[DISCUSS] FLIP-149: Introduce the KTable Connector

[Shengkai Fang]

Hi, devs.

Jark and I want to start a new FLIP to introduce the KTable connector. The
KTable is a shortcut of "Kafka Table", it also has the same semantics with
the KTable notion in Kafka Stream.

FLIP-149:
https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector

Currently many users have expressed their needs for the upsert Kafka by
mail lists and issues. The KTable connector has several benefits for users:

1. Users are able to interpret a compacted Kafka Topic as an upsert stream
in Apache Flink. And also be able to write a changelog stream to Kafka
(into a compacted topic).
2. As a part of the real time pipeline, store join or aggregate result (may
contain updates) into a Kafka topic for further calculation;
3. The semantic of the KTable connector is just the same as KTable in Kafka
Stream. So it's very handy for Kafka Stream and KSQL users. We have seen
several questions in the mailing list asking how to model a KTable and how
to join a KTable in Flink SQL.

We hope it can expand the usage of the Flink with Kafka.

I'm looking forward to your feedback.

Best,
Shengkai

[Jingsong Li]

Thanks Shengkai for your proposal.

+1 for this feature.

> Future Work: Support bounded KTable source

I don't think it should be a future work, I think it is one of the
important concepts of this FLIP. We need to understand it now.

Intuitively, a ktable in my opinion is a bounded table rather than a
stream, so select should produce a bounded table by default.

I think we can list Kafka related knowledge, because the word `ktable` is
easy to associate with ksql related concepts. (If possible, it's better to
unify with it)

What do you think?

> value.fields-include

What about the default behavior of KSQL?

Best,
Jingsong

On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <[hidden email]> wrote:

> Hi, devs.
>
> Jark and I want to start a new FLIP to introduce the KTable connector. The
> KTable is a shortcut of "Kafka Table", it also has the same semantics with
> the KTable notion in Kafka Stream.
>
> FLIP-149:
>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
>
> Currently many users have expressed their needs for the upsert Kafka by
> mail lists and issues. The KTable connector has several benefits for users:
>
> 1. Users are able to interpret a compacted Kafka Topic as an upsert stream
> in Apache Flink. And also be able to write a changelog stream to Kafka
> (into a compacted topic).
> 2. As a part of the real time pipeline, store join or aggregate result (may
> contain updates) into a Kafka topic for further calculation;
> 3. The semantic of the KTable connector is just the same as KTable in Kafka
> Stream. So it's very handy for Kafka Stream and KSQL users. We have seen
> several questions in the mailing list asking how to model a KTable and how
> to join a KTable in Flink SQL.
>
> We hope it can expand the usage of the Flink with Kafka.
>
> I'm looking forward to your feedback.
>
> Best,
> Shengkai
>

--
Best, Jingsong Lee

[Danny Chan]

The concept seems conflicts with the Flink abstraction “dynamic table”, in Flink we see both “stream” and “table” as a dynamic table,

I think we should make clear first how to express stream and table specific features on one “dynamic table”,
it is more natural for KSQL because KSQL takes stream and table as different abstractions for representing collections. In KSQL, only table is mutable and can have a primary key.

Does this connector belongs to the “table” scope or “stream” scope ?

Some of the concepts (such as the primary key on stream) should be suitable for all the connectors, not just Kafka, Shouldn’t this be an extension of existing Kafka connector instead of a totally new connector ? What about the other connectors ?

Because this touches the core abstraction of Flink, we better have a top-down overall design, following the KSQL directly is not the answer.

P.S. For the source
> Shouldn’t this be an extension of existing Kafka connector instead of a totally new connector ?

How could we achieve that (e.g. set up the parallelism correctly) ?

Best,
Danny Chan
在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]>，写道：

> Thanks Shengkai for your proposal.
>
> +1 for this feature.
>
> > Future Work: Support bounded KTable source
>
> I don't think it should be a future work, I think it is one of the
> important concepts of this FLIP. We need to understand it now.
>
> Intuitively, a ktable in my opinion is a bounded table rather than a
> stream, so select should produce a bounded table by default.
>
> I think we can list Kafka related knowledge, because the word `ktable` is
> easy to associate with ksql related concepts. (If possible, it's better to
> unify with it)
>
> What do you think?
>
> > value.fields-include
>
> What about the default behavior of KSQL?
>
> Best,
> Jingsong
>
> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <[hidden email]> wrote:
>
> > Hi, devs.
> >
> > Jark and I want to start a new FLIP to introduce the KTable connector. The
> > KTable is a shortcut of "Kafka Table", it also has the same semantics with
> > the KTable notion in Kafka Stream.
> >
> > FLIP-149:
> >
> > https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> >
> > Currently many users have expressed their needs for the upsert Kafka by
> > mail lists and issues. The KTable connector has several benefits for users:
> >
> > 1. Users are able to interpret a compacted Kafka Topic as an upsert stream
> > in Apache Flink. And also be able to write a changelog stream to Kafka
> > (into a compacted topic).
> > 2. As a part of the real time pipeline, store join or aggregate result (may
> > contain updates) into a Kafka topic for further calculation;
> > 3. The semantic of the KTable connector is just the same as KTable in Kafka
> > Stream. So it's very handy for Kafka Stream and KSQL users. We have seen
> > several questions in the mailing list asking how to model a KTable and how
> > to join a KTable in Flink SQL.
> >
> > We hope it can expand the usage of the Flink with Kafka.
> >
> > I'm looking forward to your feedback.
> >
> > Best,
> > Shengkai
> >
>
>
> --
> Best, Jingsong Lee

[Jark Wu-2]

Hi Jingsong,

As the FLIP describes, "KTable connector produces a changelog stream, where
each data record represents an update or delete event.".
Therefore, a ktable source is an unbounded stream source. Selecting a
ktable source is similar to selecting a kafka source with debezium-json
format
that it never ends and the results are continuously updated.

It's possible to have a bounded ktable source in the future, for example,
add an option 'bounded=true' or 'end-offset=xxx'.
In this way, the ktable will produce a bounded changelog stream.
So I think this can be a compatible feature in the future.

I don't think we should associate with ksql related concepts. Actually, we
didn't introduce any concepts from KSQL (e.g. Stream vs Table notion).
The "ktable" is just a connector name, we can also call it
"compacted-kafka" or something else.
Calling it "ktable" is just because KSQL users can migrate to Flink SQL
easily.

Regarding the "value.fields-include", this is an option introduced in
FLIP-107 for Kafka connector.
I think we should keep the same behavior with the Kafka connector. I'm not
sure what's the default behavior of KSQL.
But I guess it also stores the keys in value from this example docs (see
the "users_original" table) [1].

Best,
Jark

[1]:
https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table


On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]> wrote:

> The concept seems conflicts with the Flink abstraction “dynamic table”, in
> Flink we see both “stream” and “table” as a dynamic table,
>
> I think we should make clear first how to express stream and table
> specific features on one “dynamic table”,
> it is more natural for KSQL because KSQL takes stream and table as
> different abstractions for representing collections. In KSQL, only table is
> mutable and can have a primary key.
>
> Does this connector belongs to the “table” scope or “stream” scope ?
>
> Some of the concepts (such as the primary key on stream) should be
> suitable for all the connectors, not just Kafka, Shouldn’t this be an
> extension of existing Kafka connector instead of a totally new connector ?
> What about the other connectors ?
>
> Because this touches the core abstraction of Flink, we better have a
> top-down overall design, following the KSQL directly is not the answer.
>
> P.S. For the source
> > Shouldn’t this be an extension of existing Kafka connector instead of a
> totally new connector ?
>
> How could we achieve that (e.g. set up the parallelism correctly) ?
>
> Best,
> Danny Chan
> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]>，写道：
> > Thanks Shengkai for your proposal.
> >
> > +1 for this feature.
> >
> > > Future Work: Support bounded KTable source
> >
> > I don't think it should be a future work, I think it is one of the
> > important concepts of this FLIP. We need to understand it now.
> >
> > Intuitively, a ktable in my opinion is a bounded table rather than a
> > stream, so select should produce a bounded table by default.
> >
> > I think we can list Kafka related knowledge, because the word `ktable` is
> > easy to associate with ksql related concepts. (If possible, it's better
> to
> > unify with it)
> >
> > What do you think?
> >
> > > value.fields-include
> >
> > What about the default behavior of KSQL?
> >
> > Best,
> > Jingsong
> >
> > On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <[hidden email]> wrote:
> >
> > > Hi, devs.
> > >
> > > Jark and I want to start a new FLIP to introduce the KTable connector.
> The
> > > KTable is a shortcut of "Kafka Table", it also has the same semantics
> with
> > > the KTable notion in Kafka Stream.
> > >
> > > FLIP-149:
> > >
> > >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> > >
> > > Currently many users have expressed their needs for the upsert Kafka by
> > > mail lists and issues. The KTable connector has several benefits for
> users:
> > >
> > > 1. Users are able to interpret a compacted Kafka Topic as an upsert
> stream
> > > in Apache Flink. And also be able to write a changelog stream to Kafka
> > > (into a compacted topic).
> > > 2. As a part of the real time pipeline, store join or aggregate result
> (may
> > > contain updates) into a Kafka topic for further calculation;
> > > 3. The semantic of the KTable connector is just the same as KTable in
> Kafka
> > > Stream. So it's very handy for Kafka Stream and KSQL users. We have
> seen
> > > several questions in the mailing list asking how to model a KTable and
> how
> > > to join a KTable in Flink SQL.
> > >
> > > We hope it can expand the usage of the Flink with Kafka.
> > >
> > > I'm looking forward to your feedback.
> > >
> > > Best,
> > > Shengkai
> > >
> >
> >
> > --
> > Best, Jingsong Lee
>

[Jark Wu-2]

Hi Danny,

First of all, we didn't introduce any concepts from KSQL (e.g. Stream vs
Table notion).
This new connector will produce a changelog stream, so it's still a dynamic
table and doesn't conflict with Flink core concepts.

The "ktable" is just a connector name, we can also call it
"compacted-kafka" or something else.
Calling it "ktable" is just because KSQL users can migrate to Flink SQL
easily.

Regarding to why introducing a new connector vs a new property in existing
kafka connector:

I think the main reason is that we want to have a clear separation for such
two use cases, because they are very different.
We also listed reasons in the FLIP, including:

1) It's hard to explain what's the behavior when users specify the start
offset from a middle position (e.g. how to process non exist delete
events).
    It's dangerous if users do that. So we don't provide the offset option
in the new connector at the moment.
2) It's a different perspective/abstraction on the same kafka topic (append
vs. upsert). It would be easier to understand if we can separate them
    instead of mixing them in one connector. The new connector requires
hash sink partitioner, primary key declared, regular format.
    If we mix them in one connector, it might be confusing how to use the
options correctly.
3) The semantic of the KTable connector is just the same as KTable in Kafka
Stream. So it's very handy for Kafka Stream and KSQL users.
    We have seen several questions in the mailing list asking how to model
a KTable and how to join a KTable in Flink SQL.

Best,
Jark

On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:

> Hi Jingsong,
>
> As the FLIP describes, "KTable connector produces a changelog stream,
> where each data record represents an update or delete event.".
> Therefore, a ktable source is an unbounded stream source. Selecting a
> ktable source is similar to selecting a kafka source with debezium-json
> format
> that it never ends and the results are continuously updated.
>
> It's possible to have a bounded ktable source in the future, for example,
> add an option 'bounded=true' or 'end-offset=xxx'.
> In this way, the ktable will produce a bounded changelog stream.
> So I think this can be a compatible feature in the future.
>
> I don't think we should associate with ksql related concepts. Actually, we
> didn't introduce any concepts from KSQL (e.g. Stream vs Table notion).
> The "ktable" is just a connector name, we can also call it
> "compacted-kafka" or something else.
> Calling it "ktable" is just because KSQL users can migrate to Flink SQL
> easily.
>
> Regarding the "value.fields-include", this is an option introduced in
> FLIP-107 for Kafka connector.
> I think we should keep the same behavior with the Kafka connector. I'm not
> sure what's the default behavior of KSQL.
> But I guess it also stores the keys in value from this example docs (see
> the "users_original" table) [1].
>
> Best,
> Jark
>
> [1]:
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
>
>
> On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]> wrote:
>
>> The concept seems conflicts with the Flink abstraction “dynamic table”,
>> in Flink we see both “stream” and “table” as a dynamic table,
>>
>> I think we should make clear first how to express stream and table
>> specific features on one “dynamic table”,
>> it is more natural for KSQL because KSQL takes stream and table as
>> different abstractions for representing collections. In KSQL, only table is
>> mutable and can have a primary key.
>>
>> Does this connector belongs to the “table” scope or “stream” scope ?
>>
>> Some of the concepts (such as the primary key on stream) should be
>> suitable for all the connectors, not just Kafka, Shouldn’t this be an
>> extension of existing Kafka connector instead of a totally new connector ?
>> What about the other connectors ?
>>
>> Because this touches the core abstraction of Flink, we better have a
>> top-down overall design, following the KSQL directly is not the answer.
>>
>> P.S. For the source
>> > Shouldn’t this be an extension of existing Kafka connector instead of a
>> totally new connector ?
>>
>> How could we achieve that (e.g. set up the parallelism correctly) ?
>>
>> Best,
>> Danny Chan
>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]>，写道：
>> > Thanks Shengkai for your proposal.
>> >
>> > +1 for this feature.
>> >
>> > > Future Work: Support bounded KTable source
>> >
>> > I don't think it should be a future work, I think it is one of the
>> > important concepts of this FLIP. We need to understand it now.
>> >
>> > Intuitively, a ktable in my opinion is a bounded table rather than a
>> > stream, so select should produce a bounded table by default.
>> >
>> > I think we can list Kafka related knowledge, because the word `ktable`
>> is
>> > easy to associate with ksql related concepts. (If possible, it's better
>> to
>> > unify with it)
>> >
>> > What do you think?
>> >
>> > > value.fields-include
>> >
>> > What about the default behavior of KSQL?
>> >
>> > Best,
>> > Jingsong
>> >
>> > On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <[hidden email]>
>> wrote:
>> >
>> > > Hi, devs.
>> > >
>> > > Jark and I want to start a new FLIP to introduce the KTable
>> connector. The
>> > > KTable is a shortcut of "Kafka Table", it also has the same semantics
>> with
>> > > the KTable notion in Kafka Stream.
>> > >
>> > > FLIP-149:
>> > >
>> > >
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
>> > >
>> > > Currently many users have expressed their needs for the upsert Kafka
>> by
>> > > mail lists and issues. The KTable connector has several benefits for
>> users:
>> > >
>> > > 1. Users are able to interpret a compacted Kafka Topic as an upsert
>> stream
>> > > in Apache Flink. And also be able to write a changelog stream to Kafka
>> > > (into a compacted topic).
>> > > 2. As a part of the real time pipeline, store join or aggregate
>> result (may
>> > > contain updates) into a Kafka topic for further calculation;
>> > > 3. The semantic of the KTable connector is just the same as KTable in
>> Kafka
>> > > Stream. So it's very handy for Kafka Stream and KSQL users. We have
>> seen
>> > > several questions in the mailing list asking how to model a KTable
>> and how
>> > > to join a KTable in Flink SQL.
>> > >
>> > > We hope it can expand the usage of the Flink with Kafka.
>> > >
>> > > I'm looking forward to your feedback.
>> > >
>> > > Best,
>> > > Shengkai
>> > >
>> >
>> >
>> > --
>> > Best, Jingsong Lee
>>
>

[Konstantin Knauf-4]

Hi Shengkai,

Thank you for driving this effort. I believe this a very important feature
for many users who use Kafka and Flink SQL together. A few questions and
thoughts:

* Is your example "Use KTable as a reference/dimension table" correct? It
uses the "kafka" connector and does not specify a primary key.

* Will it be possible to use a "ktable" table directly as a dimensional
table in temporal join (*based on event time*) (FLIP-132)? This is not
completely clear to me from the FLIP.

* I'd personally prefer not to introduce a new connector and instead to
extend the Kafka connector. We could add an additional property "compacted"
= "true"|"false". If it is set to "true", we can add additional validation
logic (e.g. "scan.startup.mode" can not be set, primary key required,
etc.). If we stick to a separate connector I'd not call it "ktable", but
rather "compacted-kafka" or similar. KTable seems to carry more implicit
meaning than we want to imply here.

* I agree that this is not a bounded source. If we want to support a
bounded mode, this is an orthogonal concern that also applies to other
unbounded sources.

Best,

Konstantin

On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:

> Hi Danny,
>
> First of all, we didn't introduce any concepts from KSQL (e.g. Stream vs
> Table notion).
> This new connector will produce a changelog stream, so it's still a dynamic
> table and doesn't conflict with Flink core concepts.
>
> The "ktable" is just a connector name, we can also call it
> "compacted-kafka" or something else.
> Calling it "ktable" is just because KSQL users can migrate to Flink SQL
> easily.
>
> Regarding to why introducing a new connector vs a new property in existing
> kafka connector:
>
> I think the main reason is that we want to have a clear separation for such
> two use cases, because they are very different.
> We also listed reasons in the FLIP, including:
>
> 1) It's hard to explain what's the behavior when users specify the start
> offset from a middle position (e.g. how to process non exist delete
> events).
>     It's dangerous if users do that. So we don't provide the offset option
> in the new connector at the moment.
> 2) It's a different perspective/abstraction on the same kafka topic (append
> vs. upsert). It would be easier to understand if we can separate them
>     instead of mixing them in one connector. The new connector requires
> hash sink partitioner, primary key declared, regular format.
>     If we mix them in one connector, it might be confusing how to use the
> options correctly.
> 3) The semantic of the KTable connector is just the same as KTable in Kafka
> Stream. So it's very handy for Kafka Stream and KSQL users.
>     We have seen several questions in the mailing list asking how to model
> a KTable and how to join a KTable in Flink SQL.
>
> Best,
> Jark
>
> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
>
> > Hi Jingsong,
> >
> > As the FLIP describes, "KTable connector produces a changelog stream,
> > where each data record represents an update or delete event.".
> > Therefore, a ktable source is an unbounded stream source. Selecting a
> > ktable source is similar to selecting a kafka source with debezium-json
> > format
> > that it never ends and the results are continuously updated.
> >
> > It's possible to have a bounded ktable source in the future, for example,
> > add an option 'bounded=true' or 'end-offset=xxx'.
> > In this way, the ktable will produce a bounded changelog stream.
> > So I think this can be a compatible feature in the future.
> >
> > I don't think we should associate with ksql related concepts. Actually,
> we
> > didn't introduce any concepts from KSQL (e.g. Stream vs Table notion).
> > The "ktable" is just a connector name, we can also call it
> > "compacted-kafka" or something else.
> > Calling it "ktable" is just because KSQL users can migrate to Flink SQL
> > easily.
> >
> > Regarding the "value.fields-include", this is an option introduced in
> > FLIP-107 for Kafka connector.
> > I think we should keep the same behavior with the Kafka connector. I'm
> not
> > sure what's the default behavior of KSQL.
> > But I guess it also stores the keys in value from this example docs (see
> > the "users_original" table) [1].
> >
> > Best,
> > Jark
> >
> > [1]:
> >
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> >
> >
> > On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]> wrote:
> >
> >> The concept seems conflicts with the Flink abstraction “dynamic table”,
> >> in Flink we see both “stream” and “table” as a dynamic table,
> >>
> >> I think we should make clear first how to express stream and table
> >> specific features on one “dynamic table”,
> >> it is more natural for KSQL because KSQL takes stream and table as
> >> different abstractions for representing collections. In KSQL, only
> table is
> >> mutable and can have a primary key.
> >>
> >> Does this connector belongs to the “table” scope or “stream” scope ?
> >>
> >> Some of the concepts (such as the primary key on stream) should be
> >> suitable for all the connectors, not just Kafka, Shouldn’t this be an
> >> extension of existing Kafka connector instead of a totally new
> connector ?
> >> What about the other connectors ?
> >>
> >> Because this touches the core abstraction of Flink, we better have a
> >> top-down overall design, following the KSQL directly is not the answer.
> >>
> >> P.S. For the source
> >> > Shouldn’t this be an extension of existing Kafka connector instead of
> a
> >> totally new connector ?
> >>
> >> How could we achieve that (e.g. set up the parallelism correctly) ?
> >>
> >> Best,
> >> Danny Chan
> >> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]>，写道：
> >> > Thanks Shengkai for your proposal.
> >> >
> >> > +1 for this feature.
> >> >
> >> > > Future Work: Support bounded KTable source
> >> >
> >> > I don't think it should be a future work, I think it is one of the
> >> > important concepts of this FLIP. We need to understand it now.
> >> >
> >> > Intuitively, a ktable in my opinion is a bounded table rather than a
> >> > stream, so select should produce a bounded table by default.
> >> >
> >> > I think we can list Kafka related knowledge, because the word `ktable`
> >> is
> >> > easy to associate with ksql related concepts. (If possible, it's
> better
> >> to
> >> > unify with it)
> >> >
> >> > What do you think?
> >> >
> >> > > value.fields-include
> >> >
> >> > What about the default behavior of KSQL?
> >> >
> >> > Best,
> >> > Jingsong
> >> >
> >> > On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <[hidden email]>
> >> wrote:
> >> >
> >> > > Hi, devs.
> >> > >
> >> > > Jark and I want to start a new FLIP to introduce the KTable
> >> connector. The
> >> > > KTable is a shortcut of "Kafka Table", it also has the same
> semantics
> >> with
> >> > > the KTable notion in Kafka Stream.
> >> > >
> >> > > FLIP-149:
> >> > >
> >> > >
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> >> > >
> >> > > Currently many users have expressed their needs for the upsert Kafka
> >> by
> >> > > mail lists and issues. The KTable connector has several benefits for
> >> users:
> >> > >
> >> > > 1. Users are able to interpret a compacted Kafka Topic as an upsert
> >> stream
> >> > > in Apache Flink. And also be able to write a changelog stream to
> Kafka
> >> > > (into a compacted topic).
> >> > > 2. As a part of the real time pipeline, store join or aggregate
> >> result (may
> >> > > contain updates) into a Kafka topic for further calculation;
> >> > > 3. The semantic of the KTable connector is just the same as KTable
> in
> >> Kafka
> >> > > Stream. So it's very handy for Kafka Stream and KSQL users. We have
> >> seen
> >> > > several questions in the mailing list asking how to model a KTable
> >> and how
> >> > > to join a KTable in Flink SQL.
> >> > >
> >> > > We hope it can expand the usage of the Flink with Kafka.
> >> > >
> >> > > I'm looking forward to your feedback.
> >> > >
> >> > > Best,
> >> > > Shengkai
> >> > >
> >> >
> >> >
> >> > --
> >> > Best, Jingsong Lee
> >>
> >
>

--

Konstantin Knauf

https://twitter.com/snntrable

https://github.com/knaufk

[Shengkai Fang]

Hi Konstantin,

Thanks for your reply.

> It uses the "kafka" connector and does not specify a primary key.
The dimensional table `users` is a ktable connector and we can specify the
pk on the KTable.

> Will it possible to use a "ktable" as a dimensional table in FLIP-132
Yes. We can specify the watermark on the KTable and it can be used as a
dimension table in temporal join.

>Introduce a new connector vs introduce a new property
The main reason behind is that the KTable connector almost has no common
options with the Kafka connector. The options that can be reused by KTable
connectors are 'topic', 'properties.bootstrap.servers' and
'value.fields-include' . We can't set cdc format for 'key.format' and
'value.format' in KTable connector now, which is  available in Kafka
connector. Considering the difference between the options we can use, it's
more suitable to introduce an another connector rather than a property.

We are also fine to use "compacted-kafka" as the name of the new connector.
What do you think?

Best,
Shengkai

Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：

> Hi Shengkai,
>
> Thank you for driving this effort. I believe this a very important feature
> for many users who use Kafka and Flink SQL together. A few questions and
> thoughts:
>
> * Is your example "Use KTable as a reference/dimension table" correct? It
> uses the "kafka" connector and does not specify a primary key.
>
> * Will it be possible to use a "ktable" table directly as a dimensional
> table in temporal join (*based on event time*) (FLIP-132)? This is not
> completely clear to me from the FLIP.
>
> * I'd personally prefer not to introduce a new connector and instead to
> extend the Kafka connector. We could add an additional property "compacted"
> = "true"|"false". If it is set to "true", we can add additional validation
> logic (e.g. "scan.startup.mode" can not be set, primary key required,
> etc.). If we stick to a separate connector I'd not call it "ktable", but
> rather "compacted-kafka" or similar. KTable seems to carry more implicit
> meaning than we want to imply here.
>
> * I agree that this is not a bounded source. If we want to support a
> bounded mode, this is an orthogonal concern that also applies to other
> unbounded sources.
>
> Best,
>
> Konstantin
>
> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:
>
> > Hi Danny,
> >
> > First of all, we didn't introduce any concepts from KSQL (e.g. Stream vs
> > Table notion).
> > This new connector will produce a changelog stream, so it's still a
> dynamic
> > table and doesn't conflict with Flink core concepts.
> >
> > The "ktable" is just a connector name, we can also call it
> > "compacted-kafka" or something else.
> > Calling it "ktable" is just because KSQL users can migrate to Flink SQL
> > easily.
> >
> > Regarding to why introducing a new connector vs a new property in
> existing
> > kafka connector:
> >
> > I think the main reason is that we want to have a clear separation for
> such
> > two use cases, because they are very different.
> > We also listed reasons in the FLIP, including:
> >
> > 1) It's hard to explain what's the behavior when users specify the start
> > offset from a middle position (e.g. how to process non exist delete
> > events).
> >     It's dangerous if users do that. So we don't provide the offset
> option
> > in the new connector at the moment.
> > 2) It's a different perspective/abstraction on the same kafka topic
> (append
> > vs. upsert). It would be easier to understand if we can separate them
> >     instead of mixing them in one connector. The new connector requires
> > hash sink partitioner, primary key declared, regular format.
> >     If we mix them in one connector, it might be confusing how to use the
> > options correctly.
> > 3) The semantic of the KTable connector is just the same as KTable in
> Kafka
> > Stream. So it's very handy for Kafka Stream and KSQL users.
> >     We have seen several questions in the mailing list asking how to
> model
> > a KTable and how to join a KTable in Flink SQL.
> >
> > Best,
> > Jark
> >
> > On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
> >
> > > Hi Jingsong,
> > >
> > > As the FLIP describes, "KTable connector produces a changelog stream,
> > > where each data record represents an update or delete event.".
> > > Therefore, a ktable source is an unbounded stream source. Selecting a
> > > ktable source is similar to selecting a kafka source with debezium-json
> > > format
> > > that it never ends and the results are continuously updated.
> > >
> > > It's possible to have a bounded ktable source in the future, for
> example,
> > > add an option 'bounded=true' or 'end-offset=xxx'.
> > > In this way, the ktable will produce a bounded changelog stream.
> > > So I think this can be a compatible feature in the future.
> > >
> > > I don't think we should associate with ksql related concepts. Actually,
> > we
> > > didn't introduce any concepts from KSQL (e.g. Stream vs Table notion).
> > > The "ktable" is just a connector name, we can also call it
> > > "compacted-kafka" or something else.
> > > Calling it "ktable" is just because KSQL users can migrate to Flink SQL
> > > easily.
> > >
> > > Regarding the "value.fields-include", this is an option introduced in
> > > FLIP-107 for Kafka connector.
> > > I think we should keep the same behavior with the Kafka connector. I'm
> > not
> > > sure what's the default behavior of KSQL.
> > > But I guess it also stores the keys in value from this example docs
> (see
> > > the "users_original" table) [1].
> > >
> > > Best,
> > > Jark
> > >
> > > [1]:
> > >
> >
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> > >
> > >
> > > On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]> wrote:
> > >
> > >> The concept seems conflicts with the Flink abstraction “dynamic
> table”,
> > >> in Flink we see both “stream” and “table” as a dynamic table,
> > >>
> > >> I think we should make clear first how to express stream and table
> > >> specific features on one “dynamic table”,
> > >> it is more natural for KSQL because KSQL takes stream and table as
> > >> different abstractions for representing collections. In KSQL, only
> > table is
> > >> mutable and can have a primary key.
> > >>
> > >> Does this connector belongs to the “table” scope or “stream” scope ?
> > >>
> > >> Some of the concepts (such as the primary key on stream) should be
> > >> suitable for all the connectors, not just Kafka, Shouldn’t this be an
> > >> extension of existing Kafka connector instead of a totally new
> > connector ?
> > >> What about the other connectors ?
> > >>
> > >> Because this touches the core abstraction of Flink, we better have a
> > >> top-down overall design, following the KSQL directly is not the
> answer.
> > >>
> > >> P.S. For the source
> > >> > Shouldn’t this be an extension of existing Kafka connector instead
> of
> > a
> > >> totally new connector ?
> > >>
> > >> How could we achieve that (e.g. set up the parallelism correctly) ?
> > >>
> > >> Best,
> > >> Danny Chan
> > >> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]>，写道：
> > >> > Thanks Shengkai for your proposal.
> > >> >
> > >> > +1 for this feature.
> > >> >
> > >> > > Future Work: Support bounded KTable source
> > >> >
> > >> > I don't think it should be a future work, I think it is one of the
> > >> > important concepts of this FLIP. We need to understand it now.
> > >> >
> > >> > Intuitively, a ktable in my opinion is a bounded table rather than a
> > >> > stream, so select should produce a bounded table by default.
> > >> >
> > >> > I think we can list Kafka related knowledge, because the word
> `ktable`
> > >> is
> > >> > easy to associate with ksql related concepts. (If possible, it's
> > better
> > >> to
> > >> > unify with it)
> > >> >
> > >> > What do you think?
> > >> >
> > >> > > value.fields-include
> > >> >
> > >> > What about the default behavior of KSQL?
> > >> >
> > >> > Best,
> > >> > Jingsong
> > >> >
> > >> > On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <[hidden email]>
> > >> wrote:
> > >> >
> > >> > > Hi, devs.
> > >> > >
> > >> > > Jark and I want to start a new FLIP to introduce the KTable
> > >> connector. The
> > >> > > KTable is a shortcut of "Kafka Table", it also has the same
> > semantics
> > >> with
> > >> > > the KTable notion in Kafka Stream.
> > >> > >
> > >> > > FLIP-149:
> > >> > >
> > >> > >
> > >>
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> > >> > >
> > >> > > Currently many users have expressed their needs for the upsert
> Kafka
> > >> by
> > >> > > mail lists and issues. The KTable connector has several benefits
> for
> > >> users:
> > >> > >
> > >> > > 1. Users are able to interpret a compacted Kafka Topic as an
> upsert
> > >> stream
> > >> > > in Apache Flink. And also be able to write a changelog stream to
> > Kafka
> > >> > > (into a compacted topic).
> > >> > > 2. As a part of the real time pipeline, store join or aggregate
> > >> result (may
> > >> > > contain updates) into a Kafka topic for further calculation;
> > >> > > 3. The semantic of the KTable connector is just the same as KTable
> > in
> > >> Kafka
> > >> > > Stream. So it's very handy for Kafka Stream and KSQL users. We
> have
> > >> seen
> > >> > > several questions in the mailing list asking how to model a KTable
> > >> and how
> > >> > > to join a KTable in Flink SQL.
> > >> > >
> > >> > > We hope it can expand the usage of the Flink with Kafka.
> > >> > >
> > >> > > I'm looking forward to your feedback.
> > >> > >
> > >> > > Best,
> > >> > > Shengkai
> > >> > >
> > >> >
> > >> >
> > >> > --
> > >> > Best, Jingsong Lee
> > >>
> > >
> >
>
>
> --
>
> Konstantin Knauf
>
> https://twitter.com/snntrable
>
> https://github.com/knaufk
>

[Shengkai Fang]

Hi devs,

As many people are still confused about the difference option behaviours
between the Kafka connector and KTable connector, Jark and I list the
differences in the doc[1].

Best,
Shengkai

[1]
https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit

Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：

> Hi Konstantin,
>
> Thanks for your reply.
>
> > It uses the "kafka" connector and does not specify a primary key.
> The dimensional table `users` is a ktable connector and we can specify the
> pk on the KTable.
>
> > Will it possible to use a "ktable" as a dimensional table in FLIP-132
> Yes. We can specify the watermark on the KTable and it can be used as a
> dimension table in temporal join.
>
> >Introduce a new connector vs introduce a new property
> The main reason behind is that the KTable connector almost has no common
> options with the Kafka connector. The options that can be reused by KTable
> connectors are 'topic', 'properties.bootstrap.servers' and
> 'value.fields-include' . We can't set cdc format for 'key.format' and
> 'value.format' in KTable connector now, which is  available in Kafka
> connector. Considering the difference between the options we can use, it's
> more suitable to introduce an another connector rather than a property.
>
> We are also fine to use "compacted-kafka" as the name of the new
> connector. What do you think?
>
> Best,
> Shengkai
>
> Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
>
>> Hi Shengkai,
>>
>> Thank you for driving this effort. I believe this a very important feature
>> for many users who use Kafka and Flink SQL together. A few questions and
>> thoughts:
>>
>> * Is your example "Use KTable as a reference/dimension table" correct? It
>> uses the "kafka" connector and does not specify a primary key.
>>
>> * Will it be possible to use a "ktable" table directly as a dimensional
>> table in temporal join (*based on event time*) (FLIP-132)? This is not
>> completely clear to me from the FLIP.
>>
>> * I'd personally prefer not to introduce a new connector and instead to
>> extend the Kafka connector. We could add an additional property
>> "compacted"
>> = "true"|"false". If it is set to "true", we can add additional validation
>> logic (e.g. "scan.startup.mode" can not be set, primary key required,
>> etc.). If we stick to a separate connector I'd not call it "ktable", but
>> rather "compacted-kafka" or similar. KTable seems to carry more implicit
>> meaning than we want to imply here.
>>
>> * I agree that this is not a bounded source. If we want to support a
>> bounded mode, this is an orthogonal concern that also applies to other
>> unbounded sources.
>>
>> Best,
>>
>> Konstantin
>>
>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:
>>
>> > Hi Danny,
>> >
>> > First of all, we didn't introduce any concepts from KSQL (e.g. Stream vs
>> > Table notion).
>> > This new connector will produce a changelog stream, so it's still a
>> dynamic
>> > table and doesn't conflict with Flink core concepts.
>> >
>> > The "ktable" is just a connector name, we can also call it
>> > "compacted-kafka" or something else.
>> > Calling it "ktable" is just because KSQL users can migrate to Flink SQL
>> > easily.
>> >
>> > Regarding to why introducing a new connector vs a new property in
>> existing
>> > kafka connector:
>> >
>> > I think the main reason is that we want to have a clear separation for
>> such
>> > two use cases, because they are very different.
>> > We also listed reasons in the FLIP, including:
>> >
>> > 1) It's hard to explain what's the behavior when users specify the start
>> > offset from a middle position (e.g. how to process non exist delete
>> > events).
>> >     It's dangerous if users do that. So we don't provide the offset
>> option
>> > in the new connector at the moment.
>> > 2) It's a different perspective/abstraction on the same kafka topic
>> (append
>> > vs. upsert). It would be easier to understand if we can separate them
>> >     instead of mixing them in one connector. The new connector requires
>> > hash sink partitioner, primary key declared, regular format.
>> >     If we mix them in one connector, it might be confusing how to use
>> the
>> > options correctly.
>> > 3) The semantic of the KTable connector is just the same as KTable in
>> Kafka
>> > Stream. So it's very handy for Kafka Stream and KSQL users.
>> >     We have seen several questions in the mailing list asking how to
>> model
>> > a KTable and how to join a KTable in Flink SQL.
>> >
>> > Best,
>> > Jark
>> >
>> > On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
>> >
>> > > Hi Jingsong,
>> > >
>> > > As the FLIP describes, "KTable connector produces a changelog stream,
>> > > where each data record represents an update or delete event.".
>> > > Therefore, a ktable source is an unbounded stream source. Selecting a
>> > > ktable source is similar to selecting a kafka source with
>> debezium-json
>> > > format
>> > > that it never ends and the results are continuously updated.
>> > >
>> > > It's possible to have a bounded ktable source in the future, for
>> example,
>> > > add an option 'bounded=true' or 'end-offset=xxx'.
>> > > In this way, the ktable will produce a bounded changelog stream.
>> > > So I think this can be a compatible feature in the future.
>> > >
>> > > I don't think we should associate with ksql related concepts.
>> Actually,
>> > we
>> > > didn't introduce any concepts from KSQL (e.g. Stream vs Table notion).
>> > > The "ktable" is just a connector name, we can also call it
>> > > "compacted-kafka" or something else.
>> > > Calling it "ktable" is just because KSQL users can migrate to Flink
>> SQL
>> > > easily.
>> > >
>> > > Regarding the "value.fields-include", this is an option introduced in
>> > > FLIP-107 for Kafka connector.
>> > > I think we should keep the same behavior with the Kafka connector. I'm
>> > not
>> > > sure what's the default behavior of KSQL.
>> > > But I guess it also stores the keys in value from this example docs
>> (see
>> > > the "users_original" table) [1].
>> > >
>> > > Best,
>> > > Jark
>> > >
>> > > [1]:
>> > >
>> >
>> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
>> > >
>> > >
>> > > On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]>
>> wrote:
>> > >
>> > >> The concept seems conflicts with the Flink abstraction “dynamic
>> table”,
>> > >> in Flink we see both “stream” and “table” as a dynamic table,
>> > >>
>> > >> I think we should make clear first how to express stream and table
>> > >> specific features on one “dynamic table”,
>> > >> it is more natural for KSQL because KSQL takes stream and table as
>> > >> different abstractions for representing collections. In KSQL, only
>> > table is
>> > >> mutable and can have a primary key.
>> > >>
>> > >> Does this connector belongs to the “table” scope or “stream” scope ?
>> > >>
>> > >> Some of the concepts (such as the primary key on stream) should be
>> > >> suitable for all the connectors, not just Kafka, Shouldn’t this be an
>> > >> extension of existing Kafka connector instead of a totally new
>> > connector ?
>> > >> What about the other connectors ?
>> > >>
>> > >> Because this touches the core abstraction of Flink, we better have a
>> > >> top-down overall design, following the KSQL directly is not the
>> answer.
>> > >>
>> > >> P.S. For the source
>> > >> > Shouldn’t this be an extension of existing Kafka connector instead
>> of
>> > a
>> > >> totally new connector ?
>> > >>
>> > >> How could we achieve that (e.g. set up the parallelism correctly) ?
>> > >>
>> > >> Best,
>> > >> Danny Chan
>> > >> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]>，写道：
>> > >> > Thanks Shengkai for your proposal.
>> > >> >
>> > >> > +1 for this feature.
>> > >> >
>> > >> > > Future Work: Support bounded KTable source
>> > >> >
>> > >> > I don't think it should be a future work, I think it is one of the
>> > >> > important concepts of this FLIP. We need to understand it now.
>> > >> >
>> > >> > Intuitively, a ktable in my opinion is a bounded table rather than
>> a
>> > >> > stream, so select should produce a bounded table by default.
>> > >> >
>> > >> > I think we can list Kafka related knowledge, because the word
>> `ktable`
>> > >> is
>> > >> > easy to associate with ksql related concepts. (If possible, it's
>> > better
>> > >> to
>> > >> > unify with it)
>> > >> >
>> > >> > What do you think?
>> > >> >
>> > >> > > value.fields-include
>> > >> >
>> > >> > What about the default behavior of KSQL?
>> > >> >
>> > >> > Best,
>> > >> > Jingsong
>> > >> >
>> > >> > On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <[hidden email]>
>> > >> wrote:
>> > >> >
>> > >> > > Hi, devs.
>> > >> > >
>> > >> > > Jark and I want to start a new FLIP to introduce the KTable
>> > >> connector. The
>> > >> > > KTable is a shortcut of "Kafka Table", it also has the same
>> > semantics
>> > >> with
>> > >> > > the KTable notion in Kafka Stream.
>> > >> > >
>> > >> > > FLIP-149:
>> > >> > >
>> > >> > >
>> > >>
>> >
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
>> > >> > >
>> > >> > > Currently many users have expressed their needs for the upsert
>> Kafka
>> > >> by
>> > >> > > mail lists and issues. The KTable connector has several benefits
>> for
>> > >> users:
>> > >> > >
>> > >> > > 1. Users are able to interpret a compacted Kafka Topic as an
>> upsert
>> > >> stream
>> > >> > > in Apache Flink. And also be able to write a changelog stream to
>> > Kafka
>> > >> > > (into a compacted topic).
>> > >> > > 2. As a part of the real time pipeline, store join or aggregate
>> > >> result (may
>> > >> > > contain updates) into a Kafka topic for further calculation;
>> > >> > > 3. The semantic of the KTable connector is just the same as
>> KTable
>> > in
>> > >> Kafka
>> > >> > > Stream. So it's very handy for Kafka Stream and KSQL users. We
>> have
>> > >> seen
>> > >> > > several questions in the mailing list asking how to model a
>> KTable
>> > >> and how
>> > >> > > to join a KTable in Flink SQL.
>> > >> > >
>> > >> > > We hope it can expand the usage of the Flink with Kafka.
>> > >> > >
>> > >> > > I'm looking forward to your feedback.
>> > >> > >
>> > >> > > Best,
>> > >> > > Shengkai
>> > >> > >
>> > >> >
>> > >> >
>> > >> > --
>> > >> > Best, Jingsong Lee
>> > >>
>> > >
>> >
>>
>>
>> --
>>
>> Konstantin Knauf
>>
>> https://twitter.com/snntrable
>>
>> https://github.com/knaufk
>>
>

[Kurt Young]

Hi all,

I want to describe the discussion process which drove us to have such
conclusion, this might make some of
the design choices easier to understand and keep everyone on the same page.

Back to the motivation, what functionality do we want to provide in the
first place? We got a lot of feedback and
questions from mailing lists that people want to write Not-Insert-Only
messages into kafka. They might be
intentional or by accident, e.g. wrote an non-windowed aggregate query or
non-windowed left outer join. And
some users from KSQL world also asked about why Flink didn't leverage the
Key concept of every kafka topic
and make kafka as a dynamic changing keyed table.

To work with kafka better, we were thinking to extend the functionality of
the current kafka connector by letting it
accept updates and deletions. But due to the limitation of kafka, the
update has to be "update by key", aka a table
with primary key.

This introduces a couple of conflicts with current kafka table's options:
1. key.fields: as said above, we need the kafka table to have the primary
key constraint. And users can also configure
key.fields freely, this might cause friction. (Sure we can do some sanity
check on this but it also creates friction.)
2. sink.partitioner: to make the semantics right, we need to make sure all
the updates on the same key are written to
the same kafka partition, such we should force to use a hash by key
partition inside such table. Again, this has conflicts
and creates friction with current user options.

The above things are solvable, though not perfect or most user friendly.

Let's take a look at the reading side. The keyed kafka table contains two
kinds of messages: upsert or deletion. What upsert
means is "If the key doesn't exist yet, it's an insert record. Otherwise
it's an update record". For the sake of correctness or
simplicity, the Flink SQL engine also needs such information. If we
interpret all messages to "update record", some queries or
operators may not work properly. It's weird to see an update record but you
haven't seen the insert record before.

So what Flink should do is after reading out the records from such table,
it needs to create a state to record which messages have
been seen and then generate the correct row type correspondingly. This kind
of couples the state and the data of the message
queue, and it also creates conflicts with current kafka connector.

Think about if users suspend a running job (which contains some reading
state now), and then change the start offset of the reader.
By changing the reading offset, it actually change the whole story of
"which records should be insert messages and which records
should be update messages). And it will also make Flink to deal with
another weird situation that it might receive a deletion
on a non existing message.

We were unsatisfied with all the frictions and conflicts it will create if
we enable the "upsert & deletion" support to the current kafka
connector. And later we begin to realize that we shouldn't treat it as a
normal message queue, but should treat it as a changing keyed
table. We should be able to always get the whole data of such table (by
disabling the start offset option) and we can also read the
changelog out of such table. It's like a HBase table with binlog support
but doesn't have random access capability (which can be fulfilled
by Flink's state).

So our intention was instead of telling and persuading users what kind of
options they should or should not use by extending
current kafka connector when enable upsert support, we are actually create
a whole new and different connector that has total
different abstractions in SQL layer, and should be treated totally
different with current kafka connector.

Hope this can clarify some of the concerns.

Best,
Kurt


On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <[hidden email]> wrote:

> Hi devs,
>
> As many people are still confused about the difference option behaviours
> between the Kafka connector and KTable connector, Jark and I list the
> differences in the doc[1].
>
> Best,
> Shengkai
>
> [1]
>
> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
>
> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
>
> > Hi Konstantin,
> >
> > Thanks for your reply.
> >
> > > It uses the "kafka" connector and does not specify a primary key.
> > The dimensional table `users` is a ktable connector and we can specify
> the
> > pk on the KTable.
> >
> > > Will it possible to use a "ktable" as a dimensional table in FLIP-132
> > Yes. We can specify the watermark on the KTable and it can be used as a
> > dimension table in temporal join.
> >
> > >Introduce a new connector vs introduce a new property
> > The main reason behind is that the KTable connector almost has no common
> > options with the Kafka connector. The options that can be reused by
> KTable
> > connectors are 'topic', 'properties.bootstrap.servers' and
> > 'value.fields-include' . We can't set cdc format for 'key.format' and
> > 'value.format' in KTable connector now, which is  available in Kafka
> > connector. Considering the difference between the options we can use,
> it's
> > more suitable to introduce an another connector rather than a property.
> >
> > We are also fine to use "compacted-kafka" as the name of the new
> > connector. What do you think?
> >
> > Best,
> > Shengkai
> >
> > Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
> >
> >> Hi Shengkai,
> >>
> >> Thank you for driving this effort. I believe this a very important
> feature
> >> for many users who use Kafka and Flink SQL together. A few questions and
> >> thoughts:
> >>
> >> * Is your example "Use KTable as a reference/dimension table" correct?
> It
> >> uses the "kafka" connector and does not specify a primary key.
> >>
> >> * Will it be possible to use a "ktable" table directly as a dimensional
> >> table in temporal join (*based on event time*) (FLIP-132)? This is not
> >> completely clear to me from the FLIP.
> >>
> >> * I'd personally prefer not to introduce a new connector and instead to
> >> extend the Kafka connector. We could add an additional property
> >> "compacted"
> >> = "true"|"false". If it is set to "true", we can add additional
> validation
> >> logic (e.g. "scan.startup.mode" can not be set, primary key required,
> >> etc.). If we stick to a separate connector I'd not call it "ktable", but
> >> rather "compacted-kafka" or similar. KTable seems to carry more implicit
> >> meaning than we want to imply here.
> >>
> >> * I agree that this is not a bounded source. If we want to support a
> >> bounded mode, this is an orthogonal concern that also applies to other
> >> unbounded sources.
> >>
> >> Best,
> >>
> >> Konstantin
> >>
> >> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:
> >>
> >> > Hi Danny,
> >> >
> >> > First of all, we didn't introduce any concepts from KSQL (e.g. Stream
> vs
> >> > Table notion).
> >> > This new connector will produce a changelog stream, so it's still a
> >> dynamic
> >> > table and doesn't conflict with Flink core concepts.
> >> >
> >> > The "ktable" is just a connector name, we can also call it
> >> > "compacted-kafka" or something else.
> >> > Calling it "ktable" is just because KSQL users can migrate to Flink
> SQL
> >> > easily.
> >> >
> >> > Regarding to why introducing a new connector vs a new property in
> >> existing
> >> > kafka connector:
> >> >
> >> > I think the main reason is that we want to have a clear separation for
> >> such
> >> > two use cases, because they are very different.
> >> > We also listed reasons in the FLIP, including:
> >> >
> >> > 1) It's hard to explain what's the behavior when users specify the
> start
> >> > offset from a middle position (e.g. how to process non exist delete
> >> > events).
> >> >     It's dangerous if users do that. So we don't provide the offset
> >> option
> >> > in the new connector at the moment.
> >> > 2) It's a different perspective/abstraction on the same kafka topic
> >> (append
> >> > vs. upsert). It would be easier to understand if we can separate them
> >> >     instead of mixing them in one connector. The new connector
> requires
> >> > hash sink partitioner, primary key declared, regular format.
> >> >     If we mix them in one connector, it might be confusing how to use
> >> the
> >> > options correctly.
> >> > 3) The semantic of the KTable connector is just the same as KTable in
> >> Kafka
> >> > Stream. So it's very handy for Kafka Stream and KSQL users.
> >> >     We have seen several questions in the mailing list asking how to
> >> model
> >> > a KTable and how to join a KTable in Flink SQL.
> >> >
> >> > Best,
> >> > Jark
> >> >
> >> > On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
> >> >
> >> > > Hi Jingsong,
> >> > >
> >> > > As the FLIP describes, "KTable connector produces a changelog
> stream,
> >> > > where each data record represents an update or delete event.".
> >> > > Therefore, a ktable source is an unbounded stream source. Selecting
> a
> >> > > ktable source is similar to selecting a kafka source with
> >> debezium-json
> >> > > format
> >> > > that it never ends and the results are continuously updated.
> >> > >
> >> > > It's possible to have a bounded ktable source in the future, for
> >> example,
> >> > > add an option 'bounded=true' or 'end-offset=xxx'.
> >> > > In this way, the ktable will produce a bounded changelog stream.
> >> > > So I think this can be a compatible feature in the future.
> >> > >
> >> > > I don't think we should associate with ksql related concepts.
> >> Actually,
> >> > we
> >> > > didn't introduce any concepts from KSQL (e.g. Stream vs Table
> notion).
> >> > > The "ktable" is just a connector name, we can also call it
> >> > > "compacted-kafka" or something else.
> >> > > Calling it "ktable" is just because KSQL users can migrate to Flink
> >> SQL
> >> > > easily.
> >> > >
> >> > > Regarding the "value.fields-include", this is an option introduced
> in
> >> > > FLIP-107 for Kafka connector.
> >> > > I think we should keep the same behavior with the Kafka connector.
> I'm
> >> > not
> >> > > sure what's the default behavior of KSQL.
> >> > > But I guess it also stores the keys in value from this example docs
> >> (see
> >> > > the "users_original" table) [1].
> >> > >
> >> > > Best,
> >> > > Jark
> >> > >
> >> > > [1]:
> >> > >
> >> >
> >>
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> >> > >
> >> > >
> >> > > On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]>
> >> wrote:
> >> > >
> >> > >> The concept seems conflicts with the Flink abstraction “dynamic
> >> table”,
> >> > >> in Flink we see both “stream” and “table” as a dynamic table,
> >> > >>
> >> > >> I think we should make clear first how to express stream and table
> >> > >> specific features on one “dynamic table”,
> >> > >> it is more natural for KSQL because KSQL takes stream and table as
> >> > >> different abstractions for representing collections. In KSQL, only
> >> > table is
> >> > >> mutable and can have a primary key.
> >> > >>
> >> > >> Does this connector belongs to the “table” scope or “stream” scope
> ?
> >> > >>
> >> > >> Some of the concepts (such as the primary key on stream) should be
> >> > >> suitable for all the connectors, not just Kafka, Shouldn’t this be
> an
> >> > >> extension of existing Kafka connector instead of a totally new
> >> > connector ?
> >> > >> What about the other connectors ?
> >> > >>
> >> > >> Because this touches the core abstraction of Flink, we better have
> a
> >> > >> top-down overall design, following the KSQL directly is not the
> >> answer.
> >> > >>
> >> > >> P.S. For the source
> >> > >> > Shouldn’t this be an extension of existing Kafka connector
> instead
> >> of
> >> > a
> >> > >> totally new connector ?
> >> > >>
> >> > >> How could we achieve that (e.g. set up the parallelism correctly) ?
> >> > >>
> >> > >> Best,
> >> > >> Danny Chan
> >> > >> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]
> >，写道：
> >> > >> > Thanks Shengkai for your proposal.
> >> > >> >
> >> > >> > +1 for this feature.
> >> > >> >
> >> > >> > > Future Work: Support bounded KTable source
> >> > >> >
> >> > >> > I don't think it should be a future work, I think it is one of
> the
> >> > >> > important concepts of this FLIP. We need to understand it now.
> >> > >> >
> >> > >> > Intuitively, a ktable in my opinion is a bounded table rather
> than
> >> a
> >> > >> > stream, so select should produce a bounded table by default.
> >> > >> >
> >> > >> > I think we can list Kafka related knowledge, because the word
> >> `ktable`
> >> > >> is
> >> > >> > easy to associate with ksql related concepts. (If possible, it's
> >> > better
> >> > >> to
> >> > >> > unify with it)
> >> > >> >
> >> > >> > What do you think?
> >> > >> >
> >> > >> > > value.fields-include
> >> > >> >
> >> > >> > What about the default behavior of KSQL?
> >> > >> >
> >> > >> > Best,
> >> > >> > Jingsong
> >> > >> >
> >> > >> > On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <[hidden email]
> >
> >> > >> wrote:
> >> > >> >
> >> > >> > > Hi, devs.
> >> > >> > >
> >> > >> > > Jark and I want to start a new FLIP to introduce the KTable
> >> > >> connector. The
> >> > >> > > KTable is a shortcut of "Kafka Table", it also has the same
> >> > semantics
> >> > >> with
> >> > >> > > the KTable notion in Kafka Stream.
> >> > >> > >
> >> > >> > > FLIP-149:
> >> > >> > >
> >> > >> > >
> >> > >>
> >> >
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> >> > >> > >
> >> > >> > > Currently many users have expressed their needs for the upsert
> >> Kafka
> >> > >> by
> >> > >> > > mail lists and issues. The KTable connector has several
> benefits
> >> for
> >> > >> users:
> >> > >> > >
> >> > >> > > 1. Users are able to interpret a compacted Kafka Topic as an
> >> upsert
> >> > >> stream
> >> > >> > > in Apache Flink. And also be able to write a changelog stream
> to
> >> > Kafka
> >> > >> > > (into a compacted topic).
> >> > >> > > 2. As a part of the real time pipeline, store join or aggregate
> >> > >> result (may
> >> > >> > > contain updates) into a Kafka topic for further calculation;
> >> > >> > > 3. The semantic of the KTable connector is just the same as
> >> KTable
> >> > in
> >> > >> Kafka
> >> > >> > > Stream. So it's very handy for Kafka Stream and KSQL users. We
> >> have
> >> > >> seen
> >> > >> > > several questions in the mailing list asking how to model a
> >> KTable
> >> > >> and how
> >> > >> > > to join a KTable in Flink SQL.
> >> > >> > >
> >> > >> > > We hope it can expand the usage of the Flink with Kafka.
> >> > >> > >
> >> > >> > > I'm looking forward to your feedback.
> >> > >> > >
> >> > >> > > Best,
> >> > >> > > Shengkai
> >> > >> > >
> >> > >> >
> >> > >> >
> >> > >> > --
> >> > >> > Best, Jingsong Lee
> >> > >>
> >> > >
> >> >
> >>
> >>
> >> --
> >>
> >> Konstantin Knauf
> >>
> >> https://twitter.com/snntrable
> >>
> >> https://github.com/knaufk
> >>
> >
>

[Konstantin Knauf-4]

Hi Kurt, Hi Shengkai,

thanks for answering my questions and the additional clarifications. I
don't have a strong opinion on whether to extend the "kafka" connector or
to introduce a new connector. So, from my perspective feel free to go with
a separate connector. If we do introduce a new connector I wouldn't call it
"ktable" for aforementioned reasons (In addition, we might suggest that
there is also a "kstreams" connector for symmetry reasons). I don't have a
good alternative name, though, maybe "kafka-compacted" or
"compacted-kafka".

Thanks,

Konstantin


On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]> wrote:

> Hi all,
>
> I want to describe the discussion process which drove us to have such
> conclusion, this might make some of
> the design choices easier to understand and keep everyone on the same page.
>
> Back to the motivation, what functionality do we want to provide in the
> first place? We got a lot of feedback and
> questions from mailing lists that people want to write Not-Insert-Only
> messages into kafka. They might be
> intentional or by accident, e.g. wrote an non-windowed aggregate query or
> non-windowed left outer join. And
> some users from KSQL world also asked about why Flink didn't leverage the
> Key concept of every kafka topic
> and make kafka as a dynamic changing keyed table.
>
> To work with kafka better, we were thinking to extend the functionality of
> the current kafka connector by letting it
> accept updates and deletions. But due to the limitation of kafka, the
> update has to be "update by key", aka a table
> with primary key.
>
> This introduces a couple of conflicts with current kafka table's options:
> 1. key.fields: as said above, we need the kafka table to have the primary
> key constraint. And users can also configure
> key.fields freely, this might cause friction. (Sure we can do some sanity
> check on this but it also creates friction.)
> 2. sink.partitioner: to make the semantics right, we need to make sure all
> the updates on the same key are written to
> the same kafka partition, such we should force to use a hash by key
> partition inside such table. Again, this has conflicts
> and creates friction with current user options.
>
> The above things are solvable, though not perfect or most user friendly.
>
> Let's take a look at the reading side. The keyed kafka table contains two
> kinds of messages: upsert or deletion. What upsert
> means is "If the key doesn't exist yet, it's an insert record. Otherwise
> it's an update record". For the sake of correctness or
> simplicity, the Flink SQL engine also needs such information. If we
> interpret all messages to "update record", some queries or
> operators may not work properly. It's weird to see an update record but you
> haven't seen the insert record before.
>
> So what Flink should do is after reading out the records from such table,
> it needs to create a state to record which messages have
> been seen and then generate the correct row type correspondingly. This kind
> of couples the state and the data of the message
> queue, and it also creates conflicts with current kafka connector.
>
> Think about if users suspend a running job (which contains some reading
> state now), and then change the start offset of the reader.
> By changing the reading offset, it actually change the whole story of
> "which records should be insert messages and which records
> should be update messages). And it will also make Flink to deal with
> another weird situation that it might receive a deletion
> on a non existing message.
>
> We were unsatisfied with all the frictions and conflicts it will create if
> we enable the "upsert & deletion" support to the current kafka
> connector. And later we begin to realize that we shouldn't treat it as a
> normal message queue, but should treat it as a changing keyed
> table. We should be able to always get the whole data of such table (by
> disabling the start offset option) and we can also read the
> changelog out of such table. It's like a HBase table with binlog support
> but doesn't have random access capability (which can be fulfilled
> by Flink's state).
>
> So our intention was instead of telling and persuading users what kind of
> options they should or should not use by extending
> current kafka connector when enable upsert support, we are actually create
> a whole new and different connector that has total
> different abstractions in SQL layer, and should be treated totally
> different with current kafka connector.
>
> Hope this can clarify some of the concerns.
>
> Best,
> Kurt
>
>
> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <[hidden email]> wrote:
>
> > Hi devs,
> >
> > As many people are still confused about the difference option behaviours
> > between the Kafka connector and KTable connector, Jark and I list the
> > differences in the doc[1].
> >
> > Best,
> > Shengkai
> >
> > [1]
> >
> >
> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
> >
> > Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
> >
> > > Hi Konstantin,
> > >
> > > Thanks for your reply.
> > >
> > > > It uses the "kafka" connector and does not specify a primary key.
> > > The dimensional table `users` is a ktable connector and we can specify
> > the
> > > pk on the KTable.
> > >
> > > > Will it possible to use a "ktable" as a dimensional table in FLIP-132
> > > Yes. We can specify the watermark on the KTable and it can be used as a
> > > dimension table in temporal join.
> > >
> > > >Introduce a new connector vs introduce a new property
> > > The main reason behind is that the KTable connector almost has no
> common
> > > options with the Kafka connector. The options that can be reused by
> > KTable
> > > connectors are 'topic', 'properties.bootstrap.servers' and
> > > 'value.fields-include' . We can't set cdc format for 'key.format' and
> > > 'value.format' in KTable connector now, which is  available in Kafka
> > > connector. Considering the difference between the options we can use,
> > it's
> > > more suitable to introduce an another connector rather than a property.
> > >
> > > We are also fine to use "compacted-kafka" as the name of the new
> > > connector. What do you think?
> > >
> > > Best,
> > > Shengkai
> > >
> > > Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
> > >
> > >> Hi Shengkai,
> > >>
> > >> Thank you for driving this effort. I believe this a very important
> > feature
> > >> for many users who use Kafka and Flink SQL together. A few questions
> and
> > >> thoughts:
> > >>
> > >> * Is your example "Use KTable as a reference/dimension table" correct?
> > It
> > >> uses the "kafka" connector and does not specify a primary key.
> > >>
> > >> * Will it be possible to use a "ktable" table directly as a
> dimensional
> > >> table in temporal join (*based on event time*) (FLIP-132)? This is not
> > >> completely clear to me from the FLIP.
> > >>
> > >> * I'd personally prefer not to introduce a new connector and instead
> to
> > >> extend the Kafka connector. We could add an additional property
> > >> "compacted"
> > >> = "true"|"false". If it is set to "true", we can add additional
> > validation
> > >> logic (e.g. "scan.startup.mode" can not be set, primary key required,
> > >> etc.). If we stick to a separate connector I'd not call it "ktable",
> but
> > >> rather "compacted-kafka" or similar. KTable seems to carry more
> implicit
> > >> meaning than we want to imply here.
> > >>
> > >> * I agree that this is not a bounded source. If we want to support a
> > >> bounded mode, this is an orthogonal concern that also applies to other
> > >> unbounded sources.
> > >>
> > >> Best,
> > >>
> > >> Konstantin
> > >>
> > >> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:
> > >>
> > >> > Hi Danny,
> > >> >
> > >> > First of all, we didn't introduce any concepts from KSQL (e.g.
> Stream
> > vs
> > >> > Table notion).
> > >> > This new connector will produce a changelog stream, so it's still a
> > >> dynamic
> > >> > table and doesn't conflict with Flink core concepts.
> > >> >
> > >> > The "ktable" is just a connector name, we can also call it
> > >> > "compacted-kafka" or something else.
> > >> > Calling it "ktable" is just because KSQL users can migrate to Flink
> > SQL
> > >> > easily.
> > >> >
> > >> > Regarding to why introducing a new connector vs a new property in
> > >> existing
> > >> > kafka connector:
> > >> >
> > >> > I think the main reason is that we want to have a clear separation
> for
> > >> such
> > >> > two use cases, because they are very different.
> > >> > We also listed reasons in the FLIP, including:
> > >> >
> > >> > 1) It's hard to explain what's the behavior when users specify the
> > start
> > >> > offset from a middle position (e.g. how to process non exist delete
> > >> > events).
> > >> >     It's dangerous if users do that. So we don't provide the offset
> > >> option
> > >> > in the new connector at the moment.
> > >> > 2) It's a different perspective/abstraction on the same kafka topic
> > >> (append
> > >> > vs. upsert). It would be easier to understand if we can separate
> them
> > >> >     instead of mixing them in one connector. The new connector
> > requires
> > >> > hash sink partitioner, primary key declared, regular format.
> > >> >     If we mix them in one connector, it might be confusing how to
> use
> > >> the
> > >> > options correctly.
> > >> > 3) The semantic of the KTable connector is just the same as KTable
> in
> > >> Kafka
> > >> > Stream. So it's very handy for Kafka Stream and KSQL users.
> > >> >     We have seen several questions in the mailing list asking how to
> > >> model
> > >> > a KTable and how to join a KTable in Flink SQL.
> > >> >
> > >> > Best,
> > >> > Jark
> > >> >
> > >> > On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
> > >> >
> > >> > > Hi Jingsong,
> > >> > >
> > >> > > As the FLIP describes, "KTable connector produces a changelog
> > stream,
> > >> > > where each data record represents an update or delete event.".
> > >> > > Therefore, a ktable source is an unbounded stream source.
> Selecting
> > a
> > >> > > ktable source is similar to selecting a kafka source with
> > >> debezium-json
> > >> > > format
> > >> > > that it never ends and the results are continuously updated.
> > >> > >
> > >> > > It's possible to have a bounded ktable source in the future, for
> > >> example,
> > >> > > add an option 'bounded=true' or 'end-offset=xxx'.
> > >> > > In this way, the ktable will produce a bounded changelog stream.
> > >> > > So I think this can be a compatible feature in the future.
> > >> > >
> > >> > > I don't think we should associate with ksql related concepts.
> > >> Actually,
> > >> > we
> > >> > > didn't introduce any concepts from KSQL (e.g. Stream vs Table
> > notion).
> > >> > > The "ktable" is just a connector name, we can also call it
> > >> > > "compacted-kafka" or something else.
> > >> > > Calling it "ktable" is just because KSQL users can migrate to
> Flink
> > >> SQL
> > >> > > easily.
> > >> > >
> > >> > > Regarding the "value.fields-include", this is an option introduced
> > in
> > >> > > FLIP-107 for Kafka connector.
> > >> > > I think we should keep the same behavior with the Kafka connector.
> > I'm
> > >> > not
> > >> > > sure what's the default behavior of KSQL.
> > >> > > But I guess it also stores the keys in value from this example
> docs
> > >> (see
> > >> > > the "users_original" table) [1].
> > >> > >
> > >> > > Best,
> > >> > > Jark
> > >> > >
> > >> > > [1]:
> > >> > >
> > >> >
> > >>
> >
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> > >> > >
> > >> > >
> > >> > > On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]>
> > >> wrote:
> > >> > >
> > >> > >> The concept seems conflicts with the Flink abstraction “dynamic
> > >> table”,
> > >> > >> in Flink we see both “stream” and “table” as a dynamic table,
> > >> > >>
> > >> > >> I think we should make clear first how to express stream and
> table
> > >> > >> specific features on one “dynamic table”,
> > >> > >> it is more natural for KSQL because KSQL takes stream and table
> as
> > >> > >> different abstractions for representing collections. In KSQL,
> only
> > >> > table is
> > >> > >> mutable and can have a primary key.
> > >> > >>
> > >> > >> Does this connector belongs to the “table” scope or “stream”
> scope
> > ?
> > >> > >>
> > >> > >> Some of the concepts (such as the primary key on stream) should
> be
> > >> > >> suitable for all the connectors, not just Kafka, Shouldn’t this
> be
> > an
> > >> > >> extension of existing Kafka connector instead of a totally new
> > >> > connector ?
> > >> > >> What about the other connectors ?
> > >> > >>
> > >> > >> Because this touches the core abstraction of Flink, we better
> have
> > a
> > >> > >> top-down overall design, following the KSQL directly is not the
> > >> answer.
> > >> > >>
> > >> > >> P.S. For the source
> > >> > >> > Shouldn’t this be an extension of existing Kafka connector
> > instead
> > >> of
> > >> > a
> > >> > >> totally new connector ?
> > >> > >>
> > >> > >> How could we achieve that (e.g. set up the parallelism
> correctly) ?
> > >> > >>
> > >> > >> Best,
> > >> > >> Danny Chan
> > >> > >> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]
> > >，写道：
> > >> > >> > Thanks Shengkai for your proposal.
> > >> > >> >
> > >> > >> > +1 for this feature.
> > >> > >> >
> > >> > >> > > Future Work: Support bounded KTable source
> > >> > >> >
> > >> > >> > I don't think it should be a future work, I think it is one of
> > the
> > >> > >> > important concepts of this FLIP. We need to understand it now.
> > >> > >> >
> > >> > >> > Intuitively, a ktable in my opinion is a bounded table rather
> > than
> > >> a
> > >> > >> > stream, so select should produce a bounded table by default.
> > >> > >> >
> > >> > >> > I think we can list Kafka related knowledge, because the word
> > >> `ktable`
> > >> > >> is
> > >> > >> > easy to associate with ksql related concepts. (If possible,
> it's
> > >> > better
> > >> > >> to
> > >> > >> > unify with it)
> > >> > >> >
> > >> > >> > What do you think?
> > >> > >> >
> > >> > >> > > value.fields-include
> > >> > >> >
> > >> > >> > What about the default behavior of KSQL?
> > >> > >> >
> > >> > >> > Best,
> > >> > >> > Jingsong
> > >> > >> >
> > >> > >> > On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
> [hidden email]
> > >
> > >> > >> wrote:
> > >> > >> >
> > >> > >> > > Hi, devs.
> > >> > >> > >
> > >> > >> > > Jark and I want to start a new FLIP to introduce the KTable
> > >> > >> connector. The
> > >> > >> > > KTable is a shortcut of "Kafka Table", it also has the same
> > >> > semantics
> > >> > >> with
> > >> > >> > > the KTable notion in Kafka Stream.
> > >> > >> > >
> > >> > >> > > FLIP-149:
> > >> > >> > >
> > >> > >> > >
> > >> > >>
> > >> >
> > >>
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> > >> > >> > >
> > >> > >> > > Currently many users have expressed their needs for the
> upsert
> > >> Kafka
> > >> > >> by
> > >> > >> > > mail lists and issues. The KTable connector has several
> > benefits
> > >> for
> > >> > >> users:
> > >> > >> > >
> > >> > >> > > 1. Users are able to interpret a compacted Kafka Topic as an
> > >> upsert
> > >> > >> stream
> > >> > >> > > in Apache Flink. And also be able to write a changelog stream
> > to
> > >> > Kafka
> > >> > >> > > (into a compacted topic).
> > >> > >> > > 2. As a part of the real time pipeline, store join or
> aggregate
> > >> > >> result (may
> > >> > >> > > contain updates) into a Kafka topic for further calculation;
> > >> > >> > > 3. The semantic of the KTable connector is just the same as
> > >> KTable
> > >> > in
> > >> > >> Kafka
> > >> > >> > > Stream. So it's very handy for Kafka Stream and KSQL users.
> We
> > >> have
> > >> > >> seen
> > >> > >> > > several questions in the mailing list asking how to model a
> > >> KTable
> > >> > >> and how
> > >> > >> > > to join a KTable in Flink SQL.
> > >> > >> > >
> > >> > >> > > We hope it can expand the usage of the Flink with Kafka.
> > >> > >> > >
> > >> > >> > > I'm looking forward to your feedback.
> > >> > >> > >
> > >> > >> > > Best,
> > >> > >> > > Shengkai
> > >> > >> > >
> > >> > >> >
> > >> > >> >
> > >> > >> > --
> > >> > >> > Best, Jingsong Lee
> > >> > >>
> > >> > >
> > >> >
> > >>
> > >>
> > >> --
> > >>
> > >> Konstantin Knauf
> > >>
> > >> https://twitter.com/snntrable
> > >>
> > >> https://github.com/knaufk
> > >>
> > >
> >
>

--

Konstantin Knauf

https://twitter.com/snntrable

https://github.com/knaufk

[Jark Wu-2]

Hi,

IMO, if we are going to mix them in one connector,
1) either users need to set some options to a specific value explicitly,
e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
This makes the connector awkward to use. Users may face to fix options one
by one according to the exception.
Besides, in the future, it is still possible to use
"sink.partitioner=fixed" (reduce network cost) if users are aware of
the partition routing,
however, it's error-prone to have "fixed" as default for compacted mode.

2) or make those options a different default value when "compacted=true".
This would be more confusing and unpredictable if the default value of
options will change according to other options.
What happens if we have a third mode in the future?

In terms of usage and options, it's very different from the
original "kafka" connector.
It would be more handy to use and less fallible if separating them into two
connectors.
In the implementation layer, we can reuse code as much as possible.

Therefore, I'm still +1 to have a new connector.
The "kafka-compacted" name sounds good to me.

Best,
Jark


On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <[hidden email]> wrote:

> Hi Kurt, Hi Shengkai,
>
> thanks for answering my questions and the additional clarifications. I
> don't have a strong opinion on whether to extend the "kafka" connector or
> to introduce a new connector. So, from my perspective feel free to go with
> a separate connector. If we do introduce a new connector I wouldn't call it
> "ktable" for aforementioned reasons (In addition, we might suggest that
> there is also a "kstreams" connector for symmetry reasons). I don't have a
> good alternative name, though, maybe "kafka-compacted" or
> "compacted-kafka".
>
> Thanks,
>
> Konstantin
>
>
> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]> wrote:
>
> > Hi all,
> >
> > I want to describe the discussion process which drove us to have such
> > conclusion, this might make some of
> > the design choices easier to understand and keep everyone on the same
> page.
> >
> > Back to the motivation, what functionality do we want to provide in the
> > first place? We got a lot of feedback and
> > questions from mailing lists that people want to write Not-Insert-Only
> > messages into kafka. They might be
> > intentional or by accident, e.g. wrote an non-windowed aggregate query or
> > non-windowed left outer join. And
> > some users from KSQL world also asked about why Flink didn't leverage the
> > Key concept of every kafka topic
> > and make kafka as a dynamic changing keyed table.
> >
> > To work with kafka better, we were thinking to extend the functionality
> of
> > the current kafka connector by letting it
> > accept updates and deletions. But due to the limitation of kafka, the
> > update has to be "update by key", aka a table
> > with primary key.
> >
> > This introduces a couple of conflicts with current kafka table's options:
> > 1. key.fields: as said above, we need the kafka table to have the primary
> > key constraint. And users can also configure
> > key.fields freely, this might cause friction. (Sure we can do some sanity
> > check on this but it also creates friction.)
> > 2. sink.partitioner: to make the semantics right, we need to make sure
> all
> > the updates on the same key are written to
> > the same kafka partition, such we should force to use a hash by key
> > partition inside such table. Again, this has conflicts
> > and creates friction with current user options.
> >
> > The above things are solvable, though not perfect or most user friendly.
> >
> > Let's take a look at the reading side. The keyed kafka table contains two
> > kinds of messages: upsert or deletion. What upsert
> > means is "If the key doesn't exist yet, it's an insert record. Otherwise
> > it's an update record". For the sake of correctness or
> > simplicity, the Flink SQL engine also needs such information. If we
> > interpret all messages to "update record", some queries or
> > operators may not work properly. It's weird to see an update record but
> you
> > haven't seen the insert record before.
> >
> > So what Flink should do is after reading out the records from such table,
> > it needs to create a state to record which messages have
> > been seen and then generate the correct row type correspondingly. This
> kind
> > of couples the state and the data of the message
> > queue, and it also creates conflicts with current kafka connector.
> >
> > Think about if users suspend a running job (which contains some reading
> > state now), and then change the start offset of the reader.
> > By changing the reading offset, it actually change the whole story of
> > "which records should be insert messages and which records
> > should be update messages). And it will also make Flink to deal with
> > another weird situation that it might receive a deletion
> > on a non existing message.
> >
> > We were unsatisfied with all the frictions and conflicts it will create
> if
> > we enable the "upsert & deletion" support to the current kafka
> > connector. And later we begin to realize that we shouldn't treat it as a
> > normal message queue, but should treat it as a changing keyed
> > table. We should be able to always get the whole data of such table (by
> > disabling the start offset option) and we can also read the
> > changelog out of such table. It's like a HBase table with binlog support
> > but doesn't have random access capability (which can be fulfilled
> > by Flink's state).
> >
> > So our intention was instead of telling and persuading users what kind of
> > options they should or should not use by extending
> > current kafka connector when enable upsert support, we are actually
> create
> > a whole new and different connector that has total
> > different abstractions in SQL layer, and should be treated totally
> > different with current kafka connector.
> >
> > Hope this can clarify some of the concerns.
> >
> > Best,
> > Kurt
> >
> >
> > On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <[hidden email]> wrote:
> >
> > > Hi devs,
> > >
> > > As many people are still confused about the difference option
> behaviours
> > > between the Kafka connector and KTable connector, Jark and I list the
> > > differences in the doc[1].
> > >
> > > Best,
> > > Shengkai
> > >
> > > [1]
> > >
> > >
> >
> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
> > >
> > > Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
> > >
> > > > Hi Konstantin,
> > > >
> > > > Thanks for your reply.
> > > >
> > > > > It uses the "kafka" connector and does not specify a primary key.
> > > > The dimensional table `users` is a ktable connector and we can
> specify
> > > the
> > > > pk on the KTable.
> > > >
> > > > > Will it possible to use a "ktable" as a dimensional table in
> FLIP-132
> > > > Yes. We can specify the watermark on the KTable and it can be used
> as a
> > > > dimension table in temporal join.
> > > >
> > > > >Introduce a new connector vs introduce a new property
> > > > The main reason behind is that the KTable connector almost has no
> > common
> > > > options with the Kafka connector. The options that can be reused by
> > > KTable
> > > > connectors are 'topic', 'properties.bootstrap.servers' and
> > > > 'value.fields-include' . We can't set cdc format for 'key.format' and
> > > > 'value.format' in KTable connector now, which is  available in Kafka
> > > > connector. Considering the difference between the options we can use,
> > > it's
> > > > more suitable to introduce an another connector rather than a
> property.
> > > >
> > > > We are also fine to use "compacted-kafka" as the name of the new
> > > > connector. What do you think?
> > > >
> > > > Best,
> > > > Shengkai
> > > >
> > > > Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
> > > >
> > > >> Hi Shengkai,
> > > >>
> > > >> Thank you for driving this effort. I believe this a very important
> > > feature
> > > >> for many users who use Kafka and Flink SQL together. A few questions
> > and
> > > >> thoughts:
> > > >>
> > > >> * Is your example "Use KTable as a reference/dimension table"
> correct?
> > > It
> > > >> uses the "kafka" connector and does not specify a primary key.
> > > >>
> > > >> * Will it be possible to use a "ktable" table directly as a
> > dimensional
> > > >> table in temporal join (*based on event time*) (FLIP-132)? This is
> not
> > > >> completely clear to me from the FLIP.
> > > >>
> > > >> * I'd personally prefer not to introduce a new connector and instead
> > to
> > > >> extend the Kafka connector. We could add an additional property
> > > >> "compacted"
> > > >> = "true"|"false". If it is set to "true", we can add additional
> > > validation
> > > >> logic (e.g. "scan.startup.mode" can not be set, primary key
> required,
> > > >> etc.). If we stick to a separate connector I'd not call it "ktable",
> > but
> > > >> rather "compacted-kafka" or similar. KTable seems to carry more
> > implicit
> > > >> meaning than we want to imply here.
> > > >>
> > > >> * I agree that this is not a bounded source. If we want to support a
> > > >> bounded mode, this is an orthogonal concern that also applies to
> other
> > > >> unbounded sources.
> > > >>
> > > >> Best,
> > > >>
> > > >> Konstantin
> > > >>
> > > >> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:
> > > >>
> > > >> > Hi Danny,
> > > >> >
> > > >> > First of all, we didn't introduce any concepts from KSQL (e.g.
> > Stream
> > > vs
> > > >> > Table notion).
> > > >> > This new connector will produce a changelog stream, so it's still
> a
> > > >> dynamic
> > > >> > table and doesn't conflict with Flink core concepts.
> > > >> >
> > > >> > The "ktable" is just a connector name, we can also call it
> > > >> > "compacted-kafka" or something else.
> > > >> > Calling it "ktable" is just because KSQL users can migrate to
> Flink
> > > SQL
> > > >> > easily.
> > > >> >
> > > >> > Regarding to why introducing a new connector vs a new property in
> > > >> existing
> > > >> > kafka connector:
> > > >> >
> > > >> > I think the main reason is that we want to have a clear separation
> > for
> > > >> such
> > > >> > two use cases, because they are very different.
> > > >> > We also listed reasons in the FLIP, including:
> > > >> >
> > > >> > 1) It's hard to explain what's the behavior when users specify the
> > > start
> > > >> > offset from a middle position (e.g. how to process non exist
> delete
> > > >> > events).
> > > >> >     It's dangerous if users do that. So we don't provide the
> offset
> > > >> option
> > > >> > in the new connector at the moment.
> > > >> > 2) It's a different perspective/abstraction on the same kafka
> topic
> > > >> (append
> > > >> > vs. upsert). It would be easier to understand if we can separate
> > them
> > > >> >     instead of mixing them in one connector. The new connector
> > > requires
> > > >> > hash sink partitioner, primary key declared, regular format.
> > > >> >     If we mix them in one connector, it might be confusing how to
> > use
> > > >> the
> > > >> > options correctly.
> > > >> > 3) The semantic of the KTable connector is just the same as KTable
> > in
> > > >> Kafka
> > > >> > Stream. So it's very handy for Kafka Stream and KSQL users.
> > > >> >     We have seen several questions in the mailing list asking how
> to
> > > >> model
> > > >> > a KTable and how to join a KTable in Flink SQL.
> > > >> >
> > > >> > Best,
> > > >> > Jark
> > > >> >
> > > >> > On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
> > > >> >
> > > >> > > Hi Jingsong,
> > > >> > >
> > > >> > > As the FLIP describes, "KTable connector produces a changelog
> > > stream,
> > > >> > > where each data record represents an update or delete event.".
> > > >> > > Therefore, a ktable source is an unbounded stream source.
> > Selecting
> > > a
> > > >> > > ktable source is similar to selecting a kafka source with
> > > >> debezium-json
> > > >> > > format
> > > >> > > that it never ends and the results are continuously updated.
> > > >> > >
> > > >> > > It's possible to have a bounded ktable source in the future, for
> > > >> example,
> > > >> > > add an option 'bounded=true' or 'end-offset=xxx'.
> > > >> > > In this way, the ktable will produce a bounded changelog stream.
> > > >> > > So I think this can be a compatible feature in the future.
> > > >> > >
> > > >> > > I don't think we should associate with ksql related concepts.
> > > >> Actually,
> > > >> > we
> > > >> > > didn't introduce any concepts from KSQL (e.g. Stream vs Table
> > > notion).
> > > >> > > The "ktable" is just a connector name, we can also call it
> > > >> > > "compacted-kafka" or something else.
> > > >> > > Calling it "ktable" is just because KSQL users can migrate to
> > Flink
> > > >> SQL
> > > >> > > easily.
> > > >> > >
> > > >> > > Regarding the "value.fields-include", this is an option
> introduced
> > > in
> > > >> > > FLIP-107 for Kafka connector.
> > > >> > > I think we should keep the same behavior with the Kafka
> connector.
> > > I'm
> > > >> > not
> > > >> > > sure what's the default behavior of KSQL.
> > > >> > > But I guess it also stores the keys in value from this example
> > docs
> > > >> (see
> > > >> > > the "users_original" table) [1].
> > > >> > >
> > > >> > > Best,
> > > >> > > Jark
> > > >> > >
> > > >> > > [1]:
> > > >> > >
> > > >> >
> > > >>
> > >
> >
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> > > >> > >
> > > >> > >
> > > >> > > On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]>
> > > >> wrote:
> > > >> > >
> > > >> > >> The concept seems conflicts with the Flink abstraction “dynamic
> > > >> table”,
> > > >> > >> in Flink we see both “stream” and “table” as a dynamic table,
> > > >> > >>
> > > >> > >> I think we should make clear first how to express stream and
> > table
> > > >> > >> specific features on one “dynamic table”,
> > > >> > >> it is more natural for KSQL because KSQL takes stream and table
> > as
> > > >> > >> different abstractions for representing collections. In KSQL,
> > only
> > > >> > table is
> > > >> > >> mutable and can have a primary key.
> > > >> > >>
> > > >> > >> Does this connector belongs to the “table” scope or “stream”
> > scope
> > > ?
> > > >> > >>
> > > >> > >> Some of the concepts (such as the primary key on stream) should
> > be
> > > >> > >> suitable for all the connectors, not just Kafka, Shouldn’t this
> > be
> > > an
> > > >> > >> extension of existing Kafka connector instead of a totally new
> > > >> > connector ?
> > > >> > >> What about the other connectors ?
> > > >> > >>
> > > >> > >> Because this touches the core abstraction of Flink, we better
> > have
> > > a
> > > >> > >> top-down overall design, following the KSQL directly is not the
> > > >> answer.
> > > >> > >>
> > > >> > >> P.S. For the source
> > > >> > >> > Shouldn’t this be an extension of existing Kafka connector
> > > instead
> > > >> of
> > > >> > a
> > > >> > >> totally new connector ?
> > > >> > >>
> > > >> > >> How could we achieve that (e.g. set up the parallelism
> > correctly) ?
> > > >> > >>
> > > >> > >> Best,
> > > >> > >> Danny Chan
> > > >> > >> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]
> > > >，写道：
> > > >> > >> > Thanks Shengkai for your proposal.
> > > >> > >> >
> > > >> > >> > +1 for this feature.
> > > >> > >> >
> > > >> > >> > > Future Work: Support bounded KTable source
> > > >> > >> >
> > > >> > >> > I don't think it should be a future work, I think it is one
> of
> > > the
> > > >> > >> > important concepts of this FLIP. We need to understand it
> now.
> > > >> > >> >
> > > >> > >> > Intuitively, a ktable in my opinion is a bounded table rather
> > > than
> > > >> a
> > > >> > >> > stream, so select should produce a bounded table by default.
> > > >> > >> >
> > > >> > >> > I think we can list Kafka related knowledge, because the word
> > > >> `ktable`
> > > >> > >> is
> > > >> > >> > easy to associate with ksql related concepts. (If possible,
> > it's
> > > >> > better
> > > >> > >> to
> > > >> > >> > unify with it)
> > > >> > >> >
> > > >> > >> > What do you think?
> > > >> > >> >
> > > >> > >> > > value.fields-include
> > > >> > >> >
> > > >> > >> > What about the default behavior of KSQL?
> > > >> > >> >
> > > >> > >> > Best,
> > > >> > >> > Jingsong
> > > >> > >> >
> > > >> > >> > On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
> > [hidden email]
> > > >
> > > >> > >> wrote:
> > > >> > >> >
> > > >> > >> > > Hi, devs.
> > > >> > >> > >
> > > >> > >> > > Jark and I want to start a new FLIP to introduce the KTable
> > > >> > >> connector. The
> > > >> > >> > > KTable is a shortcut of "Kafka Table", it also has the same
> > > >> > semantics
> > > >> > >> with
> > > >> > >> > > the KTable notion in Kafka Stream.
> > > >> > >> > >
> > > >> > >> > > FLIP-149:
> > > >> > >> > >
> > > >> > >> > >
> > > >> > >>
> > > >> >
> > > >>
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> > > >> > >> > >
> > > >> > >> > > Currently many users have expressed their needs for the
> > upsert
> > > >> Kafka
> > > >> > >> by
> > > >> > >> > > mail lists and issues. The KTable connector has several
> > > benefits
> > > >> for
> > > >> > >> users:
> > > >> > >> > >
> > > >> > >> > > 1. Users are able to interpret a compacted Kafka Topic as
> an
> > > >> upsert
> > > >> > >> stream
> > > >> > >> > > in Apache Flink. And also be able to write a changelog
> stream
> > > to
> > > >> > Kafka
> > > >> > >> > > (into a compacted topic).
> > > >> > >> > > 2. As a part of the real time pipeline, store join or
> > aggregate
> > > >> > >> result (may
> > > >> > >> > > contain updates) into a Kafka topic for further
> calculation;
> > > >> > >> > > 3. The semantic of the KTable connector is just the same as
> > > >> KTable
> > > >> > in
> > > >> > >> Kafka
> > > >> > >> > > Stream. So it's very handy for Kafka Stream and KSQL users.
> > We
> > > >> have
> > > >> > >> seen
> > > >> > >> > > several questions in the mailing list asking how to model a
> > > >> KTable
> > > >> > >> and how
> > > >> > >> > > to join a KTable in Flink SQL.
> > > >> > >> > >
> > > >> > >> > > We hope it can expand the usage of the Flink with Kafka.
> > > >> > >> > >
> > > >> > >> > > I'm looking forward to your feedback.
> > > >> > >> > >
> > > >> > >> > > Best,
> > > >> > >> > > Shengkai
> > > >> > >> > >
> > > >> > >> >
> > > >> > >> >
> > > >> > >> > --
> > > >> > >> > Best, Jingsong Lee
> > > >> > >>
> > > >> > >
> > > >> >
> > > >>
> > > >>
> > > >> --
> > > >>
> > > >> Konstantin Knauf
> > > >>
> > > >> https://twitter.com/snntrable
> > > >>
> > > >> https://github.com/knaufk
> > > >>
> > > >
> > >
> >
>
>
> --
>
> Konstantin Knauf
>
> https://twitter.com/snntrable
>
> https://github.com/knaufk
>

[Timo Walther-2]

Hi Shengkai, Hi Jark,

thanks for this great proposal. It is time to finally connect the
changelog processor with a compacted Kafka topic.

"The operator will produce INSERT rows, or additionally generate
UPDATE_BEFORE rows for the previous image, or produce DELETE rows with
all columns filled with values."

Could you elaborate a bit on the implementation details in the FLIP? How
are UPDATE_BEFOREs are generated. How much state is required to perform
this operation.

 From a conceptual and semantical point of view, I'm fine with the
proposal. But I would like to share my opinion about how we expose this
feature:

ktable vs kafka-compacted

I'm against having an additional connector like `ktable` or
`kafka-compacted`. We recently simplified the table options to a minimum
amount of characters to be as concise as possible in the DDL. Therefore,
I would keep the `connector=kafka` and introduce an additional option.
Because a user wants to read "from Kafka". And the "how" should be
determined in the lower options.

When people read `connector=ktable` they might not know that this is
Kafka. Or they wonder where `kstream` is?

When people read `connector=kafka-compacted` they might not know that it
has ktable semantics. You don't need to enable log compaction in order
to use a KTable as far as I know. Log compaction and table semantics are
orthogonal topics.

In the end we will need 3 types of information when declaring a Kafka
connector:

CREATE TABLE ... WITH (
   connector=kafka        -- Some information about the connector
   end-offset = XXXX      -- Some information about the boundedness
   model = table/stream   -- Some information about interpretation
)


We can still apply all the constraints mentioned in the FLIP. When
`model` is set to `table`.

What do you think?

Regards,
Timo


On 21.10.20 14:19, Jark Wu wrote:

> Hi,
>
> IMO, if we are going to mix them in one connector,
> 1) either users need to set some options to a specific value explicitly,
> e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
> This makes the connector awkward to use. Users may face to fix options one
> by one according to the exception.
> Besides, in the future, it is still possible to use
> "sink.partitioner=fixed" (reduce network cost) if users are aware of
> the partition routing,
> however, it's error-prone to have "fixed" as default for compacted mode.
>
> 2) or make those options a different default value when "compacted=true".
> This would be more confusing and unpredictable if the default value of
> options will change according to other options.
> What happens if we have a third mode in the future?
>
> In terms of usage and options, it's very different from the
> original "kafka" connector.
> It would be more handy to use and less fallible if separating them into two
> connectors.
> In the implementation layer, we can reuse code as much as possible.
>
> Therefore, I'm still +1 to have a new connector.
> The "kafka-compacted" name sounds good to me.
>
> Best,
> Jark
>
>
> On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <[hidden email]> wrote:
>
>> Hi Kurt, Hi Shengkai,
>>
>> thanks for answering my questions and the additional clarifications. I
>> don't have a strong opinion on whether to extend the "kafka" connector or
>> to introduce a new connector. So, from my perspective feel free to go with
>> a separate connector. If we do introduce a new connector I wouldn't call it
>> "ktable" for aforementioned reasons (In addition, we might suggest that
>> there is also a "kstreams" connector for symmetry reasons). I don't have a
>> good alternative name, though, maybe "kafka-compacted" or
>> "compacted-kafka".
>>
>> Thanks,
>>
>> Konstantin
>>
>>
>> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]> wrote:
>>
>>> Hi all,
>>>
>>> I want to describe the discussion process which drove us to have such
>>> conclusion, this might make some of
>>> the design choices easier to understand and keep everyone on the same
>> page.
>>>
>>> Back to the motivation, what functionality do we want to provide in the
>>> first place? We got a lot of feedback and
>>> questions from mailing lists that people want to write Not-Insert-Only
>>> messages into kafka. They might be
>>> intentional or by accident, e.g. wrote an non-windowed aggregate query or
>>> non-windowed left outer join. And
>>> some users from KSQL world also asked about why Flink didn't leverage the
>>> Key concept of every kafka topic
>>> and make kafka as a dynamic changing keyed table.
>>>
>>> To work with kafka better, we were thinking to extend the functionality
>> of
>>> the current kafka connector by letting it
>>> accept updates and deletions. But due to the limitation of kafka, the
>>> update has to be "update by key", aka a table
>>> with primary key.
>>>
>>> This introduces a couple of conflicts with current kafka table's options:
>>> 1. key.fields: as said above, we need the kafka table to have the primary
>>> key constraint. And users can also configure
>>> key.fields freely, this might cause friction. (Sure we can do some sanity
>>> check on this but it also creates friction.)
>>> 2. sink.partitioner: to make the semantics right, we need to make sure
>> all
>>> the updates on the same key are written to
>>> the same kafka partition, such we should force to use a hash by key
>>> partition inside such table. Again, this has conflicts
>>> and creates friction with current user options.
>>>
>>> The above things are solvable, though not perfect or most user friendly.
>>>
>>> Let's take a look at the reading side. The keyed kafka table contains two
>>> kinds of messages: upsert or deletion. What upsert
>>> means is "If the key doesn't exist yet, it's an insert record. Otherwise
>>> it's an update record". For the sake of correctness or
>>> simplicity, the Flink SQL engine also needs such information. If we
>>> interpret all messages to "update record", some queries or
>>> operators may not work properly. It's weird to see an update record but
>> you
>>> haven't seen the insert record before.
>>>
>>> So what Flink should do is after reading out the records from such table,
>>> it needs to create a state to record which messages have
>>> been seen and then generate the correct row type correspondingly. This
>> kind
>>> of couples the state and the data of the message
>>> queue, and it also creates conflicts with current kafka connector.
>>>
>>> Think about if users suspend a running job (which contains some reading
>>> state now), and then change the start offset of the reader.
>>> By changing the reading offset, it actually change the whole story of
>>> "which records should be insert messages and which records
>>> should be update messages). And it will also make Flink to deal with
>>> another weird situation that it might receive a deletion
>>> on a non existing message.
>>>
>>> We were unsatisfied with all the frictions and conflicts it will create
>> if
>>> we enable the "upsert & deletion" support to the current kafka
>>> connector. And later we begin to realize that we shouldn't treat it as a
>>> normal message queue, but should treat it as a changing keyed
>>> table. We should be able to always get the whole data of such table (by
>>> disabling the start offset option) and we can also read the
>>> changelog out of such table. It's like a HBase table with binlog support
>>> but doesn't have random access capability (which can be fulfilled
>>> by Flink's state).
>>>
>>> So our intention was instead of telling and persuading users what kind of
>>> options they should or should not use by extending
>>> current kafka connector when enable upsert support, we are actually
>> create
>>> a whole new and different connector that has total
>>> different abstractions in SQL layer, and should be treated totally
>>> different with current kafka connector.
>>>
>>> Hope this can clarify some of the concerns.
>>>
>>> Best,
>>> Kurt
>>>
>>>
>>> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <[hidden email]> wrote:
>>>
>>>> Hi devs,
>>>>
>>>> As many people are still confused about the difference option
>> behaviours
>>>> between the Kafka connector and KTable connector, Jark and I list the
>>>> differences in the doc[1].
>>>>
>>>> Best,
>>>> Shengkai
>>>>
>>>> [1]
>>>>
>>>>
>>>
>> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
>>>>
>>>> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
>>>>
>>>>> Hi Konstantin,
>>>>>
>>>>> Thanks for your reply.
>>>>>
>>>>>> It uses the "kafka" connector and does not specify a primary key.
>>>>> The dimensional table `users` is a ktable connector and we can
>> specify
>>>> the
>>>>> pk on the KTable.
>>>>>
>>>>>> Will it possible to use a "ktable" as a dimensional table in
>> FLIP-132
>>>>> Yes. We can specify the watermark on the KTable and it can be used
>> as a
>>>>> dimension table in temporal join.
>>>>>
>>>>>> Introduce a new connector vs introduce a new property
>>>>> The main reason behind is that the KTable connector almost has no
>>> common
>>>>> options with the Kafka connector. The options that can be reused by
>>>> KTable
>>>>> connectors are 'topic', 'properties.bootstrap.servers' and
>>>>> 'value.fields-include' . We can't set cdc format for 'key.format' and
>>>>> 'value.format' in KTable connector now, which is  available in Kafka
>>>>> connector. Considering the difference between the options we can use,
>>>> it's
>>>>> more suitable to introduce an another connector rather than a
>> property.
>>>>>
>>>>> We are also fine to use "compacted-kafka" as the name of the new
>>>>> connector. What do you think?
>>>>>
>>>>> Best,
>>>>> Shengkai
>>>>>
>>>>> Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
>>>>>
>>>>>> Hi Shengkai,
>>>>>>
>>>>>> Thank you for driving this effort. I believe this a very important
>>>> feature
>>>>>> for many users who use Kafka and Flink SQL together. A few questions
>>> and
>>>>>> thoughts:
>>>>>>
>>>>>> * Is your example "Use KTable as a reference/dimension table"
>> correct?
>>>> It
>>>>>> uses the "kafka" connector and does not specify a primary key.
>>>>>>
>>>>>> * Will it be possible to use a "ktable" table directly as a
>>> dimensional
>>>>>> table in temporal join (*based on event time*) (FLIP-132)? This is
>> not
>>>>>> completely clear to me from the FLIP.
>>>>>>
>>>>>> * I'd personally prefer not to introduce a new connector and instead
>>> to
>>>>>> extend the Kafka connector. We could add an additional property
>>>>>> "compacted"
>>>>>> = "true"|"false". If it is set to "true", we can add additional
>>>> validation
>>>>>> logic (e.g. "scan.startup.mode" can not be set, primary key
>> required,
>>>>>> etc.). If we stick to a separate connector I'd not call it "ktable",
>>> but
>>>>>> rather "compacted-kafka" or similar. KTable seems to carry more
>>> implicit
>>>>>> meaning than we want to imply here.
>>>>>>
>>>>>> * I agree that this is not a bounded source. If we want to support a
>>>>>> bounded mode, this is an orthogonal concern that also applies to
>> other
>>>>>> unbounded sources.
>>>>>>
>>>>>> Best,
>>>>>>
>>>>>> Konstantin
>>>>>>
>>>>>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:
>>>>>>
>>>>>>> Hi Danny,
>>>>>>>
>>>>>>> First of all, we didn't introduce any concepts from KSQL (e.g.
>>> Stream
>>>> vs
>>>>>>> Table notion).
>>>>>>> This new connector will produce a changelog stream, so it's still
>> a
>>>>>> dynamic
>>>>>>> table and doesn't conflict with Flink core concepts.
>>>>>>>
>>>>>>> The "ktable" is just a connector name, we can also call it
>>>>>>> "compacted-kafka" or something else.
>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
>> Flink
>>>> SQL
>>>>>>> easily.
>>>>>>>
>>>>>>> Regarding to why introducing a new connector vs a new property in
>>>>>> existing
>>>>>>> kafka connector:
>>>>>>>
>>>>>>> I think the main reason is that we want to have a clear separation
>>> for
>>>>>> such
>>>>>>> two use cases, because they are very different.
>>>>>>> We also listed reasons in the FLIP, including:
>>>>>>>
>>>>>>> 1) It's hard to explain what's the behavior when users specify the
>>>> start
>>>>>>> offset from a middle position (e.g. how to process non exist
>> delete
>>>>>>> events).
>>>>>>>      It's dangerous if users do that. So we don't provide the
>> offset
>>>>>> option
>>>>>>> in the new connector at the moment.
>>>>>>> 2) It's a different perspective/abstraction on the same kafka
>> topic
>>>>>> (append
>>>>>>> vs. upsert). It would be easier to understand if we can separate
>>> them
>>>>>>>      instead of mixing them in one connector. The new connector
>>>> requires
>>>>>>> hash sink partitioner, primary key declared, regular format.
>>>>>>>      If we mix them in one connector, it might be confusing how to
>>> use
>>>>>> the
>>>>>>> options correctly.
>>>>>>> 3) The semantic of the KTable connector is just the same as KTable
>>> in
>>>>>> Kafka
>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
>>>>>>>      We have seen several questions in the mailing list asking how
>> to
>>>>>> model
>>>>>>> a KTable and how to join a KTable in Flink SQL.
>>>>>>>
>>>>>>> Best,
>>>>>>> Jark
>>>>>>>
>>>>>>> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
>>>>>>>
>>>>>>>> Hi Jingsong,
>>>>>>>>
>>>>>>>> As the FLIP describes, "KTable connector produces a changelog
>>>> stream,
>>>>>>>> where each data record represents an update or delete event.".
>>>>>>>> Therefore, a ktable source is an unbounded stream source.
>>> Selecting
>>>> a
>>>>>>>> ktable source is similar to selecting a kafka source with
>>>>>> debezium-json
>>>>>>>> format
>>>>>>>> that it never ends and the results are continuously updated.
>>>>>>>>
>>>>>>>> It's possible to have a bounded ktable source in the future, for
>>>>>> example,
>>>>>>>> add an option 'bounded=true' or 'end-offset=xxx'.
>>>>>>>> In this way, the ktable will produce a bounded changelog stream.
>>>>>>>> So I think this can be a compatible feature in the future.
>>>>>>>>
>>>>>>>> I don't think we should associate with ksql related concepts.
>>>>>> Actually,
>>>>>>> we
>>>>>>>> didn't introduce any concepts from KSQL (e.g. Stream vs Table
>>>> notion).
>>>>>>>> The "ktable" is just a connector name, we can also call it
>>>>>>>> "compacted-kafka" or something else.
>>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
>>> Flink
>>>>>> SQL
>>>>>>>> easily.
>>>>>>>>
>>>>>>>> Regarding the "value.fields-include", this is an option
>> introduced
>>>> in
>>>>>>>> FLIP-107 for Kafka connector.
>>>>>>>> I think we should keep the same behavior with the Kafka
>> connector.
>>>> I'm
>>>>>>> not
>>>>>>>> sure what's the default behavior of KSQL.
>>>>>>>> But I guess it also stores the keys in value from this example
>>> docs
>>>>>> (see
>>>>>>>> the "users_original" table) [1].
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Jark
>>>>>>>>
>>>>>>>> [1]:
>>>>>>>>
>>>>>>>
>>>>>>
>>>>
>>>
>> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
>>>>>>>>
>>>>>>>>
>>>>>>>> On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]>
>>>>>> wrote:
>>>>>>>>
>>>>>>>>> The concept seems conflicts with the Flink abstraction “dynamic
>>>>>> table”,
>>>>>>>>> in Flink we see both “stream” and “table” as a dynamic table,
>>>>>>>>>
>>>>>>>>> I think we should make clear first how to express stream and
>>> table
>>>>>>>>> specific features on one “dynamic table”,
>>>>>>>>> it is more natural for KSQL because KSQL takes stream and table
>>> as
>>>>>>>>> different abstractions for representing collections. In KSQL,
>>> only
>>>>>>> table is
>>>>>>>>> mutable and can have a primary key.
>>>>>>>>>
>>>>>>>>> Does this connector belongs to the “table” scope or “stream”
>>> scope
>>>> ?
>>>>>>>>>
>>>>>>>>> Some of the concepts (such as the primary key on stream) should
>>> be
>>>>>>>>> suitable for all the connectors, not just Kafka, Shouldn’t this
>>> be
>>>> an
>>>>>>>>> extension of existing Kafka connector instead of a totally new
>>>>>>> connector ?
>>>>>>>>> What about the other connectors ?
>>>>>>>>>
>>>>>>>>> Because this touches the core abstraction of Flink, we better
>>> have
>>>> a
>>>>>>>>> top-down overall design, following the KSQL directly is not the
>>>>>> answer.
>>>>>>>>>
>>>>>>>>> P.S. For the source
>>>>>>>>>> Shouldn’t this be an extension of existing Kafka connector
>>>> instead
>>>>>> of
>>>>>>> a
>>>>>>>>> totally new connector ?
>>>>>>>>>
>>>>>>>>> How could we achieve that (e.g. set up the parallelism
>>> correctly) ?
>>>>>>>>>
>>>>>>>>> Best,
>>>>>>>>> Danny Chan
>>>>>>>>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]
>>>>> ，写道：
>>>>>>>>>> Thanks Shengkai for your proposal.
>>>>>>>>>>
>>>>>>>>>> +1 for this feature.
>>>>>>>>>>
>>>>>>>>>>> Future Work: Support bounded KTable source
>>>>>>>>>>
>>>>>>>>>> I don't think it should be a future work, I think it is one
>> of
>>>> the
>>>>>>>>>> important concepts of this FLIP. We need to understand it
>> now.
>>>>>>>>>>
>>>>>>>>>> Intuitively, a ktable in my opinion is a bounded table rather
>>>> than
>>>>>> a
>>>>>>>>>> stream, so select should produce a bounded table by default.
>>>>>>>>>>
>>>>>>>>>> I think we can list Kafka related knowledge, because the word
>>>>>> `ktable`
>>>>>>>>> is
>>>>>>>>>> easy to associate with ksql related concepts. (If possible,
>>> it's
>>>>>>> better
>>>>>>>>> to
>>>>>>>>>> unify with it)
>>>>>>>>>>
>>>>>>>>>> What do you think?
>>>>>>>>>>
>>>>>>>>>>> value.fields-include
>>>>>>>>>>
>>>>>>>>>> What about the default behavior of KSQL?
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>> Jingsong
>>>>>>>>>>
>>>>>>>>>> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
>>> [hidden email]
>>>>>
>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi, devs.
>>>>>>>>>>>
>>>>>>>>>>> Jark and I want to start a new FLIP to introduce the KTable
>>>>>>>>> connector. The
>>>>>>>>>>> KTable is a shortcut of "Kafka Table", it also has the same
>>>>>>> semantics
>>>>>>>>> with
>>>>>>>>>>> the KTable notion in Kafka Stream.
>>>>>>>>>>>
>>>>>>>>>>> FLIP-149:
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>>
>>>>
>>>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
>>>>>>>>>>>
>>>>>>>>>>> Currently many users have expressed their needs for the
>>> upsert
>>>>>> Kafka
>>>>>>>>> by
>>>>>>>>>>> mail lists and issues. The KTable connector has several
>>>> benefits
>>>>>> for
>>>>>>>>> users:
>>>>>>>>>>>
>>>>>>>>>>> 1. Users are able to interpret a compacted Kafka Topic as
>> an
>>>>>> upsert
>>>>>>>>> stream
>>>>>>>>>>> in Apache Flink. And also be able to write a changelog
>> stream
>>>> to
>>>>>>> Kafka
>>>>>>>>>>> (into a compacted topic).
>>>>>>>>>>> 2. As a part of the real time pipeline, store join or
>>> aggregate
>>>>>>>>> result (may
>>>>>>>>>>> contain updates) into a Kafka topic for further
>> calculation;
>>>>>>>>>>> 3. The semantic of the KTable connector is just the same as
>>>>>> KTable
>>>>>>> in
>>>>>>>>> Kafka
>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
>>> We
>>>>>> have
>>>>>>>>> seen
>>>>>>>>>>> several questions in the mailing list asking how to model a
>>>>>> KTable
>>>>>>>>> and how
>>>>>>>>>>> to join a KTable in Flink SQL.
>>>>>>>>>>>
>>>>>>>>>>> We hope it can expand the usage of the Flink with Kafka.
>>>>>>>>>>>
>>>>>>>>>>> I'm looking forward to your feedback.
>>>>>>>>>>>
>>>>>>>>>>> Best,
>>>>>>>>>>> Shengkai
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> --
>>>>>>>>>> Best, Jingsong Lee
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>>
>>>>>> Konstantin Knauf
>>>>>>
>>>>>> https://twitter.com/snntrable
>>>>>>
>>>>>> https://github.com/knaufk
>>>>>>
>>>>>
>>>>
>>>
>>
>>
>> --
>>
>> Konstantin Knauf
>>
>> https://twitter.com/snntrable
>>
>> https://github.com/knaufk
>>
>

[Jark Wu-2]

Hi Timo,

Thanks for your opinions.

1) Implementation
We will have an stateful operator to generate INSERT and UPDATE_BEFORE.
This operator is keyby-ed (primary key as the shuffle key) after the source
operator.
The implementation of this operator is very similar to the existing
`DeduplicateKeepLastRowFunction`.
The operator will register a value state using the primary key fields as
keys.
When the value state is empty under current key, we will emit INSERT for
the input row.
When the value state is not empty under current key, we will emit
UPDATE_BEFORE using the row in state,
and emit UPDATE_AFTER using the input row.
When the input row is DELETE, we will clear state and emit DELETE row.

2) new option vs new connector
> We recently simplified the table options to a minimum amount of
characters to be as concise as possible in the DDL.
I think this is the reason why we want to introduce a new connector,
because we can simplify the options in DDL.
For example, if using a new option, the DDL may look like this:

CREATE TABLE users (
  user_id BIGINT,
  user_name STRING,
  user_level STRING,
  region STRING,
  PRIMARY KEY (user_id) NOT ENFORCED
) WITH (
  'connector' = 'kafka',
  'model' = 'table',
  'topic' = 'pageviews_per_region',
  'properties.bootstrap.servers' = '...',
  'properties.group.id' = 'testGroup',
  'scan.startup.mode' = 'earliest',
  'key.format' = 'csv',
  'key.fields' = 'user_id',
  'value.format' = 'avro',
  'sink.partitioner' = 'hash'
);

If using a new connector, we can have a different default value for the
options and remove unnecessary options,
the DDL can look like this which is much more concise:

CREATE TABLE pageviews_per_region (
  user_id BIGINT,
  user_name STRING,
  user_level STRING,
  region STRING,
  PRIMARY KEY (user_id) NOT ENFORCED
) WITH (
  'connector' = 'kafka-compacted',
  'topic' = 'pageviews_per_region',
  'properties.bootstrap.servers' = '...',
  'key.format' = 'csv',
  'value.format' = 'avro'
);

> When people read `connector=kafka-compacted` they might not know that it
> has ktable semantics. You don't need to enable log compaction in order
> to use a KTable as far as I know.
We don't need to let users know it has ktable semantics, as Konstantin
mentioned this may carry more implicit
meaning than we want to imply here. I agree users don't need to enable log
compaction, but from the production perspective,
log compaction should always be enabled if it is used in this purpose.
Calling it "kafka-compacted" can even remind users to enable log compaction.

I don't agree to introduce "model = table/stream" option, or
"connector=kafka-table",
because this means we are introducing Table vs Stream concept from KSQL.
However, we don't have such top-level concept in Flink SQL now, this will
further confuse users.
In Flink SQL, all the things are STREAM, the differences are whether it is
bounded or unbounded,
 whether it is insert-only or changelog.


Best,
Jark


On Thu, 22 Oct 2020 at 20:39, Timo Walther <[hidden email]> wrote:

> Hi Shengkai, Hi Jark,
>
> thanks for this great proposal. It is time to finally connect the
> changelog processor with a compacted Kafka topic.
>
> "The operator will produce INSERT rows, or additionally generate
> UPDATE_BEFORE rows for the previous image, or produce DELETE rows with
> all columns filled with values."
>
> Could you elaborate a bit on the implementation details in the FLIP? How
> are UPDATE_BEFOREs are generated. How much state is required to perform
> this operation.
>
>  From a conceptual and semantical point of view, I'm fine with the
> proposal. But I would like to share my opinion about how we expose this
> feature:
>
> ktable vs kafka-compacted
>
> I'm against having an additional connector like `ktable` or
> `kafka-compacted`. We recently simplified the table options to a minimum
> amount of characters to be as concise as possible in the DDL. Therefore,
> I would keep the `connector=kafka` and introduce an additional option.
> Because a user wants to read "from Kafka". And the "how" should be
> determined in the lower options.
>
> When people read `connector=ktable` they might not know that this is
> Kafka. Or they wonder where `kstream` is?
>
> When people read `connector=kafka-compacted` they might not know that it
> has ktable semantics. You don't need to enable log compaction in order
> to use a KTable as far as I know. Log compaction and table semantics are
> orthogonal topics.
>
> In the end we will need 3 types of information when declaring a Kafka
> connector:
>
> CREATE TABLE ... WITH (
>    connector=kafka        -- Some information about the connector
>    end-offset = XXXX      -- Some information about the boundedness
>    model = table/stream   -- Some information about interpretation
> )
>
>
> We can still apply all the constraints mentioned in the FLIP. When
> `model` is set to `table`.
>
> What do you think?
>
> Regards,
> Timo
>
>
> On 21.10.20 14:19, Jark Wu wrote:
> > Hi,
> >
> > IMO, if we are going to mix them in one connector,
> > 1) either users need to set some options to a specific value explicitly,
> > e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
> > This makes the connector awkward to use. Users may face to fix options
> one
> > by one according to the exception.
> > Besides, in the future, it is still possible to use
> > "sink.partitioner=fixed" (reduce network cost) if users are aware of
> > the partition routing,
> > however, it's error-prone to have "fixed" as default for compacted mode.
> >
> > 2) or make those options a different default value when "compacted=true".
> > This would be more confusing and unpredictable if the default value of
> > options will change according to other options.
> > What happens if we have a third mode in the future?
> >
> > In terms of usage and options, it's very different from the
> > original "kafka" connector.
> > It would be more handy to use and less fallible if separating them into
> two
> > connectors.
> > In the implementation layer, we can reuse code as much as possible.
> >
> > Therefore, I'm still +1 to have a new connector.
> > The "kafka-compacted" name sounds good to me.
> >
> > Best,
> > Jark
> >
> >
> > On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <[hidden email]>
> wrote:
> >
> >> Hi Kurt, Hi Shengkai,
> >>
> >> thanks for answering my questions and the additional clarifications. I
> >> don't have a strong opinion on whether to extend the "kafka" connector
> or
> >> to introduce a new connector. So, from my perspective feel free to go
> with
> >> a separate connector. If we do introduce a new connector I wouldn't
> call it
> >> "ktable" for aforementioned reasons (In addition, we might suggest that
> >> there is also a "kstreams" connector for symmetry reasons). I don't
> have a
> >> good alternative name, though, maybe "kafka-compacted" or
> >> "compacted-kafka".
> >>
> >> Thanks,
> >>
> >> Konstantin
> >>
> >>
> >> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]> wrote:
> >>
> >>> Hi all,
> >>>
> >>> I want to describe the discussion process which drove us to have such
> >>> conclusion, this might make some of
> >>> the design choices easier to understand and keep everyone on the same
> >> page.
> >>>
> >>> Back to the motivation, what functionality do we want to provide in the
> >>> first place? We got a lot of feedback and
> >>> questions from mailing lists that people want to write Not-Insert-Only
> >>> messages into kafka. They might be
> >>> intentional or by accident, e.g. wrote an non-windowed aggregate query
> or
> >>> non-windowed left outer join. And
> >>> some users from KSQL world also asked about why Flink didn't leverage
> the
> >>> Key concept of every kafka topic
> >>> and make kafka as a dynamic changing keyed table.
> >>>
> >>> To work with kafka better, we were thinking to extend the functionality
> >> of
> >>> the current kafka connector by letting it
> >>> accept updates and deletions. But due to the limitation of kafka, the
> >>> update has to be "update by key", aka a table
> >>> with primary key.
> >>>
> >>> This introduces a couple of conflicts with current kafka table's
> options:
> >>> 1. key.fields: as said above, we need the kafka table to have the
> primary
> >>> key constraint. And users can also configure
> >>> key.fields freely, this might cause friction. (Sure we can do some
> sanity
> >>> check on this but it also creates friction.)
> >>> 2. sink.partitioner: to make the semantics right, we need to make sure
> >> all
> >>> the updates on the same key are written to
> >>> the same kafka partition, such we should force to use a hash by key
> >>> partition inside such table. Again, this has conflicts
> >>> and creates friction with current user options.
> >>>
> >>> The above things are solvable, though not perfect or most user
> friendly.
> >>>
> >>> Let's take a look at the reading side. The keyed kafka table contains
> two
> >>> kinds of messages: upsert or deletion. What upsert
> >>> means is "If the key doesn't exist yet, it's an insert record.
> Otherwise
> >>> it's an update record". For the sake of correctness or
> >>> simplicity, the Flink SQL engine also needs such information. If we
> >>> interpret all messages to "update record", some queries or
> >>> operators may not work properly. It's weird to see an update record but
> >> you
> >>> haven't seen the insert record before.
> >>>
> >>> So what Flink should do is after reading out the records from such
> table,
> >>> it needs to create a state to record which messages have
> >>> been seen and then generate the correct row type correspondingly. This
> >> kind
> >>> of couples the state and the data of the message
> >>> queue, and it also creates conflicts with current kafka connector.
> >>>
> >>> Think about if users suspend a running job (which contains some reading
> >>> state now), and then change the start offset of the reader.
> >>> By changing the reading offset, it actually change the whole story of
> >>> "which records should be insert messages and which records
> >>> should be update messages). And it will also make Flink to deal with
> >>> another weird situation that it might receive a deletion
> >>> on a non existing message.
> >>>
> >>> We were unsatisfied with all the frictions and conflicts it will create
> >> if
> >>> we enable the "upsert & deletion" support to the current kafka
> >>> connector. And later we begin to realize that we shouldn't treat it as
> a
> >>> normal message queue, but should treat it as a changing keyed
> >>> table. We should be able to always get the whole data of such table (by
> >>> disabling the start offset option) and we can also read the
> >>> changelog out of such table. It's like a HBase table with binlog
> support
> >>> but doesn't have random access capability (which can be fulfilled
> >>> by Flink's state).
> >>>
> >>> So our intention was instead of telling and persuading users what kind
> of
> >>> options they should or should not use by extending
> >>> current kafka connector when enable upsert support, we are actually
> >> create
> >>> a whole new and different connector that has total
> >>> different abstractions in SQL layer, and should be treated totally
> >>> different with current kafka connector.
> >>>
> >>> Hope this can clarify some of the concerns.
> >>>
> >>> Best,
> >>> Kurt
> >>>
> >>>
> >>> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <[hidden email]>
> wrote:
> >>>
> >>>> Hi devs,
> >>>>
> >>>> As many people are still confused about the difference option
> >> behaviours
> >>>> between the Kafka connector and KTable connector, Jark and I list the
> >>>> differences in the doc[1].
> >>>>
> >>>> Best,
> >>>> Shengkai
> >>>>
> >>>> [1]
> >>>>
> >>>>
> >>>
> >>
> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
> >>>>
> >>>> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
> >>>>
> >>>>> Hi Konstantin,
> >>>>>
> >>>>> Thanks for your reply.
> >>>>>
> >>>>>> It uses the "kafka" connector and does not specify a primary key.
> >>>>> The dimensional table `users` is a ktable connector and we can
> >> specify
> >>>> the
> >>>>> pk on the KTable.
> >>>>>
> >>>>>> Will it possible to use a "ktable" as a dimensional table in
> >> FLIP-132
> >>>>> Yes. We can specify the watermark on the KTable and it can be used
> >> as a
> >>>>> dimension table in temporal join.
> >>>>>
> >>>>>> Introduce a new connector vs introduce a new property
> >>>>> The main reason behind is that the KTable connector almost has no
> >>> common
> >>>>> options with the Kafka connector. The options that can be reused by
> >>>> KTable
> >>>>> connectors are 'topic', 'properties.bootstrap.servers' and
> >>>>> 'value.fields-include' . We can't set cdc format for 'key.format' and
> >>>>> 'value.format' in KTable connector now, which is  available in Kafka
> >>>>> connector. Considering the difference between the options we can use,
> >>>> it's
> >>>>> more suitable to introduce an another connector rather than a
> >> property.
> >>>>>
> >>>>> We are also fine to use "compacted-kafka" as the name of the new
> >>>>> connector. What do you think?
> >>>>>
> >>>>> Best,
> >>>>> Shengkai
> >>>>>
> >>>>> Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
> >>>>>
> >>>>>> Hi Shengkai,
> >>>>>>
> >>>>>> Thank you for driving this effort. I believe this a very important
> >>>> feature
> >>>>>> for many users who use Kafka and Flink SQL together. A few questions
> >>> and
> >>>>>> thoughts:
> >>>>>>
> >>>>>> * Is your example "Use KTable as a reference/dimension table"
> >> correct?
> >>>> It
> >>>>>> uses the "kafka" connector and does not specify a primary key.
> >>>>>>
> >>>>>> * Will it be possible to use a "ktable" table directly as a
> >>> dimensional
> >>>>>> table in temporal join (*based on event time*) (FLIP-132)? This is
> >> not
> >>>>>> completely clear to me from the FLIP.
> >>>>>>
> >>>>>> * I'd personally prefer not to introduce a new connector and instead
> >>> to
> >>>>>> extend the Kafka connector. We could add an additional property
> >>>>>> "compacted"
> >>>>>> = "true"|"false". If it is set to "true", we can add additional
> >>>> validation
> >>>>>> logic (e.g. "scan.startup.mode" can not be set, primary key
> >> required,
> >>>>>> etc.). If we stick to a separate connector I'd not call it "ktable",
> >>> but
> >>>>>> rather "compacted-kafka" or similar. KTable seems to carry more
> >>> implicit
> >>>>>> meaning than we want to imply here.
> >>>>>>
> >>>>>> * I agree that this is not a bounded source. If we want to support a
> >>>>>> bounded mode, this is an orthogonal concern that also applies to
> >> other
> >>>>>> unbounded sources.
> >>>>>>
> >>>>>> Best,
> >>>>>>
> >>>>>> Konstantin
> >>>>>>
> >>>>>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:
> >>>>>>
> >>>>>>> Hi Danny,
> >>>>>>>
> >>>>>>> First of all, we didn't introduce any concepts from KSQL (e.g.
> >>> Stream
> >>>> vs
> >>>>>>> Table notion).
> >>>>>>> This new connector will produce a changelog stream, so it's still
> >> a
> >>>>>> dynamic
> >>>>>>> table and doesn't conflict with Flink core concepts.
> >>>>>>>
> >>>>>>> The "ktable" is just a connector name, we can also call it
> >>>>>>> "compacted-kafka" or something else.
> >>>>>>> Calling it "ktable" is just because KSQL users can migrate to
> >> Flink
> >>>> SQL
> >>>>>>> easily.
> >>>>>>>
> >>>>>>> Regarding to why introducing a new connector vs a new property in
> >>>>>> existing
> >>>>>>> kafka connector:
> >>>>>>>
> >>>>>>> I think the main reason is that we want to have a clear separation
> >>> for
> >>>>>> such
> >>>>>>> two use cases, because they are very different.
> >>>>>>> We also listed reasons in the FLIP, including:
> >>>>>>>
> >>>>>>> 1) It's hard to explain what's the behavior when users specify the
> >>>> start
> >>>>>>> offset from a middle position (e.g. how to process non exist
> >> delete
> >>>>>>> events).
> >>>>>>>      It's dangerous if users do that. So we don't provide the
> >> offset
> >>>>>> option
> >>>>>>> in the new connector at the moment.
> >>>>>>> 2) It's a different perspective/abstraction on the same kafka
> >> topic
> >>>>>> (append
> >>>>>>> vs. upsert). It would be easier to understand if we can separate
> >>> them
> >>>>>>>      instead of mixing them in one connector. The new connector
> >>>> requires
> >>>>>>> hash sink partitioner, primary key declared, regular format.
> >>>>>>>      If we mix them in one connector, it might be confusing how to
> >>> use
> >>>>>> the
> >>>>>>> options correctly.
> >>>>>>> 3) The semantic of the KTable connector is just the same as KTable
> >>> in
> >>>>>> Kafka
> >>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
> >>>>>>>      We have seen several questions in the mailing list asking how
> >> to
> >>>>>> model
> >>>>>>> a KTable and how to join a KTable in Flink SQL.
> >>>>>>>
> >>>>>>> Best,
> >>>>>>> Jark
> >>>>>>>
> >>>>>>> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
> >>>>>>>
> >>>>>>>> Hi Jingsong,
> >>>>>>>>
> >>>>>>>> As the FLIP describes, "KTable connector produces a changelog
> >>>> stream,
> >>>>>>>> where each data record represents an update or delete event.".
> >>>>>>>> Therefore, a ktable source is an unbounded stream source.
> >>> Selecting
> >>>> a
> >>>>>>>> ktable source is similar to selecting a kafka source with
> >>>>>> debezium-json
> >>>>>>>> format
> >>>>>>>> that it never ends and the results are continuously updated.
> >>>>>>>>
> >>>>>>>> It's possible to have a bounded ktable source in the future, for
> >>>>>> example,
> >>>>>>>> add an option 'bounded=true' or 'end-offset=xxx'.
> >>>>>>>> In this way, the ktable will produce a bounded changelog stream.
> >>>>>>>> So I think this can be a compatible feature in the future.
> >>>>>>>>
> >>>>>>>> I don't think we should associate with ksql related concepts.
> >>>>>> Actually,
> >>>>>>> we
> >>>>>>>> didn't introduce any concepts from KSQL (e.g. Stream vs Table
> >>>> notion).
> >>>>>>>> The "ktable" is just a connector name, we can also call it
> >>>>>>>> "compacted-kafka" or something else.
> >>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
> >>> Flink
> >>>>>> SQL
> >>>>>>>> easily.
> >>>>>>>>
> >>>>>>>> Regarding the "value.fields-include", this is an option
> >> introduced
> >>>> in
> >>>>>>>> FLIP-107 for Kafka connector.
> >>>>>>>> I think we should keep the same behavior with the Kafka
> >> connector.
> >>>> I'm
> >>>>>>> not
> >>>>>>>> sure what's the default behavior of KSQL.
> >>>>>>>> But I guess it also stores the keys in value from this example
> >>> docs
> >>>>>> (see
> >>>>>>>> the "users_original" table) [1].
> >>>>>>>>
> >>>>>>>> Best,
> >>>>>>>> Jark
> >>>>>>>>
> >>>>>>>> [1]:
> >>>>>>>>
> >>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]>
> >>>>>> wrote:
> >>>>>>>>
> >>>>>>>>> The concept seems conflicts with the Flink abstraction “dynamic
> >>>>>> table”,
> >>>>>>>>> in Flink we see both “stream” and “table” as a dynamic table,
> >>>>>>>>>
> >>>>>>>>> I think we should make clear first how to express stream and
> >>> table
> >>>>>>>>> specific features on one “dynamic table”,
> >>>>>>>>> it is more natural for KSQL because KSQL takes stream and table
> >>> as
> >>>>>>>>> different abstractions for representing collections. In KSQL,
> >>> only
> >>>>>>> table is
> >>>>>>>>> mutable and can have a primary key.
> >>>>>>>>>
> >>>>>>>>> Does this connector belongs to the “table” scope or “stream”
> >>> scope
> >>>> ?
> >>>>>>>>>
> >>>>>>>>> Some of the concepts (such as the primary key on stream) should
> >>> be
> >>>>>>>>> suitable for all the connectors, not just Kafka, Shouldn’t this
> >>> be
> >>>> an
> >>>>>>>>> extension of existing Kafka connector instead of a totally new
> >>>>>>> connector ?
> >>>>>>>>> What about the other connectors ?
> >>>>>>>>>
> >>>>>>>>> Because this touches the core abstraction of Flink, we better
> >>> have
> >>>> a
> >>>>>>>>> top-down overall design, following the KSQL directly is not the
> >>>>>> answer.
> >>>>>>>>>
> >>>>>>>>> P.S. For the source
> >>>>>>>>>> Shouldn’t this be an extension of existing Kafka connector
> >>>> instead
> >>>>>> of
> >>>>>>> a
> >>>>>>>>> totally new connector ?
> >>>>>>>>>
> >>>>>>>>> How could we achieve that (e.g. set up the parallelism
> >>> correctly) ?
> >>>>>>>>>
> >>>>>>>>> Best,
> >>>>>>>>> Danny Chan
> >>>>>>>>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]
> >>>>> ，写道：
> >>>>>>>>>> Thanks Shengkai for your proposal.
> >>>>>>>>>>
> >>>>>>>>>> +1 for this feature.
> >>>>>>>>>>
> >>>>>>>>>>> Future Work: Support bounded KTable source
> >>>>>>>>>>
> >>>>>>>>>> I don't think it should be a future work, I think it is one
> >> of
> >>>> the
> >>>>>>>>>> important concepts of this FLIP. We need to understand it
> >> now.
> >>>>>>>>>>
> >>>>>>>>>> Intuitively, a ktable in my opinion is a bounded table rather
> >>>> than
> >>>>>> a
> >>>>>>>>>> stream, so select should produce a bounded table by default.
> >>>>>>>>>>
> >>>>>>>>>> I think we can list Kafka related knowledge, because the word
> >>>>>> `ktable`
> >>>>>>>>> is
> >>>>>>>>>> easy to associate with ksql related concepts. (If possible,
> >>> it's
> >>>>>>> better
> >>>>>>>>> to
> >>>>>>>>>> unify with it)
> >>>>>>>>>>
> >>>>>>>>>> What do you think?
> >>>>>>>>>>
> >>>>>>>>>>> value.fields-include
> >>>>>>>>>>
> >>>>>>>>>> What about the default behavior of KSQL?
> >>>>>>>>>>
> >>>>>>>>>> Best,
> >>>>>>>>>> Jingsong
> >>>>>>>>>>
> >>>>>>>>>> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
> >>> [hidden email]
> >>>>>
> >>>>>>>>> wrote:
> >>>>>>>>>>
> >>>>>>>>>>> Hi, devs.
> >>>>>>>>>>>
> >>>>>>>>>>> Jark and I want to start a new FLIP to introduce the KTable
> >>>>>>>>> connector. The
> >>>>>>>>>>> KTable is a shortcut of "Kafka Table", it also has the same
> >>>>>>> semantics
> >>>>>>>>> with
> >>>>>>>>>>> the KTable notion in Kafka Stream.
> >>>>>>>>>>>
> >>>>>>>>>>> FLIP-149:
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>
> >>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> >>>>>>>>>>>
> >>>>>>>>>>> Currently many users have expressed their needs for the
> >>> upsert
> >>>>>> Kafka
> >>>>>>>>> by
> >>>>>>>>>>> mail lists and issues. The KTable connector has several
> >>>> benefits
> >>>>>> for
> >>>>>>>>> users:
> >>>>>>>>>>>
> >>>>>>>>>>> 1. Users are able to interpret a compacted Kafka Topic as
> >> an
> >>>>>> upsert
> >>>>>>>>> stream
> >>>>>>>>>>> in Apache Flink. And also be able to write a changelog
> >> stream
> >>>> to
> >>>>>>> Kafka
> >>>>>>>>>>> (into a compacted topic).
> >>>>>>>>>>> 2. As a part of the real time pipeline, store join or
> >>> aggregate
> >>>>>>>>> result (may
> >>>>>>>>>>> contain updates) into a Kafka topic for further
> >> calculation;
> >>>>>>>>>>> 3. The semantic of the KTable connector is just the same as
> >>>>>> KTable
> >>>>>>> in
> >>>>>>>>> Kafka
> >>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
> >>> We
> >>>>>> have
> >>>>>>>>> seen
> >>>>>>>>>>> several questions in the mailing list asking how to model a
> >>>>>> KTable
> >>>>>>>>> and how
> >>>>>>>>>>> to join a KTable in Flink SQL.
> >>>>>>>>>>>
> >>>>>>>>>>> We hope it can expand the usage of the Flink with Kafka.
> >>>>>>>>>>>
> >>>>>>>>>>> I'm looking forward to your feedback.
> >>>>>>>>>>>
> >>>>>>>>>>> Best,
> >>>>>>>>>>> Shengkai
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> --
> >>>>>>>>>> Best, Jingsong Lee
> >>>>>>>>>
> >>>>>>>>
> >>>>>>>
> >>>>>>
> >>>>>>
> >>>>>> --
> >>>>>>
> >>>>>> Konstantin Knauf
> >>>>>>
> >>>>>> https://twitter.com/snntrable
> >>>>>>
> >>>>>> https://github.com/knaufk
> >>>>>>
> >>>>>
> >>>>
> >>>
> >>
> >>
> >> --
> >>
> >> Konstantin Knauf
> >>
> >> https://twitter.com/snntrable
> >>
> >> https://github.com/knaufk
> >>
> >
>
>

[Timo Walther-2]

Hi Jark,

"calling it "kafka-compacted" can even remind users to enable log
compaction"

But sometimes users like to store a lineage of changes in their topics.
Indepent of any ktable/kstream interpretation.

I let the majority decide on this topic to not further block this
effort. But we might find a better name like:

connector = kafka
mode = updating/inserting

OR

connector = kafka-updating

...

Regards,
Timo




On 22.10.20 15:24, Jark Wu wrote:

> Hi Timo,
>
> Thanks for your opinions.
>
> 1) Implementation
> We will have an stateful operator to generate INSERT and UPDATE_BEFORE.
> This operator is keyby-ed (primary key as the shuffle key) after the source
> operator.
> The implementation of this operator is very similar to the existing
> `DeduplicateKeepLastRowFunction`.
> The operator will register a value state using the primary key fields as
> keys.
> When the value state is empty under current key, we will emit INSERT for
> the input row.
> When the value state is not empty under current key, we will emit
> UPDATE_BEFORE using the row in state,
> and emit UPDATE_AFTER using the input row.
> When the input row is DELETE, we will clear state and emit DELETE row.
>
> 2) new option vs new connector
>> We recently simplified the table options to a minimum amount of
> characters to be as concise as possible in the DDL.
> I think this is the reason why we want to introduce a new connector,
> because we can simplify the options in DDL.
> For example, if using a new option, the DDL may look like this:
>
> CREATE TABLE users (
>    user_id BIGINT,
>    user_name STRING,
>    user_level STRING,
>    region STRING,
>    PRIMARY KEY (user_id) NOT ENFORCED
> ) WITH (
>    'connector' = 'kafka',
>    'model' = 'table',
>    'topic' = 'pageviews_per_region',
>    'properties.bootstrap.servers' = '...',
>    'properties.group.id' = 'testGroup',
>    'scan.startup.mode' = 'earliest',
>    'key.format' = 'csv',
>    'key.fields' = 'user_id',
>    'value.format' = 'avro',
>    'sink.partitioner' = 'hash'
> );
>
> If using a new connector, we can have a different default value for the
> options and remove unnecessary options,
> the DDL can look like this which is much more concise:
>
> CREATE TABLE pageviews_per_region (
>    user_id BIGINT,
>    user_name STRING,
>    user_level STRING,
>    region STRING,
>    PRIMARY KEY (user_id) NOT ENFORCED
> ) WITH (
>    'connector' = 'kafka-compacted',
>    'topic' = 'pageviews_per_region',
>    'properties.bootstrap.servers' = '...',
>    'key.format' = 'csv',
>    'value.format' = 'avro'
> );
>
>> When people read `connector=kafka-compacted` they might not know that it
>> has ktable semantics. You don't need to enable log compaction in order
>> to use a KTable as far as I know.
> We don't need to let users know it has ktable semantics, as Konstantin
> mentioned this may carry more implicit
> meaning than we want to imply here. I agree users don't need to enable log
> compaction, but from the production perspective,
> log compaction should always be enabled if it is used in this purpose.
> Calling it "kafka-compacted" can even remind users to enable log compaction.
>
> I don't agree to introduce "model = table/stream" option, or
> "connector=kafka-table",
> because this means we are introducing Table vs Stream concept from KSQL.
> However, we don't have such top-level concept in Flink SQL now, this will
> further confuse users.
> In Flink SQL, all the things are STREAM, the differences are whether it is
> bounded or unbounded,
>   whether it is insert-only or changelog.
>
>
> Best,
> Jark
>
>
> On Thu, 22 Oct 2020 at 20:39, Timo Walther <[hidden email]> wrote:
>
>> Hi Shengkai, Hi Jark,
>>
>> thanks for this great proposal. It is time to finally connect the
>> changelog processor with a compacted Kafka topic.
>>
>> "The operator will produce INSERT rows, or additionally generate
>> UPDATE_BEFORE rows for the previous image, or produce DELETE rows with
>> all columns filled with values."
>>
>> Could you elaborate a bit on the implementation details in the FLIP? How
>> are UPDATE_BEFOREs are generated. How much state is required to perform
>> this operation.
>>
>>   From a conceptual and semantical point of view, I'm fine with the
>> proposal. But I would like to share my opinion about how we expose this
>> feature:
>>
>> ktable vs kafka-compacted
>>
>> I'm against having an additional connector like `ktable` or
>> `kafka-compacted`. We recently simplified the table options to a minimum
>> amount of characters to be as concise as possible in the DDL. Therefore,
>> I would keep the `connector=kafka` and introduce an additional option.
>> Because a user wants to read "from Kafka". And the "how" should be
>> determined in the lower options.
>>
>> When people read `connector=ktable` they might not know that this is
>> Kafka. Or they wonder where `kstream` is?
>>
>> When people read `connector=kafka-compacted` they might not know that it
>> has ktable semantics. You don't need to enable log compaction in order
>> to use a KTable as far as I know. Log compaction and table semantics are
>> orthogonal topics.
>>
>> In the end we will need 3 types of information when declaring a Kafka
>> connector:
>>
>> CREATE TABLE ... WITH (
>>     connector=kafka        -- Some information about the connector
>>     end-offset = XXXX      -- Some information about the boundedness
>>     model = table/stream   -- Some information about interpretation
>> )
>>
>>
>> We can still apply all the constraints mentioned in the FLIP. When
>> `model` is set to `table`.
>>
>> What do you think?
>>
>> Regards,
>> Timo
>>
>>
>> On 21.10.20 14:19, Jark Wu wrote:
>>> Hi,
>>>
>>> IMO, if we are going to mix them in one connector,
>>> 1) either users need to set some options to a specific value explicitly,
>>> e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
>>> This makes the connector awkward to use. Users may face to fix options
>> one
>>> by one according to the exception.
>>> Besides, in the future, it is still possible to use
>>> "sink.partitioner=fixed" (reduce network cost) if users are aware of
>>> the partition routing,
>>> however, it's error-prone to have "fixed" as default for compacted mode.
>>>
>>> 2) or make those options a different default value when "compacted=true".
>>> This would be more confusing and unpredictable if the default value of
>>> options will change according to other options.
>>> What happens if we have a third mode in the future?
>>>
>>> In terms of usage and options, it's very different from the
>>> original "kafka" connector.
>>> It would be more handy to use and less fallible if separating them into
>> two
>>> connectors.
>>> In the implementation layer, we can reuse code as much as possible.
>>>
>>> Therefore, I'm still +1 to have a new connector.
>>> The "kafka-compacted" name sounds good to me.
>>>
>>> Best,
>>> Jark
>>>
>>>
>>> On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <[hidden email]>
>> wrote:
>>>
>>>> Hi Kurt, Hi Shengkai,
>>>>
>>>> thanks for answering my questions and the additional clarifications. I
>>>> don't have a strong opinion on whether to extend the "kafka" connector
>> or
>>>> to introduce a new connector. So, from my perspective feel free to go
>> with
>>>> a separate connector. If we do introduce a new connector I wouldn't
>> call it
>>>> "ktable" for aforementioned reasons (In addition, we might suggest that
>>>> there is also a "kstreams" connector for symmetry reasons). I don't
>> have a
>>>> good alternative name, though, maybe "kafka-compacted" or
>>>> "compacted-kafka".
>>>>
>>>> Thanks,
>>>>
>>>> Konstantin
>>>>
>>>>
>>>> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]> wrote:
>>>>
>>>>> Hi all,
>>>>>
>>>>> I want to describe the discussion process which drove us to have such
>>>>> conclusion, this might make some of
>>>>> the design choices easier to understand and keep everyone on the same
>>>> page.
>>>>>
>>>>> Back to the motivation, what functionality do we want to provide in the
>>>>> first place? We got a lot of feedback and
>>>>> questions from mailing lists that people want to write Not-Insert-Only
>>>>> messages into kafka. They might be
>>>>> intentional or by accident, e.g. wrote an non-windowed aggregate query
>> or
>>>>> non-windowed left outer join. And
>>>>> some users from KSQL world also asked about why Flink didn't leverage
>> the
>>>>> Key concept of every kafka topic
>>>>> and make kafka as a dynamic changing keyed table.
>>>>>
>>>>> To work with kafka better, we were thinking to extend the functionality
>>>> of
>>>>> the current kafka connector by letting it
>>>>> accept updates and deletions. But due to the limitation of kafka, the
>>>>> update has to be "update by key", aka a table
>>>>> with primary key.
>>>>>
>>>>> This introduces a couple of conflicts with current kafka table's
>> options:
>>>>> 1. key.fields: as said above, we need the kafka table to have the
>> primary
>>>>> key constraint. And users can also configure
>>>>> key.fields freely, this might cause friction. (Sure we can do some
>> sanity
>>>>> check on this but it also creates friction.)
>>>>> 2. sink.partitioner: to make the semantics right, we need to make sure
>>>> all
>>>>> the updates on the same key are written to
>>>>> the same kafka partition, such we should force to use a hash by key
>>>>> partition inside such table. Again, this has conflicts
>>>>> and creates friction with current user options.
>>>>>
>>>>> The above things are solvable, though not perfect or most user
>> friendly.
>>>>>
>>>>> Let's take a look at the reading side. The keyed kafka table contains
>> two
>>>>> kinds of messages: upsert or deletion. What upsert
>>>>> means is "If the key doesn't exist yet, it's an insert record.
>> Otherwise
>>>>> it's an update record". For the sake of correctness or
>>>>> simplicity, the Flink SQL engine also needs such information. If we
>>>>> interpret all messages to "update record", some queries or
>>>>> operators may not work properly. It's weird to see an update record but
>>>> you
>>>>> haven't seen the insert record before.
>>>>>
>>>>> So what Flink should do is after reading out the records from such
>> table,
>>>>> it needs to create a state to record which messages have
>>>>> been seen and then generate the correct row type correspondingly. This
>>>> kind
>>>>> of couples the state and the data of the message
>>>>> queue, and it also creates conflicts with current kafka connector.
>>>>>
>>>>> Think about if users suspend a running job (which contains some reading
>>>>> state now), and then change the start offset of the reader.
>>>>> By changing the reading offset, it actually change the whole story of
>>>>> "which records should be insert messages and which records
>>>>> should be update messages). And it will also make Flink to deal with
>>>>> another weird situation that it might receive a deletion
>>>>> on a non existing message.
>>>>>
>>>>> We were unsatisfied with all the frictions and conflicts it will create
>>>> if
>>>>> we enable the "upsert & deletion" support to the current kafka
>>>>> connector. And later we begin to realize that we shouldn't treat it as
>> a
>>>>> normal message queue, but should treat it as a changing keyed
>>>>> table. We should be able to always get the whole data of such table (by
>>>>> disabling the start offset option) and we can also read the
>>>>> changelog out of such table. It's like a HBase table with binlog
>> support
>>>>> but doesn't have random access capability (which can be fulfilled
>>>>> by Flink's state).
>>>>>
>>>>> So our intention was instead of telling and persuading users what kind
>> of
>>>>> options they should or should not use by extending
>>>>> current kafka connector when enable upsert support, we are actually
>>>> create
>>>>> a whole new and different connector that has total
>>>>> different abstractions in SQL layer, and should be treated totally
>>>>> different with current kafka connector.
>>>>>
>>>>> Hope this can clarify some of the concerns.
>>>>>
>>>>> Best,
>>>>> Kurt
>>>>>
>>>>>
>>>>> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <[hidden email]>
>> wrote:
>>>>>
>>>>>> Hi devs,
>>>>>>
>>>>>> As many people are still confused about the difference option
>>>> behaviours
>>>>>> between the Kafka connector and KTable connector, Jark and I list the
>>>>>> differences in the doc[1].
>>>>>>
>>>>>> Best,
>>>>>> Shengkai
>>>>>>
>>>>>> [1]
>>>>>>
>>>>>>
>>>>>
>>>>
>> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
>>>>>>
>>>>>> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
>>>>>>
>>>>>>> Hi Konstantin,
>>>>>>>
>>>>>>> Thanks for your reply.
>>>>>>>
>>>>>>>> It uses the "kafka" connector and does not specify a primary key.
>>>>>>> The dimensional table `users` is a ktable connector and we can
>>>> specify
>>>>>> the
>>>>>>> pk on the KTable.
>>>>>>>
>>>>>>>> Will it possible to use a "ktable" as a dimensional table in
>>>> FLIP-132
>>>>>>> Yes. We can specify the watermark on the KTable and it can be used
>>>> as a
>>>>>>> dimension table in temporal join.
>>>>>>>
>>>>>>>> Introduce a new connector vs introduce a new property
>>>>>>> The main reason behind is that the KTable connector almost has no
>>>>> common
>>>>>>> options with the Kafka connector. The options that can be reused by
>>>>>> KTable
>>>>>>> connectors are 'topic', 'properties.bootstrap.servers' and
>>>>>>> 'value.fields-include' . We can't set cdc format for 'key.format' and
>>>>>>> 'value.format' in KTable connector now, which is  available in Kafka
>>>>>>> connector. Considering the difference between the options we can use,
>>>>>> it's
>>>>>>> more suitable to introduce an another connector rather than a
>>>> property.
>>>>>>>
>>>>>>> We are also fine to use "compacted-kafka" as the name of the new
>>>>>>> connector. What do you think?
>>>>>>>
>>>>>>> Best,
>>>>>>> Shengkai
>>>>>>>
>>>>>>> Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
>>>>>>>
>>>>>>>> Hi Shengkai,
>>>>>>>>
>>>>>>>> Thank you for driving this effort. I believe this a very important
>>>>>> feature
>>>>>>>> for many users who use Kafka and Flink SQL together. A few questions
>>>>> and
>>>>>>>> thoughts:
>>>>>>>>
>>>>>>>> * Is your example "Use KTable as a reference/dimension table"
>>>> correct?
>>>>>> It
>>>>>>>> uses the "kafka" connector and does not specify a primary key.
>>>>>>>>
>>>>>>>> * Will it be possible to use a "ktable" table directly as a
>>>>> dimensional
>>>>>>>> table in temporal join (*based on event time*) (FLIP-132)? This is
>>>> not
>>>>>>>> completely clear to me from the FLIP.
>>>>>>>>
>>>>>>>> * I'd personally prefer not to introduce a new connector and instead
>>>>> to
>>>>>>>> extend the Kafka connector. We could add an additional property
>>>>>>>> "compacted"
>>>>>>>> = "true"|"false". If it is set to "true", we can add additional
>>>>>> validation
>>>>>>>> logic (e.g. "scan.startup.mode" can not be set, primary key
>>>> required,
>>>>>>>> etc.). If we stick to a separate connector I'd not call it "ktable",
>>>>> but
>>>>>>>> rather "compacted-kafka" or similar. KTable seems to carry more
>>>>> implicit
>>>>>>>> meaning than we want to imply here.
>>>>>>>>
>>>>>>>> * I agree that this is not a bounded source. If we want to support a
>>>>>>>> bounded mode, this is an orthogonal concern that also applies to
>>>> other
>>>>>>>> unbounded sources.
>>>>>>>>
>>>>>>>> Best,
>>>>>>>>
>>>>>>>> Konstantin
>>>>>>>>
>>>>>>>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:
>>>>>>>>
>>>>>>>>> Hi Danny,
>>>>>>>>>
>>>>>>>>> First of all, we didn't introduce any concepts from KSQL (e.g.
>>>>> Stream
>>>>>> vs
>>>>>>>>> Table notion).
>>>>>>>>> This new connector will produce a changelog stream, so it's still
>>>> a
>>>>>>>> dynamic
>>>>>>>>> table and doesn't conflict with Flink core concepts.
>>>>>>>>>
>>>>>>>>> The "ktable" is just a connector name, we can also call it
>>>>>>>>> "compacted-kafka" or something else.
>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
>>>> Flink
>>>>>> SQL
>>>>>>>>> easily.
>>>>>>>>>
>>>>>>>>> Regarding to why introducing a new connector vs a new property in
>>>>>>>> existing
>>>>>>>>> kafka connector:
>>>>>>>>>
>>>>>>>>> I think the main reason is that we want to have a clear separation
>>>>> for
>>>>>>>> such
>>>>>>>>> two use cases, because they are very different.
>>>>>>>>> We also listed reasons in the FLIP, including:
>>>>>>>>>
>>>>>>>>> 1) It's hard to explain what's the behavior when users specify the
>>>>>> start
>>>>>>>>> offset from a middle position (e.g. how to process non exist
>>>> delete
>>>>>>>>> events).
>>>>>>>>>       It's dangerous if users do that. So we don't provide the
>>>> offset
>>>>>>>> option
>>>>>>>>> in the new connector at the moment.
>>>>>>>>> 2) It's a different perspective/abstraction on the same kafka
>>>> topic
>>>>>>>> (append
>>>>>>>>> vs. upsert). It would be easier to understand if we can separate
>>>>> them
>>>>>>>>>       instead of mixing them in one connector. The new connector
>>>>>> requires
>>>>>>>>> hash sink partitioner, primary key declared, regular format.
>>>>>>>>>       If we mix them in one connector, it might be confusing how to
>>>>> use
>>>>>>>> the
>>>>>>>>> options correctly.
>>>>>>>>> 3) The semantic of the KTable connector is just the same as KTable
>>>>> in
>>>>>>>> Kafka
>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
>>>>>>>>>       We have seen several questions in the mailing list asking how
>>>> to
>>>>>>>> model
>>>>>>>>> a KTable and how to join a KTable in Flink SQL.
>>>>>>>>>
>>>>>>>>> Best,
>>>>>>>>> Jark
>>>>>>>>>
>>>>>>>>> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
>>>>>>>>>
>>>>>>>>>> Hi Jingsong,
>>>>>>>>>>
>>>>>>>>>> As the FLIP describes, "KTable connector produces a changelog
>>>>>> stream,
>>>>>>>>>> where each data record represents an update or delete event.".
>>>>>>>>>> Therefore, a ktable source is an unbounded stream source.
>>>>> Selecting
>>>>>> a
>>>>>>>>>> ktable source is similar to selecting a kafka source with
>>>>>>>> debezium-json
>>>>>>>>>> format
>>>>>>>>>> that it never ends and the results are continuously updated.
>>>>>>>>>>
>>>>>>>>>> It's possible to have a bounded ktable source in the future, for
>>>>>>>> example,
>>>>>>>>>> add an option 'bounded=true' or 'end-offset=xxx'.
>>>>>>>>>> In this way, the ktable will produce a bounded changelog stream.
>>>>>>>>>> So I think this can be a compatible feature in the future.
>>>>>>>>>>
>>>>>>>>>> I don't think we should associate with ksql related concepts.
>>>>>>>> Actually,
>>>>>>>>> we
>>>>>>>>>> didn't introduce any concepts from KSQL (e.g. Stream vs Table
>>>>>> notion).
>>>>>>>>>> The "ktable" is just a connector name, we can also call it
>>>>>>>>>> "compacted-kafka" or something else.
>>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
>>>>> Flink
>>>>>>>> SQL
>>>>>>>>>> easily.
>>>>>>>>>>
>>>>>>>>>> Regarding the "value.fields-include", this is an option
>>>> introduced
>>>>>> in
>>>>>>>>>> FLIP-107 for Kafka connector.
>>>>>>>>>> I think we should keep the same behavior with the Kafka
>>>> connector.
>>>>>> I'm
>>>>>>>>> not
>>>>>>>>>> sure what's the default behavior of KSQL.
>>>>>>>>>> But I guess it also stores the keys in value from this example
>>>>> docs
>>>>>>>> (see
>>>>>>>>>> the "users_original" table) [1].
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>> Jark
>>>>>>>>>>
>>>>>>>>>> [1]:
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]>
>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> The concept seems conflicts with the Flink abstraction “dynamic
>>>>>>>> table”,
>>>>>>>>>>> in Flink we see both “stream” and “table” as a dynamic table,
>>>>>>>>>>>
>>>>>>>>>>> I think we should make clear first how to express stream and
>>>>> table
>>>>>>>>>>> specific features on one “dynamic table”,
>>>>>>>>>>> it is more natural for KSQL because KSQL takes stream and table
>>>>> as
>>>>>>>>>>> different abstractions for representing collections. In KSQL,
>>>>> only
>>>>>>>>> table is
>>>>>>>>>>> mutable and can have a primary key.
>>>>>>>>>>>
>>>>>>>>>>> Does this connector belongs to the “table” scope or “stream”
>>>>> scope
>>>>>> ?
>>>>>>>>>>>
>>>>>>>>>>> Some of the concepts (such as the primary key on stream) should
>>>>> be
>>>>>>>>>>> suitable for all the connectors, not just Kafka, Shouldn’t this
>>>>> be
>>>>>> an
>>>>>>>>>>> extension of existing Kafka connector instead of a totally new
>>>>>>>>> connector ?
>>>>>>>>>>> What about the other connectors ?
>>>>>>>>>>>
>>>>>>>>>>> Because this touches the core abstraction of Flink, we better
>>>>> have
>>>>>> a
>>>>>>>>>>> top-down overall design, following the KSQL directly is not the
>>>>>>>> answer.
>>>>>>>>>>>
>>>>>>>>>>> P.S. For the source
>>>>>>>>>>>> Shouldn’t this be an extension of existing Kafka connector
>>>>>> instead
>>>>>>>> of
>>>>>>>>> a
>>>>>>>>>>> totally new connector ?
>>>>>>>>>>>
>>>>>>>>>>> How could we achieve that (e.g. set up the parallelism
>>>>> correctly) ?
>>>>>>>>>>>
>>>>>>>>>>> Best,
>>>>>>>>>>> Danny Chan
>>>>>>>>>>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]
>>>>>>> ，写道：
>>>>>>>>>>>> Thanks Shengkai for your proposal.
>>>>>>>>>>>>
>>>>>>>>>>>> +1 for this feature.
>>>>>>>>>>>>
>>>>>>>>>>>>> Future Work: Support bounded KTable source
>>>>>>>>>>>>
>>>>>>>>>>>> I don't think it should be a future work, I think it is one
>>>> of
>>>>>> the
>>>>>>>>>>>> important concepts of this FLIP. We need to understand it
>>>> now.
>>>>>>>>>>>>
>>>>>>>>>>>> Intuitively, a ktable in my opinion is a bounded table rather
>>>>>> than
>>>>>>>> a
>>>>>>>>>>>> stream, so select should produce a bounded table by default.
>>>>>>>>>>>>
>>>>>>>>>>>> I think we can list Kafka related knowledge, because the word
>>>>>>>> `ktable`
>>>>>>>>>>> is
>>>>>>>>>>>> easy to associate with ksql related concepts. (If possible,
>>>>> it's
>>>>>>>>> better
>>>>>>>>>>> to
>>>>>>>>>>>> unify with it)
>>>>>>>>>>>>
>>>>>>>>>>>> What do you think?
>>>>>>>>>>>>
>>>>>>>>>>>>> value.fields-include
>>>>>>>>>>>>
>>>>>>>>>>>> What about the default behavior of KSQL?
>>>>>>>>>>>>
>>>>>>>>>>>> Best,
>>>>>>>>>>>> Jingsong
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
>>>>> [hidden email]
>>>>>>>
>>>>>>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Hi, devs.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Jark and I want to start a new FLIP to introduce the KTable
>>>>>>>>>>> connector. The
>>>>>>>>>>>>> KTable is a shortcut of "Kafka Table", it also has the same
>>>>>>>>> semantics
>>>>>>>>>>> with
>>>>>>>>>>>>> the KTable notion in Kafka Stream.
>>>>>>>>>>>>>
>>>>>>>>>>>>> FLIP-149:
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
>>>>>>>>>>>>>
>>>>>>>>>>>>> Currently many users have expressed their needs for the
>>>>> upsert
>>>>>>>> Kafka
>>>>>>>>>>> by
>>>>>>>>>>>>> mail lists and issues. The KTable connector has several
>>>>>> benefits
>>>>>>>> for
>>>>>>>>>>> users:
>>>>>>>>>>>>>
>>>>>>>>>>>>> 1. Users are able to interpret a compacted Kafka Topic as
>>>> an
>>>>>>>> upsert
>>>>>>>>>>> stream
>>>>>>>>>>>>> in Apache Flink. And also be able to write a changelog
>>>> stream
>>>>>> to
>>>>>>>>> Kafka
>>>>>>>>>>>>> (into a compacted topic).
>>>>>>>>>>>>> 2. As a part of the real time pipeline, store join or
>>>>> aggregate
>>>>>>>>>>> result (may
>>>>>>>>>>>>> contain updates) into a Kafka topic for further
>>>> calculation;
>>>>>>>>>>>>> 3. The semantic of the KTable connector is just the same as
>>>>>>>> KTable
>>>>>>>>> in
>>>>>>>>>>> Kafka
>>>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
>>>>> We
>>>>>>>> have
>>>>>>>>>>> seen
>>>>>>>>>>>>> several questions in the mailing list asking how to model a
>>>>>>>> KTable
>>>>>>>>>>> and how
>>>>>>>>>>>>> to join a KTable in Flink SQL.
>>>>>>>>>>>>>
>>>>>>>>>>>>> We hope it can expand the usage of the Flink with Kafka.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I'm looking forward to your feedback.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>> Shengkai
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>>> Best, Jingsong Lee
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>>
>>>>>>>> Konstantin Knauf
>>>>>>>>
>>>>>>>> https://twitter.com/snntrable
>>>>>>>>
>>>>>>>> https://github.com/knaufk
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>>
>>>> Konstantin Knauf
>>>>
>>>> https://twitter.com/snntrable
>>>>
>>>> https://github.com/knaufk
>>>>
>>>
>>
>>
>

[Seth Wiesman-3]

+1 for supporting upsert results into Kafka.

I have no comments on the implementation details.

As far as configuration goes, I tend to favor Timo's option where we add a
"mode" property to the existing Kafka table with default value "inserting".
If the mode is set to "updating" then the validation changes to the new
requirements. I personally find it more intuitive than a seperate
connector, my fear is users won't understand its the same physical kafka
sink under the hood and it will lead to other confusion like does it offer
the same persistence guarantees? I think we are capable of adding good
valdiation messaging that solves Jark and Kurts concerns.


On Thu, Oct 22, 2020 at 8:51 AM Timo Walther <[hidden email]> wrote:

> Hi Jark,
>
> "calling it "kafka-compacted" can even remind users to enable log
> compaction"
>
> But sometimes users like to store a lineage of changes in their topics.
> Indepent of any ktable/kstream interpretation.
>
> I let the majority decide on this topic to not further block this
> effort. But we might find a better name like:
>
> connector = kafka
> mode = updating/inserting
>
> OR
>
> connector = kafka-updating
>
> ...
>
> Regards,
> Timo
>
>
>
>
> On 22.10.20 15:24, Jark Wu wrote:
> > Hi Timo,
> >
> > Thanks for your opinions.
> >
> > 1) Implementation
> > We will have an stateful operator to generate INSERT and UPDATE_BEFORE.
> > This operator is keyby-ed (primary key as the shuffle key) after the
> source
> > operator.
> > The implementation of this operator is very similar to the existing
> > `DeduplicateKeepLastRowFunction`.
> > The operator will register a value state using the primary key fields as
> > keys.
> > When the value state is empty under current key, we will emit INSERT for
> > the input row.
> > When the value state is not empty under current key, we will emit
> > UPDATE_BEFORE using the row in state,
> > and emit UPDATE_AFTER using the input row.
> > When the input row is DELETE, we will clear state and emit DELETE row.
> >
> > 2) new option vs new connector
> >> We recently simplified the table options to a minimum amount of
> > characters to be as concise as possible in the DDL.
> > I think this is the reason why we want to introduce a new connector,
> > because we can simplify the options in DDL.
> > For example, if using a new option, the DDL may look like this:
> >
> > CREATE TABLE users (
> >    user_id BIGINT,
> >    user_name STRING,
> >    user_level STRING,
> >    region STRING,
> >    PRIMARY KEY (user_id) NOT ENFORCED
> > ) WITH (
> >    'connector' = 'kafka',
> >    'model' = 'table',
> >    'topic' = 'pageviews_per_region',
> >    'properties.bootstrap.servers' = '...',
> >    'properties.group.id' = 'testGroup',
> >    'scan.startup.mode' = 'earliest',
> >    'key.format' = 'csv',
> >    'key.fields' = 'user_id',
> >    'value.format' = 'avro',
> >    'sink.partitioner' = 'hash'
> > );
> >
> > If using a new connector, we can have a different default value for the
> > options and remove unnecessary options,
> > the DDL can look like this which is much more concise:
> >
> > CREATE TABLE pageviews_per_region (
> >    user_id BIGINT,
> >    user_name STRING,
> >    user_level STRING,
> >    region STRING,
> >    PRIMARY KEY (user_id) NOT ENFORCED
> > ) WITH (
> >    'connector' = 'kafka-compacted',
> >    'topic' = 'pageviews_per_region',
> >    'properties.bootstrap.servers' = '...',
> >    'key.format' = 'csv',
> >    'value.format' = 'avro'
> > );
> >
> >> When people read `connector=kafka-compacted` they might not know that it
> >> has ktable semantics. You don't need to enable log compaction in order
> >> to use a KTable as far as I know.
> > We don't need to let users know it has ktable semantics, as Konstantin
> > mentioned this may carry more implicit
> > meaning than we want to imply here. I agree users don't need to enable
> log
> > compaction, but from the production perspective,
> > log compaction should always be enabled if it is used in this purpose.
> > Calling it "kafka-compacted" can even remind users to enable log
> compaction.
> >
> > I don't agree to introduce "model = table/stream" option, or
> > "connector=kafka-table",
> > because this means we are introducing Table vs Stream concept from KSQL.
> > However, we don't have such top-level concept in Flink SQL now, this will
> > further confuse users.
> > In Flink SQL, all the things are STREAM, the differences are whether it
> is
> > bounded or unbounded,
> >   whether it is insert-only or changelog.
> >
> >
> > Best,
> > Jark
> >
> >
> > On Thu, 22 Oct 2020 at 20:39, Timo Walther <[hidden email]> wrote:
> >
> >> Hi Shengkai, Hi Jark,
> >>
> >> thanks for this great proposal. It is time to finally connect the
> >> changelog processor with a compacted Kafka topic.
> >>
> >> "The operator will produce INSERT rows, or additionally generate
> >> UPDATE_BEFORE rows for the previous image, or produce DELETE rows with
> >> all columns filled with values."
> >>
> >> Could you elaborate a bit on the implementation details in the FLIP? How
> >> are UPDATE_BEFOREs are generated. How much state is required to perform
> >> this operation.
> >>
> >>   From a conceptual and semantical point of view, I'm fine with the
> >> proposal. But I would like to share my opinion about how we expose this
> >> feature:
> >>
> >> ktable vs kafka-compacted
> >>
> >> I'm against having an additional connector like `ktable` or
> >> `kafka-compacted`. We recently simplified the table options to a minimum
> >> amount of characters to be as concise as possible in the DDL. Therefore,
> >> I would keep the `connector=kafka` and introduce an additional option.
> >> Because a user wants to read "from Kafka". And the "how" should be
> >> determined in the lower options.
> >>
> >> When people read `connector=ktable` they might not know that this is
> >> Kafka. Or they wonder where `kstream` is?
> >>
> >> When people read `connector=kafka-compacted` they might not know that it
> >> has ktable semantics. You don't need to enable log compaction in order
> >> to use a KTable as far as I know. Log compaction and table semantics are
> >> orthogonal topics.
> >>
> >> In the end we will need 3 types of information when declaring a Kafka
> >> connector:
> >>
> >> CREATE TABLE ... WITH (
> >>     connector=kafka        -- Some information about the connector
> >>     end-offset = XXXX      -- Some information about the boundedness
> >>     model = table/stream   -- Some information about interpretation
> >> )
> >>
> >>
> >> We can still apply all the constraints mentioned in the FLIP. When
> >> `model` is set to `table`.
> >>
> >> What do you think?
> >>
> >> Regards,
> >> Timo
> >>
> >>
> >> On 21.10.20 14:19, Jark Wu wrote:
> >>> Hi,
> >>>
> >>> IMO, if we are going to mix them in one connector,
> >>> 1) either users need to set some options to a specific value
> explicitly,
> >>> e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
> >>> This makes the connector awkward to use. Users may face to fix options
> >> one
> >>> by one according to the exception.
> >>> Besides, in the future, it is still possible to use
> >>> "sink.partitioner=fixed" (reduce network cost) if users are aware of
> >>> the partition routing,
> >>> however, it's error-prone to have "fixed" as default for compacted
> mode.
> >>>
> >>> 2) or make those options a different default value when
> "compacted=true".
> >>> This would be more confusing and unpredictable if the default value of
> >>> options will change according to other options.
> >>> What happens if we have a third mode in the future?
> >>>
> >>> In terms of usage and options, it's very different from the
> >>> original "kafka" connector.
> >>> It would be more handy to use and less fallible if separating them into
> >> two
> >>> connectors.
> >>> In the implementation layer, we can reuse code as much as possible.
> >>>
> >>> Therefore, I'm still +1 to have a new connector.
> >>> The "kafka-compacted" name sounds good to me.
> >>>
> >>> Best,
> >>> Jark
> >>>
> >>>
> >>> On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <[hidden email]>
> >> wrote:
> >>>
> >>>> Hi Kurt, Hi Shengkai,
> >>>>
> >>>> thanks for answering my questions and the additional clarifications. I
> >>>> don't have a strong opinion on whether to extend the "kafka" connector
> >> or
> >>>> to introduce a new connector. So, from my perspective feel free to go
> >> with
> >>>> a separate connector. If we do introduce a new connector I wouldn't
> >> call it
> >>>> "ktable" for aforementioned reasons (In addition, we might suggest
> that
> >>>> there is also a "kstreams" connector for symmetry reasons). I don't
> >> have a
> >>>> good alternative name, though, maybe "kafka-compacted" or
> >>>> "compacted-kafka".
> >>>>
> >>>> Thanks,
> >>>>
> >>>> Konstantin
> >>>>
> >>>>
> >>>> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]> wrote:
> >>>>
> >>>>> Hi all,
> >>>>>
> >>>>> I want to describe the discussion process which drove us to have such
> >>>>> conclusion, this might make some of
> >>>>> the design choices easier to understand and keep everyone on the same
> >>>> page.
> >>>>>
> >>>>> Back to the motivation, what functionality do we want to provide in
> the
> >>>>> first place? We got a lot of feedback and
> >>>>> questions from mailing lists that people want to write
> Not-Insert-Only
> >>>>> messages into kafka. They might be
> >>>>> intentional or by accident, e.g. wrote an non-windowed aggregate
> query
> >> or
> >>>>> non-windowed left outer join. And
> >>>>> some users from KSQL world also asked about why Flink didn't leverage
> >> the
> >>>>> Key concept of every kafka topic
> >>>>> and make kafka as a dynamic changing keyed table.
> >>>>>
> >>>>> To work with kafka better, we were thinking to extend the
> functionality
> >>>> of
> >>>>> the current kafka connector by letting it
> >>>>> accept updates and deletions. But due to the limitation of kafka, the
> >>>>> update has to be "update by key", aka a table
> >>>>> with primary key.
> >>>>>
> >>>>> This introduces a couple of conflicts with current kafka table's
> >> options:
> >>>>> 1. key.fields: as said above, we need the kafka table to have the
> >> primary
> >>>>> key constraint. And users can also configure
> >>>>> key.fields freely, this might cause friction. (Sure we can do some
> >> sanity
> >>>>> check on this but it also creates friction.)
> >>>>> 2. sink.partitioner: to make the semantics right, we need to make
> sure
> >>>> all
> >>>>> the updates on the same key are written to
> >>>>> the same kafka partition, such we should force to use a hash by key
> >>>>> partition inside such table. Again, this has conflicts
> >>>>> and creates friction with current user options.
> >>>>>
> >>>>> The above things are solvable, though not perfect or most user
> >> friendly.
> >>>>>
> >>>>> Let's take a look at the reading side. The keyed kafka table contains
> >> two
> >>>>> kinds of messages: upsert or deletion. What upsert
> >>>>> means is "If the key doesn't exist yet, it's an insert record.
> >> Otherwise
> >>>>> it's an update record". For the sake of correctness or
> >>>>> simplicity, the Flink SQL engine also needs such information. If we
> >>>>> interpret all messages to "update record", some queries or
> >>>>> operators may not work properly. It's weird to see an update record
> but
> >>>> you
> >>>>> haven't seen the insert record before.
> >>>>>
> >>>>> So what Flink should do is after reading out the records from such
> >> table,
> >>>>> it needs to create a state to record which messages have
> >>>>> been seen and then generate the correct row type correspondingly.
> This
> >>>> kind
> >>>>> of couples the state and the data of the message
> >>>>> queue, and it also creates conflicts with current kafka connector.
> >>>>>
> >>>>> Think about if users suspend a running job (which contains some
> reading
> >>>>> state now), and then change the start offset of the reader.
> >>>>> By changing the reading offset, it actually change the whole story of
> >>>>> "which records should be insert messages and which records
> >>>>> should be update messages). And it will also make Flink to deal with
> >>>>> another weird situation that it might receive a deletion
> >>>>> on a non existing message.
> >>>>>
> >>>>> We were unsatisfied with all the frictions and conflicts it will
> create
> >>>> if
> >>>>> we enable the "upsert & deletion" support to the current kafka
> >>>>> connector. And later we begin to realize that we shouldn't treat it
> as
> >> a
> >>>>> normal message queue, but should treat it as a changing keyed
> >>>>> table. We should be able to always get the whole data of such table
> (by
> >>>>> disabling the start offset option) and we can also read the
> >>>>> changelog out of such table. It's like a HBase table with binlog
> >> support
> >>>>> but doesn't have random access capability (which can be fulfilled
> >>>>> by Flink's state).
> >>>>>
> >>>>> So our intention was instead of telling and persuading users what
> kind
> >> of
> >>>>> options they should or should not use by extending
> >>>>> current kafka connector when enable upsert support, we are actually
> >>>> create
> >>>>> a whole new and different connector that has total
> >>>>> different abstractions in SQL layer, and should be treated totally
> >>>>> different with current kafka connector.
> >>>>>
> >>>>> Hope this can clarify some of the concerns.
> >>>>>
> >>>>> Best,
> >>>>> Kurt
> >>>>>
> >>>>>
> >>>>> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <[hidden email]>
> >> wrote:
> >>>>>
> >>>>>> Hi devs,
> >>>>>>
> >>>>>> As many people are still confused about the difference option
> >>>> behaviours
> >>>>>> between the Kafka connector and KTable connector, Jark and I list
> the
> >>>>>> differences in the doc[1].
> >>>>>>
> >>>>>> Best,
> >>>>>> Shengkai
> >>>>>>
> >>>>>> [1]
> >>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>
> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
> >>>>>>
> >>>>>> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
> >>>>>>
> >>>>>>> Hi Konstantin,
> >>>>>>>
> >>>>>>> Thanks for your reply.
> >>>>>>>
> >>>>>>>> It uses the "kafka" connector and does not specify a primary key.
> >>>>>>> The dimensional table `users` is a ktable connector and we can
> >>>> specify
> >>>>>> the
> >>>>>>> pk on the KTable.
> >>>>>>>
> >>>>>>>> Will it possible to use a "ktable" as a dimensional table in
> >>>> FLIP-132
> >>>>>>> Yes. We can specify the watermark on the KTable and it can be used
> >>>> as a
> >>>>>>> dimension table in temporal join.
> >>>>>>>
> >>>>>>>> Introduce a new connector vs introduce a new property
> >>>>>>> The main reason behind is that the KTable connector almost has no
> >>>>> common
> >>>>>>> options with the Kafka connector. The options that can be reused by
> >>>>>> KTable
> >>>>>>> connectors are 'topic', 'properties.bootstrap.servers' and
> >>>>>>> 'value.fields-include' . We can't set cdc format for 'key.format'
> and
> >>>>>>> 'value.format' in KTable connector now, which is  available in
> Kafka
> >>>>>>> connector. Considering the difference between the options we can
> use,
> >>>>>> it's
> >>>>>>> more suitable to introduce an another connector rather than a
> >>>> property.
> >>>>>>>
> >>>>>>> We are also fine to use "compacted-kafka" as the name of the new
> >>>>>>> connector. What do you think?
> >>>>>>>
> >>>>>>> Best,
> >>>>>>> Shengkai
> >>>>>>>
> >>>>>>> Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
> >>>>>>>
> >>>>>>>> Hi Shengkai,
> >>>>>>>>
> >>>>>>>> Thank you for driving this effort. I believe this a very important
> >>>>>> feature
> >>>>>>>> for many users who use Kafka and Flink SQL together. A few
> questions
> >>>>> and
> >>>>>>>> thoughts:
> >>>>>>>>
> >>>>>>>> * Is your example "Use KTable as a reference/dimension table"
> >>>> correct?
> >>>>>> It
> >>>>>>>> uses the "kafka" connector and does not specify a primary key.
> >>>>>>>>
> >>>>>>>> * Will it be possible to use a "ktable" table directly as a
> >>>>> dimensional
> >>>>>>>> table in temporal join (*based on event time*) (FLIP-132)? This is
> >>>> not
> >>>>>>>> completely clear to me from the FLIP.
> >>>>>>>>
> >>>>>>>> * I'd personally prefer not to introduce a new connector and
> instead
> >>>>> to
> >>>>>>>> extend the Kafka connector. We could add an additional property
> >>>>>>>> "compacted"
> >>>>>>>> = "true"|"false". If it is set to "true", we can add additional
> >>>>>> validation
> >>>>>>>> logic (e.g. "scan.startup.mode" can not be set, primary key
> >>>> required,
> >>>>>>>> etc.). If we stick to a separate connector I'd not call it
> "ktable",
> >>>>> but
> >>>>>>>> rather "compacted-kafka" or similar. KTable seems to carry more
> >>>>> implicit
> >>>>>>>> meaning than we want to imply here.
> >>>>>>>>
> >>>>>>>> * I agree that this is not a bounded source. If we want to
> support a
> >>>>>>>> bounded mode, this is an orthogonal concern that also applies to
> >>>> other
> >>>>>>>> unbounded sources.
> >>>>>>>>
> >>>>>>>> Best,
> >>>>>>>>
> >>>>>>>> Konstantin
> >>>>>>>>
> >>>>>>>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]> wrote:
> >>>>>>>>
> >>>>>>>>> Hi Danny,
> >>>>>>>>>
> >>>>>>>>> First of all, we didn't introduce any concepts from KSQL (e.g.
> >>>>> Stream
> >>>>>> vs
> >>>>>>>>> Table notion).
> >>>>>>>>> This new connector will produce a changelog stream, so it's still
> >>>> a
> >>>>>>>> dynamic
> >>>>>>>>> table and doesn't conflict with Flink core concepts.
> >>>>>>>>>
> >>>>>>>>> The "ktable" is just a connector name, we can also call it
> >>>>>>>>> "compacted-kafka" or something else.
> >>>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
> >>>> Flink
> >>>>>> SQL
> >>>>>>>>> easily.
> >>>>>>>>>
> >>>>>>>>> Regarding to why introducing a new connector vs a new property in
> >>>>>>>> existing
> >>>>>>>>> kafka connector:
> >>>>>>>>>
> >>>>>>>>> I think the main reason is that we want to have a clear
> separation
> >>>>> for
> >>>>>>>> such
> >>>>>>>>> two use cases, because they are very different.
> >>>>>>>>> We also listed reasons in the FLIP, including:
> >>>>>>>>>
> >>>>>>>>> 1) It's hard to explain what's the behavior when users specify
> the
> >>>>>> start
> >>>>>>>>> offset from a middle position (e.g. how to process non exist
> >>>> delete
> >>>>>>>>> events).
> >>>>>>>>>       It's dangerous if users do that. So we don't provide the
> >>>> offset
> >>>>>>>> option
> >>>>>>>>> in the new connector at the moment.
> >>>>>>>>> 2) It's a different perspective/abstraction on the same kafka
> >>>> topic
> >>>>>>>> (append
> >>>>>>>>> vs. upsert). It would be easier to understand if we can separate
> >>>>> them
> >>>>>>>>>       instead of mixing them in one connector. The new connector
> >>>>>> requires
> >>>>>>>>> hash sink partitioner, primary key declared, regular format.
> >>>>>>>>>       If we mix them in one connector, it might be confusing how
> to
> >>>>> use
> >>>>>>>> the
> >>>>>>>>> options correctly.
> >>>>>>>>> 3) The semantic of the KTable connector is just the same as
> KTable
> >>>>> in
> >>>>>>>> Kafka
> >>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
> >>>>>>>>>       We have seen several questions in the mailing list asking
> how
> >>>> to
> >>>>>>>> model
> >>>>>>>>> a KTable and how to join a KTable in Flink SQL.
> >>>>>>>>>
> >>>>>>>>> Best,
> >>>>>>>>> Jark
> >>>>>>>>>
> >>>>>>>>> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]> wrote:
> >>>>>>>>>
> >>>>>>>>>> Hi Jingsong,
> >>>>>>>>>>
> >>>>>>>>>> As the FLIP describes, "KTable connector produces a changelog
> >>>>>> stream,
> >>>>>>>>>> where each data record represents an update or delete event.".
> >>>>>>>>>> Therefore, a ktable source is an unbounded stream source.
> >>>>> Selecting
> >>>>>> a
> >>>>>>>>>> ktable source is similar to selecting a kafka source with
> >>>>>>>> debezium-json
> >>>>>>>>>> format
> >>>>>>>>>> that it never ends and the results are continuously updated.
> >>>>>>>>>>
> >>>>>>>>>> It's possible to have a bounded ktable source in the future, for
> >>>>>>>> example,
> >>>>>>>>>> add an option 'bounded=true' or 'end-offset=xxx'.
> >>>>>>>>>> In this way, the ktable will produce a bounded changelog stream.
> >>>>>>>>>> So I think this can be a compatible feature in the future.
> >>>>>>>>>>
> >>>>>>>>>> I don't think we should associate with ksql related concepts.
> >>>>>>>> Actually,
> >>>>>>>>> we
> >>>>>>>>>> didn't introduce any concepts from KSQL (e.g. Stream vs Table
> >>>>>> notion).
> >>>>>>>>>> The "ktable" is just a connector name, we can also call it
> >>>>>>>>>> "compacted-kafka" or something else.
> >>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
> >>>>> Flink
> >>>>>>>> SQL
> >>>>>>>>>> easily.
> >>>>>>>>>>
> >>>>>>>>>> Regarding the "value.fields-include", this is an option
> >>>> introduced
> >>>>>> in
> >>>>>>>>>> FLIP-107 for Kafka connector.
> >>>>>>>>>> I think we should keep the same behavior with the Kafka
> >>>> connector.
> >>>>>> I'm
> >>>>>>>>> not
> >>>>>>>>>> sure what's the default behavior of KSQL.
> >>>>>>>>>> But I guess it also stores the keys in value from this example
> >>>>> docs
> >>>>>>>> (see
> >>>>>>>>>> the "users_original" table) [1].
> >>>>>>>>>>
> >>>>>>>>>> Best,
> >>>>>>>>>> Jark
> >>>>>>>>>>
> >>>>>>>>>> [1]:
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> On Mon, 19 Oct 2020 at 18:17, Danny Chan <[hidden email]>
> >>>>>>>> wrote:
> >>>>>>>>>>
> >>>>>>>>>>> The concept seems conflicts with the Flink abstraction “dynamic
> >>>>>>>> table”,
> >>>>>>>>>>> in Flink we see both “stream” and “table” as a dynamic table,
> >>>>>>>>>>>
> >>>>>>>>>>> I think we should make clear first how to express stream and
> >>>>> table
> >>>>>>>>>>> specific features on one “dynamic table”,
> >>>>>>>>>>> it is more natural for KSQL because KSQL takes stream and table
> >>>>> as
> >>>>>>>>>>> different abstractions for representing collections. In KSQL,
> >>>>> only
> >>>>>>>>> table is
> >>>>>>>>>>> mutable and can have a primary key.
> >>>>>>>>>>>
> >>>>>>>>>>> Does this connector belongs to the “table” scope or “stream”
> >>>>> scope
> >>>>>> ?
> >>>>>>>>>>>
> >>>>>>>>>>> Some of the concepts (such as the primary key on stream) should
> >>>>> be
> >>>>>>>>>>> suitable for all the connectors, not just Kafka, Shouldn’t this
> >>>>> be
> >>>>>> an
> >>>>>>>>>>> extension of existing Kafka connector instead of a totally new
> >>>>>>>>> connector ?
> >>>>>>>>>>> What about the other connectors ?
> >>>>>>>>>>>
> >>>>>>>>>>> Because this touches the core abstraction of Flink, we better
> >>>>> have
> >>>>>> a
> >>>>>>>>>>> top-down overall design, following the KSQL directly is not the
> >>>>>>>> answer.
> >>>>>>>>>>>
> >>>>>>>>>>> P.S. For the source
> >>>>>>>>>>>> Shouldn’t this be an extension of existing Kafka connector
> >>>>>> instead
> >>>>>>>> of
> >>>>>>>>> a
> >>>>>>>>>>> totally new connector ?
> >>>>>>>>>>>
> >>>>>>>>>>> How could we achieve that (e.g. set up the parallelism
> >>>>> correctly) ?
> >>>>>>>>>>>
> >>>>>>>>>>> Best,
> >>>>>>>>>>> Danny Chan
> >>>>>>>>>>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <[hidden email]
> >>>>>>> ，写道：
> >>>>>>>>>>>> Thanks Shengkai for your proposal.
> >>>>>>>>>>>>
> >>>>>>>>>>>> +1 for this feature.
> >>>>>>>>>>>>
> >>>>>>>>>>>>> Future Work: Support bounded KTable source
> >>>>>>>>>>>>
> >>>>>>>>>>>> I don't think it should be a future work, I think it is one
> >>>> of
> >>>>>> the
> >>>>>>>>>>>> important concepts of this FLIP. We need to understand it
> >>>> now.
> >>>>>>>>>>>>
> >>>>>>>>>>>> Intuitively, a ktable in my opinion is a bounded table rather
> >>>>>> than
> >>>>>>>> a
> >>>>>>>>>>>> stream, so select should produce a bounded table by default.
> >>>>>>>>>>>>
> >>>>>>>>>>>> I think we can list Kafka related knowledge, because the word
> >>>>>>>> `ktable`
> >>>>>>>>>>> is
> >>>>>>>>>>>> easy to associate with ksql related concepts. (If possible,
> >>>>> it's
> >>>>>>>>> better
> >>>>>>>>>>> to
> >>>>>>>>>>>> unify with it)
> >>>>>>>>>>>>
> >>>>>>>>>>>> What do you think?
> >>>>>>>>>>>>
> >>>>>>>>>>>>> value.fields-include
> >>>>>>>>>>>>
> >>>>>>>>>>>> What about the default behavior of KSQL?
> >>>>>>>>>>>>
> >>>>>>>>>>>> Best,
> >>>>>>>>>>>> Jingsong
> >>>>>>>>>>>>
> >>>>>>>>>>>> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
> >>>>> [hidden email]
> >>>>>>>
> >>>>>>>>>>> wrote:
> >>>>>>>>>>>>
> >>>>>>>>>>>>> Hi, devs.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Jark and I want to start a new FLIP to introduce the KTable
> >>>>>>>>>>> connector. The
> >>>>>>>>>>>>> KTable is a shortcut of "Kafka Table", it also has the same
> >>>>>>>>> semantics
> >>>>>>>>>>> with
> >>>>>>>>>>>>> the KTable notion in Kafka Stream.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> FLIP-149:
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>
> >>>>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Currently many users have expressed their needs for the
> >>>>> upsert
> >>>>>>>> Kafka
> >>>>>>>>>>> by
> >>>>>>>>>>>>> mail lists and issues. The KTable connector has several
> >>>>>> benefits
> >>>>>>>> for
> >>>>>>>>>>> users:
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> 1. Users are able to interpret a compacted Kafka Topic as
> >>>> an
> >>>>>>>> upsert
> >>>>>>>>>>> stream
> >>>>>>>>>>>>> in Apache Flink. And also be able to write a changelog
> >>>> stream
> >>>>>> to
> >>>>>>>>> Kafka
> >>>>>>>>>>>>> (into a compacted topic).
> >>>>>>>>>>>>> 2. As a part of the real time pipeline, store join or
> >>>>> aggregate
> >>>>>>>>>>> result (may
> >>>>>>>>>>>>> contain updates) into a Kafka topic for further
> >>>> calculation;
> >>>>>>>>>>>>> 3. The semantic of the KTable connector is just the same as
> >>>>>>>> KTable
> >>>>>>>>> in
> >>>>>>>>>>> Kafka
> >>>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
> >>>>> We
> >>>>>>>> have
> >>>>>>>>>>> seen
> >>>>>>>>>>>>> several questions in the mailing list asking how to model a
> >>>>>>>> KTable
> >>>>>>>>>>> and how
> >>>>>>>>>>>>> to join a KTable in Flink SQL.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> We hope it can expand the usage of the Flink with Kafka.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> I'm looking forward to your feedback.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Best,
> >>>>>>>>>>>>> Shengkai
> >>>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>> --
> >>>>>>>>>>>> Best, Jingsong Lee
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> --
> >>>>>>>>
> >>>>>>>> Konstantin Knauf
> >>>>>>>>
> >>>>>>>> https://twitter.com/snntrable
> >>>>>>>>
> >>>>>>>> https://github.com/knaufk
> >>>>>>>>
> >>>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>>>
> >>>> --
> >>>>
> >>>> Konstantin Knauf
> >>>>
> >>>> https://twitter.com/snntrable
> >>>>
> >>>> https://github.com/knaufk
> >>>>
> >>>
> >>
> >>
> >
>
>

--

Seth Wiesman | Solutions Architect

[phone]

<https://www.ververica.com/>

Follow us @VervericaData

--

Join Flink Forward <https://flink-forward.org/> - The Apache Flink
Conference

Stream Processing | Event Driven | Real Time

[Jark Wu-2]

Hi Timo, Seth,

The default value "inserting" of "mode" might be not suitable,
because "debezium-json" emits changelog messages which include updates.

On Thu, 22 Oct 2020 at 22:10, Seth Wiesman <[hidden email]> wrote:

> +1 for supporting upsert results into Kafka.
>
> I have no comments on the implementation details.
>
> As far as configuration goes, I tend to favor Timo's option where we add a
> "mode" property to the existing Kafka table with default value "inserting".
> If the mode is set to "updating" then the validation changes to the new
> requirements. I personally find it more intuitive than a seperate
> connector, my fear is users won't understand its the same physical kafka
> sink under the hood and it will lead to other confusion like does it offer
> the same persistence guarantees? I think we are capable of adding good
> valdiation messaging that solves Jark and Kurts concerns.
>
>
> On Thu, Oct 22, 2020 at 8:51 AM Timo Walther <[hidden email]> wrote:
>
> > Hi Jark,
> >
> > "calling it "kafka-compacted" can even remind users to enable log
> > compaction"
> >
> > But sometimes users like to store a lineage of changes in their topics.
> > Indepent of any ktable/kstream interpretation.
> >
> > I let the majority decide on this topic to not further block this
> > effort. But we might find a better name like:
> >
> > connector = kafka
> > mode = updating/inserting
> >
> > OR
> >
> > connector = kafka-updating
> >
> > ...
> >
> > Regards,
> > Timo
> >
> >
> >
> >
> > On 22.10.20 15:24, Jark Wu wrote:
> > > Hi Timo,
> > >
> > > Thanks for your opinions.
> > >
> > > 1) Implementation
> > > We will have an stateful operator to generate INSERT and UPDATE_BEFORE.
> > > This operator is keyby-ed (primary key as the shuffle key) after the
> > source
> > > operator.
> > > The implementation of this operator is very similar to the existing
> > > `DeduplicateKeepLastRowFunction`.
> > > The operator will register a value state using the primary key fields
> as
> > > keys.
> > > When the value state is empty under current key, we will emit INSERT
> for
> > > the input row.
> > > When the value state is not empty under current key, we will emit
> > > UPDATE_BEFORE using the row in state,
> > > and emit UPDATE_AFTER using the input row.
> > > When the input row is DELETE, we will clear state and emit DELETE row.
> > >
> > > 2) new option vs new connector
> > >> We recently simplified the table options to a minimum amount of
> > > characters to be as concise as possible in the DDL.
> > > I think this is the reason why we want to introduce a new connector,
> > > because we can simplify the options in DDL.
> > > For example, if using a new option, the DDL may look like this:
> > >
> > > CREATE TABLE users (
> > >    user_id BIGINT,
> > >    user_name STRING,
> > >    user_level STRING,
> > >    region STRING,
> > >    PRIMARY KEY (user_id) NOT ENFORCED
> > > ) WITH (
> > >    'connector' = 'kafka',
> > >    'model' = 'table',
> > >    'topic' = 'pageviews_per_region',
> > >    'properties.bootstrap.servers' = '...',
> > >    'properties.group.id' = 'testGroup',
> > >    'scan.startup.mode' = 'earliest',
> > >    'key.format' = 'csv',
> > >    'key.fields' = 'user_id',
> > >    'value.format' = 'avro',
> > >    'sink.partitioner' = 'hash'
> > > );
> > >
> > > If using a new connector, we can have a different default value for the
> > > options and remove unnecessary options,
> > > the DDL can look like this which is much more concise:
> > >
> > > CREATE TABLE pageviews_per_region (
> > >    user_id BIGINT,
> > >    user_name STRING,
> > >    user_level STRING,
> > >    region STRING,
> > >    PRIMARY KEY (user_id) NOT ENFORCED
> > > ) WITH (
> > >    'connector' = 'kafka-compacted',
> > >    'topic' = 'pageviews_per_region',
> > >    'properties.bootstrap.servers' = '...',
> > >    'key.format' = 'csv',
> > >    'value.format' = 'avro'
> > > );
> > >
> > >> When people read `connector=kafka-compacted` they might not know that
> it
> > >> has ktable semantics. You don't need to enable log compaction in order
> > >> to use a KTable as far as I know.
> > > We don't need to let users know it has ktable semantics, as Konstantin
> > > mentioned this may carry more implicit
> > > meaning than we want to imply here. I agree users don't need to enable
> > log
> > > compaction, but from the production perspective,
> > > log compaction should always be enabled if it is used in this purpose.
> > > Calling it "kafka-compacted" can even remind users to enable log
> > compaction.
> > >
> > > I don't agree to introduce "model = table/stream" option, or
> > > "connector=kafka-table",
> > > because this means we are introducing Table vs Stream concept from
> KSQL.
> > > However, we don't have such top-level concept in Flink SQL now, this
> will
> > > further confuse users.
> > > In Flink SQL, all the things are STREAM, the differences are whether it
> > is
> > > bounded or unbounded,
> > >   whether it is insert-only or changelog.
> > >
> > >
> > > Best,
> > > Jark
> > >
> > >
> > > On Thu, 22 Oct 2020 at 20:39, Timo Walther <[hidden email]> wrote:
> > >
> > >> Hi Shengkai, Hi Jark,
> > >>
> > >> thanks for this great proposal. It is time to finally connect the
> > >> changelog processor with a compacted Kafka topic.
> > >>
> > >> "The operator will produce INSERT rows, or additionally generate
> > >> UPDATE_BEFORE rows for the previous image, or produce DELETE rows with
> > >> all columns filled with values."
> > >>
> > >> Could you elaborate a bit on the implementation details in the FLIP?
> How
> > >> are UPDATE_BEFOREs are generated. How much state is required to
> perform
> > >> this operation.
> > >>
> > >>   From a conceptual and semantical point of view, I'm fine with the
> > >> proposal. But I would like to share my opinion about how we expose
> this
> > >> feature:
> > >>
> > >> ktable vs kafka-compacted
> > >>
> > >> I'm against having an additional connector like `ktable` or
> > >> `kafka-compacted`. We recently simplified the table options to a
> minimum
> > >> amount of characters to be as concise as possible in the DDL.
> Therefore,
> > >> I would keep the `connector=kafka` and introduce an additional option.
> > >> Because a user wants to read "from Kafka". And the "how" should be
> > >> determined in the lower options.
> > >>
> > >> When people read `connector=ktable` they might not know that this is
> > >> Kafka. Or they wonder where `kstream` is?
> > >>
> > >> When people read `connector=kafka-compacted` they might not know that
> it
> > >> has ktable semantics. You don't need to enable log compaction in order
> > >> to use a KTable as far as I know. Log compaction and table semantics
> are
> > >> orthogonal topics.
> > >>
> > >> In the end we will need 3 types of information when declaring a Kafka
> > >> connector:
> > >>
> > >> CREATE TABLE ... WITH (
> > >>     connector=kafka        -- Some information about the connector
> > >>     end-offset = XXXX      -- Some information about the boundedness
> > >>     model = table/stream   -- Some information about interpretation
> > >> )
> > >>
> > >>
> > >> We can still apply all the constraints mentioned in the FLIP. When
> > >> `model` is set to `table`.
> > >>
> > >> What do you think?
> > >>
> > >> Regards,
> > >> Timo
> > >>
> > >>
> > >> On 21.10.20 14:19, Jark Wu wrote:
> > >>> Hi,
> > >>>
> > >>> IMO, if we are going to mix them in one connector,
> > >>> 1) either users need to set some options to a specific value
> > explicitly,
> > >>> e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
> > >>> This makes the connector awkward to use. Users may face to fix
> options
> > >> one
> > >>> by one according to the exception.
> > >>> Besides, in the future, it is still possible to use
> > >>> "sink.partitioner=fixed" (reduce network cost) if users are aware of
> > >>> the partition routing,
> > >>> however, it's error-prone to have "fixed" as default for compacted
> > mode.
> > >>>
> > >>> 2) or make those options a different default value when
> > "compacted=true".
> > >>> This would be more confusing and unpredictable if the default value
> of
> > >>> options will change according to other options.
> > >>> What happens if we have a third mode in the future?
> > >>>
> > >>> In terms of usage and options, it's very different from the
> > >>> original "kafka" connector.
> > >>> It would be more handy to use and less fallible if separating them
> into
> > >> two
> > >>> connectors.
> > >>> In the implementation layer, we can reuse code as much as possible.
> > >>>
> > >>> Therefore, I'm still +1 to have a new connector.
> > >>> The "kafka-compacted" name sounds good to me.
> > >>>
> > >>> Best,
> > >>> Jark
> > >>>
> > >>>
> > >>> On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <[hidden email]>
> > >> wrote:
> > >>>
> > >>>> Hi Kurt, Hi Shengkai,
> > >>>>
> > >>>> thanks for answering my questions and the additional
> clarifications. I
> > >>>> don't have a strong opinion on whether to extend the "kafka"
> connector
> > >> or
> > >>>> to introduce a new connector. So, from my perspective feel free to
> go
> > >> with
> > >>>> a separate connector. If we do introduce a new connector I wouldn't
> > >> call it
> > >>>> "ktable" for aforementioned reasons (In addition, we might suggest
> > that
> > >>>> there is also a "kstreams" connector for symmetry reasons). I don't
> > >> have a
> > >>>> good alternative name, though, maybe "kafka-compacted" or
> > >>>> "compacted-kafka".
> > >>>>
> > >>>> Thanks,
> > >>>>
> > >>>> Konstantin
> > >>>>
> > >>>>
> > >>>> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]>
> wrote:
> > >>>>
> > >>>>> Hi all,
> > >>>>>
> > >>>>> I want to describe the discussion process which drove us to have
> such
> > >>>>> conclusion, this might make some of
> > >>>>> the design choices easier to understand and keep everyone on the
> same
> > >>>> page.
> > >>>>>
> > >>>>> Back to the motivation, what functionality do we want to provide in
> > the
> > >>>>> first place? We got a lot of feedback and
> > >>>>> questions from mailing lists that people want to write
> > Not-Insert-Only
> > >>>>> messages into kafka. They might be
> > >>>>> intentional or by accident, e.g. wrote an non-windowed aggregate
> > query
> > >> or
> > >>>>> non-windowed left outer join. And
> > >>>>> some users from KSQL world also asked about why Flink didn't
> leverage
> > >> the
> > >>>>> Key concept of every kafka topic
> > >>>>> and make kafka as a dynamic changing keyed table.
> > >>>>>
> > >>>>> To work with kafka better, we were thinking to extend the
> > functionality
> > >>>> of
> > >>>>> the current kafka connector by letting it
> > >>>>> accept updates and deletions. But due to the limitation of kafka,
> the
> > >>>>> update has to be "update by key", aka a table
> > >>>>> with primary key.
> > >>>>>
> > >>>>> This introduces a couple of conflicts with current kafka table's
> > >> options:
> > >>>>> 1. key.fields: as said above, we need the kafka table to have the
> > >> primary
> > >>>>> key constraint. And users can also configure
> > >>>>> key.fields freely, this might cause friction. (Sure we can do some
> > >> sanity
> > >>>>> check on this but it also creates friction.)
> > >>>>> 2. sink.partitioner: to make the semantics right, we need to make
> > sure
> > >>>> all
> > >>>>> the updates on the same key are written to
> > >>>>> the same kafka partition, such we should force to use a hash by key
> > >>>>> partition inside such table. Again, this has conflicts
> > >>>>> and creates friction with current user options.
> > >>>>>
> > >>>>> The above things are solvable, though not perfect or most user
> > >> friendly.
> > >>>>>
> > >>>>> Let's take a look at the reading side. The keyed kafka table
> contains
> > >> two
> > >>>>> kinds of messages: upsert or deletion. What upsert
> > >>>>> means is "If the key doesn't exist yet, it's an insert record.
> > >> Otherwise
> > >>>>> it's an update record". For the sake of correctness or
> > >>>>> simplicity, the Flink SQL engine also needs such information. If we
> > >>>>> interpret all messages to "update record", some queries or
> > >>>>> operators may not work properly. It's weird to see an update record
> > but
> > >>>> you
> > >>>>> haven't seen the insert record before.
> > >>>>>
> > >>>>> So what Flink should do is after reading out the records from such
> > >> table,
> > >>>>> it needs to create a state to record which messages have
> > >>>>> been seen and then generate the correct row type correspondingly.
> > This
> > >>>> kind
> > >>>>> of couples the state and the data of the message
> > >>>>> queue, and it also creates conflicts with current kafka connector.
> > >>>>>
> > >>>>> Think about if users suspend a running job (which contains some
> > reading
> > >>>>> state now), and then change the start offset of the reader.
> > >>>>> By changing the reading offset, it actually change the whole story
> of
> > >>>>> "which records should be insert messages and which records
> > >>>>> should be update messages). And it will also make Flink to deal
> with
> > >>>>> another weird situation that it might receive a deletion
> > >>>>> on a non existing message.
> > >>>>>
> > >>>>> We were unsatisfied with all the frictions and conflicts it will
> > create
> > >>>> if
> > >>>>> we enable the "upsert & deletion" support to the current kafka
> > >>>>> connector. And later we begin to realize that we shouldn't treat it
> > as
> > >> a
> > >>>>> normal message queue, but should treat it as a changing keyed
> > >>>>> table. We should be able to always get the whole data of such table
> > (by
> > >>>>> disabling the start offset option) and we can also read the
> > >>>>> changelog out of such table. It's like a HBase table with binlog
> > >> support
> > >>>>> but doesn't have random access capability (which can be fulfilled
> > >>>>> by Flink's state).
> > >>>>>
> > >>>>> So our intention was instead of telling and persuading users what
> > kind
> > >> of
> > >>>>> options they should or should not use by extending
> > >>>>> current kafka connector when enable upsert support, we are actually
> > >>>> create
> > >>>>> a whole new and different connector that has total
> > >>>>> different abstractions in SQL layer, and should be treated totally
> > >>>>> different with current kafka connector.
> > >>>>>
> > >>>>> Hope this can clarify some of the concerns.
> > >>>>>
> > >>>>> Best,
> > >>>>> Kurt
> > >>>>>
> > >>>>>
> > >>>>> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <[hidden email]>
> > >> wrote:
> > >>>>>
> > >>>>>> Hi devs,
> > >>>>>>
> > >>>>>> As many people are still confused about the difference option
> > >>>> behaviours
> > >>>>>> between the Kafka connector and KTable connector, Jark and I list
> > the
> > >>>>>> differences in the doc[1].
> > >>>>>>
> > >>>>>> Best,
> > >>>>>> Shengkai
> > >>>>>>
> > >>>>>> [1]
> > >>>>>>
> > >>>>>>
> > >>>>>
> > >>>>
> > >>
> >
> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
> > >>>>>>
> > >>>>>> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
> > >>>>>>
> > >>>>>>> Hi Konstantin,
> > >>>>>>>
> > >>>>>>> Thanks for your reply.
> > >>>>>>>
> > >>>>>>>> It uses the "kafka" connector and does not specify a primary
> key.
> > >>>>>>> The dimensional table `users` is a ktable connector and we can
> > >>>> specify
> > >>>>>> the
> > >>>>>>> pk on the KTable.
> > >>>>>>>
> > >>>>>>>> Will it possible to use a "ktable" as a dimensional table in
> > >>>> FLIP-132
> > >>>>>>> Yes. We can specify the watermark on the KTable and it can be
> used
> > >>>> as a
> > >>>>>>> dimension table in temporal join.
> > >>>>>>>
> > >>>>>>>> Introduce a new connector vs introduce a new property
> > >>>>>>> The main reason behind is that the KTable connector almost has no
> > >>>>> common
> > >>>>>>> options with the Kafka connector. The options that can be reused
> by
> > >>>>>> KTable
> > >>>>>>> connectors are 'topic', 'properties.bootstrap.servers' and
> > >>>>>>> 'value.fields-include' . We can't set cdc format for 'key.format'
> > and
> > >>>>>>> 'value.format' in KTable connector now, which is  available in
> > Kafka
> > >>>>>>> connector. Considering the difference between the options we can
> > use,
> > >>>>>> it's
> > >>>>>>> more suitable to introduce an another connector rather than a
> > >>>> property.
> > >>>>>>>
> > >>>>>>> We are also fine to use "compacted-kafka" as the name of the new
> > >>>>>>> connector. What do you think?
> > >>>>>>>
> > >>>>>>> Best,
> > >>>>>>> Shengkai
> > >>>>>>>
> > >>>>>>> Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
> > >>>>>>>
> > >>>>>>>> Hi Shengkai,
> > >>>>>>>>
> > >>>>>>>> Thank you for driving this effort. I believe this a very
> important
> > >>>>>> feature
> > >>>>>>>> for many users who use Kafka and Flink SQL together. A few
> > questions
> > >>>>> and
> > >>>>>>>> thoughts:
> > >>>>>>>>
> > >>>>>>>> * Is your example "Use KTable as a reference/dimension table"
> > >>>> correct?
> > >>>>>> It
> > >>>>>>>> uses the "kafka" connector and does not specify a primary key.
> > >>>>>>>>
> > >>>>>>>> * Will it be possible to use a "ktable" table directly as a
> > >>>>> dimensional
> > >>>>>>>> table in temporal join (*based on event time*) (FLIP-132)? This
> is
> > >>>> not
> > >>>>>>>> completely clear to me from the FLIP.
> > >>>>>>>>
> > >>>>>>>> * I'd personally prefer not to introduce a new connector and
> > instead
> > >>>>> to
> > >>>>>>>> extend the Kafka connector. We could add an additional property
> > >>>>>>>> "compacted"
> > >>>>>>>> = "true"|"false". If it is set to "true", we can add additional
> > >>>>>> validation
> > >>>>>>>> logic (e.g. "scan.startup.mode" can not be set, primary key
> > >>>> required,
> > >>>>>>>> etc.). If we stick to a separate connector I'd not call it
> > "ktable",
> > >>>>> but
> > >>>>>>>> rather "compacted-kafka" or similar. KTable seems to carry more
> > >>>>> implicit
> > >>>>>>>> meaning than we want to imply here.
> > >>>>>>>>
> > >>>>>>>> * I agree that this is not a bounded source. If we want to
> > support a
> > >>>>>>>> bounded mode, this is an orthogonal concern that also applies to
> > >>>> other
> > >>>>>>>> unbounded sources.
> > >>>>>>>>
> > >>>>>>>> Best,
> > >>>>>>>>
> > >>>>>>>> Konstantin
> > >>>>>>>>
> > >>>>>>>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]>
> wrote:
> > >>>>>>>>
> > >>>>>>>>> Hi Danny,
> > >>>>>>>>>
> > >>>>>>>>> First of all, we didn't introduce any concepts from KSQL (e.g.
> > >>>>> Stream
> > >>>>>> vs
> > >>>>>>>>> Table notion).
> > >>>>>>>>> This new connector will produce a changelog stream, so it's
> still
> > >>>> a
> > >>>>>>>> dynamic
> > >>>>>>>>> table and doesn't conflict with Flink core concepts.
> > >>>>>>>>>
> > >>>>>>>>> The "ktable" is just a connector name, we can also call it
> > >>>>>>>>> "compacted-kafka" or something else.
> > >>>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
> > >>>> Flink
> > >>>>>> SQL
> > >>>>>>>>> easily.
> > >>>>>>>>>
> > >>>>>>>>> Regarding to why introducing a new connector vs a new property
> in
> > >>>>>>>> existing
> > >>>>>>>>> kafka connector:
> > >>>>>>>>>
> > >>>>>>>>> I think the main reason is that we want to have a clear
> > separation
> > >>>>> for
> > >>>>>>>> such
> > >>>>>>>>> two use cases, because they are very different.
> > >>>>>>>>> We also listed reasons in the FLIP, including:
> > >>>>>>>>>
> > >>>>>>>>> 1) It's hard to explain what's the behavior when users specify
> > the
> > >>>>>> start
> > >>>>>>>>> offset from a middle position (e.g. how to process non exist
> > >>>> delete
> > >>>>>>>>> events).
> > >>>>>>>>>       It's dangerous if users do that. So we don't provide the
> > >>>> offset
> > >>>>>>>> option
> > >>>>>>>>> in the new connector at the moment.
> > >>>>>>>>> 2) It's a different perspective/abstraction on the same kafka
> > >>>> topic
> > >>>>>>>> (append
> > >>>>>>>>> vs. upsert). It would be easier to understand if we can
> separate
> > >>>>> them
> > >>>>>>>>>       instead of mixing them in one connector. The new
> connector
> > >>>>>> requires
> > >>>>>>>>> hash sink partitioner, primary key declared, regular format.
> > >>>>>>>>>       If we mix them in one connector, it might be confusing
> how
> > to
> > >>>>> use
> > >>>>>>>> the
> > >>>>>>>>> options correctly.
> > >>>>>>>>> 3) The semantic of the KTable connector is just the same as
> > KTable
> > >>>>> in
> > >>>>>>>> Kafka
> > >>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
> > >>>>>>>>>       We have seen several questions in the mailing list asking
> > how
> > >>>> to
> > >>>>>>>> model
> > >>>>>>>>> a KTable and how to join a KTable in Flink SQL.
> > >>>>>>>>>
> > >>>>>>>>> Best,
> > >>>>>>>>> Jark
> > >>>>>>>>>
> > >>>>>>>>> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]>
> wrote:
> > >>>>>>>>>
> > >>>>>>>>>> Hi Jingsong,
> > >>>>>>>>>>
> > >>>>>>>>>> As the FLIP describes, "KTable connector produces a changelog
> > >>>>>> stream,
> > >>>>>>>>>> where each data record represents an update or delete event.".
> > >>>>>>>>>> Therefore, a ktable source is an unbounded stream source.
> > >>>>> Selecting
> > >>>>>> a
> > >>>>>>>>>> ktable source is similar to selecting a kafka source with
> > >>>>>>>> debezium-json
> > >>>>>>>>>> format
> > >>>>>>>>>> that it never ends and the results are continuously updated.
> > >>>>>>>>>>
> > >>>>>>>>>> It's possible to have a bounded ktable source in the future,
> for
> > >>>>>>>> example,
> > >>>>>>>>>> add an option 'bounded=true' or 'end-offset=xxx'.
> > >>>>>>>>>> In this way, the ktable will produce a bounded changelog
> stream.
> > >>>>>>>>>> So I think this can be a compatible feature in the future.
> > >>>>>>>>>>
> > >>>>>>>>>> I don't think we should associate with ksql related concepts.
> > >>>>>>>> Actually,
> > >>>>>>>>> we
> > >>>>>>>>>> didn't introduce any concepts from KSQL (e.g. Stream vs Table
> > >>>>>> notion).
> > >>>>>>>>>> The "ktable" is just a connector name, we can also call it
> > >>>>>>>>>> "compacted-kafka" or something else.
> > >>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
> > >>>>> Flink
> > >>>>>>>> SQL
> > >>>>>>>>>> easily.
> > >>>>>>>>>>
> > >>>>>>>>>> Regarding the "value.fields-include", this is an option
> > >>>> introduced
> > >>>>>> in
> > >>>>>>>>>> FLIP-107 for Kafka connector.
> > >>>>>>>>>> I think we should keep the same behavior with the Kafka
> > >>>> connector.
> > >>>>>> I'm
> > >>>>>>>>> not
> > >>>>>>>>>> sure what's the default behavior of KSQL.
> > >>>>>>>>>> But I guess it also stores the keys in value from this example
> > >>>>> docs
> > >>>>>>>> (see
> > >>>>>>>>>> the "users_original" table) [1].
> > >>>>>>>>>>
> > >>>>>>>>>> Best,
> > >>>>>>>>>> Jark
> > >>>>>>>>>>
> > >>>>>>>>>> [1]:
> > >>>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>
> > >>>>>>
> > >>>>>
> > >>>>
> > >>
> >
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> > >>>>>>>>>>
> > >>>>>>>>>>
> > >>>>>>>>>> On Mon, 19 Oct 2020 at 18:17, Danny Chan <
> [hidden email]>
> > >>>>>>>> wrote:
> > >>>>>>>>>>
> > >>>>>>>>>>> The concept seems conflicts with the Flink abstraction
> “dynamic
> > >>>>>>>> table”,
> > >>>>>>>>>>> in Flink we see both “stream” and “table” as a dynamic table,
> > >>>>>>>>>>>
> > >>>>>>>>>>> I think we should make clear first how to express stream and
> > >>>>> table
> > >>>>>>>>>>> specific features on one “dynamic table”,
> > >>>>>>>>>>> it is more natural for KSQL because KSQL takes stream and
> table
> > >>>>> as
> > >>>>>>>>>>> different abstractions for representing collections. In KSQL,
> > >>>>> only
> > >>>>>>>>> table is
> > >>>>>>>>>>> mutable and can have a primary key.
> > >>>>>>>>>>>
> > >>>>>>>>>>> Does this connector belongs to the “table” scope or “stream”
> > >>>>> scope
> > >>>>>> ?
> > >>>>>>>>>>>
> > >>>>>>>>>>> Some of the concepts (such as the primary key on stream)
> should
> > >>>>> be
> > >>>>>>>>>>> suitable for all the connectors, not just Kafka, Shouldn’t
> this
> > >>>>> be
> > >>>>>> an
> > >>>>>>>>>>> extension of existing Kafka connector instead of a totally
> new
> > >>>>>>>>> connector ?
> > >>>>>>>>>>> What about the other connectors ?
> > >>>>>>>>>>>
> > >>>>>>>>>>> Because this touches the core abstraction of Flink, we better
> > >>>>> have
> > >>>>>> a
> > >>>>>>>>>>> top-down overall design, following the KSQL directly is not
> the
> > >>>>>>>> answer.
> > >>>>>>>>>>>
> > >>>>>>>>>>> P.S. For the source
> > >>>>>>>>>>>> Shouldn’t this be an extension of existing Kafka connector
> > >>>>>> instead
> > >>>>>>>> of
> > >>>>>>>>> a
> > >>>>>>>>>>> totally new connector ?
> > >>>>>>>>>>>
> > >>>>>>>>>>> How could we achieve that (e.g. set up the parallelism
> > >>>>> correctly) ?
> > >>>>>>>>>>>
> > >>>>>>>>>>> Best,
> > >>>>>>>>>>> Danny Chan
> > >>>>>>>>>>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <
> [hidden email]
> > >>>>>>> ，写道：
> > >>>>>>>>>>>> Thanks Shengkai for your proposal.
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> +1 for this feature.
> > >>>>>>>>>>>>
> > >>>>>>>>>>>>> Future Work: Support bounded KTable source
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> I don't think it should be a future work, I think it is one
> > >>>> of
> > >>>>>> the
> > >>>>>>>>>>>> important concepts of this FLIP. We need to understand it
> > >>>> now.
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> Intuitively, a ktable in my opinion is a bounded table
> rather
> > >>>>>> than
> > >>>>>>>> a
> > >>>>>>>>>>>> stream, so select should produce a bounded table by default.
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> I think we can list Kafka related knowledge, because the
> word
> > >>>>>>>> `ktable`
> > >>>>>>>>>>> is
> > >>>>>>>>>>>> easy to associate with ksql related concepts. (If possible,
> > >>>>> it's
> > >>>>>>>>> better
> > >>>>>>>>>>> to
> > >>>>>>>>>>>> unify with it)
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> What do you think?
> > >>>>>>>>>>>>
> > >>>>>>>>>>>>> value.fields-include
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> What about the default behavior of KSQL?
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> Best,
> > >>>>>>>>>>>> Jingsong
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
> > >>>>> [hidden email]
> > >>>>>>>
> > >>>>>>>>>>> wrote:
> > >>>>>>>>>>>>
> > >>>>>>>>>>>>> Hi, devs.
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> Jark and I want to start a new FLIP to introduce the KTable
> > >>>>>>>>>>> connector. The
> > >>>>>>>>>>>>> KTable is a shortcut of "Kafka Table", it also has the same
> > >>>>>>>>> semantics
> > >>>>>>>>>>> with
> > >>>>>>>>>>>>> the KTable notion in Kafka Stream.
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> FLIP-149:
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>
> > >>>>>>
> > >>>>>
> > >>>>
> > >>
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> Currently many users have expressed their needs for the
> > >>>>> upsert
> > >>>>>>>> Kafka
> > >>>>>>>>>>> by
> > >>>>>>>>>>>>> mail lists and issues. The KTable connector has several
> > >>>>>> benefits
> > >>>>>>>> for
> > >>>>>>>>>>> users:
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> 1. Users are able to interpret a compacted Kafka Topic as
> > >>>> an
> > >>>>>>>> upsert
> > >>>>>>>>>>> stream
> > >>>>>>>>>>>>> in Apache Flink. And also be able to write a changelog
> > >>>> stream
> > >>>>>> to
> > >>>>>>>>> Kafka
> > >>>>>>>>>>>>> (into a compacted topic).
> > >>>>>>>>>>>>> 2. As a part of the real time pipeline, store join or
> > >>>>> aggregate
> > >>>>>>>>>>> result (may
> > >>>>>>>>>>>>> contain updates) into a Kafka topic for further
> > >>>> calculation;
> > >>>>>>>>>>>>> 3. The semantic of the KTable connector is just the same as
> > >>>>>>>> KTable
> > >>>>>>>>> in
> > >>>>>>>>>>> Kafka
> > >>>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
> > >>>>> We
> > >>>>>>>> have
> > >>>>>>>>>>> seen
> > >>>>>>>>>>>>> several questions in the mailing list asking how to model a
> > >>>>>>>> KTable
> > >>>>>>>>>>> and how
> > >>>>>>>>>>>>> to join a KTable in Flink SQL.
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> We hope it can expand the usage of the Flink with Kafka.
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> I'm looking forward to your feedback.
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> Best,
> > >>>>>>>>>>>>> Shengkai
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> --
> > >>>>>>>>>>>> Best, Jingsong Lee
> > >>>>>>>>>>>
> > >>>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>
> > >>>>>>>>
> > >>>>>>>> --
> > >>>>>>>>
> > >>>>>>>> Konstantin Knauf
> > >>>>>>>>
> > >>>>>>>> https://twitter.com/snntrable
> > >>>>>>>>
> > >>>>>>>> https://github.com/knaufk
> > >>>>>>>>
> > >>>>>>>
> > >>>>>>
> > >>>>>
> > >>>>
> > >>>>
> > >>>> --
> > >>>>
> > >>>> Konstantin Knauf
> > >>>>
> > >>>> https://twitter.com/snntrable
> > >>>>
> > >>>> https://github.com/knaufk
> > >>>>
> > >>>
> > >>
> > >>
> > >
> >
> >
>
> --
>
> Seth Wiesman | Solutions Architect
>
> [phone]
>
> <https://www.ververica.com/>
>
> Follow us @VervericaData
>
> --
>
> Join Flink Forward <https://flink-forward.org/> - The Apache Flink
> Conference
>
> Stream Processing | Event Driven | Real Time
>

[Kurt Young]

Good validation messages can't solve the broken user experience, especially
that
such update mode option will implicitly make half of current kafka options
invalid or doesn't
make sense.

Best,
Kurt


On Thu, Oct 22, 2020 at 10:31 PM Jark Wu <[hidden email]> wrote:

> Hi Timo, Seth,
>
> The default value "inserting" of "mode" might be not suitable,
> because "debezium-json" emits changelog messages which include updates.
>
> On Thu, 22 Oct 2020 at 22:10, Seth Wiesman <[hidden email]> wrote:
>
> > +1 for supporting upsert results into Kafka.
> >
> > I have no comments on the implementation details.
> >
> > As far as configuration goes, I tend to favor Timo's option where we add
> a
> > "mode" property to the existing Kafka table with default value
> "inserting".
> > If the mode is set to "updating" then the validation changes to the new
> > requirements. I personally find it more intuitive than a seperate
> > connector, my fear is users won't understand its the same physical kafka
> > sink under the hood and it will lead to other confusion like does it
> offer
> > the same persistence guarantees? I think we are capable of adding good
> > valdiation messaging that solves Jark and Kurts concerns.
> >
> >
> > On Thu, Oct 22, 2020 at 8:51 AM Timo Walther <[hidden email]> wrote:
> >
> > > Hi Jark,
> > >
> > > "calling it "kafka-compacted" can even remind users to enable log
> > > compaction"
> > >
> > > But sometimes users like to store a lineage of changes in their topics.
> > > Indepent of any ktable/kstream interpretation.
> > >
> > > I let the majority decide on this topic to not further block this
> > > effort. But we might find a better name like:
> > >
> > > connector = kafka
> > > mode = updating/inserting
> > >
> > > OR
> > >
> > > connector = kafka-updating
> > >
> > > ...
> > >
> > > Regards,
> > > Timo
> > >
> > >
> > >
> > >
> > > On 22.10.20 15:24, Jark Wu wrote:
> > > > Hi Timo,
> > > >
> > > > Thanks for your opinions.
> > > >
> > > > 1) Implementation
> > > > We will have an stateful operator to generate INSERT and
> UPDATE_BEFORE.
> > > > This operator is keyby-ed (primary key as the shuffle key) after the
> > > source
> > > > operator.
> > > > The implementation of this operator is very similar to the existing
> > > > `DeduplicateKeepLastRowFunction`.
> > > > The operator will register a value state using the primary key fields
> > as
> > > > keys.
> > > > When the value state is empty under current key, we will emit INSERT
> > for
> > > > the input row.
> > > > When the value state is not empty under current key, we will emit
> > > > UPDATE_BEFORE using the row in state,
> > > > and emit UPDATE_AFTER using the input row.
> > > > When the input row is DELETE, we will clear state and emit DELETE
> row.
> > > >
> > > > 2) new option vs new connector
> > > >> We recently simplified the table options to a minimum amount of
> > > > characters to be as concise as possible in the DDL.
> > > > I think this is the reason why we want to introduce a new connector,
> > > > because we can simplify the options in DDL.
> > > > For example, if using a new option, the DDL may look like this:
> > > >
> > > > CREATE TABLE users (
> > > >    user_id BIGINT,
> > > >    user_name STRING,
> > > >    user_level STRING,
> > > >    region STRING,
> > > >    PRIMARY KEY (user_id) NOT ENFORCED
> > > > ) WITH (
> > > >    'connector' = 'kafka',
> > > >    'model' = 'table',
> > > >    'topic' = 'pageviews_per_region',
> > > >    'properties.bootstrap.servers' = '...',
> > > >    'properties.group.id' = 'testGroup',
> > > >    'scan.startup.mode' = 'earliest',
> > > >    'key.format' = 'csv',
> > > >    'key.fields' = 'user_id',
> > > >    'value.format' = 'avro',
> > > >    'sink.partitioner' = 'hash'
> > > > );
> > > >
> > > > If using a new connector, we can have a different default value for
> the
> > > > options and remove unnecessary options,
> > > > the DDL can look like this which is much more concise:
> > > >
> > > > CREATE TABLE pageviews_per_region (
> > > >    user_id BIGINT,
> > > >    user_name STRING,
> > > >    user_level STRING,
> > > >    region STRING,
> > > >    PRIMARY KEY (user_id) NOT ENFORCED
> > > > ) WITH (
> > > >    'connector' = 'kafka-compacted',
> > > >    'topic' = 'pageviews_per_region',
> > > >    'properties.bootstrap.servers' = '...',
> > > >    'key.format' = 'csv',
> > > >    'value.format' = 'avro'
> > > > );
> > > >
> > > >> When people read `connector=kafka-compacted` they might not know
> that
> > it
> > > >> has ktable semantics. You don't need to enable log compaction in
> order
> > > >> to use a KTable as far as I know.
> > > > We don't need to let users know it has ktable semantics, as
> Konstantin
> > > > mentioned this may carry more implicit
> > > > meaning than we want to imply here. I agree users don't need to
> enable
> > > log
> > > > compaction, but from the production perspective,
> > > > log compaction should always be enabled if it is used in this
> purpose.
> > > > Calling it "kafka-compacted" can even remind users to enable log
> > > compaction.
> > > >
> > > > I don't agree to introduce "model = table/stream" option, or
> > > > "connector=kafka-table",
> > > > because this means we are introducing Table vs Stream concept from
> > KSQL.
> > > > However, we don't have such top-level concept in Flink SQL now, this
> > will
> > > > further confuse users.
> > > > In Flink SQL, all the things are STREAM, the differences are whether
> it
> > > is
> > > > bounded or unbounded,
> > > >   whether it is insert-only or changelog.
> > > >
> > > >
> > > > Best,
> > > > Jark
> > > >
> > > >
> > > > On Thu, 22 Oct 2020 at 20:39, Timo Walther <[hidden email]>
> wrote:
> > > >
> > > >> Hi Shengkai, Hi Jark,
> > > >>
> > > >> thanks for this great proposal. It is time to finally connect the
> > > >> changelog processor with a compacted Kafka topic.
> > > >>
> > > >> "The operator will produce INSERT rows, or additionally generate
> > > >> UPDATE_BEFORE rows for the previous image, or produce DELETE rows
> with
> > > >> all columns filled with values."
> > > >>
> > > >> Could you elaborate a bit on the implementation details in the FLIP?
> > How
> > > >> are UPDATE_BEFOREs are generated. How much state is required to
> > perform
> > > >> this operation.
> > > >>
> > > >>   From a conceptual and semantical point of view, I'm fine with the
> > > >> proposal. But I would like to share my opinion about how we expose
> > this
> > > >> feature:
> > > >>
> > > >> ktable vs kafka-compacted
> > > >>
> > > >> I'm against having an additional connector like `ktable` or
> > > >> `kafka-compacted`. We recently simplified the table options to a
> > minimum
> > > >> amount of characters to be as concise as possible in the DDL.
> > Therefore,
> > > >> I would keep the `connector=kafka` and introduce an additional
> option.
> > > >> Because a user wants to read "from Kafka". And the "how" should be
> > > >> determined in the lower options.
> > > >>
> > > >> When people read `connector=ktable` they might not know that this is
> > > >> Kafka. Or they wonder where `kstream` is?
> > > >>
> > > >> When people read `connector=kafka-compacted` they might not know
> that
> > it
> > > >> has ktable semantics. You don't need to enable log compaction in
> order
> > > >> to use a KTable as far as I know. Log compaction and table semantics
> > are
> > > >> orthogonal topics.
> > > >>
> > > >> In the end we will need 3 types of information when declaring a
> Kafka
> > > >> connector:
> > > >>
> > > >> CREATE TABLE ... WITH (
> > > >>     connector=kafka        -- Some information about the connector
> > > >>     end-offset = XXXX      -- Some information about the boundedness
> > > >>     model = table/stream   -- Some information about interpretation
> > > >> )
> > > >>
> > > >>
> > > >> We can still apply all the constraints mentioned in the FLIP. When
> > > >> `model` is set to `table`.
> > > >>
> > > >> What do you think?
> > > >>
> > > >> Regards,
> > > >> Timo
> > > >>
> > > >>
> > > >> On 21.10.20 14:19, Jark Wu wrote:
> > > >>> Hi,
> > > >>>
> > > >>> IMO, if we are going to mix them in one connector,
> > > >>> 1) either users need to set some options to a specific value
> > > explicitly,
> > > >>> e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
> > > >>> This makes the connector awkward to use. Users may face to fix
> > options
> > > >> one
> > > >>> by one according to the exception.
> > > >>> Besides, in the future, it is still possible to use
> > > >>> "sink.partitioner=fixed" (reduce network cost) if users are aware
> of
> > > >>> the partition routing,
> > > >>> however, it's error-prone to have "fixed" as default for compacted
> > > mode.
> > > >>>
> > > >>> 2) or make those options a different default value when
> > > "compacted=true".
> > > >>> This would be more confusing and unpredictable if the default value
> > of
> > > >>> options will change according to other options.
> > > >>> What happens if we have a third mode in the future?
> > > >>>
> > > >>> In terms of usage and options, it's very different from the
> > > >>> original "kafka" connector.
> > > >>> It would be more handy to use and less fallible if separating them
> > into
> > > >> two
> > > >>> connectors.
> > > >>> In the implementation layer, we can reuse code as much as possible.
> > > >>>
> > > >>> Therefore, I'm still +1 to have a new connector.
> > > >>> The "kafka-compacted" name sounds good to me.
> > > >>>
> > > >>> Best,
> > > >>> Jark
> > > >>>
> > > >>>
> > > >>> On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <[hidden email]>
> > > >> wrote:
> > > >>>
> > > >>>> Hi Kurt, Hi Shengkai,
> > > >>>>
> > > >>>> thanks for answering my questions and the additional
> > clarifications. I
> > > >>>> don't have a strong opinion on whether to extend the "kafka"
> > connector
> > > >> or
> > > >>>> to introduce a new connector. So, from my perspective feel free to
> > go
> > > >> with
> > > >>>> a separate connector. If we do introduce a new connector I
> wouldn't
> > > >> call it
> > > >>>> "ktable" for aforementioned reasons (In addition, we might suggest
> > > that
> > > >>>> there is also a "kstreams" connector for symmetry reasons). I
> don't
> > > >> have a
> > > >>>> good alternative name, though, maybe "kafka-compacted" or
> > > >>>> "compacted-kafka".
> > > >>>>
> > > >>>> Thanks,
> > > >>>>
> > > >>>> Konstantin
> > > >>>>
> > > >>>>
> > > >>>> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]>
> > wrote:
> > > >>>>
> > > >>>>> Hi all,
> > > >>>>>
> > > >>>>> I want to describe the discussion process which drove us to have
> > such
> > > >>>>> conclusion, this might make some of
> > > >>>>> the design choices easier to understand and keep everyone on the
> > same
> > > >>>> page.
> > > >>>>>
> > > >>>>> Back to the motivation, what functionality do we want to provide
> in
> > > the
> > > >>>>> first place? We got a lot of feedback and
> > > >>>>> questions from mailing lists that people want to write
> > > Not-Insert-Only
> > > >>>>> messages into kafka. They might be
> > > >>>>> intentional or by accident, e.g. wrote an non-windowed aggregate
> > > query
> > > >> or
> > > >>>>> non-windowed left outer join. And
> > > >>>>> some users from KSQL world also asked about why Flink didn't
> > leverage
> > > >> the
> > > >>>>> Key concept of every kafka topic
> > > >>>>> and make kafka as a dynamic changing keyed table.
> > > >>>>>
> > > >>>>> To work with kafka better, we were thinking to extend the
> > > functionality
> > > >>>> of
> > > >>>>> the current kafka connector by letting it
> > > >>>>> accept updates and deletions. But due to the limitation of kafka,
> > the
> > > >>>>> update has to be "update by key", aka a table
> > > >>>>> with primary key.
> > > >>>>>
> > > >>>>> This introduces a couple of conflicts with current kafka table's
> > > >> options:
> > > >>>>> 1. key.fields: as said above, we need the kafka table to have the
> > > >> primary
> > > >>>>> key constraint. And users can also configure
> > > >>>>> key.fields freely, this might cause friction. (Sure we can do
> some
> > > >> sanity
> > > >>>>> check on this but it also creates friction.)
> > > >>>>> 2. sink.partitioner: to make the semantics right, we need to make
> > > sure
> > > >>>> all
> > > >>>>> the updates on the same key are written to
> > > >>>>> the same kafka partition, such we should force to use a hash by
> key
> > > >>>>> partition inside such table. Again, this has conflicts
> > > >>>>> and creates friction with current user options.
> > > >>>>>
> > > >>>>> The above things are solvable, though not perfect or most user
> > > >> friendly.
> > > >>>>>
> > > >>>>> Let's take a look at the reading side. The keyed kafka table
> > contains
> > > >> two
> > > >>>>> kinds of messages: upsert or deletion. What upsert
> > > >>>>> means is "If the key doesn't exist yet, it's an insert record.
> > > >> Otherwise
> > > >>>>> it's an update record". For the sake of correctness or
> > > >>>>> simplicity, the Flink SQL engine also needs such information. If
> we
> > > >>>>> interpret all messages to "update record", some queries or
> > > >>>>> operators may not work properly. It's weird to see an update
> record
> > > but
> > > >>>> you
> > > >>>>> haven't seen the insert record before.
> > > >>>>>
> > > >>>>> So what Flink should do is after reading out the records from
> such
> > > >> table,
> > > >>>>> it needs to create a state to record which messages have
> > > >>>>> been seen and then generate the correct row type correspondingly.
> > > This
> > > >>>> kind
> > > >>>>> of couples the state and the data of the message
> > > >>>>> queue, and it also creates conflicts with current kafka
> connector.
> > > >>>>>
> > > >>>>> Think about if users suspend a running job (which contains some
> > > reading
> > > >>>>> state now), and then change the start offset of the reader.
> > > >>>>> By changing the reading offset, it actually change the whole
> story
> > of
> > > >>>>> "which records should be insert messages and which records
> > > >>>>> should be update messages). And it will also make Flink to deal
> > with
> > > >>>>> another weird situation that it might receive a deletion
> > > >>>>> on a non existing message.
> > > >>>>>
> > > >>>>> We were unsatisfied with all the frictions and conflicts it will
> > > create
> > > >>>> if
> > > >>>>> we enable the "upsert & deletion" support to the current kafka
> > > >>>>> connector. And later we begin to realize that we shouldn't treat
> it
> > > as
> > > >> a
> > > >>>>> normal message queue, but should treat it as a changing keyed
> > > >>>>> table. We should be able to always get the whole data of such
> table
> > > (by
> > > >>>>> disabling the start offset option) and we can also read the
> > > >>>>> changelog out of such table. It's like a HBase table with binlog
> > > >> support
> > > >>>>> but doesn't have random access capability (which can be fulfilled
> > > >>>>> by Flink's state).
> > > >>>>>
> > > >>>>> So our intention was instead of telling and persuading users what
> > > kind
> > > >> of
> > > >>>>> options they should or should not use by extending
> > > >>>>> current kafka connector when enable upsert support, we are
> actually
> > > >>>> create
> > > >>>>> a whole new and different connector that has total
> > > >>>>> different abstractions in SQL layer, and should be treated
> totally
> > > >>>>> different with current kafka connector.
> > > >>>>>
> > > >>>>> Hope this can clarify some of the concerns.
> > > >>>>>
> > > >>>>> Best,
> > > >>>>> Kurt
> > > >>>>>
> > > >>>>>
> > > >>>>> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <[hidden email]
> >
> > > >> wrote:
> > > >>>>>
> > > >>>>>> Hi devs,
> > > >>>>>>
> > > >>>>>> As many people are still confused about the difference option
> > > >>>> behaviours
> > > >>>>>> between the Kafka connector and KTable connector, Jark and I
> list
> > > the
> > > >>>>>> differences in the doc[1].
> > > >>>>>>
> > > >>>>>> Best,
> > > >>>>>> Shengkai
> > > >>>>>>
> > > >>>>>> [1]
> > > >>>>>>
> > > >>>>>>
> > > >>>>>
> > > >>>>
> > > >>
> > >
> >
> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
> > > >>>>>>
> > > >>>>>> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
> > > >>>>>>
> > > >>>>>>> Hi Konstantin,
> > > >>>>>>>
> > > >>>>>>> Thanks for your reply.
> > > >>>>>>>
> > > >>>>>>>> It uses the "kafka" connector and does not specify a primary
> > key.
> > > >>>>>>> The dimensional table `users` is a ktable connector and we can
> > > >>>> specify
> > > >>>>>> the
> > > >>>>>>> pk on the KTable.
> > > >>>>>>>
> > > >>>>>>>> Will it possible to use a "ktable" as a dimensional table in
> > > >>>> FLIP-132
> > > >>>>>>> Yes. We can specify the watermark on the KTable and it can be
> > used
> > > >>>> as a
> > > >>>>>>> dimension table in temporal join.
> > > >>>>>>>
> > > >>>>>>>> Introduce a new connector vs introduce a new property
> > > >>>>>>> The main reason behind is that the KTable connector almost has
> no
> > > >>>>> common
> > > >>>>>>> options with the Kafka connector. The options that can be
> reused
> > by
> > > >>>>>> KTable
> > > >>>>>>> connectors are 'topic', 'properties.bootstrap.servers' and
> > > >>>>>>> 'value.fields-include' . We can't set cdc format for
> 'key.format'
> > > and
> > > >>>>>>> 'value.format' in KTable connector now, which is  available in
> > > Kafka
> > > >>>>>>> connector. Considering the difference between the options we
> can
> > > use,
> > > >>>>>> it's
> > > >>>>>>> more suitable to introduce an another connector rather than a
> > > >>>> property.
> > > >>>>>>>
> > > >>>>>>> We are also fine to use "compacted-kafka" as the name of the
> new
> > > >>>>>>> connector. What do you think?
> > > >>>>>>>
> > > >>>>>>> Best,
> > > >>>>>>> Shengkai
> > > >>>>>>>
> > > >>>>>>> Konstantin Knauf <[hidden email]> 于2020年10月19日周一 下午10:15写道：
> > > >>>>>>>
> > > >>>>>>>> Hi Shengkai,
> > > >>>>>>>>
> > > >>>>>>>> Thank you for driving this effort. I believe this a very
> > important
> > > >>>>>> feature
> > > >>>>>>>> for many users who use Kafka and Flink SQL together. A few
> > > questions
> > > >>>>> and
> > > >>>>>>>> thoughts:
> > > >>>>>>>>
> > > >>>>>>>> * Is your example "Use KTable as a reference/dimension table"
> > > >>>> correct?
> > > >>>>>> It
> > > >>>>>>>> uses the "kafka" connector and does not specify a primary key.
> > > >>>>>>>>
> > > >>>>>>>> * Will it be possible to use a "ktable" table directly as a
> > > >>>>> dimensional
> > > >>>>>>>> table in temporal join (*based on event time*) (FLIP-132)?
> This
> > is
> > > >>>> not
> > > >>>>>>>> completely clear to me from the FLIP.
> > > >>>>>>>>
> > > >>>>>>>> * I'd personally prefer not to introduce a new connector and
> > > instead
> > > >>>>> to
> > > >>>>>>>> extend the Kafka connector. We could add an additional
> property
> > > >>>>>>>> "compacted"
> > > >>>>>>>> = "true"|"false". If it is set to "true", we can add
> additional
> > > >>>>>> validation
> > > >>>>>>>> logic (e.g. "scan.startup.mode" can not be set, primary key
> > > >>>> required,
> > > >>>>>>>> etc.). If we stick to a separate connector I'd not call it
> > > "ktable",
> > > >>>>> but
> > > >>>>>>>> rather "compacted-kafka" or similar. KTable seems to carry
> more
> > > >>>>> implicit
> > > >>>>>>>> meaning than we want to imply here.
> > > >>>>>>>>
> > > >>>>>>>> * I agree that this is not a bounded source. If we want to
> > > support a
> > > >>>>>>>> bounded mode, this is an orthogonal concern that also applies
> to
> > > >>>> other
> > > >>>>>>>> unbounded sources.
> > > >>>>>>>>
> > > >>>>>>>> Best,
> > > >>>>>>>>
> > > >>>>>>>> Konstantin
> > > >>>>>>>>
> > > >>>>>>>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]>
> > wrote:
> > > >>>>>>>>
> > > >>>>>>>>> Hi Danny,
> > > >>>>>>>>>
> > > >>>>>>>>> First of all, we didn't introduce any concepts from KSQL
> (e.g.
> > > >>>>> Stream
> > > >>>>>> vs
> > > >>>>>>>>> Table notion).
> > > >>>>>>>>> This new connector will produce a changelog stream, so it's
> > still
> > > >>>> a
> > > >>>>>>>> dynamic
> > > >>>>>>>>> table and doesn't conflict with Flink core concepts.
> > > >>>>>>>>>
> > > >>>>>>>>> The "ktable" is just a connector name, we can also call it
> > > >>>>>>>>> "compacted-kafka" or something else.
> > > >>>>>>>>> Calling it "ktable" is just because KSQL users can migrate to
> > > >>>> Flink
> > > >>>>>> SQL
> > > >>>>>>>>> easily.
> > > >>>>>>>>>
> > > >>>>>>>>> Regarding to why introducing a new connector vs a new
> property
> > in
> > > >>>>>>>> existing
> > > >>>>>>>>> kafka connector:
> > > >>>>>>>>>
> > > >>>>>>>>> I think the main reason is that we want to have a clear
> > > separation
> > > >>>>> for
> > > >>>>>>>> such
> > > >>>>>>>>> two use cases, because they are very different.
> > > >>>>>>>>> We also listed reasons in the FLIP, including:
> > > >>>>>>>>>
> > > >>>>>>>>> 1) It's hard to explain what's the behavior when users
> specify
> > > the
> > > >>>>>> start
> > > >>>>>>>>> offset from a middle position (e.g. how to process non exist
> > > >>>> delete
> > > >>>>>>>>> events).
> > > >>>>>>>>>       It's dangerous if users do that. So we don't provide
> the
> > > >>>> offset
> > > >>>>>>>> option
> > > >>>>>>>>> in the new connector at the moment.
> > > >>>>>>>>> 2) It's a different perspective/abstraction on the same kafka
> > > >>>> topic
> > > >>>>>>>> (append
> > > >>>>>>>>> vs. upsert). It would be easier to understand if we can
> > separate
> > > >>>>> them
> > > >>>>>>>>>       instead of mixing them in one connector. The new
> > connector
> > > >>>>>> requires
> > > >>>>>>>>> hash sink partitioner, primary key declared, regular format.
> > > >>>>>>>>>       If we mix them in one connector, it might be confusing
> > how
> > > to
> > > >>>>> use
> > > >>>>>>>> the
> > > >>>>>>>>> options correctly.
> > > >>>>>>>>> 3) The semantic of the KTable connector is just the same as
> > > KTable
> > > >>>>> in
> > > >>>>>>>> Kafka
> > > >>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
> > > >>>>>>>>>       We have seen several questions in the mailing list
> asking
> > > how
> > > >>>> to
> > > >>>>>>>> model
> > > >>>>>>>>> a KTable and how to join a KTable in Flink SQL.
> > > >>>>>>>>>
> > > >>>>>>>>> Best,
> > > >>>>>>>>> Jark
> > > >>>>>>>>>
> > > >>>>>>>>> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]>
> > wrote:
> > > >>>>>>>>>
> > > >>>>>>>>>> Hi Jingsong,
> > > >>>>>>>>>>
> > > >>>>>>>>>> As the FLIP describes, "KTable connector produces a
> changelog
> > > >>>>>> stream,
> > > >>>>>>>>>> where each data record represents an update or delete
> event.".
> > > >>>>>>>>>> Therefore, a ktable source is an unbounded stream source.
> > > >>>>> Selecting
> > > >>>>>> a
> > > >>>>>>>>>> ktable source is similar to selecting a kafka source with
> > > >>>>>>>> debezium-json
> > > >>>>>>>>>> format
> > > >>>>>>>>>> that it never ends and the results are continuously updated.
> > > >>>>>>>>>>
> > > >>>>>>>>>> It's possible to have a bounded ktable source in the future,
> > for
> > > >>>>>>>> example,
> > > >>>>>>>>>> add an option 'bounded=true' or 'end-offset=xxx'.
> > > >>>>>>>>>> In this way, the ktable will produce a bounded changelog
> > stream.
> > > >>>>>>>>>> So I think this can be a compatible feature in the future.
> > > >>>>>>>>>>
> > > >>>>>>>>>> I don't think we should associate with ksql related
> concepts.
> > > >>>>>>>> Actually,
> > > >>>>>>>>> we
> > > >>>>>>>>>> didn't introduce any concepts from KSQL (e.g. Stream vs
> Table
> > > >>>>>> notion).
> > > >>>>>>>>>> The "ktable" is just a connector name, we can also call it
> > > >>>>>>>>>> "compacted-kafka" or something else.
> > > >>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate
> to
> > > >>>>> Flink
> > > >>>>>>>> SQL
> > > >>>>>>>>>> easily.
> > > >>>>>>>>>>
> > > >>>>>>>>>> Regarding the "value.fields-include", this is an option
> > > >>>> introduced
> > > >>>>>> in
> > > >>>>>>>>>> FLIP-107 for Kafka connector.
> > > >>>>>>>>>> I think we should keep the same behavior with the Kafka
> > > >>>> connector.
> > > >>>>>> I'm
> > > >>>>>>>>> not
> > > >>>>>>>>>> sure what's the default behavior of KSQL.
> > > >>>>>>>>>> But I guess it also stores the keys in value from this
> example
> > > >>>>> docs
> > > >>>>>>>> (see
> > > >>>>>>>>>> the "users_original" table) [1].
> > > >>>>>>>>>>
> > > >>>>>>>>>> Best,
> > > >>>>>>>>>> Jark
> > > >>>>>>>>>>
> > > >>>>>>>>>> [1]:
> > > >>>>>>>>>>
> > > >>>>>>>>>
> > > >>>>>>>>
> > > >>>>>>
> > > >>>>>
> > > >>>>
> > > >>
> > >
> >
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> > > >>>>>>>>>>
> > > >>>>>>>>>>
> > > >>>>>>>>>> On Mon, 19 Oct 2020 at 18:17, Danny Chan <
> > [hidden email]>
> > > >>>>>>>> wrote:
> > > >>>>>>>>>>
> > > >>>>>>>>>>> The concept seems conflicts with the Flink abstraction
> > “dynamic
> > > >>>>>>>> table”,
> > > >>>>>>>>>>> in Flink we see both “stream” and “table” as a dynamic
> table,
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> I think we should make clear first how to express stream
> and
> > > >>>>> table
> > > >>>>>>>>>>> specific features on one “dynamic table”,
> > > >>>>>>>>>>> it is more natural for KSQL because KSQL takes stream and
> > table
> > > >>>>> as
> > > >>>>>>>>>>> different abstractions for representing collections. In
> KSQL,
> > > >>>>> only
> > > >>>>>>>>> table is
> > > >>>>>>>>>>> mutable and can have a primary key.
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> Does this connector belongs to the “table” scope or
> “stream”
> > > >>>>> scope
> > > >>>>>> ?
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> Some of the concepts (such as the primary key on stream)
> > should
> > > >>>>> be
> > > >>>>>>>>>>> suitable for all the connectors, not just Kafka, Shouldn’t
> > this
> > > >>>>> be
> > > >>>>>> an
> > > >>>>>>>>>>> extension of existing Kafka connector instead of a totally
> > new
> > > >>>>>>>>> connector ?
> > > >>>>>>>>>>> What about the other connectors ?
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> Because this touches the core abstraction of Flink, we
> better
> > > >>>>> have
> > > >>>>>> a
> > > >>>>>>>>>>> top-down overall design, following the KSQL directly is not
> > the
> > > >>>>>>>> answer.
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> P.S. For the source
> > > >>>>>>>>>>>> Shouldn’t this be an extension of existing Kafka connector
> > > >>>>>> instead
> > > >>>>>>>> of
> > > >>>>>>>>> a
> > > >>>>>>>>>>> totally new connector ?
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> How could we achieve that (e.g. set up the parallelism
> > > >>>>> correctly) ?
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> Best,
> > > >>>>>>>>>>> Danny Chan
> > > >>>>>>>>>>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <
> > [hidden email]
> > > >>>>>>> ，写道：
> > > >>>>>>>>>>>> Thanks Shengkai for your proposal.
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> +1 for this feature.
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>>> Future Work: Support bounded KTable source
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> I don't think it should be a future work, I think it is
> one
> > > >>>> of
> > > >>>>>> the
> > > >>>>>>>>>>>> important concepts of this FLIP. We need to understand it
> > > >>>> now.
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> Intuitively, a ktable in my opinion is a bounded table
> > rather
> > > >>>>>> than
> > > >>>>>>>> a
> > > >>>>>>>>>>>> stream, so select should produce a bounded table by
> default.
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> I think we can list Kafka related knowledge, because the
> > word
> > > >>>>>>>> `ktable`
> > > >>>>>>>>>>> is
> > > >>>>>>>>>>>> easy to associate with ksql related concepts. (If
> possible,
> > > >>>>> it's
> > > >>>>>>>>> better
> > > >>>>>>>>>>> to
> > > >>>>>>>>>>>> unify with it)
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> What do you think?
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>>> value.fields-include
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> What about the default behavior of KSQL?
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> Best,
> > > >>>>>>>>>>>> Jingsong
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
> > > >>>>> [hidden email]
> > > >>>>>>>
> > > >>>>>>>>>>> wrote:
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>>> Hi, devs.
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> Jark and I want to start a new FLIP to introduce the
> KTable
> > > >>>>>>>>>>> connector. The
> > > >>>>>>>>>>>>> KTable is a shortcut of "Kafka Table", it also has the
> same
> > > >>>>>>>>> semantics
> > > >>>>>>>>>>> with
> > > >>>>>>>>>>>>> the KTable notion in Kafka Stream.
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> FLIP-149:
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>
> > > >>>>>>>>>
> > > >>>>>>>>
> > > >>>>>>
> > > >>>>>
> > > >>>>
> > > >>
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> Currently many users have expressed their needs for the
> > > >>>>> upsert
> > > >>>>>>>> Kafka
> > > >>>>>>>>>>> by
> > > >>>>>>>>>>>>> mail lists and issues. The KTable connector has several
> > > >>>>>> benefits
> > > >>>>>>>> for
> > > >>>>>>>>>>> users:
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> 1. Users are able to interpret a compacted Kafka Topic as
> > > >>>> an
> > > >>>>>>>> upsert
> > > >>>>>>>>>>> stream
> > > >>>>>>>>>>>>> in Apache Flink. And also be able to write a changelog
> > > >>>> stream
> > > >>>>>> to
> > > >>>>>>>>> Kafka
> > > >>>>>>>>>>>>> (into a compacted topic).
> > > >>>>>>>>>>>>> 2. As a part of the real time pipeline, store join or
> > > >>>>> aggregate
> > > >>>>>>>>>>> result (may
> > > >>>>>>>>>>>>> contain updates) into a Kafka topic for further
> > > >>>> calculation;
> > > >>>>>>>>>>>>> 3. The semantic of the KTable connector is just the same
> as
> > > >>>>>>>> KTable
> > > >>>>>>>>> in
> > > >>>>>>>>>>> Kafka
> > > >>>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL
> users.
> > > >>>>> We
> > > >>>>>>>> have
> > > >>>>>>>>>>> seen
> > > >>>>>>>>>>>>> several questions in the mailing list asking how to
> model a
> > > >>>>>>>> KTable
> > > >>>>>>>>>>> and how
> > > >>>>>>>>>>>>> to join a KTable in Flink SQL.
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> We hope it can expand the usage of the Flink with Kafka.
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> I'm looking forward to your feedback.
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> Best,
> > > >>>>>>>>>>>>> Shengkai
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> --
> > > >>>>>>>>>>>> Best, Jingsong Lee
> > > >>>>>>>>>>>
> > > >>>>>>>>>>
> > > >>>>>>>>>
> > > >>>>>>>>
> > > >>>>>>>>
> > > >>>>>>>> --
> > > >>>>>>>>
> > > >>>>>>>> Konstantin Knauf
> > > >>>>>>>>
> > > >>>>>>>> https://twitter.com/snntrable
> > > >>>>>>>>
> > > >>>>>>>> https://github.com/knaufk
> > > >>>>>>>>
> > > >>>>>>>
> > > >>>>>>
> > > >>>>>
> > > >>>>
> > > >>>>
> > > >>>> --
> > > >>>>
> > > >>>> Konstantin Knauf
> > > >>>>
> > > >>>> https://twitter.com/snntrable
> > > >>>>
> > > >>>> https://github.com/knaufk
> > > >>>>
> > > >>>
> > > >>
> > > >>
> > > >
> > >
> > >
> >
> > --
> >
> > Seth Wiesman | Solutions Architect
> >
> > [phone]
> >
> > <https://www.ververica.com/>
> >
> > Follow us @VervericaData
> >
> > --
> >
> > Join Flink Forward <https://flink-forward.org/> - The Apache Flink
> > Conference
> >
> > Stream Processing | Event Driven | Real Time
> >
>

[Jark Wu-2]

Another name is "connector=upsert-kafka', I think this can solve Timo's
concern on the "compacted" word.

Materialize also uses "ENVELOPE UPSERT" [1] keyword to identify such kafka
sources.
I think "upsert" is a well-known terminology widely used in many systems
and matches the
 behavior of how we handle the kafka messages.

What do you think?

Best,
Jark

[1]:
https://materialize.io/docs/sql/create-source/text-kafka/#upsert-on-a-kafka-topic




On Thu, 22 Oct 2020 at 22:53, Kurt Young <[hidden email]> wrote:

> Good validation messages can't solve the broken user experience, especially
> that
> such update mode option will implicitly make half of current kafka options
> invalid or doesn't
> make sense.
>
> Best,
> Kurt
>
>
> On Thu, Oct 22, 2020 at 10:31 PM Jark Wu <[hidden email]> wrote:
>
> > Hi Timo, Seth,
> >
> > The default value "inserting" of "mode" might be not suitable,
> > because "debezium-json" emits changelog messages which include updates.
> >
> > On Thu, 22 Oct 2020 at 22:10, Seth Wiesman <[hidden email]> wrote:
> >
> > > +1 for supporting upsert results into Kafka.
> > >
> > > I have no comments on the implementation details.
> > >
> > > As far as configuration goes, I tend to favor Timo's option where we
> add
> > a
> > > "mode" property to the existing Kafka table with default value
> > "inserting".
> > > If the mode is set to "updating" then the validation changes to the new
> > > requirements. I personally find it more intuitive than a seperate
> > > connector, my fear is users won't understand its the same physical
> kafka
> > > sink under the hood and it will lead to other confusion like does it
> > offer
> > > the same persistence guarantees? I think we are capable of adding good
> > > valdiation messaging that solves Jark and Kurts concerns.
> > >
> > >
> > > On Thu, Oct 22, 2020 at 8:51 AM Timo Walther <[hidden email]>
> wrote:
> > >
> > > > Hi Jark,
> > > >
> > > > "calling it "kafka-compacted" can even remind users to enable log
> > > > compaction"
> > > >
> > > > But sometimes users like to store a lineage of changes in their
> topics.
> > > > Indepent of any ktable/kstream interpretation.
> > > >
> > > > I let the majority decide on this topic to not further block this
> > > > effort. But we might find a better name like:
> > > >
> > > > connector = kafka
> > > > mode = updating/inserting
> > > >
> > > > OR
> > > >
> > > > connector = kafka-updating
> > > >
> > > > ...
> > > >
> > > > Regards,
> > > > Timo
> > > >
> > > >
> > > >
> > > >
> > > > On 22.10.20 15:24, Jark Wu wrote:
> > > > > Hi Timo,
> > > > >
> > > > > Thanks for your opinions.
> > > > >
> > > > > 1) Implementation
> > > > > We will have an stateful operator to generate INSERT and
> > UPDATE_BEFORE.
> > > > > This operator is keyby-ed (primary key as the shuffle key) after
> the
> > > > source
> > > > > operator.
> > > > > The implementation of this operator is very similar to the existing
> > > > > `DeduplicateKeepLastRowFunction`.
> > > > > The operator will register a value state using the primary key
> fields
> > > as
> > > > > keys.
> > > > > When the value state is empty under current key, we will emit
> INSERT
> > > for
> > > > > the input row.
> > > > > When the value state is not empty under current key, we will emit
> > > > > UPDATE_BEFORE using the row in state,
> > > > > and emit UPDATE_AFTER using the input row.
> > > > > When the input row is DELETE, we will clear state and emit DELETE
> > row.
> > > > >
> > > > > 2) new option vs new connector
> > > > >> We recently simplified the table options to a minimum amount of
> > > > > characters to be as concise as possible in the DDL.
> > > > > I think this is the reason why we want to introduce a new
> connector,
> > > > > because we can simplify the options in DDL.
> > > > > For example, if using a new option, the DDL may look like this:
> > > > >
> > > > > CREATE TABLE users (
> > > > >    user_id BIGINT,
> > > > >    user_name STRING,
> > > > >    user_level STRING,
> > > > >    region STRING,
> > > > >    PRIMARY KEY (user_id) NOT ENFORCED
> > > > > ) WITH (
> > > > >    'connector' = 'kafka',
> > > > >    'model' = 'table',
> > > > >    'topic' = 'pageviews_per_region',
> > > > >    'properties.bootstrap.servers' = '...',
> > > > >    'properties.group.id' = 'testGroup',
> > > > >    'scan.startup.mode' = 'earliest',
> > > > >    'key.format' = 'csv',
> > > > >    'key.fields' = 'user_id',
> > > > >    'value.format' = 'avro',
> > > > >    'sink.partitioner' = 'hash'
> > > > > );
> > > > >
> > > > > If using a new connector, we can have a different default value for
> > the
> > > > > options and remove unnecessary options,
> > > > > the DDL can look like this which is much more concise:
> > > > >
> > > > > CREATE TABLE pageviews_per_region (
> > > > >    user_id BIGINT,
> > > > >    user_name STRING,
> > > > >    user_level STRING,
> > > > >    region STRING,
> > > > >    PRIMARY KEY (user_id) NOT ENFORCED
> > > > > ) WITH (
> > > > >    'connector' = 'kafka-compacted',
> > > > >    'topic' = 'pageviews_per_region',
> > > > >    'properties.bootstrap.servers' = '...',
> > > > >    'key.format' = 'csv',
> > > > >    'value.format' = 'avro'
> > > > > );
> > > > >
> > > > >> When people read `connector=kafka-compacted` they might not know
> > that
> > > it
> > > > >> has ktable semantics. You don't need to enable log compaction in
> > order
> > > > >> to use a KTable as far as I know.
> > > > > We don't need to let users know it has ktable semantics, as
> > Konstantin
> > > > > mentioned this may carry more implicit
> > > > > meaning than we want to imply here. I agree users don't need to
> > enable
> > > > log
> > > > > compaction, but from the production perspective,
> > > > > log compaction should always be enabled if it is used in this
> > purpose.
> > > > > Calling it "kafka-compacted" can even remind users to enable log
> > > > compaction.
> > > > >
> > > > > I don't agree to introduce "model = table/stream" option, or
> > > > > "connector=kafka-table",
> > > > > because this means we are introducing Table vs Stream concept from
> > > KSQL.
> > > > > However, we don't have such top-level concept in Flink SQL now,
> this
> > > will
> > > > > further confuse users.
> > > > > In Flink SQL, all the things are STREAM, the differences are
> whether
> > it
> > > > is
> > > > > bounded or unbounded,
> > > > >   whether it is insert-only or changelog.
> > > > >
> > > > >
> > > > > Best,
> > > > > Jark
> > > > >
> > > > >
> > > > > On Thu, 22 Oct 2020 at 20:39, Timo Walther <[hidden email]>
> > wrote:
> > > > >
> > > > >> Hi Shengkai, Hi Jark,
> > > > >>
> > > > >> thanks for this great proposal. It is time to finally connect the
> > > > >> changelog processor with a compacted Kafka topic.
> > > > >>
> > > > >> "The operator will produce INSERT rows, or additionally generate
> > > > >> UPDATE_BEFORE rows for the previous image, or produce DELETE rows
> > with
> > > > >> all columns filled with values."
> > > > >>
> > > > >> Could you elaborate a bit on the implementation details in the
> FLIP?
> > > How
> > > > >> are UPDATE_BEFOREs are generated. How much state is required to
> > > perform
> > > > >> this operation.
> > > > >>
> > > > >>   From a conceptual and semantical point of view, I'm fine with
> the
> > > > >> proposal. But I would like to share my opinion about how we expose
> > > this
> > > > >> feature:
> > > > >>
> > > > >> ktable vs kafka-compacted
> > > > >>
> > > > >> I'm against having an additional connector like `ktable` or
> > > > >> `kafka-compacted`. We recently simplified the table options to a
> > > minimum
> > > > >> amount of characters to be as concise as possible in the DDL.
> > > Therefore,
> > > > >> I would keep the `connector=kafka` and introduce an additional
> > option.
> > > > >> Because a user wants to read "from Kafka". And the "how" should be
> > > > >> determined in the lower options.
> > > > >>
> > > > >> When people read `connector=ktable` they might not know that this
> is
> > > > >> Kafka. Or they wonder where `kstream` is?
> > > > >>
> > > > >> When people read `connector=kafka-compacted` they might not know
> > that
> > > it
> > > > >> has ktable semantics. You don't need to enable log compaction in
> > order
> > > > >> to use a KTable as far as I know. Log compaction and table
> semantics
> > > are
> > > > >> orthogonal topics.
> > > > >>
> > > > >> In the end we will need 3 types of information when declaring a
> > Kafka
> > > > >> connector:
> > > > >>
> > > > >> CREATE TABLE ... WITH (
> > > > >>     connector=kafka        -- Some information about the connector
> > > > >>     end-offset = XXXX      -- Some information about the
> boundedness
> > > > >>     model = table/stream   -- Some information about
> interpretation
> > > > >> )
> > > > >>
> > > > >>
> > > > >> We can still apply all the constraints mentioned in the FLIP. When
> > > > >> `model` is set to `table`.
> > > > >>
> > > > >> What do you think?
> > > > >>
> > > > >> Regards,
> > > > >> Timo
> > > > >>
> > > > >>
> > > > >> On 21.10.20 14:19, Jark Wu wrote:
> > > > >>> Hi,
> > > > >>>
> > > > >>> IMO, if we are going to mix them in one connector,
> > > > >>> 1) either users need to set some options to a specific value
> > > > explicitly,
> > > > >>> e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
> > > > >>> This makes the connector awkward to use. Users may face to fix
> > > options
> > > > >> one
> > > > >>> by one according to the exception.
> > > > >>> Besides, in the future, it is still possible to use
> > > > >>> "sink.partitioner=fixed" (reduce network cost) if users are aware
> > of
> > > > >>> the partition routing,
> > > > >>> however, it's error-prone to have "fixed" as default for
> compacted
> > > > mode.
> > > > >>>
> > > > >>> 2) or make those options a different default value when
> > > > "compacted=true".
> > > > >>> This would be more confusing and unpredictable if the default
> value
> > > of
> > > > >>> options will change according to other options.
> > > > >>> What happens if we have a third mode in the future?
> > > > >>>
> > > > >>> In terms of usage and options, it's very different from the
> > > > >>> original "kafka" connector.
> > > > >>> It would be more handy to use and less fallible if separating
> them
> > > into
> > > > >> two
> > > > >>> connectors.
> > > > >>> In the implementation layer, we can reuse code as much as
> possible.
> > > > >>>
> > > > >>> Therefore, I'm still +1 to have a new connector.
> > > > >>> The "kafka-compacted" name sounds good to me.
> > > > >>>
> > > > >>> Best,
> > > > >>> Jark
> > > > >>>
> > > > >>>
> > > > >>> On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <
> [hidden email]>
> > > > >> wrote:
> > > > >>>
> > > > >>>> Hi Kurt, Hi Shengkai,
> > > > >>>>
> > > > >>>> thanks for answering my questions and the additional
> > > clarifications. I
> > > > >>>> don't have a strong opinion on whether to extend the "kafka"
> > > connector
> > > > >> or
> > > > >>>> to introduce a new connector. So, from my perspective feel free
> to
> > > go
> > > > >> with
> > > > >>>> a separate connector. If we do introduce a new connector I
> > wouldn't
> > > > >> call it
> > > > >>>> "ktable" for aforementioned reasons (In addition, we might
> suggest
> > > > that
> > > > >>>> there is also a "kstreams" connector for symmetry reasons). I
> > don't
> > > > >> have a
> > > > >>>> good alternative name, though, maybe "kafka-compacted" or
> > > > >>>> "compacted-kafka".
> > > > >>>>
> > > > >>>> Thanks,
> > > > >>>>
> > > > >>>> Konstantin
> > > > >>>>
> > > > >>>>
> > > > >>>> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]>
> > > wrote:
> > > > >>>>
> > > > >>>>> Hi all,
> > > > >>>>>
> > > > >>>>> I want to describe the discussion process which drove us to
> have
> > > such
> > > > >>>>> conclusion, this might make some of
> > > > >>>>> the design choices easier to understand and keep everyone on
> the
> > > same
> > > > >>>> page.
> > > > >>>>>
> > > > >>>>> Back to the motivation, what functionality do we want to
> provide
> > in
> > > > the
> > > > >>>>> first place? We got a lot of feedback and
> > > > >>>>> questions from mailing lists that people want to write
> > > > Not-Insert-Only
> > > > >>>>> messages into kafka. They might be
> > > > >>>>> intentional or by accident, e.g. wrote an non-windowed
> aggregate
> > > > query
> > > > >> or
> > > > >>>>> non-windowed left outer join. And
> > > > >>>>> some users from KSQL world also asked about why Flink didn't
> > > leverage
> > > > >> the
> > > > >>>>> Key concept of every kafka topic
> > > > >>>>> and make kafka as a dynamic changing keyed table.
> > > > >>>>>
> > > > >>>>> To work with kafka better, we were thinking to extend the
> > > > functionality
> > > > >>>> of
> > > > >>>>> the current kafka connector by letting it
> > > > >>>>> accept updates and deletions. But due to the limitation of
> kafka,
> > > the
> > > > >>>>> update has to be "update by key", aka a table
> > > > >>>>> with primary key.
> > > > >>>>>
> > > > >>>>> This introduces a couple of conflicts with current kafka
> table's
> > > > >> options:
> > > > >>>>> 1. key.fields: as said above, we need the kafka table to have
> the
> > > > >> primary
> > > > >>>>> key constraint. And users can also configure
> > > > >>>>> key.fields freely, this might cause friction. (Sure we can do
> > some
> > > > >> sanity
> > > > >>>>> check on this but it also creates friction.)
> > > > >>>>> 2. sink.partitioner: to make the semantics right, we need to
> make
> > > > sure
> > > > >>>> all
> > > > >>>>> the updates on the same key are written to
> > > > >>>>> the same kafka partition, such we should force to use a hash by
> > key
> > > > >>>>> partition inside such table. Again, this has conflicts
> > > > >>>>> and creates friction with current user options.
> > > > >>>>>
> > > > >>>>> The above things are solvable, though not perfect or most user
> > > > >> friendly.
> > > > >>>>>
> > > > >>>>> Let's take a look at the reading side. The keyed kafka table
> > > contains
> > > > >> two
> > > > >>>>> kinds of messages: upsert or deletion. What upsert
> > > > >>>>> means is "If the key doesn't exist yet, it's an insert record.
> > > > >> Otherwise
> > > > >>>>> it's an update record". For the sake of correctness or
> > > > >>>>> simplicity, the Flink SQL engine also needs such information.
> If
> > we
> > > > >>>>> interpret all messages to "update record", some queries or
> > > > >>>>> operators may not work properly. It's weird to see an update
> > record
> > > > but
> > > > >>>> you
> > > > >>>>> haven't seen the insert record before.
> > > > >>>>>
> > > > >>>>> So what Flink should do is after reading out the records from
> > such
> > > > >> table,
> > > > >>>>> it needs to create a state to record which messages have
> > > > >>>>> been seen and then generate the correct row type
> correspondingly.
> > > > This
> > > > >>>> kind
> > > > >>>>> of couples the state and the data of the message
> > > > >>>>> queue, and it also creates conflicts with current kafka
> > connector.
> > > > >>>>>
> > > > >>>>> Think about if users suspend a running job (which contains some
> > > > reading
> > > > >>>>> state now), and then change the start offset of the reader.
> > > > >>>>> By changing the reading offset, it actually change the whole
> > story
> > > of
> > > > >>>>> "which records should be insert messages and which records
> > > > >>>>> should be update messages). And it will also make Flink to deal
> > > with
> > > > >>>>> another weird situation that it might receive a deletion
> > > > >>>>> on a non existing message.
> > > > >>>>>
> > > > >>>>> We were unsatisfied with all the frictions and conflicts it
> will
> > > > create
> > > > >>>> if
> > > > >>>>> we enable the "upsert & deletion" support to the current kafka
> > > > >>>>> connector. And later we begin to realize that we shouldn't
> treat
> > it
> > > > as
> > > > >> a
> > > > >>>>> normal message queue, but should treat it as a changing keyed
> > > > >>>>> table. We should be able to always get the whole data of such
> > table
> > > > (by
> > > > >>>>> disabling the start offset option) and we can also read the
> > > > >>>>> changelog out of such table. It's like a HBase table with
> binlog
> > > > >> support
> > > > >>>>> but doesn't have random access capability (which can be
> fulfilled
> > > > >>>>> by Flink's state).
> > > > >>>>>
> > > > >>>>> So our intention was instead of telling and persuading users
> what
> > > > kind
> > > > >> of
> > > > >>>>> options they should or should not use by extending
> > > > >>>>> current kafka connector when enable upsert support, we are
> > actually
> > > > >>>> create
> > > > >>>>> a whole new and different connector that has total
> > > > >>>>> different abstractions in SQL layer, and should be treated
> > totally
> > > > >>>>> different with current kafka connector.
> > > > >>>>>
> > > > >>>>> Hope this can clarify some of the concerns.
> > > > >>>>>
> > > > >>>>> Best,
> > > > >>>>> Kurt
> > > > >>>>>
> > > > >>>>>
> > > > >>>>> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <
> [hidden email]
> > >
> > > > >> wrote:
> > > > >>>>>
> > > > >>>>>> Hi devs,
> > > > >>>>>>
> > > > >>>>>> As many people are still confused about the difference option
> > > > >>>> behaviours
> > > > >>>>>> between the Kafka connector and KTable connector, Jark and I
> > list
> > > > the
> > > > >>>>>> differences in the doc[1].
> > > > >>>>>>
> > > > >>>>>> Best,
> > > > >>>>>> Shengkai
> > > > >>>>>>
> > > > >>>>>> [1]
> > > > >>>>>>
> > > > >>>>>>
> > > > >>>>>
> > > > >>>>
> > > > >>
> > > >
> > >
> >
> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
> > > > >>>>>>
> > > > >>>>>> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
> > > > >>>>>>
> > > > >>>>>>> Hi Konstantin,
> > > > >>>>>>>
> > > > >>>>>>> Thanks for your reply.
> > > > >>>>>>>
> > > > >>>>>>>> It uses the "kafka" connector and does not specify a primary
> > > key.
> > > > >>>>>>> The dimensional table `users` is a ktable connector and we
> can
> > > > >>>> specify
> > > > >>>>>> the
> > > > >>>>>>> pk on the KTable.
> > > > >>>>>>>
> > > > >>>>>>>> Will it possible to use a "ktable" as a dimensional table in
> > > > >>>> FLIP-132
> > > > >>>>>>> Yes. We can specify the watermark on the KTable and it can be
> > > used
> > > > >>>> as a
> > > > >>>>>>> dimension table in temporal join.
> > > > >>>>>>>
> > > > >>>>>>>> Introduce a new connector vs introduce a new property
> > > > >>>>>>> The main reason behind is that the KTable connector almost
> has
> > no
> > > > >>>>> common
> > > > >>>>>>> options with the Kafka connector. The options that can be
> > reused
> > > by
> > > > >>>>>> KTable
> > > > >>>>>>> connectors are 'topic', 'properties.bootstrap.servers' and
> > > > >>>>>>> 'value.fields-include' . We can't set cdc format for
> > 'key.format'
> > > > and
> > > > >>>>>>> 'value.format' in KTable connector now, which is  available
> in
> > > > Kafka
> > > > >>>>>>> connector. Considering the difference between the options we
> > can
> > > > use,
> > > > >>>>>> it's
> > > > >>>>>>> more suitable to introduce an another connector rather than a
> > > > >>>> property.
> > > > >>>>>>>
> > > > >>>>>>> We are also fine to use "compacted-kafka" as the name of the
> > new
> > > > >>>>>>> connector. What do you think?
> > > > >>>>>>>
> > > > >>>>>>> Best,
> > > > >>>>>>> Shengkai
> > > > >>>>>>>
> > > > >>>>>>> Konstantin Knauf <[hidden email]> 于2020年10月19日周一
> 下午10:15写道：
> > > > >>>>>>>
> > > > >>>>>>>> Hi Shengkai,
> > > > >>>>>>>>
> > > > >>>>>>>> Thank you for driving this effort. I believe this a very
> > > important
> > > > >>>>>> feature
> > > > >>>>>>>> for many users who use Kafka and Flink SQL together. A few
> > > > questions
> > > > >>>>> and
> > > > >>>>>>>> thoughts:
> > > > >>>>>>>>
> > > > >>>>>>>> * Is your example "Use KTable as a reference/dimension
> table"
> > > > >>>> correct?
> > > > >>>>>> It
> > > > >>>>>>>> uses the "kafka" connector and does not specify a primary
> key.
> > > > >>>>>>>>
> > > > >>>>>>>> * Will it be possible to use a "ktable" table directly as a
> > > > >>>>> dimensional
> > > > >>>>>>>> table in temporal join (*based on event time*) (FLIP-132)?
> > This
> > > is
> > > > >>>> not
> > > > >>>>>>>> completely clear to me from the FLIP.
> > > > >>>>>>>>
> > > > >>>>>>>> * I'd personally prefer not to introduce a new connector and
> > > > instead
> > > > >>>>> to
> > > > >>>>>>>> extend the Kafka connector. We could add an additional
> > property
> > > > >>>>>>>> "compacted"
> > > > >>>>>>>> = "true"|"false". If it is set to "true", we can add
> > additional
> > > > >>>>>> validation
> > > > >>>>>>>> logic (e.g. "scan.startup.mode" can not be set, primary key
> > > > >>>> required,
> > > > >>>>>>>> etc.). If we stick to a separate connector I'd not call it
> > > > "ktable",
> > > > >>>>> but
> > > > >>>>>>>> rather "compacted-kafka" or similar. KTable seems to carry
> > more
> > > > >>>>> implicit
> > > > >>>>>>>> meaning than we want to imply here.
> > > > >>>>>>>>
> > > > >>>>>>>> * I agree that this is not a bounded source. If we want to
> > > > support a
> > > > >>>>>>>> bounded mode, this is an orthogonal concern that also
> applies
> > to
> > > > >>>> other
> > > > >>>>>>>> unbounded sources.
> > > > >>>>>>>>
> > > > >>>>>>>> Best,
> > > > >>>>>>>>
> > > > >>>>>>>> Konstantin
> > > > >>>>>>>>
> > > > >>>>>>>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]>
> > > wrote:
> > > > >>>>>>>>
> > > > >>>>>>>>> Hi Danny,
> > > > >>>>>>>>>
> > > > >>>>>>>>> First of all, we didn't introduce any concepts from KSQL
> > (e.g.
> > > > >>>>> Stream
> > > > >>>>>> vs
> > > > >>>>>>>>> Table notion).
> > > > >>>>>>>>> This new connector will produce a changelog stream, so it's
> > > still
> > > > >>>> a
> > > > >>>>>>>> dynamic
> > > > >>>>>>>>> table and doesn't conflict with Flink core concepts.
> > > > >>>>>>>>>
> > > > >>>>>>>>> The "ktable" is just a connector name, we can also call it
> > > > >>>>>>>>> "compacted-kafka" or something else.
> > > > >>>>>>>>> Calling it "ktable" is just because KSQL users can migrate
> to
> > > > >>>> Flink
> > > > >>>>>> SQL
> > > > >>>>>>>>> easily.
> > > > >>>>>>>>>
> > > > >>>>>>>>> Regarding to why introducing a new connector vs a new
> > property
> > > in
> > > > >>>>>>>> existing
> > > > >>>>>>>>> kafka connector:
> > > > >>>>>>>>>
> > > > >>>>>>>>> I think the main reason is that we want to have a clear
> > > > separation
> > > > >>>>> for
> > > > >>>>>>>> such
> > > > >>>>>>>>> two use cases, because they are very different.
> > > > >>>>>>>>> We also listed reasons in the FLIP, including:
> > > > >>>>>>>>>
> > > > >>>>>>>>> 1) It's hard to explain what's the behavior when users
> > specify
> > > > the
> > > > >>>>>> start
> > > > >>>>>>>>> offset from a middle position (e.g. how to process non
> exist
> > > > >>>> delete
> > > > >>>>>>>>> events).
> > > > >>>>>>>>>       It's dangerous if users do that. So we don't provide
> > the
> > > > >>>> offset
> > > > >>>>>>>> option
> > > > >>>>>>>>> in the new connector at the moment.
> > > > >>>>>>>>> 2) It's a different perspective/abstraction on the same
> kafka
> > > > >>>> topic
> > > > >>>>>>>> (append
> > > > >>>>>>>>> vs. upsert). It would be easier to understand if we can
> > > separate
> > > > >>>>> them
> > > > >>>>>>>>>       instead of mixing them in one connector. The new
> > > connector
> > > > >>>>>> requires
> > > > >>>>>>>>> hash sink partitioner, primary key declared, regular
> format.
> > > > >>>>>>>>>       If we mix them in one connector, it might be
> confusing
> > > how
> > > > to
> > > > >>>>> use
> > > > >>>>>>>> the
> > > > >>>>>>>>> options correctly.
> > > > >>>>>>>>> 3) The semantic of the KTable connector is just the same as
> > > > KTable
> > > > >>>>> in
> > > > >>>>>>>> Kafka
> > > > >>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
> > > > >>>>>>>>>       We have seen several questions in the mailing list
> > asking
> > > > how
> > > > >>>> to
> > > > >>>>>>>> model
> > > > >>>>>>>>> a KTable and how to join a KTable in Flink SQL.
> > > > >>>>>>>>>
> > > > >>>>>>>>> Best,
> > > > >>>>>>>>> Jark
> > > > >>>>>>>>>
> > > > >>>>>>>>> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]>
> > > wrote:
> > > > >>>>>>>>>
> > > > >>>>>>>>>> Hi Jingsong,
> > > > >>>>>>>>>>
> > > > >>>>>>>>>> As the FLIP describes, "KTable connector produces a
> > changelog
> > > > >>>>>> stream,
> > > > >>>>>>>>>> where each data record represents an update or delete
> > event.".
> > > > >>>>>>>>>> Therefore, a ktable source is an unbounded stream source.
> > > > >>>>> Selecting
> > > > >>>>>> a
> > > > >>>>>>>>>> ktable source is similar to selecting a kafka source with
> > > > >>>>>>>> debezium-json
> > > > >>>>>>>>>> format
> > > > >>>>>>>>>> that it never ends and the results are continuously
> updated.
> > > > >>>>>>>>>>
> > > > >>>>>>>>>> It's possible to have a bounded ktable source in the
> future,
> > > for
> > > > >>>>>>>> example,
> > > > >>>>>>>>>> add an option 'bounded=true' or 'end-offset=xxx'.
> > > > >>>>>>>>>> In this way, the ktable will produce a bounded changelog
> > > stream.
> > > > >>>>>>>>>> So I think this can be a compatible feature in the future.
> > > > >>>>>>>>>>
> > > > >>>>>>>>>> I don't think we should associate with ksql related
> > concepts.
> > > > >>>>>>>> Actually,
> > > > >>>>>>>>> we
> > > > >>>>>>>>>> didn't introduce any concepts from KSQL (e.g. Stream vs
> > Table
> > > > >>>>>> notion).
> > > > >>>>>>>>>> The "ktable" is just a connector name, we can also call it
> > > > >>>>>>>>>> "compacted-kafka" or something else.
> > > > >>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate
> > to
> > > > >>>>> Flink
> > > > >>>>>>>> SQL
> > > > >>>>>>>>>> easily.
> > > > >>>>>>>>>>
> > > > >>>>>>>>>> Regarding the "value.fields-include", this is an option
> > > > >>>> introduced
> > > > >>>>>> in
> > > > >>>>>>>>>> FLIP-107 for Kafka connector.
> > > > >>>>>>>>>> I think we should keep the same behavior with the Kafka
> > > > >>>> connector.
> > > > >>>>>> I'm
> > > > >>>>>>>>> not
> > > > >>>>>>>>>> sure what's the default behavior of KSQL.
> > > > >>>>>>>>>> But I guess it also stores the keys in value from this
> > example
> > > > >>>>> docs
> > > > >>>>>>>> (see
> > > > >>>>>>>>>> the "users_original" table) [1].
> > > > >>>>>>>>>>
> > > > >>>>>>>>>> Best,
> > > > >>>>>>>>>> Jark
> > > > >>>>>>>>>>
> > > > >>>>>>>>>> [1]:
> > > > >>>>>>>>>>
> > > > >>>>>>>>>
> > > > >>>>>>>>
> > > > >>>>>>
> > > > >>>>>
> > > > >>>>
> > > > >>
> > > >
> > >
> >
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> > > > >>>>>>>>>>
> > > > >>>>>>>>>>
> > > > >>>>>>>>>> On Mon, 19 Oct 2020 at 18:17, Danny Chan <
> > > [hidden email]>
> > > > >>>>>>>> wrote:
> > > > >>>>>>>>>>
> > > > >>>>>>>>>>> The concept seems conflicts with the Flink abstraction
> > > “dynamic
> > > > >>>>>>>> table”,
> > > > >>>>>>>>>>> in Flink we see both “stream” and “table” as a dynamic
> > table,
> > > > >>>>>>>>>>>
> > > > >>>>>>>>>>> I think we should make clear first how to express stream
> > and
> > > > >>>>> table
> > > > >>>>>>>>>>> specific features on one “dynamic table”,
> > > > >>>>>>>>>>> it is more natural for KSQL because KSQL takes stream and
> > > table
> > > > >>>>> as
> > > > >>>>>>>>>>> different abstractions for representing collections. In
> > KSQL,
> > > > >>>>> only
> > > > >>>>>>>>> table is
> > > > >>>>>>>>>>> mutable and can have a primary key.
> > > > >>>>>>>>>>>
> > > > >>>>>>>>>>> Does this connector belongs to the “table” scope or
> > “stream”
> > > > >>>>> scope
> > > > >>>>>> ?
> > > > >>>>>>>>>>>
> > > > >>>>>>>>>>> Some of the concepts (such as the primary key on stream)
> > > should
> > > > >>>>> be
> > > > >>>>>>>>>>> suitable for all the connectors, not just Kafka,
> Shouldn’t
> > > this
> > > > >>>>> be
> > > > >>>>>> an
> > > > >>>>>>>>>>> extension of existing Kafka connector instead of a
> totally
> > > new
> > > > >>>>>>>>> connector ?
> > > > >>>>>>>>>>> What about the other connectors ?
> > > > >>>>>>>>>>>
> > > > >>>>>>>>>>> Because this touches the core abstraction of Flink, we
> > better
> > > > >>>>> have
> > > > >>>>>> a
> > > > >>>>>>>>>>> top-down overall design, following the KSQL directly is
> not
> > > the
> > > > >>>>>>>> answer.
> > > > >>>>>>>>>>>
> > > > >>>>>>>>>>> P.S. For the source
> > > > >>>>>>>>>>>> Shouldn’t this be an extension of existing Kafka
> connector
> > > > >>>>>> instead
> > > > >>>>>>>> of
> > > > >>>>>>>>> a
> > > > >>>>>>>>>>> totally new connector ?
> > > > >>>>>>>>>>>
> > > > >>>>>>>>>>> How could we achieve that (e.g. set up the parallelism
> > > > >>>>> correctly) ?
> > > > >>>>>>>>>>>
> > > > >>>>>>>>>>> Best,
> > > > >>>>>>>>>>> Danny Chan
> > > > >>>>>>>>>>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <
> > > [hidden email]
> > > > >>>>>>> ，写道：
> > > > >>>>>>>>>>>> Thanks Shengkai for your proposal.
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>> +1 for this feature.
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>>> Future Work: Support bounded KTable source
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>> I don't think it should be a future work, I think it is
> > one
> > > > >>>> of
> > > > >>>>>> the
> > > > >>>>>>>>>>>> important concepts of this FLIP. We need to understand
> it
> > > > >>>> now.
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>> Intuitively, a ktable in my opinion is a bounded table
> > > rather
> > > > >>>>>> than
> > > > >>>>>>>> a
> > > > >>>>>>>>>>>> stream, so select should produce a bounded table by
> > default.
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>> I think we can list Kafka related knowledge, because the
> > > word
> > > > >>>>>>>> `ktable`
> > > > >>>>>>>>>>> is
> > > > >>>>>>>>>>>> easy to associate with ksql related concepts. (If
> > possible,
> > > > >>>>> it's
> > > > >>>>>>>>> better
> > > > >>>>>>>>>>> to
> > > > >>>>>>>>>>>> unify with it)
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>> What do you think?
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>>> value.fields-include
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>> What about the default behavior of KSQL?
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>> Best,
> > > > >>>>>>>>>>>> Jingsong
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
> > > > >>>>> [hidden email]
> > > > >>>>>>>
> > > > >>>>>>>>>>> wrote:
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>>> Hi, devs.
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>>> Jark and I want to start a new FLIP to introduce the
> > KTable
> > > > >>>>>>>>>>> connector. The
> > > > >>>>>>>>>>>>> KTable is a shortcut of "Kafka Table", it also has the
> > same
> > > > >>>>>>>>> semantics
> > > > >>>>>>>>>>> with
> > > > >>>>>>>>>>>>> the KTable notion in Kafka Stream.
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>>> FLIP-149:
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>
> > > > >>>>>>>>>
> > > > >>>>>>>>
> > > > >>>>>>
> > > > >>>>>
> > > > >>>>
> > > > >>
> > > >
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>>> Currently many users have expressed their needs for the
> > > > >>>>> upsert
> > > > >>>>>>>> Kafka
> > > > >>>>>>>>>>> by
> > > > >>>>>>>>>>>>> mail lists and issues. The KTable connector has several
> > > > >>>>>> benefits
> > > > >>>>>>>> for
> > > > >>>>>>>>>>> users:
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>>> 1. Users are able to interpret a compacted Kafka Topic
> as
> > > > >>>> an
> > > > >>>>>>>> upsert
> > > > >>>>>>>>>>> stream
> > > > >>>>>>>>>>>>> in Apache Flink. And also be able to write a changelog
> > > > >>>> stream
> > > > >>>>>> to
> > > > >>>>>>>>> Kafka
> > > > >>>>>>>>>>>>> (into a compacted topic).
> > > > >>>>>>>>>>>>> 2. As a part of the real time pipeline, store join or
> > > > >>>>> aggregate
> > > > >>>>>>>>>>> result (may
> > > > >>>>>>>>>>>>> contain updates) into a Kafka topic for further
> > > > >>>> calculation;
> > > > >>>>>>>>>>>>> 3. The semantic of the KTable connector is just the
> same
> > as
> > > > >>>>>>>> KTable
> > > > >>>>>>>>> in
> > > > >>>>>>>>>>> Kafka
> > > > >>>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL
> > users.
> > > > >>>>> We
> > > > >>>>>>>> have
> > > > >>>>>>>>>>> seen
> > > > >>>>>>>>>>>>> several questions in the mailing list asking how to
> > model a
> > > > >>>>>>>> KTable
> > > > >>>>>>>>>>> and how
> > > > >>>>>>>>>>>>> to join a KTable in Flink SQL.
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>>> We hope it can expand the usage of the Flink with
> Kafka.
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>>> I'm looking forward to your feedback.
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>>> Best,
> > > > >>>>>>>>>>>>> Shengkai
> > > > >>>>>>>>>>>>>
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>>
> > > > >>>>>>>>>>>> --
> > > > >>>>>>>>>>>> Best, Jingsong Lee
> > > > >>>>>>>>>>>
> > > > >>>>>>>>>>
> > > > >>>>>>>>>
> > > > >>>>>>>>
> > > > >>>>>>>>
> > > > >>>>>>>> --
> > > > >>>>>>>>
> > > > >>>>>>>> Konstantin Knauf
> > > > >>>>>>>>
> > > > >>>>>>>> https://twitter.com/snntrable
> > > > >>>>>>>>
> > > > >>>>>>>> https://github.com/knaufk
> > > > >>>>>>>>
> > > > >>>>>>>
> > > > >>>>>>
> > > > >>>>>
> > > > >>>>
> > > > >>>>
> > > > >>>> --
> > > > >>>>
> > > > >>>> Konstantin Knauf
> > > > >>>>
> > > > >>>> https://twitter.com/snntrable
> > > > >>>>
> > > > >>>> https://github.com/knaufk
> > > > >>>>
> > > > >>>
> > > > >>
> > > > >>
> > > > >
> > > >
> > > >
> > >
> > > --
> > >
> > > Seth Wiesman | Solutions Architect
> > >
> > > [phone]
> > >
> > > <https://www.ververica.com/>
> > >
> > > Follow us @VervericaData
> > >
> > > --
> > >
> > > Join Flink Forward <https://flink-forward.org/> - The Apache Flink
> > > Conference
> > >
> > > Stream Processing | Event Driven | Real Time
> > >
> >
>

[Timo Walther-2]

Hi Jark,

I would be fine with `connector=upsert-kafka`. Another idea would be to
align the name to other available Flink connectors [1]:

`connector=kafka-cdc`.

Regards,
Timo

[1] https://github.com/ververica/flink-cdc-connectors

On 22.10.20 17:17, Jark Wu wrote:

> Another name is "connector=upsert-kafka', I think this can solve Timo's
> concern on the "compacted" word.
>
> Materialize also uses "ENVELOPE UPSERT" [1] keyword to identify such kafka
> sources.
> I think "upsert" is a well-known terminology widely used in many systems
> and matches the
>   behavior of how we handle the kafka messages.
>
> What do you think?
>
> Best,
> Jark
>
> [1]:
> https://materialize.io/docs/sql/create-source/text-kafka/#upsert-on-a-kafka-topic
>
>
>
>
> On Thu, 22 Oct 2020 at 22:53, Kurt Young <[hidden email]> wrote:
>
>> Good validation messages can't solve the broken user experience, especially
>> that
>> such update mode option will implicitly make half of current kafka options
>> invalid or doesn't
>> make sense.
>>
>> Best,
>> Kurt
>>
>>
>> On Thu, Oct 22, 2020 at 10:31 PM Jark Wu <[hidden email]> wrote:
>>
>>> Hi Timo, Seth,
>>>
>>> The default value "inserting" of "mode" might be not suitable,
>>> because "debezium-json" emits changelog messages which include updates.
>>>
>>> On Thu, 22 Oct 2020 at 22:10, Seth Wiesman <[hidden email]> wrote:
>>>
>>>> +1 for supporting upsert results into Kafka.
>>>>
>>>> I have no comments on the implementation details.
>>>>
>>>> As far as configuration goes, I tend to favor Timo's option where we
>> add
>>> a
>>>> "mode" property to the existing Kafka table with default value
>>> "inserting".
>>>> If the mode is set to "updating" then the validation changes to the new
>>>> requirements. I personally find it more intuitive than a seperate
>>>> connector, my fear is users won't understand its the same physical
>> kafka
>>>> sink under the hood and it will lead to other confusion like does it
>>> offer
>>>> the same persistence guarantees? I think we are capable of adding good
>>>> valdiation messaging that solves Jark and Kurts concerns.
>>>>
>>>>
>>>> On Thu, Oct 22, 2020 at 8:51 AM Timo Walther <[hidden email]>
>> wrote:
>>>>
>>>>> Hi Jark,
>>>>>
>>>>> "calling it "kafka-compacted" can even remind users to enable log
>>>>> compaction"
>>>>>
>>>>> But sometimes users like to store a lineage of changes in their
>> topics.
>>>>> Indepent of any ktable/kstream interpretation.
>>>>>
>>>>> I let the majority decide on this topic to not further block this
>>>>> effort. But we might find a better name like:
>>>>>
>>>>> connector = kafka
>>>>> mode = updating/inserting
>>>>>
>>>>> OR
>>>>>
>>>>> connector = kafka-updating
>>>>>
>>>>> ...
>>>>>
>>>>> Regards,
>>>>> Timo
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On 22.10.20 15:24, Jark Wu wrote:
>>>>>> Hi Timo,
>>>>>>
>>>>>> Thanks for your opinions.
>>>>>>
>>>>>> 1) Implementation
>>>>>> We will have an stateful operator to generate INSERT and
>>> UPDATE_BEFORE.
>>>>>> This operator is keyby-ed (primary key as the shuffle key) after
>> the
>>>>> source
>>>>>> operator.
>>>>>> The implementation of this operator is very similar to the existing
>>>>>> `DeduplicateKeepLastRowFunction`.
>>>>>> The operator will register a value state using the primary key
>> fields
>>>> as
>>>>>> keys.
>>>>>> When the value state is empty under current key, we will emit
>> INSERT
>>>> for
>>>>>> the input row.
>>>>>> When the value state is not empty under current key, we will emit
>>>>>> UPDATE_BEFORE using the row in state,
>>>>>> and emit UPDATE_AFTER using the input row.
>>>>>> When the input row is DELETE, we will clear state and emit DELETE
>>> row.
>>>>>>
>>>>>> 2) new option vs new connector
>>>>>>> We recently simplified the table options to a minimum amount of
>>>>>> characters to be as concise as possible in the DDL.
>>>>>> I think this is the reason why we want to introduce a new
>> connector,
>>>>>> because we can simplify the options in DDL.
>>>>>> For example, if using a new option, the DDL may look like this:
>>>>>>
>>>>>> CREATE TABLE users (
>>>>>>     user_id BIGINT,
>>>>>>     user_name STRING,
>>>>>>     user_level STRING,
>>>>>>     region STRING,
>>>>>>     PRIMARY KEY (user_id) NOT ENFORCED
>>>>>> ) WITH (
>>>>>>     'connector' = 'kafka',
>>>>>>     'model' = 'table',
>>>>>>     'topic' = 'pageviews_per_region',
>>>>>>     'properties.bootstrap.servers' = '...',
>>>>>>     'properties.group.id' = 'testGroup',
>>>>>>     'scan.startup.mode' = 'earliest',
>>>>>>     'key.format' = 'csv',
>>>>>>     'key.fields' = 'user_id',
>>>>>>     'value.format' = 'avro',
>>>>>>     'sink.partitioner' = 'hash'
>>>>>> );
>>>>>>
>>>>>> If using a new connector, we can have a different default value for
>>> the
>>>>>> options and remove unnecessary options,
>>>>>> the DDL can look like this which is much more concise:
>>>>>>
>>>>>> CREATE TABLE pageviews_per_region (
>>>>>>     user_id BIGINT,
>>>>>>     user_name STRING,
>>>>>>     user_level STRING,
>>>>>>     region STRING,
>>>>>>     PRIMARY KEY (user_id) NOT ENFORCED
>>>>>> ) WITH (
>>>>>>     'connector' = 'kafka-compacted',
>>>>>>     'topic' = 'pageviews_per_region',
>>>>>>     'properties.bootstrap.servers' = '...',
>>>>>>     'key.format' = 'csv',
>>>>>>     'value.format' = 'avro'
>>>>>> );
>>>>>>
>>>>>>> When people read `connector=kafka-compacted` they might not know
>>> that
>>>> it
>>>>>>> has ktable semantics. You don't need to enable log compaction in
>>> order
>>>>>>> to use a KTable as far as I know.
>>>>>> We don't need to let users know it has ktable semantics, as
>>> Konstantin
>>>>>> mentioned this may carry more implicit
>>>>>> meaning than we want to imply here. I agree users don't need to
>>> enable
>>>>> log
>>>>>> compaction, but from the production perspective,
>>>>>> log compaction should always be enabled if it is used in this
>>> purpose.
>>>>>> Calling it "kafka-compacted" can even remind users to enable log
>>>>> compaction.
>>>>>>
>>>>>> I don't agree to introduce "model = table/stream" option, or
>>>>>> "connector=kafka-table",
>>>>>> because this means we are introducing Table vs Stream concept from
>>>> KSQL.
>>>>>> However, we don't have such top-level concept in Flink SQL now,
>> this
>>>> will
>>>>>> further confuse users.
>>>>>> In Flink SQL, all the things are STREAM, the differences are
>> whether
>>> it
>>>>> is
>>>>>> bounded or unbounded,
>>>>>>    whether it is insert-only or changelog.
>>>>>>
>>>>>>
>>>>>> Best,
>>>>>> Jark
>>>>>>
>>>>>>
>>>>>> On Thu, 22 Oct 2020 at 20:39, Timo Walther <[hidden email]>
>>> wrote:
>>>>>>
>>>>>>> Hi Shengkai, Hi Jark,
>>>>>>>
>>>>>>> thanks for this great proposal. It is time to finally connect the
>>>>>>> changelog processor with a compacted Kafka topic.
>>>>>>>
>>>>>>> "The operator will produce INSERT rows, or additionally generate
>>>>>>> UPDATE_BEFORE rows for the previous image, or produce DELETE rows
>>> with
>>>>>>> all columns filled with values."
>>>>>>>
>>>>>>> Could you elaborate a bit on the implementation details in the
>> FLIP?
>>>> How
>>>>>>> are UPDATE_BEFOREs are generated. How much state is required to
>>>> perform
>>>>>>> this operation.
>>>>>>>
>>>>>>>    From a conceptual and semantical point of view, I'm fine with
>> the
>>>>>>> proposal. But I would like to share my opinion about how we expose
>>>> this
>>>>>>> feature:
>>>>>>>
>>>>>>> ktable vs kafka-compacted
>>>>>>>
>>>>>>> I'm against having an additional connector like `ktable` or
>>>>>>> `kafka-compacted`. We recently simplified the table options to a
>>>> minimum
>>>>>>> amount of characters to be as concise as possible in the DDL.
>>>> Therefore,
>>>>>>> I would keep the `connector=kafka` and introduce an additional
>>> option.
>>>>>>> Because a user wants to read "from Kafka". And the "how" should be
>>>>>>> determined in the lower options.
>>>>>>>
>>>>>>> When people read `connector=ktable` they might not know that this
>> is
>>>>>>> Kafka. Or they wonder where `kstream` is?
>>>>>>>
>>>>>>> When people read `connector=kafka-compacted` they might not know
>>> that
>>>> it
>>>>>>> has ktable semantics. You don't need to enable log compaction in
>>> order
>>>>>>> to use a KTable as far as I know. Log compaction and table
>> semantics
>>>> are
>>>>>>> orthogonal topics.
>>>>>>>
>>>>>>> In the end we will need 3 types of information when declaring a
>>> Kafka
>>>>>>> connector:
>>>>>>>
>>>>>>> CREATE TABLE ... WITH (
>>>>>>>      connector=kafka        -- Some information about the connector
>>>>>>>      end-offset = XXXX      -- Some information about the
>> boundedness
>>>>>>>      model = table/stream   -- Some information about
>> interpretation
>>>>>>> )
>>>>>>>
>>>>>>>
>>>>>>> We can still apply all the constraints mentioned in the FLIP. When
>>>>>>> `model` is set to `table`.
>>>>>>>
>>>>>>> What do you think?
>>>>>>>
>>>>>>> Regards,
>>>>>>> Timo
>>>>>>>
>>>>>>>
>>>>>>> On 21.10.20 14:19, Jark Wu wrote:
>>>>>>>> Hi,
>>>>>>>>
>>>>>>>> IMO, if we are going to mix them in one connector,
>>>>>>>> 1) either users need to set some options to a specific value
>>>>> explicitly,
>>>>>>>> e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
>>>>>>>> This makes the connector awkward to use. Users may face to fix
>>>> options
>>>>>>> one
>>>>>>>> by one according to the exception.
>>>>>>>> Besides, in the future, it is still possible to use
>>>>>>>> "sink.partitioner=fixed" (reduce network cost) if users are aware
>>> of
>>>>>>>> the partition routing,
>>>>>>>> however, it's error-prone to have "fixed" as default for
>> compacted
>>>>> mode.
>>>>>>>>
>>>>>>>> 2) or make those options a different default value when
>>>>> "compacted=true".
>>>>>>>> This would be more confusing and unpredictable if the default
>> value
>>>> of
>>>>>>>> options will change according to other options.
>>>>>>>> What happens if we have a third mode in the future?
>>>>>>>>
>>>>>>>> In terms of usage and options, it's very different from the
>>>>>>>> original "kafka" connector.
>>>>>>>> It would be more handy to use and less fallible if separating
>> them
>>>> into
>>>>>>> two
>>>>>>>> connectors.
>>>>>>>> In the implementation layer, we can reuse code as much as
>> possible.
>>>>>>>>
>>>>>>>> Therefore, I'm still +1 to have a new connector.
>>>>>>>> The "kafka-compacted" name sounds good to me.
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Jark
>>>>>>>>
>>>>>>>>
>>>>>>>> On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <
>> [hidden email]>
>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi Kurt, Hi Shengkai,
>>>>>>>>>
>>>>>>>>> thanks for answering my questions and the additional
>>>> clarifications. I
>>>>>>>>> don't have a strong opinion on whether to extend the "kafka"
>>>> connector
>>>>>>> or
>>>>>>>>> to introduce a new connector. So, from my perspective feel free
>> to
>>>> go
>>>>>>> with
>>>>>>>>> a separate connector. If we do introduce a new connector I
>>> wouldn't
>>>>>>> call it
>>>>>>>>> "ktable" for aforementioned reasons (In addition, we might
>> suggest
>>>>> that
>>>>>>>>> there is also a "kstreams" connector for symmetry reasons). I
>>> don't
>>>>>>> have a
>>>>>>>>> good alternative name, though, maybe "kafka-compacted" or
>>>>>>>>> "compacted-kafka".
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>>
>>>>>>>>> Konstantin
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]>
>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi all,
>>>>>>>>>>
>>>>>>>>>> I want to describe the discussion process which drove us to
>> have
>>>> such
>>>>>>>>>> conclusion, this might make some of
>>>>>>>>>> the design choices easier to understand and keep everyone on
>> the
>>>> same
>>>>>>>>> page.
>>>>>>>>>>
>>>>>>>>>> Back to the motivation, what functionality do we want to
>> provide
>>> in
>>>>> the
>>>>>>>>>> first place? We got a lot of feedback and
>>>>>>>>>> questions from mailing lists that people want to write
>>>>> Not-Insert-Only
>>>>>>>>>> messages into kafka. They might be
>>>>>>>>>> intentional or by accident, e.g. wrote an non-windowed
>> aggregate
>>>>> query
>>>>>>> or
>>>>>>>>>> non-windowed left outer join. And
>>>>>>>>>> some users from KSQL world also asked about why Flink didn't
>>>> leverage
>>>>>>> the
>>>>>>>>>> Key concept of every kafka topic
>>>>>>>>>> and make kafka as a dynamic changing keyed table.
>>>>>>>>>>
>>>>>>>>>> To work with kafka better, we were thinking to extend the
>>>>> functionality
>>>>>>>>> of
>>>>>>>>>> the current kafka connector by letting it
>>>>>>>>>> accept updates and deletions. But due to the limitation of
>> kafka,
>>>> the
>>>>>>>>>> update has to be "update by key", aka a table
>>>>>>>>>> with primary key.
>>>>>>>>>>
>>>>>>>>>> This introduces a couple of conflicts with current kafka
>> table's
>>>>>>> options:
>>>>>>>>>> 1. key.fields: as said above, we need the kafka table to have
>> the
>>>>>>> primary
>>>>>>>>>> key constraint. And users can also configure
>>>>>>>>>> key.fields freely, this might cause friction. (Sure we can do
>>> some
>>>>>>> sanity
>>>>>>>>>> check on this but it also creates friction.)
>>>>>>>>>> 2. sink.partitioner: to make the semantics right, we need to
>> make
>>>>> sure
>>>>>>>>> all
>>>>>>>>>> the updates on the same key are written to
>>>>>>>>>> the same kafka partition, such we should force to use a hash by
>>> key
>>>>>>>>>> partition inside such table. Again, this has conflicts
>>>>>>>>>> and creates friction with current user options.
>>>>>>>>>>
>>>>>>>>>> The above things are solvable, though not perfect or most user
>>>>>>> friendly.
>>>>>>>>>>
>>>>>>>>>> Let's take a look at the reading side. The keyed kafka table
>>>> contains
>>>>>>> two
>>>>>>>>>> kinds of messages: upsert or deletion. What upsert
>>>>>>>>>> means is "If the key doesn't exist yet, it's an insert record.
>>>>>>> Otherwise
>>>>>>>>>> it's an update record". For the sake of correctness or
>>>>>>>>>> simplicity, the Flink SQL engine also needs such information.
>> If
>>> we
>>>>>>>>>> interpret all messages to "update record", some queries or
>>>>>>>>>> operators may not work properly. It's weird to see an update
>>> record
>>>>> but
>>>>>>>>> you
>>>>>>>>>> haven't seen the insert record before.
>>>>>>>>>>
>>>>>>>>>> So what Flink should do is after reading out the records from
>>> such
>>>>>>> table,
>>>>>>>>>> it needs to create a state to record which messages have
>>>>>>>>>> been seen and then generate the correct row type
>> correspondingly.
>>>>> This
>>>>>>>>> kind
>>>>>>>>>> of couples the state and the data of the message
>>>>>>>>>> queue, and it also creates conflicts with current kafka
>>> connector.
>>>>>>>>>>
>>>>>>>>>> Think about if users suspend a running job (which contains some
>>>>> reading
>>>>>>>>>> state now), and then change the start offset of the reader.
>>>>>>>>>> By changing the reading offset, it actually change the whole
>>> story
>>>> of
>>>>>>>>>> "which records should be insert messages and which records
>>>>>>>>>> should be update messages). And it will also make Flink to deal
>>>> with
>>>>>>>>>> another weird situation that it might receive a deletion
>>>>>>>>>> on a non existing message.
>>>>>>>>>>
>>>>>>>>>> We were unsatisfied with all the frictions and conflicts it
>> will
>>>>> create
>>>>>>>>> if
>>>>>>>>>> we enable the "upsert & deletion" support to the current kafka
>>>>>>>>>> connector. And later we begin to realize that we shouldn't
>> treat
>>> it
>>>>> as
>>>>>>> a
>>>>>>>>>> normal message queue, but should treat it as a changing keyed
>>>>>>>>>> table. We should be able to always get the whole data of such
>>> table
>>>>> (by
>>>>>>>>>> disabling the start offset option) and we can also read the
>>>>>>>>>> changelog out of such table. It's like a HBase table with
>> binlog
>>>>>>> support
>>>>>>>>>> but doesn't have random access capability (which can be
>> fulfilled
>>>>>>>>>> by Flink's state).
>>>>>>>>>>
>>>>>>>>>> So our intention was instead of telling and persuading users
>> what
>>>>> kind
>>>>>>> of
>>>>>>>>>> options they should or should not use by extending
>>>>>>>>>> current kafka connector when enable upsert support, we are
>>> actually
>>>>>>>>> create
>>>>>>>>>> a whole new and different connector that has total
>>>>>>>>>> different abstractions in SQL layer, and should be treated
>>> totally
>>>>>>>>>> different with current kafka connector.
>>>>>>>>>>
>>>>>>>>>> Hope this can clarify some of the concerns.
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>> Kurt
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <
>> [hidden email]
>>>>
>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi devs,
>>>>>>>>>>>
>>>>>>>>>>> As many people are still confused about the difference option
>>>>>>>>> behaviours
>>>>>>>>>>> between the Kafka connector and KTable connector, Jark and I
>>> list
>>>>> the
>>>>>>>>>>> differences in the doc[1].
>>>>>>>>>>>
>>>>>>>>>>> Best,
>>>>>>>>>>> Shengkai
>>>>>>>>>>>
>>>>>>>>>>> [1]
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>>
>> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
>>>>>>>>>>>
>>>>>>>>>>> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
>>>>>>>>>>>
>>>>>>>>>>>> Hi Konstantin,
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks for your reply.
>>>>>>>>>>>>
>>>>>>>>>>>>> It uses the "kafka" connector and does not specify a primary
>>>> key.
>>>>>>>>>>>> The dimensional table `users` is a ktable connector and we
>> can
>>>>>>>>> specify
>>>>>>>>>>> the
>>>>>>>>>>>> pk on the KTable.
>>>>>>>>>>>>
>>>>>>>>>>>>> Will it possible to use a "ktable" as a dimensional table in
>>>>>>>>> FLIP-132
>>>>>>>>>>>> Yes. We can specify the watermark on the KTable and it can be
>>>> used
>>>>>>>>> as a
>>>>>>>>>>>> dimension table in temporal join.
>>>>>>>>>>>>
>>>>>>>>>>>>> Introduce a new connector vs introduce a new property
>>>>>>>>>>>> The main reason behind is that the KTable connector almost
>> has
>>> no
>>>>>>>>>> common
>>>>>>>>>>>> options with the Kafka connector. The options that can be
>>> reused
>>>> by
>>>>>>>>>>> KTable
>>>>>>>>>>>> connectors are 'topic', 'properties.bootstrap.servers' and
>>>>>>>>>>>> 'value.fields-include' . We can't set cdc format for
>>> 'key.format'
>>>>> and
>>>>>>>>>>>> 'value.format' in KTable connector now, which is  available
>> in
>>>>> Kafka
>>>>>>>>>>>> connector. Considering the difference between the options we
>>> can
>>>>> use,
>>>>>>>>>>> it's
>>>>>>>>>>>> more suitable to introduce an another connector rather than a
>>>>>>>>> property.
>>>>>>>>>>>>
>>>>>>>>>>>> We are also fine to use "compacted-kafka" as the name of the
>>> new
>>>>>>>>>>>> connector. What do you think?
>>>>>>>>>>>>
>>>>>>>>>>>> Best,
>>>>>>>>>>>> Shengkai
>>>>>>>>>>>>
>>>>>>>>>>>> Konstantin Knauf <[hidden email]> 于2020年10月19日周一
>> 下午10:15写道：
>>>>>>>>>>>>
>>>>>>>>>>>>> Hi Shengkai,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thank you for driving this effort. I believe this a very
>>>> important
>>>>>>>>>>> feature
>>>>>>>>>>>>> for many users who use Kafka and Flink SQL together. A few
>>>>> questions
>>>>>>>>>> and
>>>>>>>>>>>>> thoughts:
>>>>>>>>>>>>>
>>>>>>>>>>>>> * Is your example "Use KTable as a reference/dimension
>> table"
>>>>>>>>> correct?
>>>>>>>>>>> It
>>>>>>>>>>>>> uses the "kafka" connector and does not specify a primary
>> key.
>>>>>>>>>>>>>
>>>>>>>>>>>>> * Will it be possible to use a "ktable" table directly as a
>>>>>>>>>> dimensional
>>>>>>>>>>>>> table in temporal join (*based on event time*) (FLIP-132)?
>>> This
>>>> is
>>>>>>>>> not
>>>>>>>>>>>>> completely clear to me from the FLIP.
>>>>>>>>>>>>>
>>>>>>>>>>>>> * I'd personally prefer not to introduce a new connector and
>>>>> instead
>>>>>>>>>> to
>>>>>>>>>>>>> extend the Kafka connector. We could add an additional
>>> property
>>>>>>>>>>>>> "compacted"
>>>>>>>>>>>>> = "true"|"false". If it is set to "true", we can add
>>> additional
>>>>>>>>>>> validation
>>>>>>>>>>>>> logic (e.g. "scan.startup.mode" can not be set, primary key
>>>>>>>>> required,
>>>>>>>>>>>>> etc.). If we stick to a separate connector I'd not call it
>>>>> "ktable",
>>>>>>>>>> but
>>>>>>>>>>>>> rather "compacted-kafka" or similar. KTable seems to carry
>>> more
>>>>>>>>>> implicit
>>>>>>>>>>>>> meaning than we want to imply here.
>>>>>>>>>>>>>
>>>>>>>>>>>>> * I agree that this is not a bounded source. If we want to
>>>>> support a
>>>>>>>>>>>>> bounded mode, this is an orthogonal concern that also
>> applies
>>> to
>>>>>>>>> other
>>>>>>>>>>>>> unbounded sources.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Konstantin
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]>
>>>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi Danny,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> First of all, we didn't introduce any concepts from KSQL
>>> (e.g.
>>>>>>>>>> Stream
>>>>>>>>>>> vs
>>>>>>>>>>>>>> Table notion).
>>>>>>>>>>>>>> This new connector will produce a changelog stream, so it's
>>>> still
>>>>>>>>> a
>>>>>>>>>>>>> dynamic
>>>>>>>>>>>>>> table and doesn't conflict with Flink core concepts.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The "ktable" is just a connector name, we can also call it
>>>>>>>>>>>>>> "compacted-kafka" or something else.
>>>>>>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate
>> to
>>>>>>>>> Flink
>>>>>>>>>>> SQL
>>>>>>>>>>>>>> easily.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Regarding to why introducing a new connector vs a new
>>> property
>>>> in
>>>>>>>>>>>>> existing
>>>>>>>>>>>>>> kafka connector:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I think the main reason is that we want to have a clear
>>>>> separation
>>>>>>>>>> for
>>>>>>>>>>>>> such
>>>>>>>>>>>>>> two use cases, because they are very different.
>>>>>>>>>>>>>> We also listed reasons in the FLIP, including:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 1) It's hard to explain what's the behavior when users
>>> specify
>>>>> the
>>>>>>>>>>> start
>>>>>>>>>>>>>> offset from a middle position (e.g. how to process non
>> exist
>>>>>>>>> delete
>>>>>>>>>>>>>> events).
>>>>>>>>>>>>>>        It's dangerous if users do that. So we don't provide
>>> the
>>>>>>>>> offset
>>>>>>>>>>>>> option
>>>>>>>>>>>>>> in the new connector at the moment.
>>>>>>>>>>>>>> 2) It's a different perspective/abstraction on the same
>> kafka
>>>>>>>>> topic
>>>>>>>>>>>>> (append
>>>>>>>>>>>>>> vs. upsert). It would be easier to understand if we can
>>>> separate
>>>>>>>>>> them
>>>>>>>>>>>>>>        instead of mixing them in one connector. The new
>>>> connector
>>>>>>>>>>> requires
>>>>>>>>>>>>>> hash sink partitioner, primary key declared, regular
>> format.
>>>>>>>>>>>>>>        If we mix them in one connector, it might be
>> confusing
>>>> how
>>>>> to
>>>>>>>>>> use
>>>>>>>>>>>>> the
>>>>>>>>>>>>>> options correctly.
>>>>>>>>>>>>>> 3) The semantic of the KTable connector is just the same as
>>>>> KTable
>>>>>>>>>> in
>>>>>>>>>>>>> Kafka
>>>>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
>>>>>>>>>>>>>>        We have seen several questions in the mailing list
>>> asking
>>>>> how
>>>>>>>>> to
>>>>>>>>>>>>> model
>>>>>>>>>>>>>> a KTable and how to join a KTable in Flink SQL.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]>
>>>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi Jingsong,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> As the FLIP describes, "KTable connector produces a
>>> changelog
>>>>>>>>>>> stream,
>>>>>>>>>>>>>>> where each data record represents an update or delete
>>> event.".
>>>>>>>>>>>>>>> Therefore, a ktable source is an unbounded stream source.
>>>>>>>>>> Selecting
>>>>>>>>>>> a
>>>>>>>>>>>>>>> ktable source is similar to selecting a kafka source with
>>>>>>>>>>>>> debezium-json
>>>>>>>>>>>>>>> format
>>>>>>>>>>>>>>> that it never ends and the results are continuously
>> updated.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It's possible to have a bounded ktable source in the
>> future,
>>>> for
>>>>>>>>>>>>> example,
>>>>>>>>>>>>>>> add an option 'bounded=true' or 'end-offset=xxx'.
>>>>>>>>>>>>>>> In this way, the ktable will produce a bounded changelog
>>>> stream.
>>>>>>>>>>>>>>> So I think this can be a compatible feature in the future.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I don't think we should associate with ksql related
>>> concepts.
>>>>>>>>>>>>> Actually,
>>>>>>>>>>>>>> we
>>>>>>>>>>>>>>> didn't introduce any concepts from KSQL (e.g. Stream vs
>>> Table
>>>>>>>>>>> notion).
>>>>>>>>>>>>>>> The "ktable" is just a connector name, we can also call it
>>>>>>>>>>>>>>> "compacted-kafka" or something else.
>>>>>>>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate
>>> to
>>>>>>>>>> Flink
>>>>>>>>>>>>> SQL
>>>>>>>>>>>>>>> easily.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Regarding the "value.fields-include", this is an option
>>>>>>>>> introduced
>>>>>>>>>>> in
>>>>>>>>>>>>>>> FLIP-107 for Kafka connector.
>>>>>>>>>>>>>>> I think we should keep the same behavior with the Kafka
>>>>>>>>> connector.
>>>>>>>>>>> I'm
>>>>>>>>>>>>>> not
>>>>>>>>>>>>>>> sure what's the default behavior of KSQL.
>>>>>>>>>>>>>>> But I guess it also stores the keys in value from this
>>> example
>>>>>>>>>> docs
>>>>>>>>>>>>> (see
>>>>>>>>>>>>>>> the "users_original" table) [1].
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> [1]:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>>
>> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Mon, 19 Oct 2020 at 18:17, Danny Chan <
>>>> [hidden email]>
>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The concept seems conflicts with the Flink abstraction
>>>> “dynamic
>>>>>>>>>>>>> table”,
>>>>>>>>>>>>>>>> in Flink we see both “stream” and “table” as a dynamic
>>> table,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I think we should make clear first how to express stream
>>> and
>>>>>>>>>> table
>>>>>>>>>>>>>>>> specific features on one “dynamic table”,
>>>>>>>>>>>>>>>> it is more natural for KSQL because KSQL takes stream and
>>>> table
>>>>>>>>>> as
>>>>>>>>>>>>>>>> different abstractions for representing collections. In
>>> KSQL,
>>>>>>>>>> only
>>>>>>>>>>>>>> table is
>>>>>>>>>>>>>>>> mutable and can have a primary key.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Does this connector belongs to the “table” scope or
>>> “stream”
>>>>>>>>>> scope
>>>>>>>>>>> ?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Some of the concepts (such as the primary key on stream)
>>>> should
>>>>>>>>>> be
>>>>>>>>>>>>>>>> suitable for all the connectors, not just Kafka,
>> Shouldn’t
>>>> this
>>>>>>>>>> be
>>>>>>>>>>> an
>>>>>>>>>>>>>>>> extension of existing Kafka connector instead of a
>> totally
>>>> new
>>>>>>>>>>>>>> connector ?
>>>>>>>>>>>>>>>> What about the other connectors ?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Because this touches the core abstraction of Flink, we
>>> better
>>>>>>>>>> have
>>>>>>>>>>> a
>>>>>>>>>>>>>>>> top-down overall design, following the KSQL directly is
>> not
>>>> the
>>>>>>>>>>>>> answer.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> P.S. For the source
>>>>>>>>>>>>>>>>> Shouldn’t this be an extension of existing Kafka
>> connector
>>>>>>>>>>> instead
>>>>>>>>>>>>> of
>>>>>>>>>>>>>> a
>>>>>>>>>>>>>>>> totally new connector ?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> How could we achieve that (e.g. set up the parallelism
>>>>>>>>>> correctly) ?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>>>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <
>>>> [hidden email]
>>>>>>>>>>>> ，写道：
>>>>>>>>>>>>>>>>> Thanks Shengkai for your proposal.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> +1 for this feature.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Future Work: Support bounded KTable source
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I don't think it should be a future work, I think it is
>>> one
>>>>>>>>> of
>>>>>>>>>>> the
>>>>>>>>>>>>>>>>> important concepts of this FLIP. We need to understand
>> it
>>>>>>>>> now.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Intuitively, a ktable in my opinion is a bounded table
>>>> rather
>>>>>>>>>>> than
>>>>>>>>>>>>> a
>>>>>>>>>>>>>>>>> stream, so select should produce a bounded table by
>>> default.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I think we can list Kafka related knowledge, because the
>>>> word
>>>>>>>>>>>>> `ktable`
>>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>> easy to associate with ksql related concepts. (If
>>> possible,
>>>>>>>>>> it's
>>>>>>>>>>>>>> better
>>>>>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>> unify with it)
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> What do you think?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> value.fields-include
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> What about the default behavior of KSQL?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>> Jingsong
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
>>>>>>>>>> [hidden email]
>>>>>>>>>>>>
>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hi, devs.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Jark and I want to start a new FLIP to introduce the
>>> KTable
>>>>>>>>>>>>>>>> connector. The
>>>>>>>>>>>>>>>>>> KTable is a shortcut of "Kafka Table", it also has the
>>> same
>>>>>>>>>>>>>> semantics
>>>>>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>> the KTable notion in Kafka Stream.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> FLIP-149:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Currently many users have expressed their needs for the
>>>>>>>>>> upsert
>>>>>>>>>>>>> Kafka
>>>>>>>>>>>>>>>> by
>>>>>>>>>>>>>>>>>> mail lists and issues. The KTable connector has several
>>>>>>>>>>> benefits
>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>> users:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> 1. Users are able to interpret a compacted Kafka Topic
>> as
>>>>>>>>> an
>>>>>>>>>>>>> upsert
>>>>>>>>>>>>>>>> stream
>>>>>>>>>>>>>>>>>> in Apache Flink. And also be able to write a changelog
>>>>>>>>> stream
>>>>>>>>>>> to
>>>>>>>>>>>>>> Kafka
>>>>>>>>>>>>>>>>>> (into a compacted topic).
>>>>>>>>>>>>>>>>>> 2. As a part of the real time pipeline, store join or
>>>>>>>>>> aggregate
>>>>>>>>>>>>>>>> result (may
>>>>>>>>>>>>>>>>>> contain updates) into a Kafka topic for further
>>>>>>>>> calculation;
>>>>>>>>>>>>>>>>>> 3. The semantic of the KTable connector is just the
>> same
>>> as
>>>>>>>>>>>>> KTable
>>>>>>>>>>>>>> in
>>>>>>>>>>>>>>>> Kafka
>>>>>>>>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL
>>> users.
>>>>>>>>>> We
>>>>>>>>>>>>> have
>>>>>>>>>>>>>>>> seen
>>>>>>>>>>>>>>>>>> several questions in the mailing list asking how to
>>> model a
>>>>>>>>>>>>> KTable
>>>>>>>>>>>>>>>> and how
>>>>>>>>>>>>>>>>>> to join a KTable in Flink SQL.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> We hope it can expand the usage of the Flink with
>> Kafka.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I'm looking forward to your feedback.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>> Shengkai
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> --
>>>>>>>>>>>>>>>>> Best, Jingsong Lee
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> --
>>>>>>>>>>>>>
>>>>>>>>>>>>> Konstantin Knauf
>>>>>>>>>>>>>
>>>>>>>>>>>>> https://twitter.com/snntrable
>>>>>>>>>>>>>
>>>>>>>>>>>>> https://github.com/knaufk
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>>
>>>>>>>>> Konstantin Knauf
>>>>>>>>>
>>>>>>>>> https://twitter.com/snntrable
>>>>>>>>>
>>>>>>>>> https://github.com/knaufk
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>> --
>>>>
>>>> Seth Wiesman | Solutions Architect
>>>>
>>>> [phone]
>>>>
>>>> <https://www.ververica.com/>
>>>>
>>>> Follow us @VervericaData
>>>>
>>>> --
>>>>
>>>> Join Flink Forward <https://flink-forward.org/> - The Apache Flink
>>>> Conference
>>>>
>>>> Stream Processing | Event Driven | Real Time
>>>>
>>>
>>
>

[Jingsong Li]

The `kafka-cdc` looks good to me.
We can even give options to indicate whether to turn on compact, because
compact is just an optimization?

- ktable let me think about KSQL.
- kafka-compacted it is not just compacted, more than that, it still has
the ability of CDC
- upsert-kafka , upsert is back, and I don't really want to see it again
since we have CDC

Best,
Jingsong

On Fri, Oct 23, 2020 at 2:21 AM Timo Walther <[hidden email]> wrote:

> Hi Jark,
>
> I would be fine with `connector=upsert-kafka`. Another idea would be to
> align the name to other available Flink connectors [1]:
>
> `connector=kafka-cdc`.
>
> Regards,
> Timo
>
> [1] https://github.com/ververica/flink-cdc-connectors
>
> On 22.10.20 17:17, Jark Wu wrote:
> > Another name is "connector=upsert-kafka', I think this can solve Timo's
> > concern on the "compacted" word.
> >
> > Materialize also uses "ENVELOPE UPSERT" [1] keyword to identify such
> kafka
> > sources.
> > I think "upsert" is a well-known terminology widely used in many systems
> > and matches the
> >   behavior of how we handle the kafka messages.
> >
> > What do you think?
> >
> > Best,
> > Jark
> >
> > [1]:
> >
> https://materialize.io/docs/sql/create-source/text-kafka/#upsert-on-a-kafka-topic
> >
> >
> >
> >
> > On Thu, 22 Oct 2020 at 22:53, Kurt Young <[hidden email]> wrote:
> >
> >> Good validation messages can't solve the broken user experience,
> especially
> >> that
> >> such update mode option will implicitly make half of current kafka
> options
> >> invalid or doesn't
> >> make sense.
> >>
> >> Best,
> >> Kurt
> >>
> >>
> >> On Thu, Oct 22, 2020 at 10:31 PM Jark Wu <[hidden email]> wrote:
> >>
> >>> Hi Timo, Seth,
> >>>
> >>> The default value "inserting" of "mode" might be not suitable,
> >>> because "debezium-json" emits changelog messages which include updates.
> >>>
> >>> On Thu, 22 Oct 2020 at 22:10, Seth Wiesman <[hidden email]> wrote:
> >>>
> >>>> +1 for supporting upsert results into Kafka.
> >>>>
> >>>> I have no comments on the implementation details.
> >>>>
> >>>> As far as configuration goes, I tend to favor Timo's option where we
> >> add
> >>> a
> >>>> "mode" property to the existing Kafka table with default value
> >>> "inserting".
> >>>> If the mode is set to "updating" then the validation changes to the
> new
> >>>> requirements. I personally find it more intuitive than a seperate
> >>>> connector, my fear is users won't understand its the same physical
> >> kafka
> >>>> sink under the hood and it will lead to other confusion like does it
> >>> offer
> >>>> the same persistence guarantees? I think we are capable of adding good
> >>>> valdiation messaging that solves Jark and Kurts concerns.
> >>>>
> >>>>
> >>>> On Thu, Oct 22, 2020 at 8:51 AM Timo Walther <[hidden email]>
> >> wrote:
> >>>>
> >>>>> Hi Jark,
> >>>>>
> >>>>> "calling it "kafka-compacted" can even remind users to enable log
> >>>>> compaction"
> >>>>>
> >>>>> But sometimes users like to store a lineage of changes in their
> >> topics.
> >>>>> Indepent of any ktable/kstream interpretation.
> >>>>>
> >>>>> I let the majority decide on this topic to not further block this
> >>>>> effort. But we might find a better name like:
> >>>>>
> >>>>> connector = kafka
> >>>>> mode = updating/inserting
> >>>>>
> >>>>> OR
> >>>>>
> >>>>> connector = kafka-updating
> >>>>>
> >>>>> ...
> >>>>>
> >>>>> Regards,
> >>>>> Timo
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>> On 22.10.20 15:24, Jark Wu wrote:
> >>>>>> Hi Timo,
> >>>>>>
> >>>>>> Thanks for your opinions.
> >>>>>>
> >>>>>> 1) Implementation
> >>>>>> We will have an stateful operator to generate INSERT and
> >>> UPDATE_BEFORE.
> >>>>>> This operator is keyby-ed (primary key as the shuffle key) after
> >> the
> >>>>> source
> >>>>>> operator.
> >>>>>> The implementation of this operator is very similar to the existing
> >>>>>> `DeduplicateKeepLastRowFunction`.
> >>>>>> The operator will register a value state using the primary key
> >> fields
> >>>> as
> >>>>>> keys.
> >>>>>> When the value state is empty under current key, we will emit
> >> INSERT
> >>>> for
> >>>>>> the input row.
> >>>>>> When the value state is not empty under current key, we will emit
> >>>>>> UPDATE_BEFORE using the row in state,
> >>>>>> and emit UPDATE_AFTER using the input row.
> >>>>>> When the input row is DELETE, we will clear state and emit DELETE
> >>> row.
> >>>>>>
> >>>>>> 2) new option vs new connector
> >>>>>>> We recently simplified the table options to a minimum amount of
> >>>>>> characters to be as concise as possible in the DDL.
> >>>>>> I think this is the reason why we want to introduce a new
> >> connector,
> >>>>>> because we can simplify the options in DDL.
> >>>>>> For example, if using a new option, the DDL may look like this:
> >>>>>>
> >>>>>> CREATE TABLE users (
> >>>>>>     user_id BIGINT,
> >>>>>>     user_name STRING,
> >>>>>>     user_level STRING,
> >>>>>>     region STRING,
> >>>>>>     PRIMARY KEY (user_id) NOT ENFORCED
> >>>>>> ) WITH (
> >>>>>>     'connector' = 'kafka',
> >>>>>>     'model' = 'table',
> >>>>>>     'topic' = 'pageviews_per_region',
> >>>>>>     'properties.bootstrap.servers' = '...',
> >>>>>>     'properties.group.id' = 'testGroup',
> >>>>>>     'scan.startup.mode' = 'earliest',
> >>>>>>     'key.format' = 'csv',
> >>>>>>     'key.fields' = 'user_id',
> >>>>>>     'value.format' = 'avro',
> >>>>>>     'sink.partitioner' = 'hash'
> >>>>>> );
> >>>>>>
> >>>>>> If using a new connector, we can have a different default value for
> >>> the
> >>>>>> options and remove unnecessary options,
> >>>>>> the DDL can look like this which is much more concise:
> >>>>>>
> >>>>>> CREATE TABLE pageviews_per_region (
> >>>>>>     user_id BIGINT,
> >>>>>>     user_name STRING,
> >>>>>>     user_level STRING,
> >>>>>>     region STRING,
> >>>>>>     PRIMARY KEY (user_id) NOT ENFORCED
> >>>>>> ) WITH (
> >>>>>>     'connector' = 'kafka-compacted',
> >>>>>>     'topic' = 'pageviews_per_region',
> >>>>>>     'properties.bootstrap.servers' = '...',
> >>>>>>     'key.format' = 'csv',
> >>>>>>     'value.format' = 'avro'
> >>>>>> );
> >>>>>>
> >>>>>>> When people read `connector=kafka-compacted` they might not know
> >>> that
> >>>> it
> >>>>>>> has ktable semantics. You don't need to enable log compaction in
> >>> order
> >>>>>>> to use a KTable as far as I know.
> >>>>>> We don't need to let users know it has ktable semantics, as
> >>> Konstantin
> >>>>>> mentioned this may carry more implicit
> >>>>>> meaning than we want to imply here. I agree users don't need to
> >>> enable
> >>>>> log
> >>>>>> compaction, but from the production perspective,
> >>>>>> log compaction should always be enabled if it is used in this
> >>> purpose.
> >>>>>> Calling it "kafka-compacted" can even remind users to enable log
> >>>>> compaction.
> >>>>>>
> >>>>>> I don't agree to introduce "model = table/stream" option, or
> >>>>>> "connector=kafka-table",
> >>>>>> because this means we are introducing Table vs Stream concept from
> >>>> KSQL.
> >>>>>> However, we don't have such top-level concept in Flink SQL now,
> >> this
> >>>> will
> >>>>>> further confuse users.
> >>>>>> In Flink SQL, all the things are STREAM, the differences are
> >> whether
> >>> it
> >>>>> is
> >>>>>> bounded or unbounded,
> >>>>>>    whether it is insert-only or changelog.
> >>>>>>
> >>>>>>
> >>>>>> Best,
> >>>>>> Jark
> >>>>>>
> >>>>>>
> >>>>>> On Thu, 22 Oct 2020 at 20:39, Timo Walther <[hidden email]>
> >>> wrote:
> >>>>>>
> >>>>>>> Hi Shengkai, Hi Jark,
> >>>>>>>
> >>>>>>> thanks for this great proposal. It is time to finally connect the
> >>>>>>> changelog processor with a compacted Kafka topic.
> >>>>>>>
> >>>>>>> "The operator will produce INSERT rows, or additionally generate
> >>>>>>> UPDATE_BEFORE rows for the previous image, or produce DELETE rows
> >>> with
> >>>>>>> all columns filled with values."
> >>>>>>>
> >>>>>>> Could you elaborate a bit on the implementation details in the
> >> FLIP?
> >>>> How
> >>>>>>> are UPDATE_BEFOREs are generated. How much state is required to
> >>>> perform
> >>>>>>> this operation.
> >>>>>>>
> >>>>>>>    From a conceptual and semantical point of view, I'm fine with
> >> the
> >>>>>>> proposal. But I would like to share my opinion about how we expose
> >>>> this
> >>>>>>> feature:
> >>>>>>>
> >>>>>>> ktable vs kafka-compacted
> >>>>>>>
> >>>>>>> I'm against having an additional connector like `ktable` or
> >>>>>>> `kafka-compacted`. We recently simplified the table options to a
> >>>> minimum
> >>>>>>> amount of characters to be as concise as possible in the DDL.
> >>>> Therefore,
> >>>>>>> I would keep the `connector=kafka` and introduce an additional
> >>> option.
> >>>>>>> Because a user wants to read "from Kafka". And the "how" should be
> >>>>>>> determined in the lower options.
> >>>>>>>
> >>>>>>> When people read `connector=ktable` they might not know that this
> >> is
> >>>>>>> Kafka. Or they wonder where `kstream` is?
> >>>>>>>
> >>>>>>> When people read `connector=kafka-compacted` they might not know
> >>> that
> >>>> it
> >>>>>>> has ktable semantics. You don't need to enable log compaction in
> >>> order
> >>>>>>> to use a KTable as far as I know. Log compaction and table
> >> semantics
> >>>> are
> >>>>>>> orthogonal topics.
> >>>>>>>
> >>>>>>> In the end we will need 3 types of information when declaring a
> >>> Kafka
> >>>>>>> connector:
> >>>>>>>
> >>>>>>> CREATE TABLE ... WITH (
> >>>>>>>      connector=kafka        -- Some information about the connector
> >>>>>>>      end-offset = XXXX      -- Some information about the
> >> boundedness
> >>>>>>>      model = table/stream   -- Some information about
> >> interpretation
> >>>>>>> )
> >>>>>>>
> >>>>>>>
> >>>>>>> We can still apply all the constraints mentioned in the FLIP. When
> >>>>>>> `model` is set to `table`.
> >>>>>>>
> >>>>>>> What do you think?
> >>>>>>>
> >>>>>>> Regards,
> >>>>>>> Timo
> >>>>>>>
> >>>>>>>
> >>>>>>> On 21.10.20 14:19, Jark Wu wrote:
> >>>>>>>> Hi,
> >>>>>>>>
> >>>>>>>> IMO, if we are going to mix them in one connector,
> >>>>>>>> 1) either users need to set some options to a specific value
> >>>>> explicitly,
> >>>>>>>> e.g. "scan.startup.mode=earliest", "sink.partitioner=hash", etc..
> >>>>>>>> This makes the connector awkward to use. Users may face to fix
> >>>> options
> >>>>>>> one
> >>>>>>>> by one according to the exception.
> >>>>>>>> Besides, in the future, it is still possible to use
> >>>>>>>> "sink.partitioner=fixed" (reduce network cost) if users are aware
> >>> of
> >>>>>>>> the partition routing,
> >>>>>>>> however, it's error-prone to have "fixed" as default for
> >> compacted
> >>>>> mode.
> >>>>>>>>
> >>>>>>>> 2) or make those options a different default value when
> >>>>> "compacted=true".
> >>>>>>>> This would be more confusing and unpredictable if the default
> >> value
> >>>> of
> >>>>>>>> options will change according to other options.
> >>>>>>>> What happens if we have a third mode in the future?
> >>>>>>>>
> >>>>>>>> In terms of usage and options, it's very different from the
> >>>>>>>> original "kafka" connector.
> >>>>>>>> It would be more handy to use and less fallible if separating
> >> them
> >>>> into
> >>>>>>> two
> >>>>>>>> connectors.
> >>>>>>>> In the implementation layer, we can reuse code as much as
> >> possible.
> >>>>>>>>
> >>>>>>>> Therefore, I'm still +1 to have a new connector.
> >>>>>>>> The "kafka-compacted" name sounds good to me.
> >>>>>>>>
> >>>>>>>> Best,
> >>>>>>>> Jark
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> On Wed, 21 Oct 2020 at 17:58, Konstantin Knauf <
> >> [hidden email]>
> >>>>>>> wrote:
> >>>>>>>>
> >>>>>>>>> Hi Kurt, Hi Shengkai,
> >>>>>>>>>
> >>>>>>>>> thanks for answering my questions and the additional
> >>>> clarifications. I
> >>>>>>>>> don't have a strong opinion on whether to extend the "kafka"
> >>>> connector
> >>>>>>> or
> >>>>>>>>> to introduce a new connector. So, from my perspective feel free
> >> to
> >>>> go
> >>>>>>> with
> >>>>>>>>> a separate connector. If we do introduce a new connector I
> >>> wouldn't
> >>>>>>> call it
> >>>>>>>>> "ktable" for aforementioned reasons (In addition, we might
> >> suggest
> >>>>> that
> >>>>>>>>> there is also a "kstreams" connector for symmetry reasons). I
> >>> don't
> >>>>>>> have a
> >>>>>>>>> good alternative name, though, maybe "kafka-compacted" or
> >>>>>>>>> "compacted-kafka".
> >>>>>>>>>
> >>>>>>>>> Thanks,
> >>>>>>>>>
> >>>>>>>>> Konstantin
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> On Wed, Oct 21, 2020 at 4:43 AM Kurt Young <[hidden email]>
> >>>> wrote:
> >>>>>>>>>
> >>>>>>>>>> Hi all,
> >>>>>>>>>>
> >>>>>>>>>> I want to describe the discussion process which drove us to
> >> have
> >>>> such
> >>>>>>>>>> conclusion, this might make some of
> >>>>>>>>>> the design choices easier to understand and keep everyone on
> >> the
> >>>> same
> >>>>>>>>> page.
> >>>>>>>>>>
> >>>>>>>>>> Back to the motivation, what functionality do we want to
> >> provide
> >>> in
> >>>>> the
> >>>>>>>>>> first place? We got a lot of feedback and
> >>>>>>>>>> questions from mailing lists that people want to write
> >>>>> Not-Insert-Only
> >>>>>>>>>> messages into kafka. They might be
> >>>>>>>>>> intentional or by accident, e.g. wrote an non-windowed
> >> aggregate
> >>>>> query
> >>>>>>> or
> >>>>>>>>>> non-windowed left outer join. And
> >>>>>>>>>> some users from KSQL world also asked about why Flink didn't
> >>>> leverage
> >>>>>>> the
> >>>>>>>>>> Key concept of every kafka topic
> >>>>>>>>>> and make kafka as a dynamic changing keyed table.
> >>>>>>>>>>
> >>>>>>>>>> To work with kafka better, we were thinking to extend the
> >>>>> functionality
> >>>>>>>>> of
> >>>>>>>>>> the current kafka connector by letting it
> >>>>>>>>>> accept updates and deletions. But due to the limitation of
> >> kafka,
> >>>> the
> >>>>>>>>>> update has to be "update by key", aka a table
> >>>>>>>>>> with primary key.
> >>>>>>>>>>
> >>>>>>>>>> This introduces a couple of conflicts with current kafka
> >> table's
> >>>>>>> options:
> >>>>>>>>>> 1. key.fields: as said above, we need the kafka table to have
> >> the
> >>>>>>> primary
> >>>>>>>>>> key constraint. And users can also configure
> >>>>>>>>>> key.fields freely, this might cause friction. (Sure we can do
> >>> some
> >>>>>>> sanity
> >>>>>>>>>> check on this but it also creates friction.)
> >>>>>>>>>> 2. sink.partitioner: to make the semantics right, we need to
> >> make
> >>>>> sure
> >>>>>>>>> all
> >>>>>>>>>> the updates on the same key are written to
> >>>>>>>>>> the same kafka partition, such we should force to use a hash by
> >>> key
> >>>>>>>>>> partition inside such table. Again, this has conflicts
> >>>>>>>>>> and creates friction with current user options.
> >>>>>>>>>>
> >>>>>>>>>> The above things are solvable, though not perfect or most user
> >>>>>>> friendly.
> >>>>>>>>>>
> >>>>>>>>>> Let's take a look at the reading side. The keyed kafka table
> >>>> contains
> >>>>>>> two
> >>>>>>>>>> kinds of messages: upsert or deletion. What upsert
> >>>>>>>>>> means is "If the key doesn't exist yet, it's an insert record.
> >>>>>>> Otherwise
> >>>>>>>>>> it's an update record". For the sake of correctness or
> >>>>>>>>>> simplicity, the Flink SQL engine also needs such information.
> >> If
> >>> we
> >>>>>>>>>> interpret all messages to "update record", some queries or
> >>>>>>>>>> operators may not work properly. It's weird to see an update
> >>> record
> >>>>> but
> >>>>>>>>> you
> >>>>>>>>>> haven't seen the insert record before.
> >>>>>>>>>>
> >>>>>>>>>> So what Flink should do is after reading out the records from
> >>> such
> >>>>>>> table,
> >>>>>>>>>> it needs to create a state to record which messages have
> >>>>>>>>>> been seen and then generate the correct row type
> >> correspondingly.
> >>>>> This
> >>>>>>>>> kind
> >>>>>>>>>> of couples the state and the data of the message
> >>>>>>>>>> queue, and it also creates conflicts with current kafka
> >>> connector.
> >>>>>>>>>>
> >>>>>>>>>> Think about if users suspend a running job (which contains some
> >>>>> reading
> >>>>>>>>>> state now), and then change the start offset of the reader.
> >>>>>>>>>> By changing the reading offset, it actually change the whole
> >>> story
> >>>> of
> >>>>>>>>>> "which records should be insert messages and which records
> >>>>>>>>>> should be update messages). And it will also make Flink to deal
> >>>> with
> >>>>>>>>>> another weird situation that it might receive a deletion
> >>>>>>>>>> on a non existing message.
> >>>>>>>>>>
> >>>>>>>>>> We were unsatisfied with all the frictions and conflicts it
> >> will
> >>>>> create
> >>>>>>>>> if
> >>>>>>>>>> we enable the "upsert & deletion" support to the current kafka
> >>>>>>>>>> connector. And later we begin to realize that we shouldn't
> >> treat
> >>> it
> >>>>> as
> >>>>>>> a
> >>>>>>>>>> normal message queue, but should treat it as a changing keyed
> >>>>>>>>>> table. We should be able to always get the whole data of such
> >>> table
> >>>>> (by
> >>>>>>>>>> disabling the start offset option) and we can also read the
> >>>>>>>>>> changelog out of such table. It's like a HBase table with
> >> binlog
> >>>>>>> support
> >>>>>>>>>> but doesn't have random access capability (which can be
> >> fulfilled
> >>>>>>>>>> by Flink's state).
> >>>>>>>>>>
> >>>>>>>>>> So our intention was instead of telling and persuading users
> >> what
> >>>>> kind
> >>>>>>> of
> >>>>>>>>>> options they should or should not use by extending
> >>>>>>>>>> current kafka connector when enable upsert support, we are
> >>> actually
> >>>>>>>>> create
> >>>>>>>>>> a whole new and different connector that has total
> >>>>>>>>>> different abstractions in SQL layer, and should be treated
> >>> totally
> >>>>>>>>>> different with current kafka connector.
> >>>>>>>>>>
> >>>>>>>>>> Hope this can clarify some of the concerns.
> >>>>>>>>>>
> >>>>>>>>>> Best,
> >>>>>>>>>> Kurt
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> On Tue, Oct 20, 2020 at 5:20 PM Shengkai Fang <
> >> [hidden email]
> >>>>
> >>>>>>> wrote:
> >>>>>>>>>>
> >>>>>>>>>>> Hi devs,
> >>>>>>>>>>>
> >>>>>>>>>>> As many people are still confused about the difference option
> >>>>>>>>> behaviours
> >>>>>>>>>>> between the Kafka connector and KTable connector, Jark and I
> >>> list
> >>>>> the
> >>>>>>>>>>> differences in the doc[1].
> >>>>>>>>>>>
> >>>>>>>>>>> Best,
> >>>>>>>>>>> Shengkai
> >>>>>>>>>>>
> >>>>>>>>>>> [1]
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>
> >>>>>
> >>>>
> >>>
> >>
> https://docs.google.com/document/d/13oAWAwQez0lZLsyfV21BfTEze1fc2cz4AZKiNOyBNPk/edit
> >>>>>>>>>>>
> >>>>>>>>>>> Shengkai Fang <[hidden email]> 于2020年10月20日周二 下午12:05写道：
> >>>>>>>>>>>
> >>>>>>>>>>>> Hi Konstantin,
> >>>>>>>>>>>>
> >>>>>>>>>>>> Thanks for your reply.
> >>>>>>>>>>>>
> >>>>>>>>>>>>> It uses the "kafka" connector and does not specify a primary
> >>>> key.
> >>>>>>>>>>>> The dimensional table `users` is a ktable connector and we
> >> can
> >>>>>>>>> specify
> >>>>>>>>>>> the
> >>>>>>>>>>>> pk on the KTable.
> >>>>>>>>>>>>
> >>>>>>>>>>>>> Will it possible to use a "ktable" as a dimensional table in
> >>>>>>>>> FLIP-132
> >>>>>>>>>>>> Yes. We can specify the watermark on the KTable and it can be
> >>>> used
> >>>>>>>>> as a
> >>>>>>>>>>>> dimension table in temporal join.
> >>>>>>>>>>>>
> >>>>>>>>>>>>> Introduce a new connector vs introduce a new property
> >>>>>>>>>>>> The main reason behind is that the KTable connector almost
> >> has
> >>> no
> >>>>>>>>>> common
> >>>>>>>>>>>> options with the Kafka connector. The options that can be
> >>> reused
> >>>> by
> >>>>>>>>>>> KTable
> >>>>>>>>>>>> connectors are 'topic', 'properties.bootstrap.servers' and
> >>>>>>>>>>>> 'value.fields-include' . We can't set cdc format for
> >>> 'key.format'
> >>>>> and
> >>>>>>>>>>>> 'value.format' in KTable connector now, which is  available
> >> in
> >>>>> Kafka
> >>>>>>>>>>>> connector. Considering the difference between the options we
> >>> can
> >>>>> use,
> >>>>>>>>>>> it's
> >>>>>>>>>>>> more suitable to introduce an another connector rather than a
> >>>>>>>>> property.
> >>>>>>>>>>>>
> >>>>>>>>>>>> We are also fine to use "compacted-kafka" as the name of the
> >>> new
> >>>>>>>>>>>> connector. What do you think?
> >>>>>>>>>>>>
> >>>>>>>>>>>> Best,
> >>>>>>>>>>>> Shengkai
> >>>>>>>>>>>>
> >>>>>>>>>>>> Konstantin Knauf <[hidden email]> 于2020年10月19日周一
> >> 下午10:15写道：
> >>>>>>>>>>>>
> >>>>>>>>>>>>> Hi Shengkai,
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Thank you for driving this effort. I believe this a very
> >>>> important
> >>>>>>>>>>> feature
> >>>>>>>>>>>>> for many users who use Kafka and Flink SQL together. A few
> >>>>> questions
> >>>>>>>>>> and
> >>>>>>>>>>>>> thoughts:
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> * Is your example "Use KTable as a reference/dimension
> >> table"
> >>>>>>>>> correct?
> >>>>>>>>>>> It
> >>>>>>>>>>>>> uses the "kafka" connector and does not specify a primary
> >> key.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> * Will it be possible to use a "ktable" table directly as a
> >>>>>>>>>> dimensional
> >>>>>>>>>>>>> table in temporal join (*based on event time*) (FLIP-132)?
> >>> This
> >>>> is
> >>>>>>>>> not
> >>>>>>>>>>>>> completely clear to me from the FLIP.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> * I'd personally prefer not to introduce a new connector and
> >>>>> instead
> >>>>>>>>>> to
> >>>>>>>>>>>>> extend the Kafka connector. We could add an additional
> >>> property
> >>>>>>>>>>>>> "compacted"
> >>>>>>>>>>>>> = "true"|"false". If it is set to "true", we can add
> >>> additional
> >>>>>>>>>>> validation
> >>>>>>>>>>>>> logic (e.g. "scan.startup.mode" can not be set, primary key
> >>>>>>>>> required,
> >>>>>>>>>>>>> etc.). If we stick to a separate connector I'd not call it
> >>>>> "ktable",
> >>>>>>>>>> but
> >>>>>>>>>>>>> rather "compacted-kafka" or similar. KTable seems to carry
> >>> more
> >>>>>>>>>> implicit
> >>>>>>>>>>>>> meaning than we want to imply here.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> * I agree that this is not a bounded source. If we want to
> >>>>> support a
> >>>>>>>>>>>>> bounded mode, this is an orthogonal concern that also
> >> applies
> >>> to
> >>>>>>>>> other
> >>>>>>>>>>>>> unbounded sources.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Konstantin
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> On Mon, Oct 19, 2020 at 3:26 PM Jark Wu <[hidden email]>
> >>>> wrote:
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>> Hi Danny,
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> First of all, we didn't introduce any concepts from KSQL
> >>> (e.g.
> >>>>>>>>>> Stream
> >>>>>>>>>>> vs
> >>>>>>>>>>>>>> Table notion).
> >>>>>>>>>>>>>> This new connector will produce a changelog stream, so it's
> >>>> still
> >>>>>>>>> a
> >>>>>>>>>>>>> dynamic
> >>>>>>>>>>>>>> table and doesn't conflict with Flink core concepts.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> The "ktable" is just a connector name, we can also call it
> >>>>>>>>>>>>>> "compacted-kafka" or something else.
> >>>>>>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate
> >> to
> >>>>>>>>> Flink
> >>>>>>>>>>> SQL
> >>>>>>>>>>>>>> easily.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Regarding to why introducing a new connector vs a new
> >>> property
> >>>> in
> >>>>>>>>>>>>> existing
> >>>>>>>>>>>>>> kafka connector:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> I think the main reason is that we want to have a clear
> >>>>> separation
> >>>>>>>>>> for
> >>>>>>>>>>>>> such
> >>>>>>>>>>>>>> two use cases, because they are very different.
> >>>>>>>>>>>>>> We also listed reasons in the FLIP, including:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> 1) It's hard to explain what's the behavior when users
> >>> specify
> >>>>> the
> >>>>>>>>>>> start
> >>>>>>>>>>>>>> offset from a middle position (e.g. how to process non
> >> exist
> >>>>>>>>> delete
> >>>>>>>>>>>>>> events).
> >>>>>>>>>>>>>>        It's dangerous if users do that. So we don't provide
> >>> the
> >>>>>>>>> offset
> >>>>>>>>>>>>> option
> >>>>>>>>>>>>>> in the new connector at the moment.
> >>>>>>>>>>>>>> 2) It's a different perspective/abstraction on the same
> >> kafka
> >>>>>>>>> topic
> >>>>>>>>>>>>> (append
> >>>>>>>>>>>>>> vs. upsert). It would be easier to understand if we can
> >>>> separate
> >>>>>>>>>> them
> >>>>>>>>>>>>>>        instead of mixing them in one connector. The new
> >>>> connector
> >>>>>>>>>>> requires
> >>>>>>>>>>>>>> hash sink partitioner, primary key declared, regular
> >> format.
> >>>>>>>>>>>>>>        If we mix them in one connector, it might be
> >> confusing
> >>>> how
> >>>>> to
> >>>>>>>>>> use
> >>>>>>>>>>>>> the
> >>>>>>>>>>>>>> options correctly.
> >>>>>>>>>>>>>> 3) The semantic of the KTable connector is just the same as
> >>>>> KTable
> >>>>>>>>>> in
> >>>>>>>>>>>>> Kafka
> >>>>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL users.
> >>>>>>>>>>>>>>        We have seen several questions in the mailing list
> >>> asking
> >>>>> how
> >>>>>>>>> to
> >>>>>>>>>>>>> model
> >>>>>>>>>>>>>> a KTable and how to join a KTable in Flink SQL.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>> Jark
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> On Mon, 19 Oct 2020 at 19:53, Jark Wu <[hidden email]>
> >>>> wrote:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Hi Jingsong,
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> As the FLIP describes, "KTable connector produces a
> >>> changelog
> >>>>>>>>>>> stream,
> >>>>>>>>>>>>>>> where each data record represents an update or delete
> >>> event.".
> >>>>>>>>>>>>>>> Therefore, a ktable source is an unbounded stream source.
> >>>>>>>>>> Selecting
> >>>>>>>>>>> a
> >>>>>>>>>>>>>>> ktable source is similar to selecting a kafka source with
> >>>>>>>>>>>>> debezium-json
> >>>>>>>>>>>>>>> format
> >>>>>>>>>>>>>>> that it never ends and the results are continuously
> >> updated.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> It's possible to have a bounded ktable source in the
> >> future,
> >>>> for
> >>>>>>>>>>>>> example,
> >>>>>>>>>>>>>>> add an option 'bounded=true' or 'end-offset=xxx'.
> >>>>>>>>>>>>>>> In this way, the ktable will produce a bounded changelog
> >>>> stream.
> >>>>>>>>>>>>>>> So I think this can be a compatible feature in the future.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> I don't think we should associate with ksql related
> >>> concepts.
> >>>>>>>>>>>>> Actually,
> >>>>>>>>>>>>>> we
> >>>>>>>>>>>>>>> didn't introduce any concepts from KSQL (e.g. Stream vs
> >>> Table
> >>>>>>>>>>> notion).
> >>>>>>>>>>>>>>> The "ktable" is just a connector name, we can also call it
> >>>>>>>>>>>>>>> "compacted-kafka" or something else.
> >>>>>>>>>>>>>>> Calling it "ktable" is just because KSQL users can migrate
> >>> to
> >>>>>>>>>> Flink
> >>>>>>>>>>>>> SQL
> >>>>>>>>>>>>>>> easily.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Regarding the "value.fields-include", this is an option
> >>>>>>>>> introduced
> >>>>>>>>>>> in
> >>>>>>>>>>>>>>> FLIP-107 for Kafka connector.
> >>>>>>>>>>>>>>> I think we should keep the same behavior with the Kafka
> >>>>>>>>> connector.
> >>>>>>>>>>> I'm
> >>>>>>>>>>>>>> not
> >>>>>>>>>>>>>>> sure what's the default behavior of KSQL.
> >>>>>>>>>>>>>>> But I guess it also stores the keys in value from this
> >>> example
> >>>>>>>>>> docs
> >>>>>>>>>>>>> (see
> >>>>>>>>>>>>>>> the "users_original" table) [1].
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>> Jark
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> [1]:
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>
> >>>>>
> >>>>
> >>>
> >>
> https://docs.confluent.io/current/ksqldb/tutorials/basics-local.html#create-a-stream-and-table
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> On Mon, 19 Oct 2020 at 18:17, Danny Chan <
> >>>> [hidden email]>
> >>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> The concept seems conflicts with the Flink abstraction
> >>>> “dynamic
> >>>>>>>>>>>>> table”,
> >>>>>>>>>>>>>>>> in Flink we see both “stream” and “table” as a dynamic
> >>> table,
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> I think we should make clear first how to express stream
> >>> and
> >>>>>>>>>> table
> >>>>>>>>>>>>>>>> specific features on one “dynamic table”,
> >>>>>>>>>>>>>>>> it is more natural for KSQL because KSQL takes stream and
> >>>> table
> >>>>>>>>>> as
> >>>>>>>>>>>>>>>> different abstractions for representing collections. In
> >>> KSQL,
> >>>>>>>>>> only
> >>>>>>>>>>>>>> table is
> >>>>>>>>>>>>>>>> mutable and can have a primary key.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Does this connector belongs to the “table” scope or
> >>> “stream”
> >>>>>>>>>> scope
> >>>>>>>>>>> ?
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Some of the concepts (such as the primary key on stream)
> >>>> should
> >>>>>>>>>> be
> >>>>>>>>>>>>>>>> suitable for all the connectors, not just Kafka,
> >> Shouldn’t
> >>>> this
> >>>>>>>>>> be
> >>>>>>>>>>> an
> >>>>>>>>>>>>>>>> extension of existing Kafka connector instead of a
> >> totally
> >>>> new
> >>>>>>>>>>>>>> connector ?
> >>>>>>>>>>>>>>>> What about the other connectors ?
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Because this touches the core abstraction of Flink, we
> >>> better
> >>>>>>>>>> have
> >>>>>>>>>>> a
> >>>>>>>>>>>>>>>> top-down overall design, following the KSQL directly is
> >> not
> >>>> the
> >>>>>>>>>>>>> answer.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> P.S. For the source
> >>>>>>>>>>>>>>>>> Shouldn’t this be an extension of existing Kafka
> >> connector
> >>>>>>>>>>> instead
> >>>>>>>>>>>>> of
> >>>>>>>>>>>>>> a
> >>>>>>>>>>>>>>>> totally new connector ?
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> How could we achieve that (e.g. set up the parallelism
> >>>>>>>>>> correctly) ?
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>> Danny Chan
> >>>>>>>>>>>>>>>> 在 2020年10月19日 +0800 PM5:17，Jingsong Li <
> >>>> [hidden email]
> >>>>>>>>>>>> ，写道：
> >>>>>>>>>>>>>>>>> Thanks Shengkai for your proposal.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> +1 for this feature.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Future Work: Support bounded KTable source
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> I don't think it should be a future work, I think it is
> >>> one
> >>>>>>>>> of
> >>>>>>>>>>> the
> >>>>>>>>>>>>>>>>> important concepts of this FLIP. We need to understand
> >> it
> >>>>>>>>> now.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Intuitively, a ktable in my opinion is a bounded table
> >>>> rather
> >>>>>>>>>>> than
> >>>>>>>>>>>>> a
> >>>>>>>>>>>>>>>>> stream, so select should produce a bounded table by
> >>> default.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> I think we can list Kafka related knowledge, because the
> >>>> word
> >>>>>>>>>>>>> `ktable`
> >>>>>>>>>>>>>>>> is
> >>>>>>>>>>>>>>>>> easy to associate with ksql related concepts. (If
> >>> possible,
> >>>>>>>>>> it's
> >>>>>>>>>>>>>> better
> >>>>>>>>>>>>>>>> to
> >>>>>>>>>>>>>>>>> unify with it)
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> What do you think?
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> value.fields-include
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> What about the default behavior of KSQL?
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>> Jingsong
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> On Mon, Oct 19, 2020 at 4:33 PM Shengkai Fang <
> >>>>>>>>>> [hidden email]
> >>>>>>>>>>>>
> >>>>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Hi, devs.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Jark and I want to start a new FLIP to introduce the
> >>> KTable
> >>>>>>>>>>>>>>>> connector. The
> >>>>>>>>>>>>>>>>>> KTable is a shortcut of "Kafka Table", it also has the
> >>> same
> >>>>>>>>>>>>>> semantics
> >>>>>>>>>>>>>>>> with
> >>>>>>>>>>>>>>>>>> the KTable notion in Kafka Stream.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> FLIP-149:
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>
> >>>>>
> >>>>
> >>>
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-149%3A+Introduce+the+KTable+Connector
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Currently many users have expressed their needs for the
> >>>>>>>>>> upsert
> >>>>>>>>>>>>> Kafka
> >>>>>>>>>>>>>>>> by
> >>>>>>>>>>>>>>>>>> mail lists and issues. The KTable connector has several
> >>>>>>>>>>> benefits
> >>>>>>>>>>>>> for
> >>>>>>>>>>>>>>>> users:
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> 1. Users are able to interpret a compacted Kafka Topic
> >> as
> >>>>>>>>> an
> >>>>>>>>>>>>> upsert
> >>>>>>>>>>>>>>>> stream
> >>>>>>>>>>>>>>>>>> in Apache Flink. And also be able to write a changelog
> >>>>>>>>> stream
> >>>>>>>>>>> to
> >>>>>>>>>>>>>> Kafka
> >>>>>>>>>>>>>>>>>> (into a compacted topic).
> >>>>>>>>>>>>>>>>>> 2. As a part of the real time pipeline, store join or
> >>>>>>>>>> aggregate
> >>>>>>>>>>>>>>>> result (may
> >>>>>>>>>>>>>>>>>> contain updates) into a Kafka topic for further
> >>>>>>>>> calculation;
> >>>>>>>>>>>>>>>>>> 3. The semantic of the KTable connector is just the
> >> same
> >>> as
> >>>>>>>>>>>>> KTable
> >>>>>>>>>>>>>> in
> >>>>>>>>>>>>>>>> Kafka
> >>>>>>>>>>>>>>>>>> Stream. So it's very handy for Kafka Stream and KSQL
> >>> users.
> >>>>>>>>>> We
> >>>>>>>>>>>>> have
> >>>>>>>>>>>>>>>> seen
> >>>>>>>>>>>>>>>>>> several questions in the mailing list asking how to
> >>> model a
> >>>>>>>>>>>>> KTable
> >>>>>>>>>>>>>>>> and how
> >>>>>>>>>>>>>>>>>> to join a KTable in Flink SQL.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> We hope it can expand the usage of the Flink with
> >> Kafka.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> I'm looking forward to your feedback.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>>> Shengkai
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> --
> >>>>>>>>>>>>>>>>> Best, Jingsong Lee
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> --
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Konstantin Knauf
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> https://twitter.com/snntrable
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> https://github.com/knaufk
> >>>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> --
> >>>>>>>>>
> >>>>>>>>> Konstantin Knauf
> >>>>>>>>>
> >>>>>>>>> https://twitter.com/snntrable
> >>>>>>>>>
> >>>>>>>>> https://github.com/knaufk
> >>>>>>>>>
> >>>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>
> >>>>>
> >>>>>
> >>>>
> >>>> --
> >>>>
> >>>> Seth Wiesman | Solutions Architect
> >>>>
> >>>> [phone]
> >>>>
> >>>> <https://www.ververica.com/>
> >>>>
> >>>> Follow us @VervericaData
> >>>>
> >>>> --
> >>>>
> >>>> Join Flink Forward <https://flink-forward.org/> - The Apache Flink
> >>>> Conference
> >>>>
> >>>> Stream Processing | Event Driven | Real Time
> >>>>
> >>>
> >>
> >
>
>

--
Best, Jingsong Lee