBy(0)                             //
>> > > Further
>> > > > >> > keyby
>> > > > >> > > it
>> > > > >> > > >> > with
>> > > > >> > > >> > > > > > >> required
>> > > > >> > > >> > > > > > >>> key
>> > > > >> > > >> > > > > > >>>    .aggregate(1)                      //
>> This
>> > > will
>> > > > >> merge
>> > > > >> > > all
>> > > > >> > > >> > the
>> > > > >> > > >> > > > > > partial
>> > > > >> > > >> > > > > > >>> results and get the final average.
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>> (This is only a draft, only trying to
>> explain
>> > > what
>> > > > it
>> > > > >> > > looks
>> > > > >> > > >> > > like. )
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>> The local aggregate operator can be
>> stateless, we
>> > > > can
>> > > > >> > > keep a
>> > > > >> > > >> > > memory
>> > > > >> > > >> > > > > > >> buffer
>> > > > >> > > >> > > > > > >>> or other efficient data structure to
>> improve the
>> > > > >> > aggregate
>> > > > >> > > >> > > > > performance.
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>> Let me know if you have any other questions.
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>> Best,
>> > > > >> > > >> > > > > > >>> Kurt
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
>> > > > >> > > >> > [hidden email] <mailto:[hidden email]>
>> > > > >> > > >> > > >
>> > > > >> > > >> > > > > > wrote:
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>>> Hi Kurt,
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> Thanks for your reply.
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> Actually, I am not against you to raise
>> your
>> > > > design.
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> From your description before, I just can
>> imagine
>> > > > >> your
>> > > > >> > > >> > high-level
>> > > > >> > > >> > > > > > >>>> implementation is about SQL and the
>> optimization
>> > > > is
>> > > > >> > inner
>> > > > >> > > >> of
>> > > > >> > > >> > the
>> > > > >> > > >> > > > > API.
>> > > > >> > > >> > > > > > >> Is
>> > > > >> > > >> > > > > > >>> it
>> > > > >> > > >> > > > > > >>>> automatically? how to give the
>> configuration
>> > > > option
>> > > > >> > about
>> > > > >> > > >> > > trigger
>> > > > >> > > >> > > > > > >>>> pre-aggregation?
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> Maybe after I get more information, it
>> sounds
>> > > more
>> > > > >> > > >> reasonable.
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> IMO, first of all, it would be better to
>> make
>> > > your
>> > > > >> user
>> > > > >> > > >> > > interface
>> > > > >> > > >> > > > > > >>> concrete,
>> > > > >> > > >> > > > > > >>>> it's the basis of the discussion.
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> For example, can you give an example code
>> > > snippet
>> > > > to
>> > > > >> > > >> introduce
>> > > > >> > > >> > > how
>> > > > >> > > >> > > > > to
>> > > > >> > > >> > > > > > >>> help
>> > > > >> > > >> > > > > > >>>> users to process data skew caused by the
>> jobs
>> > > > which
>> > > > >> > built
>> > > > >> > > >> with
>> > > > >> > > >> > > > > > >> DataStream
>> > > > >> > > >> > > > > > >>>> API?
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> If you give more details we can discuss
>> further
>> > > > >> more. I
>> > > > >> > > >> think
>> > > > >> > > >> > if
>> > > > >> > > >> > > > one
>> > > > >> > > >> > > > > > >>> design
>> > > > >> > > >> > > > > > >>>> introduces an exact interface and another
>> does
>> > > > not.
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> The implementation has an obvious
>> difference.
>> > > For
>> > > > >> > > example,
>> > > > >> > > >> we
>> > > > >> > > >> > > > > > introduce
>> > > > >> > > >> > > > > > >>> an
>> > > > >> > > >> > > > > > >>>> exact API in DataStream named localKeyBy,
>> about
>> > > > the
>> > > > >> > > >> > > > pre-aggregation
>> > > > >> > > >> > > > > we
>> > > > >> > > >> > > > > > >>> need
>> > > > >> > > >> > > > > > >>>> to define the trigger mechanism of local
>> > > > >> aggregation,
>> > > > >> > so
>> > > > >> > > we
>> > > > >> > > >> > find
>> > > > >> > > >> > > > > > reused
>> > > > >> > > >> > > > > > >>>> window API and operator is a good choice.
>> This
>> > > is
>> > > > a
>> > > > >> > > >> reasoning
>> > > > >> > > >> > > link
>> > > > >> > > >> > > > > > from
>> > > > >> > > >> > > > > > >>>> design to implementation.
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> What do you think?
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> Best,
>> > > > >> > > >> > > > > > >>>> Vino
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>> Kurt Young <[hidden email] <mailto:
>> [hidden email]>> 于2019年6月18日周二
>> > > > >> 上午11:58写道：
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>>> Hi Vino,
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>> Now I feel that we may have different
>> > > > >> understandings
>> > > > >> > > about
>> > > > >> > > >> > what
>> > > > >> > > >> > > > > kind
>> > > > >> > > >> > > > > > >> of
>> > > > >> > > >> > > > > > >>>>> problems or improvements you want to
>> > > > >> > > >> > > > > > >>>>> resolve. Currently, most of the feedback
>> are
>> > > > >> focusing
>> > > > >> > on
>> > > > >> > > >> *how
>> > > > >> > > >> > > to
>> > > > >> > > >> > > > > do a
>> > > > >> > > >> > > > > > >>>>> proper local aggregation to improve
>> performance
>> > > > >> > > >> > > > > > >>>>> and maybe solving the data skew issue*.
>> And my
>> > > > gut
>> > > > >> > > >> feeling is
>> > > > >> > > >> > > > this
>> > > > >> > > >> > > > > is
>> > > > >> > > >> > > > > > >>>>> exactly what users want at the first
>> place,
>> > > > >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to
>> > > summarize
>> > > > >> here,
>> > > > >> > > >> please
>> > > > >> > > >> > > > > correct
>> > > > >> > > >> > > > > > >>> me
>> > > > >> > > >> > > > > > >>>> if
>> > > > >> > > >> > > > > > >>>>> i'm wrong).
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>> But I still think the design is somehow
>> > > diverged
>> > > > >> from
>> > > > >> > > the
>> > > > >> > > >> > goal.
>> > > > >> > > >> > > > If
>> > > > >> > > >> > > > > we
>> > > > >> > > >> > > > > > >>>> want
>> > > > >> > > >> > > > > > >>>>> to have an efficient and powerful way to
>> > > > >> > > >> > > > > > >>>>> have local aggregation, supporting
>> intermedia
>> > > > >> result
>> > > > >> > > type
>> > > > >> > > >> is
>> > > > >> > > >> > > > > > >> essential
>> > > > >> > > >> > > > > > >>>> IMO.
>> > > > >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
>> > > > >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction` have
>> a
>> > > > proper
>> > > > >> > > >> support of
>> > > > >> > > >> > > > > > >>>> intermediate
>> > > > >> > > >> > > > > > >>>>> result type and can do `merge` operation
>> > > > >> > > >> > > > > > >>>>> on them.
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>> Now, we have a lightweight alternatives
>> which
>> > > > >> performs
>> > > > >> > > >> well,
>> > > > >> > > >> > > and
>> > > > >> > > >> > > > > > >> have a
>> > > > >> > > >> > > > > > >>>>> nice fit with the local aggregate
>> requirements.
>> > > > >> > > >> > > > > > >>>>> Mostly importantly,  it's much less
>> complex
>> > > > because
>> > > > >> > it's
>> > > > >> > > >> > > > stateless.
>> > > > >> > > >> > > > > > >> And
>> > > > >> > > >> > > > > > >>>> it
>> > > > >> > > >> > > > > > >>>>> can also achieve the similar
>> > > multiple-aggregation
>> > > > >> > > >> > > > > > >>>>> scenario.
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>> I still not convinced why we shouldn't
>> consider
>> > > > it
>> > > > >> as
>> > > > >> > a
>> > > > >> > > >> first
>> > > > >> > > >> > > > step.
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>> Best,
>> > > > >> > > >> > > > > > >>>>> Kurt
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino
>> yang <
>> > > > >> > > >> > > > [hidden email] <mailto:[hidden email]
>> >>
>> > > > >> > > >> > > > > > >>>> wrote:
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>>> Hi Kurt,
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> Thanks for your comments.
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> It seems we both implemented local
>> aggregation
>> > > > >> > feature
>> > > > >> > > to
>> > > > >> > > >> > > > optimize
>> > > > >> > > >> > > > > > >>> the
>> > > > >> > > >> > > > > > >>>>>> issue of data skew.
>> > > > >> > > >> > > > > > >>>>>> However, IMHO, the API level of
>> optimizing
>> > > > >> revenue is
>> > > > >> > > >> > > different.
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> *Your optimization benefits from Flink
>> SQL and
>> > > > >> it's
>> > > > >> > not
>> > > > >> > > >> > user's
>> > > > >> > > >> > > > > > >>>> faces.(If
>> > > > >> > > >> > > > > > >>>>> I
>> > > > >> > > >> > > > > > >>>>>> understand it incorrectly, please correct
>> > > > this.)*
>> > > > >> > > >> > > > > > >>>>>> *Our implementation employs it as an
>> > > > optimization
>> > > > >> > tool
>> > > > >> > > >> API
>> > > > >> > > >> > for
>> > > > >> > > >> > > > > > >>>>> DataStream,
>> > > > >> > > >> > > > > > >>>>>> it just like a local version of the keyBy
>> > > API.*
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> Based on this, I want to say support it
>> as a
>> > > > >> > DataStream
>> > > > >> > > >> API
>> > > > >> > > >> > > can
>> > > > >> > > >> > > > > > >>> provide
>> > > > >> > > >> > > > > > >>>>>> these advantages:
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear
>> semantic
>> > > and
>> > > > >> it's
>> > > > >> > > >> > flexible
>> > > > >> > > >> > > > not
>> > > > >> > > >> > > > > > >>> only
>> > > > >> > > >> > > > > > >>>>> for
>> > > > >> > > >> > > > > > >>>>>>   processing data skew but also for
>> > > implementing
>> > > > >> some
>> > > > >> > > >> user
>> > > > >> > > >> > > > cases,
>> > > > >> > > >> > > > > > >>> for
>> > > > >> > > >> > > > > > >>>>>>   example, if we want to calculate the
>> > > > >> multiple-level
>> > > > >> > > >> > > > aggregation,
>> > > > >> > > >> > > > > > >>> we
>> > > > >> > > >> > > > > > >>>>> can
>> > > > >> > > >> > > > > > >>>>>> do
>> > > > >> > > >> > > > > > >>>>>>   multiple-level aggregation in the local
>> > > > >> > aggregation:
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
>> > > > >> > > >> //
>> > > > >> > > >> > > here
>> > > > >> > > >> > > > > > >> "a"
>> > > > >> > > >> > > > > > >>>> is
>> > > > >> > > >> > > > > > >>>>> a
>> > > > >> > > >> > > > > > >>>>>>   sub-category, while "b" is a category,
>> here
>> > > we
>> > > > >> do
>> > > > >> > not
>> > > > >> > > >> need
>> > > > >> > > >> > > to
>> > > > >> > > >> > > > > > >>>> shuffle
>> > > > >> > > >> > > > > > >>>>>> data
>> > > > >> > > >> > > > > > >>>>>>   in the network.
>> > > > >> > > >> > > > > > >>>>>>   - The users of DataStream API will
>> benefit
>> > > > from
>> > > > >> > this.
>> > > > >> > > >> > > > Actually,
>> > > > >> > > >> > > > > > >> we
>> > > > >> > > >> > > > > > >>>>> have
>> > > > >> > > >> > > > > > >>>>>>   a lot of scenes need to use DataStream
>> API.
>> > > > >> > > Currently,
>> > > > >> > > >> > > > > > >> DataStream
>> > > > >> > > >> > > > > > >>>> API
>> > > > >> > > >> > > > > > >>>>> is
>> > > > >> > > >> > > > > > >>>>>>   the cornerstone of the physical plan of
>> > > Flink
>> > > > >> SQL.
>> > > > >> > > >> With a
>> > > > >> > > >> > > > > > >>> localKeyBy
>> > > > >> > > >> > > > > > >>>>>> API,
>> > > > >> > > >> > > > > > >>>>>>   the optimization of SQL at least may
>> use
>> > > this
>> > > > >> > > optimized
>> > > > >> > > >> > API,
>> > > > >> > > >> > > > > > >> this
>> > > > >> > > >> > > > > > >>>> is a
>> > > > >> > > >> > > > > > >>>>>>   further topic.
>> > > > >> > > >> > > > > > >>>>>>   - Based on the window operator, our
>> state
>> > > > would
>> > > > >> > > benefit
>> > > > >> > > >> > from
>> > > > >> > > >> > > > > > >> Flink
>> > > > >> > > >> > > > > > >>>>> State
>> > > > >> > > >> > > > > > >>>>>>   and checkpoint, we do not need to worry
>> > > about
>> > > > >> OOM
>> > > > >> > and
>> > > > >> > > >> job
>> > > > >> > > >> > > > > > >> failed.
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> Now, about your questions:
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> 1. About our design cannot change the
>> data
>> > > type
>> > > > >> and
>> > > > >> > > about
>> > > > >> > > >> > the
>> > > > >> > > >> > > > > > >>>>>> implementation of average:
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the
>> localKeyBy is
>> > > > an
>> > > > >> API
>> > > > >> > > >> > provides
>> > > > >> > > >> > > > to
>> > > > >> > > >> > > > > > >> the
>> > > > >> > > >> > > > > > >>>>> users
>> > > > >> > > >> > > > > > >>>>>> who use DataStream API to build their
>> jobs.
>> > > > >> > > >> > > > > > >>>>>> Users should know its semantics and the
>> > > > difference
>> > > > >> > with
>> > > > >> > > >> > keyBy
>> > > > >> > > >> > > > API,
>> > > > >> > > >> > > > > > >> so
>> > > > >> > > >> > > > > > >>>> if
>> > > > >> > > >> > > > > > >>>>>> they want to the average aggregation,
>> they
>> > > > should
>> > > > >> > carry
>> > > > >> > > >> > local
>> > > > >> > > >> > > > sum
>> > > > >> > > >> > > > > > >>>> result
>> > > > >> > > >> > > > > > >>>>>> and local count result.
>> > > > >> > > >> > > > > > >>>>>> I admit that it will be convenient to use
>> > > keyBy
>> > > > >> > > directly.
>> > > > >> > > >> > But
>> > > > >> > > >> > > we
>> > > > >> > > >> > > > > > >> need
>> > > > >> > > >> > > > > > >>>> to
>> > > > >> > > >> > > > > > >>>>>> pay a little price when we get some
>> benefits.
>> > > I
>> > > > >> think
>> > > > >> > > >> this
>> > > > >> > > >> > > price
>> > > > >> > > >> > > > > is
>> > > > >> > > >> > > > > > >>>>>> reasonable. Considering that the
>> DataStream
>> > > API
>> > > > >> > itself
>> > > > >> > > >> is a
>> > > > >> > > >> > > > > > >> low-level
>> > > > >> > > >> > > > > > >>>> API
>> > > > >> > > >> > > > > > >>>>>> (at least for now).
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> 2. About stateless operator and
>> > > > >> > > >> > > > > > >>>>>>
>> `StreamOperator::prepareSnapshotPreBarrier()`:
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> Actually, I have discussed this opinion
>> with
>> > > > >> @dianfu
>> > > > >> > in
>> > > > >> > > >> the
>> > > > >> > > >> > > old
>> > > > >> > > >> > > > > > >> mail
>> > > > >> > > >> > > > > > >>>>>> thread. I will copy my opinion from
>> there:
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>>   - for your design, you still need
>> somewhere
>> > > to
>> > > > >> give
>> > > > >> > > the
>> > > > >> > > >> > > users
>> > > > >> > > >> > > > > > >>>>> configure
>> > > > >> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
>> > > > >> availability?),
>> > > > >> > > >> this
>> > > > >> > > >> > > > design
>> > > > >> > > >> > > > > > >>>> cannot
>> > > > >> > > >> > > > > > >>>>>>   guarantee a deterministic semantics
>> (it will
>> > > > >> bring
>> > > > >> > > >> trouble
>> > > > >> > > >> > > for
>> > > > >> > > >> > > > > > >>>> testing
>> > > > >> > > >> > > > > > >>>>>> and
>> > > > >> > > >> > > > > > >>>>>>   debugging).
>> > > > >> > > >> > > > > > >>>>>>   - if the implementation depends on the
>> > > timing
>> > > > of
>> > > > >> > > >> > checkpoint,
>> > > > >> > > >> > > > it
>> > > > >> > > >> > > > > > >>>> would
>> > > > >> > > >> > > > > > >>>>>>   affect the checkpoint's progress, and
>> the
>> > > > >> buffered
>> > > > >> > > data
>> > > > >> > > >> > may
>> > > > >> > > >> > > > > > >> cause
>> > > > >> > > >> > > > > > >>>> OOM
>> > > > >> > > >> > > > > > >>>>>>   issue. In addition, if the operator is
>> > > > >> stateless,
>> > > > >> > it
>> > > > >> > > >> can
>> > > > >> > > >> > not
>> > > > >> > > >> > > > > > >>> provide
>> > > > >> > > >> > > > > > >>>>>> fault
>> > > > >> > > >> > > > > > >>>>>>   tolerance.
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> Best,
>> > > > >> > > >> > > > > > >>>>>> Vino
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>> Kurt Young <[hidden email] <mailto:
>> [hidden email]>> 于2019年6月18日周二
>> > > > >> > 上午9:22写道：
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>>> Hi Vino,
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the
>> general
>> > > > idea
>> > > > >> and
>> > > > >> > > IMO
>> > > > >> > > >> > it's
>> > > > >> > > >> > > > > > >> very
>> > > > >> > > >> > > > > > >>>>> useful
>> > > > >> > > >> > > > > > >>>>>>> feature.
>> > > > >> > > >> > > > > > >>>>>>> But after reading through the document,
>> I
>> > > feel
>> > > > >> that
>> > > > >> > we
>> > > > >> > > >> may
>> > > > >> > > >> > > over
>> > > > >> > > >> > > > > > >>>> design
>> > > > >> > > >> > > > > > >>>>>> the
>> > > > >> > > >> > > > > > >>>>>>> required
>> > > > >> > > >> > > > > > >>>>>>> operator for proper local aggregation.
>> The
>> > > main
>> > > > >> > reason
>> > > > >> > > >> is
>> > > > >> > > >> > we
>> > > > >> > > >> > > > want
>> > > > >> > > >> > > > > > >>> to
>> > > > >> > > >> > > > > > >>>>>> have a
>> > > > >> > > >> > > > > > >>>>>>> clear definition and behavior about the
>> > > "local
>> > > > >> keyed
>> > > > >> > > >> state"
>> > > > >> > > >> > > > which
>> > > > >> > > >> > > > > > >>> in
>> > > > >> > > >> > > > > > >>>> my
>> > > > >> > > >> > > > > > >>>>>>> opinion is not
>> > > > >> > > >> > > > > > >>>>>>> necessary for local aggregation, at
>> least for
>> > > > >> start.
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>> Another issue I noticed is the local
>> key by
>> > > > >> operator
>> > > > >> > > >> cannot
>> > > > >> > > >> > > > > > >> change
>> > > > >> > > >> > > > > > >>>>>> element
>> > > > >> > > >> > > > > > >>>>>>> type, it will
>> > > > >> > > >> > > > > > >>>>>>> also restrict a lot of use cases which
>> can be
>> > > > >> > benefit
>> > > > >> > > >> from
>> > > > >> > > >> > > > local
>> > > > >> > > >> > > > > > >>>>>>> aggregation, like "average".
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and
>> the only
>> > > > >> thing
>> > > > >> > > >> need to
>> > > > >> > > >> > > be
>> > > > >> > > >> > > > > > >> done
>> > > > >> > > >> > > > > > >>>> is
>> > > > >> > > >> > > > > > >>>>>>> introduce
>> > > > >> > > >> > > > > > >>>>>>> a stateless lightweight operator which
>> is
>> > > > >> *chained*
>> > > > >> > > >> before
>> > > > >> > > >> > > > > > >>> `keyby()`.
>> > > > >> > > >> > > > > > >>>>> The
>> > > > >> > > >> > > > > > >>>>>>> operator will flush all buffered
>> > > > >> > > >> > > > > > >>>>>>> elements during
>> > > > >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
>> > > > >> > > >> > > > and
>> > > > >> > > >> > > > > > >>>> make
>> > > > >> > > >> > > > > > >>>>>>> himself stateless.
>> > > > >> > > >> > > > > > >>>>>>> By the way, in the earlier version we
>> also
>> > > did
>> > > > >> the
>> > > > >> > > >> similar
>> > > > >> > > >> > > > > > >> approach
>> > > > >> > > >> > > > > > >>>> by
>> > > > >> > > >> > > > > > >>>>>>> introducing a stateful
>> > > > >> > > >> > > > > > >>>>>>> local aggregation operator but it's not
>> > > > >> performed as
>> > > > >> > > >> well
>> > > > >> > > >> > as
>> > > > >> > > >> > > > the
>> > > > >> > > >> > > > > > >>>> later
>> > > > >> > > >> > > > > > >>>>>> one,
>> > > > >> > > >> > > > > > >>>>>>> and also effect the barrie
>> > > > >> > > >> > > > > > >>>>>>> alignment time. The later one is fairly
>> > > simple
>> > > > >> and
>> > > > >> > > more
>> > > > >> > > >> > > > > > >> efficient.
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>> I would highly suggest you to consider
>> to
>> > > have
>> > > > a
>> > > > >> > > >> stateless
>> > > > >> > > >> > > > > > >> approach
>> > > > >> > > >> > > > > > >>>> at
>> > > > >> > > >> > > > > > >>>>>> the
>> > > > >> > > >> > > > > > >>>>>>> first step.
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>> Best,
>> > > > >> > > >> > > > > > >>>>>>> Kurt
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark Wu
>> <
>> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
>> > > > >> > > >> > > > > > >> wrote:
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>> Hi Vino,
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>> Regarding to the
>> "input.keyBy(0).sum(1)" vs
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> >
>> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
>> > > > >> > > >> > > > > > >> have
>> > > > >> > > >> > > > > > >>>> you
>> > > > >> > > >> > > > > > >>>>>>> done
>> > > > >> > > >> > > > > > >>>>>>>> some benchmark?
>> > > > >> > > >> > > > > > >>>>>>>> Because I'm curious about how much
>> > > performance
>> > > > >> > > >> improvement
>> > > > >> > > >> > > can
>> > > > >> > > >> > > > > > >> we
>> > > > >> > > >> > > > > > >>>> get
>> > > > >> > > >> > > > > > >>>>>> by
>> > > > >> > > >> > > > > > >>>>>>>> using count window as the local
>> operator.
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>> Best,
>> > > > >> > > >> > > > > > >>>>>>>> Jark
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino
>> yang <
>> > > > >> > > >> > > > [hidden email] <mailto:[hidden email]
>> >
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>>>> wrote:
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> Hi Hequn,
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to
>> > > provide a
>> > > > >> tool
>> > > > >> > > >> which
>> > > > >> > > >> > > can
>> > > > >> > > >> > > > > > >>> let
>> > > > >> > > >> > > > > > >>>>>> users
>> > > > >> > > >> > > > > > >>>>>>> do
>> > > > >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The
>> behavior
>> > > of
>> > > > >> the
>> > > > >> > > >> > > > > > >>> pre-aggregation
>> > > > >> > > >> > > > > > >>>>> is
>> > > > >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> So the three cases are different, I
>> will
>> > > > >> describe
>> > > > >> > > them
>> > > > >> > > >> > one
>> > > > >> > > >> > > by
>> > > > >> > > >> > > > > > >>>> one:
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the result is
>> event-driven,
>> > > > each
>> > > > >> > > event
>> > > > >> > > >> can
>> > > > >> > > >> > > > > > >>> produce
>> > > > >> > > >> > > > > > >>>>> one
>> > > > >> > > >> > > > > > >>>>>>> sum
>> > > > >> > > >> > > > > > >>>>>>>>> aggregation result and it is the
>> latest one
>> > > > >> from
>> > > > >> > the
>> > > > >> > > >> > source
>> > > > >> > > >> > > > > > >>>> start.*
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> 2.
>> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have a
>> > > > >> problem, it
>> > > > >> > > >> would
>> > > > >> > > >> > do
>> > > > >> > > >> > > > > > >> the
>> > > > >> > > >> > > > > > >>>>> local
>> > > > >> > > >> > > > > > >>>>>>> sum
>> > > > >> > > >> > > > > > >>>>>>>>> aggregation and will produce the
>> latest
>> > > > partial
>> > > > >> > > result
>> > > > >> > > >> > from
>> > > > >> > > >> > > > > > >> the
>> > > > >> > > >> > > > > > >>>>>> source
>> > > > >> > > >> > > > > > >>>>>>>>> start for every event. *
>> > > > >> > > >> > > > > > >>>>>>>>> *These latest partial results from
>> the same
>> > > > key
>> > > > >> > are
>> > > > >> > > >> > hashed
>> > > > >> > > >> > > to
>> > > > >> > > >> > > > > > >>> one
>> > > > >> > > >> > > > > > >>>>>> node
>> > > > >> > > >> > > > > > >>>>>>> to
>> > > > >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
>> > > > >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it
>> > > received
>> > > > >> > > multiple
>> > > > >> > > >> > > partial
>> > > > >> > > >> > > > > > >>>>> results
>> > > > >> > > >> > > > > > >>>>>>>> (they
>> > > > >> > > >> > > > > > >>>>>>>>> are all calculated from the source
>> start)
>> > > and
>> > > > >> sum
>> > > > >> > > them
>> > > > >> > > >> > will
>> > > > >> > > >> > > > > > >> get
>> > > > >> > > >> > > > > > >>>> the
>> > > > >> > > >> > > > > > >>>>>>> wrong
>> > > > >> > > >> > > > > > >>>>>>>>> result.*
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> 3.
>> > > > >> > > >> > >
>> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a
>> partial
>> > > > >> > > aggregation
>> > > > >> > > >> > > result
>> > > > >> > > >> > > > > > >>> for
>> > > > >> > > >> > > > > > >>>>>> the 5
>> > > > >> > > >> > > > > > >>>>>>>>> records in the count window. The
>> partial
>> > > > >> > aggregation
>> > > > >> > > >> > > results
>> > > > >> > > >> > > > > > >>> from
>> > > > >> > > >> > > > > > >>>>> the
>> > > > >> > > >> > > > > > >>>>>>>> same
>> > > > >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> So the first case and the third case
>> can
>> > > get
>> > > > >> the
>> > > > >> > > >> *same*
>> > > > >> > > >> > > > > > >> result,
>> > > > >> > > >> > > > > > >>>> the
>> > > > >> > > >> > > > > > >>>>>>>>> difference is the output-style and the
>> > > > latency.
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key API
>> is
>> > > just
>> > > > >> an
>> > > > >> > > >> > > optimization
>> > > > >> > > >> > > > > > >>>> API.
>> > > > >> > > >> > > > > > >>>>> We
>> > > > >> > > >> > > > > > >>>>>>> do
>> > > > >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the
>> user
>> > > has
>> > > > to
>> > > > >> > > >> > understand
>> > > > >> > > >> > > > > > >> its
>> > > > >> > > >> > > > > > >>>>>>> semantics
>> > > > >> > > >> > > > > > >>>>>>>>> and use it correctly.
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> Best,
>> > > > >> > > >> > > > > > >>>>>>>>> Vino
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]
>> <mailto:[hidden email]>>
>> > > > >> 于2019年6月17日周一
>> > > > >> > > >> > 下午4:18写道：
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>> Hi Vino,
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it
>> is a
>> > > > very
>> > > > >> > good
>> > > > >> > > >> > > feature!
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the
>> > > > semantics
>> > > > >> > for
>> > > > >> > > >> the
>> > > > >> > > >> > > > > > >>>>>> `localKeyBy`.
>> > > > >> > > >> > > > > > >>>>>>>> From
>> > > > >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API
>> returns
>> > > > an
>> > > > >> > > >> instance
>> > > > >> > > >> > of
>> > > > >> > > >> > > > > > >>>>>>> `KeyedStream`
>> > > > >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in
>> this
>> > > > case,
>> > > > >> > > what's
>> > > > >> > > >> > the
>> > > > >> > > >> > > > > > >>>>> semantics
>> > > > >> > > >> > > > > > >>>>>>> for
>> > > > >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will the
>> > > > >> following
>> > > > >> > > code
>> > > > >> > > >> > share
>> > > > >> > > >> > > > > > >>> the
>> > > > >> > > >> > > > > > >>>>> same
>> > > > >> > > >> > > > > > >>>>>>>>> result?
>> > > > >> > > >> > > > > > >>>>>>>>>> and what're the differences between
>> them?
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
>> > > > >> > > >> > > > > > >>>>>>>>>> 2.
>> > > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>> > > > >> > > >> > > > > > >>>>>>>>>> 3.
>> > > > >> > > >> > > > > > >>
>> > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add
>> this
>> > > into
>> > > > >> the
>> > > > >> > > >> > document.
>> > > > >> > > >> > > > > > >>> Thank
>> > > > >> > > >> > > > > > >>>>> you
>> > > > >> > > >> > > > > > >>>>>>>> very
>> > > > >> > > >> > > > > > >>>>>>>>>> much.
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>> Best, Hequn
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM vino
>> > > yang <
>> > > > >> > > >> > > > > > >>>>> [hidden email] <mailto:
>> [hidden email]>>
>> > > > >> > > >> > > > > > >>>>>>>>> wrote:
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*"
>> section
>> > > of
>> > > > >> FLIP
>> > > > >> > > >> wiki
>> > > > >> > > >> > > > > > >>>> page.[1]
>> > > > >> > > >> > > > > > >>>>>> This
>> > > > >> > > >> > > > > > >>>>>>>>> mail
>> > > > >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has
>> proceeded to
>> > > > the
>> > > > >> > > third
>> > > > >> > > >> > step.
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth
>> step(vote
>> > > > step),
>> > > > >> I
>> > > > >> > > >> didn't
>> > > > >> > > >> > > > > > >> find
>> > > > >> > > >> > > > > > >>>> the
>> > > > >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the
>> voting
>> > > > >> process.
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of
>> this
>> > > > >> feature
>> > > > >> > > has
>> > > > >> > > >> > been
>> > > > >> > > >> > > > > > >>> done
>> > > > >> > > >> > > > > > >>>>> in
>> > > > >> > > >> > > > > > >>>>>>> the
>> > > > >> > > >> > > > > > >>>>>>>>> old
>> > > > >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when
>> > > should
>> > > > I
>> > > > >> > start
>> > > > >> > > >> > > > > > >> voting?
>> > > > >> > > >> > > > > > >>>> Can
>> > > > >> > > >> > > > > > >>>>> I
>> > > > >> > > >> > > > > > >>>>>>>> start
>> > > > >> > > >> > > > > > >>>>>>>>>> now?
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>> Best,
>> > > > >> > > >> > > > > > >>>>>>>>>>> Vino
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>> [1]:
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > >
>> > > > >> > > >> > >
>> > > > >> > > >> >
>> > > > >> > > >>
>> > > > >> > >
>> > > > >> >
>> > > > >>
>> > > >
>> > >
>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>> <
>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>> >
>> > > > >> > > >> > > > > > >>>>>>>>>>> [2]:
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > >
>> > > > >> > > >> > >
>> > > > >> > > >> >
>> > > > >> > > >>
>> > > > >> > >
>> > > > >> >
>> > > > >>
>> > > >
>> > >
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> <
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> >
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email] <mailto:
>> [hidden email]>>
>> > > 于2019年6月13日周四
>> > > > >> > > 上午9:19写道：
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for
>> your
>> > > > >> efforts.
>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>> Best,
>> > > > >> > > >> > > > > > >>>>>>>>>>>> Leesf
>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]
>> <mailto:[hidden email]>>
>> > > > >> > 于2019年6月12日周三
>> > > > >> > > >> > > > > > >>> 下午5:46写道：
>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP
>> > > discussion
>> > > > >> > thread
>> > > > >> > > >> > > > > > >> about
>> > > > >> > > >> > > > > > >>>>>>> supporting
>> > > > >> > > >> > > > > > >>>>>>>>>> local
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can
>> effectively
>> > > > >> > alleviate
>> > > > >> > > >> data
>> > > > >> > > >> > > > > > >>>> skew.
>> > > > >> > > >> > > > > > >>>>>>> This
>> > > > >> > > >> > > > > > >>>>>>>> is
>> > > > >> > > >> > > > > > >>>>>>>>>> the
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > >
>> > > > >> > > >> > >
>> > > > >> > > >> >
>> > > > >> > > >>
>> > > > >> > >
>> > > > >> >
>> > > > >>
>> > > >
>> > >
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>> <
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>> >
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are
>> widely
>> > > used
>> > > > to
>> > > > >> > > >> perform
>> > > > >> > > >> > > > > > >>>>>> aggregating
>> > > > >> > > >> > > > > > >>>>>>>>>>>> operations
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window) on
>> the
>> > > > >> elements
>> > > > >> > > >> that
>> > > > >> > > >> > > > > > >>> have
>> > > > >> > > >> > > > > > >>>>> the
>> > > > >> > > >> > > > > > >>>>>>> same
>> > > > >> > > >> > > > > > >>>>>>>>>> key.
>> > > > >> > > >> > > > > > >>>>>>>>>>>> When
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the elements
>> with
>> > > > the
>> > > > >> > same
>> > > > >> > > >> key
>> > > > >> > > >> > > > > > >>> will
>> > > > >> > > >> > > > > > >>>> be
>> > > > >> > > >> > > > > > >>>>>>> sent
>> > > > >> > > >> > > > > > >>>>>>>> to
>> > > > >> > > >> > > > > > >>>>>>>>>> and
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> The performance of these
>> aggregating
>> > > > >> > operations
>> > > > >> > > is
>> > > > >> > > >> > > > > > >> very
>> > > > >> > > >> > > > > > >>>>>>> sensitive
>> > > > >> > > >> > > > > > >>>>>>>>> to
>> > > > >> > > >> > > > > > >>>>>>>>>>> the
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the cases
>> > > where
>> > > > >> the
>> > > > >> > > >> > > > > > >>> distribution
>> > > > >> > > >> > > > > > >>>>> of
>> > > > >> > > >> > > > > > >>>>>>> keys
>> > > > >> > > >> > > > > > >>>>>>>>>>>> follows a
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance
>> will be
>> > > > >> > > >> significantly
>> > > > >> > > >> > > > > > >>>>>> downgraded.
>> > > > >> > > >> > > > > > >>>>>>>>> More
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree
>> of
>> > > > >> > parallelism
>> > > > >> > > >> does
>> > > > >> > > >> > > > > > >>> not
>> > > > >> > > >> > > > > > >>>>> help
>> > > > >> > > >> > > > > > >>>>>>>> when
>> > > > >> > > >> > > > > > >>>>>>>>> a
>> > > > >> > > >> > > > > > >>>>>>>>>>> task
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a
>> widely-adopted
>> > > > >> method
>> > > > >> > to
>> > > > >> > > >> > > > > > >> reduce
>> > > > >> > > >> > > > > > >>>> the
>> > > > >> > > >> > > > > > >>>>>>>>>> performance
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can
>> decompose
>> > > > the
>> > > > >> > > >> > > > > > >> aggregating
>> > > > >> > > >> > > > > > >>>>>>>> operations
>> > > > >> > > >> > > > > > >>>>>>>>>> into
>> > > > >> > > >> > > > > > >>>>>>>>>>>> two
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we
>> > > aggregate
>> > > > >> the
>> > > > >> > > >> elements
>> > > > >> > > >> > > > > > >>> of
>> > > > >> > > >> > > > > > >>>>> the
>> > > > >> > > >> > > > > > >>>>>>> same
>> > > > >> > > >> > > > > > >>>>>>>>> key
>> > > > >> > > >> > > > > > >>>>>>>>>>> at
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial
>> > > > results.
>> > > > >> > Then
>> > > > >> > > at
>> > > > >> > > >> > > > > > >> the
>> > > > >> > > >> > > > > > >>>>> second
>> > > > >> > > >> > > > > > >>>>>>>>> phase,
>> > > > >> > > >> > > > > > >>>>>>>>>>>> these
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to
>> receivers
>> > > > >> > according
>> > > > >> > > to
>> > > > >> > > >> > > > > > >>> their
>> > > > >> > > >> > > > > > >>>>> keys
>> > > > >> > > >> > > > > > >>>>>>> and
>> > > > >> > > >> > > > > > >>>>>>>>> are
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final
>> result.
>> > > > Since
>> > > > >> the
>> > > > >> > > >> number
>> > > > >> > > >> > > > > > >>> of
>> > > > >> > > >> > > > > > >>>>>>> partial
>> > > > >> > > >> > > > > > >>>>>>>>>>> results
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is
>> limited by
>> > > > the
>> > > > >> > > >> number of
>> > > > >> > > >> > > > > > >>>>>> senders,
>> > > > >> > > >> > > > > > >>>>>>>> the
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be
>> > > reduced.
>> > > > >> > > >> Besides, by
>> > > > >> > > >> > > > > > >>>>>> reducing
>> > > > >> > > >> > > > > > >>>>>>>> the
>> > > > >> > > >> > > > > > >>>>>>>>>>> amount
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the
>> performance can
>> > > > be
>> > > > >> > > further
>> > > > >> > > >> > > > > > >>>>> improved.
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > >
>> > > > >> > > >> > >
>> > > > >> > > >> >
>> > > > >> > > >>
>> > > > >> > >
>> > > > >> >
>> > > > >>
>> > > >
>> > >
>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>> <
>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>> >
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > >
>> > > > >> > > >> > >
>> > > > >> > > >> >
>> > > > >> > > >>
>> > > > >> > >
>> > > > >> >
>> > > > >>
>> > > >
>> > >
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> <
>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>> >
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> https://issues.apache.org/jira/browse/FLINK-12786 <
>> https://issues.apache.org/jira/browse/FLINK-12786>
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your
>> > > feedback!
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Best,
>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Vino
>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>>
>> > > > >> > > >> > > > > > >>>>>>>
>> > > > >> > > >> > > > > > >>>>>>
>> > > > >> > > >> > > > > > >>>>>
>> > > > >> > > >> > > > > > >>>>
>> > > > >> > > >> > > > > > >>>
>> > > > >> > > >> > > > > > >>
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > > >
>> > > > >> > > >> > > > >
>> > > > >> > > >> > > >
>> > > > >> > > >> > >
>> > > > >> > > >> >
>> > > > >> > > >>
>> > > > >> > > >
>> > > > >> > >
>> > > > >> >
>> > > > >>
>> > > > >
>> > > >
>> > >
>>
>>

[vino yang]

Hi all,

I also think it's a good idea that we need to agree on the API level first.

I am sorry, we did not give some usage examples of the API in the FLIP
documentation before. This may have caused some misunderstandings about the
discussion of this mail thread.

So, now I have added some usage examples in the "Public Interfaces" section
of the FLIP-44 documentation.

Let us first know the API through its use examples.

Any feedback and questions please let me know.

Best,
Vino

vino yang <[hidden email]> 于2019年6月27日周四 下午12:51写道：

> Hi Jark,
>
> `DataStream.localKeyBy().process()` has some key difference with
> `DataStream.process()`. The former API receive `KeyedProcessFunction`
> (sorry my previous reply may let you misunderstood), the latter receive API
> receive `ProcessFunction`. When you read the java doc of ProcessFunction,
> you can find a "*Note*" statement:
>
> Access to keyed state and timers (which are also scoped to a key) is only
>> available if the ProcessFunction is applied on a KeyedStream.
>
>
> In addition, you can also compare the two
> implementations(`ProcessOperator` and `KeyedProcessOperator`) of them to
> view the difference.
>
> IMO, the "Note" statement means a lot for many use scenarios. For example,
> if we cannot access keyed state, we can only use heap memory to buffer data
> while it does not guarantee the semantics of correctness! And the timer is
> also very important in some scenarios.
>
> That's why we say our API is flexible, it can get most benefits (even
> subsequent potential benefits in the future) from KeyedStream.
>
> I have added some instructions on the use of localKeyBy in the FLIP-44
> documentation.
>
> Best,
> Vino
>
>
> Jark Wu <[hidden email]> 于2019年6月27日周四 上午10:44写道：
>
>> Hi Piotr,
>>
>> I think the state migration you raised is a good point. Having
>> "stream.enableLocalAggregation(Trigger)” might add some implicit operators
>> which users can't set uid and cause the state compatibility/evolution
>> problems.
>> So let's put this in rejected alternatives.
>>
>> Hi Vino,
>>
>> You mentioned several times that "DataStream.localKeyBy().process()" can
>> solve the data skew problem of "DataStream.keyBy().process()".
>> I'm curious about what's the differences between "DataStream.process()"
>> and "DataStream.localKeyBy().process()"?
>> Can't "DataStream.process()" solve the data skew problem?
>>
>> Best,
>> Jark
>>
>>
>> On Wed, 26 Jun 2019 at 18:20, Piotr Nowojski <[hidden email]> wrote:
>>
>>> Hi Jark and Vino,
>>>
>>> I agree fully with Jark, that in order to have the discussion focused
>>> and to limit the number of parallel topics, we should first focus on one
>>> topic. We can first decide on the API and later we can discuss the runtime
>>> details. At least as long as we keep the potential requirements of the
>>> runtime part in mind while designing the API.
>>>
>>> Regarding the automatic optimisation and proposed by Jark:
>>>
>>> "stream.enableLocalAggregation(Trigger)”
>>>
>>> I would be against that in the DataStream API for the reasons that Vino
>>> presented. There was a discussion thread about future directions of Table
>>> API vs DataStream API and the consensus was that the automatic
>>> optimisations are one of the dividing lines between those two, for at least
>>> a couple of reasons. Flexibility and full control over the program was one
>>> of them. Another is state migration. Having
>>> "stream.enableLocalAggregation(Trigger)” that might add some implicit
>>> operators in the job graph can cause problems with savepoint/checkpoint
>>> compatibility.
>>>
>>> However I haven’t thought about/looked into the details of the Vino’s
>>> API proposal, so I can not fully judge it.
>>>
>>> Piotrek
>>>
>>> > On 26 Jun 2019, at 09:17, vino yang <[hidden email]> wrote:
>>> >
>>> > Hi Jark,
>>> >
>>> > Similar questions and responses have been repeated many times.
>>> >
>>> > Why didn't we spend more sections discussing the API?
>>> >
>>> > Because we try to reuse the ability of KeyedStream. The localKeyBy API
>>> just returns the KeyedStream, that's our design, we can get all the benefit
>>> from the KeyedStream and get further benefit from WindowedStream. The APIs
>>> come from KeyedStream and WindowedStream is long-tested and flexible. Yes,
>>> we spend much space discussing the local keyed state, that's not the goal
>>> and motivation, that's the way to implement local aggregation. It is much
>>> more complicated than the API we introduced, so we spent more section. Of
>>> course, this is the implementation level of the Operator. We also agreed to
>>> support the implementation of buffer+flush and added related instructions
>>> to the documentation. This needs to wait for the community to recognize,
>>> and if the community agrees, we will give more instructions. What's more, I
>>> have indicated before that we welcome state-related commenters to
>>> participate in the discussion, but it is not wise to modify the FLIP title.
>>> >
>>> > About the API of local aggregation:
>>> >
>>> > I don't object to ease of use is very important. But IMHO flexibility
>>> is the most important at the DataStream API level. Otherwise, what does
>>> DataStream mean? The significance of the DataStream API is that it is more
>>> flexible than Table/SQL, if it cannot provide this point then everyone
>>> would just use Table/SQL.
>>> >
>>> > The DataStream API should focus more on flexibility than on automatic
>>> optimization, which allows users to have more possibilities to implement
>>> complex programs and meet specific scenarios. There are a lot of programs
>>> written using the DataStream API that are far more complex than we think.
>>> It is very difficult to optimize at the API level and the benefit is very
>>> low.
>>> >
>>> > I want to say that we support a more generalized local aggregation. I
>>> mentioned in the previous reply that not only the UDF that implements
>>> AggregateFunction is called aggregation. In some complex scenarios, we have
>>> to support local aggregation through ProcessFunction and
>>> ProcessWindowFunction to solve the data skew problem. How do you support
>>> them in the API implementation and optimization you mentioned?
>>> >
>>> > Flexible APIs are arbitrarily combined to result in erroneous
>>> semantics, which does not prove that flexibility is meaningless because the
>>> user is the decision maker. I have been exemplified many times, for many
>>> APIs in DataStream, if we arbitrarily combined them, they also do not have
>>> much practical significance. So, users who use flexible APIs need to
>>> understand what they are doing and what is the right choice.
>>> >
>>> > I think that if we discuss this, there will be no result.
>>> >
>>> > @Stephan Ewen <mailto:[hidden email]> , @Aljoscha Krettek <mailto:
>>> [hidden email]> and @Piotr Nowojski <mailto:[hidden email]>
>>> Do you have further comments?
>>> >
>>> >
>>> > Jark Wu <[hidden email] <mailto:[hidden email]>> 于2019年6月26日周三
>>> 上午11:46写道：
>>> > Thanks for the long discussion Vino, Kurt, Hequn, Piotr and others,
>>> >
>>> > It seems that we still have some different ideas about the API
>>> > (localKeyBy()?) and implementation details (reuse window operator?
>>> local
>>> > keyed state?).
>>> > And the discussion is stalled and mixed with motivation and API and
>>> > implementation discussion.
>>> >
>>> > In order to make some progress in this topic, I want to summarize the
>>> > points (pls correct me if I'm wrong or missing sth) and would suggest
>>> to
>>> > split
>>> >  the topic into following aspects and discuss them one by one.
>>> >
>>> > 1) What's the main purpose of this FLIP?
>>> >  - From the title of this FLIP, it is to support local aggregate.
>>> However
>>> > from the content of the FLIP, 80% are introducing a new state called
>>> local
>>> > keyed state.
>>> >  - If we mainly want to introduce local keyed state, then we should
>>> > re-title the FLIP and involve in more people who works on state.
>>> >  - If we mainly want to support local aggregate, then we can jump to
>>> step 2
>>> > to discuss the API design.
>>> >
>>> > 2) What does the API look like?
>>> >  - Vino proposed to use "localKeyBy()" to do local process, the output
>>> of
>>> > local process is the result type of aggregate function.
>>> >   a) For non-windowed aggregate:
>>> > input.localKeyBy(..).aggregate(agg1).keyBy(..).aggregate(agg2)    **NOT
>>> > SUPPORT**
>>> >   b) For windowed aggregate:
>>> >
>>> input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)
>>> >
>>> > 3) What's the implementation detail?
>>> >  - may reuse window operator or not.
>>> >  - may introduce a new state concepts or not.
>>> >  - may not have state in local operator by flushing buffers in
>>> > prepareSnapshotPreBarrier
>>> >  - and so on...
>>> >  - we can discuss these later when we reach a consensus on API
>>> >
>>> > --------------------
>>> >
>>> > Here are my thoughts:
>>> >
>>> > 1) Purpose of this FLIP
>>> >  - From the motivation section in the FLIP, I think the purpose is to
>>> > support local aggregation to solve the data skew issue.
>>> >    Then I think we should focus on how to provide a easy to use and
>>> clear
>>> > API to support **local aggregation**.
>>> >  - Vino's point is centered around the local keyed state API (or
>>> > localKeyBy()), and how to leverage the local keyed state API to support
>>> > local aggregation.
>>> >    But I'm afraid it's not a good way to design API for local
>>> aggregation.
>>> >
>>> > 2) local aggregation API
>>> >  - IMO, the method call chain
>>> >
>>> "input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)"
>>> > is not such easy to use.
>>> >    Because we have to provide two implementation for an aggregation
>>> (one
>>> > for partial agg, another for final agg). And we have to take care of
>>> >    the first window call, an inappropriate window call will break the
>>> > sematics.
>>> >  - From my point of view, local aggregation is a mature concept which
>>> > should output the intermediate accumulator (ACC) in the past period of
>>> time
>>> > (a trigger).
>>> >    And the downstream final aggregation will merge ACCs received from
>>> local
>>> > side, and output the current final result.
>>> >  - The current "AggregateFunction" API in DataStream already has the
>>> > accumulator type and "merge" method. So the only thing user need to do
>>> is
>>> > how to enable
>>> >    local aggregation opimization and set a trigger.
>>> >  - One idea comes to my head is that, assume we have a windowed
>>> aggregation
>>> > stream: "val stream = input.keyBy().window(w).aggregate(agg)". We can
>>> > provide an API on the stream.
>>> >    For exmaple, "stream.enableLocalAggregation(Trigger)", the trigger
>>> can
>>> > be "ContinuousEventTimeTrigger.of(Time.of(Time.minutes(1)))". Then it
>>> will
>>> > be optmized into
>>> >    local operator + final operator, and local operator will combine
>>> records
>>> > every minute on event time.
>>> >  - In this way, there is only one line added, and the output is the
>>> same
>>> > with before, because it is just an opimization.
>>> >
>>> >
>>> > Regards,
>>> > Jark
>>> >
>>> >
>>> >
>>> > On Tue, 25 Jun 2019 at 14:34, vino yang <[hidden email]
>>> <mailto:[hidden email]>> wrote:
>>> >
>>> > > Hi Kurt,
>>> > >
>>> > > Answer your questions:
>>> > >
>>> > > a) Sorry, I just updated the Google doc, still have no time update
>>> the
>>> > > FLIP, will update FLIP as soon as possible.
>>> > > About your description at this point, I have a question, what does
>>> it mean:
>>> > > how do we combine with
>>> > > `AggregateFunction`?
>>> > >
>>> > > I have shown you the examples which Flink has supported:
>>> > >
>>> > >    - input.localKeyBy(0).aggregate()
>>> > >    - input.localKeyBy(0).window().aggregate()
>>> > >
>>> > > You can show me a example about how do we combine with
>>> `AggregateFuncion`
>>> > > through your localAggregate API.
>>> > >
>>> > > About the example, how to do the local aggregation for AVG, consider
>>> this
>>> > > code:
>>> > >
>>> > >
>>> > >
>>> > >
>>> > >
>>> > >
>>> > >
>>> > >
>>> > >
>>> > > *DataStream<Tuple2<String, Long>> source = null; source
>>> .localKeyBy(0)
>>> > > .timeWindow(Time.seconds(60)) .aggregate(agg1, new
>>> > > WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>,
>>> String,
>>> > > TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60))
>>> .aggregate(agg2,
>>> > > new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, String,
>>> > > TimeWindow>());*
>>> > >
>>> > > *agg1:*
>>> > > *signature : new AggregateFunction<Tuple2<String, Long>, Tuple2<Long,
>>> > > Long>, Tuple2<Long, Long>>() {}*
>>> > > *input param type: Tuple2<String, Long> f0: key, f1: value*
>>> > > *intermediate result type: Tuple2<Long, Long>, f0: local aggregated
>>> sum;
>>> > > f1: local aggregated count*
>>> > > *output param type:  Tuple2<Long, Long>, f0: local aggregated sum;
>>> f1:
>>> > > local aggregated count*
>>> > >
>>> > > *agg2:*
>>> > > *signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
>>> > > Tuple2<String, Long>>() {},*
>>> > > *input param type: Tuple3<String, Long, Long>, f0: key, f1:  local
>>> > > aggregated sum; f2: local aggregated count*
>>> > >
>>> > > *intermediate result type: Long  avg result*
>>> > > *output param type:  Tuple2<String, Long> f0: key, f1 avg result*
>>> > >
>>> > > For sliding window, we just need to change the window type if users
>>> want to
>>> > > do.
>>> > > Again, we try to give the design and implementation in the DataStream
>>> > > level. So I believe we can match all the requirements(It's just that
>>> the
>>> > > implementation may be different) comes from the SQL level.
>>> > >
>>> > > b) Yes, Theoretically, your thought is right. But in reality, it
>>> cannot
>>> > > bring many benefits.
>>> > > If we want to get the benefits from the window API, while we do not
>>> reuse
>>> > > the window operator? And just copy some many duplicated code to
>>> another
>>> > > operator?
>>> > >
>>> > > c) OK, I agree to let the state backend committers join this
>>> discussion.
>>> > >
>>> > > Best,
>>> > > Vino
>>> > >
>>> > >
>>> > > Kurt Young <[hidden email] <mailto:[hidden email]>>
>>> 于2019年6月24日周一 下午6:53写道：
>>> > >
>>> > > > Hi vino,
>>> > > >
>>> > > > One thing to add,  for a), I think use one or two examples like
>>> how to do
>>> > > > local aggregation on a sliding window,
>>> > > > and how do we do local aggregation on an unbounded aggregate, will
>>> do a
>>> > > lot
>>> > > > help.
>>> > > >
>>> > > > Best,
>>> > > > Kurt
>>> > > >
>>> > > >
>>> > > > On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email]
>>> <mailto:[hidden email]>> wrote:
>>> > > >
>>> > > > > Hi vino,
>>> > > > >
>>> > > > > I think there are several things still need discussion.
>>> > > > >
>>> > > > > a) We all agree that we should first go with a unified
>>> abstraction, but
>>> > > > > the abstraction is not reflected by the FLIP.
>>> > > > > If your answer is "locakKeyBy" API, then I would ask how do we
>>> combine
>>> > > > > with `AggregateFunction`, and how do
>>> > > > > we do proper local aggregation for those have different
>>> intermediate
>>> > > > > result type, like AVG. Could you add these
>>> > > > > to the document?
>>> > > > >
>>> > > > > b) From implementation side, reusing window operator is one of
>>> the
>>> > > > > possible solutions, but not we base on window
>>> > > > > operator to have two different implementations. What I
>>> understanding
>>> > > is,
>>> > > > > one of the possible implementations should
>>> > > > > not touch window operator.
>>> > > > >
>>> > > > > c) 80% of your FLIP content is actually describing how do we
>>> support
>>> > > > local
>>> > > > > keyed state. I don't know if this is necessary
>>> > > > > to introduce at the first step and we should also involve
>>> committers
>>> > > work
>>> > > > > on state backend to share their thoughts.
>>> > > > >
>>> > > > > Best,
>>> > > > > Kurt
>>> > > > >
>>> > > > >
>>> > > > > On Mon, Jun 24, 2019 at 5:17 PM vino yang <[hidden email]
>>> <mailto:[hidden email]>>
>>> > > wrote:
>>> > > > >
>>> > > > >> Hi Kurt,
>>> > > > >>
>>> > > > >> You did not give more further different opinions, so I thought
>>> you
>>> > > have
>>> > > > >> agreed with the design after we promised to support two kinds of
>>> > > > >> implementation.
>>> > > > >>
>>> > > > >> In API level, we have answered your question about pass an
>>> > > > >> AggregateFunction to do the aggregation. No matter introduce
>>> > > localKeyBy
>>> > > > >> API
>>> > > > >> or not, we can support AggregateFunction.
>>> > > > >>
>>> > > > >> So what's your different opinion now? Can you share it with us?
>>> > > > >>
>>> > > > >> Best,
>>> > > > >> Vino
>>> > > > >>
>>> > > > >> Kurt Young <[hidden email] <mailto:[hidden email]>>
>>> 于2019年6月24日周一 下午4:24写道：
>>> > > > >>
>>> > > > >> > Hi vino,
>>> > > > >> >
>>> > > > >> > Sorry I don't see the consensus about reusing window operator
>>> and
>>> > > keep
>>> > > > >> the
>>> > > > >> > API design of localKeyBy. But I think we should definitely
>>> more
>>> > > > thoughts
>>> > > > >> > about this topic.
>>> > > > >> >
>>> > > > >> > I also try to loop in Stephan for this discussion.
>>> > > > >> >
>>> > > > >> > Best,
>>> > > > >> > Kurt
>>> > > > >> >
>>> > > > >> >
>>> > > > >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <
>>> [hidden email] <mailto:[hidden email]>>
>>> > > > >> wrote:
>>> > > > >> >
>>> > > > >> > > Hi all,
>>> > > > >> > >
>>> > > > >> > > I am happy we have a wonderful discussion and received many
>>> > > valuable
>>> > > > >> > > opinions in the last few days.
>>> > > > >> > >
>>> > > > >> > > Now, let me try to summarize what we have reached consensus
>>> about
>>> > > > the
>>> > > > >> > > changes in the design.
>>> > > > >> > >
>>> > > > >> > >    - provide a unified abstraction to support two kinds of
>>> > > > >> > implementation;
>>> > > > >> > >    - reuse WindowOperator and try to enhance it so that we
>>> can
>>> > > make
>>> > > > >> the
>>> > > > >> > >    intermediate result of the local aggregation can be
>>> buffered
>>> > > and
>>> > > > >> > > flushed to
>>> > > > >> > >    support two kinds of implementation;
>>> > > > >> > >    - keep the API design of localKeyBy, but declare the
>>> disabled
>>> > > > some
>>> > > > >> > APIs
>>> > > > >> > >    we cannot support currently, and provide a configurable
>>> API for
>>> > > > >> users
>>> > > > >> > to
>>> > > > >> > >    choose how to handle intermediate result;
>>> > > > >> > >
>>> > > > >> > > The above three points have been updated in the design doc.
>>> Any
>>> > > > >> > > questions, please let me know.
>>> > > > >> > >
>>> > > > >> > > @Aljoscha Krettek <[hidden email] <mailto:
>>> [hidden email]>> What do you think? Any
>>> > > > >> further
>>> > > > >> > > comments?
>>> > > > >> > >
>>> > > > >> > > Best,
>>> > > > >> > > Vino
>>> > > > >> > >
>>> > > > >> > > vino yang <[hidden email] <mailto:
>>> [hidden email]>> 于2019年6月20日周四 下午2:02写道：
>>> > > > >> > >
>>> > > > >> > > > Hi Kurt,
>>> > > > >> > > >
>>> > > > >> > > > Thanks for your comments.
>>> > > > >> > > >
>>> > > > >> > > > It seems we come to a consensus that we should alleviate
>>> the
>>> > > > >> > performance
>>> > > > >> > > > degraded by data skew with local aggregation. In this
>>> FLIP, our
>>> > > > key
>>> > > > >> > > > solution is to introduce local keyed partition to achieve
>>> this
>>> > > > goal.
>>> > > > >> > > >
>>> > > > >> > > > I also agree that we can benefit a lot from the usage of
>>> > > > >> > > > AggregateFunction. In combination with localKeyBy, We can
>>> easily
>>> > > > >> use it
>>> > > > >> > > to
>>> > > > >> > > > achieve local aggregation:
>>> > > > >> > > >
>>> > > > >> > > >    - input.localKeyBy(0).aggregate()
>>> > > > >> > > >    - input.localKeyBy(0).window().aggregate()
>>> > > > >> > > >
>>> > > > >> > > >
>>> > > > >> > > > I think the only problem here is the choices between
>>> > > > >> > > >
>>> > > > >> > > >    - (1) Introducing a new primitive called localKeyBy and
>>> > > > implement
>>> > > > >> > > >    local aggregation with existing operators, or
>>> > > > >> > > >    - (2) Introducing an operator called localAggregation
>>> which
>>> > > is
>>> > > > >> > > >    composed of a key selector, a window-like operator,
>>> and an
>>> > > > >> aggregate
>>> > > > >> > > >    function.
>>> > > > >> > > >
>>> > > > >> > > >
>>> > > > >> > > > There may exist some optimization opportunities by
>>> providing a
>>> > > > >> > composited
>>> > > > >> > > > interface for local aggregation. But at the same time, in
>>> my
>>> > > > >> opinion,
>>> > > > >> > we
>>> > > > >> > > > lose flexibility (Or we need certain efforts to achieve
>>> the same
>>> > > > >> > > > flexibility).
>>> > > > >> > > >
>>> > > > >> > > > As said in the previous mails, we have many use cases
>>> where the
>>> > > > >> > > > aggregation is very complicated and cannot be performed
>>> with
>>> > > > >> > > > AggregateFunction. For example, users may perform windowed
>>> > > > >> aggregations
>>> > > > >> > > > according to time, data values, or even external storage.
>>> > > > Typically,
>>> > > > >> > they
>>> > > > >> > > > now use KeyedProcessFunction or customized triggers to
>>> implement
>>> > > > >> these
>>> > > > >> > > > aggregations. It's not easy to address data skew in such
>>> cases
>>> > > > with
>>> > > > >> a
>>> > > > >> > > > composited interface for local aggregation.
>>> > > > >> > > >
>>> > > > >> > > > Given that Data Stream API is exactly targeted at these
>>> cases
>>> > > > where
>>> > > > >> the
>>> > > > >> > > > application logic is very complicated and optimization
>>> does not
>>> > > > >> > matter, I
>>> > > > >> > > > think it's a better choice to provide a relatively
>>> low-level and
>>> > > > >> > > canonical
>>> > > > >> > > > interface.
>>> > > > >> > > >
>>> > > > >> > > > The composited interface, on the other side, may be a good
>>> > > choice
>>> > > > in
>>> > > > >> > > > declarative interfaces, including SQL and Table API, as it
>>> > > allows
>>> > > > >> more
>>> > > > >> > > > optimization opportunities.
>>> > > > >> > > >
>>> > > > >> > > > Best,
>>> > > > >> > > > Vino
>>> > > > >> > > >
>>> > > > >> > > >
>>> > > > >> > > > Kurt Young <[hidden email] <mailto:[hidden email]>>
>>> 于2019年6月20日周四 上午10:15写道：
>>> > > > >> > > >
>>> > > > >> > > >> Hi all,
>>> > > > >> > > >>
>>> > > > >> > > >> As vino said in previous emails, I think we should first
>>> > > discuss
>>> > > > >> and
>>> > > > >> > > >> decide
>>> > > > >> > > >> what kind of use cases this FLIP want to
>>> > > > >> > > >> resolve, and what the API should look like. From my
>>> side, I
>>> > > think
>>> > > > >> this
>>> > > > >> > > is
>>> > > > >> > > >> probably the root cause of current divergence.
>>> > > > >> > > >>
>>> > > > >> > > >> My understand is (from the FLIP title and motivation
>>> section of
>>> > > > the
>>> > > > >> > > >> document), we want to have a proper support of
>>> > > > >> > > >> local aggregation, or pre aggregation. This is not a
>>> very new
>>> > > > idea,
>>> > > > >> > most
>>> > > > >> > > >> SQL engine already did this improvement. And
>>> > > > >> > > >> the core concept about this is, there should be an
>>> > > > >> AggregateFunction,
>>> > > > >> > no
>>> > > > >> > > >> matter it's a Flink runtime's AggregateFunction or
>>> > > > >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation have
>>> > > concept
>>> > > > >> of
>>> > > > >> > > >> intermediate data type, sometimes we call it ACC.
>>> > > > >> > > >> I quickly went through the POC piotr did before [1], it
>>> also
>>> > > > >> directly
>>> > > > >> > > uses
>>> > > > >> > > >> AggregateFunction.
>>> > > > >> > > >>
>>> > > > >> > > >> But the thing is, after reading the design of this FLIP,
>>> I
>>> > > can't
>>> > > > >> help
>>> > > > >> > > >> myself feeling that this FLIP is not targeting to have a
>>> proper
>>> > > > >> > > >> local aggregation support. It actually want to introduce
>>> > > another
>>> > > > >> > > concept:
>>> > > > >> > > >> LocalKeyBy, and how to split and merge local key groups,
>>> > > > >> > > >> and how to properly support state on local key. Local
>>> > > aggregation
>>> > > > >> just
>>> > > > >> > > >> happened to be one possible use case of LocalKeyBy.
>>> > > > >> > > >> But it lacks supporting the essential concept of local
>>> > > > aggregation,
>>> > > > >> > > which
>>> > > > >> > > >> is intermediate data type. Without this, I really don't
>>> thing
>>> > > > >> > > >> it is a good fit of local aggregation.
>>> > > > >> > > >>
>>> > > > >> > > >> Here I want to make sure of the scope or the goal about
>>> this
>>> > > > FLIP,
>>> > > > >> do
>>> > > > >> > we
>>> > > > >> > > >> want to have a proper local aggregation engine, or we
>>> > > > >> > > >> just want to introduce a new concept called LocalKeyBy?
>>> > > > >> > > >>
>>> > > > >> > > >> [1]: https://github.com/apache/flink/pull/4626 <
>>> https://github.com/apache/flink/pull/4626>
>>> > > > >> > > >>
>>> > > > >> > > >> Best,
>>> > > > >> > > >> Kurt
>>> > > > >> > > >>
>>> > > > >> > > >>
>>> > > > >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <
>>> > > [hidden email] <mailto:[hidden email]>
>>> > > > >
>>> > > > >> > > wrote:
>>> > > > >> > > >>
>>> > > > >> > > >> > Hi Hequn,
>>> > > > >> > > >> >
>>> > > > >> > > >> > Thanks for your comments!
>>> > > > >> > > >> >
>>> > > > >> > > >> > I agree that allowing local aggregation reusing window
>>> API
>>> > > and
>>> > > > >> > > refining
>>> > > > >> > > >> > window operator to make it match both requirements
>>> (come from
>>> > > > our
>>> > > > >> > and
>>> > > > >> > > >> Kurt)
>>> > > > >> > > >> > is a good decision!
>>> > > > >> > > >> >
>>> > > > >> > > >> > Concerning your questions:
>>> > > > >> > > >> >
>>> > > > >> > > >> > 1. The result of
>>> input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>>> > > may
>>> > > > >> be
>>> > > > >> > > >> > meaningless.
>>> > > > >> > > >> >
>>> > > > >> > > >> > Yes, it does not make sense in most cases. However, I
>>> also
>>> > > want
>>> > > > >> to
>>> > > > >> > > note
>>> > > > >> > > >> > users should know the right semantics of localKeyBy
>>> and use
>>> > > it
>>> > > > >> > > >> correctly.
>>> > > > >> > > >> > Because this issue also exists for the global keyBy,
>>> consider
>>> > > > >> this
>>> > > > >> > > >> example:
>>> > > > >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is
>>> also
>>> > > > >> > meaningless.
>>> > > > >> > > >> >
>>> > > > >> > > >> > 2. About the semantics of
>>> > > > >> > > >> >
>>> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
>>> > > > >> > > >> >
>>> > > > >> > > >> > Good catch! I agree with you that it's not good to
>>> enable all
>>> > > > >> > > >> > functionalities for localKeyBy from KeyedStream.
>>> > > > >> > > >> > Currently, We do not support some APIs such as
>>> > > > >> > > >> > connect/join/intervalJoin/coGroup. This is due to that
>>> we
>>> > > force
>>> > > > >> the
>>> > > > >> > > >> > operators on LocalKeyedStreams chained with the inputs.
>>> > > > >> > > >> >
>>> > > > >> > > >> > Best,
>>> > > > >> > > >> > Vino
>>> > > > >> > > >> >
>>> > > > >> > > >> >
>>> > > > >> > > >> > Hequn Cheng <[hidden email] <mailto:
>>> [hidden email]>> 于2019年6月19日周三 下午3:42写道：
>>> > > > >> > > >> >
>>> > > > >> > > >> > > Hi,
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > Thanks a lot for your great discussion and great to
>>> see
>>> > > that
>>> > > > >> some
>>> > > > >> > > >> > agreement
>>> > > > >> > > >> > > has been reached on the "local aggregate engine"!
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > ===> Considering the abstract engine,
>>> > > > >> > > >> > > I'm thinking is it valuable for us to extend the
>>> current
>>> > > > >> window to
>>> > > > >> > > >> meet
>>> > > > >> > > >> > > both demands raised by Kurt and Vino? There are some
>>> > > benefits
>>> > > > >> we
>>> > > > >> > can
>>> > > > >> > > >> get:
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > 1. The interfaces of the window are complete and
>>> clear.
>>> > > With
>>> > > > >> > > windows,
>>> > > > >> > > >> we
>>> > > > >> > > >> > > can define a lot of ways to split the data and
>>> perform
>>> > > > >> different
>>> > > > >> > > >> > > computations.
>>> > > > >> > > >> > > 2. We can also leverage the window to do miniBatch
>>> for the
>>> > > > >> global
>>> > > > >> > > >> > > aggregation, i.e, we can use the window to bundle
>>> data
>>> > > belong
>>> > > > >> to
>>> > > > >> > the
>>> > > > >> > > >> same
>>> > > > >> > > >> > > key, for every bundle we only need to read and write
>>> once
>>> > > > >> state.
>>> > > > >> > > This
>>> > > > >> > > >> can
>>> > > > >> > > >> > > greatly reduce state IO and improve performance.
>>> > > > >> > > >> > > 3. A lot of other use cases can also benefit from the
>>> > > window
>>> > > > >> base
>>> > > > >> > on
>>> > > > >> > > >> > memory
>>> > > > >> > > >> > > or stateless.
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > ===> As for the API,
>>> > > > >> > > >> > > I think it is good to make our API more flexible.
>>> However,
>>> > > we
>>> > > > >> may
>>> > > > >> > > >> need to
>>> > > > >> > > >> > > make our API meaningful.
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > Take my previous reply as an example,
>>> > > > >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The
>>> result may
>>> > > be
>>> > > > >> > > >> > meaningless.
>>> > > > >> > > >> > > Another example I find is the intervalJoin, e.g.,
>>> > > > >> > > >> > >
>>> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
>>> > > > >> this
>>> > > > >> > > >> case, it
>>> > > > >> > > >> > > will bring problems if input1 and input2 share
>>> different
>>> > > > >> > > parallelism.
>>> > > > >> > > >> We
>>> > > > >> > > >> > > don't know which input should the join chained with?
>>> Even
>>> > > if
>>> > > > >> they
>>> > > > >> > > >> share
>>> > > > >> > > >> > the
>>> > > > >> > > >> > > same parallelism, it's hard to tell what the join is
>>> doing.
>>> > > > >> There
>>> > > > >> > > are
>>> > > > >> > > >> > maybe
>>> > > > >> > > >> > > some other problems.
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > From this point of view, it's at least not good to
>>> enable
>>> > > all
>>> > > > >> > > >> > > functionalities for localKeyBy from KeyedStream?
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > Great to also have your opinions.
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > Best, Hequn
>>> > > > >> > > >> > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
>>> > > > >> [hidden email] <mailto:[hidden email]>
>>> > > > >> > >
>>> > > > >> > > >> > wrote:
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > > Hi Kurt and Piotrek,
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > > > Thanks for your comments.
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > > > I agree that we can provide a better abstraction
>>> to be
>>> > > > >> > compatible
>>> > > > >> > > >> with
>>> > > > >> > > >> > > two
>>> > > > >> > > >> > > > different implementations.
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > > > First of all, I think we should consider what kind
>>> of
>>> > > > >> scenarios
>>> > > > >> > we
>>> > > > >> > > >> need
>>> > > > >> > > >> > > to
>>> > > > >> > > >> > > > support in *API* level?
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > > > We have some use cases which need to a customized
>>> > > > aggregation
>>> > > > >> > > >> through
>>> > > > >> > > >> > > > KeyedProcessFunction, (in the usage of our
>>> > > > localKeyBy.window
>>> > > > >> > they
>>> > > > >> > > >> can
>>> > > > >> > > >> > use
>>> > > > >> > > >> > > > ProcessWindowFunction).
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > > > Shall we support these flexible use scenarios?
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > > > Best,
>>> > > > >> > > >> > > > Vino
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > > > Kurt Young <[hidden email] <mailto:
>>> [hidden email]>> 于2019年6月18日周二 下午8:37写道：
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > > > > Hi Piotr,
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > > > Thanks for joining the discussion. Make “local
>>> > > > aggregation"
>>> > > > >> > > >> abstract
>>> > > > >> > > >> > > > enough
>>> > > > >> > > >> > > > > sounds good to me, we could
>>> > > > >> > > >> > > > > implement and verify alternative solutions for
>>> use
>>> > > cases
>>> > > > of
>>> > > > >> > > local
>>> > > > >> > > >> > > > > aggregation. Maybe we will find both solutions
>>> > > > >> > > >> > > > > are appropriate for different scenarios.
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > > > Starting from a simple one sounds a practical
>>> way to
>>> > > go.
>>> > > > >> What
>>> > > > >> > do
>>> > > > >> > > >> you
>>> > > > >> > > >> > > > think,
>>> > > > >> > > >> > > > > vino?
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > > > Best,
>>> > > > >> > > >> > > > > Kurt
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
>>> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
>>> > > > >> > > >> > > > > wrote:
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > > > > Hi Kurt and Vino,
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > > > I think there is a trade of hat we need to
>>> consider
>>> > > for
>>> > > > >> the
>>> > > > >> > > >> local
>>> > > > >> > > >> > > > > > aggregation.
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > > > Generally speaking I would agree with Kurt
>>> about
>>> > > local
>>> > > > >> > > >> > > aggregation/pre
>>> > > > >> > > >> > > > > > aggregation not using Flink's state flush the
>>> > > operator
>>> > > > >> on a
>>> > > > >> > > >> > > checkpoint.
>>> > > > >> > > >> > > > > > Network IO is usually cheaper compared to
>>> Disks IO.
>>> > > > This
>>> > > > >> has
>>> > > > >> > > >> > however
>>> > > > >> > > >> > > > > couple
>>> > > > >> > > >> > > > > > of issues:
>>> > > > >> > > >> > > > > > 1. It can explode number of in-flight records
>>> during
>>> > > > >> > > checkpoint
>>> > > > >> > > >> > > barrier
>>> > > > >> > > >> > > > > > alignment, making checkpointing slower and
>>> decrease
>>> > > the
>>> > > > >> > actual
>>> > > > >> > > >> > > > > throughput.
>>> > > > >> > > >> > > > > > 2. This trades Disks IO on the local
>>> aggregation
>>> > > > machine
>>> > > > >> > with
>>> > > > >> > > >> CPU
>>> > > > >> > > >> > > (and
>>> > > > >> > > >> > > > > > Disks IO in case of RocksDB) on the final
>>> aggregation
>>> > > > >> > machine.
>>> > > > >> > > >> This
>>> > > > >> > > >> > > is
>>> > > > >> > > >> > > > > > fine, as long there is no huge data skew. If
>>> there is
>>> > > > >> only a
>>> > > > >> > > >> > handful
>>> > > > >> > > >> > > > (or
>>> > > > >> > > >> > > > > > even one single) hot keys, it might be better
>>> to keep
>>> > > > the
>>> > > > >> > > >> > persistent
>>> > > > >> > > >> > > > > state
>>> > > > >> > > >> > > > > > in the LocalAggregationOperator to offload
>>> final
>>> > > > >> aggregation
>>> > > > >> > > as
>>> > > > >> > > >> > much
>>> > > > >> > > >> > > as
>>> > > > >> > > >> > > > > > possible.
>>> > > > >> > > >> > > > > > 3. With frequent checkpointing local
>>> aggregation
>>> > > > >> > effectiveness
>>> > > > >> > > >> > would
>>> > > > >> > > >> > > > > > degrade.
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > > > I assume Kurt is correct, that in your use
>>> cases
>>> > > > >> stateless
>>> > > > >> > > >> operator
>>> > > > >> > > >> > > was
>>> > > > >> > > >> > > > > > behaving better, but I could easily see other
>>> use
>>> > > cases
>>> > > > >> as
>>> > > > >> > > well.
>>> > > > >> > > >> > For
>>> > > > >> > > >> > > > > > example someone is already using RocksDB, and
>>> his job
>>> > > > is
>>> > > > >> > > >> > bottlenecked
>>> > > > >> > > >> > > > on
>>> > > > >> > > >> > > > > a
>>> > > > >> > > >> > > > > > single window operator instance because of the
>>> data
>>> > > > >> skew. In
>>> > > > >> > > >> that
>>> > > > >> > > >> > > case
>>> > > > >> > > >> > > > > > stateful local aggregation would be probably a
>>> better
>>> > > > >> > choice.
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > > > Because of that, I think we should eventually
>>> provide
>>> > > > >> both
>>> > > > >> > > >> versions
>>> > > > >> > > >> > > and
>>> > > > >> > > >> > > > > in
>>> > > > >> > > >> > > > > > the initial version we should at least make the
>>> > > “local
>>> > > > >> > > >> aggregation
>>> > > > >> > > >> > > > > engine”
>>> > > > >> > > >> > > > > > abstract enough, that one could easily provide
>>> > > > different
>>> > > > >> > > >> > > implementation
>>> > > > >> > > >> > > > > > strategy.
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > > > Piotrek
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
>>> > > > [hidden email] <mailto:[hidden email]>
>>> > > > >> >
>>> > > > >> > > >> wrote:
>>> > > > >> > > >> > > > > > >
>>> > > > >> > > >> > > > > > > Hi,
>>> > > > >> > > >> > > > > > >
>>> > > > >> > > >> > > > > > > For the trigger, it depends on what operator
>>> we
>>> > > want
>>> > > > to
>>> > > > >> > use
>>> > > > >> > > >> under
>>> > > > >> > > >> > > the
>>> > > > >> > > >> > > > > > API.
>>> > > > >> > > >> > > > > > > If we choose to use window operator,
>>> > > > >> > > >> > > > > > > we should also use window's trigger.
>>> However, I
>>> > > also
>>> > > > >> think
>>> > > > >> > > >> reuse
>>> > > > >> > > >> > > > window
>>> > > > >> > > >> > > > > > > operator for this scenario may not be
>>> > > > >> > > >> > > > > > > the best choice. The reasons are the
>>> following:
>>> > > > >> > > >> > > > > > >
>>> > > > >> > > >> > > > > > > 1. As a lot of people already pointed out,
>>> window
>>> > > > >> relies
>>> > > > >> > > >> heavily
>>> > > > >> > > >> > on
>>> > > > >> > > >> > > > > state
>>> > > > >> > > >> > > > > > > and it will definitely effect performance.
>>> You can
>>> > > > >> > > >> > > > > > > argue that one can use heap based
>>> statebackend, but
>>> > > > >> this
>>> > > > >> > > will
>>> > > > >> > > >> > > > introduce
>>> > > > >> > > >> > > > > > > extra coupling. Especially we have a chance
>>> to
>>> > > > >> > > >> > > > > > > design a pure stateless operator.
>>> > > > >> > > >> > > > > > > 2. The window operator is *the most*
>>> complicated
>>> > > > >> operator
>>> > > > >> > > >> Flink
>>> > > > >> > > >> > > > > currently
>>> > > > >> > > >> > > > > > > have. Maybe we only need to pick a subset of
>>> > > > >> > > >> > > > > > > window operator to achieve the goal, but
>>> once the
>>> > > > user
>>> > > > >> > wants
>>> > > > >> > > >> to
>>> > > > >> > > >> > > have
>>> > > > >> > > >> > > > a
>>> > > > >> > > >> > > > > > deep
>>> > > > >> > > >> > > > > > > look at the localAggregation operator, it's
>>> still
>>> > > > >> > > >> > > > > > > hard to find out what's going on under the
>>> window
>>> > > > >> > operator.
>>> > > > >> > > >> For
>>> > > > >> > > >> > > > > > simplicity,
>>> > > > >> > > >> > > > > > > I would also recommend we introduce a
>>> dedicated
>>> > > > >> > > >> > > > > > > lightweight operator, which also much easier
>>> for a
>>> > > > >> user to
>>> > > > >> > > >> learn
>>> > > > >> > > >> > > and
>>> > > > >> > > >> > > > > use.
>>> > > > >> > > >> > > > > > >
>>> > > > >> > > >> > > > > > > For your question about increasing the
>>> burden in
>>> > > > >> > > >> > > > > > >
>>> `StreamOperator::prepareSnapshotPreBarrier()`, the
>>> > > > only
>>> > > > >> > > thing
>>> > > > >> > > >> > this
>>> > > > >> > > >> > > > > > function
>>> > > > >> > > >> > > > > > > need
>>> > > > >> > > >> > > > > > > to do is output all the partial results, it's
>>> > > purely
>>> > > > >> cpu
>>> > > > >> > > >> > workload,
>>> > > > >> > > >> > > > not
>>> > > > >> > > >> > > > > > > introducing any IO. I want to point out that
>>> even
>>> > > if
>>> > > > we
>>> > > > >> > have
>>> > > > >> > > >> this
>>> > > > >> > > >> > > > > > > cost, we reduced another barrier align cost
>>> of the
>>> > > > >> > operator,
>>> > > > >> > > >> > which
>>> > > > >> > > >> > > is
>>> > > > >> > > >> > > > > the
>>> > > > >> > > >> > > > > > > sync flush stage of the state, if you
>>> introduced
>>> > > > state.
>>> > > > >> > This
>>> > > > >> > > >> > > > > > > flush actually will introduce disk IO, and I
>>> think
>>> > > > it's
>>> > > > >> > > >> worthy to
>>> > > > >> > > >> > > > > > exchange
>>> > > > >> > > >> > > > > > > this cost with purely CPU workload. And we
>>> do have
>>> > > > some
>>> > > > >> > > >> > > > > > > observations about these two behavior (as i
>>> said
>>> > > > >> before,
>>> > > > >> > we
>>> > > > >> > > >> > > actually
>>> > > > >> > > >> > > > > > > implemented both solutions), the stateless
>>> one
>>> > > > actually
>>> > > > >> > > >> performs
>>> > > > >> > > >> > > > > > > better both in performance and barrier align
>>> time.
>>> > > > >> > > >> > > > > > >
>>> > > > >> > > >> > > > > > > Best,
>>> > > > >> > > >> > > > > > > Kurt
>>> > > > >> > > >> > > > > > >
>>> > > > >> > > >> > > > > > >
>>> > > > >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
>>> > > > >> > > >> [hidden email] <mailto:[hidden email]>
>>> > > > >> > > >> > >
>>> > > > >> > > >> > > > > wrote:
>>> > > > >> > > >> > > > > > >
>>> > > > >> > > >> > > > > > >> Hi Kurt,
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> Thanks for your example. Now, it looks more
>>> > > clearly
>>> > > > >> for
>>> > > > >> > me.
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> From your example code snippet, I saw the
>>> > > > >> localAggregate
>>> > > > >> > > API
>>> > > > >> > > >> has
>>> > > > >> > > >> > > > three
>>> > > > >> > > >> > > > > > >> parameters:
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >>   1. key field
>>> > > > >> > > >> > > > > > >>   2. PartitionAvg
>>> > > > >> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes
>>> from
>>> > > > window
>>> > > > >> > > >> package?
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> I will compare our and your design from API
>>> and
>>> > > > >> operator
>>> > > > >> > > >> level:
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> *From the API level:*
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> As I replied to @dianfu in the old email
>>> > > thread,[1]
>>> > > > >> the
>>> > > > >> > > >> Window
>>> > > > >> > > >> > API
>>> > > > >> > > >> > > > can
>>> > > > >> > > >> > > > > > >> provide the second and the third parameter
>>> right
>>> > > > now.
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> If you reuse specified interface or class,
>>> such as
>>> > > > >> > > *Trigger*
>>> > > > >> > > >> or
>>> > > > >> > > >> > > > > > >> *CounterTrigger* provided by window
>>> package, but
>>> > > do
>>> > > > >> not
>>> > > > >> > use
>>> > > > >> > > >> > window
>>> > > > >> > > >> > > > > API,
>>> > > > >> > > >> > > > > > >> it's not reasonable.
>>> > > > >> > > >> > > > > > >> And if you do not reuse these interface or
>>> class,
>>> > > > you
>>> > > > >> > would
>>> > > > >> > > >> need
>>> > > > >> > > >> > > to
>>> > > > >> > > >> > > > > > >> introduce more things however they are
>>> looked
>>> > > > similar
>>> > > > >> to
>>> > > > >> > > the
>>> > > > >> > > >> > > things
>>> > > > >> > > >> > > > > > >> provided by window package.
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> The window package has provided several
>>> types of
>>> > > the
>>> > > > >> > window
>>> > > > >> > > >> and
>>> > > > >> > > >> > > many
>>> > > > >> > > >> > > > > > >> triggers and let users customize it. What's
>>> more,
>>> > > > the
>>> > > > >> > user
>>> > > > >> > > is
>>> > > > >> > > >> > more
>>> > > > >> > > >> > > > > > familiar
>>> > > > >> > > >> > > > > > >> with Window API.
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> This is the reason why we just provide
>>> localKeyBy
>>> > > > API
>>> > > > >> and
>>> > > > >> > > >> reuse
>>> > > > >> > > >> > > the
>>> > > > >> > > >> > > > > > window
>>> > > > >> > > >> > > > > > >> API. It reduces unnecessary components such
>>> as
>>> > > > >> triggers
>>> > > > >> > and
>>> > > > >> > > >> the
>>> > > > >> > > >> > > > > > mechanism
>>> > > > >> > > >> > > > > > >> of buffer (based on count num or time).
>>> > > > >> > > >> > > > > > >> And it has a clear and easy to understand
>>> > > semantics.
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> *From the operator level:*
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> We reused window operator, so we can get
>>> all the
>>> > > > >> benefits
>>> > > > >> > > >> from
>>> > > > >> > > >> > > state
>>> > > > >> > > >> > > > > and
>>> > > > >> > > >> > > > > > >> checkpoint.
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> From your design, you named the operator
>>> under
>>> > > > >> > > localAggregate
>>> > > > >> > > >> > API
>>> > > > >> > > >> > > > is a
>>> > > > >> > > >> > > > > > >> *stateless* operator. IMO, it is still a
>>> state, it
>>> > > > is
>>> > > > >> > just
>>> > > > >> > > >> not
>>> > > > >> > > >> > > Flink
>>> > > > >> > > >> > > > > > >> managed state.
>>> > > > >> > > >> > > > > > >> About the memory buffer (I think it's still
>>> not
>>> > > very
>>> > > > >> > clear,
>>> > > > >> > > >> if
>>> > > > >> > > >> > you
>>> > > > >> > > >> > > > > have
>>> > > > >> > > >> > > > > > >> time, can you give more detail information
>>> or
>>> > > answer
>>> > > > >> my
>>> > > > >> > > >> > > questions),
>>> > > > >> > > >> > > > I
>>> > > > >> > > >> > > > > > have
>>> > > > >> > > >> > > > > > >> some questions:
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >>   - if it just a raw JVM heap memory
>>> buffer, how
>>> > > to
>>> > > > >> > support
>>> > > > >> > > >> > fault
>>> > > > >> > > >> > > > > > >>   tolerance, if the job is configured
>>> EXACTLY-ONCE
>>> > > > >> > semantic
>>> > > > >> > > >> > > > guarantee?
>>> > > > >> > > >> > > > > > >>   - if you thought the memory
>>> buffer(non-Flink
>>> > > > state),
>>> > > > >> > has
>>> > > > >> > > >> > better
>>> > > > >> > > >> > > > > > >>   performance. In our design, users can also
>>> > > config
>>> > > > >> HEAP
>>> > > > >> > > >> state
>>> > > > >> > > >> > > > backend
>>> > > > >> > > >> > > > > > to
>>> > > > >> > > >> > > > > > >>   provide the performance close to your
>>> mechanism.
>>> > > > >> > > >> > > > > > >>   -
>>> `StreamOperator::prepareSnapshotPreBarrier()`
>>> > > > >> related
>>> > > > >> > > to
>>> > > > >> > > >> the
>>> > > > >> > > >> > > > > timing
>>> > > > >> > > >> > > > > > of
>>> > > > >> > > >> > > > > > >>   snapshot. IMO, the flush action should be
>>> a
>>> > > > >> > synchronized
>>> > > > >> > > >> > action?
>>> > > > >> > > >> > > > (if
>>> > > > >> > > >> > > > > > >> not,
>>> > > > >> > > >> > > > > > >>   please point out my mistake) I still
>>> think we
>>> > > > should
>>> > > > >> > not
>>> > > > >> > > >> > depend
>>> > > > >> > > >> > > on
>>> > > > >> > > >> > > > > the
>>> > > > >> > > >> > > > > > >>   timing of checkpoint. Checkpoint related
>>> > > > operations
>>> > > > >> are
>>> > > > >> > > >> > inherent
>>> > > > >> > > >> > > > > > >>   performance sensitive, we should not
>>> increase
>>> > > its
>>> > > > >> > burden
>>> > > > >> > > >> > > anymore.
>>> > > > >> > > >> > > > > Our
>>> > > > >> > > >> > > > > > >>   implementation based on the mechanism of
>>> Flink's
>>> > > > >> > > >> checkpoint,
>>> > > > >> > > >> > > which
>>> > > > >> > > >> > > > > can
>>> > > > >> > > >> > > > > > >>   benefit from the asnyc snapshot and
>>> incremental
>>> > > > >> > > checkpoint.
>>> > > > >> > > >> > IMO,
>>> > > > >> > > >> > > > the
>>> > > > >> > > >> > > > > > >>   performance is not a problem, and we also
>>> do not
>>> > > > >> find
>>> > > > >> > the
>>> > > > >> > > >> > > > > performance
>>> > > > >> > > >> > > > > > >> issue
>>> > > > >> > > >> > > > > > >>   in our production.
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> [1]:
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> >
>>> > > > >> > > >>
>>> > > > >> > >
>>> > > > >> >
>>> > > > >>
>>> > > >
>>> > >
>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>> <
>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>> >
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> Best,
>>> > > > >> > > >> > > > > > >> Vino
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >> Kurt Young <[hidden email] <mailto:
>>> [hidden email]>> 于2019年6月18日周二
>>> > > > 下午2:27写道：
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > > >>> Yeah, sorry for not expressing myself
>>> clearly. I
>>> > > > will
>>> > > > >> > try
>>> > > > >> > > to
>>> > > > >> > > >> > > > provide
>>> > > > >> > > >> > > > > > more
>>> > > > >> > > >> > > > > > >>> details to make sure we are on the same
>>> page.
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>> For DataStream API, it shouldn't be
>>> optimized
>>> > > > >> > > automatically.
>>> > > > >> > > >> > You
>>> > > > >> > > >> > > > have
>>> > > > >> > > >> > > > > > to
>>> > > > >> > > >> > > > > > >>> explicitly call API to do local aggregation
>>> > > > >> > > >> > > > > > >>> as well as the trigger policy of the local
>>> > > > >> aggregation.
>>> > > > >> > > Take
>>> > > > >> > > >> > > > average
>>> > > > >> > > >> > > > > > for
>>> > > > >> > > >> > > > > > >>> example, the user program may look like
>>> this
>>> > > (just
>>> > > > a
>>> > > > >> > > draft):
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>> assuming the input type is
>>> > > DataStream<Tupl2<String,
>>> > > > >> > Int>>
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>> ds.localAggregate(
>>> > > > >> > > >> > > > > > >>>        0,
>>> > >  //
>>> > > > >> The
>>> > > > >> > > local
>>> > > > >> > > >> > key,
>>> > > > >> > > >> > > > > which
>>> > > > >> > > >> > > > > > >> is
>>> > > > >> > > >> > > > > > >>> the String from Tuple2
>>> > > > >> > > >> > > > > > >>>        PartitionAvg(1),                 //
>>> The
>>> > > > >> partial
>>> > > > >> > > >> > > aggregation
>>> > > > >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>,
>>> indicating
>>> > > > sum
>>> > > > >> and
>>> > > > >> > > >> count
>>> > > > >> > > >> > > > > > >>>        CountTrigger.of(1000L)    // Trigger
>>> > > policy,
>>> > > > >> note
>>> > > > >> > > >> this
>>> > > > >> > > >> > > > should
>>> > > > >> > > >> > > > > be
>>> > > > >> > > >> > > > > > >>> best effort, and also be composited with
>>> time
>>> > > based
>>> > > > >> or
>>> > > > >> > > >> memory
>>> > > > >> > > >> > > size
>>> > > > >> > > >> > > > > > based
>>> > > > >> > > >> > > > > > >>> trigger
>>> > > > >> > > >> > > > > > >>>    )
>>>      //
>>> > > > The
>>> > > > >> > > return
>>> > > > >> > > >> > type
>>> > > > >> > > >> > > > is
>>> > > > >> > > >> > > > > > >> local
>>> > > > >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
>>> > > > >> > > >> > > > > > >>>    .keyBy(0)                             //
>>> > > Further
>>> > > > >> > keyby
>>> > > > >> > > it
>>> > > > >> > > >> > with
>>> > > > >> > > >> > > > > > >> required
>>> > > > >> > > >> > > > > > >>> key
>>> > > > >> > > >> > > > > > >>>    .aggregate(1)                      //
>>> This
>>> > > will
>>> > > > >> merge
>>> > > > >> > > all
>>> > > > >> > > >> > the
>>> > > > >> > > >> > > > > > partial
>>> > > > >> > > >> > > > > > >>> results and get the final average.
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>> (This is only a draft, only trying to
>>> explain
>>> > > what
>>> > > > it
>>> > > > >> > > looks
>>> > > > >> > > >> > > like. )
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>> The local aggregate operator can be
>>> stateless, we
>>> > > > can
>>> > > > >> > > keep a
>>> > > > >> > > >> > > memory
>>> > > > >> > > >> > > > > > >> buffer
>>> > > > >> > > >> > > > > > >>> or other efficient data structure to
>>> improve the
>>> > > > >> > aggregate
>>> > > > >> > > >> > > > > performance.
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>> Let me know if you have any other
>>> questions.
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>> Best,
>>> > > > >> > > >> > > > > > >>> Kurt
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang <
>>> > > > >> > > >> > [hidden email] <mailto:[hidden email]>
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > > > > > wrote:
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>>> Hi Kurt,
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> Thanks for your reply.
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> Actually, I am not against you to raise
>>> your
>>> > > > design.
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> From your description before, I just can
>>> imagine
>>> > > > >> your
>>> > > > >> > > >> > high-level
>>> > > > >> > > >> > > > > > >>>> implementation is about SQL and the
>>> optimization
>>> > > > is
>>> > > > >> > inner
>>> > > > >> > > >> of
>>> > > > >> > > >> > the
>>> > > > >> > > >> > > > > API.
>>> > > > >> > > >> > > > > > >> Is
>>> > > > >> > > >> > > > > > >>> it
>>> > > > >> > > >> > > > > > >>>> automatically? how to give the
>>> configuration
>>> > > > option
>>> > > > >> > about
>>> > > > >> > > >> > > trigger
>>> > > > >> > > >> > > > > > >>>> pre-aggregation?
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> Maybe after I get more information, it
>>> sounds
>>> > > more
>>> > > > >> > > >> reasonable.
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> IMO, first of all, it would be better to
>>> make
>>> > > your
>>> > > > >> user
>>> > > > >> > > >> > > interface
>>> > > > >> > > >> > > > > > >>> concrete,
>>> > > > >> > > >> > > > > > >>>> it's the basis of the discussion.
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> For example, can you give an example code
>>> > > snippet
>>> > > > to
>>> > > > >> > > >> introduce
>>> > > > >> > > >> > > how
>>> > > > >> > > >> > > > > to
>>> > > > >> > > >> > > > > > >>> help
>>> > > > >> > > >> > > > > > >>>> users to process data skew caused by the
>>> jobs
>>> > > > which
>>> > > > >> > built
>>> > > > >> > > >> with
>>> > > > >> > > >> > > > > > >> DataStream
>>> > > > >> > > >> > > > > > >>>> API?
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> If you give more details we can discuss
>>> further
>>> > > > >> more. I
>>> > > > >> > > >> think
>>> > > > >> > > >> > if
>>> > > > >> > > >> > > > one
>>> > > > >> > > >> > > > > > >>> design
>>> > > > >> > > >> > > > > > >>>> introduces an exact interface and another
>>> does
>>> > > > not.
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> The implementation has an obvious
>>> difference.
>>> > > For
>>> > > > >> > > example,
>>> > > > >> > > >> we
>>> > > > >> > > >> > > > > > introduce
>>> > > > >> > > >> > > > > > >>> an
>>> > > > >> > > >> > > > > > >>>> exact API in DataStream named localKeyBy,
>>> about
>>> > > > the
>>> > > > >> > > >> > > > pre-aggregation
>>> > > > >> > > >> > > > > we
>>> > > > >> > > >> > > > > > >>> need
>>> > > > >> > > >> > > > > > >>>> to define the trigger mechanism of local
>>> > > > >> aggregation,
>>> > > > >> > so
>>> > > > >> > > we
>>> > > > >> > > >> > find
>>> > > > >> > > >> > > > > > reused
>>> > > > >> > > >> > > > > > >>>> window API and operator is a good choice.
>>> This
>>> > > is
>>> > > > a
>>> > > > >> > > >> reasoning
>>> > > > >> > > >> > > link
>>> > > > >> > > >> > > > > > from
>>> > > > >> > > >> > > > > > >>>> design to implementation.
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> What do you think?
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> Best,
>>> > > > >> > > >> > > > > > >>>> Vino
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>> Kurt Young <[hidden email] <mailto:
>>> [hidden email]>> 于2019年6月18日周二
>>> > > > >> 上午11:58写道：
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>>> Hi Vino,
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>> Now I feel that we may have different
>>> > > > >> understandings
>>> > > > >> > > about
>>> > > > >> > > >> > what
>>> > > > >> > > >> > > > > kind
>>> > > > >> > > >> > > > > > >> of
>>> > > > >> > > >> > > > > > >>>>> problems or improvements you want to
>>> > > > >> > > >> > > > > > >>>>> resolve. Currently, most of the feedback
>>> are
>>> > > > >> focusing
>>> > > > >> > on
>>> > > > >> > > >> *how
>>> > > > >> > > >> > > to
>>> > > > >> > > >> > > > > do a
>>> > > > >> > > >> > > > > > >>>>> proper local aggregation to improve
>>> performance
>>> > > > >> > > >> > > > > > >>>>> and maybe solving the data skew issue*.
>>> And my
>>> > > > gut
>>> > > > >> > > >> feeling is
>>> > > > >> > > >> > > > this
>>> > > > >> > > >> > > > > is
>>> > > > >> > > >> > > > > > >>>>> exactly what users want at the first
>>> place,
>>> > > > >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to
>>> > > summarize
>>> > > > >> here,
>>> > > > >> > > >> please
>>> > > > >> > > >> > > > > correct
>>> > > > >> > > >> > > > > > >>> me
>>> > > > >> > > >> > > > > > >>>> if
>>> > > > >> > > >> > > > > > >>>>> i'm wrong).
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>> But I still think the design is somehow
>>> > > diverged
>>> > > > >> from
>>> > > > >> > > the
>>> > > > >> > > >> > goal.
>>> > > > >> > > >> > > > If
>>> > > > >> > > >> > > > > we
>>> > > > >> > > >> > > > > > >>>> want
>>> > > > >> > > >> > > > > > >>>>> to have an efficient and powerful way to
>>> > > > >> > > >> > > > > > >>>>> have local aggregation, supporting
>>> intermedia
>>> > > > >> result
>>> > > > >> > > type
>>> > > > >> > > >> is
>>> > > > >> > > >> > > > > > >> essential
>>> > > > >> > > >> > > > > > >>>> IMO.
>>> > > > >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
>>> > > > >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction`
>>> have a
>>> > > > proper
>>> > > > >> > > >> support of
>>> > > > >> > > >> > > > > > >>>> intermediate
>>> > > > >> > > >> > > > > > >>>>> result type and can do `merge` operation
>>> > > > >> > > >> > > > > > >>>>> on them.
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>> Now, we have a lightweight alternatives
>>> which
>>> > > > >> performs
>>> > > > >> > > >> well,
>>> > > > >> > > >> > > and
>>> > > > >> > > >> > > > > > >> have a
>>> > > > >> > > >> > > > > > >>>>> nice fit with the local aggregate
>>> requirements.
>>> > > > >> > > >> > > > > > >>>>> Mostly importantly,  it's much less
>>> complex
>>> > > > because
>>> > > > >> > it's
>>> > > > >> > > >> > > > stateless.
>>> > > > >> > > >> > > > > > >> And
>>> > > > >> > > >> > > > > > >>>> it
>>> > > > >> > > >> > > > > > >>>>> can also achieve the similar
>>> > > multiple-aggregation
>>> > > > >> > > >> > > > > > >>>>> scenario.
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>> I still not convinced why we shouldn't
>>> consider
>>> > > > it
>>> > > > >> as
>>> > > > >> > a
>>> > > > >> > > >> first
>>> > > > >> > > >> > > > step.
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>> Best,
>>> > > > >> > > >> > > > > > >>>>> Kurt
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino
>>> yang <
>>> > > > >> > > >> > > > [hidden email] <mailto:
>>> [hidden email]>>
>>> > > > >> > > >> > > > > > >>>> wrote:
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>>> Hi Kurt,
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> Thanks for your comments.
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> It seems we both implemented local
>>> aggregation
>>> > > > >> > feature
>>> > > > >> > > to
>>> > > > >> > > >> > > > optimize
>>> > > > >> > > >> > > > > > >>> the
>>> > > > >> > > >> > > > > > >>>>>> issue of data skew.
>>> > > > >> > > >> > > > > > >>>>>> However, IMHO, the API level of
>>> optimizing
>>> > > > >> revenue is
>>> > > > >> > > >> > > different.
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> *Your optimization benefits from Flink
>>> SQL and
>>> > > > >> it's
>>> > > > >> > not
>>> > > > >> > > >> > user's
>>> > > > >> > > >> > > > > > >>>> faces.(If
>>> > > > >> > > >> > > > > > >>>>> I
>>> > > > >> > > >> > > > > > >>>>>> understand it incorrectly, please
>>> correct
>>> > > > this.)*
>>> > > > >> > > >> > > > > > >>>>>> *Our implementation employs it as an
>>> > > > optimization
>>> > > > >> > tool
>>> > > > >> > > >> API
>>> > > > >> > > >> > for
>>> > > > >> > > >> > > > > > >>>>> DataStream,
>>> > > > >> > > >> > > > > > >>>>>> it just like a local version of the
>>> keyBy
>>> > > API.*
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> Based on this, I want to say support it
>>> as a
>>> > > > >> > DataStream
>>> > > > >> > > >> API
>>> > > > >> > > >> > > can
>>> > > > >> > > >> > > > > > >>> provide
>>> > > > >> > > >> > > > > > >>>>>> these advantages:
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear
>>> semantic
>>> > > and
>>> > > > >> it's
>>> > > > >> > > >> > flexible
>>> > > > >> > > >> > > > not
>>> > > > >> > > >> > > > > > >>> only
>>> > > > >> > > >> > > > > > >>>>> for
>>> > > > >> > > >> > > > > > >>>>>>   processing data skew but also for
>>> > > implementing
>>> > > > >> some
>>> > > > >> > > >> user
>>> > > > >> > > >> > > > cases,
>>> > > > >> > > >> > > > > > >>> for
>>> > > > >> > > >> > > > > > >>>>>>   example, if we want to calculate the
>>> > > > >> multiple-level
>>> > > > >> > > >> > > > aggregation,
>>> > > > >> > > >> > > > > > >>> we
>>> > > > >> > > >> > > > > > >>>>> can
>>> > > > >> > > >> > > > > > >>>>>> do
>>> > > > >> > > >> > > > > > >>>>>>   multiple-level aggregation in the
>>> local
>>> > > > >> > aggregation:
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
>>> > > > >> > > >> //
>>> > > > >> > > >> > > here
>>> > > > >> > > >> > > > > > >> "a"
>>> > > > >> > > >> > > > > > >>>> is
>>> > > > >> > > >> > > > > > >>>>> a
>>> > > > >> > > >> > > > > > >>>>>>   sub-category, while "b" is a
>>> category, here
>>> > > we
>>> > > > >> do
>>> > > > >> > not
>>> > > > >> > > >> need
>>> > > > >> > > >> > > to
>>> > > > >> > > >> > > > > > >>>> shuffle
>>> > > > >> > > >> > > > > > >>>>>> data
>>> > > > >> > > >> > > > > > >>>>>>   in the network.
>>> > > > >> > > >> > > > > > >>>>>>   - The users of DataStream API will
>>> benefit
>>> > > > from
>>> > > > >> > this.
>>> > > > >> > > >> > > > Actually,
>>> > > > >> > > >> > > > > > >> we
>>> > > > >> > > >> > > > > > >>>>> have
>>> > > > >> > > >> > > > > > >>>>>>   a lot of scenes need to use
>>> DataStream API.
>>> > > > >> > > Currently,
>>> > > > >> > > >> > > > > > >> DataStream
>>> > > > >> > > >> > > > > > >>>> API
>>> > > > >> > > >> > > > > > >>>>> is
>>> > > > >> > > >> > > > > > >>>>>>   the cornerstone of the physical plan
>>> of
>>> > > Flink
>>> > > > >> SQL.
>>> > > > >> > > >> With a
>>> > > > >> > > >> > > > > > >>> localKeyBy
>>> > > > >> > > >> > > > > > >>>>>> API,
>>> > > > >> > > >> > > > > > >>>>>>   the optimization of SQL at least may
>>> use
>>> > > this
>>> > > > >> > > optimized
>>> > > > >> > > >> > API,
>>> > > > >> > > >> > > > > > >> this
>>> > > > >> > > >> > > > > > >>>> is a
>>> > > > >> > > >> > > > > > >>>>>>   further topic.
>>> > > > >> > > >> > > > > > >>>>>>   - Based on the window operator, our
>>> state
>>> > > > would
>>> > > > >> > > benefit
>>> > > > >> > > >> > from
>>> > > > >> > > >> > > > > > >> Flink
>>> > > > >> > > >> > > > > > >>>>> State
>>> > > > >> > > >> > > > > > >>>>>>   and checkpoint, we do not need to
>>> worry
>>> > > about
>>> > > > >> OOM
>>> > > > >> > and
>>> > > > >> > > >> job
>>> > > > >> > > >> > > > > > >> failed.
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> Now, about your questions:
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> 1. About our design cannot change the
>>> data
>>> > > type
>>> > > > >> and
>>> > > > >> > > about
>>> > > > >> > > >> > the
>>> > > > >> > > >> > > > > > >>>>>> implementation of average:
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the
>>> localKeyBy is
>>> > > > an
>>> > > > >> API
>>> > > > >> > > >> > provides
>>> > > > >> > > >> > > > to
>>> > > > >> > > >> > > > > > >> the
>>> > > > >> > > >> > > > > > >>>>> users
>>> > > > >> > > >> > > > > > >>>>>> who use DataStream API to build their
>>> jobs.
>>> > > > >> > > >> > > > > > >>>>>> Users should know its semantics and the
>>> > > > difference
>>> > > > >> > with
>>> > > > >> > > >> > keyBy
>>> > > > >> > > >> > > > API,
>>> > > > >> > > >> > > > > > >> so
>>> > > > >> > > >> > > > > > >>>> if
>>> > > > >> > > >> > > > > > >>>>>> they want to the average aggregation,
>>> they
>>> > > > should
>>> > > > >> > carry
>>> > > > >> > > >> > local
>>> > > > >> > > >> > > > sum
>>> > > > >> > > >> > > > > > >>>> result
>>> > > > >> > > >> > > > > > >>>>>> and local count result.
>>> > > > >> > > >> > > > > > >>>>>> I admit that it will be convenient to
>>> use
>>> > > keyBy
>>> > > > >> > > directly.
>>> > > > >> > > >> > But
>>> > > > >> > > >> > > we
>>> > > > >> > > >> > > > > > >> need
>>> > > > >> > > >> > > > > > >>>> to
>>> > > > >> > > >> > > > > > >>>>>> pay a little price when we get some
>>> benefits.
>>> > > I
>>> > > > >> think
>>> > > > >> > > >> this
>>> > > > >> > > >> > > price
>>> > > > >> > > >> > > > > is
>>> > > > >> > > >> > > > > > >>>>>> reasonable. Considering that the
>>> DataStream
>>> > > API
>>> > > > >> > itself
>>> > > > >> > > >> is a
>>> > > > >> > > >> > > > > > >> low-level
>>> > > > >> > > >> > > > > > >>>> API
>>> > > > >> > > >> > > > > > >>>>>> (at least for now).
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> 2. About stateless operator and
>>> > > > >> > > >> > > > > > >>>>>>
>>> `StreamOperator::prepareSnapshotPreBarrier()`:
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> Actually, I have discussed this opinion
>>> with
>>> > > > >> @dianfu
>>> > > > >> > in
>>> > > > >> > > >> the
>>> > > > >> > > >> > > old
>>> > > > >> > > >> > > > > > >> mail
>>> > > > >> > > >> > > > > > >>>>>> thread. I will copy my opinion from
>>> there:
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>>   - for your design, you still need
>>> somewhere
>>> > > to
>>> > > > >> give
>>> > > > >> > > the
>>> > > > >> > > >> > > users
>>> > > > >> > > >> > > > > > >>>>> configure
>>> > > > >> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
>>> > > > >> availability?),
>>> > > > >> > > >> this
>>> > > > >> > > >> > > > design
>>> > > > >> > > >> > > > > > >>>> cannot
>>> > > > >> > > >> > > > > > >>>>>>   guarantee a deterministic semantics
>>> (it will
>>> > > > >> bring
>>> > > > >> > > >> trouble
>>> > > > >> > > >> > > for
>>> > > > >> > > >> > > > > > >>>> testing
>>> > > > >> > > >> > > > > > >>>>>> and
>>> > > > >> > > >> > > > > > >>>>>>   debugging).
>>> > > > >> > > >> > > > > > >>>>>>   - if the implementation depends on the
>>> > > timing
>>> > > > of
>>> > > > >> > > >> > checkpoint,
>>> > > > >> > > >> > > > it
>>> > > > >> > > >> > > > > > >>>> would
>>> > > > >> > > >> > > > > > >>>>>>   affect the checkpoint's progress, and
>>> the
>>> > > > >> buffered
>>> > > > >> > > data
>>> > > > >> > > >> > may
>>> > > > >> > > >> > > > > > >> cause
>>> > > > >> > > >> > > > > > >>>> OOM
>>> > > > >> > > >> > > > > > >>>>>>   issue. In addition, if the operator is
>>> > > > >> stateless,
>>> > > > >> > it
>>> > > > >> > > >> can
>>> > > > >> > > >> > not
>>> > > > >> > > >> > > > > > >>> provide
>>> > > > >> > > >> > > > > > >>>>>> fault
>>> > > > >> > > >> > > > > > >>>>>>   tolerance.
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> Best,
>>> > > > >> > > >> > > > > > >>>>>> Vino
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>> Kurt Young <[hidden email] <mailto:
>>> [hidden email]>> 于2019年6月18日周二
>>> > > > >> > 上午9:22写道：
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>>> Hi Vino,
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the
>>> general
>>> > > > idea
>>> > > > >> and
>>> > > > >> > > IMO
>>> > > > >> > > >> > it's
>>> > > > >> > > >> > > > > > >> very
>>> > > > >> > > >> > > > > > >>>>> useful
>>> > > > >> > > >> > > > > > >>>>>>> feature.
>>> > > > >> > > >> > > > > > >>>>>>> But after reading through the
>>> document, I
>>> > > feel
>>> > > > >> that
>>> > > > >> > we
>>> > > > >> > > >> may
>>> > > > >> > > >> > > over
>>> > > > >> > > >> > > > > > >>>> design
>>> > > > >> > > >> > > > > > >>>>>> the
>>> > > > >> > > >> > > > > > >>>>>>> required
>>> > > > >> > > >> > > > > > >>>>>>> operator for proper local aggregation.
>>> The
>>> > > main
>>> > > > >> > reason
>>> > > > >> > > >> is
>>> > > > >> > > >> > we
>>> > > > >> > > >> > > > want
>>> > > > >> > > >> > > > > > >>> to
>>> > > > >> > > >> > > > > > >>>>>> have a
>>> > > > >> > > >> > > > > > >>>>>>> clear definition and behavior about the
>>> > > "local
>>> > > > >> keyed
>>> > > > >> > > >> state"
>>> > > > >> > > >> > > > which
>>> > > > >> > > >> > > > > > >>> in
>>> > > > >> > > >> > > > > > >>>> my
>>> > > > >> > > >> > > > > > >>>>>>> opinion is not
>>> > > > >> > > >> > > > > > >>>>>>> necessary for local aggregation, at
>>> least for
>>> > > > >> start.
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>> Another issue I noticed is the local
>>> key by
>>> > > > >> operator
>>> > > > >> > > >> cannot
>>> > > > >> > > >> > > > > > >> change
>>> > > > >> > > >> > > > > > >>>>>> element
>>> > > > >> > > >> > > > > > >>>>>>> type, it will
>>> > > > >> > > >> > > > > > >>>>>>> also restrict a lot of use cases which
>>> can be
>>> > > > >> > benefit
>>> > > > >> > > >> from
>>> > > > >> > > >> > > > local
>>> > > > >> > > >> > > > > > >>>>>>> aggregation, like "average".
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and
>>> the only
>>> > > > >> thing
>>> > > > >> > > >> need to
>>> > > > >> > > >> > > be
>>> > > > >> > > >> > > > > > >> done
>>> > > > >> > > >> > > > > > >>>> is
>>> > > > >> > > >> > > > > > >>>>>>> introduce
>>> > > > >> > > >> > > > > > >>>>>>> a stateless lightweight operator which
>>> is
>>> > > > >> *chained*
>>> > > > >> > > >> before
>>> > > > >> > > >> > > > > > >>> `keyby()`.
>>> > > > >> > > >> > > > > > >>>>> The
>>> > > > >> > > >> > > > > > >>>>>>> operator will flush all buffered
>>> > > > >> > > >> > > > > > >>>>>>> elements during
>>> > > > >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
>>> > > > >> > > >> > > > and
>>> > > > >> > > >> > > > > > >>>> make
>>> > > > >> > > >> > > > > > >>>>>>> himself stateless.
>>> > > > >> > > >> > > > > > >>>>>>> By the way, in the earlier version we
>>> also
>>> > > did
>>> > > > >> the
>>> > > > >> > > >> similar
>>> > > > >> > > >> > > > > > >> approach
>>> > > > >> > > >> > > > > > >>>> by
>>> > > > >> > > >> > > > > > >>>>>>> introducing a stateful
>>> > > > >> > > >> > > > > > >>>>>>> local aggregation operator but it's not
>>> > > > >> performed as
>>> > > > >> > > >> well
>>> > > > >> > > >> > as
>>> > > > >> > > >> > > > the
>>> > > > >> > > >> > > > > > >>>> later
>>> > > > >> > > >> > > > > > >>>>>> one,
>>> > > > >> > > >> > > > > > >>>>>>> and also effect the barrie
>>> > > > >> > > >> > > > > > >>>>>>> alignment time. The later one is fairly
>>> > > simple
>>> > > > >> and
>>> > > > >> > > more
>>> > > > >> > > >> > > > > > >> efficient.
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>> I would highly suggest you to consider
>>> to
>>> > > have
>>> > > > a
>>> > > > >> > > >> stateless
>>> > > > >> > > >> > > > > > >> approach
>>> > > > >> > > >> > > > > > >>>> at
>>> > > > >> > > >> > > > > > >>>>>> the
>>> > > > >> > > >> > > > > > >>>>>>> first step.
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>> Best,
>>> > > > >> > > >> > > > > > >>>>>>> Kurt
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark
>>> Wu <
>>> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
>>> > > > >> > > >> > > > > > >> wrote:
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>> Hi Vino,
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>> Regarding to the
>>> "input.keyBy(0).sum(1)" vs
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> >
>>> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
>>> > > > >> > > >> > > > > > >> have
>>> > > > >> > > >> > > > > > >>>> you
>>> > > > >> > > >> > > > > > >>>>>>> done
>>> > > > >> > > >> > > > > > >>>>>>>> some benchmark?
>>> > > > >> > > >> > > > > > >>>>>>>> Because I'm curious about how much
>>> > > performance
>>> > > > >> > > >> improvement
>>> > > > >> > > >> > > can
>>> > > > >> > > >> > > > > > >> we
>>> > > > >> > > >> > > > > > >>>> get
>>> > > > >> > > >> > > > > > >>>>>> by
>>> > > > >> > > >> > > > > > >>>>>>>> using count window as the local
>>> operator.
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>> Best,
>>> > > > >> > > >> > > > > > >>>>>>>> Jark
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino
>>> yang <
>>> > > > >> > > >> > > > [hidden email] <mailto:
>>> [hidden email]>
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>>>> wrote:
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> Hi Hequn,
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to
>>> > > provide a
>>> > > > >> tool
>>> > > > >> > > >> which
>>> > > > >> > > >> > > can
>>> > > > >> > > >> > > > > > >>> let
>>> > > > >> > > >> > > > > > >>>>>> users
>>> > > > >> > > >> > > > > > >>>>>>> do
>>> > > > >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The
>>> behavior
>>> > > of
>>> > > > >> the
>>> > > > >> > > >> > > > > > >>> pre-aggregation
>>> > > > >> > > >> > > > > > >>>>> is
>>> > > > >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> So the three cases are different, I
>>> will
>>> > > > >> describe
>>> > > > >> > > them
>>> > > > >> > > >> > one
>>> > > > >> > > >> > > by
>>> > > > >> > > >> > > > > > >>>> one:
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the result is
>>> event-driven,
>>> > > > each
>>> > > > >> > > event
>>> > > > >> > > >> can
>>> > > > >> > > >> > > > > > >>> produce
>>> > > > >> > > >> > > > > > >>>>> one
>>> > > > >> > > >> > > > > > >>>>>>> sum
>>> > > > >> > > >> > > > > > >>>>>>>>> aggregation result and it is the
>>> latest one
>>> > > > >> from
>>> > > > >> > the
>>> > > > >> > > >> > source
>>> > > > >> > > >> > > > > > >>>> start.*
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> 2.
>>> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may have
>>> a
>>> > > > >> problem, it
>>> > > > >> > > >> would
>>> > > > >> > > >> > do
>>> > > > >> > > >> > > > > > >> the
>>> > > > >> > > >> > > > > > >>>>> local
>>> > > > >> > > >> > > > > > >>>>>>> sum
>>> > > > >> > > >> > > > > > >>>>>>>>> aggregation and will produce the
>>> latest
>>> > > > partial
>>> > > > >> > > result
>>> > > > >> > > >> > from
>>> > > > >> > > >> > > > > > >> the
>>> > > > >> > > >> > > > > > >>>>>> source
>>> > > > >> > > >> > > > > > >>>>>>>>> start for every event. *
>>> > > > >> > > >> > > > > > >>>>>>>>> *These latest partial results from
>>> the same
>>> > > > key
>>> > > > >> > are
>>> > > > >> > > >> > hashed
>>> > > > >> > > >> > > to
>>> > > > >> > > >> > > > > > >>> one
>>> > > > >> > > >> > > > > > >>>>>> node
>>> > > > >> > > >> > > > > > >>>>>>> to
>>> > > > >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
>>> > > > >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it
>>> > > received
>>> > > > >> > > multiple
>>> > > > >> > > >> > > partial
>>> > > > >> > > >> > > > > > >>>>> results
>>> > > > >> > > >> > > > > > >>>>>>>> (they
>>> > > > >> > > >> > > > > > >>>>>>>>> are all calculated from the source
>>> start)
>>> > > and
>>> > > > >> sum
>>> > > > >> > > them
>>> > > > >> > > >> > will
>>> > > > >> > > >> > > > > > >> get
>>> > > > >> > > >> > > > > > >>>> the
>>> > > > >> > > >> > > > > > >>>>>>> wrong
>>> > > > >> > > >> > > > > > >>>>>>>>> result.*
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> 3.
>>> > > > >> > > >> > >
>>> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a
>>> partial
>>> > > > >> > > aggregation
>>> > > > >> > > >> > > result
>>> > > > >> > > >> > > > > > >>> for
>>> > > > >> > > >> > > > > > >>>>>> the 5
>>> > > > >> > > >> > > > > > >>>>>>>>> records in the count window. The
>>> partial
>>> > > > >> > aggregation
>>> > > > >> > > >> > > results
>>> > > > >> > > >> > > > > > >>> from
>>> > > > >> > > >> > > > > > >>>>> the
>>> > > > >> > > >> > > > > > >>>>>>>> same
>>> > > > >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> So the first case and the third case
>>> can
>>> > > get
>>> > > > >> the
>>> > > > >> > > >> *same*
>>> > > > >> > > >> > > > > > >> result,
>>> > > > >> > > >> > > > > > >>>> the
>>> > > > >> > > >> > > > > > >>>>>>>>> difference is the output-style and
>>> the
>>> > > > latency.
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key
>>> API is
>>> > > just
>>> > > > >> an
>>> > > > >> > > >> > > optimization
>>> > > > >> > > >> > > > > > >>>> API.
>>> > > > >> > > >> > > > > > >>>>> We
>>> > > > >> > > >> > > > > > >>>>>>> do
>>> > > > >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the
>>> user
>>> > > has
>>> > > > to
>>> > > > >> > > >> > understand
>>> > > > >> > > >> > > > > > >> its
>>> > > > >> > > >> > > > > > >>>>>>> semantics
>>> > > > >> > > >> > > > > > >>>>>>>>> and use it correctly.
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> Best,
>>> > > > >> > > >> > > > > > >>>>>>>>> Vino
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]
>>> <mailto:[hidden email]>>
>>> > > > >> 于2019年6月17日周一
>>> > > > >> > > >> > 下午4:18写道：
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>> Hi Vino,
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think it
>>> is a
>>> > > > very
>>> > > > >> > good
>>> > > > >> > > >> > > feature!
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is the
>>> > > > semantics
>>> > > > >> > for
>>> > > > >> > > >> the
>>> > > > >> > > >> > > > > > >>>>>> `localKeyBy`.
>>> > > > >> > > >> > > > > > >>>>>>>> From
>>> > > > >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API
>>> returns
>>> > > > an
>>> > > > >> > > >> instance
>>> > > > >> > > >> > of
>>> > > > >> > > >> > > > > > >>>>>>> `KeyedStream`
>>> > > > >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so in
>>> this
>>> > > > case,
>>> > > > >> > > what's
>>> > > > >> > > >> > the
>>> > > > >> > > >> > > > > > >>>>> semantics
>>> > > > >> > > >> > > > > > >>>>>>> for
>>> > > > >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will
>>> the
>>> > > > >> following
>>> > > > >> > > code
>>> > > > >> > > >> > share
>>> > > > >> > > >> > > > > > >>> the
>>> > > > >> > > >> > > > > > >>>>> same
>>> > > > >> > > >> > > > > > >>>>>>>>> result?
>>> > > > >> > > >> > > > > > >>>>>>>>>> and what're the differences between
>>> them?
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
>>> > > > >> > > >> > > > > > >>>>>>>>>> 2.
>>> > > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>>> > > > >> > > >> > > > > > >>>>>>>>>> 3.
>>> > > > >> > > >> > > > > > >>
>>> > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add
>>> this
>>> > > into
>>> > > > >> the
>>> > > > >> > > >> > document.
>>> > > > >> > > >> > > > > > >>> Thank
>>> > > > >> > > >> > > > > > >>>>> you
>>> > > > >> > > >> > > > > > >>>>>>>> very
>>> > > > >> > > >> > > > > > >>>>>>>>>> much.
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>> Best, Hequn
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM
>>> vino
>>> > > yang <
>>> > > > >> > > >> > > > > > >>>>> [hidden email] <mailto:
>>> [hidden email]>>
>>> > > > >> > > >> > > > > > >>>>>>>>> wrote:
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*"
>>> section
>>> > > of
>>> > > > >> FLIP
>>> > > > >> > > >> wiki
>>> > > > >> > > >> > > > > > >>>> page.[1]
>>> > > > >> > > >> > > > > > >>>>>> This
>>> > > > >> > > >> > > > > > >>>>>>>>> mail
>>> > > > >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has
>>> proceeded to
>>> > > > the
>>> > > > >> > > third
>>> > > > >> > > >> > step.
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth
>>> step(vote
>>> > > > step),
>>> > > > >> I
>>> > > > >> > > >> didn't
>>> > > > >> > > >> > > > > > >> find
>>> > > > >> > > >> > > > > > >>>> the
>>> > > > >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the
>>> voting
>>> > > > >> process.
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion of
>>> this
>>> > > > >> feature
>>> > > > >> > > has
>>> > > > >> > > >> > been
>>> > > > >> > > >> > > > > > >>> done
>>> > > > >> > > >> > > > > > >>>>> in
>>> > > > >> > > >> > > > > > >>>>>>> the
>>> > > > >> > > >> > > > > > >>>>>>>>> old
>>> > > > >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me when
>>> > > should
>>> > > > I
>>> > > > >> > start
>>> > > > >> > > >> > > > > > >> voting?
>>> > > > >> > > >> > > > > > >>>> Can
>>> > > > >> > > >> > > > > > >>>>> I
>>> > > > >> > > >> > > > > > >>>>>>>> start
>>> > > > >> > > >> > > > > > >>>>>>>>>> now?
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>> Best,
>>> > > > >> > > >> > > > > > >>>>>>>>>>> Vino
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>> [1]:
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> >
>>> > > > >> > > >>
>>> > > > >> > >
>>> > > > >> >
>>> > > > >>
>>> > > >
>>> > >
>>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>>> <
>>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>>> >
>>> > > > >> > > >> > > > > > >>>>>>>>>>> [2]:
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> >
>>> > > > >> > > >>
>>> > > > >> > >
>>> > > > >> >
>>> > > > >>
>>> > > >
>>> > >
>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>> <
>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>> >
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email]
>>> <mailto:[hidden email]>>
>>> > > 于2019年6月13日周四
>>> > > > >> > > 上午9:19写道：
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for
>>> your
>>> > > > >> efforts.
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>> Best,
>>> > > > >> > > >> > > > > > >>>>>>>>>>>> Leesf
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]
>>> <mailto:[hidden email]>>
>>> > > > >> > 于2019年6月12日周三
>>> > > > >> > > >> > > > > > >>> 下午5:46写道：
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP
>>> > > discussion
>>> > > > >> > thread
>>> > > > >> > > >> > > > > > >> about
>>> > > > >> > > >> > > > > > >>>>>>> supporting
>>> > > > >> > > >> > > > > > >>>>>>>>>> local
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can
>>> effectively
>>> > > > >> > alleviate
>>> > > > >> > > >> data
>>> > > > >> > > >> > > > > > >>>> skew.
>>> > > > >> > > >> > > > > > >>>>>>> This
>>> > > > >> > > >> > > > > > >>>>>>>> is
>>> > > > >> > > >> > > > > > >>>>>>>>>> the
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> >
>>> > > > >> > > >>
>>> > > > >> > >
>>> > > > >> >
>>> > > > >>
>>> > > >
>>> > >
>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>>> <
>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>>> >
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are
>>> widely
>>> > > used
>>> > > > to
>>> > > > >> > > >> perform
>>> > > > >> > > >> > > > > > >>>>>> aggregating
>>> > > > >> > > >> > > > > > >>>>>>>>>>>> operations
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window)
>>> on the
>>> > > > >> elements
>>> > > > >> > > >> that
>>> > > > >> > > >> > > > > > >>> have
>>> > > > >> > > >> > > > > > >>>>> the
>>> > > > >> > > >> > > > > > >>>>>>> same
>>> > > > >> > > >> > > > > > >>>>>>>>>> key.
>>> > > > >> > > >> > > > > > >>>>>>>>>>>> When
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the
>>> elements with
>>> > > > the
>>> > > > >> > same
>>> > > > >> > > >> key
>>> > > > >> > > >> > > > > > >>> will
>>> > > > >> > > >> > > > > > >>>> be
>>> > > > >> > > >> > > > > > >>>>>>> sent
>>> > > > >> > > >> > > > > > >>>>>>>> to
>>> > > > >> > > >> > > > > > >>>>>>>>>> and
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> The performance of these
>>> aggregating
>>> > > > >> > operations
>>> > > > >> > > is
>>> > > > >> > > >> > > > > > >> very
>>> > > > >> > > >> > > > > > >>>>>>> sensitive
>>> > > > >> > > >> > > > > > >>>>>>>>> to
>>> > > > >> > > >> > > > > > >>>>>>>>>>> the
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the
>>> cases
>>> > > where
>>> > > > >> the
>>> > > > >> > > >> > > > > > >>> distribution
>>> > > > >> > > >> > > > > > >>>>> of
>>> > > > >> > > >> > > > > > >>>>>>> keys
>>> > > > >> > > >> > > > > > >>>>>>>>>>>> follows a
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance
>>> will be
>>> > > > >> > > >> significantly
>>> > > > >> > > >> > > > > > >>>>>> downgraded.
>>> > > > >> > > >> > > > > > >>>>>>>>> More
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the degree
>>> of
>>> > > > >> > parallelism
>>> > > > >> > > >> does
>>> > > > >> > > >> > > > > > >>> not
>>> > > > >> > > >> > > > > > >>>>> help
>>> > > > >> > > >> > > > > > >>>>>>>> when
>>> > > > >> > > >> > > > > > >>>>>>>>> a
>>> > > > >> > > >> > > > > > >>>>>>>>>>> task
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a
>>> widely-adopted
>>> > > > >> method
>>> > > > >> > to
>>> > > > >> > > >> > > > > > >> reduce
>>> > > > >> > > >> > > > > > >>>> the
>>> > > > >> > > >> > > > > > >>>>>>>>>> performance
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can
>>> decompose
>>> > > > the
>>> > > > >> > > >> > > > > > >> aggregating
>>> > > > >> > > >> > > > > > >>>>>>>> operations
>>> > > > >> > > >> > > > > > >>>>>>>>>> into
>>> > > > >> > > >> > > > > > >>>>>>>>>>>> two
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we
>>> > > aggregate
>>> > > > >> the
>>> > > > >> > > >> elements
>>> > > > >> > > >> > > > > > >>> of
>>> > > > >> > > >> > > > > > >>>>> the
>>> > > > >> > > >> > > > > > >>>>>>> same
>>> > > > >> > > >> > > > > > >>>>>>>>> key
>>> > > > >> > > >> > > > > > >>>>>>>>>>> at
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain partial
>>> > > > results.
>>> > > > >> > Then
>>> > > > >> > > at
>>> > > > >> > > >> > > > > > >> the
>>> > > > >> > > >> > > > > > >>>>> second
>>> > > > >> > > >> > > > > > >>>>>>>>> phase,
>>> > > > >> > > >> > > > > > >>>>>>>>>>>> these
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to
>>> receivers
>>> > > > >> > according
>>> > > > >> > > to
>>> > > > >> > > >> > > > > > >>> their
>>> > > > >> > > >> > > > > > >>>>> keys
>>> > > > >> > > >> > > > > > >>>>>>> and
>>> > > > >> > > >> > > > > > >>>>>>>>> are
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final
>>> result.
>>> > > > Since
>>> > > > >> the
>>> > > > >> > > >> number
>>> > > > >> > > >> > > > > > >>> of
>>> > > > >> > > >> > > > > > >>>>>>> partial
>>> > > > >> > > >> > > > > > >>>>>>>>>>> results
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is
>>> limited by
>>> > > > the
>>> > > > >> > > >> number of
>>> > > > >> > > >> > > > > > >>>>>> senders,
>>> > > > >> > > >> > > > > > >>>>>>>> the
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be
>>> > > reduced.
>>> > > > >> > > >> Besides, by
>>> > > > >> > > >> > > > > > >>>>>> reducing
>>> > > > >> > > >> > > > > > >>>>>>>> the
>>> > > > >> > > >> > > > > > >>>>>>>>>>> amount
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the
>>> performance can
>>> > > > be
>>> > > > >> > > further
>>> > > > >> > > >> > > > > > >>>>> improved.
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> >
>>> > > > >> > > >>
>>> > > > >> > >
>>> > > > >> >
>>> > > > >>
>>> > > >
>>> > >
>>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>>> <
>>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>>> >
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> >
>>> > > > >> > > >>
>>> > > > >> > >
>>> > > > >> >
>>> > > > >>
>>> > > >
>>> > >
>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>> <
>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>> >
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> https://issues.apache.org/jira/browse/FLINK-12786 <
>>> https://issues.apache.org/jira/browse/FLINK-12786>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your
>>> > > feedback!
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Best,
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Vino
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>>
>>> > > > >> > > >> > > > > > >>>>>>
>>> > > > >> > > >> > > > > > >>>>>
>>> > > > >> > > >> > > > > > >>>>
>>> > > > >> > > >> > > > > > >>>
>>> > > > >> > > >> > > > > > >>
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > > >
>>> > > > >> > > >> > > > >
>>> > > > >> > > >> > > >
>>> > > > >> > > >> > >
>>> > > > >> > > >> >
>>> > > > >> > > >>
>>> > > > >> > > >
>>> > > > >> > >
>>> > > > >> >
>>> > > > >>
>>> > > > >
>>> > > >
>>> > >
>>>
>>>

[Jark Wu-2]

Hi Vino,

So the difference between `DataStream.localKeyBy().process()` with
`DataStream.process()` is that the former can access keyed state and the
latter can only access operator state.
I think it's out of the scope of designing a local aggregation API. It
might be an extension of state API, i.e. local keyed state.
The difference between local keyed state with operator state (if I
understand correctly) is local keyed state can be backed on RocksDB? or
making "keyed state" locally?
IMO, it's a larger topic than local aggregation and should be discussed
separately. I cc-ed people who works on states @Tzu-Li (Gordon) Tai
<[hidden email]>  @Seth @Yu Li to give some feedback from the
perspective of state.

Regarding to the API designing updated in your FLIP, I have some concerns:

1) The "localKeyBy()" method returns a "KeyedStream" which exposes all
method of it.
However, not every method makes sense or have a clear definition on local
stream.
For example, "countWindow(long, long)", "timeWindow(long, long)",
"window(WindowAssigner)", and "intervalJoin" Hequn mentioned before.
I would suggest we can expose the only APIs we needed for local aggregation
and leave the others later.
We can return a "LocalKeyedStream" and may expose only some dedicated
methods: for example, "aggregate()", "trigger()".
These APIs do not need to expose local keyed state to support local
aggregation.

2) I think `localKeyBy().process()` is something called "local process",
not just "local aggregate".
It needs more discussion about local keyed state, and I would like to put
it out of this FLIP.


Regards,
Jark


On Thu, 27 Jun 2019 at 13:03, vino yang <[hidden email]> wrote:

> Hi all,
>
> I also think it's a good idea that we need to agree on the API level first.
>
> I am sorry, we did not give some usage examples of the API in the FLIP
> documentation before. This may have caused some misunderstandings about the
> discussion of this mail thread.
>
> So, now I have added some usage examples in the "Public Interfaces"
> section of the FLIP-44 documentation.
>
> Let us first know the API through its use examples.
>
> Any feedback and questions please let me know.
>
> Best,
> Vino
>
> vino yang <[hidden email]> 于2019年6月27日周四 下午12:51写道：
>
>> Hi Jark,
>>
>> `DataStream.localKeyBy().process()` has some key difference with
>> `DataStream.process()`. The former API receive `KeyedProcessFunction`
>> (sorry my previous reply may let you misunderstood), the latter receive API
>> receive `ProcessFunction`. When you read the java doc of ProcessFunction,
>> you can find a "*Note*" statement:
>>
>> Access to keyed state and timers (which are also scoped to a key) is only
>>> available if the ProcessFunction is applied on a KeyedStream.
>>
>>
>> In addition, you can also compare the two
>> implementations(`ProcessOperator` and `KeyedProcessOperator`) of them to
>> view the difference.
>>
>> IMO, the "Note" statement means a lot for many use scenarios.
>> For example, if we cannot access keyed state, we can only use heap memory
>> to buffer data while it does not guarantee the semantics of correctness!
>> And the timer is also very important in some scenarios.
>>
>> That's why we say our API is flexible, it can get most benefits (even
>> subsequent potential benefits in the future) from KeyedStream.
>>
>> I have added some instructions on the use of localKeyBy in the FLIP-44
>> documentation.
>>
>> Best,
>> Vino
>>
>>
>> Jark Wu <[hidden email]> 于2019年6月27日周四 上午10:44写道：
>>
>>> Hi Piotr,
>>>
>>> I think the state migration you raised is a good point. Having
>>> "stream.enableLocalAggregation(Trigger)” might add some implicit operators
>>> which users can't set uid and cause the state compatibility/evolution
>>> problems.
>>> So let's put this in rejected alternatives.
>>>
>>> Hi Vino,
>>>
>>> You mentioned several times that "DataStream.localKeyBy().process()" can
>>> solve the data skew problem of "DataStream.keyBy().process()".
>>> I'm curious about what's the differences between "DataStream.process()"
>>> and "DataStream.localKeyBy().process()"?
>>> Can't "DataStream.process()" solve the data skew problem?
>>>
>>> Best,
>>> Jark
>>>
>>>
>>> On Wed, 26 Jun 2019 at 18:20, Piotr Nowojski <[hidden email]>
>>> wrote:
>>>
>>>> Hi Jark and Vino,
>>>>
>>>> I agree fully with Jark, that in order to have the discussion focused
>>>> and to limit the number of parallel topics, we should first focus on one
>>>> topic. We can first decide on the API and later we can discuss the runtime
>>>> details. At least as long as we keep the potential requirements of the
>>>> runtime part in mind while designing the API.
>>>>
>>>> Regarding the automatic optimisation and proposed by Jark:
>>>>
>>>> "stream.enableLocalAggregation(Trigger)”
>>>>
>>>> I would be against that in the DataStream API for the reasons that Vino
>>>> presented. There was a discussion thread about future directions of Table
>>>> API vs DataStream API and the consensus was that the automatic
>>>> optimisations are one of the dividing lines between those two, for at least
>>>> a couple of reasons. Flexibility and full control over the program was one
>>>> of them. Another is state migration. Having
>>>> "stream.enableLocalAggregation(Trigger)” that might add some implicit
>>>> operators in the job graph can cause problems with savepoint/checkpoint
>>>> compatibility.
>>>>
>>>> However I haven’t thought about/looked into the details of the Vino’s
>>>> API proposal, so I can not fully judge it.
>>>>
>>>> Piotrek
>>>>
>>>> > On 26 Jun 2019, at 09:17, vino yang <[hidden email]> wrote:
>>>> >
>>>> > Hi Jark,
>>>> >
>>>> > Similar questions and responses have been repeated many times.
>>>> >
>>>> > Why didn't we spend more sections discussing the API?
>>>> >
>>>> > Because we try to reuse the ability of KeyedStream. The localKeyBy
>>>> API just returns the KeyedStream, that's our design, we can get all the
>>>> benefit from the KeyedStream and get further benefit from WindowedStream.
>>>> The APIs come from KeyedStream and WindowedStream is long-tested and
>>>> flexible. Yes, we spend much space discussing the local keyed state, that's
>>>> not the goal and motivation, that's the way to implement local aggregation.
>>>> It is much more complicated than the API we introduced, so we spent more
>>>> section. Of course, this is the implementation level of the Operator. We
>>>> also agreed to support the implementation of buffer+flush and added related
>>>> instructions to the documentation. This needs to wait for the community to
>>>> recognize, and if the community agrees, we will give more instructions.
>>>> What's more, I have indicated before that we welcome state-related
>>>> commenters to participate in the discussion, but it is not wise to modify
>>>> the FLIP title.
>>>> >
>>>> > About the API of local aggregation:
>>>> >
>>>> > I don't object to ease of use is very important. But IMHO flexibility
>>>> is the most important at the DataStream API level. Otherwise, what does
>>>> DataStream mean? The significance of the DataStream API is that it is more
>>>> flexible than Table/SQL, if it cannot provide this point then everyone
>>>> would just use Table/SQL.
>>>> >
>>>> > The DataStream API should focus more on flexibility than on automatic
>>>> optimization, which allows users to have more possibilities to implement
>>>> complex programs and meet specific scenarios. There are a lot of programs
>>>> written using the DataStream API that are far more complex than we think.
>>>> It is very difficult to optimize at the API level and the benefit is very
>>>> low.
>>>> >
>>>> > I want to say that we support a more generalized local aggregation. I
>>>> mentioned in the previous reply that not only the UDF that implements
>>>> AggregateFunction is called aggregation. In some complex scenarios, we have
>>>> to support local aggregation through ProcessFunction and
>>>> ProcessWindowFunction to solve the data skew problem. How do you support
>>>> them in the API implementation and optimization you mentioned?
>>>> >
>>>> > Flexible APIs are arbitrarily combined to result in erroneous
>>>> semantics, which does not prove that flexibility is meaningless because the
>>>> user is the decision maker. I have been exemplified many times, for many
>>>> APIs in DataStream, if we arbitrarily combined them, they also do not have
>>>> much practical significance. So, users who use flexible APIs need to
>>>> understand what they are doing and what is the right choice.
>>>> >
>>>> > I think that if we discuss this, there will be no result.
>>>> >
>>>> > @Stephan Ewen <mailto:[hidden email]> , @Aljoscha Krettek <mailto:
>>>> [hidden email]> and @Piotr Nowojski <mailto:[hidden email]>
>>>> Do you have further comments?
>>>> >
>>>> >
>>>> > Jark Wu <[hidden email] <mailto:[hidden email]>> 于2019年6月26日周三
>>>> 上午11:46写道：
>>>> > Thanks for the long discussion Vino, Kurt, Hequn, Piotr and others,
>>>> >
>>>> > It seems that we still have some different ideas about the API
>>>> > (localKeyBy()?) and implementation details (reuse window operator?
>>>> local
>>>> > keyed state?).
>>>> > And the discussion is stalled and mixed with motivation and API and
>>>> > implementation discussion.
>>>> >
>>>> > In order to make some progress in this topic, I want to summarize the
>>>> > points (pls correct me if I'm wrong or missing sth) and would suggest
>>>> to
>>>> > split
>>>> >  the topic into following aspects and discuss them one by one.
>>>> >
>>>> > 1) What's the main purpose of this FLIP?
>>>> >  - From the title of this FLIP, it is to support local aggregate.
>>>> However
>>>> > from the content of the FLIP, 80% are introducing a new state called
>>>> local
>>>> > keyed state.
>>>> >  - If we mainly want to introduce local keyed state, then we should
>>>> > re-title the FLIP and involve in more people who works on state.
>>>> >  - If we mainly want to support local aggregate, then we can jump to
>>>> step 2
>>>> > to discuss the API design.
>>>> >
>>>> > 2) What does the API look like?
>>>> >  - Vino proposed to use "localKeyBy()" to do local process, the
>>>> output of
>>>> > local process is the result type of aggregate function.
>>>> >   a) For non-windowed aggregate:
>>>> > input.localKeyBy(..).aggregate(agg1).keyBy(..).aggregate(agg2)
>>>> **NOT
>>>> > SUPPORT**
>>>> >   b) For windowed aggregate:
>>>> >
>>>> input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)
>>>> >
>>>> > 3) What's the implementation detail?
>>>> >  - may reuse window operator or not.
>>>> >  - may introduce a new state concepts or not.
>>>> >  - may not have state in local operator by flushing buffers in
>>>> > prepareSnapshotPreBarrier
>>>> >  - and so on...
>>>> >  - we can discuss these later when we reach a consensus on API
>>>> >
>>>> > --------------------
>>>> >
>>>> > Here are my thoughts:
>>>> >
>>>> > 1) Purpose of this FLIP
>>>> >  - From the motivation section in the FLIP, I think the purpose is to
>>>> > support local aggregation to solve the data skew issue.
>>>> >    Then I think we should focus on how to provide a easy to use and
>>>> clear
>>>> > API to support **local aggregation**.
>>>> >  - Vino's point is centered around the local keyed state API (or
>>>> > localKeyBy()), and how to leverage the local keyed state API to
>>>> support
>>>> > local aggregation.
>>>> >    But I'm afraid it's not a good way to design API for local
>>>> aggregation.
>>>> >
>>>> > 2) local aggregation API
>>>> >  - IMO, the method call chain
>>>> >
>>>> "input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)"
>>>> > is not such easy to use.
>>>> >    Because we have to provide two implementation for an aggregation
>>>> (one
>>>> > for partial agg, another for final agg). And we have to take care of
>>>> >    the first window call, an inappropriate window call will break the
>>>> > sematics.
>>>> >  - From my point of view, local aggregation is a mature concept which
>>>> > should output the intermediate accumulator (ACC) in the past period
>>>> of time
>>>> > (a trigger).
>>>> >    And the downstream final aggregation will merge ACCs received from
>>>> local
>>>> > side, and output the current final result.
>>>> >  - The current "AggregateFunction" API in DataStream already has the
>>>> > accumulator type and "merge" method. So the only thing user need to
>>>> do is
>>>> > how to enable
>>>> >    local aggregation opimization and set a trigger.
>>>> >  - One idea comes to my head is that, assume we have a windowed
>>>> aggregation
>>>> > stream: "val stream = input.keyBy().window(w).aggregate(agg)". We can
>>>> > provide an API on the stream.
>>>> >    For exmaple, "stream.enableLocalAggregation(Trigger)", the trigger
>>>> can
>>>> > be "ContinuousEventTimeTrigger.of(Time.of(Time.minutes(1)))". Then it
>>>> will
>>>> > be optmized into
>>>> >    local operator + final operator, and local operator will combine
>>>> records
>>>> > every minute on event time.
>>>> >  - In this way, there is only one line added, and the output is the
>>>> same
>>>> > with before, because it is just an opimization.
>>>> >
>>>> >
>>>> > Regards,
>>>> > Jark
>>>> >
>>>> >
>>>> >
>>>> > On Tue, 25 Jun 2019 at 14:34, vino yang <[hidden email]
>>>> <mailto:[hidden email]>> wrote:
>>>> >
>>>> > > Hi Kurt,
>>>> > >
>>>> > > Answer your questions:
>>>> > >
>>>> > > a) Sorry, I just updated the Google doc, still have no time update
>>>> the
>>>> > > FLIP, will update FLIP as soon as possible.
>>>> > > About your description at this point, I have a question, what does
>>>> it mean:
>>>> > > how do we combine with
>>>> > > `AggregateFunction`?
>>>> > >
>>>> > > I have shown you the examples which Flink has supported:
>>>> > >
>>>> > >    - input.localKeyBy(0).aggregate()
>>>> > >    - input.localKeyBy(0).window().aggregate()
>>>> > >
>>>> > > You can show me a example about how do we combine with
>>>> `AggregateFuncion`
>>>> > > through your localAggregate API.
>>>> > >
>>>> > > About the example, how to do the local aggregation for AVG,
>>>> consider this
>>>> > > code:
>>>> > >
>>>> > >
>>>> > >
>>>> > >
>>>> > >
>>>> > >
>>>> > >
>>>> > >
>>>> > >
>>>> > > *DataStream<Tuple2<String, Long>> source = null; source
>>>> .localKeyBy(0)
>>>> > > .timeWindow(Time.seconds(60)) .aggregate(agg1, new
>>>> > > WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>,
>>>> String,
>>>> > > TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60))
>>>> .aggregate(agg2,
>>>> > > new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>, String,
>>>> > > TimeWindow>());*
>>>> > >
>>>> > > *agg1:*
>>>> > > *signature : new AggregateFunction<Tuple2<String, Long>,
>>>> Tuple2<Long,
>>>> > > Long>, Tuple2<Long, Long>>() {}*
>>>> > > *input param type: Tuple2<String, Long> f0: key, f1: value*
>>>> > > *intermediate result type: Tuple2<Long, Long>, f0: local aggregated
>>>> sum;
>>>> > > f1: local aggregated count*
>>>> > > *output param type:  Tuple2<Long, Long>, f0: local aggregated sum;
>>>> f1:
>>>> > > local aggregated count*
>>>> > >
>>>> > > *agg2:*
>>>> > > *signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
>>>> > > Tuple2<String, Long>>() {},*
>>>> > > *input param type: Tuple3<String, Long, Long>, f0: key, f1:  local
>>>> > > aggregated sum; f2: local aggregated count*
>>>> > >
>>>> > > *intermediate result type: Long  avg result*
>>>> > > *output param type:  Tuple2<String, Long> f0: key, f1 avg result*
>>>> > >
>>>> > > For sliding window, we just need to change the window type if users
>>>> want to
>>>> > > do.
>>>> > > Again, we try to give the design and implementation in the
>>>> DataStream
>>>> > > level. So I believe we can match all the requirements(It's just
>>>> that the
>>>> > > implementation may be different) comes from the SQL level.
>>>> > >
>>>> > > b) Yes, Theoretically, your thought is right. But in reality, it
>>>> cannot
>>>> > > bring many benefits.
>>>> > > If we want to get the benefits from the window API, while we do not
>>>> reuse
>>>> > > the window operator? And just copy some many duplicated code to
>>>> another
>>>> > > operator?
>>>> > >
>>>> > > c) OK, I agree to let the state backend committers join this
>>>> discussion.
>>>> > >
>>>> > > Best,
>>>> > > Vino
>>>> > >
>>>> > >
>>>> > > Kurt Young <[hidden email] <mailto:[hidden email]>>
>>>> 于2019年6月24日周一 下午6:53写道：
>>>> > >
>>>> > > > Hi vino,
>>>> > > >
>>>> > > > One thing to add,  for a), I think use one or two examples like
>>>> how to do
>>>> > > > local aggregation on a sliding window,
>>>> > > > and how do we do local aggregation on an unbounded aggregate,
>>>> will do a
>>>> > > lot
>>>> > > > help.
>>>> > > >
>>>> > > > Best,
>>>> > > > Kurt
>>>> > > >
>>>> > > >
>>>> > > > On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email]
>>>> <mailto:[hidden email]>> wrote:
>>>> > > >
>>>> > > > > Hi vino,
>>>> > > > >
>>>> > > > > I think there are several things still need discussion.
>>>> > > > >
>>>> > > > > a) We all agree that we should first go with a unified
>>>> abstraction, but
>>>> > > > > the abstraction is not reflected by the FLIP.
>>>> > > > > If your answer is "locakKeyBy" API, then I would ask how do we
>>>> combine
>>>> > > > > with `AggregateFunction`, and how do
>>>> > > > > we do proper local aggregation for those have different
>>>> intermediate
>>>> > > > > result type, like AVG. Could you add these
>>>> > > > > to the document?
>>>> > > > >
>>>> > > > > b) From implementation side, reusing window operator is one of
>>>> the
>>>> > > > > possible solutions, but not we base on window
>>>> > > > > operator to have two different implementations. What I
>>>> understanding
>>>> > > is,
>>>> > > > > one of the possible implementations should
>>>> > > > > not touch window operator.
>>>> > > > >
>>>> > > > > c) 80% of your FLIP content is actually describing how do we
>>>> support
>>>> > > > local
>>>> > > > > keyed state. I don't know if this is necessary
>>>> > > > > to introduce at the first step and we should also involve
>>>> committers
>>>> > > work
>>>> > > > > on state backend to share their thoughts.
>>>> > > > >
>>>> > > > > Best,
>>>> > > > > Kurt
>>>> > > > >
>>>> > > > >
>>>> > > > > On Mon, Jun 24, 2019 at 5:17 PM vino yang <
>>>> [hidden email] <mailto:[hidden email]>>
>>>> > > wrote:
>>>> > > > >
>>>> > > > >> Hi Kurt,
>>>> > > > >>
>>>> > > > >> You did not give more further different opinions, so I thought
>>>> you
>>>> > > have
>>>> > > > >> agreed with the design after we promised to support two kinds
>>>> of
>>>> > > > >> implementation.
>>>> > > > >>
>>>> > > > >> In API level, we have answered your question about pass an
>>>> > > > >> AggregateFunction to do the aggregation. No matter introduce
>>>> > > localKeyBy
>>>> > > > >> API
>>>> > > > >> or not, we can support AggregateFunction.
>>>> > > > >>
>>>> > > > >> So what's your different opinion now? Can you share it with us?
>>>> > > > >>
>>>> > > > >> Best,
>>>> > > > >> Vino
>>>> > > > >>
>>>> > > > >> Kurt Young <[hidden email] <mailto:[hidden email]>>
>>>> 于2019年6月24日周一 下午4:24写道：
>>>> > > > >>
>>>> > > > >> > Hi vino,
>>>> > > > >> >
>>>> > > > >> > Sorry I don't see the consensus about reusing window
>>>> operator and
>>>> > > keep
>>>> > > > >> the
>>>> > > > >> > API design of localKeyBy. But I think we should definitely
>>>> more
>>>> > > > thoughts
>>>> > > > >> > about this topic.
>>>> > > > >> >
>>>> > > > >> > I also try to loop in Stephan for this discussion.
>>>> > > > >> >
>>>> > > > >> > Best,
>>>> > > > >> > Kurt
>>>> > > > >> >
>>>> > > > >> >
>>>> > > > >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <
>>>> [hidden email] <mailto:[hidden email]>>
>>>> > > > >> wrote:
>>>> > > > >> >
>>>> > > > >> > > Hi all,
>>>> > > > >> > >
>>>> > > > >> > > I am happy we have a wonderful discussion and received many
>>>> > > valuable
>>>> > > > >> > > opinions in the last few days.
>>>> > > > >> > >
>>>> > > > >> > > Now, let me try to summarize what we have reached
>>>> consensus about
>>>> > > > the
>>>> > > > >> > > changes in the design.
>>>> > > > >> > >
>>>> > > > >> > >    - provide a unified abstraction to support two kinds of
>>>> > > > >> > implementation;
>>>> > > > >> > >    - reuse WindowOperator and try to enhance it so that we
>>>> can
>>>> > > make
>>>> > > > >> the
>>>> > > > >> > >    intermediate result of the local aggregation can be
>>>> buffered
>>>> > > and
>>>> > > > >> > > flushed to
>>>> > > > >> > >    support two kinds of implementation;
>>>> > > > >> > >    - keep the API design of localKeyBy, but declare the
>>>> disabled
>>>> > > > some
>>>> > > > >> > APIs
>>>> > > > >> > >    we cannot support currently, and provide a configurable
>>>> API for
>>>> > > > >> users
>>>> > > > >> > to
>>>> > > > >> > >    choose how to handle intermediate result;
>>>> > > > >> > >
>>>> > > > >> > > The above three points have been updated in the design
>>>> doc. Any
>>>> > > > >> > > questions, please let me know.
>>>> > > > >> > >
>>>> > > > >> > > @Aljoscha Krettek <[hidden email] <mailto:
>>>> [hidden email]>> What do you think? Any
>>>> > > > >> further
>>>> > > > >> > > comments?
>>>> > > > >> > >
>>>> > > > >> > > Best,
>>>> > > > >> > > Vino
>>>> > > > >> > >
>>>> > > > >> > > vino yang <[hidden email] <mailto:
>>>> [hidden email]>> 于2019年6月20日周四 下午2:02写道：
>>>> > > > >> > >
>>>> > > > >> > > > Hi Kurt,
>>>> > > > >> > > >
>>>> > > > >> > > > Thanks for your comments.
>>>> > > > >> > > >
>>>> > > > >> > > > It seems we come to a consensus that we should alleviate
>>>> the
>>>> > > > >> > performance
>>>> > > > >> > > > degraded by data skew with local aggregation. In this
>>>> FLIP, our
>>>> > > > key
>>>> > > > >> > > > solution is to introduce local keyed partition to
>>>> achieve this
>>>> > > > goal.
>>>> > > > >> > > >
>>>> > > > >> > > > I also agree that we can benefit a lot from the usage of
>>>> > > > >> > > > AggregateFunction. In combination with localKeyBy, We
>>>> can easily
>>>> > > > >> use it
>>>> > > > >> > > to
>>>> > > > >> > > > achieve local aggregation:
>>>> > > > >> > > >
>>>> > > > >> > > >    - input.localKeyBy(0).aggregate()
>>>> > > > >> > > >    - input.localKeyBy(0).window().aggregate()
>>>> > > > >> > > >
>>>> > > > >> > > >
>>>> > > > >> > > > I think the only problem here is the choices between
>>>> > > > >> > > >
>>>> > > > >> > > >    - (1) Introducing a new primitive called localKeyBy
>>>> and
>>>> > > > implement
>>>> > > > >> > > >    local aggregation with existing operators, or
>>>> > > > >> > > >    - (2) Introducing an operator called localAggregation
>>>> which
>>>> > > is
>>>> > > > >> > > >    composed of a key selector, a window-like operator,
>>>> and an
>>>> > > > >> aggregate
>>>> > > > >> > > >    function.
>>>> > > > >> > > >
>>>> > > > >> > > >
>>>> > > > >> > > > There may exist some optimization opportunities by
>>>> providing a
>>>> > > > >> > composited
>>>> > > > >> > > > interface for local aggregation. But at the same time,
>>>> in my
>>>> > > > >> opinion,
>>>> > > > >> > we
>>>> > > > >> > > > lose flexibility (Or we need certain efforts to achieve
>>>> the same
>>>> > > > >> > > > flexibility).
>>>> > > > >> > > >
>>>> > > > >> > > > As said in the previous mails, we have many use cases
>>>> where the
>>>> > > > >> > > > aggregation is very complicated and cannot be performed
>>>> with
>>>> > > > >> > > > AggregateFunction. For example, users may perform
>>>> windowed
>>>> > > > >> aggregations
>>>> > > > >> > > > according to time, data values, or even external storage.
>>>> > > > Typically,
>>>> > > > >> > they
>>>> > > > >> > > > now use KeyedProcessFunction or customized triggers to
>>>> implement
>>>> > > > >> these
>>>> > > > >> > > > aggregations. It's not easy to address data skew in such
>>>> cases
>>>> > > > with
>>>> > > > >> a
>>>> > > > >> > > > composited interface for local aggregation.
>>>> > > > >> > > >
>>>> > > > >> > > > Given that Data Stream API is exactly targeted at these
>>>> cases
>>>> > > > where
>>>> > > > >> the
>>>> > > > >> > > > application logic is very complicated and optimization
>>>> does not
>>>> > > > >> > matter, I
>>>> > > > >> > > > think it's a better choice to provide a relatively
>>>> low-level and
>>>> > > > >> > > canonical
>>>> > > > >> > > > interface.
>>>> > > > >> > > >
>>>> > > > >> > > > The composited interface, on the other side, may be a
>>>> good
>>>> > > choice
>>>> > > > in
>>>> > > > >> > > > declarative interfaces, including SQL and Table API, as
>>>> it
>>>> > > allows
>>>> > > > >> more
>>>> > > > >> > > > optimization opportunities.
>>>> > > > >> > > >
>>>> > > > >> > > > Best,
>>>> > > > >> > > > Vino
>>>> > > > >> > > >
>>>> > > > >> > > >
>>>> > > > >> > > > Kurt Young <[hidden email] <mailto:[hidden email]>>
>>>> 于2019年6月20日周四 上午10:15写道：
>>>> > > > >> > > >
>>>> > > > >> > > >> Hi all,
>>>> > > > >> > > >>
>>>> > > > >> > > >> As vino said in previous emails, I think we should first
>>>> > > discuss
>>>> > > > >> and
>>>> > > > >> > > >> decide
>>>> > > > >> > > >> what kind of use cases this FLIP want to
>>>> > > > >> > > >> resolve, and what the API should look like. From my
>>>> side, I
>>>> > > think
>>>> > > > >> this
>>>> > > > >> > > is
>>>> > > > >> > > >> probably the root cause of current divergence.
>>>> > > > >> > > >>
>>>> > > > >> > > >> My understand is (from the FLIP title and motivation
>>>> section of
>>>> > > > the
>>>> > > > >> > > >> document), we want to have a proper support of
>>>> > > > >> > > >> local aggregation, or pre aggregation. This is not a
>>>> very new
>>>> > > > idea,
>>>> > > > >> > most
>>>> > > > >> > > >> SQL engine already did this improvement. And
>>>> > > > >> > > >> the core concept about this is, there should be an
>>>> > > > >> AggregateFunction,
>>>> > > > >> > no
>>>> > > > >> > > >> matter it's a Flink runtime's AggregateFunction or
>>>> > > > >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation
>>>> have
>>>> > > concept
>>>> > > > >> of
>>>> > > > >> > > >> intermediate data type, sometimes we call it ACC.
>>>> > > > >> > > >> I quickly went through the POC piotr did before [1], it
>>>> also
>>>> > > > >> directly
>>>> > > > >> > > uses
>>>> > > > >> > > >> AggregateFunction.
>>>> > > > >> > > >>
>>>> > > > >> > > >> But the thing is, after reading the design of this
>>>> FLIP, I
>>>> > > can't
>>>> > > > >> help
>>>> > > > >> > > >> myself feeling that this FLIP is not targeting to have
>>>> a proper
>>>> > > > >> > > >> local aggregation support. It actually want to introduce
>>>> > > another
>>>> > > > >> > > concept:
>>>> > > > >> > > >> LocalKeyBy, and how to split and merge local key groups,
>>>> > > > >> > > >> and how to properly support state on local key. Local
>>>> > > aggregation
>>>> > > > >> just
>>>> > > > >> > > >> happened to be one possible use case of LocalKeyBy.
>>>> > > > >> > > >> But it lacks supporting the essential concept of local
>>>> > > > aggregation,
>>>> > > > >> > > which
>>>> > > > >> > > >> is intermediate data type. Without this, I really don't
>>>> thing
>>>> > > > >> > > >> it is a good fit of local aggregation.
>>>> > > > >> > > >>
>>>> > > > >> > > >> Here I want to make sure of the scope or the goal about
>>>> this
>>>> > > > FLIP,
>>>> > > > >> do
>>>> > > > >> > we
>>>> > > > >> > > >> want to have a proper local aggregation engine, or we
>>>> > > > >> > > >> just want to introduce a new concept called LocalKeyBy?
>>>> > > > >> > > >>
>>>> > > > >> > > >> [1]: https://github.com/apache/flink/pull/4626 <
>>>> https://github.com/apache/flink/pull/4626>
>>>> > > > >> > > >>
>>>> > > > >> > > >> Best,
>>>> > > > >> > > >> Kurt
>>>> > > > >> > > >>
>>>> > > > >> > > >>
>>>> > > > >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <
>>>> > > [hidden email] <mailto:[hidden email]>
>>>> > > > >
>>>> > > > >> > > wrote:
>>>> > > > >> > > >>
>>>> > > > >> > > >> > Hi Hequn,
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > Thanks for your comments!
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > I agree that allowing local aggregation reusing
>>>> window API
>>>> > > and
>>>> > > > >> > > refining
>>>> > > > >> > > >> > window operator to make it match both requirements
>>>> (come from
>>>> > > > our
>>>> > > > >> > and
>>>> > > > >> > > >> Kurt)
>>>> > > > >> > > >> > is a good decision!
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > Concerning your questions:
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > 1. The result of
>>>> input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>>>> > > may
>>>> > > > >> be
>>>> > > > >> > > >> > meaningless.
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > Yes, it does not make sense in most cases. However, I
>>>> also
>>>> > > want
>>>> > > > >> to
>>>> > > > >> > > note
>>>> > > > >> > > >> > users should know the right semantics of localKeyBy
>>>> and use
>>>> > > it
>>>> > > > >> > > >> correctly.
>>>> > > > >> > > >> > Because this issue also exists for the global keyBy,
>>>> consider
>>>> > > > >> this
>>>> > > > >> > > >> example:
>>>> > > > >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is
>>>> also
>>>> > > > >> > meaningless.
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > 2. About the semantics of
>>>> > > > >> > > >> >
>>>> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > Good catch! I agree with you that it's not good to
>>>> enable all
>>>> > > > >> > > >> > functionalities for localKeyBy from KeyedStream.
>>>> > > > >> > > >> > Currently, We do not support some APIs such as
>>>> > > > >> > > >> > connect/join/intervalJoin/coGroup. This is due to
>>>> that we
>>>> > > force
>>>> > > > >> the
>>>> > > > >> > > >> > operators on LocalKeyedStreams chained with the
>>>> inputs.
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > Best,
>>>> > > > >> > > >> > Vino
>>>> > > > >> > > >> >
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > Hequn Cheng <[hidden email] <mailto:
>>>> [hidden email]>> 于2019年6月19日周三 下午3:42写道：
>>>> > > > >> > > >> >
>>>> > > > >> > > >> > > Hi,
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > Thanks a lot for your great discussion and great to
>>>> see
>>>> > > that
>>>> > > > >> some
>>>> > > > >> > > >> > agreement
>>>> > > > >> > > >> > > has been reached on the "local aggregate engine"!
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > ===> Considering the abstract engine,
>>>> > > > >> > > >> > > I'm thinking is it valuable for us to extend the
>>>> current
>>>> > > > >> window to
>>>> > > > >> > > >> meet
>>>> > > > >> > > >> > > both demands raised by Kurt and Vino? There are some
>>>> > > benefits
>>>> > > > >> we
>>>> > > > >> > can
>>>> > > > >> > > >> get:
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > 1. The interfaces of the window are complete and
>>>> clear.
>>>> > > With
>>>> > > > >> > > windows,
>>>> > > > >> > > >> we
>>>> > > > >> > > >> > > can define a lot of ways to split the data and
>>>> perform
>>>> > > > >> different
>>>> > > > >> > > >> > > computations.
>>>> > > > >> > > >> > > 2. We can also leverage the window to do miniBatch
>>>> for the
>>>> > > > >> global
>>>> > > > >> > > >> > > aggregation, i.e, we can use the window to bundle
>>>> data
>>>> > > belong
>>>> > > > >> to
>>>> > > > >> > the
>>>> > > > >> > > >> same
>>>> > > > >> > > >> > > key, for every bundle we only need to read and
>>>> write once
>>>> > > > >> state.
>>>> > > > >> > > This
>>>> > > > >> > > >> can
>>>> > > > >> > > >> > > greatly reduce state IO and improve performance.
>>>> > > > >> > > >> > > 3. A lot of other use cases can also benefit from
>>>> the
>>>> > > window
>>>> > > > >> base
>>>> > > > >> > on
>>>> > > > >> > > >> > memory
>>>> > > > >> > > >> > > or stateless.
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > ===> As for the API,
>>>> > > > >> > > >> > > I think it is good to make our API more flexible.
>>>> However,
>>>> > > we
>>>> > > > >> may
>>>> > > > >> > > >> need to
>>>> > > > >> > > >> > > make our API meaningful.
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > Take my previous reply as an example,
>>>> > > > >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The
>>>> result may
>>>> > > be
>>>> > > > >> > > >> > meaningless.
>>>> > > > >> > > >> > > Another example I find is the intervalJoin, e.g.,
>>>> > > > >> > > >> > >
>>>> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
>>>> > > > >> this
>>>> > > > >> > > >> case, it
>>>> > > > >> > > >> > > will bring problems if input1 and input2 share
>>>> different
>>>> > > > >> > > parallelism.
>>>> > > > >> > > >> We
>>>> > > > >> > > >> > > don't know which input should the join chained
>>>> with? Even
>>>> > > if
>>>> > > > >> they
>>>> > > > >> > > >> share
>>>> > > > >> > > >> > the
>>>> > > > >> > > >> > > same parallelism, it's hard to tell what the join
>>>> is doing.
>>>> > > > >> There
>>>> > > > >> > > are
>>>> > > > >> > > >> > maybe
>>>> > > > >> > > >> > > some other problems.
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > From this point of view, it's at least not good to
>>>> enable
>>>> > > all
>>>> > > > >> > > >> > > functionalities for localKeyBy from KeyedStream?
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > Great to also have your opinions.
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > Best, Hequn
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
>>>> > > > >> [hidden email] <mailto:[hidden email]>
>>>> > > > >> > >
>>>> > > > >> > > >> > wrote:
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > > Hi Kurt and Piotrek,
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > > > Thanks for your comments.
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > > > I agree that we can provide a better abstraction
>>>> to be
>>>> > > > >> > compatible
>>>> > > > >> > > >> with
>>>> > > > >> > > >> > > two
>>>> > > > >> > > >> > > > different implementations.
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > > > First of all, I think we should consider what
>>>> kind of
>>>> > > > >> scenarios
>>>> > > > >> > we
>>>> > > > >> > > >> need
>>>> > > > >> > > >> > > to
>>>> > > > >> > > >> > > > support in *API* level?
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > > > We have some use cases which need to a customized
>>>> > > > aggregation
>>>> > > > >> > > >> through
>>>> > > > >> > > >> > > > KeyedProcessFunction, (in the usage of our
>>>> > > > localKeyBy.window
>>>> > > > >> > they
>>>> > > > >> > > >> can
>>>> > > > >> > > >> > use
>>>> > > > >> > > >> > > > ProcessWindowFunction).
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > > > Shall we support these flexible use scenarios?
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > > > Best,
>>>> > > > >> > > >> > > > Vino
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > > > Kurt Young <[hidden email] <mailto:
>>>> [hidden email]>> 于2019年6月18日周二 下午8:37写道：
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > > > > Hi Piotr,
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > > > Thanks for joining the discussion. Make “local
>>>> > > > aggregation"
>>>> > > > >> > > >> abstract
>>>> > > > >> > > >> > > > enough
>>>> > > > >> > > >> > > > > sounds good to me, we could
>>>> > > > >> > > >> > > > > implement and verify alternative solutions for
>>>> use
>>>> > > cases
>>>> > > > of
>>>> > > > >> > > local
>>>> > > > >> > > >> > > > > aggregation. Maybe we will find both solutions
>>>> > > > >> > > >> > > > > are appropriate for different scenarios.
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > > > Starting from a simple one sounds a practical
>>>> way to
>>>> > > go.
>>>> > > > >> What
>>>> > > > >> > do
>>>> > > > >> > > >> you
>>>> > > > >> > > >> > > > think,
>>>> > > > >> > > >> > > > > vino?
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > > > Best,
>>>> > > > >> > > >> > > > > Kurt
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski <
>>>> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
>>>> > > > >> > > >> > > > > wrote:
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > > > > Hi Kurt and Vino,
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > > > I think there is a trade of hat we need to
>>>> consider
>>>> > > for
>>>> > > > >> the
>>>> > > > >> > > >> local
>>>> > > > >> > > >> > > > > > aggregation.
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > > > Generally speaking I would agree with Kurt
>>>> about
>>>> > > local
>>>> > > > >> > > >> > > aggregation/pre
>>>> > > > >> > > >> > > > > > aggregation not using Flink's state flush the
>>>> > > operator
>>>> > > > >> on a
>>>> > > > >> > > >> > > checkpoint.
>>>> > > > >> > > >> > > > > > Network IO is usually cheaper compared to
>>>> Disks IO.
>>>> > > > This
>>>> > > > >> has
>>>> > > > >> > > >> > however
>>>> > > > >> > > >> > > > > couple
>>>> > > > >> > > >> > > > > > of issues:
>>>> > > > >> > > >> > > > > > 1. It can explode number of in-flight records
>>>> during
>>>> > > > >> > > checkpoint
>>>> > > > >> > > >> > > barrier
>>>> > > > >> > > >> > > > > > alignment, making checkpointing slower and
>>>> decrease
>>>> > > the
>>>> > > > >> > actual
>>>> > > > >> > > >> > > > > throughput.
>>>> > > > >> > > >> > > > > > 2. This trades Disks IO on the local
>>>> aggregation
>>>> > > > machine
>>>> > > > >> > with
>>>> > > > >> > > >> CPU
>>>> > > > >> > > >> > > (and
>>>> > > > >> > > >> > > > > > Disks IO in case of RocksDB) on the final
>>>> aggregation
>>>> > > > >> > machine.
>>>> > > > >> > > >> This
>>>> > > > >> > > >> > > is
>>>> > > > >> > > >> > > > > > fine, as long there is no huge data skew. If
>>>> there is
>>>> > > > >> only a
>>>> > > > >> > > >> > handful
>>>> > > > >> > > >> > > > (or
>>>> > > > >> > > >> > > > > > even one single) hot keys, it might be better
>>>> to keep
>>>> > > > the
>>>> > > > >> > > >> > persistent
>>>> > > > >> > > >> > > > > state
>>>> > > > >> > > >> > > > > > in the LocalAggregationOperator to offload
>>>> final
>>>> > > > >> aggregation
>>>> > > > >> > > as
>>>> > > > >> > > >> > much
>>>> > > > >> > > >> > > as
>>>> > > > >> > > >> > > > > > possible.
>>>> > > > >> > > >> > > > > > 3. With frequent checkpointing local
>>>> aggregation
>>>> > > > >> > effectiveness
>>>> > > > >> > > >> > would
>>>> > > > >> > > >> > > > > > degrade.
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > > > I assume Kurt is correct, that in your use
>>>> cases
>>>> > > > >> stateless
>>>> > > > >> > > >> operator
>>>> > > > >> > > >> > > was
>>>> > > > >> > > >> > > > > > behaving better, but I could easily see other
>>>> use
>>>> > > cases
>>>> > > > >> as
>>>> > > > >> > > well.
>>>> > > > >> > > >> > For
>>>> > > > >> > > >> > > > > > example someone is already using RocksDB, and
>>>> his job
>>>> > > > is
>>>> > > > >> > > >> > bottlenecked
>>>> > > > >> > > >> > > > on
>>>> > > > >> > > >> > > > > a
>>>> > > > >> > > >> > > > > > single window operator instance because of
>>>> the data
>>>> > > > >> skew. In
>>>> > > > >> > > >> that
>>>> > > > >> > > >> > > case
>>>> > > > >> > > >> > > > > > stateful local aggregation would be probably
>>>> a better
>>>> > > > >> > choice.
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > > > Because of that, I think we should eventually
>>>> provide
>>>> > > > >> both
>>>> > > > >> > > >> versions
>>>> > > > >> > > >> > > and
>>>> > > > >> > > >> > > > > in
>>>> > > > >> > > >> > > > > > the initial version we should at least make
>>>> the
>>>> > > “local
>>>> > > > >> > > >> aggregation
>>>> > > > >> > > >> > > > > engine”
>>>> > > > >> > > >> > > > > > abstract enough, that one could easily provide
>>>> > > > different
>>>> > > > >> > > >> > > implementation
>>>> > > > >> > > >> > > > > > strategy.
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > > > Piotrek
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
>>>> > > > [hidden email] <mailto:[hidden email]>
>>>> > > > >> >
>>>> > > > >> > > >> wrote:
>>>> > > > >> > > >> > > > > > >
>>>> > > > >> > > >> > > > > > > Hi,
>>>> > > > >> > > >> > > > > > >
>>>> > > > >> > > >> > > > > > > For the trigger, it depends on what
>>>> operator we
>>>> > > want
>>>> > > > to
>>>> > > > >> > use
>>>> > > > >> > > >> under
>>>> > > > >> > > >> > > the
>>>> > > > >> > > >> > > > > > API.
>>>> > > > >> > > >> > > > > > > If we choose to use window operator,
>>>> > > > >> > > >> > > > > > > we should also use window's trigger.
>>>> However, I
>>>> > > also
>>>> > > > >> think
>>>> > > > >> > > >> reuse
>>>> > > > >> > > >> > > > window
>>>> > > > >> > > >> > > > > > > operator for this scenario may not be
>>>> > > > >> > > >> > > > > > > the best choice. The reasons are the
>>>> following:
>>>> > > > >> > > >> > > > > > >
>>>> > > > >> > > >> > > > > > > 1. As a lot of people already pointed out,
>>>> window
>>>> > > > >> relies
>>>> > > > >> > > >> heavily
>>>> > > > >> > > >> > on
>>>> > > > >> > > >> > > > > state
>>>> > > > >> > > >> > > > > > > and it will definitely effect performance.
>>>> You can
>>>> > > > >> > > >> > > > > > > argue that one can use heap based
>>>> statebackend, but
>>>> > > > >> this
>>>> > > > >> > > will
>>>> > > > >> > > >> > > > introduce
>>>> > > > >> > > >> > > > > > > extra coupling. Especially we have a chance
>>>> to
>>>> > > > >> > > >> > > > > > > design a pure stateless operator.
>>>> > > > >> > > >> > > > > > > 2. The window operator is *the most*
>>>> complicated
>>>> > > > >> operator
>>>> > > > >> > > >> Flink
>>>> > > > >> > > >> > > > > currently
>>>> > > > >> > > >> > > > > > > have. Maybe we only need to pick a subset of
>>>> > > > >> > > >> > > > > > > window operator to achieve the goal, but
>>>> once the
>>>> > > > user
>>>> > > > >> > wants
>>>> > > > >> > > >> to
>>>> > > > >> > > >> > > have
>>>> > > > >> > > >> > > > a
>>>> > > > >> > > >> > > > > > deep
>>>> > > > >> > > >> > > > > > > look at the localAggregation operator, it's
>>>> still
>>>> > > > >> > > >> > > > > > > hard to find out what's going on under the
>>>> window
>>>> > > > >> > operator.
>>>> > > > >> > > >> For
>>>> > > > >> > > >> > > > > > simplicity,
>>>> > > > >> > > >> > > > > > > I would also recommend we introduce a
>>>> dedicated
>>>> > > > >> > > >> > > > > > > lightweight operator, which also much
>>>> easier for a
>>>> > > > >> user to
>>>> > > > >> > > >> learn
>>>> > > > >> > > >> > > and
>>>> > > > >> > > >> > > > > use.
>>>> > > > >> > > >> > > > > > >
>>>> > > > >> > > >> > > > > > > For your question about increasing the
>>>> burden in
>>>> > > > >> > > >> > > > > > >
>>>> `StreamOperator::prepareSnapshotPreBarrier()`, the
>>>> > > > only
>>>> > > > >> > > thing
>>>> > > > >> > > >> > this
>>>> > > > >> > > >> > > > > > function
>>>> > > > >> > > >> > > > > > > need
>>>> > > > >> > > >> > > > > > > to do is output all the partial results,
>>>> it's
>>>> > > purely
>>>> > > > >> cpu
>>>> > > > >> > > >> > workload,
>>>> > > > >> > > >> > > > not
>>>> > > > >> > > >> > > > > > > introducing any IO. I want to point out
>>>> that even
>>>> > > if
>>>> > > > we
>>>> > > > >> > have
>>>> > > > >> > > >> this
>>>> > > > >> > > >> > > > > > > cost, we reduced another barrier align cost
>>>> of the
>>>> > > > >> > operator,
>>>> > > > >> > > >> > which
>>>> > > > >> > > >> > > is
>>>> > > > >> > > >> > > > > the
>>>> > > > >> > > >> > > > > > > sync flush stage of the state, if you
>>>> introduced
>>>> > > > state.
>>>> > > > >> > This
>>>> > > > >> > > >> > > > > > > flush actually will introduce disk IO, and
>>>> I think
>>>> > > > it's
>>>> > > > >> > > >> worthy to
>>>> > > > >> > > >> > > > > > exchange
>>>> > > > >> > > >> > > > > > > this cost with purely CPU workload. And we
>>>> do have
>>>> > > > some
>>>> > > > >> > > >> > > > > > > observations about these two behavior (as i
>>>> said
>>>> > > > >> before,
>>>> > > > >> > we
>>>> > > > >> > > >> > > actually
>>>> > > > >> > > >> > > > > > > implemented both solutions), the stateless
>>>> one
>>>> > > > actually
>>>> > > > >> > > >> performs
>>>> > > > >> > > >> > > > > > > better both in performance and barrier
>>>> align time.
>>>> > > > >> > > >> > > > > > >
>>>> > > > >> > > >> > > > > > > Best,
>>>> > > > >> > > >> > > > > > > Kurt
>>>> > > > >> > > >> > > > > > >
>>>> > > > >> > > >> > > > > > >
>>>> > > > >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
>>>> > > > >> > > >> [hidden email] <mailto:[hidden email]>
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> > > > > wrote:
>>>> > > > >> > > >> > > > > > >
>>>> > > > >> > > >> > > > > > >> Hi Kurt,
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> Thanks for your example. Now, it looks more
>>>> > > clearly
>>>> > > > >> for
>>>> > > > >> > me.
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> From your example code snippet, I saw the
>>>> > > > >> localAggregate
>>>> > > > >> > > API
>>>> > > > >> > > >> has
>>>> > > > >> > > >> > > > three
>>>> > > > >> > > >> > > > > > >> parameters:
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >>   1. key field
>>>> > > > >> > > >> > > > > > >>   2. PartitionAvg
>>>> > > > >> > > >> > > > > > >>   3. CountTrigger: Does this trigger comes
>>>> from
>>>> > > > window
>>>> > > > >> > > >> package?
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> I will compare our and your design from
>>>> API and
>>>> > > > >> operator
>>>> > > > >> > > >> level:
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> *From the API level:*
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> As I replied to @dianfu in the old email
>>>> > > thread,[1]
>>>> > > > >> the
>>>> > > > >> > > >> Window
>>>> > > > >> > > >> > API
>>>> > > > >> > > >> > > > can
>>>> > > > >> > > >> > > > > > >> provide the second and the third parameter
>>>> right
>>>> > > > now.
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> If you reuse specified interface or class,
>>>> such as
>>>> > > > >> > > *Trigger*
>>>> > > > >> > > >> or
>>>> > > > >> > > >> > > > > > >> *CounterTrigger* provided by window
>>>> package, but
>>>> > > do
>>>> > > > >> not
>>>> > > > >> > use
>>>> > > > >> > > >> > window
>>>> > > > >> > > >> > > > > API,
>>>> > > > >> > > >> > > > > > >> it's not reasonable.
>>>> > > > >> > > >> > > > > > >> And if you do not reuse these interface or
>>>> class,
>>>> > > > you
>>>> > > > >> > would
>>>> > > > >> > > >> need
>>>> > > > >> > > >> > > to
>>>> > > > >> > > >> > > > > > >> introduce more things however they are
>>>> looked
>>>> > > > similar
>>>> > > > >> to
>>>> > > > >> > > the
>>>> > > > >> > > >> > > things
>>>> > > > >> > > >> > > > > > >> provided by window package.
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> The window package has provided several
>>>> types of
>>>> > > the
>>>> > > > >> > window
>>>> > > > >> > > >> and
>>>> > > > >> > > >> > > many
>>>> > > > >> > > >> > > > > > >> triggers and let users customize it.
>>>> What's more,
>>>> > > > the
>>>> > > > >> > user
>>>> > > > >> > > is
>>>> > > > >> > > >> > more
>>>> > > > >> > > >> > > > > > familiar
>>>> > > > >> > > >> > > > > > >> with Window API.
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> This is the reason why we just provide
>>>> localKeyBy
>>>> > > > API
>>>> > > > >> and
>>>> > > > >> > > >> reuse
>>>> > > > >> > > >> > > the
>>>> > > > >> > > >> > > > > > window
>>>> > > > >> > > >> > > > > > >> API. It reduces unnecessary components
>>>> such as
>>>> > > > >> triggers
>>>> > > > >> > and
>>>> > > > >> > > >> the
>>>> > > > >> > > >> > > > > > mechanism
>>>> > > > >> > > >> > > > > > >> of buffer (based on count num or time).
>>>> > > > >> > > >> > > > > > >> And it has a clear and easy to understand
>>>> > > semantics.
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> *From the operator level:*
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> We reused window operator, so we can get
>>>> all the
>>>> > > > >> benefits
>>>> > > > >> > > >> from
>>>> > > > >> > > >> > > state
>>>> > > > >> > > >> > > > > and
>>>> > > > >> > > >> > > > > > >> checkpoint.
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> From your design, you named the operator
>>>> under
>>>> > > > >> > > localAggregate
>>>> > > > >> > > >> > API
>>>> > > > >> > > >> > > > is a
>>>> > > > >> > > >> > > > > > >> *stateless* operator. IMO, it is still a
>>>> state, it
>>>> > > > is
>>>> > > > >> > just
>>>> > > > >> > > >> not
>>>> > > > >> > > >> > > Flink
>>>> > > > >> > > >> > > > > > >> managed state.
>>>> > > > >> > > >> > > > > > >> About the memory buffer (I think it's
>>>> still not
>>>> > > very
>>>> > > > >> > clear,
>>>> > > > >> > > >> if
>>>> > > > >> > > >> > you
>>>> > > > >> > > >> > > > > have
>>>> > > > >> > > >> > > > > > >> time, can you give more detail information
>>>> or
>>>> > > answer
>>>> > > > >> my
>>>> > > > >> > > >> > > questions),
>>>> > > > >> > > >> > > > I
>>>> > > > >> > > >> > > > > > have
>>>> > > > >> > > >> > > > > > >> some questions:
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >>   - if it just a raw JVM heap memory
>>>> buffer, how
>>>> > > to
>>>> > > > >> > support
>>>> > > > >> > > >> > fault
>>>> > > > >> > > >> > > > > > >>   tolerance, if the job is configured
>>>> EXACTLY-ONCE
>>>> > > > >> > semantic
>>>> > > > >> > > >> > > > guarantee?
>>>> > > > >> > > >> > > > > > >>   - if you thought the memory
>>>> buffer(non-Flink
>>>> > > > state),
>>>> > > > >> > has
>>>> > > > >> > > >> > better
>>>> > > > >> > > >> > > > > > >>   performance. In our design, users can
>>>> also
>>>> > > config
>>>> > > > >> HEAP
>>>> > > > >> > > >> state
>>>> > > > >> > > >> > > > backend
>>>> > > > >> > > >> > > > > > to
>>>> > > > >> > > >> > > > > > >>   provide the performance close to your
>>>> mechanism.
>>>> > > > >> > > >> > > > > > >>   -
>>>> `StreamOperator::prepareSnapshotPreBarrier()`
>>>> > > > >> related
>>>> > > > >> > > to
>>>> > > > >> > > >> the
>>>> > > > >> > > >> > > > > timing
>>>> > > > >> > > >> > > > > > of
>>>> > > > >> > > >> > > > > > >>   snapshot. IMO, the flush action should
>>>> be a
>>>> > > > >> > synchronized
>>>> > > > >> > > >> > action?
>>>> > > > >> > > >> > > > (if
>>>> > > > >> > > >> > > > > > >> not,
>>>> > > > >> > > >> > > > > > >>   please point out my mistake) I still
>>>> think we
>>>> > > > should
>>>> > > > >> > not
>>>> > > > >> > > >> > depend
>>>> > > > >> > > >> > > on
>>>> > > > >> > > >> > > > > the
>>>> > > > >> > > >> > > > > > >>   timing of checkpoint. Checkpoint related
>>>> > > > operations
>>>> > > > >> are
>>>> > > > >> > > >> > inherent
>>>> > > > >> > > >> > > > > > >>   performance sensitive, we should not
>>>> increase
>>>> > > its
>>>> > > > >> > burden
>>>> > > > >> > > >> > > anymore.
>>>> > > > >> > > >> > > > > Our
>>>> > > > >> > > >> > > > > > >>   implementation based on the mechanism of
>>>> Flink's
>>>> > > > >> > > >> checkpoint,
>>>> > > > >> > > >> > > which
>>>> > > > >> > > >> > > > > can
>>>> > > > >> > > >> > > > > > >>   benefit from the asnyc snapshot and
>>>> incremental
>>>> > > > >> > > checkpoint.
>>>> > > > >> > > >> > IMO,
>>>> > > > >> > > >> > > > the
>>>> > > > >> > > >> > > > > > >>   performance is not a problem, and we
>>>> also do not
>>>> > > > >> find
>>>> > > > >> > the
>>>> > > > >> > > >> > > > > performance
>>>> > > > >> > > >> > > > > > >> issue
>>>> > > > >> > > >> > > > > > >>   in our production.
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> [1]:
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> >
>>>> > > > >> > > >>
>>>> > > > >> > >
>>>> > > > >> >
>>>> > > > >>
>>>> > > >
>>>> > >
>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>> <
>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>> >
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> Best,
>>>> > > > >> > > >> > > > > > >> Vino
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >> Kurt Young <[hidden email] <mailto:
>>>> [hidden email]>> 于2019年6月18日周二
>>>> > > > 下午2:27写道：
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > > >>> Yeah, sorry for not expressing myself
>>>> clearly. I
>>>> > > > will
>>>> > > > >> > try
>>>> > > > >> > > to
>>>> > > > >> > > >> > > > provide
>>>> > > > >> > > >> > > > > > more
>>>> > > > >> > > >> > > > > > >>> details to make sure we are on the same
>>>> page.
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>> For DataStream API, it shouldn't be
>>>> optimized
>>>> > > > >> > > automatically.
>>>> > > > >> > > >> > You
>>>> > > > >> > > >> > > > have
>>>> > > > >> > > >> > > > > > to
>>>> > > > >> > > >> > > > > > >>> explicitly call API to do local
>>>> aggregation
>>>> > > > >> > > >> > > > > > >>> as well as the trigger policy of the local
>>>> > > > >> aggregation.
>>>> > > > >> > > Take
>>>> > > > >> > > >> > > > average
>>>> > > > >> > > >> > > > > > for
>>>> > > > >> > > >> > > > > > >>> example, the user program may look like
>>>> this
>>>> > > (just
>>>> > > > a
>>>> > > > >> > > draft):
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>> assuming the input type is
>>>> > > DataStream<Tupl2<String,
>>>> > > > >> > Int>>
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>> ds.localAggregate(
>>>> > > > >> > > >> > > > > > >>>        0,
>>>> > >  //
>>>> > > > >> The
>>>> > > > >> > > local
>>>> > > > >> > > >> > key,
>>>> > > > >> > > >> > > > > which
>>>> > > > >> > > >> > > > > > >> is
>>>> > > > >> > > >> > > > > > >>> the String from Tuple2
>>>> > > > >> > > >> > > > > > >>>        PartitionAvg(1),
>>>>  // The
>>>> > > > >> partial
>>>> > > > >> > > >> > > aggregation
>>>> > > > >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>,
>>>> indicating
>>>> > > > sum
>>>> > > > >> and
>>>> > > > >> > > >> count
>>>> > > > >> > > >> > > > > > >>>        CountTrigger.of(1000L)    //
>>>> Trigger
>>>> > > policy,
>>>> > > > >> note
>>>> > > > >> > > >> this
>>>> > > > >> > > >> > > > should
>>>> > > > >> > > >> > > > > be
>>>> > > > >> > > >> > > > > > >>> best effort, and also be composited with
>>>> time
>>>> > > based
>>>> > > > >> or
>>>> > > > >> > > >> memory
>>>> > > > >> > > >> > > size
>>>> > > > >> > > >> > > > > > based
>>>> > > > >> > > >> > > > > > >>> trigger
>>>> > > > >> > > >> > > > > > >>>    )
>>>>      //
>>>> > > > The
>>>> > > > >> > > return
>>>> > > > >> > > >> > type
>>>> > > > >> > > >> > > > is
>>>> > > > >> > > >> > > > > > >> local
>>>> > > > >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long, Int>>
>>>> > > > >> > > >> > > > > > >>>    .keyBy(0)
>>>>  //
>>>> > > Further
>>>> > > > >> > keyby
>>>> > > > >> > > it
>>>> > > > >> > > >> > with
>>>> > > > >> > > >> > > > > > >> required
>>>> > > > >> > > >> > > > > > >>> key
>>>> > > > >> > > >> > > > > > >>>    .aggregate(1)                      //
>>>> This
>>>> > > will
>>>> > > > >> merge
>>>> > > > >> > > all
>>>> > > > >> > > >> > the
>>>> > > > >> > > >> > > > > > partial
>>>> > > > >> > > >> > > > > > >>> results and get the final average.
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>> (This is only a draft, only trying to
>>>> explain
>>>> > > what
>>>> > > > it
>>>> > > > >> > > looks
>>>> > > > >> > > >> > > like. )
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>> The local aggregate operator can be
>>>> stateless, we
>>>> > > > can
>>>> > > > >> > > keep a
>>>> > > > >> > > >> > > memory
>>>> > > > >> > > >> > > > > > >> buffer
>>>> > > > >> > > >> > > > > > >>> or other efficient data structure to
>>>> improve the
>>>> > > > >> > aggregate
>>>> > > > >> > > >> > > > > performance.
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>> Let me know if you have any other
>>>> questions.
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>> Best,
>>>> > > > >> > > >> > > > > > >>> Kurt
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino yang
>>>> <
>>>> > > > >> > > >> > [hidden email] <mailto:[hidden email]>
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > > > > > wrote:
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>>> Hi Kurt,
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> Thanks for your reply.
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> Actually, I am not against you to raise
>>>> your
>>>> > > > design.
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> From your description before, I just can
>>>> imagine
>>>> > > > >> your
>>>> > > > >> > > >> > high-level
>>>> > > > >> > > >> > > > > > >>>> implementation is about SQL and the
>>>> optimization
>>>> > > > is
>>>> > > > >> > inner
>>>> > > > >> > > >> of
>>>> > > > >> > > >> > the
>>>> > > > >> > > >> > > > > API.
>>>> > > > >> > > >> > > > > > >> Is
>>>> > > > >> > > >> > > > > > >>> it
>>>> > > > >> > > >> > > > > > >>>> automatically? how to give the
>>>> configuration
>>>> > > > option
>>>> > > > >> > about
>>>> > > > >> > > >> > > trigger
>>>> > > > >> > > >> > > > > > >>>> pre-aggregation?
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> Maybe after I get more information, it
>>>> sounds
>>>> > > more
>>>> > > > >> > > >> reasonable.
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> IMO, first of all, it would be better to
>>>> make
>>>> > > your
>>>> > > > >> user
>>>> > > > >> > > >> > > interface
>>>> > > > >> > > >> > > > > > >>> concrete,
>>>> > > > >> > > >> > > > > > >>>> it's the basis of the discussion.
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> For example, can you give an example code
>>>> > > snippet
>>>> > > > to
>>>> > > > >> > > >> introduce
>>>> > > > >> > > >> > > how
>>>> > > > >> > > >> > > > > to
>>>> > > > >> > > >> > > > > > >>> help
>>>> > > > >> > > >> > > > > > >>>> users to process data skew caused by the
>>>> jobs
>>>> > > > which
>>>> > > > >> > built
>>>> > > > >> > > >> with
>>>> > > > >> > > >> > > > > > >> DataStream
>>>> > > > >> > > >> > > > > > >>>> API?
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> If you give more details we can discuss
>>>> further
>>>> > > > >> more. I
>>>> > > > >> > > >> think
>>>> > > > >> > > >> > if
>>>> > > > >> > > >> > > > one
>>>> > > > >> > > >> > > > > > >>> design
>>>> > > > >> > > >> > > > > > >>>> introduces an exact interface and
>>>> another does
>>>> > > > not.
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> The implementation has an obvious
>>>> difference.
>>>> > > For
>>>> > > > >> > > example,
>>>> > > > >> > > >> we
>>>> > > > >> > > >> > > > > > introduce
>>>> > > > >> > > >> > > > > > >>> an
>>>> > > > >> > > >> > > > > > >>>> exact API in DataStream named
>>>> localKeyBy, about
>>>> > > > the
>>>> > > > >> > > >> > > > pre-aggregation
>>>> > > > >> > > >> > > > > we
>>>> > > > >> > > >> > > > > > >>> need
>>>> > > > >> > > >> > > > > > >>>> to define the trigger mechanism of local
>>>> > > > >> aggregation,
>>>> > > > >> > so
>>>> > > > >> > > we
>>>> > > > >> > > >> > find
>>>> > > > >> > > >> > > > > > reused
>>>> > > > >> > > >> > > > > > >>>> window API and operator is a good
>>>> choice. This
>>>> > > is
>>>> > > > a
>>>> > > > >> > > >> reasoning
>>>> > > > >> > > >> > > link
>>>> > > > >> > > >> > > > > > from
>>>> > > > >> > > >> > > > > > >>>> design to implementation.
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> What do you think?
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> Best,
>>>> > > > >> > > >> > > > > > >>>> Vino
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>> Kurt Young <[hidden email] <mailto:
>>>> [hidden email]>> 于2019年6月18日周二
>>>> > > > >> 上午11:58写道：
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>>> Hi Vino,
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>> Now I feel that we may have different
>>>> > > > >> understandings
>>>> > > > >> > > about
>>>> > > > >> > > >> > what
>>>> > > > >> > > >> > > > > kind
>>>> > > > >> > > >> > > > > > >> of
>>>> > > > >> > > >> > > > > > >>>>> problems or improvements you want to
>>>> > > > >> > > >> > > > > > >>>>> resolve. Currently, most of the
>>>> feedback are
>>>> > > > >> focusing
>>>> > > > >> > on
>>>> > > > >> > > >> *how
>>>> > > > >> > > >> > > to
>>>> > > > >> > > >> > > > > do a
>>>> > > > >> > > >> > > > > > >>>>> proper local aggregation to improve
>>>> performance
>>>> > > > >> > > >> > > > > > >>>>> and maybe solving the data skew issue*.
>>>> And my
>>>> > > > gut
>>>> > > > >> > > >> feeling is
>>>> > > > >> > > >> > > > this
>>>> > > > >> > > >> > > > > is
>>>> > > > >> > > >> > > > > > >>>>> exactly what users want at the first
>>>> place,
>>>> > > > >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to
>>>> > > summarize
>>>> > > > >> here,
>>>> > > > >> > > >> please
>>>> > > > >> > > >> > > > > correct
>>>> > > > >> > > >> > > > > > >>> me
>>>> > > > >> > > >> > > > > > >>>> if
>>>> > > > >> > > >> > > > > > >>>>> i'm wrong).
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>> But I still think the design is somehow
>>>> > > diverged
>>>> > > > >> from
>>>> > > > >> > > the
>>>> > > > >> > > >> > goal.
>>>> > > > >> > > >> > > > If
>>>> > > > >> > > >> > > > > we
>>>> > > > >> > > >> > > > > > >>>> want
>>>> > > > >> > > >> > > > > > >>>>> to have an efficient and powerful way to
>>>> > > > >> > > >> > > > > > >>>>> have local aggregation, supporting
>>>> intermedia
>>>> > > > >> result
>>>> > > > >> > > type
>>>> > > > >> > > >> is
>>>> > > > >> > > >> > > > > > >> essential
>>>> > > > >> > > >> > > > > > >>>> IMO.
>>>> > > > >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
>>>> > > > >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction`
>>>> have a
>>>> > > > proper
>>>> > > > >> > > >> support of
>>>> > > > >> > > >> > > > > > >>>> intermediate
>>>> > > > >> > > >> > > > > > >>>>> result type and can do `merge` operation
>>>> > > > >> > > >> > > > > > >>>>> on them.
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>> Now, we have a lightweight alternatives
>>>> which
>>>> > > > >> performs
>>>> > > > >> > > >> well,
>>>> > > > >> > > >> > > and
>>>> > > > >> > > >> > > > > > >> have a
>>>> > > > >> > > >> > > > > > >>>>> nice fit with the local aggregate
>>>> requirements.
>>>> > > > >> > > >> > > > > > >>>>> Mostly importantly,  it's much less
>>>> complex
>>>> > > > because
>>>> > > > >> > it's
>>>> > > > >> > > >> > > > stateless.
>>>> > > > >> > > >> > > > > > >> And
>>>> > > > >> > > >> > > > > > >>>> it
>>>> > > > >> > > >> > > > > > >>>>> can also achieve the similar
>>>> > > multiple-aggregation
>>>> > > > >> > > >> > > > > > >>>>> scenario.
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>> I still not convinced why we shouldn't
>>>> consider
>>>> > > > it
>>>> > > > >> as
>>>> > > > >> > a
>>>> > > > >> > > >> first
>>>> > > > >> > > >> > > > step.
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>> Best,
>>>> > > > >> > > >> > > > > > >>>>> Kurt
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino
>>>> yang <
>>>> > > > >> > > >> > > > [hidden email] <mailto:
>>>> [hidden email]>>
>>>> > > > >> > > >> > > > > > >>>> wrote:
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>>> Hi Kurt,
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> Thanks for your comments.
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> It seems we both implemented local
>>>> aggregation
>>>> > > > >> > feature
>>>> > > > >> > > to
>>>> > > > >> > > >> > > > optimize
>>>> > > > >> > > >> > > > > > >>> the
>>>> > > > >> > > >> > > > > > >>>>>> issue of data skew.
>>>> > > > >> > > >> > > > > > >>>>>> However, IMHO, the API level of
>>>> optimizing
>>>> > > > >> revenue is
>>>> > > > >> > > >> > > different.
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> *Your optimization benefits from Flink
>>>> SQL and
>>>> > > > >> it's
>>>> > > > >> > not
>>>> > > > >> > > >> > user's
>>>> > > > >> > > >> > > > > > >>>> faces.(If
>>>> > > > >> > > >> > > > > > >>>>> I
>>>> > > > >> > > >> > > > > > >>>>>> understand it incorrectly, please
>>>> correct
>>>> > > > this.)*
>>>> > > > >> > > >> > > > > > >>>>>> *Our implementation employs it as an
>>>> > > > optimization
>>>> > > > >> > tool
>>>> > > > >> > > >> API
>>>> > > > >> > > >> > for
>>>> > > > >> > > >> > > > > > >>>>> DataStream,
>>>> > > > >> > > >> > > > > > >>>>>> it just like a local version of the
>>>> keyBy
>>>> > > API.*
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> Based on this, I want to say support
>>>> it as a
>>>> > > > >> > DataStream
>>>> > > > >> > > >> API
>>>> > > > >> > > >> > > can
>>>> > > > >> > > >> > > > > > >>> provide
>>>> > > > >> > > >> > > > > > >>>>>> these advantages:
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear
>>>> semantic
>>>> > > and
>>>> > > > >> it's
>>>> > > > >> > > >> > flexible
>>>> > > > >> > > >> > > > not
>>>> > > > >> > > >> > > > > > >>> only
>>>> > > > >> > > >> > > > > > >>>>> for
>>>> > > > >> > > >> > > > > > >>>>>>   processing data skew but also for
>>>> > > implementing
>>>> > > > >> some
>>>> > > > >> > > >> user
>>>> > > > >> > > >> > > > cases,
>>>> > > > >> > > >> > > > > > >>> for
>>>> > > > >> > > >> > > > > > >>>>>>   example, if we want to calculate the
>>>> > > > >> multiple-level
>>>> > > > >> > > >> > > > aggregation,
>>>> > > > >> > > >> > > > > > >>> we
>>>> > > > >> > > >> > > > > > >>>>> can
>>>> > > > >> > > >> > > > > > >>>>>> do
>>>> > > > >> > > >> > > > > > >>>>>>   multiple-level aggregation in the
>>>> local
>>>> > > > >> > aggregation:
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
>>>> > > > >> > > >> //
>>>> > > > >> > > >> > > here
>>>> > > > >> > > >> > > > > > >> "a"
>>>> > > > >> > > >> > > > > > >>>> is
>>>> > > > >> > > >> > > > > > >>>>> a
>>>> > > > >> > > >> > > > > > >>>>>>   sub-category, while "b" is a
>>>> category, here
>>>> > > we
>>>> > > > >> do
>>>> > > > >> > not
>>>> > > > >> > > >> need
>>>> > > > >> > > >> > > to
>>>> > > > >> > > >> > > > > > >>>> shuffle
>>>> > > > >> > > >> > > > > > >>>>>> data
>>>> > > > >> > > >> > > > > > >>>>>>   in the network.
>>>> > > > >> > > >> > > > > > >>>>>>   - The users of DataStream API will
>>>> benefit
>>>> > > > from
>>>> > > > >> > this.
>>>> > > > >> > > >> > > > Actually,
>>>> > > > >> > > >> > > > > > >> we
>>>> > > > >> > > >> > > > > > >>>>> have
>>>> > > > >> > > >> > > > > > >>>>>>   a lot of scenes need to use
>>>> DataStream API.
>>>> > > > >> > > Currently,
>>>> > > > >> > > >> > > > > > >> DataStream
>>>> > > > >> > > >> > > > > > >>>> API
>>>> > > > >> > > >> > > > > > >>>>> is
>>>> > > > >> > > >> > > > > > >>>>>>   the cornerstone of the physical plan
>>>> of
>>>> > > Flink
>>>> > > > >> SQL.
>>>> > > > >> > > >> With a
>>>> > > > >> > > >> > > > > > >>> localKeyBy
>>>> > > > >> > > >> > > > > > >>>>>> API,
>>>> > > > >> > > >> > > > > > >>>>>>   the optimization of SQL at least may
>>>> use
>>>> > > this
>>>> > > > >> > > optimized
>>>> > > > >> > > >> > API,
>>>> > > > >> > > >> > > > > > >> this
>>>> > > > >> > > >> > > > > > >>>> is a
>>>> > > > >> > > >> > > > > > >>>>>>   further topic.
>>>> > > > >> > > >> > > > > > >>>>>>   - Based on the window operator, our
>>>> state
>>>> > > > would
>>>> > > > >> > > benefit
>>>> > > > >> > > >> > from
>>>> > > > >> > > >> > > > > > >> Flink
>>>> > > > >> > > >> > > > > > >>>>> State
>>>> > > > >> > > >> > > > > > >>>>>>   and checkpoint, we do not need to
>>>> worry
>>>> > > about
>>>> > > > >> OOM
>>>> > > > >> > and
>>>> > > > >> > > >> job
>>>> > > > >> > > >> > > > > > >> failed.
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> Now, about your questions:
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> 1. About our design cannot change the
>>>> data
>>>> > > type
>>>> > > > >> and
>>>> > > > >> > > about
>>>> > > > >> > > >> > the
>>>> > > > >> > > >> > > > > > >>>>>> implementation of average:
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the
>>>> localKeyBy is
>>>> > > > an
>>>> > > > >> API
>>>> > > > >> > > >> > provides
>>>> > > > >> > > >> > > > to
>>>> > > > >> > > >> > > > > > >> the
>>>> > > > >> > > >> > > > > > >>>>> users
>>>> > > > >> > > >> > > > > > >>>>>> who use DataStream API to build their
>>>> jobs.
>>>> > > > >> > > >> > > > > > >>>>>> Users should know its semantics and the
>>>> > > > difference
>>>> > > > >> > with
>>>> > > > >> > > >> > keyBy
>>>> > > > >> > > >> > > > API,
>>>> > > > >> > > >> > > > > > >> so
>>>> > > > >> > > >> > > > > > >>>> if
>>>> > > > >> > > >> > > > > > >>>>>> they want to the average aggregation,
>>>> they
>>>> > > > should
>>>> > > > >> > carry
>>>> > > > >> > > >> > local
>>>> > > > >> > > >> > > > sum
>>>> > > > >> > > >> > > > > > >>>> result
>>>> > > > >> > > >> > > > > > >>>>>> and local count result.
>>>> > > > >> > > >> > > > > > >>>>>> I admit that it will be convenient to
>>>> use
>>>> > > keyBy
>>>> > > > >> > > directly.
>>>> > > > >> > > >> > But
>>>> > > > >> > > >> > > we
>>>> > > > >> > > >> > > > > > >> need
>>>> > > > >> > > >> > > > > > >>>> to
>>>> > > > >> > > >> > > > > > >>>>>> pay a little price when we get some
>>>> benefits.
>>>> > > I
>>>> > > > >> think
>>>> > > > >> > > >> this
>>>> > > > >> > > >> > > price
>>>> > > > >> > > >> > > > > is
>>>> > > > >> > > >> > > > > > >>>>>> reasonable. Considering that the
>>>> DataStream
>>>> > > API
>>>> > > > >> > itself
>>>> > > > >> > > >> is a
>>>> > > > >> > > >> > > > > > >> low-level
>>>> > > > >> > > >> > > > > > >>>> API
>>>> > > > >> > > >> > > > > > >>>>>> (at least for now).
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> 2. About stateless operator and
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> Actually, I have discussed this
>>>> opinion with
>>>> > > > >> @dianfu
>>>> > > > >> > in
>>>> > > > >> > > >> the
>>>> > > > >> > > >> > > old
>>>> > > > >> > > >> > > > > > >> mail
>>>> > > > >> > > >> > > > > > >>>>>> thread. I will copy my opinion from
>>>> there:
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>   - for your design, you still need
>>>> somewhere
>>>> > > to
>>>> > > > >> give
>>>> > > > >> > > the
>>>> > > > >> > > >> > > users
>>>> > > > >> > > >> > > > > > >>>>> configure
>>>> > > > >> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
>>>> > > > >> availability?),
>>>> > > > >> > > >> this
>>>> > > > >> > > >> > > > design
>>>> > > > >> > > >> > > > > > >>>> cannot
>>>> > > > >> > > >> > > > > > >>>>>>   guarantee a deterministic semantics
>>>> (it will
>>>> > > > >> bring
>>>> > > > >> > > >> trouble
>>>> > > > >> > > >> > > for
>>>> > > > >> > > >> > > > > > >>>> testing
>>>> > > > >> > > >> > > > > > >>>>>> and
>>>> > > > >> > > >> > > > > > >>>>>>   debugging).
>>>> > > > >> > > >> > > > > > >>>>>>   - if the implementation depends on
>>>> the
>>>> > > timing
>>>> > > > of
>>>> > > > >> > > >> > checkpoint,
>>>> > > > >> > > >> > > > it
>>>> > > > >> > > >> > > > > > >>>> would
>>>> > > > >> > > >> > > > > > >>>>>>   affect the checkpoint's progress,
>>>> and the
>>>> > > > >> buffered
>>>> > > > >> > > data
>>>> > > > >> > > >> > may
>>>> > > > >> > > >> > > > > > >> cause
>>>> > > > >> > > >> > > > > > >>>> OOM
>>>> > > > >> > > >> > > > > > >>>>>>   issue. In addition, if the operator
>>>> is
>>>> > > > >> stateless,
>>>> > > > >> > it
>>>> > > > >> > > >> can
>>>> > > > >> > > >> > not
>>>> > > > >> > > >> > > > > > >>> provide
>>>> > > > >> > > >> > > > > > >>>>>> fault
>>>> > > > >> > > >> > > > > > >>>>>>   tolerance.
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> Best,
>>>> > > > >> > > >> > > > > > >>>>>> Vino
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>> Kurt Young <[hidden email] <mailto:
>>>> [hidden email]>> 于2019年6月18日周二
>>>> > > > >> > 上午9:22写道：
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>> Hi Vino,
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the
>>>> general
>>>> > > > idea
>>>> > > > >> and
>>>> > > > >> > > IMO
>>>> > > > >> > > >> > it's
>>>> > > > >> > > >> > > > > > >> very
>>>> > > > >> > > >> > > > > > >>>>> useful
>>>> > > > >> > > >> > > > > > >>>>>>> feature.
>>>> > > > >> > > >> > > > > > >>>>>>> But after reading through the
>>>> document, I
>>>> > > feel
>>>> > > > >> that
>>>> > > > >> > we
>>>> > > > >> > > >> may
>>>> > > > >> > > >> > > over
>>>> > > > >> > > >> > > > > > >>>> design
>>>> > > > >> > > >> > > > > > >>>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>> required
>>>> > > > >> > > >> > > > > > >>>>>>> operator for proper local
>>>> aggregation. The
>>>> > > main
>>>> > > > >> > reason
>>>> > > > >> > > >> is
>>>> > > > >> > > >> > we
>>>> > > > >> > > >> > > > want
>>>> > > > >> > > >> > > > > > >>> to
>>>> > > > >> > > >> > > > > > >>>>>> have a
>>>> > > > >> > > >> > > > > > >>>>>>> clear definition and behavior about
>>>> the
>>>> > > "local
>>>> > > > >> keyed
>>>> > > > >> > > >> state"
>>>> > > > >> > > >> > > > which
>>>> > > > >> > > >> > > > > > >>> in
>>>> > > > >> > > >> > > > > > >>>> my
>>>> > > > >> > > >> > > > > > >>>>>>> opinion is not
>>>> > > > >> > > >> > > > > > >>>>>>> necessary for local aggregation, at
>>>> least for
>>>> > > > >> start.
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>> Another issue I noticed is the local
>>>> key by
>>>> > > > >> operator
>>>> > > > >> > > >> cannot
>>>> > > > >> > > >> > > > > > >> change
>>>> > > > >> > > >> > > > > > >>>>>> element
>>>> > > > >> > > >> > > > > > >>>>>>> type, it will
>>>> > > > >> > > >> > > > > > >>>>>>> also restrict a lot of use cases
>>>> which can be
>>>> > > > >> > benefit
>>>> > > > >> > > >> from
>>>> > > > >> > > >> > > > local
>>>> > > > >> > > >> > > > > > >>>>>>> aggregation, like "average".
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and
>>>> the only
>>>> > > > >> thing
>>>> > > > >> > > >> need to
>>>> > > > >> > > >> > > be
>>>> > > > >> > > >> > > > > > >> done
>>>> > > > >> > > >> > > > > > >>>> is
>>>> > > > >> > > >> > > > > > >>>>>>> introduce
>>>> > > > >> > > >> > > > > > >>>>>>> a stateless lightweight operator
>>>> which is
>>>> > > > >> *chained*
>>>> > > > >> > > >> before
>>>> > > > >> > > >> > > > > > >>> `keyby()`.
>>>> > > > >> > > >> > > > > > >>>>> The
>>>> > > > >> > > >> > > > > > >>>>>>> operator will flush all buffered
>>>> > > > >> > > >> > > > > > >>>>>>> elements during
>>>> > > > >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
>>>> > > > >> > > >> > > > and
>>>> > > > >> > > >> > > > > > >>>> make
>>>> > > > >> > > >> > > > > > >>>>>>> himself stateless.
>>>> > > > >> > > >> > > > > > >>>>>>> By the way, in the earlier version we
>>>> also
>>>> > > did
>>>> > > > >> the
>>>> > > > >> > > >> similar
>>>> > > > >> > > >> > > > > > >> approach
>>>> > > > >> > > >> > > > > > >>>> by
>>>> > > > >> > > >> > > > > > >>>>>>> introducing a stateful
>>>> > > > >> > > >> > > > > > >>>>>>> local aggregation operator but it's
>>>> not
>>>> > > > >> performed as
>>>> > > > >> > > >> well
>>>> > > > >> > > >> > as
>>>> > > > >> > > >> > > > the
>>>> > > > >> > > >> > > > > > >>>> later
>>>> > > > >> > > >> > > > > > >>>>>> one,
>>>> > > > >> > > >> > > > > > >>>>>>> and also effect the barrie
>>>> > > > >> > > >> > > > > > >>>>>>> alignment time. The later one is
>>>> fairly
>>>> > > simple
>>>> > > > >> and
>>>> > > > >> > > more
>>>> > > > >> > > >> > > > > > >> efficient.
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>> I would highly suggest you to
>>>> consider to
>>>> > > have
>>>> > > > a
>>>> > > > >> > > >> stateless
>>>> > > > >> > > >> > > > > > >> approach
>>>> > > > >> > > >> > > > > > >>>> at
>>>> > > > >> > > >> > > > > > >>>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>> first step.
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>> Best,
>>>> > > > >> > > >> > > > > > >>>>>>> Kurt
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark
>>>> Wu <
>>>> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
>>>> > > > >> > > >> > > > > > >> wrote:
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>> Hi Vino,
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>> Regarding to the
>>>> "input.keyBy(0).sum(1)" vs
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> >
>>>> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
>>>> > > > >> > > >> > > > > > >> have
>>>> > > > >> > > >> > > > > > >>>> you
>>>> > > > >> > > >> > > > > > >>>>>>> done
>>>> > > > >> > > >> > > > > > >>>>>>>> some benchmark?
>>>> > > > >> > > >> > > > > > >>>>>>>> Because I'm curious about how much
>>>> > > performance
>>>> > > > >> > > >> improvement
>>>> > > > >> > > >> > > can
>>>> > > > >> > > >> > > > > > >> we
>>>> > > > >> > > >> > > > > > >>>> get
>>>> > > > >> > > >> > > > > > >>>>>> by
>>>> > > > >> > > >> > > > > > >>>>>>>> using count window as the local
>>>> operator.
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>> Best,
>>>> > > > >> > > >> > > > > > >>>>>>>> Jark
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino
>>>> yang <
>>>> > > > >> > > >> > > > [hidden email] <mailto:
>>>> [hidden email]>
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>>>> wrote:
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> Hi Hequn,
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to
>>>> > > provide a
>>>> > > > >> tool
>>>> > > > >> > > >> which
>>>> > > > >> > > >> > > can
>>>> > > > >> > > >> > > > > > >>> let
>>>> > > > >> > > >> > > > > > >>>>>> users
>>>> > > > >> > > >> > > > > > >>>>>>> do
>>>> > > > >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The
>>>> behavior
>>>> > > of
>>>> > > > >> the
>>>> > > > >> > > >> > > > > > >>> pre-aggregation
>>>> > > > >> > > >> > > > > > >>>>> is
>>>> > > > >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> So the three cases are different, I
>>>> will
>>>> > > > >> describe
>>>> > > > >> > > them
>>>> > > > >> > > >> > one
>>>> > > > >> > > >> > > by
>>>> > > > >> > > >> > > > > > >>>> one:
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the result is
>>>> event-driven,
>>>> > > > each
>>>> > > > >> > > event
>>>> > > > >> > > >> can
>>>> > > > >> > > >> > > > > > >>> produce
>>>> > > > >> > > >> > > > > > >>>>> one
>>>> > > > >> > > >> > > > > > >>>>>>> sum
>>>> > > > >> > > >> > > > > > >>>>>>>>> aggregation result and it is the
>>>> latest one
>>>> > > > >> from
>>>> > > > >> > the
>>>> > > > >> > > >> > source
>>>> > > > >> > > >> > > > > > >>>> start.*
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> 2.
>>>> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may
>>>> have a
>>>> > > > >> problem, it
>>>> > > > >> > > >> would
>>>> > > > >> > > >> > do
>>>> > > > >> > > >> > > > > > >> the
>>>> > > > >> > > >> > > > > > >>>>> local
>>>> > > > >> > > >> > > > > > >>>>>>> sum
>>>> > > > >> > > >> > > > > > >>>>>>>>> aggregation and will produce the
>>>> latest
>>>> > > > partial
>>>> > > > >> > > result
>>>> > > > >> > > >> > from
>>>> > > > >> > > >> > > > > > >> the
>>>> > > > >> > > >> > > > > > >>>>>> source
>>>> > > > >> > > >> > > > > > >>>>>>>>> start for every event. *
>>>> > > > >> > > >> > > > > > >>>>>>>>> *These latest partial results from
>>>> the same
>>>> > > > key
>>>> > > > >> > are
>>>> > > > >> > > >> > hashed
>>>> > > > >> > > >> > > to
>>>> > > > >> > > >> > > > > > >>> one
>>>> > > > >> > > >> > > > > > >>>>>> node
>>>> > > > >> > > >> > > > > > >>>>>>> to
>>>> > > > >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
>>>> > > > >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it
>>>> > > received
>>>> > > > >> > > multiple
>>>> > > > >> > > >> > > partial
>>>> > > > >> > > >> > > > > > >>>>> results
>>>> > > > >> > > >> > > > > > >>>>>>>> (they
>>>> > > > >> > > >> > > > > > >>>>>>>>> are all calculated from the source
>>>> start)
>>>> > > and
>>>> > > > >> sum
>>>> > > > >> > > them
>>>> > > > >> > > >> > will
>>>> > > > >> > > >> > > > > > >> get
>>>> > > > >> > > >> > > > > > >>>> the
>>>> > > > >> > > >> > > > > > >>>>>>> wrong
>>>> > > > >> > > >> > > > > > >>>>>>>>> result.*
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> 3.
>>>> > > > >> > > >> > >
>>>> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a
>>>> partial
>>>> > > > >> > > aggregation
>>>> > > > >> > > >> > > result
>>>> > > > >> > > >> > > > > > >>> for
>>>> > > > >> > > >> > > > > > >>>>>> the 5
>>>> > > > >> > > >> > > > > > >>>>>>>>> records in the count window. The
>>>> partial
>>>> > > > >> > aggregation
>>>> > > > >> > > >> > > results
>>>> > > > >> > > >> > > > > > >>> from
>>>> > > > >> > > >> > > > > > >>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>>> same
>>>> > > > >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> So the first case and the third
>>>> case can
>>>> > > get
>>>> > > > >> the
>>>> > > > >> > > >> *same*
>>>> > > > >> > > >> > > > > > >> result,
>>>> > > > >> > > >> > > > > > >>>> the
>>>> > > > >> > > >> > > > > > >>>>>>>>> difference is the output-style and
>>>> the
>>>> > > > latency.
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key
>>>> API is
>>>> > > just
>>>> > > > >> an
>>>> > > > >> > > >> > > optimization
>>>> > > > >> > > >> > > > > > >>>> API.
>>>> > > > >> > > >> > > > > > >>>>> We
>>>> > > > >> > > >> > > > > > >>>>>>> do
>>>> > > > >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but the
>>>> user
>>>> > > has
>>>> > > > to
>>>> > > > >> > > >> > understand
>>>> > > > >> > > >> > > > > > >> its
>>>> > > > >> > > >> > > > > > >>>>>>> semantics
>>>> > > > >> > > >> > > > > > >>>>>>>>> and use it correctly.
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> Best,
>>>> > > > >> > > >> > > > > > >>>>>>>>> Vino
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]
>>>> <mailto:[hidden email]>>
>>>> > > > >> 于2019年6月17日周一
>>>> > > > >> > > >> > 下午4:18写道：
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>> Hi Vino,
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think
>>>> it is a
>>>> > > > very
>>>> > > > >> > good
>>>> > > > >> > > >> > > feature!
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is
>>>> the
>>>> > > > semantics
>>>> > > > >> > for
>>>> > > > >> > > >> the
>>>> > > > >> > > >> > > > > > >>>>>> `localKeyBy`.
>>>> > > > >> > > >> > > > > > >>>>>>>> From
>>>> > > > >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy` API
>>>> returns
>>>> > > > an
>>>> > > > >> > > >> instance
>>>> > > > >> > > >> > of
>>>> > > > >> > > >> > > > > > >>>>>>> `KeyedStream`
>>>> > > > >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so
>>>> in this
>>>> > > > case,
>>>> > > > >> > > what's
>>>> > > > >> > > >> > the
>>>> > > > >> > > >> > > > > > >>>>> semantics
>>>> > > > >> > > >> > > > > > >>>>>>> for
>>>> > > > >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will
>>>> the
>>>> > > > >> following
>>>> > > > >> > > code
>>>> > > > >> > > >> > share
>>>> > > > >> > > >> > > > > > >>> the
>>>> > > > >> > > >> > > > > > >>>>> same
>>>> > > > >> > > >> > > > > > >>>>>>>>> result?
>>>> > > > >> > > >> > > > > > >>>>>>>>>> and what're the differences
>>>> between them?
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
>>>> > > > >> > > >> > > > > > >>>>>>>>>> 2.
>>>> > > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>>>> > > > >> > > >> > > > > > >>>>>>>>>> 3.
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add
>>>> this
>>>> > > into
>>>> > > > >> the
>>>> > > > >> > > >> > document.
>>>> > > > >> > > >> > > > > > >>> Thank
>>>> > > > >> > > >> > > > > > >>>>> you
>>>> > > > >> > > >> > > > > > >>>>>>>> very
>>>> > > > >> > > >> > > > > > >>>>>>>>>> much.
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>> Best, Hequn
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM
>>>> vino
>>>> > > yang <
>>>> > > > >> > > >> > > > > > >>>>> [hidden email] <mailto:
>>>> [hidden email]>>
>>>> > > > >> > > >> > > > > > >>>>>>>>> wrote:
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*"
>>>> section
>>>> > > of
>>>> > > > >> FLIP
>>>> > > > >> > > >> wiki
>>>> > > > >> > > >> > > > > > >>>> page.[1]
>>>> > > > >> > > >> > > > > > >>>>>> This
>>>> > > > >> > > >> > > > > > >>>>>>>>> mail
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has
>>>> proceeded to
>>>> > > > the
>>>> > > > >> > > third
>>>> > > > >> > > >> > step.
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth
>>>> step(vote
>>>> > > > step),
>>>> > > > >> I
>>>> > > > >> > > >> didn't
>>>> > > > >> > > >> > > > > > >> find
>>>> > > > >> > > >> > > > > > >>>> the
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the
>>>> voting
>>>> > > > >> process.
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion
>>>> of this
>>>> > > > >> feature
>>>> > > > >> > > has
>>>> > > > >> > > >> > been
>>>> > > > >> > > >> > > > > > >>> done
>>>> > > > >> > > >> > > > > > >>>>> in
>>>> > > > >> > > >> > > > > > >>>>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>>>> old
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me
>>>> when
>>>> > > should
>>>> > > > I
>>>> > > > >> > start
>>>> > > > >> > > >> > > > > > >> voting?
>>>> > > > >> > > >> > > > > > >>>> Can
>>>> > > > >> > > >> > > > > > >>>>> I
>>>> > > > >> > > >> > > > > > >>>>>>>> start
>>>> > > > >> > > >> > > > > > >>>>>>>>>> now?
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Best,
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Vino
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> [1]:
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> >
>>>> > > > >> > > >>
>>>> > > > >> > >
>>>> > > > >> >
>>>> > > > >>
>>>> > > >
>>>> > >
>>>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>>>> <
>>>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>>>> >
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> [2]:
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> >
>>>> > > > >> > > >>
>>>> > > > >> > >
>>>> > > > >> >
>>>> > > > >>
>>>> > > >
>>>> > >
>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>> <
>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>> >
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email]
>>>> <mailto:[hidden email]>>
>>>> > > 于2019年6月13日周四
>>>> > > > >> > > 上午9:19写道：
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for
>>>> your
>>>> > > > >> efforts.
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> Best,
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> Leesf
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> vino yang <[hidden email]
>>>> <mailto:[hidden email]>>
>>>> > > > >> > 于2019年6月12日周三
>>>> > > > >> > > >> > > > > > >>> 下午5:46写道：
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP
>>>> > > discussion
>>>> > > > >> > thread
>>>> > > > >> > > >> > > > > > >> about
>>>> > > > >> > > >> > > > > > >>>>>>> supporting
>>>> > > > >> > > >> > > > > > >>>>>>>>>> local
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can
>>>> effectively
>>>> > > > >> > alleviate
>>>> > > > >> > > >> data
>>>> > > > >> > > >> > > > > > >>>> skew.
>>>> > > > >> > > >> > > > > > >>>>>>> This
>>>> > > > >> > > >> > > > > > >>>>>>>> is
>>>> > > > >> > > >> > > > > > >>>>>>>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> >
>>>> > > > >> > > >>
>>>> > > > >> > >
>>>> > > > >> >
>>>> > > > >>
>>>> > > >
>>>> > >
>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>>>> <
>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>>>> >
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are
>>>> widely
>>>> > > used
>>>> > > > to
>>>> > > > >> > > >> perform
>>>> > > > >> > > >> > > > > > >>>>>> aggregating
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> operations
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window)
>>>> on the
>>>> > > > >> elements
>>>> > > > >> > > >> that
>>>> > > > >> > > >> > > > > > >>> have
>>>> > > > >> > > >> > > > > > >>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>> same
>>>> > > > >> > > >> > > > > > >>>>>>>>>> key.
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> When
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the
>>>> elements with
>>>> > > > the
>>>> > > > >> > same
>>>> > > > >> > > >> key
>>>> > > > >> > > >> > > > > > >>> will
>>>> > > > >> > > >> > > > > > >>>> be
>>>> > > > >> > > >> > > > > > >>>>>>> sent
>>>> > > > >> > > >> > > > > > >>>>>>>> to
>>>> > > > >> > > >> > > > > > >>>>>>>>>> and
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> The performance of these
>>>> aggregating
>>>> > > > >> > operations
>>>> > > > >> > > is
>>>> > > > >> > > >> > > > > > >> very
>>>> > > > >> > > >> > > > > > >>>>>>> sensitive
>>>> > > > >> > > >> > > > > > >>>>>>>>> to
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the
>>>> cases
>>>> > > where
>>>> > > > >> the
>>>> > > > >> > > >> > > > > > >>> distribution
>>>> > > > >> > > >> > > > > > >>>>> of
>>>> > > > >> > > >> > > > > > >>>>>>> keys
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> follows a
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance
>>>> will be
>>>> > > > >> > > >> significantly
>>>> > > > >> > > >> > > > > > >>>>>> downgraded.
>>>> > > > >> > > >> > > > > > >>>>>>>>> More
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the
>>>> degree of
>>>> > > > >> > parallelism
>>>> > > > >> > > >> does
>>>> > > > >> > > >> > > > > > >>> not
>>>> > > > >> > > >> > > > > > >>>>> help
>>>> > > > >> > > >> > > > > > >>>>>>>> when
>>>> > > > >> > > >> > > > > > >>>>>>>>> a
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> task
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a
>>>> widely-adopted
>>>> > > > >> method
>>>> > > > >> > to
>>>> > > > >> > > >> > > > > > >> reduce
>>>> > > > >> > > >> > > > > > >>>> the
>>>> > > > >> > > >> > > > > > >>>>>>>>>> performance
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can
>>>> decompose
>>>> > > > the
>>>> > > > >> > > >> > > > > > >> aggregating
>>>> > > > >> > > >> > > > > > >>>>>>>> operations
>>>> > > > >> > > >> > > > > > >>>>>>>>>> into
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> two
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we
>>>> > > aggregate
>>>> > > > >> the
>>>> > > > >> > > >> elements
>>>> > > > >> > > >> > > > > > >>> of
>>>> > > > >> > > >> > > > > > >>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>> same
>>>> > > > >> > > >> > > > > > >>>>>>>>> key
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> at
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain
>>>> partial
>>>> > > > results.
>>>> > > > >> > Then
>>>> > > > >> > > at
>>>> > > > >> > > >> > > > > > >> the
>>>> > > > >> > > >> > > > > > >>>>> second
>>>> > > > >> > > >> > > > > > >>>>>>>>> phase,
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> these
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to
>>>> receivers
>>>> > > > >> > according
>>>> > > > >> > > to
>>>> > > > >> > > >> > > > > > >>> their
>>>> > > > >> > > >> > > > > > >>>>> keys
>>>> > > > >> > > >> > > > > > >>>>>>> and
>>>> > > > >> > > >> > > > > > >>>>>>>>> are
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final
>>>> result.
>>>> > > > Since
>>>> > > > >> the
>>>> > > > >> > > >> number
>>>> > > > >> > > >> > > > > > >>> of
>>>> > > > >> > > >> > > > > > >>>>>>> partial
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> results
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is
>>>> limited by
>>>> > > > the
>>>> > > > >> > > >> number of
>>>> > > > >> > > >> > > > > > >>>>>> senders,
>>>> > > > >> > > >> > > > > > >>>>>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can be
>>>> > > reduced.
>>>> > > > >> > > >> Besides, by
>>>> > > > >> > > >> > > > > > >>>>>> reducing
>>>> > > > >> > > >> > > > > > >>>>>>>> the
>>>> > > > >> > > >> > > > > > >>>>>>>>>>> amount
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the
>>>> performance can
>>>> > > > be
>>>> > > > >> > > further
>>>> > > > >> > > >> > > > > > >>>>> improved.
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> >
>>>> > > > >> > > >>
>>>> > > > >> > >
>>>> > > > >> >
>>>> > > > >>
>>>> > > >
>>>> > >
>>>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>>>> <
>>>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>>>> >
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> >
>>>> > > > >> > > >>
>>>> > > > >> > >
>>>> > > > >> >
>>>> > > > >>
>>>> > > >
>>>> > >
>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>> <
>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>> >
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> https://issues.apache.org/jira/browse/FLINK-12786 <
>>>> https://issues.apache.org/jira/browse/FLINK-12786>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your
>>>> > > feedback!
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Best,
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Vino
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>>
>>>> > > > >> > > >> > > > > > >>>>>>
>>>> > > > >> > > >> > > > > > >>>>>
>>>> > > > >> > > >> > > > > > >>>>
>>>> > > > >> > > >> > > > > > >>>
>>>> > > > >> > > >> > > > > > >>
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > > >
>>>> > > > >> > > >> > > > >
>>>> > > > >> > > >> > > >
>>>> > > > >> > > >> > >
>>>> > > > >> > > >> >
>>>> > > > >> > > >>
>>>> > > > >> > > >
>>>> > > > >> > >
>>>> > > > >> >
>>>> > > > >>
>>>> > > > >
>>>> > > >
>>>> > >
>>>>
>>>>

[vino yang]

Hi Jark,

*About local keyed state:*

I object to moving it out of this FLIP. It's one of the ways we support
Local aggregation on the implementation of operator level, though not the
only one.

I guess you have misunderstood my last reply. I just tell you the
difference between `DataStream#process` and `KeyedStream#process`. Users
who use `localKeyBy#process` API are completely unaware of the differences
when using the Stateful API. The local keyed state we introduced is not
exposed to the outside! It exists only internally. When calling localKeyBy
to returns an instance of `KeyedStream`, we introduce `KeyScope` in
`KeyedStream` to distinguish them. I suggest you take a look at our design
documentation.

*About your concerns:*

1) I agree that not all exposed APIs are meaningful if localKeyBy returns
`KeyedStream`. I did not find a signature of timeWindow(long, long) API.
IMO, all the window related APIs are useful and meaningful, It is one of
the main means of our local aggregation, and we should not limit its
flexibility. I am not against localKeyBy returns `LocalKeyedStream` if you
agree `localKeyBy` is reasonable.

2) I have replied more than one times that we are trying to support a more
general local aggregation. The meaning of aggregation here is not limited
to the implementation of AggregateFunction. And that's exactly what we got
from `KeyedStream#process`. Why do you need a "local process" concept? I
don't think it is necessary at all. I don't want to say that the
aggregation you think is narrow, but we want to use this API to provide
enough flexibility. This is the primary focus of DataStream, as @Piotr
Nowojski <[hidden email]>  also agrees. I also agree "local process"
is more than "local aggregate", that's users' choice if they want to
use. Again, it should not be removed from this FLIP because it is the added
value of localKeyBy.

Best,
Vino


Jark Wu <[hidden email]> 于2019年6月27日周四 下午8:47写道：

> Hi Vino,
>
> So the difference between `DataStream.localKeyBy().process()` with
> `DataStream.process()` is that the former can access keyed state and the
> latter can only access operator state.
> I think it's out of the scope of designing a local aggregation API. It
> might be an extension of state API, i.e. local keyed state.
> The difference between local keyed state with operator state (if I
> understand correctly) is local keyed state can be backed on RocksDB? or
> making "keyed state" locally?
> IMO, it's a larger topic than local aggregation and should be discussed
> separately. I cc-ed people who works on states @Tzu-Li (Gordon) Tai
> <[hidden email]>  @Seth @Yu Li to give some feedback from the
> perspective of state.
>
> Regarding to the API designing updated in your FLIP, I have some concerns:
>
> 1) The "localKeyBy()" method returns a "KeyedStream" which exposes all
> method of it.
> However, not every method makes sense or have a clear definition on local
> stream.
> For example, "countWindow(long, long)", "timeWindow(long, long)",
> "window(WindowAssigner)", and "intervalJoin" Hequn mentioned before.
> I would suggest we can expose the only APIs we needed for local
> aggregation and leave the others later.
> We can return a "LocalKeyedStream" and may expose only some dedicated
> methods: for example, "aggregate()", "trigger()".
> These APIs do not need to expose local keyed state to support local
> aggregation.
>
> 2) I think `localKeyBy().process()` is something called "local process",
> not just "local aggregate".
> It needs more discussion about local keyed state, and I would like to put
> it out of this FLIP.
>
>
> Regards,
> Jark
>
>
> On Thu, 27 Jun 2019 at 13:03, vino yang <[hidden email]> wrote:
>
>> Hi all,
>>
>> I also think it's a good idea that we need to agree on the API level
>> first.
>>
>> I am sorry, we did not give some usage examples of the API in the FLIP
>> documentation before. This may have caused some misunderstandings about the
>> discussion of this mail thread.
>>
>> So, now I have added some usage examples in the "Public Interfaces"
>> section of the FLIP-44 documentation.
>>
>> Let us first know the API through its use examples.
>>
>> Any feedback and questions please let me know.
>>
>> Best,
>> Vino
>>
>> vino yang <[hidden email]> 于2019年6月27日周四 下午12:51写道：
>>
>>> Hi Jark,
>>>
>>> `DataStream.localKeyBy().process()` has some key difference with
>>> `DataStream.process()`. The former API receive `KeyedProcessFunction`
>>> (sorry my previous reply may let you misunderstood), the latter receive API
>>> receive `ProcessFunction`. When you read the java doc of ProcessFunction,
>>> you can find a "*Note*" statement:
>>>
>>> Access to keyed state and timers (which are also scoped to a key) is
>>>> only available if the ProcessFunction is applied on a KeyedStream.
>>>
>>>
>>> In addition, you can also compare the two
>>> implementations(`ProcessOperator` and `KeyedProcessOperator`) of them to
>>> view the difference.
>>>
>>> IMO, the "Note" statement means a lot for many use scenarios.
>>> For example, if we cannot access keyed state, we can only use heap memory
>>> to buffer data while it does not guarantee the semantics of correctness!
>>> And the timer is also very important in some scenarios.
>>>
>>> That's why we say our API is flexible, it can get most benefits (even
>>> subsequent potential benefits in the future) from KeyedStream.
>>>
>>> I have added some instructions on the use of localKeyBy in the FLIP-44
>>> documentation.
>>>
>>> Best,
>>> Vino
>>>
>>>
>>> Jark Wu <[hidden email]> 于2019年6月27日周四 上午10:44写道：
>>>
>>>> Hi Piotr,
>>>>
>>>> I think the state migration you raised is a good point. Having
>>>> "stream.enableLocalAggregation(Trigger)” might add some implicit operators
>>>> which users can't set uid and cause the state compatibility/evolution
>>>> problems.
>>>> So let's put this in rejected alternatives.
>>>>
>>>> Hi Vino,
>>>>
>>>> You mentioned several times that "DataStream.localKeyBy().process()"
>>>> can solve the data skew problem of "DataStream.keyBy().process()".
>>>> I'm curious about what's the differences between "DataStream.process()"
>>>> and "DataStream.localKeyBy().process()"?
>>>> Can't "DataStream.process()" solve the data skew problem?
>>>>
>>>> Best,
>>>> Jark
>>>>
>>>>
>>>> On Wed, 26 Jun 2019 at 18:20, Piotr Nowojski <[hidden email]>
>>>> wrote:
>>>>
>>>>> Hi Jark and Vino,
>>>>>
>>>>> I agree fully with Jark, that in order to have the discussion focused
>>>>> and to limit the number of parallel topics, we should first focus on one
>>>>> topic. We can first decide on the API and later we can discuss the runtime
>>>>> details. At least as long as we keep the potential requirements of the
>>>>> runtime part in mind while designing the API.
>>>>>
>>>>> Regarding the automatic optimisation and proposed by Jark:
>>>>>
>>>>> "stream.enableLocalAggregation(Trigger)”
>>>>>
>>>>> I would be against that in the DataStream API for the reasons that
>>>>> Vino presented. There was a discussion thread about future directions of
>>>>> Table API vs DataStream API and the consensus was that the automatic
>>>>> optimisations are one of the dividing lines between those two, for at least
>>>>> a couple of reasons. Flexibility and full control over the program was one
>>>>> of them. Another is state migration. Having
>>>>> "stream.enableLocalAggregation(Trigger)” that might add some implicit
>>>>> operators in the job graph can cause problems with savepoint/checkpoint
>>>>> compatibility.
>>>>>
>>>>> However I haven’t thought about/looked into the details of the Vino’s
>>>>> API proposal, so I can not fully judge it.
>>>>>
>>>>> Piotrek
>>>>>
>>>>> > On 26 Jun 2019, at 09:17, vino yang <[hidden email]> wrote:
>>>>> >
>>>>> > Hi Jark,
>>>>> >
>>>>> > Similar questions and responses have been repeated many times.
>>>>> >
>>>>> > Why didn't we spend more sections discussing the API?
>>>>> >
>>>>> > Because we try to reuse the ability of KeyedStream. The localKeyBy
>>>>> API just returns the KeyedStream, that's our design, we can get all the
>>>>> benefit from the KeyedStream and get further benefit from WindowedStream.
>>>>> The APIs come from KeyedStream and WindowedStream is long-tested and
>>>>> flexible. Yes, we spend much space discussing the local keyed state, that's
>>>>> not the goal and motivation, that's the way to implement local aggregation.
>>>>> It is much more complicated than the API we introduced, so we spent more
>>>>> section. Of course, this is the implementation level of the Operator. We
>>>>> also agreed to support the implementation of buffer+flush and added related
>>>>> instructions to the documentation. This needs to wait for the community to
>>>>> recognize, and if the community agrees, we will give more instructions.
>>>>> What's more, I have indicated before that we welcome state-related
>>>>> commenters to participate in the discussion, but it is not wise to modify
>>>>> the FLIP title.
>>>>> >
>>>>> > About the API of local aggregation:
>>>>> >
>>>>> > I don't object to ease of use is very important. But IMHO
>>>>> flexibility is the most important at the DataStream API level. Otherwise,
>>>>> what does DataStream mean? The significance of the DataStream API is that
>>>>> it is more flexible than Table/SQL, if it cannot provide this point then
>>>>> everyone would just use Table/SQL.
>>>>> >
>>>>> > The DataStream API should focus more on flexibility than on
>>>>> automatic optimization, which allows users to have more possibilities to
>>>>> implement complex programs and meet specific scenarios. There are a lot of
>>>>> programs written using the DataStream API that are far more complex than we
>>>>> think. It is very difficult to optimize at the API level and the benefit is
>>>>> very low.
>>>>> >
>>>>> > I want to say that we support a more generalized local aggregation.
>>>>> I mentioned in the previous reply that not only the UDF that implements
>>>>> AggregateFunction is called aggregation. In some complex scenarios, we have
>>>>> to support local aggregation through ProcessFunction and
>>>>> ProcessWindowFunction to solve the data skew problem. How do you support
>>>>> them in the API implementation and optimization you mentioned?
>>>>> >
>>>>> > Flexible APIs are arbitrarily combined to result in erroneous
>>>>> semantics, which does not prove that flexibility is meaningless because the
>>>>> user is the decision maker. I have been exemplified many times, for many
>>>>> APIs in DataStream, if we arbitrarily combined them, they also do not have
>>>>> much practical significance. So, users who use flexible APIs need to
>>>>> understand what they are doing and what is the right choice.
>>>>> >
>>>>> > I think that if we discuss this, there will be no result.
>>>>> >
>>>>> > @Stephan Ewen <mailto:[hidden email]> , @Aljoscha Krettek <mailto:
>>>>> [hidden email]> and @Piotr Nowojski <mailto:[hidden email]>
>>>>> Do you have further comments?
>>>>> >
>>>>> >
>>>>> > Jark Wu <[hidden email] <mailto:[hidden email]>> 于2019年6月26日周三
>>>>> 上午11:46写道：
>>>>> > Thanks for the long discussion Vino, Kurt, Hequn, Piotr and others,
>>>>> >
>>>>> > It seems that we still have some different ideas about the API
>>>>> > (localKeyBy()?) and implementation details (reuse window operator?
>>>>> local
>>>>> > keyed state?).
>>>>> > And the discussion is stalled and mixed with motivation and API and
>>>>> > implementation discussion.
>>>>> >
>>>>> > In order to make some progress in this topic, I want to summarize the
>>>>> > points (pls correct me if I'm wrong or missing sth) and would
>>>>> suggest to
>>>>> > split
>>>>> >  the topic into following aspects and discuss them one by one.
>>>>> >
>>>>> > 1) What's the main purpose of this FLIP?
>>>>> >  - From the title of this FLIP, it is to support local aggregate.
>>>>> However
>>>>> > from the content of the FLIP, 80% are introducing a new state called
>>>>> local
>>>>> > keyed state.
>>>>> >  - If we mainly want to introduce local keyed state, then we should
>>>>> > re-title the FLIP and involve in more people who works on state.
>>>>> >  - If we mainly want to support local aggregate, then we can jump to
>>>>> step 2
>>>>> > to discuss the API design.
>>>>> >
>>>>> > 2) What does the API look like?
>>>>> >  - Vino proposed to use "localKeyBy()" to do local process, the
>>>>> output of
>>>>> > local process is the result type of aggregate function.
>>>>> >   a) For non-windowed aggregate:
>>>>> > input.localKeyBy(..).aggregate(agg1).keyBy(..).aggregate(agg2)
>>>>> **NOT
>>>>> > SUPPORT**
>>>>> >   b) For windowed aggregate:
>>>>> >
>>>>> input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)
>>>>> >
>>>>> > 3) What's the implementation detail?
>>>>> >  - may reuse window operator or not.
>>>>> >  - may introduce a new state concepts or not.
>>>>> >  - may not have state in local operator by flushing buffers in
>>>>> > prepareSnapshotPreBarrier
>>>>> >  - and so on...
>>>>> >  - we can discuss these later when we reach a consensus on API
>>>>> >
>>>>> > --------------------
>>>>> >
>>>>> > Here are my thoughts:
>>>>> >
>>>>> > 1) Purpose of this FLIP
>>>>> >  - From the motivation section in the FLIP, I think the purpose is to
>>>>> > support local aggregation to solve the data skew issue.
>>>>> >    Then I think we should focus on how to provide a easy to use and
>>>>> clear
>>>>> > API to support **local aggregation**.
>>>>> >  - Vino's point is centered around the local keyed state API (or
>>>>> > localKeyBy()), and how to leverage the local keyed state API to
>>>>> support
>>>>> > local aggregation.
>>>>> >    But I'm afraid it's not a good way to design API for local
>>>>> aggregation.
>>>>> >
>>>>> > 2) local aggregation API
>>>>> >  - IMO, the method call chain
>>>>> >
>>>>> "input.localKeyBy(..).window(w1).aggregate(agg1).keyBy(..).window(w2).aggregate(agg2)"
>>>>> > is not such easy to use.
>>>>> >    Because we have to provide two implementation for an aggregation
>>>>> (one
>>>>> > for partial agg, another for final agg). And we have to take care of
>>>>> >    the first window call, an inappropriate window call will break the
>>>>> > sematics.
>>>>> >  - From my point of view, local aggregation is a mature concept which
>>>>> > should output the intermediate accumulator (ACC) in the past period
>>>>> of time
>>>>> > (a trigger).
>>>>> >    And the downstream final aggregation will merge ACCs received
>>>>> from local
>>>>> > side, and output the current final result.
>>>>> >  - The current "AggregateFunction" API in DataStream already has the
>>>>> > accumulator type and "merge" method. So the only thing user need to
>>>>> do is
>>>>> > how to enable
>>>>> >    local aggregation opimization and set a trigger.
>>>>> >  - One idea comes to my head is that, assume we have a windowed
>>>>> aggregation
>>>>> > stream: "val stream = input.keyBy().window(w).aggregate(agg)". We can
>>>>> > provide an API on the stream.
>>>>> >    For exmaple, "stream.enableLocalAggregation(Trigger)", the
>>>>> trigger can
>>>>> > be "ContinuousEventTimeTrigger.of(Time.of(Time.minutes(1)))". Then
>>>>> it will
>>>>> > be optmized into
>>>>> >    local operator + final operator, and local operator will combine
>>>>> records
>>>>> > every minute on event time.
>>>>> >  - In this way, there is only one line added, and the output is the
>>>>> same
>>>>> > with before, because it is just an opimization.
>>>>> >
>>>>> >
>>>>> > Regards,
>>>>> > Jark
>>>>> >
>>>>> >
>>>>> >
>>>>> > On Tue, 25 Jun 2019 at 14:34, vino yang <[hidden email]
>>>>> <mailto:[hidden email]>> wrote:
>>>>> >
>>>>> > > Hi Kurt,
>>>>> > >
>>>>> > > Answer your questions:
>>>>> > >
>>>>> > > a) Sorry, I just updated the Google doc, still have no time update
>>>>> the
>>>>> > > FLIP, will update FLIP as soon as possible.
>>>>> > > About your description at this point, I have a question, what does
>>>>> it mean:
>>>>> > > how do we combine with
>>>>> > > `AggregateFunction`?
>>>>> > >
>>>>> > > I have shown you the examples which Flink has supported:
>>>>> > >
>>>>> > >    - input.localKeyBy(0).aggregate()
>>>>> > >    - input.localKeyBy(0).window().aggregate()
>>>>> > >
>>>>> > > You can show me a example about how do we combine with
>>>>> `AggregateFuncion`
>>>>> > > through your localAggregate API.
>>>>> > >
>>>>> > > About the example, how to do the local aggregation for AVG,
>>>>> consider this
>>>>> > > code:
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > > *DataStream<Tuple2<String, Long>> source = null; source
>>>>> .localKeyBy(0)
>>>>> > > .timeWindow(Time.seconds(60)) .aggregate(agg1, new
>>>>> > > WindowFunction<Tuple2<Long, Long>, Tuple3<String, Long, Long>,
>>>>> String,
>>>>> > > TimeWindow>() {}) .keyBy(0) .timeWindow(Time.seconds(60))
>>>>> .aggregate(agg2,
>>>>> > > new WindowFunction<Tuple2<Long, Long>, Tuple2<String, Long>,
>>>>> String,
>>>>> > > TimeWindow>());*
>>>>> > >
>>>>> > > *agg1:*
>>>>> > > *signature : new AggregateFunction<Tuple2<String, Long>,
>>>>> Tuple2<Long,
>>>>> > > Long>, Tuple2<Long, Long>>() {}*
>>>>> > > *input param type: Tuple2<String, Long> f0: key, f1: value*
>>>>> > > *intermediate result type: Tuple2<Long, Long>, f0: local
>>>>> aggregated sum;
>>>>> > > f1: local aggregated count*
>>>>> > > *output param type:  Tuple2<Long, Long>, f0: local aggregated sum;
>>>>> f1:
>>>>> > > local aggregated count*
>>>>> > >
>>>>> > > *agg2:*
>>>>> > > *signature: new AggregateFunction<Tuple3<String, Long, Long>, Long,
>>>>> > > Tuple2<String, Long>>() {},*
>>>>> > > *input param type: Tuple3<String, Long, Long>, f0: key, f1:  local
>>>>> > > aggregated sum; f2: local aggregated count*
>>>>> > >
>>>>> > > *intermediate result type: Long  avg result*
>>>>> > > *output param type:  Tuple2<String, Long> f0: key, f1 avg result*
>>>>> > >
>>>>> > > For sliding window, we just need to change the window type if
>>>>> users want to
>>>>> > > do.
>>>>> > > Again, we try to give the design and implementation in the
>>>>> DataStream
>>>>> > > level. So I believe we can match all the requirements(It's just
>>>>> that the
>>>>> > > implementation may be different) comes from the SQL level.
>>>>> > >
>>>>> > > b) Yes, Theoretically, your thought is right. But in reality, it
>>>>> cannot
>>>>> > > bring many benefits.
>>>>> > > If we want to get the benefits from the window API, while we do
>>>>> not reuse
>>>>> > > the window operator? And just copy some many duplicated code to
>>>>> another
>>>>> > > operator?
>>>>> > >
>>>>> > > c) OK, I agree to let the state backend committers join this
>>>>> discussion.
>>>>> > >
>>>>> > > Best,
>>>>> > > Vino
>>>>> > >
>>>>> > >
>>>>> > > Kurt Young <[hidden email] <mailto:[hidden email]>>
>>>>> 于2019年6月24日周一 下午6:53写道：
>>>>> > >
>>>>> > > > Hi vino,
>>>>> > > >
>>>>> > > > One thing to add,  for a), I think use one or two examples like
>>>>> how to do
>>>>> > > > local aggregation on a sliding window,
>>>>> > > > and how do we do local aggregation on an unbounded aggregate,
>>>>> will do a
>>>>> > > lot
>>>>> > > > help.
>>>>> > > >
>>>>> > > > Best,
>>>>> > > > Kurt
>>>>> > > >
>>>>> > > >
>>>>> > > > On Mon, Jun 24, 2019 at 6:06 PM Kurt Young <[hidden email]
>>>>> <mailto:[hidden email]>> wrote:
>>>>> > > >
>>>>> > > > > Hi vino,
>>>>> > > > >
>>>>> > > > > I think there are several things still need discussion.
>>>>> > > > >
>>>>> > > > > a) We all agree that we should first go with a unified
>>>>> abstraction, but
>>>>> > > > > the abstraction is not reflected by the FLIP.
>>>>> > > > > If your answer is "locakKeyBy" API, then I would ask how do we
>>>>> combine
>>>>> > > > > with `AggregateFunction`, and how do
>>>>> > > > > we do proper local aggregation for those have different
>>>>> intermediate
>>>>> > > > > result type, like AVG. Could you add these
>>>>> > > > > to the document?
>>>>> > > > >
>>>>> > > > > b) From implementation side, reusing window operator is one of
>>>>> the
>>>>> > > > > possible solutions, but not we base on window
>>>>> > > > > operator to have two different implementations. What I
>>>>> understanding
>>>>> > > is,
>>>>> > > > > one of the possible implementations should
>>>>> > > > > not touch window operator.
>>>>> > > > >
>>>>> > > > > c) 80% of your FLIP content is actually describing how do we
>>>>> support
>>>>> > > > local
>>>>> > > > > keyed state. I don't know if this is necessary
>>>>> > > > > to introduce at the first step and we should also involve
>>>>> committers
>>>>> > > work
>>>>> > > > > on state backend to share their thoughts.
>>>>> > > > >
>>>>> > > > > Best,
>>>>> > > > > Kurt
>>>>> > > > >
>>>>> > > > >
>>>>> > > > > On Mon, Jun 24, 2019 at 5:17 PM vino yang <
>>>>> [hidden email] <mailto:[hidden email]>>
>>>>> > > wrote:
>>>>> > > > >
>>>>> > > > >> Hi Kurt,
>>>>> > > > >>
>>>>> > > > >> You did not give more further different opinions, so I
>>>>> thought you
>>>>> > > have
>>>>> > > > >> agreed with the design after we promised to support two kinds
>>>>> of
>>>>> > > > >> implementation.
>>>>> > > > >>
>>>>> > > > >> In API level, we have answered your question about pass an
>>>>> > > > >> AggregateFunction to do the aggregation. No matter introduce
>>>>> > > localKeyBy
>>>>> > > > >> API
>>>>> > > > >> or not, we can support AggregateFunction.
>>>>> > > > >>
>>>>> > > > >> So what's your different opinion now? Can you share it with
>>>>> us?
>>>>> > > > >>
>>>>> > > > >> Best,
>>>>> > > > >> Vino
>>>>> > > > >>
>>>>> > > > >> Kurt Young <[hidden email] <mailto:[hidden email]>>
>>>>> 于2019年6月24日周一 下午4:24写道：
>>>>> > > > >>
>>>>> > > > >> > Hi vino,
>>>>> > > > >> >
>>>>> > > > >> > Sorry I don't see the consensus about reusing window
>>>>> operator and
>>>>> > > keep
>>>>> > > > >> the
>>>>> > > > >> > API design of localKeyBy. But I think we should definitely
>>>>> more
>>>>> > > > thoughts
>>>>> > > > >> > about this topic.
>>>>> > > > >> >
>>>>> > > > >> > I also try to loop in Stephan for this discussion.
>>>>> > > > >> >
>>>>> > > > >> > Best,
>>>>> > > > >> > Kurt
>>>>> > > > >> >
>>>>> > > > >> >
>>>>> > > > >> > On Mon, Jun 24, 2019 at 3:26 PM vino yang <
>>>>> [hidden email] <mailto:[hidden email]>>
>>>>> > > > >> wrote:
>>>>> > > > >> >
>>>>> > > > >> > > Hi all,
>>>>> > > > >> > >
>>>>> > > > >> > > I am happy we have a wonderful discussion and received
>>>>> many
>>>>> > > valuable
>>>>> > > > >> > > opinions in the last few days.
>>>>> > > > >> > >
>>>>> > > > >> > > Now, let me try to summarize what we have reached
>>>>> consensus about
>>>>> > > > the
>>>>> > > > >> > > changes in the design.
>>>>> > > > >> > >
>>>>> > > > >> > >    - provide a unified abstraction to support two kinds of
>>>>> > > > >> > implementation;
>>>>> > > > >> > >    - reuse WindowOperator and try to enhance it so that
>>>>> we can
>>>>> > > make
>>>>> > > > >> the
>>>>> > > > >> > >    intermediate result of the local aggregation can be
>>>>> buffered
>>>>> > > and
>>>>> > > > >> > > flushed to
>>>>> > > > >> > >    support two kinds of implementation;
>>>>> > > > >> > >    - keep the API design of localKeyBy, but declare the
>>>>> disabled
>>>>> > > > some
>>>>> > > > >> > APIs
>>>>> > > > >> > >    we cannot support currently, and provide a
>>>>> configurable API for
>>>>> > > > >> users
>>>>> > > > >> > to
>>>>> > > > >> > >    choose how to handle intermediate result;
>>>>> > > > >> > >
>>>>> > > > >> > > The above three points have been updated in the design
>>>>> doc. Any
>>>>> > > > >> > > questions, please let me know.
>>>>> > > > >> > >
>>>>> > > > >> > > @Aljoscha Krettek <[hidden email] <mailto:
>>>>> [hidden email]>> What do you think? Any
>>>>> > > > >> further
>>>>> > > > >> > > comments?
>>>>> > > > >> > >
>>>>> > > > >> > > Best,
>>>>> > > > >> > > Vino
>>>>> > > > >> > >
>>>>> > > > >> > > vino yang <[hidden email] <mailto:
>>>>> [hidden email]>> 于2019年6月20日周四 下午2:02写道：
>>>>> > > > >> > >
>>>>> > > > >> > > > Hi Kurt,
>>>>> > > > >> > > >
>>>>> > > > >> > > > Thanks for your comments.
>>>>> > > > >> > > >
>>>>> > > > >> > > > It seems we come to a consensus that we should
>>>>> alleviate the
>>>>> > > > >> > performance
>>>>> > > > >> > > > degraded by data skew with local aggregation. In this
>>>>> FLIP, our
>>>>> > > > key
>>>>> > > > >> > > > solution is to introduce local keyed partition to
>>>>> achieve this
>>>>> > > > goal.
>>>>> > > > >> > > >
>>>>> > > > >> > > > I also agree that we can benefit a lot from the usage of
>>>>> > > > >> > > > AggregateFunction. In combination with localKeyBy, We
>>>>> can easily
>>>>> > > > >> use it
>>>>> > > > >> > > to
>>>>> > > > >> > > > achieve local aggregation:
>>>>> > > > >> > > >
>>>>> > > > >> > > >    - input.localKeyBy(0).aggregate()
>>>>> > > > >> > > >    - input.localKeyBy(0).window().aggregate()
>>>>> > > > >> > > >
>>>>> > > > >> > > >
>>>>> > > > >> > > > I think the only problem here is the choices between
>>>>> > > > >> > > >
>>>>> > > > >> > > >    - (1) Introducing a new primitive called localKeyBy
>>>>> and
>>>>> > > > implement
>>>>> > > > >> > > >    local aggregation with existing operators, or
>>>>> > > > >> > > >    - (2) Introducing an operator called
>>>>> localAggregation which
>>>>> > > is
>>>>> > > > >> > > >    composed of a key selector, a window-like operator,
>>>>> and an
>>>>> > > > >> aggregate
>>>>> > > > >> > > >    function.
>>>>> > > > >> > > >
>>>>> > > > >> > > >
>>>>> > > > >> > > > There may exist some optimization opportunities by
>>>>> providing a
>>>>> > > > >> > composited
>>>>> > > > >> > > > interface for local aggregation. But at the same time,
>>>>> in my
>>>>> > > > >> opinion,
>>>>> > > > >> > we
>>>>> > > > >> > > > lose flexibility (Or we need certain efforts to achieve
>>>>> the same
>>>>> > > > >> > > > flexibility).
>>>>> > > > >> > > >
>>>>> > > > >> > > > As said in the previous mails, we have many use cases
>>>>> where the
>>>>> > > > >> > > > aggregation is very complicated and cannot be performed
>>>>> with
>>>>> > > > >> > > > AggregateFunction. For example, users may perform
>>>>> windowed
>>>>> > > > >> aggregations
>>>>> > > > >> > > > according to time, data values, or even external
>>>>> storage.
>>>>> > > > Typically,
>>>>> > > > >> > they
>>>>> > > > >> > > > now use KeyedProcessFunction or customized triggers to
>>>>> implement
>>>>> > > > >> these
>>>>> > > > >> > > > aggregations. It's not easy to address data skew in
>>>>> such cases
>>>>> > > > with
>>>>> > > > >> a
>>>>> > > > >> > > > composited interface for local aggregation.
>>>>> > > > >> > > >
>>>>> > > > >> > > > Given that Data Stream API is exactly targeted at these
>>>>> cases
>>>>> > > > where
>>>>> > > > >> the
>>>>> > > > >> > > > application logic is very complicated and optimization
>>>>> does not
>>>>> > > > >> > matter, I
>>>>> > > > >> > > > think it's a better choice to provide a relatively
>>>>> low-level and
>>>>> > > > >> > > canonical
>>>>> > > > >> > > > interface.
>>>>> > > > >> > > >
>>>>> > > > >> > > > The composited interface, on the other side, may be a
>>>>> good
>>>>> > > choice
>>>>> > > > in
>>>>> > > > >> > > > declarative interfaces, including SQL and Table API, as
>>>>> it
>>>>> > > allows
>>>>> > > > >> more
>>>>> > > > >> > > > optimization opportunities.
>>>>> > > > >> > > >
>>>>> > > > >> > > > Best,
>>>>> > > > >> > > > Vino
>>>>> > > > >> > > >
>>>>> > > > >> > > >
>>>>> > > > >> > > > Kurt Young <[hidden email] <mailto:[hidden email]>>
>>>>> 于2019年6月20日周四 上午10:15写道：
>>>>> > > > >> > > >
>>>>> > > > >> > > >> Hi all,
>>>>> > > > >> > > >>
>>>>> > > > >> > > >> As vino said in previous emails, I think we should
>>>>> first
>>>>> > > discuss
>>>>> > > > >> and
>>>>> > > > >> > > >> decide
>>>>> > > > >> > > >> what kind of use cases this FLIP want to
>>>>> > > > >> > > >> resolve, and what the API should look like. From my
>>>>> side, I
>>>>> > > think
>>>>> > > > >> this
>>>>> > > > >> > > is
>>>>> > > > >> > > >> probably the root cause of current divergence.
>>>>> > > > >> > > >>
>>>>> > > > >> > > >> My understand is (from the FLIP title and motivation
>>>>> section of
>>>>> > > > the
>>>>> > > > >> > > >> document), we want to have a proper support of
>>>>> > > > >> > > >> local aggregation, or pre aggregation. This is not a
>>>>> very new
>>>>> > > > idea,
>>>>> > > > >> > most
>>>>> > > > >> > > >> SQL engine already did this improvement. And
>>>>> > > > >> > > >> the core concept about this is, there should be an
>>>>> > > > >> AggregateFunction,
>>>>> > > > >> > no
>>>>> > > > >> > > >> matter it's a Flink runtime's AggregateFunction or
>>>>> > > > >> > > >> SQL's UserDefinedAggregateFunction. Both aggregation
>>>>> have
>>>>> > > concept
>>>>> > > > >> of
>>>>> > > > >> > > >> intermediate data type, sometimes we call it ACC.
>>>>> > > > >> > > >> I quickly went through the POC piotr did before [1],
>>>>> it also
>>>>> > > > >> directly
>>>>> > > > >> > > uses
>>>>> > > > >> > > >> AggregateFunction.
>>>>> > > > >> > > >>
>>>>> > > > >> > > >> But the thing is, after reading the design of this
>>>>> FLIP, I
>>>>> > > can't
>>>>> > > > >> help
>>>>> > > > >> > > >> myself feeling that this FLIP is not targeting to have
>>>>> a proper
>>>>> > > > >> > > >> local aggregation support. It actually want to
>>>>> introduce
>>>>> > > another
>>>>> > > > >> > > concept:
>>>>> > > > >> > > >> LocalKeyBy, and how to split and merge local key
>>>>> groups,
>>>>> > > > >> > > >> and how to properly support state on local key. Local
>>>>> > > aggregation
>>>>> > > > >> just
>>>>> > > > >> > > >> happened to be one possible use case of LocalKeyBy.
>>>>> > > > >> > > >> But it lacks supporting the essential concept of local
>>>>> > > > aggregation,
>>>>> > > > >> > > which
>>>>> > > > >> > > >> is intermediate data type. Without this, I really
>>>>> don't thing
>>>>> > > > >> > > >> it is a good fit of local aggregation.
>>>>> > > > >> > > >>
>>>>> > > > >> > > >> Here I want to make sure of the scope or the goal
>>>>> about this
>>>>> > > > FLIP,
>>>>> > > > >> do
>>>>> > > > >> > we
>>>>> > > > >> > > >> want to have a proper local aggregation engine, or we
>>>>> > > > >> > > >> just want to introduce a new concept called LocalKeyBy?
>>>>> > > > >> > > >>
>>>>> > > > >> > > >> [1]: https://github.com/apache/flink/pull/4626 <
>>>>> https://github.com/apache/flink/pull/4626>
>>>>> > > > >> > > >>
>>>>> > > > >> > > >> Best,
>>>>> > > > >> > > >> Kurt
>>>>> > > > >> > > >>
>>>>> > > > >> > > >>
>>>>> > > > >> > > >> On Wed, Jun 19, 2019 at 5:13 PM vino yang <
>>>>> > > [hidden email] <mailto:[hidden email]>
>>>>> > > > >
>>>>> > > > >> > > wrote:
>>>>> > > > >> > > >>
>>>>> > > > >> > > >> > Hi Hequn,
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > Thanks for your comments!
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > I agree that allowing local aggregation reusing
>>>>> window API
>>>>> > > and
>>>>> > > > >> > > refining
>>>>> > > > >> > > >> > window operator to make it match both requirements
>>>>> (come from
>>>>> > > > our
>>>>> > > > >> > and
>>>>> > > > >> > > >> Kurt)
>>>>> > > > >> > > >> > is a good decision!
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > Concerning your questions:
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > 1. The result of
>>>>> input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>>>>> > > may
>>>>> > > > >> be
>>>>> > > > >> > > >> > meaningless.
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > Yes, it does not make sense in most cases. However,
>>>>> I also
>>>>> > > want
>>>>> > > > >> to
>>>>> > > > >> > > note
>>>>> > > > >> > > >> > users should know the right semantics of localKeyBy
>>>>> and use
>>>>> > > it
>>>>> > > > >> > > >> correctly.
>>>>> > > > >> > > >> > Because this issue also exists for the global keyBy,
>>>>> consider
>>>>> > > > >> this
>>>>> > > > >> > > >> example:
>>>>> > > > >> > > >> > input.keyBy(0).sum(1).keyBy(0).sum(1), the result is
>>>>> also
>>>>> > > > >> > meaningless.
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > 2. About the semantics of
>>>>> > > > >> > > >> >
>>>>> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)).
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > Good catch! I agree with you that it's not good to
>>>>> enable all
>>>>> > > > >> > > >> > functionalities for localKeyBy from KeyedStream.
>>>>> > > > >> > > >> > Currently, We do not support some APIs such as
>>>>> > > > >> > > >> > connect/join/intervalJoin/coGroup. This is due to
>>>>> that we
>>>>> > > force
>>>>> > > > >> the
>>>>> > > > >> > > >> > operators on LocalKeyedStreams chained with the
>>>>> inputs.
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > Best,
>>>>> > > > >> > > >> > Vino
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > Hequn Cheng <[hidden email] <mailto:
>>>>> [hidden email]>> 于2019年6月19日周三 下午3:42写道：
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >> > > Hi,
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > Thanks a lot for your great discussion and great
>>>>> to see
>>>>> > > that
>>>>> > > > >> some
>>>>> > > > >> > > >> > agreement
>>>>> > > > >> > > >> > > has been reached on the "local aggregate engine"!
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > ===> Considering the abstract engine,
>>>>> > > > >> > > >> > > I'm thinking is it valuable for us to extend the
>>>>> current
>>>>> > > > >> window to
>>>>> > > > >> > > >> meet
>>>>> > > > >> > > >> > > both demands raised by Kurt and Vino? There are
>>>>> some
>>>>> > > benefits
>>>>> > > > >> we
>>>>> > > > >> > can
>>>>> > > > >> > > >> get:
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > 1. The interfaces of the window are complete and
>>>>> clear.
>>>>> > > With
>>>>> > > > >> > > windows,
>>>>> > > > >> > > >> we
>>>>> > > > >> > > >> > > can define a lot of ways to split the data and
>>>>> perform
>>>>> > > > >> different
>>>>> > > > >> > > >> > > computations.
>>>>> > > > >> > > >> > > 2. We can also leverage the window to do miniBatch
>>>>> for the
>>>>> > > > >> global
>>>>> > > > >> > > >> > > aggregation, i.e, we can use the window to bundle
>>>>> data
>>>>> > > belong
>>>>> > > > >> to
>>>>> > > > >> > the
>>>>> > > > >> > > >> same
>>>>> > > > >> > > >> > > key, for every bundle we only need to read and
>>>>> write once
>>>>> > > > >> state.
>>>>> > > > >> > > This
>>>>> > > > >> > > >> can
>>>>> > > > >> > > >> > > greatly reduce state IO and improve performance.
>>>>> > > > >> > > >> > > 3. A lot of other use cases can also benefit from
>>>>> the
>>>>> > > window
>>>>> > > > >> base
>>>>> > > > >> > on
>>>>> > > > >> > > >> > memory
>>>>> > > > >> > > >> > > or stateless.
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > ===> As for the API,
>>>>> > > > >> > > >> > > I think it is good to make our API more flexible.
>>>>> However,
>>>>> > > we
>>>>> > > > >> may
>>>>> > > > >> > > >> need to
>>>>> > > > >> > > >> > > make our API meaningful.
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > Take my previous reply as an example,
>>>>> > > > >> > > >> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1). The
>>>>> result may
>>>>> > > be
>>>>> > > > >> > > >> > meaningless.
>>>>> > > > >> > > >> > > Another example I find is the intervalJoin, e.g.,
>>>>> > > > >> > > >> > >
>>>>> input1.localKeyBy(0).intervalJoin(input2.localKeyBy(1)). In
>>>>> > > > >> this
>>>>> > > > >> > > >> case, it
>>>>> > > > >> > > >> > > will bring problems if input1 and input2 share
>>>>> different
>>>>> > > > >> > > parallelism.
>>>>> > > > >> > > >> We
>>>>> > > > >> > > >> > > don't know which input should the join chained
>>>>> with? Even
>>>>> > > if
>>>>> > > > >> they
>>>>> > > > >> > > >> share
>>>>> > > > >> > > >> > the
>>>>> > > > >> > > >> > > same parallelism, it's hard to tell what the join
>>>>> is doing.
>>>>> > > > >> There
>>>>> > > > >> > > are
>>>>> > > > >> > > >> > maybe
>>>>> > > > >> > > >> > > some other problems.
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > From this point of view, it's at least not good to
>>>>> enable
>>>>> > > all
>>>>> > > > >> > > >> > > functionalities for localKeyBy from KeyedStream?
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > Great to also have your opinions.
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > Best, Hequn
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > On Wed, Jun 19, 2019 at 10:24 AM vino yang <
>>>>> > > > >> [hidden email] <mailto:[hidden email]>
>>>>> > > > >> > >
>>>>> > > > >> > > >> > wrote:
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > > Hi Kurt and Piotrek,
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > > > Thanks for your comments.
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > > > I agree that we can provide a better abstraction
>>>>> to be
>>>>> > > > >> > compatible
>>>>> > > > >> > > >> with
>>>>> > > > >> > > >> > > two
>>>>> > > > >> > > >> > > > different implementations.
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > > > First of all, I think we should consider what
>>>>> kind of
>>>>> > > > >> scenarios
>>>>> > > > >> > we
>>>>> > > > >> > > >> need
>>>>> > > > >> > > >> > > to
>>>>> > > > >> > > >> > > > support in *API* level?
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > > > We have some use cases which need to a customized
>>>>> > > > aggregation
>>>>> > > > >> > > >> through
>>>>> > > > >> > > >> > > > KeyedProcessFunction, (in the usage of our
>>>>> > > > localKeyBy.window
>>>>> > > > >> > they
>>>>> > > > >> > > >> can
>>>>> > > > >> > > >> > use
>>>>> > > > >> > > >> > > > ProcessWindowFunction).
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > > > Shall we support these flexible use scenarios?
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > > > Best,
>>>>> > > > >> > > >> > > > Vino
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > > > Kurt Young <[hidden email] <mailto:
>>>>> [hidden email]>> 于2019年6月18日周二 下午8:37写道：
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > > > > Hi Piotr,
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > > > Thanks for joining the discussion. Make “local
>>>>> > > > aggregation"
>>>>> > > > >> > > >> abstract
>>>>> > > > >> > > >> > > > enough
>>>>> > > > >> > > >> > > > > sounds good to me, we could
>>>>> > > > >> > > >> > > > > implement and verify alternative solutions for
>>>>> use
>>>>> > > cases
>>>>> > > > of
>>>>> > > > >> > > local
>>>>> > > > >> > > >> > > > > aggregation. Maybe we will find both solutions
>>>>> > > > >> > > >> > > > > are appropriate for different scenarios.
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > > > Starting from a simple one sounds a practical
>>>>> way to
>>>>> > > go.
>>>>> > > > >> What
>>>>> > > > >> > do
>>>>> > > > >> > > >> you
>>>>> > > > >> > > >> > > > think,
>>>>> > > > >> > > >> > > > > vino?
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > > > Best,
>>>>> > > > >> > > >> > > > > Kurt
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > > > On Tue, Jun 18, 2019 at 8:10 PM Piotr Nowojski
>>>>> <
>>>>> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
>>>>> > > > >> > > >> > > > > wrote:
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > > > > Hi Kurt and Vino,
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > > > I think there is a trade of hat we need to
>>>>> consider
>>>>> > > for
>>>>> > > > >> the
>>>>> > > > >> > > >> local
>>>>> > > > >> > > >> > > > > > aggregation.
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > > > Generally speaking I would agree with Kurt
>>>>> about
>>>>> > > local
>>>>> > > > >> > > >> > > aggregation/pre
>>>>> > > > >> > > >> > > > > > aggregation not using Flink's state flush the
>>>>> > > operator
>>>>> > > > >> on a
>>>>> > > > >> > > >> > > checkpoint.
>>>>> > > > >> > > >> > > > > > Network IO is usually cheaper compared to
>>>>> Disks IO.
>>>>> > > > This
>>>>> > > > >> has
>>>>> > > > >> > > >> > however
>>>>> > > > >> > > >> > > > > couple
>>>>> > > > >> > > >> > > > > > of issues:
>>>>> > > > >> > > >> > > > > > 1. It can explode number of in-flight
>>>>> records during
>>>>> > > > >> > > checkpoint
>>>>> > > > >> > > >> > > barrier
>>>>> > > > >> > > >> > > > > > alignment, making checkpointing slower and
>>>>> decrease
>>>>> > > the
>>>>> > > > >> > actual
>>>>> > > > >> > > >> > > > > throughput.
>>>>> > > > >> > > >> > > > > > 2. This trades Disks IO on the local
>>>>> aggregation
>>>>> > > > machine
>>>>> > > > >> > with
>>>>> > > > >> > > >> CPU
>>>>> > > > >> > > >> > > (and
>>>>> > > > >> > > >> > > > > > Disks IO in case of RocksDB) on the final
>>>>> aggregation
>>>>> > > > >> > machine.
>>>>> > > > >> > > >> This
>>>>> > > > >> > > >> > > is
>>>>> > > > >> > > >> > > > > > fine, as long there is no huge data skew. If
>>>>> there is
>>>>> > > > >> only a
>>>>> > > > >> > > >> > handful
>>>>> > > > >> > > >> > > > (or
>>>>> > > > >> > > >> > > > > > even one single) hot keys, it might be
>>>>> better to keep
>>>>> > > > the
>>>>> > > > >> > > >> > persistent
>>>>> > > > >> > > >> > > > > state
>>>>> > > > >> > > >> > > > > > in the LocalAggregationOperator to offload
>>>>> final
>>>>> > > > >> aggregation
>>>>> > > > >> > > as
>>>>> > > > >> > > >> > much
>>>>> > > > >> > > >> > > as
>>>>> > > > >> > > >> > > > > > possible.
>>>>> > > > >> > > >> > > > > > 3. With frequent checkpointing local
>>>>> aggregation
>>>>> > > > >> > effectiveness
>>>>> > > > >> > > >> > would
>>>>> > > > >> > > >> > > > > > degrade.
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > > > I assume Kurt is correct, that in your use
>>>>> cases
>>>>> > > > >> stateless
>>>>> > > > >> > > >> operator
>>>>> > > > >> > > >> > > was
>>>>> > > > >> > > >> > > > > > behaving better, but I could easily see
>>>>> other use
>>>>> > > cases
>>>>> > > > >> as
>>>>> > > > >> > > well.
>>>>> > > > >> > > >> > For
>>>>> > > > >> > > >> > > > > > example someone is already using RocksDB,
>>>>> and his job
>>>>> > > > is
>>>>> > > > >> > > >> > bottlenecked
>>>>> > > > >> > > >> > > > on
>>>>> > > > >> > > >> > > > > a
>>>>> > > > >> > > >> > > > > > single window operator instance because of
>>>>> the data
>>>>> > > > >> skew. In
>>>>> > > > >> > > >> that
>>>>> > > > >> > > >> > > case
>>>>> > > > >> > > >> > > > > > stateful local aggregation would be probably
>>>>> a better
>>>>> > > > >> > choice.
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > > > Because of that, I think we should
>>>>> eventually provide
>>>>> > > > >> both
>>>>> > > > >> > > >> versions
>>>>> > > > >> > > >> > > and
>>>>> > > > >> > > >> > > > > in
>>>>> > > > >> > > >> > > > > > the initial version we should at least make
>>>>> the
>>>>> > > “local
>>>>> > > > >> > > >> aggregation
>>>>> > > > >> > > >> > > > > engine”
>>>>> > > > >> > > >> > > > > > abstract enough, that one could easily
>>>>> provide
>>>>> > > > different
>>>>> > > > >> > > >> > > implementation
>>>>> > > > >> > > >> > > > > > strategy.
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > > > Piotrek
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > > > > On 18 Jun 2019, at 11:46, Kurt Young <
>>>>> > > > [hidden email] <mailto:[hidden email]>
>>>>> > > > >> >
>>>>> > > > >> > > >> wrote:
>>>>> > > > >> > > >> > > > > > >
>>>>> > > > >> > > >> > > > > > > Hi,
>>>>> > > > >> > > >> > > > > > >
>>>>> > > > >> > > >> > > > > > > For the trigger, it depends on what
>>>>> operator we
>>>>> > > want
>>>>> > > > to
>>>>> > > > >> > use
>>>>> > > > >> > > >> under
>>>>> > > > >> > > >> > > the
>>>>> > > > >> > > >> > > > > > API.
>>>>> > > > >> > > >> > > > > > > If we choose to use window operator,
>>>>> > > > >> > > >> > > > > > > we should also use window's trigger.
>>>>> However, I
>>>>> > > also
>>>>> > > > >> think
>>>>> > > > >> > > >> reuse
>>>>> > > > >> > > >> > > > window
>>>>> > > > >> > > >> > > > > > > operator for this scenario may not be
>>>>> > > > >> > > >> > > > > > > the best choice. The reasons are the
>>>>> following:
>>>>> > > > >> > > >> > > > > > >
>>>>> > > > >> > > >> > > > > > > 1. As a lot of people already pointed out,
>>>>> window
>>>>> > > > >> relies
>>>>> > > > >> > > >> heavily
>>>>> > > > >> > > >> > on
>>>>> > > > >> > > >> > > > > state
>>>>> > > > >> > > >> > > > > > > and it will definitely effect performance.
>>>>> You can
>>>>> > > > >> > > >> > > > > > > argue that one can use heap based
>>>>> statebackend, but
>>>>> > > > >> this
>>>>> > > > >> > > will
>>>>> > > > >> > > >> > > > introduce
>>>>> > > > >> > > >> > > > > > > extra coupling. Especially we have a
>>>>> chance to
>>>>> > > > >> > > >> > > > > > > design a pure stateless operator.
>>>>> > > > >> > > >> > > > > > > 2. The window operator is *the most*
>>>>> complicated
>>>>> > > > >> operator
>>>>> > > > >> > > >> Flink
>>>>> > > > >> > > >> > > > > currently
>>>>> > > > >> > > >> > > > > > > have. Maybe we only need to pick a subset
>>>>> of
>>>>> > > > >> > > >> > > > > > > window operator to achieve the goal, but
>>>>> once the
>>>>> > > > user
>>>>> > > > >> > wants
>>>>> > > > >> > > >> to
>>>>> > > > >> > > >> > > have
>>>>> > > > >> > > >> > > > a
>>>>> > > > >> > > >> > > > > > deep
>>>>> > > > >> > > >> > > > > > > look at the localAggregation operator,
>>>>> it's still
>>>>> > > > >> > > >> > > > > > > hard to find out what's going on under the
>>>>> window
>>>>> > > > >> > operator.
>>>>> > > > >> > > >> For
>>>>> > > > >> > > >> > > > > > simplicity,
>>>>> > > > >> > > >> > > > > > > I would also recommend we introduce a
>>>>> dedicated
>>>>> > > > >> > > >> > > > > > > lightweight operator, which also much
>>>>> easier for a
>>>>> > > > >> user to
>>>>> > > > >> > > >> learn
>>>>> > > > >> > > >> > > and
>>>>> > > > >> > > >> > > > > use.
>>>>> > > > >> > > >> > > > > > >
>>>>> > > > >> > > >> > > > > > > For your question about increasing the
>>>>> burden in
>>>>> > > > >> > > >> > > > > > >
>>>>> `StreamOperator::prepareSnapshotPreBarrier()`, the
>>>>> > > > only
>>>>> > > > >> > > thing
>>>>> > > > >> > > >> > this
>>>>> > > > >> > > >> > > > > > function
>>>>> > > > >> > > >> > > > > > > need
>>>>> > > > >> > > >> > > > > > > to do is output all the partial results,
>>>>> it's
>>>>> > > purely
>>>>> > > > >> cpu
>>>>> > > > >> > > >> > workload,
>>>>> > > > >> > > >> > > > not
>>>>> > > > >> > > >> > > > > > > introducing any IO. I want to point out
>>>>> that even
>>>>> > > if
>>>>> > > > we
>>>>> > > > >> > have
>>>>> > > > >> > > >> this
>>>>> > > > >> > > >> > > > > > > cost, we reduced another barrier align
>>>>> cost of the
>>>>> > > > >> > operator,
>>>>> > > > >> > > >> > which
>>>>> > > > >> > > >> > > is
>>>>> > > > >> > > >> > > > > the
>>>>> > > > >> > > >> > > > > > > sync flush stage of the state, if you
>>>>> introduced
>>>>> > > > state.
>>>>> > > > >> > This
>>>>> > > > >> > > >> > > > > > > flush actually will introduce disk IO, and
>>>>> I think
>>>>> > > > it's
>>>>> > > > >> > > >> worthy to
>>>>> > > > >> > > >> > > > > > exchange
>>>>> > > > >> > > >> > > > > > > this cost with purely CPU workload. And we
>>>>> do have
>>>>> > > > some
>>>>> > > > >> > > >> > > > > > > observations about these two behavior (as
>>>>> i said
>>>>> > > > >> before,
>>>>> > > > >> > we
>>>>> > > > >> > > >> > > actually
>>>>> > > > >> > > >> > > > > > > implemented both solutions), the stateless
>>>>> one
>>>>> > > > actually
>>>>> > > > >> > > >> performs
>>>>> > > > >> > > >> > > > > > > better both in performance and barrier
>>>>> align time.
>>>>> > > > >> > > >> > > > > > >
>>>>> > > > >> > > >> > > > > > > Best,
>>>>> > > > >> > > >> > > > > > > Kurt
>>>>> > > > >> > > >> > > > > > >
>>>>> > > > >> > > >> > > > > > >
>>>>> > > > >> > > >> > > > > > > On Tue, Jun 18, 2019 at 3:40 PM vino yang <
>>>>> > > > >> > > >> [hidden email] <mailto:[hidden email]>
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> > > > > wrote:
>>>>> > > > >> > > >> > > > > > >
>>>>> > > > >> > > >> > > > > > >> Hi Kurt,
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> Thanks for your example. Now, it looks
>>>>> more
>>>>> > > clearly
>>>>> > > > >> for
>>>>> > > > >> > me.
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> From your example code snippet, I saw the
>>>>> > > > >> localAggregate
>>>>> > > > >> > > API
>>>>> > > > >> > > >> has
>>>>> > > > >> > > >> > > > three
>>>>> > > > >> > > >> > > > > > >> parameters:
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >>   1. key field
>>>>> > > > >> > > >> > > > > > >>   2. PartitionAvg
>>>>> > > > >> > > >> > > > > > >>   3. CountTrigger: Does this trigger
>>>>> comes from
>>>>> > > > window
>>>>> > > > >> > > >> package?
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> I will compare our and your design from
>>>>> API and
>>>>> > > > >> operator
>>>>> > > > >> > > >> level:
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> *From the API level:*
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> As I replied to @dianfu in the old email
>>>>> > > thread,[1]
>>>>> > > > >> the
>>>>> > > > >> > > >> Window
>>>>> > > > >> > > >> > API
>>>>> > > > >> > > >> > > > can
>>>>> > > > >> > > >> > > > > > >> provide the second and the third
>>>>> parameter right
>>>>> > > > now.
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> If you reuse specified interface or
>>>>> class, such as
>>>>> > > > >> > > *Trigger*
>>>>> > > > >> > > >> or
>>>>> > > > >> > > >> > > > > > >> *CounterTrigger* provided by window
>>>>> package, but
>>>>> > > do
>>>>> > > > >> not
>>>>> > > > >> > use
>>>>> > > > >> > > >> > window
>>>>> > > > >> > > >> > > > > API,
>>>>> > > > >> > > >> > > > > > >> it's not reasonable.
>>>>> > > > >> > > >> > > > > > >> And if you do not reuse these interface
>>>>> or class,
>>>>> > > > you
>>>>> > > > >> > would
>>>>> > > > >> > > >> need
>>>>> > > > >> > > >> > > to
>>>>> > > > >> > > >> > > > > > >> introduce more things however they are
>>>>> looked
>>>>> > > > similar
>>>>> > > > >> to
>>>>> > > > >> > > the
>>>>> > > > >> > > >> > > things
>>>>> > > > >> > > >> > > > > > >> provided by window package.
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> The window package has provided several
>>>>> types of
>>>>> > > the
>>>>> > > > >> > window
>>>>> > > > >> > > >> and
>>>>> > > > >> > > >> > > many
>>>>> > > > >> > > >> > > > > > >> triggers and let users customize it.
>>>>> What's more,
>>>>> > > > the
>>>>> > > > >> > user
>>>>> > > > >> > > is
>>>>> > > > >> > > >> > more
>>>>> > > > >> > > >> > > > > > familiar
>>>>> > > > >> > > >> > > > > > >> with Window API.
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> This is the reason why we just provide
>>>>> localKeyBy
>>>>> > > > API
>>>>> > > > >> and
>>>>> > > > >> > > >> reuse
>>>>> > > > >> > > >> > > the
>>>>> > > > >> > > >> > > > > > window
>>>>> > > > >> > > >> > > > > > >> API. It reduces unnecessary components
>>>>> such as
>>>>> > > > >> triggers
>>>>> > > > >> > and
>>>>> > > > >> > > >> the
>>>>> > > > >> > > >> > > > > > mechanism
>>>>> > > > >> > > >> > > > > > >> of buffer (based on count num or time).
>>>>> > > > >> > > >> > > > > > >> And it has a clear and easy to understand
>>>>> > > semantics.
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> *From the operator level:*
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> We reused window operator, so we can get
>>>>> all the
>>>>> > > > >> benefits
>>>>> > > > >> > > >> from
>>>>> > > > >> > > >> > > state
>>>>> > > > >> > > >> > > > > and
>>>>> > > > >> > > >> > > > > > >> checkpoint.
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> From your design, you named the operator
>>>>> under
>>>>> > > > >> > > localAggregate
>>>>> > > > >> > > >> > API
>>>>> > > > >> > > >> > > > is a
>>>>> > > > >> > > >> > > > > > >> *stateless* operator. IMO, it is still a
>>>>> state, it
>>>>> > > > is
>>>>> > > > >> > just
>>>>> > > > >> > > >> not
>>>>> > > > >> > > >> > > Flink
>>>>> > > > >> > > >> > > > > > >> managed state.
>>>>> > > > >> > > >> > > > > > >> About the memory buffer (I think it's
>>>>> still not
>>>>> > > very
>>>>> > > > >> > clear,
>>>>> > > > >> > > >> if
>>>>> > > > >> > > >> > you
>>>>> > > > >> > > >> > > > > have
>>>>> > > > >> > > >> > > > > > >> time, can you give more detail
>>>>> information or
>>>>> > > answer
>>>>> > > > >> my
>>>>> > > > >> > > >> > > questions),
>>>>> > > > >> > > >> > > > I
>>>>> > > > >> > > >> > > > > > have
>>>>> > > > >> > > >> > > > > > >> some questions:
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >>   - if it just a raw JVM heap memory
>>>>> buffer, how
>>>>> > > to
>>>>> > > > >> > support
>>>>> > > > >> > > >> > fault
>>>>> > > > >> > > >> > > > > > >>   tolerance, if the job is configured
>>>>> EXACTLY-ONCE
>>>>> > > > >> > semantic
>>>>> > > > >> > > >> > > > guarantee?
>>>>> > > > >> > > >> > > > > > >>   - if you thought the memory
>>>>> buffer(non-Flink
>>>>> > > > state),
>>>>> > > > >> > has
>>>>> > > > >> > > >> > better
>>>>> > > > >> > > >> > > > > > >>   performance. In our design, users can
>>>>> also
>>>>> > > config
>>>>> > > > >> HEAP
>>>>> > > > >> > > >> state
>>>>> > > > >> > > >> > > > backend
>>>>> > > > >> > > >> > > > > > to
>>>>> > > > >> > > >> > > > > > >>   provide the performance close to your
>>>>> mechanism.
>>>>> > > > >> > > >> > > > > > >>   -
>>>>> `StreamOperator::prepareSnapshotPreBarrier()`
>>>>> > > > >> related
>>>>> > > > >> > > to
>>>>> > > > >> > > >> the
>>>>> > > > >> > > >> > > > > timing
>>>>> > > > >> > > >> > > > > > of
>>>>> > > > >> > > >> > > > > > >>   snapshot. IMO, the flush action should
>>>>> be a
>>>>> > > > >> > synchronized
>>>>> > > > >> > > >> > action?
>>>>> > > > >> > > >> > > > (if
>>>>> > > > >> > > >> > > > > > >> not,
>>>>> > > > >> > > >> > > > > > >>   please point out my mistake) I still
>>>>> think we
>>>>> > > > should
>>>>> > > > >> > not
>>>>> > > > >> > > >> > depend
>>>>> > > > >> > > >> > > on
>>>>> > > > >> > > >> > > > > the
>>>>> > > > >> > > >> > > > > > >>   timing of checkpoint. Checkpoint related
>>>>> > > > operations
>>>>> > > > >> are
>>>>> > > > >> > > >> > inherent
>>>>> > > > >> > > >> > > > > > >>   performance sensitive, we should not
>>>>> increase
>>>>> > > its
>>>>> > > > >> > burden
>>>>> > > > >> > > >> > > anymore.
>>>>> > > > >> > > >> > > > > Our
>>>>> > > > >> > > >> > > > > > >>   implementation based on the mechanism
>>>>> of Flink's
>>>>> > > > >> > > >> checkpoint,
>>>>> > > > >> > > >> > > which
>>>>> > > > >> > > >> > > > > can
>>>>> > > > >> > > >> > > > > > >>   benefit from the asnyc snapshot and
>>>>> incremental
>>>>> > > > >> > > checkpoint.
>>>>> > > > >> > > >> > IMO,
>>>>> > > > >> > > >> > > > the
>>>>> > > > >> > > >> > > > > > >>   performance is not a problem, and we
>>>>> also do not
>>>>> > > > >> find
>>>>> > > > >> > the
>>>>> > > > >> > > >> > > > > performance
>>>>> > > > >> > > >> > > > > > >> issue
>>>>> > > > >> > > >> > > > > > >>   in our production.
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> [1]:
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >>
>>>>> > > > >> > >
>>>>> > > > >> >
>>>>> > > > >>
>>>>> > > >
>>>>> > >
>>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>>> <
>>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>>> >
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> Best,
>>>>> > > > >> > > >> > > > > > >> Vino
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >> Kurt Young <[hidden email] <mailto:
>>>>> [hidden email]>> 于2019年6月18日周二
>>>>> > > > 下午2:27写道：
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > > >>> Yeah, sorry for not expressing myself
>>>>> clearly. I
>>>>> > > > will
>>>>> > > > >> > try
>>>>> > > > >> > > to
>>>>> > > > >> > > >> > > > provide
>>>>> > > > >> > > >> > > > > > more
>>>>> > > > >> > > >> > > > > > >>> details to make sure we are on the same
>>>>> page.
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>> For DataStream API, it shouldn't be
>>>>> optimized
>>>>> > > > >> > > automatically.
>>>>> > > > >> > > >> > You
>>>>> > > > >> > > >> > > > have
>>>>> > > > >> > > >> > > > > > to
>>>>> > > > >> > > >> > > > > > >>> explicitly call API to do local
>>>>> aggregation
>>>>> > > > >> > > >> > > > > > >>> as well as the trigger policy of the
>>>>> local
>>>>> > > > >> aggregation.
>>>>> > > > >> > > Take
>>>>> > > > >> > > >> > > > average
>>>>> > > > >> > > >> > > > > > for
>>>>> > > > >> > > >> > > > > > >>> example, the user program may look like
>>>>> this
>>>>> > > (just
>>>>> > > > a
>>>>> > > > >> > > draft):
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>> assuming the input type is
>>>>> > > DataStream<Tupl2<String,
>>>>> > > > >> > Int>>
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>> ds.localAggregate(
>>>>> > > > >> > > >> > > > > > >>>        0,
>>>>> > >  //
>>>>> > > > >> The
>>>>> > > > >> > > local
>>>>> > > > >> > > >> > key,
>>>>> > > > >> > > >> > > > > which
>>>>> > > > >> > > >> > > > > > >> is
>>>>> > > > >> > > >> > > > > > >>> the String from Tuple2
>>>>> > > > >> > > >> > > > > > >>>        PartitionAvg(1),
>>>>>  // The
>>>>> > > > >> partial
>>>>> > > > >> > > >> > > aggregation
>>>>> > > > >> > > >> > > > > > >>> function, produces Tuple2<Long, Int>,
>>>>> indicating
>>>>> > > > sum
>>>>> > > > >> and
>>>>> > > > >> > > >> count
>>>>> > > > >> > > >> > > > > > >>>        CountTrigger.of(1000L)    //
>>>>> Trigger
>>>>> > > policy,
>>>>> > > > >> note
>>>>> > > > >> > > >> this
>>>>> > > > >> > > >> > > > should
>>>>> > > > >> > > >> > > > > be
>>>>> > > > >> > > >> > > > > > >>> best effort, and also be composited with
>>>>> time
>>>>> > > based
>>>>> > > > >> or
>>>>> > > > >> > > >> memory
>>>>> > > > >> > > >> > > size
>>>>> > > > >> > > >> > > > > > based
>>>>> > > > >> > > >> > > > > > >>> trigger
>>>>> > > > >> > > >> > > > > > >>>    )
>>>>>        //
>>>>> > > > The
>>>>> > > > >> > > return
>>>>> > > > >> > > >> > type
>>>>> > > > >> > > >> > > > is
>>>>> > > > >> > > >> > > > > > >> local
>>>>> > > > >> > > >> > > > > > >>> aggregate Tuple2<String, Tupl2<Long,
>>>>> Int>>
>>>>> > > > >> > > >> > > > > > >>>    .keyBy(0)
>>>>>  //
>>>>> > > Further
>>>>> > > > >> > keyby
>>>>> > > > >> > > it
>>>>> > > > >> > > >> > with
>>>>> > > > >> > > >> > > > > > >> required
>>>>> > > > >> > > >> > > > > > >>> key
>>>>> > > > >> > > >> > > > > > >>>    .aggregate(1)                      //
>>>>> This
>>>>> > > will
>>>>> > > > >> merge
>>>>> > > > >> > > all
>>>>> > > > >> > > >> > the
>>>>> > > > >> > > >> > > > > > partial
>>>>> > > > >> > > >> > > > > > >>> results and get the final average.
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>> (This is only a draft, only trying to
>>>>> explain
>>>>> > > what
>>>>> > > > it
>>>>> > > > >> > > looks
>>>>> > > > >> > > >> > > like. )
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>> The local aggregate operator can be
>>>>> stateless, we
>>>>> > > > can
>>>>> > > > >> > > keep a
>>>>> > > > >> > > >> > > memory
>>>>> > > > >> > > >> > > > > > >> buffer
>>>>> > > > >> > > >> > > > > > >>> or other efficient data structure to
>>>>> improve the
>>>>> > > > >> > aggregate
>>>>> > > > >> > > >> > > > > performance.
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>> Let me know if you have any other
>>>>> questions.
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>> Best,
>>>>> > > > >> > > >> > > > > > >>> Kurt
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>> On Tue, Jun 18, 2019 at 1:29 PM vino
>>>>> yang <
>>>>> > > > >> > > >> > [hidden email] <mailto:[hidden email]>
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > > > > > wrote:
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>>> Hi Kurt,
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> Thanks for your reply.
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> Actually, I am not against you to raise
>>>>> your
>>>>> > > > design.
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> From your description before, I just
>>>>> can imagine
>>>>> > > > >> your
>>>>> > > > >> > > >> > high-level
>>>>> > > > >> > > >> > > > > > >>>> implementation is about SQL and the
>>>>> optimization
>>>>> > > > is
>>>>> > > > >> > inner
>>>>> > > > >> > > >> of
>>>>> > > > >> > > >> > the
>>>>> > > > >> > > >> > > > > API.
>>>>> > > > >> > > >> > > > > > >> Is
>>>>> > > > >> > > >> > > > > > >>> it
>>>>> > > > >> > > >> > > > > > >>>> automatically? how to give the
>>>>> configuration
>>>>> > > > option
>>>>> > > > >> > about
>>>>> > > > >> > > >> > > trigger
>>>>> > > > >> > > >> > > > > > >>>> pre-aggregation?
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> Maybe after I get more information, it
>>>>> sounds
>>>>> > > more
>>>>> > > > >> > > >> reasonable.
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> IMO, first of all, it would be better
>>>>> to make
>>>>> > > your
>>>>> > > > >> user
>>>>> > > > >> > > >> > > interface
>>>>> > > > >> > > >> > > > > > >>> concrete,
>>>>> > > > >> > > >> > > > > > >>>> it's the basis of the discussion.
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> For example, can you give an example
>>>>> code
>>>>> > > snippet
>>>>> > > > to
>>>>> > > > >> > > >> introduce
>>>>> > > > >> > > >> > > how
>>>>> > > > >> > > >> > > > > to
>>>>> > > > >> > > >> > > > > > >>> help
>>>>> > > > >> > > >> > > > > > >>>> users to process data skew caused by
>>>>> the jobs
>>>>> > > > which
>>>>> > > > >> > built
>>>>> > > > >> > > >> with
>>>>> > > > >> > > >> > > > > > >> DataStream
>>>>> > > > >> > > >> > > > > > >>>> API?
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> If you give more details we can discuss
>>>>> further
>>>>> > > > >> more. I
>>>>> > > > >> > > >> think
>>>>> > > > >> > > >> > if
>>>>> > > > >> > > >> > > > one
>>>>> > > > >> > > >> > > > > > >>> design
>>>>> > > > >> > > >> > > > > > >>>> introduces an exact interface and
>>>>> another does
>>>>> > > > not.
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> The implementation has an obvious
>>>>> difference.
>>>>> > > For
>>>>> > > > >> > > example,
>>>>> > > > >> > > >> we
>>>>> > > > >> > > >> > > > > > introduce
>>>>> > > > >> > > >> > > > > > >>> an
>>>>> > > > >> > > >> > > > > > >>>> exact API in DataStream named
>>>>> localKeyBy, about
>>>>> > > > the
>>>>> > > > >> > > >> > > > pre-aggregation
>>>>> > > > >> > > >> > > > > we
>>>>> > > > >> > > >> > > > > > >>> need
>>>>> > > > >> > > >> > > > > > >>>> to define the trigger mechanism of local
>>>>> > > > >> aggregation,
>>>>> > > > >> > so
>>>>> > > > >> > > we
>>>>> > > > >> > > >> > find
>>>>> > > > >> > > >> > > > > > reused
>>>>> > > > >> > > >> > > > > > >>>> window API and operator is a good
>>>>> choice. This
>>>>> > > is
>>>>> > > > a
>>>>> > > > >> > > >> reasoning
>>>>> > > > >> > > >> > > link
>>>>> > > > >> > > >> > > > > > from
>>>>> > > > >> > > >> > > > > > >>>> design to implementation.
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> What do you think?
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> Best,
>>>>> > > > >> > > >> > > > > > >>>> Vino
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>> Kurt Young <[hidden email] <mailto:
>>>>> [hidden email]>> 于2019年6月18日周二
>>>>> > > > >> 上午11:58写道：
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>>> Hi Vino,
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>> Now I feel that we may have different
>>>>> > > > >> understandings
>>>>> > > > >> > > about
>>>>> > > > >> > > >> > what
>>>>> > > > >> > > >> > > > > kind
>>>>> > > > >> > > >> > > > > > >> of
>>>>> > > > >> > > >> > > > > > >>>>> problems or improvements you want to
>>>>> > > > >> > > >> > > > > > >>>>> resolve. Currently, most of the
>>>>> feedback are
>>>>> > > > >> focusing
>>>>> > > > >> > on
>>>>> > > > >> > > >> *how
>>>>> > > > >> > > >> > > to
>>>>> > > > >> > > >> > > > > do a
>>>>> > > > >> > > >> > > > > > >>>>> proper local aggregation to improve
>>>>> performance
>>>>> > > > >> > > >> > > > > > >>>>> and maybe solving the data skew
>>>>> issue*. And my
>>>>> > > > gut
>>>>> > > > >> > > >> feeling is
>>>>> > > > >> > > >> > > > this
>>>>> > > > >> > > >> > > > > is
>>>>> > > > >> > > >> > > > > > >>>>> exactly what users want at the first
>>>>> place,
>>>>> > > > >> > > >> > > > > > >>>>> especially those +1s. (Sorry to try to
>>>>> > > summarize
>>>>> > > > >> here,
>>>>> > > > >> > > >> please
>>>>> > > > >> > > >> > > > > correct
>>>>> > > > >> > > >> > > > > > >>> me
>>>>> > > > >> > > >> > > > > > >>>> if
>>>>> > > > >> > > >> > > > > > >>>>> i'm wrong).
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>> But I still think the design is somehow
>>>>> > > diverged
>>>>> > > > >> from
>>>>> > > > >> > > the
>>>>> > > > >> > > >> > goal.
>>>>> > > > >> > > >> > > > If
>>>>> > > > >> > > >> > > > > we
>>>>> > > > >> > > >> > > > > > >>>> want
>>>>> > > > >> > > >> > > > > > >>>>> to have an efficient and powerful way
>>>>> to
>>>>> > > > >> > > >> > > > > > >>>>> have local aggregation, supporting
>>>>> intermedia
>>>>> > > > >> result
>>>>> > > > >> > > type
>>>>> > > > >> > > >> is
>>>>> > > > >> > > >> > > > > > >> essential
>>>>> > > > >> > > >> > > > > > >>>> IMO.
>>>>> > > > >> > > >> > > > > > >>>>> Both runtime's `AggregateFunction` and
>>>>> > > > >> > > >> > > > > > >>>>> SQL`s `UserDefinedAggregateFunction`
>>>>> have a
>>>>> > > > proper
>>>>> > > > >> > > >> support of
>>>>> > > > >> > > >> > > > > > >>>> intermediate
>>>>> > > > >> > > >> > > > > > >>>>> result type and can do `merge`
>>>>> operation
>>>>> > > > >> > > >> > > > > > >>>>> on them.
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>> Now, we have a lightweight
>>>>> alternatives which
>>>>> > > > >> performs
>>>>> > > > >> > > >> well,
>>>>> > > > >> > > >> > > and
>>>>> > > > >> > > >> > > > > > >> have a
>>>>> > > > >> > > >> > > > > > >>>>> nice fit with the local aggregate
>>>>> requirements.
>>>>> > > > >> > > >> > > > > > >>>>> Mostly importantly,  it's much less
>>>>> complex
>>>>> > > > because
>>>>> > > > >> > it's
>>>>> > > > >> > > >> > > > stateless.
>>>>> > > > >> > > >> > > > > > >> And
>>>>> > > > >> > > >> > > > > > >>>> it
>>>>> > > > >> > > >> > > > > > >>>>> can also achieve the similar
>>>>> > > multiple-aggregation
>>>>> > > > >> > > >> > > > > > >>>>> scenario.
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>> I still not convinced why we shouldn't
>>>>> consider
>>>>> > > > it
>>>>> > > > >> as
>>>>> > > > >> > a
>>>>> > > > >> > > >> first
>>>>> > > > >> > > >> > > > step.
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>> Best,
>>>>> > > > >> > > >> > > > > > >>>>> Kurt
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>> On Tue, Jun 18, 2019 at 11:35 AM vino
>>>>> yang <
>>>>> > > > >> > > >> > > > [hidden email] <mailto:
>>>>> [hidden email]>>
>>>>> > > > >> > > >> > > > > > >>>> wrote:
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> Hi Kurt,
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> Thanks for your comments.
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> It seems we both implemented local
>>>>> aggregation
>>>>> > > > >> > feature
>>>>> > > > >> > > to
>>>>> > > > >> > > >> > > > optimize
>>>>> > > > >> > > >> > > > > > >>> the
>>>>> > > > >> > > >> > > > > > >>>>>> issue of data skew.
>>>>> > > > >> > > >> > > > > > >>>>>> However, IMHO, the API level of
>>>>> optimizing
>>>>> > > > >> revenue is
>>>>> > > > >> > > >> > > different.
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> *Your optimization benefits from
>>>>> Flink SQL and
>>>>> > > > >> it's
>>>>> > > > >> > not
>>>>> > > > >> > > >> > user's
>>>>> > > > >> > > >> > > > > > >>>> faces.(If
>>>>> > > > >> > > >> > > > > > >>>>> I
>>>>> > > > >> > > >> > > > > > >>>>>> understand it incorrectly, please
>>>>> correct
>>>>> > > > this.)*
>>>>> > > > >> > > >> > > > > > >>>>>> *Our implementation employs it as an
>>>>> > > > optimization
>>>>> > > > >> > tool
>>>>> > > > >> > > >> API
>>>>> > > > >> > > >> > for
>>>>> > > > >> > > >> > > > > > >>>>> DataStream,
>>>>> > > > >> > > >> > > > > > >>>>>> it just like a local version of the
>>>>> keyBy
>>>>> > > API.*
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> Based on this, I want to say support
>>>>> it as a
>>>>> > > > >> > DataStream
>>>>> > > > >> > > >> API
>>>>> > > > >> > > >> > > can
>>>>> > > > >> > > >> > > > > > >>> provide
>>>>> > > > >> > > >> > > > > > >>>>>> these advantages:
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>   - The localKeyBy API has a clear
>>>>> semantic
>>>>> > > and
>>>>> > > > >> it's
>>>>> > > > >> > > >> > flexible
>>>>> > > > >> > > >> > > > not
>>>>> > > > >> > > >> > > > > > >>> only
>>>>> > > > >> > > >> > > > > > >>>>> for
>>>>> > > > >> > > >> > > > > > >>>>>>   processing data skew but also for
>>>>> > > implementing
>>>>> > > > >> some
>>>>> > > > >> > > >> user
>>>>> > > > >> > > >> > > > cases,
>>>>> > > > >> > > >> > > > > > >>> for
>>>>> > > > >> > > >> > > > > > >>>>>>   example, if we want to calculate the
>>>>> > > > >> multiple-level
>>>>> > > > >> > > >> > > > aggregation,
>>>>> > > > >> > > >> > > > > > >>> we
>>>>> > > > >> > > >> > > > > > >>>>> can
>>>>> > > > >> > > >> > > > > > >>>>>> do
>>>>> > > > >> > > >> > > > > > >>>>>>   multiple-level aggregation in the
>>>>> local
>>>>> > > > >> > aggregation:
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > >  input.localKeyBy("a").sum(1).localKeyBy("b").window();
>>>>> > > > >> > > >> //
>>>>> > > > >> > > >> > > here
>>>>> > > > >> > > >> > > > > > >> "a"
>>>>> > > > >> > > >> > > > > > >>>> is
>>>>> > > > >> > > >> > > > > > >>>>> a
>>>>> > > > >> > > >> > > > > > >>>>>>   sub-category, while "b" is a
>>>>> category, here
>>>>> > > we
>>>>> > > > >> do
>>>>> > > > >> > not
>>>>> > > > >> > > >> need
>>>>> > > > >> > > >> > > to
>>>>> > > > >> > > >> > > > > > >>>> shuffle
>>>>> > > > >> > > >> > > > > > >>>>>> data
>>>>> > > > >> > > >> > > > > > >>>>>>   in the network.
>>>>> > > > >> > > >> > > > > > >>>>>>   - The users of DataStream API will
>>>>> benefit
>>>>> > > > from
>>>>> > > > >> > this.
>>>>> > > > >> > > >> > > > Actually,
>>>>> > > > >> > > >> > > > > > >> we
>>>>> > > > >> > > >> > > > > > >>>>> have
>>>>> > > > >> > > >> > > > > > >>>>>>   a lot of scenes need to use
>>>>> DataStream API.
>>>>> > > > >> > > Currently,
>>>>> > > > >> > > >> > > > > > >> DataStream
>>>>> > > > >> > > >> > > > > > >>>> API
>>>>> > > > >> > > >> > > > > > >>>>> is
>>>>> > > > >> > > >> > > > > > >>>>>>   the cornerstone of the physical
>>>>> plan of
>>>>> > > Flink
>>>>> > > > >> SQL.
>>>>> > > > >> > > >> With a
>>>>> > > > >> > > >> > > > > > >>> localKeyBy
>>>>> > > > >> > > >> > > > > > >>>>>> API,
>>>>> > > > >> > > >> > > > > > >>>>>>   the optimization of SQL at least
>>>>> may use
>>>>> > > this
>>>>> > > > >> > > optimized
>>>>> > > > >> > > >> > API,
>>>>> > > > >> > > >> > > > > > >> this
>>>>> > > > >> > > >> > > > > > >>>> is a
>>>>> > > > >> > > >> > > > > > >>>>>>   further topic.
>>>>> > > > >> > > >> > > > > > >>>>>>   - Based on the window operator, our
>>>>> state
>>>>> > > > would
>>>>> > > > >> > > benefit
>>>>> > > > >> > > >> > from
>>>>> > > > >> > > >> > > > > > >> Flink
>>>>> > > > >> > > >> > > > > > >>>>> State
>>>>> > > > >> > > >> > > > > > >>>>>>   and checkpoint, we do not need to
>>>>> worry
>>>>> > > about
>>>>> > > > >> OOM
>>>>> > > > >> > and
>>>>> > > > >> > > >> job
>>>>> > > > >> > > >> > > > > > >> failed.
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> Now, about your questions:
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> 1. About our design cannot change the
>>>>> data
>>>>> > > type
>>>>> > > > >> and
>>>>> > > > >> > > about
>>>>> > > > >> > > >> > the
>>>>> > > > >> > > >> > > > > > >>>>>> implementation of average:
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> Just like my reply to Hequn, the
>>>>> localKeyBy is
>>>>> > > > an
>>>>> > > > >> API
>>>>> > > > >> > > >> > provides
>>>>> > > > >> > > >> > > > to
>>>>> > > > >> > > >> > > > > > >> the
>>>>> > > > >> > > >> > > > > > >>>>> users
>>>>> > > > >> > > >> > > > > > >>>>>> who use DataStream API to build their
>>>>> jobs.
>>>>> > > > >> > > >> > > > > > >>>>>> Users should know its semantics and
>>>>> the
>>>>> > > > difference
>>>>> > > > >> > with
>>>>> > > > >> > > >> > keyBy
>>>>> > > > >> > > >> > > > API,
>>>>> > > > >> > > >> > > > > > >> so
>>>>> > > > >> > > >> > > > > > >>>> if
>>>>> > > > >> > > >> > > > > > >>>>>> they want to the average aggregation,
>>>>> they
>>>>> > > > should
>>>>> > > > >> > carry
>>>>> > > > >> > > >> > local
>>>>> > > > >> > > >> > > > sum
>>>>> > > > >> > > >> > > > > > >>>> result
>>>>> > > > >> > > >> > > > > > >>>>>> and local count result.
>>>>> > > > >> > > >> > > > > > >>>>>> I admit that it will be convenient to
>>>>> use
>>>>> > > keyBy
>>>>> > > > >> > > directly.
>>>>> > > > >> > > >> > But
>>>>> > > > >> > > >> > > we
>>>>> > > > >> > > >> > > > > > >> need
>>>>> > > > >> > > >> > > > > > >>>> to
>>>>> > > > >> > > >> > > > > > >>>>>> pay a little price when we get some
>>>>> benefits.
>>>>> > > I
>>>>> > > > >> think
>>>>> > > > >> > > >> this
>>>>> > > > >> > > >> > > price
>>>>> > > > >> > > >> > > > > is
>>>>> > > > >> > > >> > > > > > >>>>>> reasonable. Considering that the
>>>>> DataStream
>>>>> > > API
>>>>> > > > >> > itself
>>>>> > > > >> > > >> is a
>>>>> > > > >> > > >> > > > > > >> low-level
>>>>> > > > >> > > >> > > > > > >>>> API
>>>>> > > > >> > > >> > > > > > >>>>>> (at least for now).
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> 2. About stateless operator and
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> `StreamOperator::prepareSnapshotPreBarrier()`:
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> Actually, I have discussed this
>>>>> opinion with
>>>>> > > > >> @dianfu
>>>>> > > > >> > in
>>>>> > > > >> > > >> the
>>>>> > > > >> > > >> > > old
>>>>> > > > >> > > >> > > > > > >> mail
>>>>> > > > >> > > >> > > > > > >>>>>> thread. I will copy my opinion from
>>>>> there:
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>   - for your design, you still need
>>>>> somewhere
>>>>> > > to
>>>>> > > > >> give
>>>>> > > > >> > > the
>>>>> > > > >> > > >> > > users
>>>>> > > > >> > > >> > > > > > >>>>> configure
>>>>> > > > >> > > >> > > > > > >>>>>>   the trigger threshold (maybe memory
>>>>> > > > >> availability?),
>>>>> > > > >> > > >> this
>>>>> > > > >> > > >> > > > design
>>>>> > > > >> > > >> > > > > > >>>> cannot
>>>>> > > > >> > > >> > > > > > >>>>>>   guarantee a deterministic semantics
>>>>> (it will
>>>>> > > > >> bring
>>>>> > > > >> > > >> trouble
>>>>> > > > >> > > >> > > for
>>>>> > > > >> > > >> > > > > > >>>> testing
>>>>> > > > >> > > >> > > > > > >>>>>> and
>>>>> > > > >> > > >> > > > > > >>>>>>   debugging).
>>>>> > > > >> > > >> > > > > > >>>>>>   - if the implementation depends on
>>>>> the
>>>>> > > timing
>>>>> > > > of
>>>>> > > > >> > > >> > checkpoint,
>>>>> > > > >> > > >> > > > it
>>>>> > > > >> > > >> > > > > > >>>> would
>>>>> > > > >> > > >> > > > > > >>>>>>   affect the checkpoint's progress,
>>>>> and the
>>>>> > > > >> buffered
>>>>> > > > >> > > data
>>>>> > > > >> > > >> > may
>>>>> > > > >> > > >> > > > > > >> cause
>>>>> > > > >> > > >> > > > > > >>>> OOM
>>>>> > > > >> > > >> > > > > > >>>>>>   issue. In addition, if the operator
>>>>> is
>>>>> > > > >> stateless,
>>>>> > > > >> > it
>>>>> > > > >> > > >> can
>>>>> > > > >> > > >> > not
>>>>> > > > >> > > >> > > > > > >>> provide
>>>>> > > > >> > > >> > > > > > >>>>>> fault
>>>>> > > > >> > > >> > > > > > >>>>>>   tolerance.
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> Best,
>>>>> > > > >> > > >> > > > > > >>>>>> Vino
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>> Kurt Young <[hidden email] <mailto:
>>>>> [hidden email]>> 于2019年6月18日周二
>>>>> > > > >> > 上午9:22写道：
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>> Hi Vino,
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>> Thanks for the proposal, I like the
>>>>> general
>>>>> > > > idea
>>>>> > > > >> and
>>>>> > > > >> > > IMO
>>>>> > > > >> > > >> > it's
>>>>> > > > >> > > >> > > > > > >> very
>>>>> > > > >> > > >> > > > > > >>>>> useful
>>>>> > > > >> > > >> > > > > > >>>>>>> feature.
>>>>> > > > >> > > >> > > > > > >>>>>>> But after reading through the
>>>>> document, I
>>>>> > > feel
>>>>> > > > >> that
>>>>> > > > >> > we
>>>>> > > > >> > > >> may
>>>>> > > > >> > > >> > > over
>>>>> > > > >> > > >> > > > > > >>>> design
>>>>> > > > >> > > >> > > > > > >>>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>> required
>>>>> > > > >> > > >> > > > > > >>>>>>> operator for proper local
>>>>> aggregation. The
>>>>> > > main
>>>>> > > > >> > reason
>>>>> > > > >> > > >> is
>>>>> > > > >> > > >> > we
>>>>> > > > >> > > >> > > > want
>>>>> > > > >> > > >> > > > > > >>> to
>>>>> > > > >> > > >> > > > > > >>>>>> have a
>>>>> > > > >> > > >> > > > > > >>>>>>> clear definition and behavior about
>>>>> the
>>>>> > > "local
>>>>> > > > >> keyed
>>>>> > > > >> > > >> state"
>>>>> > > > >> > > >> > > > which
>>>>> > > > >> > > >> > > > > > >>> in
>>>>> > > > >> > > >> > > > > > >>>> my
>>>>> > > > >> > > >> > > > > > >>>>>>> opinion is not
>>>>> > > > >> > > >> > > > > > >>>>>>> necessary for local aggregation, at
>>>>> least for
>>>>> > > > >> start.
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>> Another issue I noticed is the local
>>>>> key by
>>>>> > > > >> operator
>>>>> > > > >> > > >> cannot
>>>>> > > > >> > > >> > > > > > >> change
>>>>> > > > >> > > >> > > > > > >>>>>> element
>>>>> > > > >> > > >> > > > > > >>>>>>> type, it will
>>>>> > > > >> > > >> > > > > > >>>>>>> also restrict a lot of use cases
>>>>> which can be
>>>>> > > > >> > benefit
>>>>> > > > >> > > >> from
>>>>> > > > >> > > >> > > > local
>>>>> > > > >> > > >> > > > > > >>>>>>> aggregation, like "average".
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>> We also did similar logic in SQL and
>>>>> the only
>>>>> > > > >> thing
>>>>> > > > >> > > >> need to
>>>>> > > > >> > > >> > > be
>>>>> > > > >> > > >> > > > > > >> done
>>>>> > > > >> > > >> > > > > > >>>> is
>>>>> > > > >> > > >> > > > > > >>>>>>> introduce
>>>>> > > > >> > > >> > > > > > >>>>>>> a stateless lightweight operator
>>>>> which is
>>>>> > > > >> *chained*
>>>>> > > > >> > > >> before
>>>>> > > > >> > > >> > > > > > >>> `keyby()`.
>>>>> > > > >> > > >> > > > > > >>>>> The
>>>>> > > > >> > > >> > > > > > >>>>>>> operator will flush all buffered
>>>>> > > > >> > > >> > > > > > >>>>>>> elements during
>>>>> > > > >> > > >> > `StreamOperator::prepareSnapshotPreBarrier()`
>>>>> > > > >> > > >> > > > and
>>>>> > > > >> > > >> > > > > > >>>> make
>>>>> > > > >> > > >> > > > > > >>>>>>> himself stateless.
>>>>> > > > >> > > >> > > > > > >>>>>>> By the way, in the earlier version
>>>>> we also
>>>>> > > did
>>>>> > > > >> the
>>>>> > > > >> > > >> similar
>>>>> > > > >> > > >> > > > > > >> approach
>>>>> > > > >> > > >> > > > > > >>>> by
>>>>> > > > >> > > >> > > > > > >>>>>>> introducing a stateful
>>>>> > > > >> > > >> > > > > > >>>>>>> local aggregation operator but it's
>>>>> not
>>>>> > > > >> performed as
>>>>> > > > >> > > >> well
>>>>> > > > >> > > >> > as
>>>>> > > > >> > > >> > > > the
>>>>> > > > >> > > >> > > > > > >>>> later
>>>>> > > > >> > > >> > > > > > >>>>>> one,
>>>>> > > > >> > > >> > > > > > >>>>>>> and also effect the barrie
>>>>> > > > >> > > >> > > > > > >>>>>>> alignment time. The later one is
>>>>> fairly
>>>>> > > simple
>>>>> > > > >> and
>>>>> > > > >> > > more
>>>>> > > > >> > > >> > > > > > >> efficient.
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>> I would highly suggest you to
>>>>> consider to
>>>>> > > have
>>>>> > > > a
>>>>> > > > >> > > >> stateless
>>>>> > > > >> > > >> > > > > > >> approach
>>>>> > > > >> > > >> > > > > > >>>> at
>>>>> > > > >> > > >> > > > > > >>>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>> first step.
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>> Best,
>>>>> > > > >> > > >> > > > > > >>>>>>> Kurt
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>> On Mon, Jun 17, 2019 at 7:32 PM Jark
>>>>> Wu <
>>>>> > > > >> > > >> [hidden email] <mailto:[hidden email]>>
>>>>> > > > >> > > >> > > > > > >> wrote:
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>> Hi Vino,
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>> Thanks for the proposal.
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>> Regarding to the
>>>>> "input.keyBy(0).sum(1)" vs
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> >
>>>>> "input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)",
>>>>> > > > >> > > >> > > > > > >> have
>>>>> > > > >> > > >> > > > > > >>>> you
>>>>> > > > >> > > >> > > > > > >>>>>>> done
>>>>> > > > >> > > >> > > > > > >>>>>>>> some benchmark?
>>>>> > > > >> > > >> > > > > > >>>>>>>> Because I'm curious about how much
>>>>> > > performance
>>>>> > > > >> > > >> improvement
>>>>> > > > >> > > >> > > can
>>>>> > > > >> > > >> > > > > > >> we
>>>>> > > > >> > > >> > > > > > >>>> get
>>>>> > > > >> > > >> > > > > > >>>>>> by
>>>>> > > > >> > > >> > > > > > >>>>>>>> using count window as the local
>>>>> operator.
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>> Best,
>>>>> > > > >> > > >> > > > > > >>>>>>>> Jark
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>> On Mon, 17 Jun 2019 at 17:48, vino
>>>>> yang <
>>>>> > > > >> > > >> > > > [hidden email] <mailto:
>>>>> [hidden email]>
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>>>> wrote:
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> Hi Hequn,
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> Thanks for your reply.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> The purpose of localKeyBy API is to
>>>>> > > provide a
>>>>> > > > >> tool
>>>>> > > > >> > > >> which
>>>>> > > > >> > > >> > > can
>>>>> > > > >> > > >> > > > > > >>> let
>>>>> > > > >> > > >> > > > > > >>>>>> users
>>>>> > > > >> > > >> > > > > > >>>>>>> do
>>>>> > > > >> > > >> > > > > > >>>>>>>>> pre-aggregation in the local. The
>>>>> behavior
>>>>> > > of
>>>>> > > > >> the
>>>>> > > > >> > > >> > > > > > >>> pre-aggregation
>>>>> > > > >> > > >> > > > > > >>>>> is
>>>>> > > > >> > > >> > > > > > >>>>>>>>> similar to keyBy API.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> So the three cases are different,
>>>>> I will
>>>>> > > > >> describe
>>>>> > > > >> > > them
>>>>> > > > >> > > >> > one
>>>>> > > > >> > > >> > > by
>>>>> > > > >> > > >> > > > > > >>>> one:
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> 1. input.keyBy(0).sum(1)
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the result is
>>>>> event-driven,
>>>>> > > > each
>>>>> > > > >> > > event
>>>>> > > > >> > > >> can
>>>>> > > > >> > > >> > > > > > >>> produce
>>>>> > > > >> > > >> > > > > > >>>>> one
>>>>> > > > >> > > >> > > > > > >>>>>>> sum
>>>>> > > > >> > > >> > > > > > >>>>>>>>> aggregation result and it is the
>>>>> latest one
>>>>> > > > >> from
>>>>> > > > >> > the
>>>>> > > > >> > > >> > source
>>>>> > > > >> > > >> > > > > > >>>> start.*
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> 2.
>>>>> > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, the semantic may
>>>>> have a
>>>>> > > > >> problem, it
>>>>> > > > >> > > >> would
>>>>> > > > >> > > >> > do
>>>>> > > > >> > > >> > > > > > >> the
>>>>> > > > >> > > >> > > > > > >>>>> local
>>>>> > > > >> > > >> > > > > > >>>>>>> sum
>>>>> > > > >> > > >> > > > > > >>>>>>>>> aggregation and will produce the
>>>>> latest
>>>>> > > > partial
>>>>> > > > >> > > result
>>>>> > > > >> > > >> > from
>>>>> > > > >> > > >> > > > > > >> the
>>>>> > > > >> > > >> > > > > > >>>>>> source
>>>>> > > > >> > > >> > > > > > >>>>>>>>> start for every event. *
>>>>> > > > >> > > >> > > > > > >>>>>>>>> *These latest partial results from
>>>>> the same
>>>>> > > > key
>>>>> > > > >> > are
>>>>> > > > >> > > >> > hashed
>>>>> > > > >> > > >> > > to
>>>>> > > > >> > > >> > > > > > >>> one
>>>>> > > > >> > > >> > > > > > >>>>>> node
>>>>> > > > >> > > >> > > > > > >>>>>>> to
>>>>> > > > >> > > >> > > > > > >>>>>>>>> do the global sum aggregation.*
>>>>> > > > >> > > >> > > > > > >>>>>>>>> *In the global aggregation, when it
>>>>> > > received
>>>>> > > > >> > > multiple
>>>>> > > > >> > > >> > > partial
>>>>> > > > >> > > >> > > > > > >>>>> results
>>>>> > > > >> > > >> > > > > > >>>>>>>> (they
>>>>> > > > >> > > >> > > > > > >>>>>>>>> are all calculated from the source
>>>>> start)
>>>>> > > and
>>>>> > > > >> sum
>>>>> > > > >> > > them
>>>>> > > > >> > > >> > will
>>>>> > > > >> > > >> > > > > > >> get
>>>>> > > > >> > > >> > > > > > >>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>> wrong
>>>>> > > > >> > > >> > > > > > >>>>>>>>> result.*
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> 3.
>>>>> > > > >> > > >> > >
>>>>> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> *In this case, it would just get a
>>>>> partial
>>>>> > > > >> > > aggregation
>>>>> > > > >> > > >> > > result
>>>>> > > > >> > > >> > > > > > >>> for
>>>>> > > > >> > > >> > > > > > >>>>>> the 5
>>>>> > > > >> > > >> > > > > > >>>>>>>>> records in the count window. The
>>>>> partial
>>>>> > > > >> > aggregation
>>>>> > > > >> > > >> > > results
>>>>> > > > >> > > >> > > > > > >>> from
>>>>> > > > >> > > >> > > > > > >>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>>> same
>>>>> > > > >> > > >> > > > > > >>>>>>>>> key will be aggregated globally.*
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> So the first case and the third
>>>>> case can
>>>>> > > get
>>>>> > > > >> the
>>>>> > > > >> > > >> *same*
>>>>> > > > >> > > >> > > > > > >> result,
>>>>> > > > >> > > >> > > > > > >>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>>>> difference is the output-style and
>>>>> the
>>>>> > > > latency.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> Generally speaking, the local key
>>>>> API is
>>>>> > > just
>>>>> > > > >> an
>>>>> > > > >> > > >> > > optimization
>>>>> > > > >> > > >> > > > > > >>>> API.
>>>>> > > > >> > > >> > > > > > >>>>> We
>>>>> > > > >> > > >> > > > > > >>>>>>> do
>>>>> > > > >> > > >> > > > > > >>>>>>>>> not limit the user's usage, but
>>>>> the user
>>>>> > > has
>>>>> > > > to
>>>>> > > > >> > > >> > understand
>>>>> > > > >> > > >> > > > > > >> its
>>>>> > > > >> > > >> > > > > > >>>>>>> semantics
>>>>> > > > >> > > >> > > > > > >>>>>>>>> and use it correctly.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> Best,
>>>>> > > > >> > > >> > > > > > >>>>>>>>> Vino
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> Hequn Cheng <[hidden email]
>>>>> <mailto:[hidden email]>>
>>>>> > > > >> 于2019年6月17日周一
>>>>> > > > >> > > >> > 下午4:18写道：
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> Hi Vino,
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> Thanks for the proposal, I think
>>>>> it is a
>>>>> > > > very
>>>>> > > > >> > good
>>>>> > > > >> > > >> > > feature!
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> One thing I want to make sure is
>>>>> the
>>>>> > > > semantics
>>>>> > > > >> > for
>>>>> > > > >> > > >> the
>>>>> > > > >> > > >> > > > > > >>>>>> `localKeyBy`.
>>>>> > > > >> > > >> > > > > > >>>>>>>> From
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> the document, the `localKeyBy`
>>>>> API returns
>>>>> > > > an
>>>>> > > > >> > > >> instance
>>>>> > > > >> > > >> > of
>>>>> > > > >> > > >> > > > > > >>>>>>> `KeyedStream`
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> which can also perform sum(), so
>>>>> in this
>>>>> > > > case,
>>>>> > > > >> > > what's
>>>>> > > > >> > > >> > the
>>>>> > > > >> > > >> > > > > > >>>>> semantics
>>>>> > > > >> > > >> > > > > > >>>>>>> for
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> `localKeyBy()`. For example, will
>>>>> the
>>>>> > > > >> following
>>>>> > > > >> > > code
>>>>> > > > >> > > >> > share
>>>>> > > > >> > > >> > > > > > >>> the
>>>>> > > > >> > > >> > > > > > >>>>> same
>>>>> > > > >> > > >> > > > > > >>>>>>>>> result?
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> and what're the differences
>>>>> between them?
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> 1. input.keyBy(0).sum(1)
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> 2.
>>>>> > > > input.localKeyBy(0).sum(1).keyBy(0).sum(1)
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> 3.
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> input.localKeyBy(0).countWindow(5).sum(1).keyBy(0).sum(1)
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> Would also be great if we can add
>>>>> this
>>>>> > > into
>>>>> > > > >> the
>>>>> > > > >> > > >> > document.
>>>>> > > > >> > > >> > > > > > >>> Thank
>>>>> > > > >> > > >> > > > > > >>>>> you
>>>>> > > > >> > > >> > > > > > >>>>>>>> very
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> much.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> Best, Hequn
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> On Fri, Jun 14, 2019 at 11:34 AM
>>>>> vino
>>>>> > > yang <
>>>>> > > > >> > > >> > > > > > >>>>> [hidden email] <mailto:
>>>>> [hidden email]>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>> wrote:
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Hi Aljoscha,
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> I have looked at the "*Process*"
>>>>> section
>>>>> > > of
>>>>> > > > >> FLIP
>>>>> > > > >> > > >> wiki
>>>>> > > > >> > > >> > > > > > >>>> page.[1]
>>>>> > > > >> > > >> > > > > > >>>>>> This
>>>>> > > > >> > > >> > > > > > >>>>>>>>> mail
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> thread indicates that it has
>>>>> proceeded to
>>>>> > > > the
>>>>> > > > >> > > third
>>>>> > > > >> > > >> > step.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> When I looked at the fourth
>>>>> step(vote
>>>>> > > > step),
>>>>> > > > >> I
>>>>> > > > >> > > >> didn't
>>>>> > > > >> > > >> > > > > > >> find
>>>>> > > > >> > > >> > > > > > >>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> prerequisites for starting the
>>>>> voting
>>>>> > > > >> process.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Considering that the discussion
>>>>> of this
>>>>> > > > >> feature
>>>>> > > > >> > > has
>>>>> > > > >> > > >> > been
>>>>> > > > >> > > >> > > > > > >>> done
>>>>> > > > >> > > >> > > > > > >>>>> in
>>>>> > > > >> > > >> > > > > > >>>>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>>>> old
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> thread. [2] So can you tell me
>>>>> when
>>>>> > > should
>>>>> > > > I
>>>>> > > > >> > start
>>>>> > > > >> > > >> > > > > > >> voting?
>>>>> > > > >> > > >> > > > > > >>>> Can
>>>>> > > > >> > > >> > > > > > >>>>> I
>>>>> > > > >> > > >> > > > > > >>>>>>>> start
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> now?
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Best,
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> Vino
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> [1]:
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >>
>>>>> > > > >> > >
>>>>> > > > >> >
>>>>> > > > >>
>>>>> > > >
>>>>> > >
>>>>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>>>>> <
>>>>> https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals#FlinkImprovementProposals-FLIPround-up
>>>>> >
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> [2]:
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >>
>>>>> > > > >> > >
>>>>> > > > >> >
>>>>> > > > >>
>>>>> > > >
>>>>> > >
>>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>>> <
>>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>>> >
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> leesf <[hidden email]
>>>>> <mailto:[hidden email]>>
>>>>> > > 于2019年6月13日周四
>>>>> > > > >> > > 上午9:19写道：
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> +1 for the FLIP, thank vino for
>>>>> your
>>>>> > > > >> efforts.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> Best,
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> Leesf
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> vino yang <
>>>>> [hidden email] <mailto:[hidden email]>>
>>>>> > > > >> > 于2019年6月12日周三
>>>>> > > > >> > > >> > > > > > >>> 下午5:46写道：
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Hi folks,
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> I would like to start the FLIP
>>>>> > > discussion
>>>>> > > > >> > thread
>>>>> > > > >> > > >> > > > > > >> about
>>>>> > > > >> > > >> > > > > > >>>>>>> supporting
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> local
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregation in Flink.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> In short, this feature can
>>>>> effectively
>>>>> > > > >> > alleviate
>>>>> > > > >> > > >> data
>>>>> > > > >> > > >> > > > > > >>>> skew.
>>>>> > > > >> > > >> > > > > > >>>>>>> This
>>>>> > > > >> > > >> > > > > > >>>>>>>> is
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> FLIP:
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >>
>>>>> > > > >> > >
>>>>> > > > >> >
>>>>> > > > >>
>>>>> > > >
>>>>> > >
>>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>>>>> <
>>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-44%3A+Support+Local+Aggregation+in+Flink
>>>>> >
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *Motivation* (copied from FLIP)
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Currently, keyed streams are
>>>>> widely
>>>>> > > used
>>>>> > > > to
>>>>> > > > >> > > >> perform
>>>>> > > > >> > > >> > > > > > >>>>>> aggregating
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> operations
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> (e.g., reduce, sum and window)
>>>>> on the
>>>>> > > > >> elements
>>>>> > > > >> > > >> that
>>>>> > > > >> > > >> > > > > > >>> have
>>>>> > > > >> > > >> > > > > > >>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>> same
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> key.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> When
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> executed at runtime, the
>>>>> elements with
>>>>> > > > the
>>>>> > > > >> > same
>>>>> > > > >> > > >> key
>>>>> > > > >> > > >> > > > > > >>> will
>>>>> > > > >> > > >> > > > > > >>>> be
>>>>> > > > >> > > >> > > > > > >>>>>>> sent
>>>>> > > > >> > > >> > > > > > >>>>>>>> to
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> and
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> aggregated by the same task.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> The performance of these
>>>>> aggregating
>>>>> > > > >> > operations
>>>>> > > > >> > > is
>>>>> > > > >> > > >> > > > > > >> very
>>>>> > > > >> > > >> > > > > > >>>>>>> sensitive
>>>>> > > > >> > > >> > > > > > >>>>>>>>> to
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> distribution of keys. In the
>>>>> cases
>>>>> > > where
>>>>> > > > >> the
>>>>> > > > >> > > >> > > > > > >>> distribution
>>>>> > > > >> > > >> > > > > > >>>>> of
>>>>> > > > >> > > >> > > > > > >>>>>>> keys
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> follows a
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> powerful law, the performance
>>>>> will be
>>>>> > > > >> > > >> significantly
>>>>> > > > >> > > >> > > > > > >>>>>> downgraded.
>>>>> > > > >> > > >> > > > > > >>>>>>>>> More
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> unluckily, increasing the
>>>>> degree of
>>>>> > > > >> > parallelism
>>>>> > > > >> > > >> does
>>>>> > > > >> > > >> > > > > > >>> not
>>>>> > > > >> > > >> > > > > > >>>>> help
>>>>> > > > >> > > >> > > > > > >>>>>>>> when
>>>>> > > > >> > > >> > > > > > >>>>>>>>> a
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> task
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> is overloaded by a single key.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Local aggregation is a
>>>>> widely-adopted
>>>>> > > > >> method
>>>>> > > > >> > to
>>>>> > > > >> > > >> > > > > > >> reduce
>>>>> > > > >> > > >> > > > > > >>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> performance
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> degraded by data skew. We can
>>>>> decompose
>>>>> > > > the
>>>>> > > > >> > > >> > > > > > >> aggregating
>>>>> > > > >> > > >> > > > > > >>>>>>>> operations
>>>>> > > > >> > > >> > > > > > >>>>>>>>>> into
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> two
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> phases. In the first phase, we
>>>>> > > aggregate
>>>>> > > > >> the
>>>>> > > > >> > > >> elements
>>>>> > > > >> > > >> > > > > > >>> of
>>>>> > > > >> > > >> > > > > > >>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>> same
>>>>> > > > >> > > >> > > > > > >>>>>>>>> key
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> at
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> the sender side to obtain
>>>>> partial
>>>>> > > > results.
>>>>> > > > >> > Then
>>>>> > > > >> > > at
>>>>> > > > >> > > >> > > > > > >> the
>>>>> > > > >> > > >> > > > > > >>>>> second
>>>>> > > > >> > > >> > > > > > >>>>>>>>> phase,
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>> these
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> partial results are sent to
>>>>> receivers
>>>>> > > > >> > according
>>>>> > > > >> > > to
>>>>> > > > >> > > >> > > > > > >>> their
>>>>> > > > >> > > >> > > > > > >>>>> keys
>>>>> > > > >> > > >> > > > > > >>>>>>> and
>>>>> > > > >> > > >> > > > > > >>>>>>>>> are
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> combined to obtain the final
>>>>> result.
>>>>> > > > Since
>>>>> > > > >> the
>>>>> > > > >> > > >> number
>>>>> > > > >> > > >> > > > > > >>> of
>>>>> > > > >> > > >> > > > > > >>>>>>> partial
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> results
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> received by each receiver is
>>>>> limited by
>>>>> > > > the
>>>>> > > > >> > > >> number of
>>>>> > > > >> > > >> > > > > > >>>>>> senders,
>>>>> > > > >> > > >> > > > > > >>>>>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> imbalance among receivers can
>>>>> be
>>>>> > > reduced.
>>>>> > > > >> > > >> Besides, by
>>>>> > > > >> > > >> > > > > > >>>>>> reducing
>>>>> > > > >> > > >> > > > > > >>>>>>>> the
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>> amount
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> of transferred data the
>>>>> performance can
>>>>> > > > be
>>>>> > > > >> > > further
>>>>> > > > >> > > >> > > > > > >>>>> improved.
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> *More details*:
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Design documentation:
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>
>>>>> > > > >> > > >> > > > > > >>>>
>>>>> > > > >> > > >> > > > > > >>>
>>>>> > > > >> > > >> > > > > > >>
>>>>> > > > >> > > >> > > > > >
>>>>> > > > >> > > >> > > > >
>>>>> > > > >> > > >> > > >
>>>>> > > > >> > > >> > >
>>>>> > > > >> > > >> >
>>>>> > > > >> > > >>
>>>>> > > > >> > >
>>>>> > > > >> >
>>>>> > > > >>
>>>>> > > >
>>>>> > >
>>>>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>>>>> <
>>>>> https://docs.google.com/document/d/1gizbbFPVtkPZPRS8AIuH8596BmgkfEa7NRwR6n3pQes/edit?usp=sharing
>>>>> >
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Old discussion thread:
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>
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>>>>> > > >
>>>>> > >
>>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>>> <
>>>>> http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-Support-Local-Aggregation-in-Flink-td29307.html#a29308
>>>>> >
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> JIRA: FLINK-12786 <
>>>>> > > > >> > > >> > > > > > >>>>>>>>>
>>>>> > > > >> https://issues.apache.org/jira/browse/FLINK-12786 <
>>>>> https://issues.apache.org/jira/browse/FLINK-12786>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> We are looking forwards to your
>>>>> > > feedback!
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Best,
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>> Vino
>>>>> > > > >> > > >> > > > > > >>>>>>>>>>>>>
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>>>>>