Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
 
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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@Seth That's a very good point. I agree that RocksDB has the same
problem. I think we can use the same approach for the sorted shuffles then. @Aljoscha I agree we should think about making it more resilient, as I guess users might have problems already if they use keys with non-deterministic binary representation. How do you feel about addressing that separately purely to limit the scope of this FLIP? @Aljoscha I tend to agree with you that the best place to actually place the sorting would be in the InputProcessor(s). If there are no more suggestions in respect to that issue. I'll put this proposal for voting. @all Thank you for the feedback so far. I'd like to start a voting thread on the proposal tomorrow. Therefore I'd appreciate if you comment before that, if you still have some outstanding ideas. Best, Dawid On 04/09/2020 17:13, Aljoscha Krettek wrote: > Seth is right, I was just about to write that as well. There is a > problem, though, because some of our TypeSerializers are not > deterministic even though we use them as if they were. Beam excludes > the FloatCoder, for example, and the AvroCoder in certain cases. I'm > pretty sure there is also weirdness going on in our KryoSerializer. > > On 04.09.20 14:59, Seth Wiesman wrote: >> There is already an implicit assumption the TypeSerializer for keys is >> stable/deterministic, RocksDB compares keys using their serialized byte >> strings. I think this is a non-issue (or at least it's not changing the >> status quo). >> >> On Fri, Sep 4, 2020 at 6:39 AM Timo Walther <[hidden email]> wrote: >> >>> +1 for getting rid of the TypeComparator interface and rely on the >>> serialized representation for grouping. >>> >>> Adding a new type to DataStream API is quite difficult at the moment >>> due >>> to too many components that are required: TypeInformation (tries to >>> deal >>> with logical fields for TypeComparators), TypeSerializer (incl. it's >>> snapshot interfaces), and TypeComparator (with many methods and >>> internals such normalized keys etc.). >>> >>> If necessary, we can add more simple comparison-related methods to the >>> TypeSerializer interface itself in the future (like >>> TypeSerializer.isDeterministic). >>> >>> Regards, >>> Timo >>> >>> >>> On 04.09.20 11:48, Aljoscha Krettek wrote: >>>> Thanks for publishing the FLIP! >>>> >>>> On 2020/09/01 06:49:06, Dawid Wysakowicz <[hidden email]> >>>> wrote: >>>>> 1. How to sort/group keys? What representation of the key >>>>> should we >>>>> use? Should we sort on the binary form or should we depend on >>>>> Comparators being available. >>>> >>>> Initially, I suggested to Dawid (in private) to do the >>>> sorting/grouping >>> by using the binary representation. Then my opinion switched and I >>> thought >>> we should use TypeComparator/Comparator because that's what the >>> DataSet API >>> uses. After talking to Stephan, I'm again encouraged in my opinion >>> to use >>> the binary representation because it means we can eventually get rid >>> of the >>> TypeComparator interface, which is a bit complicated, and because we >>> don't >>> need any good order in our sort, we only need the grouping. >>>> >>>> This comes with some problems, though: we need to ensure that the >>> TypeSerializer of the type we're sorting is stable/deterministic. >>> Beam has >>> infrastructure for this in the form of Coder.verifyDeterministic() [1] >>> which we don't have right now and should add if we go down this path. >>>> >>>>> 2. Where in the stack should we apply the sorting (this rather a >>>>> discussion about internals) >>>> >>>> Here, I'm gravitating towards the third option of implementing it >>>> in the >>> layer of the StreamTask, which probably means implementing a custom >>> InputProcessor. I think it's best to do it in this layer because we >>> would >>> not mix concerns of different layers as we would if we implemented >>> this as >>> a custom StreamOperator. I think this solution is also best when it >>> comes >>> to multi-input operators. >>>> >>>>> 3. How should we deal with custom implementations of >>>>> StreamOperators >>>> >>>> I think the cleanest solution would be to go through the complete >>> operator lifecycle for every key, because then the watermark would not >>> oscillate between -Inf and +Inf and we would not break the semantical >>> guarantees that we gave to operators so far, in that the watermark is >>> strictly monotonically increasing. However, I don't think this >>> solution is >>> feasible because it would come with too much overhead. We should >>> solve this >>> problem via documentation and maybe educate people to not query the >>> current >>> watermark or not rely on the watermark being monotonically >>> increasing in >>> operator implementations to allow the framework more freedoms in how >>> user >>> programs are executed. >>>> >>>> Aljoscha >>>> >>>> [1] >>> https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/coders/Coder.java#L184 >>> >>>> >>> >>> >> > |
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Hey Kurt,
Thank you for comments! Ad. 1 I might have missed something here, but as far as I see it is that using the current execution stack with regular state backends (RocksDB in particular if we want to have spilling capabilities) is equivalent to hash-based execution. I can see a different spilling state backend implementation in the future, but I think it is not batch specifc. Or am I missing something? Ad. 2 Totally agree that normalized keys are important to the performance. I think though TypeComparators are not a necessity to have that. Actually this proposal is heading towards only ever performing "normalized keys" comparison. I have not included in the proposal the binary format which we will use for sorting (partially because I forgot, and partially because I thought it was too much of an implementation detail). Let me include it here though, as it might clear the situation a bit here. In DataSet, at times we have KeySelectors which extract keys based on field indices or names. This allows in certain situation to extract the key from serialized records. Compared to DataSet, in DataStream, the key is always described with a black-box KeySelector, or differently with a function which extracts a key from a deserialized record. In turn there is no way to create a comparator that could compare records by extracting the key from a serialized record (neither with, nor without key normalization). We suggest that the input for the sorter will be <key> + <timestamp> + <record> Without having the key prepended we would have to deserialize the record for every key comparison. Therefore if we agree that we perform binary comparison for keys (which are always prepended), it is actually equivalent to a DataSet with TypeComparators that support key normalization. Let me know if that is clear, or I have missed something here. Best, Dawid On 08/09/2020 03:39, Kurt Young wrote: > Hi Dawid, thanks for bringing this up, it's really exciting to see that > batch execution is introduced in DataStream. From the flip, it seems > we are sticking with sort based execution mode (at least for now), which > will sort the whole input data before any *keyed* operation is > executed. I have two comments here: > > 1. Do we want to introduce hash-based execution in the future? Sort is a > safe choice but not the best in lots of cases. IIUC we only need > to make sure that before the framework finishes dealing with one key, the > operator doesn't see any data belonging to other keys, thus > hash-based execution would also do the trick. Oon tricky thing the > framework might need to deal with is memory constraint and spilling > in the hash map, but Flink also has some good knowledge about these stuff. > > 2. Going back to sort-based execution and how to sort keys. From my > experience, the performance of sorting would be one the most important > things if we want to achieve good performance of batch execution. And > normalized keys are actually the key of the performance of sorting. > If we want to get rid of TypeComparator, I think we still need to find a > way to introduce this back. > > Best, > Kurt > > > On Tue, Sep 8, 2020 at 3:04 AM Aljoscha Krettek <[hidden email]> wrote: > >> Yes, I think we can address the problem of indeterminacy in a separate >> FLIP because we're already in it. >> >> Aljoscha >> >> On 07.09.20 17:00, Dawid Wysakowicz wrote: >>> @Seth That's a very good point. I agree that RocksDB has the same >>> problem. I think we can use the same approach for the sorted shuffles >>> then. @Aljoscha I agree we should think about making it more resilient, >>> as I guess users might have problems already if they use keys with >>> non-deterministic binary representation. How do you feel about >>> addressing that separately purely to limit the scope of this FLIP? >>> >>> @Aljoscha I tend to agree with you that the best place to actually place >>> the sorting would be in the InputProcessor(s). If there are no more >>> suggestions in respect to that issue. I'll put this proposal for voting. >>> >>> @all Thank you for the feedback so far. I'd like to start a voting >>> thread on the proposal tomorrow. Therefore I'd appreciate if you comment >>> before that, if you still have some outstanding ideas. >>> >>> Best, >>> >>> Dawid >>> >>> On 04/09/2020 17:13, Aljoscha Krettek wrote: >>>> Seth is right, I was just about to write that as well. There is a >>>> problem, though, because some of our TypeSerializers are not >>>> deterministic even though we use them as if they were. Beam excludes >>>> the FloatCoder, for example, and the AvroCoder in certain cases. I'm >>>> pretty sure there is also weirdness going on in our KryoSerializer. >>>> >>>> On 04.09.20 14:59, Seth Wiesman wrote: >>>>> There is already an implicit assumption the TypeSerializer for keys is >>>>> stable/deterministic, RocksDB compares keys using their serialized byte >>>>> strings. I think this is a non-issue (or at least it's not changing the >>>>> status quo). >>>>> >>>>> On Fri, Sep 4, 2020 at 6:39 AM Timo Walther <[hidden email]> >> wrote: >>>>>> +1 for getting rid of the TypeComparator interface and rely on the >>>>>> serialized representation for grouping. >>>>>> >>>>>> Adding a new type to DataStream API is quite difficult at the moment >>>>>> due >>>>>> to too many components that are required: TypeInformation (tries to >>>>>> deal >>>>>> with logical fields for TypeComparators), TypeSerializer (incl. it's >>>>>> snapshot interfaces), and TypeComparator (with many methods and >>>>>> internals such normalized keys etc.). >>>>>> >>>>>> If necessary, we can add more simple comparison-related methods to the >>>>>> TypeSerializer interface itself in the future (like >>>>>> TypeSerializer.isDeterministic). >>>>>> >>>>>> Regards, >>>>>> Timo >>>>>> >>>>>> >>>>>> On 04.09.20 11:48, Aljoscha Krettek wrote: >>>>>>> Thanks for publishing the FLIP! >>>>>>> >>>>>>> On 2020/09/01 06:49:06, Dawid Wysakowicz <[hidden email]> >>>>>>> wrote: >>>>>>>> 1. How to sort/group keys? What representation of the key >>>>>>>> should we >>>>>>>> use? Should we sort on the binary form or should we depend on >>>>>>>> Comparators being available. >>>>>>> Initially, I suggested to Dawid (in private) to do the >>>>>>> sorting/grouping >>>>>> by using the binary representation. Then my opinion switched and I >>>>>> thought >>>>>> we should use TypeComparator/Comparator because that's what the >>>>>> DataSet API >>>>>> uses. After talking to Stephan, I'm again encouraged in my opinion >>>>>> to use >>>>>> the binary representation because it means we can eventually get rid >>>>>> of the >>>>>> TypeComparator interface, which is a bit complicated, and because we >>>>>> don't >>>>>> need any good order in our sort, we only need the grouping. >>>>>>> This comes with some problems, though: we need to ensure that the >>>>>> TypeSerializer of the type we're sorting is stable/deterministic. >>>>>> Beam has >>>>>> infrastructure for this in the form of Coder.verifyDeterministic() [1] >>>>>> which we don't have right now and should add if we go down this path. >>>>>>>> 2. Where in the stack should we apply the sorting (this rather a >>>>>>>> discussion about internals) >>>>>>> Here, I'm gravitating towards the third option of implementing it >>>>>>> in the >>>>>> layer of the StreamTask, which probably means implementing a custom >>>>>> InputProcessor. I think it's best to do it in this layer because we >>>>>> would >>>>>> not mix concerns of different layers as we would if we implemented >>>>>> this as >>>>>> a custom StreamOperator. I think this solution is also best when it >>>>>> comes >>>>>> to multi-input operators. >>>>>>>> 3. How should we deal with custom implementations of >>>>>>>> StreamOperators >>>>>>> I think the cleanest solution would be to go through the complete >>>>>> operator lifecycle for every key, because then the watermark would not >>>>>> oscillate between -Inf and +Inf and we would not break the semantical >>>>>> guarantees that we gave to operators so far, in that the watermark is >>>>>> strictly monotonically increasing. However, I don't think this >>>>>> solution is >>>>>> feasible because it would come with too much overhead. We should >>>>>> solve this >>>>>> problem via documentation and maybe educate people to not query the >>>>>> current >>>>>> watermark or not rely on the watermark being monotonically >>>>>> increasing in >>>>>> operator implementations to allow the framework more freedoms in how >>>>>> user >>>>>> programs are executed. >>>>>>> Aljoscha >>>>>>> >>>>>>> [1] >> https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/coders/Coder.java#L184 >>>>>> >> |
Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Ad. 1
Yes, you are right in principle. Let me though clarify my proposal a bit. The proposed sort-style execution aims at a generic KeyedProcessFunction were all the "aggregations" are actually performed in the user code. It tries to improve the performance by actually removing the need to use RocksDB e.g.: private static final class Summer<K> extends KeyedProcessFunction<K, Tuple2<K, Integer>, Tuple2<K, Integer>> { .... @Override public void processElement( Tuple2<K, Integer> value, Context ctx, Collector<Tuple2<K, Integer>> out) throws Exception { if (!Objects.equals(timerRegistered.value(), Boolean.TRUE)) { ctx.timerService().registerEventTimeTimer(Long.MAX_VALUE); timerRegistered.update(true); } Integer v = counter.value(); Integer incomingValue = value.f1; if (v != null) { v += incomingValue; } else { v = incomingValue; } counter.update(v); } .... } Therefore I don't think the first part of your reply with separating the write and read workload applies here. We do not aim to create a competing API with the Table API. We think operations such as joins or analytical aggregations should be performed in Table API. As for the second part I agree it would be nice to fall back to the sorting approach only if a certain threshold of memory in a State Backend is used. This has some problems though. We would need a way to estimate the size of the occupied memory to tell when the threshold is reached. That is not easily doable by default e.g. in a MemoryStateBackend, as we do not serialize the values in the state backend by default. We would have to add that, but this would add the overhead of the serialization. This proposal aims at the cases where we do have a large state that will not fit into the memory and without the change users are forced to use RocksDB. If the state fits in memory I agree it will be better to do hash-based aggregations e.g. using the MemoryStateBackend. Therefore I think it is important to give users the choice to use one or the other approach. We might discuss which approach should be the default for RuntimeMode.BATCH proposed in FLIP-134. Should it be hash-based with user configured state backend or sorting-based with a single key at a time backend. Moreover we could think if we should let users choose the sort vs hash "state backend" per operator. Would that suffice? Ad. 2 I still think we can just use the first X bytes of the serialized form as the normalized key and fallback to comparing full keys on clashes. It is because we are actually not interested in a logical order, but we care only about the "grouping" aspect of the sorting. Therefore I think its enough to compare only parts of the full key as the normalized key. Thanks again for the really nice and thorough feedback! Best, Dawid On 08/09/2020 14:47, Kurt Young wrote: > Regarding #1, yes the state backend is definitely hash-based execution. > However there are some differences between > batch hash-based execution. The key difference is *random access & > read/write mixed workload". For example, by using > state backend in streaming execution, one have to mix the read and write > operations and all of them are actually random > access. But in a batch hash execution, we could divide the phases into > write and read. For example, we can build the > hash table first, with only write operations. And once the build is done, > we can start to read and trigger the user codes. > Take hash aggregation which blink planner implemented as an example, during > building phase, as long as the hash map > could fit into memory, we will update the accumulators directly in the hash > map. And once we are running out of memory, > we then fall back to sort based execution. It improves the performance a > lot if the incoming data can be processed in > memory. > > Regarding #2, IIUC you are actually describing a binary format of key, not > normalized key which is used in DataSet. I will > take String for example. If we have lots of keys with length all greater > than, let's say 20. In your proposal, you will encode > the whole string in the prefix of your composed data ( <key> + <timestamp> > + <record> ). And when you compare > records, you will actually compare the *whole* key of the record. For > normalized key, it's fixed-length in this case, IIRC it will > take 8 bytes to represent the string. And the sorter will store the > normalized key and offset in a dedicated array. When doing > the sorting, it only sorts this *small* array. If the normalized keys are > different, you could immediately tell which is greater from > normalized keys. You only have to compare the full keys if the normalized > keys are equal and you know in this case the normalized > key couldn't represent the full key. The reason why Dataset is doing this > is it's super cache efficient by sorting the *small* array. > The idea is borrowed from this paper [1]. Let me know if I missed or > misunderstood anything. > > [1] https://dl.acm.org/doi/10.5555/615232.615237 (AlphaSort: a > cache-sensitive parallel external sort) > > Best, > Kurt > > > On Tue, Sep 8, 2020 at 5:05 PM Dawid Wysakowicz <[hidden email]> > wrote: > >> Hey Kurt, >> >> Thank you for comments! >> >> Ad. 1 I might have missed something here, but as far as I see it is that >> using the current execution stack with regular state backends (RocksDB >> in particular if we want to have spilling capabilities) is equivalent to >> hash-based execution. I can see a different spilling state backend >> implementation in the future, but I think it is not batch specifc. Or am >> I missing something? >> >> Ad. 2 Totally agree that normalized keys are important to the >> performance. I think though TypeComparators are not a necessity to have >> that. Actually this proposal is heading towards only ever performing >> "normalized keys" comparison. I have not included in the proposal the >> binary format which we will use for sorting (partially because I forgot, >> and partially because I thought it was too much of an implementation >> detail). Let me include it here though, as it might clear the situation >> a bit here. >> >> In DataSet, at times we have KeySelectors which extract keys based on >> field indices or names. This allows in certain situation to extract the >> key from serialized records. Compared to DataSet, in DataStream, the key >> is always described with a black-box KeySelector, or differently with a >> function which extracts a key from a deserialized record. In turn there >> is no way to create a comparator that could compare records by >> extracting the key from a serialized record (neither with, nor without >> key normalization). We suggest that the input for the sorter will be >> >> <key> + <timestamp> + <record> >> >> Without having the key prepended we would have to deserialize the record >> for every key comparison. >> >> Therefore if we agree that we perform binary comparison for keys (which >> are always prepended), it is actually equivalent to a DataSet with >> TypeComparators that support key normalization. >> >> Let me know if that is clear, or I have missed something here. >> >> Best, >> >> Dawid >> >> On 08/09/2020 03:39, Kurt Young wrote: >>> Hi Dawid, thanks for bringing this up, it's really exciting to see that >>> batch execution is introduced in DataStream. From the flip, it seems >>> we are sticking with sort based execution mode (at least for now), which >>> will sort the whole input data before any *keyed* operation is >>> executed. I have two comments here: >>> >>> 1. Do we want to introduce hash-based execution in the future? Sort is a >>> safe choice but not the best in lots of cases. IIUC we only need >>> to make sure that before the framework finishes dealing with one key, the >>> operator doesn't see any data belonging to other keys, thus >>> hash-based execution would also do the trick. Oon tricky thing the >>> framework might need to deal with is memory constraint and spilling >>> in the hash map, but Flink also has some good knowledge about these >> stuff. >>> 2. Going back to sort-based execution and how to sort keys. From my >>> experience, the performance of sorting would be one the most important >>> things if we want to achieve good performance of batch execution. And >>> normalized keys are actually the key of the performance of sorting. >>> If we want to get rid of TypeComparator, I think we still need to find a >>> way to introduce this back. >>> >>> Best, >>> Kurt >>> >>> >>> On Tue, Sep 8, 2020 at 3:04 AM Aljoscha Krettek <[hidden email]> >> wrote: >>>> Yes, I think we can address the problem of indeterminacy in a separate >>>> FLIP because we're already in it. >>>> >>>> Aljoscha >>>> >>>> On 07.09.20 17:00, Dawid Wysakowicz wrote: >>>>> @Seth That's a very good point. I agree that RocksDB has the same >>>>> problem. I think we can use the same approach for the sorted shuffles >>>>> then. @Aljoscha I agree we should think about making it more resilient, >>>>> as I guess users might have problems already if they use keys with >>>>> non-deterministic binary representation. How do you feel about >>>>> addressing that separately purely to limit the scope of this FLIP? >>>>> >>>>> @Aljoscha I tend to agree with you that the best place to actually >> place >>>>> the sorting would be in the InputProcessor(s). If there are no more >>>>> suggestions in respect to that issue. I'll put this proposal for >> voting. >>>>> @all Thank you for the feedback so far. I'd like to start a voting >>>>> thread on the proposal tomorrow. Therefore I'd appreciate if you >> comment >>>>> before that, if you still have some outstanding ideas. >>>>> >>>>> Best, >>>>> >>>>> Dawid >>>>> >>>>> On 04/09/2020 17:13, Aljoscha Krettek wrote: >>>>>> Seth is right, I was just about to write that as well. There is a >>>>>> problem, though, because some of our TypeSerializers are not >>>>>> deterministic even though we use them as if they were. Beam excludes >>>>>> the FloatCoder, for example, and the AvroCoder in certain cases. I'm >>>>>> pretty sure there is also weirdness going on in our KryoSerializer. >>>>>> >>>>>> On 04.09.20 14:59, Seth Wiesman wrote: >>>>>>> There is already an implicit assumption the TypeSerializer for keys >> is >>>>>>> stable/deterministic, RocksDB compares keys using their serialized >> byte >>>>>>> strings. I think this is a non-issue (or at least it's not changing >> the >>>>>>> status quo). >>>>>>> >>>>>>> On Fri, Sep 4, 2020 at 6:39 AM Timo Walther <[hidden email]> >>>> wrote: >>>>>>>> +1 for getting rid of the TypeComparator interface and rely on the >>>>>>>> serialized representation for grouping. >>>>>>>> >>>>>>>> Adding a new type to DataStream API is quite difficult at the moment >>>>>>>> due >>>>>>>> to too many components that are required: TypeInformation (tries to >>>>>>>> deal >>>>>>>> with logical fields for TypeComparators), TypeSerializer (incl. it's >>>>>>>> snapshot interfaces), and TypeComparator (with many methods and >>>>>>>> internals such normalized keys etc.). >>>>>>>> >>>>>>>> If necessary, we can add more simple comparison-related methods to >> the >>>>>>>> TypeSerializer interface itself in the future (like >>>>>>>> TypeSerializer.isDeterministic). >>>>>>>> >>>>>>>> Regards, >>>>>>>> Timo >>>>>>>> >>>>>>>> >>>>>>>> On 04.09.20 11:48, Aljoscha Krettek wrote: >>>>>>>>> Thanks for publishing the FLIP! >>>>>>>>> >>>>>>>>> On 2020/09/01 06:49:06, Dawid Wysakowicz <[hidden email]> >>>>>>>>> wrote: >>>>>>>>>> 1. How to sort/group keys? What representation of the key >>>>>>>>>> should we >>>>>>>>>> use? Should we sort on the binary form or should we depend >> on >>>>>>>>>> Comparators being available. >>>>>>>>> Initially, I suggested to Dawid (in private) to do the >>>>>>>>> sorting/grouping >>>>>>>> by using the binary representation. Then my opinion switched and I >>>>>>>> thought >>>>>>>> we should use TypeComparator/Comparator because that's what the >>>>>>>> DataSet API >>>>>>>> uses. After talking to Stephan, I'm again encouraged in my opinion >>>>>>>> to use >>>>>>>> the binary representation because it means we can eventually get rid >>>>>>>> of the >>>>>>>> TypeComparator interface, which is a bit complicated, and because we >>>>>>>> don't >>>>>>>> need any good order in our sort, we only need the grouping. >>>>>>>>> This comes with some problems, though: we need to ensure that the >>>>>>>> TypeSerializer of the type we're sorting is stable/deterministic. >>>>>>>> Beam has >>>>>>>> infrastructure for this in the form of Coder.verifyDeterministic() >> [1] >>>>>>>> which we don't have right now and should add if we go down this >> path. >>>>>>>>>> 2. Where in the stack should we apply the sorting (this >> rather a >>>>>>>>>> discussion about internals) >>>>>>>>> Here, I'm gravitating towards the third option of implementing it >>>>>>>>> in the >>>>>>>> layer of the StreamTask, which probably means implementing a custom >>>>>>>> InputProcessor. I think it's best to do it in this layer because we >>>>>>>> would >>>>>>>> not mix concerns of different layers as we would if we implemented >>>>>>>> this as >>>>>>>> a custom StreamOperator. I think this solution is also best when it >>>>>>>> comes >>>>>>>> to multi-input operators. >>>>>>>>>> 3. How should we deal with custom implementations of >>>>>>>>>> StreamOperators >>>>>>>>> I think the cleanest solution would be to go through the complete >>>>>>>> operator lifecycle for every key, because then the watermark would >> not >>>>>>>> oscillate between -Inf and +Inf and we would not break the >> semantical >>>>>>>> guarantees that we gave to operators so far, in that the watermark >> is >>>>>>>> strictly monotonically increasing. However, I don't think this >>>>>>>> solution is >>>>>>>> feasible because it would come with too much overhead. We should >>>>>>>> solve this >>>>>>>> problem via documentation and maybe educate people to not query the >>>>>>>> current >>>>>>>> watermark or not rely on the watermark being monotonically >>>>>>>> increasing in >>>>>>>> operator implementations to allow the framework more freedoms in how >>>>>>>> user >>>>>>>> programs are executed. >>>>>>>>> Aljoscha >>>>>>>>> >>>>>>>>> [1] >> https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/coders/Coder.java#L184 >> |
Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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That's for sure. I am not claiming against it. What I am saying
is that we don't necessarily need a true "sorting" in this
particular use case. We only need to cluster records with the same
keys together. We don't need the keys to be logically sorted. What
I am saying is that for clustering the keys a binary order is
enough. I agree this would not work if we we were to implement an
operation such as DataStream#sort. Best, Dawid On 09/09/2020 08:22, Kurt Young wrote:
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Thanks for the comments Yu.
> First of all, for the performance testing result, I'm wondering whether the > sorting cost is counted in the result for both DataSet and refined > DataStream implementations. I could think of the saving of hash computation > and final iteration to emit the word-count result (processing a key at a > time could save such iteration), but not sure whether these cost savings > are at the same grade of comparing the key bytes. The results are times for end-to-end execution of a job. Therefore the sorting part is included. The actual target of the replacement is RocksDB, which does the serialization and key bytes comparison as well. On top of that it adds all the RocksDB bookkeeping. > However, I'm not fully convinced to introduce a new > `InternalKeyedStateBackend` interface. I agree that we don't need to take > the overhead of `AbstractKeyedStateBackend` since we don't plan to support > checkpoint for now, but why don't we directly write a state backend > implementation for bounded stream? Or are we planning to introduce more > internal state backends in future? What's more, the current design of > `InternalKeyedStateBackend` in the FLIP document seems to be extending as > many interfaces as `AbstractedKeyedStateBackend` implements, which I guess > is a typo. Maybe I was not clear enough about the change. This change does not "strip" the AbstractKeyedStateBackend of any functionalities. My intent is not to remove any methods of the AbstractKeyedStateBackend. The problem here is that the AbstractKeyedStateBackend is an abstract class (duh ;)), which does have some predefined implementation. Moreover it requires objects such as InternalKeyContex, CloseableRegistry etc. to be constructed, which we don't need/want e.g. in the single key state backend. My intention here is to make the StateBackend return only pure interfaces. (AbstractKeyedStateBackend is the only non-interface that StateBackend returns). In other words I just want to make AbstractKeyedStateBackend a proper interface. It is not a typo that InternalKeyedStateBackend extends the same interfaces as AbstractKeyedStateBackend does. > Thirdly, I suggest we name the special state backend as > `BoundedStreamInternalStateBackend`. And from our existing javadoc of > `StateBackend` it actually cannot be called a complete state backend...: "A > State Backend defines how the state of a streaming application is stored > and checkpointed". Thanks for the suggestion. Sure I can use that name. Yes I do agree it is not a full fledged StateBackend. I do want it to be an internal class, that is never used explicitly by users. > Lastly, I didn't find a detailed design of the "SingleKeyStateBackend" in > the FLIP, I did not put it into the design, because 1) I found it internal. It does not touch any public facing interfaces. 2) It is rather straightforward. Let me though quickly summarize and if you find it useful I can add it to the FLIP itself. > as how to detect the key switching That is rather straightforwad. The state backend works only with the assumption that the keys are sorted/grouped together. We keep the current key and in the setCurrentKey we check if the new key is different then the current one. Side note: yes, custom user operators which call setCurrentKey explicitly might not work in this setup. > remove the data (especially in the non-windowing > case), etc. We only ever keep a single value for a state object. Therefore ValueState is a very thin wrapper for a value, MapState for a HashMap, ListState for a List etc. When the key changes we simply set the wrapped value/map/state to null. I hope this clarifies a few things. Let me know if you have any questions. Best, Dawid On 17/09/2020 15:28, Yu Li wrote: > Hi all, > > Sorry for being late to the discussion, but I just noticed there are some > state backend related changes proposed in this FLIP, so would like to share > my two cents. > > First of all, for the performance testing result, I'm wondering whether the > sorting cost is counted in the result for both DataSet and refined > DataStream implementations. I could think of the saving of hash computation > and final iteration to emit the word-count result (processing a key at a > time could save such iteration), but not sure whether these cost savings > are at the same grade of comparing the key bytes. > > Regardless of the performance result, I agree that the capability of > removing the data after processing a key could prominently reduce the space > required by state, so introducing a new state backend for bounded stream > makes sense. > > However, I'm not fully convinced to introduce a new > `InternalKeyedStateBackend` interface. I agree that we don't need to take > the overhead of `AbstractKeyedStateBackend` since we don't plan to support > checkpoint for now, but why don't we directly write a state backend > implementation for bounded stream? Or are we planning to introduce more > internal state backends in future? What's more, the current design of > `InternalKeyedStateBackend` in the FLIP document seems to be extending as > many interfaces as `AbstractedKeyedStateBackend` implements, which I guess > is a typo. > > Thirdly, I suggest we name the special state backend as > `BoundedStreamInternalStateBackend`. And from our existing javadoc of > `StateBackend` it actually cannot be called a complete state backend...: "A > State Backend defines how the state of a streaming application is stored > and checkpointed". > > Lastly, I didn't find a detailed design of the "SingleKeyStateBackend" in > the FLIP, and suggest we write the key design down, such as how to detect > the key switching and remove the data (especially in the non-windowing > case), etc. > > Thanks. > > Best Regards, > Yu > > > On Wed, 9 Sep 2020 at 17:18, Kurt Young <[hidden email]> wrote: > >> Yes, I didn't intend to block this FLIP, and some of the comments are >> actually implementation details. >> And all of them are handled internally, not visible to users, thus we can >> also change or improve them >> in the future. >> >> Best, >> Kurt >> >> >> On Wed, Sep 9, 2020 at 5:03 PM Aljoscha Krettek <[hidden email]> >> wrote: >> >>> I think Kurts concerns/comments are very valid and we need to implement >>> such things in the future. However, I also think that we need to get >>> started somewhere and I think what's proposed in this FLIP is a good >>> starting point that we can build on. So we should not get paralyzed by >>> thinking too far ahead into the future. Does that make sense? >>> >>> Best, >>> Aljoscha >>> >>> On 08.09.20 16:59, Dawid Wysakowicz wrote: >>>> Ad. 1 >>>> >>>> Yes, you are right in principle. >>>> >>>> Let me though clarify my proposal a bit. The proposed sort-style >>>> execution aims at a generic KeyedProcessFunction were all the >>>> "aggregations" are actually performed in the user code. It tries to >>>> improve the performance by actually removing the need to use RocksDB >>> e.g.: >>>> private static final class Summer<K> >>>> extends KeyedProcessFunction<K, Tuple2<K, Integer>, >>>> Tuple2<K, Integer>> { >>>> >>>> .... >>>> >>>> @Override >>>> public void processElement( >>>> Tuple2<K, Integer> value, >>>> Context ctx, >>>> Collector<Tuple2<K, Integer>> out) throws Exception { >>>> if (!Objects.equals(timerRegistered.value(), >> Boolean.TRUE)) >>> { >>> ctx.timerService().registerEventTimeTimer(Long.MAX_VALUE); >>>> timerRegistered.update(true); >>>> } >>>> Integer v = counter.value(); >>>> Integer incomingValue = value.f1; >>>> if (v != null) { >>>> v += incomingValue; >>>> } else { >>>> v = incomingValue; >>>> } >>>> counter.update(v); >>>> } >>>> >>>> .... >>>> >>>> } >>>> >>>> Therefore I don't think the first part of your reply with separating >> the >>>> write and read workload applies here. We do not aim to create a >>>> competing API with the Table API. We think operations such as joins or >>>> analytical aggregations should be performed in Table API. >>>> >>>> As for the second part I agree it would be nice to fall back to the >>>> sorting approach only if a certain threshold of memory in a State >>>> Backend is used. This has some problems though. We would need a way to >>>> estimate the size of the occupied memory to tell when the threshold is >>>> reached. That is not easily doable by default e.g. in a >>>> MemoryStateBackend, as we do not serialize the values in the state >>>> backend by default. We would have to add that, but this would add the >>>> overhead of the serialization. >>>> >>>> This proposal aims at the cases where we do have a large state that >> will >>>> not fit into the memory and without the change users are forced to use >>>> RocksDB. If the state fits in memory I agree it will be better to do >>>> hash-based aggregations e.g. using the MemoryStateBackend. Therefore I >>>> think it is important to give users the choice to use one or the other >>>> approach. We might discuss which approach should be the default for >>>> RuntimeMode.BATCH proposed in FLIP-134. Should it be hash-based with >>>> user configured state backend or sorting-based with a single key at a >>>> time backend. Moreover we could think if we should let users choose the >>>> sort vs hash "state backend" per operator. Would that suffice? >>>> >>>> Ad. 2 >>>> >>>> I still think we can just use the first X bytes of the serialized form >>>> as the normalized key and fallback to comparing full keys on clashes. >> It >>>> is because we are actually not interested in a logical order, but we >>>> care only about the "grouping" aspect of the sorting. Therefore I think >>>> its enough to compare only parts of the full key as the normalized key. >>>> >>>> Thanks again for the really nice and thorough feedback! >>>> >>>> Best, >>>> >>>> Dawid >>>> >>>> On 08/09/2020 14:47, Kurt Young wrote: >>>>> Regarding #1, yes the state backend is definitely hash-based >> execution. >>>>> However there are some differences between >>>>> batch hash-based execution. The key difference is *random access & >>>>> read/write mixed workload". For example, by using >>>>> state backend in streaming execution, one have to mix the read and >> write >>>>> operations and all of them are actually random >>>>> access. But in a batch hash execution, we could divide the phases into >>>>> write and read. For example, we can build the >>>>> hash table first, with only write operations. And once the build is >>> done, >>>>> we can start to read and trigger the user codes. >>>>> Take hash aggregation which blink planner implemented as an example, >>> during >>>>> building phase, as long as the hash map >>>>> could fit into memory, we will update the accumulators directly in the >>> hash >>>>> map. And once we are running out of memory, >>>>> we then fall back to sort based execution. It improves the >> performance a >>>>> lot if the incoming data can be processed in >>>>> memory. >>>>> >>>>> Regarding #2, IIUC you are actually describing a binary format of key, >>> not >>>>> normalized key which is used in DataSet. I will >>>>> take String for example. If we have lots of keys with length all >> greater >>>>> than, let's say 20. In your proposal, you will encode >>>>> the whole string in the prefix of your composed data ( <key> + >>> <timestamp> >>>>> + <record> ). And when you compare >>>>> records, you will actually compare the *whole* key of the record. For >>>>> normalized key, it's fixed-length in this case, IIRC it will >>>>> take 8 bytes to represent the string. And the sorter will store the >>>>> normalized key and offset in a dedicated array. When doing >>>>> the sorting, it only sorts this *small* array. If the normalized keys >>> are >>>>> different, you could immediately tell which is greater from >>>>> normalized keys. You only have to compare the full keys if the >>> normalized >>>>> keys are equal and you know in this case the normalized >>>>> key couldn't represent the full key. The reason why Dataset is doing >>> this >>>>> is it's super cache efficient by sorting the *small* array. >>>>> The idea is borrowed from this paper [1]. Let me know if I missed or >>>>> misunderstood anything. >>>>> >>>>> [1] https://dl.acm.org/doi/10.5555/615232.615237 (AlphaSort: a >>>>> cache-sensitive parallel external sort) >>>>> >>>>> Best, >>>>> Kurt >>>>> >>>>> >>>>> On Tue, Sep 8, 2020 at 5:05 PM Dawid Wysakowicz < >> [hidden email] >>>>> wrote: >>>>> >>>>>> Hey Kurt, >>>>>> >>>>>> Thank you for comments! >>>>>> >>>>>> Ad. 1 I might have missed something here, but as far as I see it is >>> that >>>>>> using the current execution stack with regular state backends >> (RocksDB >>>>>> in particular if we want to have spilling capabilities) is equivalent >>> to >>>>>> hash-based execution. I can see a different spilling state backend >>>>>> implementation in the future, but I think it is not batch specifc. Or >>> am >>>>>> I missing something? >>>>>> >>>>>> Ad. 2 Totally agree that normalized keys are important to the >>>>>> performance. I think though TypeComparators are not a necessity to >> have >>>>>> that. Actually this proposal is heading towards only ever performing >>>>>> "normalized keys" comparison. I have not included in the proposal the >>>>>> binary format which we will use for sorting (partially because I >>> forgot, >>>>>> and partially because I thought it was too much of an implementation >>>>>> detail). Let me include it here though, as it might clear the >> situation >>>>>> a bit here. >>>>>> >>>>>> In DataSet, at times we have KeySelectors which extract keys based on >>>>>> field indices or names. This allows in certain situation to extract >> the >>>>>> key from serialized records. Compared to DataSet, in DataStream, the >>> key >>>>>> is always described with a black-box KeySelector, or differently >> with a >>>>>> function which extracts a key from a deserialized record. In turn >>> there >>>>>> is no way to create a comparator that could compare records by >>>>>> extracting the key from a serialized record (neither with, nor >> without >>>>>> key normalization). We suggest that the input for the sorter will be >>>>>> >>>>>> <key> + <timestamp> + <record> >>>>>> >>>>>> Without having the key prepended we would have to deserialize the >>> record >>>>>> for every key comparison. >>>>>> >>>>>> Therefore if we agree that we perform binary comparison for keys >> (which >>>>>> are always prepended), it is actually equivalent to a DataSet with >>>>>> TypeComparators that support key normalization. >>>>>> >>>>>> Let me know if that is clear, or I have missed something here. >>>>>> >>>>>> Best, >>>>>> >>>>>> Dawid >>>>>> >>>>>> On 08/09/2020 03:39, Kurt Young wrote: >>>>>>> Hi Dawid, thanks for bringing this up, it's really exciting to see >>> that >>>>>>> batch execution is introduced in DataStream. From the flip, it seems >>>>>>> we are sticking with sort based execution mode (at least for now), >>> which >>>>>>> will sort the whole input data before any *keyed* operation is >>>>>>> executed. I have two comments here: >>>>>>> >>>>>>> 1. Do we want to introduce hash-based execution in the future? Sort >>> is a >>>>>>> safe choice but not the best in lots of cases. IIUC we only need >>>>>>> to make sure that before the framework finishes dealing with one >> key, >>> the >>>>>>> operator doesn't see any data belonging to other keys, thus >>>>>>> hash-based execution would also do the trick. Oon tricky thing the >>>>>>> framework might need to deal with is memory constraint and spilling >>>>>>> in the hash map, but Flink also has some good knowledge about these >>>>>> stuff. >>>>>>> 2. Going back to sort-based execution and how to sort keys. From my >>>>>>> experience, the performance of sorting would be one the most >> important >>>>>>> things if we want to achieve good performance of batch execution. >> And >>>>>>> normalized keys are actually the key of the performance of sorting. >>>>>>> If we want to get rid of TypeComparator, I think we still need to >>> find a >>>>>>> way to introduce this back. >>>>>>> >>>>>>> Best, >>>>>>> Kurt >>>>>>> >>>>>>> >>>>>>> On Tue, Sep 8, 2020 at 3:04 AM Aljoscha Krettek < >> [hidden email]> >>>>>> wrote: >>>>>>>> Yes, I think we can address the problem of indeterminacy in a >>> separate >>>>>>>> FLIP because we're already in it. >>>>>>>> >>>>>>>> Aljoscha >>>>>>>> >>>>>>>> On 07.09.20 17:00, Dawid Wysakowicz wrote: >>>>>>>>> @Seth That's a very good point. I agree that RocksDB has the same >>>>>>>>> problem. I think we can use the same approach for the sorted >>> shuffles >>>>>>>>> then. @Aljoscha I agree we should think about making it more >>> resilient, >>>>>>>>> as I guess users might have problems already if they use keys with >>>>>>>>> non-deterministic binary representation. How do you feel about >>>>>>>>> addressing that separately purely to limit the scope of this FLIP? >>>>>>>>> >>>>>>>>> @Aljoscha I tend to agree with you that the best place to actually >>>>>> place >>>>>>>>> the sorting would be in the InputProcessor(s). If there are no >> more >>>>>>>>> suggestions in respect to that issue. I'll put this proposal for >>>>>> voting. >>>>>>>>> @all Thank you for the feedback so far. I'd like to start a voting >>>>>>>>> thread on the proposal tomorrow. Therefore I'd appreciate if you >>>>>> comment >>>>>>>>> before that, if you still have some outstanding ideas. >>>>>>>>> >>>>>>>>> Best, >>>>>>>>> >>>>>>>>> Dawid >>>>>>>>> >>>>>>>>> On 04/09/2020 17:13, Aljoscha Krettek wrote: >>>>>>>>>> Seth is right, I was just about to write that as well. There is a >>>>>>>>>> problem, though, because some of our TypeSerializers are not >>>>>>>>>> deterministic even though we use them as if they were. Beam >>> excludes >>>>>>>>>> the FloatCoder, for example, and the AvroCoder in certain cases. >>> I'm >>>>>>>>>> pretty sure there is also weirdness going on in our >> KryoSerializer. >>>>>>>>>> On 04.09.20 14:59, Seth Wiesman wrote: >>>>>>>>>>> There is already an implicit assumption the TypeSerializer for >>> keys >>>>>> is >>>>>>>>>>> stable/deterministic, RocksDB compares keys using their >> serialized >>>>>> byte >>>>>>>>>>> strings. I think this is a non-issue (or at least it's not >>> changing >>>>>> the >>>>>>>>>>> status quo). >>>>>>>>>>> >>>>>>>>>>> On Fri, Sep 4, 2020 at 6:39 AM Timo Walther <[hidden email] >>>>>>>> wrote: >>>>>>>>>>>> +1 for getting rid of the TypeComparator interface and rely on >>> the >>>>>>>>>>>> serialized representation for grouping. >>>>>>>>>>>> >>>>>>>>>>>> Adding a new type to DataStream API is quite difficult at the >>> moment >>>>>>>>>>>> due >>>>>>>>>>>> to too many components that are required: TypeInformation >> (tries >>> to >>>>>>>>>>>> deal >>>>>>>>>>>> with logical fields for TypeComparators), TypeSerializer (incl. >>> it's >>>>>>>>>>>> snapshot interfaces), and TypeComparator (with many methods and >>>>>>>>>>>> internals such normalized keys etc.). >>>>>>>>>>>> >>>>>>>>>>>> If necessary, we can add more simple comparison-related methods >>> to >>>>>> the >>>>>>>>>>>> TypeSerializer interface itself in the future (like >>>>>>>>>>>> TypeSerializer.isDeterministic). >>>>>>>>>>>> >>>>>>>>>>>> Regards, >>>>>>>>>>>> Timo >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> On 04.09.20 11:48, Aljoscha Krettek wrote: >>>>>>>>>>>>> Thanks for publishing the FLIP! >>>>>>>>>>>>> >>>>>>>>>>>>> On 2020/09/01 06:49:06, Dawid Wysakowicz < >>> [hidden email]> >>>>>>>>>>>>> wrote: >>>>>>>>>>>>>> 1. How to sort/group keys? What representation of the >> key >>>>>>>>>>>>>> should we >>>>>>>>>>>>>> use? Should we sort on the binary form or should we >>> depend >>>>>> on >>>>>>>>>>>>>> Comparators being available. >>>>>>>>>>>>> Initially, I suggested to Dawid (in private) to do the >>>>>>>>>>>>> sorting/grouping >>>>>>>>>>>> by using the binary representation. Then my opinion switched >> and >>> I >>>>>>>>>>>> thought >>>>>>>>>>>> we should use TypeComparator/Comparator because that's what the >>>>>>>>>>>> DataSet API >>>>>>>>>>>> uses. After talking to Stephan, I'm again encouraged in my >>> opinion >>>>>>>>>>>> to use >>>>>>>>>>>> the binary representation because it means we can eventually >> get >>> rid >>>>>>>>>>>> of the >>>>>>>>>>>> TypeComparator interface, which is a bit complicated, and >>> because we >>>>>>>>>>>> don't >>>>>>>>>>>> need any good order in our sort, we only need the grouping. >>>>>>>>>>>>> This comes with some problems, though: we need to ensure that >>> the >>>>>>>>>>>> TypeSerializer of the type we're sorting is >> stable/deterministic. >>>>>>>>>>>> Beam has >>>>>>>>>>>> infrastructure for this in the form of >>> Coder.verifyDeterministic() >>>>>> [1] >>>>>>>>>>>> which we don't have right now and should add if we go down this >>>>>> path. >>>>>>>>>>>>>> 2. Where in the stack should we apply the sorting (this >>>>>> rather a >>>>>>>>>>>>>> discussion about internals) >>>>>>>>>>>>> Here, I'm gravitating towards the third option of implementing >>> it >>>>>>>>>>>>> in the >>>>>>>>>>>> layer of the StreamTask, which probably means implementing a >>> custom >>>>>>>>>>>> InputProcessor. I think it's best to do it in this layer >> because >>> we >>>>>>>>>>>> would >>>>>>>>>>>> not mix concerns of different layers as we would if we >>> implemented >>>>>>>>>>>> this as >>>>>>>>>>>> a custom StreamOperator. I think this solution is also best >> when >>> it >>>>>>>>>>>> comes >>>>>>>>>>>> to multi-input operators. >>>>>>>>>>>>>> 3. How should we deal with custom implementations of >>>>>>>>>>>>>> StreamOperators >>>>>>>>>>>>> I think the cleanest solution would be to go through the >>> complete >>>>>>>>>>>> operator lifecycle for every key, because then the watermark >>> would >>>>>> not >>>>>>>>>>>> oscillate between -Inf and +Inf and we would not break the >>>>>> semantical >>>>>>>>>>>> guarantees that we gave to operators so far, in that the >>> watermark >>>>>> is >>>>>>>>>>>> strictly monotonically increasing. However, I don't think this >>>>>>>>>>>> solution is >>>>>>>>>>>> feasible because it would come with too much overhead. We >> should >>>>>>>>>>>> solve this >>>>>>>>>>>> problem via documentation and maybe educate people to not query >>> the >>>>>>>>>>>> current >>>>>>>>>>>> watermark or not rely on the watermark being monotonically >>>>>>>>>>>> increasing in >>>>>>>>>>>> operator implementations to allow the framework more freedoms >> in >>> how >>>>>>>>>>>> user >>>>>>>>>>>> programs are executed. >>>>>>>>>>>>> Aljoscha >>>>>>>>>>>>> >>>>>>>>>>>>> [1] >> https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/coders/Coder.java#L184 >>> |
Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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The problem is that I could not use this "state backend" in a StreamOperator. The goal of this effort is that it is mostly transparent to all the implementations of StreamOperator(s). Right now StreamOperator retrieves AbstractKeyedStateBackend through StreamOperatorContext which instantiates it in StreamTaskInitializer etc. The problem is that a big chunk of the current code base uses the AbstractKeyedStateBackend, whereas it really just needs an interface not that particular implementation. The change is really only about separating the contract (InternalKeyedStateBackend) from the implementation (AbstractKeyedStateBackend). My thinking is that it is only an approach to fix a mistake of the past that StateBackend returns a particular implementation rather than a contract. I do agree I don't need the `SnapshotStrategy` and
`CheckpointListener` interfaces. The thing though is that the
runtime expects those contracts from an AbstractKeyedStateBackend.
BTW, If you'd like to see how does this change really looks like you can check the PR I already opened for it: https://github.com/apache/flink/pull/13405/files
Checking the FLIP more closely I found below description: "With a high number of keys it (HeapStateBackend) suffers a significant penalty and becomes even less performant for that particular case than the sorting approach", does it mean "HeapStateBackend" outperformed "SingleKeyStateBackend" when the number of keys is relatively smallCorrect, the goal though is not to outperform the HeapStateBackend. The single key state backend requires sorted inputs which come with a price. The main goal is to outperform RocksDBStateBackend, which is necessary for large states.
Thanks for the summary. I think it's more specific and could help readers to better understand why we cannot use HeapKeyedStateBackend directly, than the single line description "when the StateBackend observes a new incoming key it will reset all acquired state objects so far". What do you think?Sure, I can add it to the document. Best, Dawid On 18/09/2020 14:29, Yu Li wrote:
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Re: [DISCUSS] FLIP-140: Introduce bounded style execution for keyed streams
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Ok, got your point now. I agree that it makes more sense to make StateBackend return a contract instead of a particular implementation. How about we name the new interface as `CheckpointableKeyedStateBackend`? We could make `BoundedStreamStateBackend` implement `CheckpointableKeyedStateBackend` but without checkpoint related operations yet, whereas reserving the possibility that the bounded stream also supports checkpoint in future. What do you think?Sounds good to me. Will update the FLIP with the new name. On 18/09/2020 15:31, Yu Li wrote:
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