Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Yun Gao]

Hi all,

Very thanks Jark for the new scenarios. Based on the these new scenarios, I 
think these scenarios and iteration should be able to represent a type of 
scenarios that requires broadcasting events.

I also totally agree with Piotr that all the scenarios we have discussed should 
be clearly motivated. From what we learned from the discussion, now we think 
that broadcasting events seems to be most suitable for iteration and also some 
other scenarios, therefore, we would rewrite a motivation design doc for 
broadcasting events first and reinitiate a separate discussion for that. The 
current discussion would be then continue for scenarios require actual 
multicasting. Very thanks for all the valuable points raised, and I think now 
the comparison of different methods and scenarios are more clear. :)

Best,
Yun


--
From:Jark Wu 
Send Time:2019 Aug. 27 (Tue.) 16:27
To:dev 
Cc:Yun Gao 
Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

Hi all,

Thanks Yun for bringing this topic. I missed this discussion because of the 
"multicast" title. 
After reading the design, if I understand correctly, it is proposing a custom 
event mach mechanism, i.e. broadcasting custom event. 
It is a orthogonality topic with multicasting. So I would suggest to start a 
new thread to discuss about it. 

Regarding to broadcasting custom event:

I would +1 for motivation, because we also encountered similar requirements 
when improving Table API & SQL before. 

For example, the mini-batch mechanism in blink planner will emit a special 
mini-batch event to the data stream to indicate this is a start of a new 
mini-batch. 
The downstream aggregation operator will buffer the data records until it 
receive the mini-batch event, and then process the buffer at once. This will 
reduce a lot of state access. 
However, we don't have a proper custom event mechanism currently, so we 
leverage the watermark as the mini-batch event (which is a little hack in my 
opinion).

Another case is joining a huge dimension table which is stored/produced in hive 
daily. We can scan the hive table and shuffle to the JOIN operators by the join 
key to join with the main stream.
Note that the dimension table is changed every day, we want to join the latest 
version of the hive table. Then we need to re-scan and re-shuffle the hive 
table once a new daily partition is produced. 
However, we need some special events to distinguish the boundary of different 
version of the dimension table. The events will be used to notify downstream 
operators (mainly the JOIN operator)
 to know "ok, I will receive a new version of the dimension table", "ok, I 
received the all the data of this version."

From my understanding, in order to support this feature, we might need to:
 1) expose collectEvent(CustomEvent) or broadcastEvent(CustomEvent) API to 
users. 
 2) support to register the serialization and deserialization of the custom 
event
 3) expose processEvent(int channel, CustomEvent) API to StreamOperator


Regards,
Jark


On Tue, 27 Aug 2019 at 15:18, Piotr Nowojski  wrote:
Hi,

 Before starting a work on the design doc, I would suggest to find someone to 
shepherd this project. Otherwise this effort might drown among other parallel 
things. I could take care of that from the runtime perspective, however most of 
the changes are about the API and changes, which are outside of my area of 
expertise.

 Regarding the multicast, before we start working on that, I would also prefer 
to see a motivation design doc, how that feature would be used for example for 
cross or theta joins in the Table API, since very similar questions would apply 
to that as well.

 Piotrek

 > On 27 Aug 2019, at 08:10, SHI Xiaogang  wrote:
 > 
 > Hi Yun Gao,
 > 
 > Thanks a lot for your clarification.
 > 
 > Now that the notification of broadcast events requires alignment whose
 > implementation, in my opinion, will affect the correctness of synchronous
 > iterations, I prefer to postpone the discussion until you have completed
 > the design of the new iteration library, or at least the progress tracking
 > part. Otherwise, the discussion for broadcasting events may become an empty
 > talk if it does not fit in with the final design.
 > 
 > What do you think?
 > 
 > Regards,
 > Xiaogang
 > 
 > Yun Gao  于2019年8月27日周二 上午11:33写道：
 > 
 >> Hi Xiaogang,
 >> 
 >>  Very thanks for also considering the iteration case! :) These points
 >> are really important for iteration. As a whole, we are implementing a new
 >> iteration library on top of Stream API. As a library, most of its
 >> implementation does not need to touch Runtime layer, but it really has some
 >> new requirements on the API, like the one for being able to broadcast the
 >> progressive events. To be more detail, these events indeed carry the
 >> sender's index and the downstream operators need to do alignment the events
 >> from all the 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Jark Wu]

Hi all,

Thanks Yun for bringing this topic. I missed this discussion because of the
"multicast" title.
After reading the design, if I understand correctly, it is proposing a
custom event mach mechanism, i.e. broadcasting custom event.
It is a orthogonality topic with multicasting. So I would suggest to start
a new thread to discuss about it.

Regarding to broadcasting custom event:

I would +1 for motivation, because we also encountered similar requirements
when improving Table API & SQL before.

For example, the mini-batch mechanism in blink planner will emit a special
mini-batch event to the data stream to indicate this is a start of a new
mini-batch.
The downstream aggregation operator will buffer the data records until it
receive the mini-batch event, and then process the buffer at once. This
will reduce a lot of state access.
However, we don't have a proper custom event mechanism currently, so we
leverage the watermark as the mini-batch event (which is a little hack in
my opinion).

Another case is joining a huge dimension table which is stored/produced in
hive daily. We can scan the hive table and shuffle to the JOIN operators by
the join key to join with the main stream.
Note that the dimension table is changed every day, we want to join the
latest version of the hive table. Then we need to re-scan and re-shuffle
the hive table once a new daily partition is produced.
However, we need some special events to distinguish the boundary of
different version of the dimension table. The events will be used to notify
downstream operators (mainly the JOIN operator)
 to know "ok, I will receive a new version of the dimension table", "ok, I
received the all the data of this version."

>From my understanding, in order to support this feature, we might need to:
 1) expose collectEvent(CustomEvent) or broadcastEvent(CustomEvent) API to
users.
 2) support to register the serialization and deserialization of the custom
event
 3) expose processEvent(int channel, CustomEvent) API to StreamOperator


Regards,
Jark


On Tue, 27 Aug 2019 at 15:18, Piotr Nowojski  wrote:

> Hi,
>
> Before starting a work on the design doc, I would suggest to find someone
> to shepherd this project. Otherwise this effort might drown among other
> parallel things. I could take care of that from the runtime perspective,
> however most of the changes are about the API and changes, which are
> outside of my area of expertise.
>
> Regarding the multicast, before we start working on that, I would also
> prefer to see a motivation design doc, how that feature would be used for
> example for cross or theta joins in the Table API, since very similar
> questions would apply to that as well.
>
> Piotrek
>
> > On 27 Aug 2019, at 08:10, SHI Xiaogang  wrote:
> >
> > Hi Yun Gao,
> >
> > Thanks a lot for your clarification.
> >
> > Now that the notification of broadcast events requires alignment whose
> > implementation, in my opinion, will affect the correctness of synchronous
> > iterations, I prefer to postpone the discussion until you have completed
> > the design of the new iteration library, or at least the progress
> tracking
> > part. Otherwise, the discussion for broadcasting events may become an
> empty
> > talk if it does not fit in with the final design.
> >
> > What do you think?
> >
> > Regards,
> > Xiaogang
> >
> > Yun Gao  于2019年8月27日周二 上午11:33写道：
> >
> >> Hi Xiaogang,
> >>
> >>  Very thanks for also considering the iteration case! :) These
> points
> >> are really important for iteration. As a whole, we are implementing a
> new
> >> iteration library on top of Stream API. As a library, most of its
> >> implementation does not need to touch Runtime layer, but it really has
> some
> >> new requirements on the API, like the one for being able to broadcast
> the
> >> progressive events. To be more detail, these events indeed carry the
> >> sender's index and the downstream operators need to do alignment the
> events
> >> from all the upstream operators. It works very similar to watermark,
> thus
> >> these events do not need to be contained in checkpoints.
> >>
> >> Some other points are also under implementation. However, since some
> part
> >> of the design is still under discussion internally, we may not be able
> to
> >> start a new discussion on iteration immediately. Besides, we should also
> >> need to fix the problems that may have new requirements on the Runtime,
> >> like broadcasting events, to have a complete design. Therefore, I think
> we
> >> may still first have the broadcasting problem settled in this thread?
> Based
> >> on the points learned in the discussion, now I think that we might be
> able
> >> to decouple the broadcasting events requirements and more generalized
> >> multicasting mechanism. :)
> >>
> >> Best,
> >> Yun
> >>
> >>
> >>
> >> --
> >> From:SHI Xiaogang 
> >> Send Time:2019 Aug. 27 (Tue.) 09:16
> >> To:dev ; Yun Gao 
> >> Cc:Piotr 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Piotr Nowojski]

Hi,

Before starting a work on the design doc, I would suggest to find someone to 
shepherd this project. Otherwise this effort might drown among other parallel 
things. I could take care of that from the runtime perspective, however most of 
the changes are about the API and changes, which are outside of my area of 
expertise.

Regarding the multicast, before we start working on that, I would also prefer 
to see a motivation design doc, how that feature would be used for example for 
cross or theta joins in the Table API, since very similar questions would apply 
to that as well.

Piotrek

> On 27 Aug 2019, at 08:10, SHI Xiaogang  wrote:
> 
> Hi Yun Gao,
> 
> Thanks a lot for your clarification.
> 
> Now that the notification of broadcast events requires alignment whose
> implementation, in my opinion, will affect the correctness of synchronous
> iterations, I prefer to postpone the discussion until you have completed
> the design of the new iteration library, or at least the progress tracking
> part. Otherwise, the discussion for broadcasting events may become an empty
> talk if it does not fit in with the final design.
> 
> What do you think?
> 
> Regards,
> Xiaogang
> 
> Yun Gao  于2019年8月27日周二 上午11:33写道：
> 
>> Hi Xiaogang,
>> 
>>  Very thanks for also considering the iteration case! :) These points
>> are really important for iteration. As a whole, we are implementing a new
>> iteration library on top of Stream API. As a library, most of its
>> implementation does not need to touch Runtime layer, but it really has some
>> new requirements on the API, like the one for being able to broadcast the
>> progressive events. To be more detail, these events indeed carry the
>> sender's index and the downstream operators need to do alignment the events
>> from all the upstream operators. It works very similar to watermark, thus
>> these events do not need to be contained in checkpoints.
>> 
>> Some other points are also under implementation. However, since some part
>> of the design is still under discussion internally, we may not be able to
>> start a new discussion on iteration immediately. Besides, we should also
>> need to fix the problems that may have new requirements on the Runtime,
>> like broadcasting events, to have a complete design. Therefore, I think we
>> may still first have the broadcasting problem settled in this thread? Based
>> on the points learned in the discussion, now I think that we might be able
>> to decouple the broadcasting events requirements and more generalized
>> multicasting mechanism. :)
>> 
>> Best,
>> Yun
>> 
>> 
>> 
>> --
>> From:SHI Xiaogang 
>> Send Time:2019 Aug. 27 (Tue.) 09:16
>> To:dev ; Yun Gao 
>> Cc:Piotr Nowojski 
>> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
>> 
>> Hi, Yun Gao
>> 
>> The discussion seems to move in a different direction, changing from
>> supporting multicasting to implementing new iteration libraries on data
>> streams.
>> 
>> Regarding the broadcast events in iterations, many details of new
>> iteration libraries are unclear,
>> 1. How the iteration progress is determined and notified? The iterations
>> are synchronous or asynchronous? As far as i know, progress tracking for
>> asynchronous iterations is very difficult.
>> 2. Do async I/O operators allowed in the iterations? If so, how the
>> broadcast events are checkpointed and restored? How broadcast events are
>> distributed when the degree of parallelism changes?
>> 3. Do the emitted broadcast events carry the sender's index? Will they be
>> aligned in a similar way to checkpoint barriers in downstream operators?
>> 4. In the case of synchronous iterations, do we need something similar to
>> barrier buffers to guarantee the correctness of iterations?
>> 5. Will checkpointing be enabled in iterations? If checkpointing is
>> enabled, how will checkpoint barriers interact with broadcast events?
>> 
>> I think a detailed design document for iterations will help understand
>> these problems, hencing improving the discussion.
>> 
>> I also suggest a new thread for the discussion on iterations.
>> This thread should focus on multicasting and discuss those problems
>> related to multicasting, including how data is delivered and states are
>> partitioned.
>> 
>> Regards,
>> Xiaogang
>> Yun Gao  于2019年8月26日周一 下午11:35写道：
>> 
>> Hi,
>> 
>> Very thanks for all the points raised !
>> 
>> @Piotr For using another edge to broadcast the event, I think it may not
>> be able to address the iteration case. The primary problem is that with
>> two edges we cannot ensure the order of records. However, In the iteration
>> case, the broadcasted event is used to mark the progress of the iteration
>> and it works like watermark, thus its position relative to the normal
>> records can not change.
>> And @Piotr, @Xiaogang, for the requirements on the state, I think
>> different options seems vary. The first option is 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[SHI Xiaogang]

Hi Yun Gao,

Thanks a lot for your clarification.

Now that the notification of broadcast events requires alignment whose
implementation, in my opinion, will affect the correctness of synchronous
iterations, I prefer to postpone the discussion until you have completed
the design of the new iteration library, or at least the progress tracking
part. Otherwise, the discussion for broadcasting events may become an empty
talk if it does not fit in with the final design.

What do you think?

Regards,
Xiaogang

Yun Gao  于2019年8月27日周二 上午11:33写道：

>  Hi Xiaogang,
>
>   Very thanks for also considering the iteration case! :) These points
> are really important for iteration. As a whole, we are implementing a new
> iteration library on top of Stream API. As a library, most of its
> implementation does not need to touch Runtime layer, but it really has some
> new requirements on the API, like the one for being able to broadcast the
> progressive events. To be more detail, these events indeed carry the
> sender's index and the downstream operators need to do alignment the events
> from all the upstream operators. It works very similar to watermark, thus
> these events do not need to be contained in checkpoints.
>
> Some other points are also under implementation. However, since some part
> of the design is still under discussion internally, we may not be able to
> start a new discussion on iteration immediately. Besides, we should also
> need to fix the problems that may have new requirements on the Runtime,
> like broadcasting events, to have a complete design. Therefore, I think we
> may still first have the broadcasting problem settled in this thread? Based
> on the points learned in the discussion, now I think that we might be able
> to decouple the broadcasting events requirements and more generalized
> multicasting mechanism. :)
>
> Best,
> Yun
>
>
>
> --
> From:SHI Xiaogang 
> Send Time:2019 Aug. 27 (Tue.) 09:16
> To:dev ; Yun Gao 
> Cc:Piotr Nowojski 
> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
>
> Hi, Yun Gao
>
> The discussion seems to move in a different direction, changing from
> supporting multicasting to implementing new iteration libraries on data
> streams.
>
> Regarding the broadcast events in iterations, many details of new
> iteration libraries are unclear,
> 1. How the iteration progress is determined and notified? The iterations
> are synchronous or asynchronous? As far as i know, progress tracking for
> asynchronous iterations is very difficult.
> 2. Do async I/O operators allowed in the iterations? If so, how the
> broadcast events are checkpointed and restored? How broadcast events are
> distributed when the degree of parallelism changes?
> 3. Do the emitted broadcast events carry the sender's index? Will they be
> aligned in a similar way to checkpoint barriers in downstream operators?
> 4. In the case of synchronous iterations, do we need something similar to
> barrier buffers to guarantee the correctness of iterations?
> 5. Will checkpointing be enabled in iterations? If checkpointing is
> enabled, how will checkpoint barriers interact with broadcast events?
>
> I think a detailed design document for iterations will help understand
> these problems, hencing improving the discussion.
>
> I also suggest a new thread for the discussion on iterations.
> This thread should focus on multicasting and discuss those problems
> related to multicasting, including how data is delivered and states are
> partitioned.
>
> Regards,
> Xiaogang
> Yun Gao  于2019年8月26日周一 下午11:35写道：
>
>  Hi,
>
>  Very thanks for all the points raised !
>
>  @Piotr For using another edge to broadcast the event, I think it may not
> be able to address the iteration case. The primary problem is that with
> two edges we cannot ensure the order of records. However, In the iteration
> case, the broadcasted event is used to mark the progress of the iteration
> and it works like watermark, thus its position relative to the normal
> records can not change.
>  And @Piotr, @Xiaogang, for the requirements on the state, I think
> different options seems vary. The first option is to allow Operator to
> broadcast a separate event and have a separate process method for this
> event. To be detail, we may add a new type of StreamElement called Event
> and allow Operator to broadcastEmit Event. Then in the received side, we
> could add a new `processEvent` method to the (Keyed)ProcessFunction.
> Similar to the broadcast side of KeyedBroadcastProcessFunction, in this new
> method users cannot access keyed state with specific key, but can register
> a state function to touch all the elements in the keyed state. This option
> needs to modify the runtime to support the new type of StreamElement, but
> it does not affect the semantics of states and thus it has no requirements
> on state.
>  The second option is to allow Operator to broadcastEmit T 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Yun Gao]

 Hi Xiaogang,

  Very thanks for also considering the iteration case! :) These points are 
really important for iteration. As a whole, we are implementing a new iteration 
library on top of Stream API. As a library, most of its implementation does not 
need to touch Runtime layer, but it really has some new requirements on the 
API, like the one for being able to broadcast the progressive events. To be 
more detail, these events indeed carry the sender's index and the downstream 
operators need to do alignment the events from all the upstream operators. It 
works very similar to watermark, thus these events do not need to be contained 
in checkpoints. 

Some other points are also under implementation. However, since some part of 
the design is still under discussion internally, we may not be able to start a 
new discussion on iteration immediately. Besides, we should also need to fix 
the problems that may have new requirements on the Runtime, like broadcasting 
events, to have a complete design. Therefore, I think we may still first have 
the broadcasting problem settled in this thread? Based on the points learned in 
the discussion, now I think that we might be able to decouple the broadcasting 
events requirements and more generalized multicasting mechanism. :)

Best,
Yun



--
From:SHI Xiaogang 
Send Time:2019 Aug. 27 (Tue.) 09:16
To:dev ; Yun Gao 
Cc:Piotr Nowojski 
Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

Hi, Yun Gao

The discussion seems to move in a different direction, changing from supporting 
multicasting to implementing new iteration libraries on data streams. 

Regarding the broadcast events in iterations, many details of new iteration 
libraries are unclear,
1. How the iteration progress is determined and notified? The iterations are 
synchronous or asynchronous? As far as i know, progress tracking for 
asynchronous iterations is very difficult.
2. Do async I/O operators allowed in the iterations? If so, how the broadcast 
events are checkpointed and restored? How broadcast events are distributed when 
the degree of parallelism changes?
3. Do the emitted broadcast events carry the sender's index? Will they be 
aligned in a similar way to checkpoint barriers in downstream operators?
4. In the case of synchronous iterations, do we need something similar to 
barrier buffers to guarantee the correctness of iterations?
5. Will checkpointing be enabled in iterations? If checkpointing is enabled, 
how will checkpoint barriers interact with broadcast events?

I think a detailed design document for iterations will help understand these 
problems, hencing improving the discussion. 

I also suggest a new thread for the discussion on iterations. 
This thread should focus on multicasting and discuss those problems related to 
multicasting, including how data is delivered and states are partitioned.

Regards,
Xiaogang
Yun Gao  于2019年8月26日周一 下午11:35写道：

 Hi,

 Very thanks for all the points raised ! 

 @Piotr For using another edge to broadcast the event, I think it may not be 
able to address the iteration case. The primary problem is that with  two edges 
we cannot ensure the order of records. However, In the iteration case, the 
broadcasted event is used to mark the progress of the iteration and it works 
like watermark, thus its position relative to the normal records can not change.
 And @Piotr, @Xiaogang, for the requirements on the state, I think different 
options seems vary. The first option is to allow Operator to broadcast a 
separate event and have a separate process method for this event. To be detail, 
we may add a new type of StreamElement called Event and allow Operator to 
broadcastEmit Event. Then in the received side, we could add a new 
`processEvent` method to the (Keyed)ProcessFunction. Similar to the broadcast 
side of KeyedBroadcastProcessFunction, in this new method users cannot access 
keyed state with specific key, but can register a state function to touch all 
the elements in the keyed state. This option needs to modify the runtime to 
support the new type of StreamElement, but it does not affect the semantics of 
states and thus it has no requirements on state.
 The second option is to allow Operator to broadcastEmit T and in the 
receiver side, user can process the broadcast element with the existing process 
method. This option is consistent with the OperatorState, but for keyedState we 
may send a record to tasks that do not containing the corresponding keyed 
state, thus it should require some changes on the State.
 The third option is to support the generic Multicast. For keyedState it also 
meets the problem of inconsistency between network partitioner and keyed state 
partitioner, and if we want to rely on it to implement the non-key join, it 
should be also meet the problem of cannot control the partitioning of operator 
state. Therefore, it should also require 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[SHI Xiaogang]

Hi, Yun Gao

The discussion seems to move in a different direction, changing from
supporting multicasting to implementing new iteration libraries on data
streams.

Regarding the broadcast events in iterations, many details of new iteration
libraries are unclear,
1. How the iteration progress is determined and notified? The iterations
are synchronous or asynchronous? As far as i know, progress tracking for
asynchronous iterations is very difficult.
2. Do async I/O operators allowed in the iterations? If so, how the
broadcast events are checkpointed and restored? How broadcast events are
distributed when the degree of parallelism changes?
3. Do the emitted broadcast events carry the sender's index? Will they be
aligned in a similar way to checkpoint barriers in downstream operators?
4. In the case of synchronous iterations, do we need something similar to
barrier buffers to guarantee the correctness of iterations?
5. Will checkpointing be enabled in iterations? If checkpointing is
enabled, how will checkpoint barriers interact with broadcast events?

I think a detailed design document for iterations will help understand
these problems, hencing improving the discussion.

I also suggest a new thread for the discussion on iterations.
This thread should focus on multicasting and discuss those problems related
to multicasting, including how data is delivered and states are partitioned.

Regards,
Xiaogang

Yun Gao  于2019年8月26日周一 下午11:35写道：

>
> Hi,
>
> Very thanks for all the points raised !
>
> @Piotr For using another edge to broadcast the event, I think it may not
> be able to address the iteration case. The primary problem is that with
> two edges we cannot ensure the order of records. However, In the iteration
> case, the broadcasted event is used to mark the progress of the iteration
> and it works like watermark, thus its position relative to the normal
> records can not change.
> And @Piotr, @Xiaogang, for the requirements on the state, I think
> different options seems vary. The first option is to allow Operator to
> broadcast a separate event and have a separate process method for this
> event. To be detail, we may add a new type of StreamElement called Event
> and allow Operator to broadcastEmit Event. Then in the received side, we
> could add a new `processEvent` method to the (Keyed)ProcessFunction.
> Similar to the broadcast side of KeyedBroadcastProcessFunction, in this new
> method users cannot access keyed state with specific key, but can register
> a state function to touch all the elements in the keyed state. This option
> needs to modify the runtime to support the new type of StreamElement, but
> it does not affect the semantics of states and thus it has no requirements
> on state.
> The second option is to allow Operator to broadcastEmit T and in the
> receiver side, user can process the broadcast element with the existing
> process method. This option is consistent with the OperatorState, but for
> keyedState we may send a record to tasks that do not containing the
> corresponding keyed state, thus it should require some changes on the State.
> The third option is to support the generic Multicast. For keyedState it
> also meets the problem of inconsistency between network partitioner and
> keyed state partitioner, and if we want to rely on it to implement the
> non-key join, it should be also meet the problem of cannot control the
> partitioning of operator state. Therefore, it should also require some
> changes on the State.
> Then for the different scenarios proposed, the iteration case in fact
> requires exactly the ability to broadcast a different event type. In the
> iteration the fields of the progress event are in fact different from that
> of normal records. It does not contain actual value but contains some
> fields for the downstream operators to align the events and track the
> progress. Therefore, broadcasting a different event type is able to solve
> the iteration case without the requirements on the state. Besides, allowing
> the operator to broadcast a separate event may also facilitate some other
> user cases, for example, users may notify the downstream operators to
> change logic if some patterns are matched. The notification might be
> different from the normal records and users do not need to uniform them
> with a wrapper type manually if the operators are able to broadcast a
> separate event. However, it truly cannot address the non-key join
> scenarios.
> Since allowing broadcasting a separate event seems to be able to serve as
> a standalone functionality, and it does not require change on the state, I
> am thinking that is it possible for us to partition to multiple steps and
> supports broadcasting events first ? At the same time we could also
> continue working on other options to support more scenarios like non-key
> join and they seems to requires more thoughts.
>
> Best,
> Yun
>
>
>
> --
> From:Piotr 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Yun Gao]

Hi,

Very thanks for all the points raised ! 

@Piotr For using another edge to broadcast the event, I think it may not be 
able to address the iteration case. The primary problem is that with  two edges 
we cannot ensure the order of records. However, In the iteration case, the 
broadcasted event is used to mark the progress of the iteration and it works 
like watermark, thus its position relative to the normal records can not change.
And @Piotr, @Xiaogang, for the requirements on the state, I think different 
options seems vary. The first option is to allow Operator to broadcast a 
separate event and have a separate process method for this event. To be detail, 
we may add a new type of StreamElement called Event and allow Operator to 
broadcastEmit Event. Then in the received side, we could add a new 
`processEvent` method to the (Keyed)ProcessFunction. Similar to the broadcast 
side of KeyedBroadcastProcessFunction, in this new method users cannot access 
keyed state with specific key, but can register a state function to touch all 
the elements in the keyed state. This option needs to modify the runtime to 
support the new type of StreamElement, but it does not affect the semantics of 
states and thus it has no requirements on state.
The second option is to allow Operator to broadcastEmit T and in the 
receiver side, user can process the broadcast element with the existing process 
method. This option is consistent with the OperatorState, but for keyedState we 
may send a record to tasks that do not containing the corresponding keyed 
state, thus it should require some changes on the State.
The third option is to support the generic Multicast. For keyedState it also 
meets the problem of inconsistency between network partitioner and keyed state 
partitioner, and if we want to rely on it to implement the non-key join, it 
should be also meet the problem of cannot control the partitioning of operator 
state. Therefore, it should also require some changes on the State.
Then for the different scenarios proposed, the iteration case in fact requires 
exactly the ability to broadcast a different event type. In the iteration the 
fields of the progress event are in fact different from that of normal records. 
It does not contain actual value but contains some fields for the downstream 
operators to align the events and track the progress. Therefore, broadcasting a 
different event type is able to solve the iteration case without the 
requirements on the state. Besides, allowing the operator to broadcast a 
separate event may also facilitate some other user cases, for example, users 
may notify the downstream operators to change logic if some patterns are 
matched. The notification might be different from the normal records and users 
do not need to uniform them with a wrapper type manually if the operators are 
able to broadcast a separate event. However, it truly cannot address the 
non-key join scenarios. 
Since allowing broadcasting a separate event seems to be able to serve as a 
standalone functionality, and it does not require change on the state, I am 
thinking that is it possible for us to partition to multiple steps and supports 
broadcasting events first ? At the same time we could also continue working on 
other options to support more scenarios like non-key join and they seems to 
requires more thoughts.

Best,
Yun



--
From:Piotr Nowojski 
Send Time:2019 Aug. 26 (Mon.) 18:59
To:dev 
Cc:Yun Gao 
Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

Hi,

Xiaogang, those things worry me the most.
1. Regarding the broadcasting, doesn’t the BroadcastState [1] cover our issues? 
Can not we construct a job graph, where one operator has two outputs, one keyed 
another broadcasted, which are wired together back to the 
KeyedBroadcastProcessFunction or BroadcastProcessFunction? 

2. Multicast on keyed streams, might be done by iterating over all of the keys. 
However I have a feeling that might not be the feature which distributed 
cross/theta joins would want, since they would probably need a guarantee to 
have only a single key per operator instance.

Kurt, by broadcast optimisation do you mean [2]?

I’m not sure if we should split the discussion yet. Most of the changes 
required by either multicast or broadcast will be in the API/state layers. 
Runtime changes for broadcast would be almost none (just exposing existing 
features) and for multicast they shouldn't be huge as well. However maybe we 
should consider those two things together at the API level, so that we do not 
make wrong decisions when just looking at the simpler/more narrow broadcast 
support?

Piotrek

[1] 
https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/state/broadcast_state.html
[2] https://github.com/apache/flink/pull/7713


On 26 Aug 2019, at 09:35, Kurt Young  wrote:
From SQL's perspective, distributed cross join is a valid 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Kurt Young]

Yes, glad to see that there is already a PR for such optimization.

Best,
Kurt


On Mon, Aug 26, 2019 at 6:59 PM Piotr Nowojski  wrote:

> Hi,
>
> Xiaogang, those things worry me the most.
>
> 1. Regarding the broadcasting, doesn’t the BroadcastState [1] cover our
> issues? Can not we construct a job graph, where one operator has two
> outputs, one keyed another broadcasted, which are wired together back to
> the KeyedBroadcastProcessFunction or BroadcastProcessFunction?
>
> 2. Multicast on keyed streams, might be done by iterating over all of the
> keys. However I have a feeling that might not be the feature which
> distributed cross/theta joins would want, since they would probably need a
> guarantee to have only a single key per operator instance.
>
> Kurt, by broadcast optimisation do you mean [2]?
>
> I’m not sure if we should split the discussion yet. Most of the changes
> required by either multicast or broadcast will be in the API/state layers.
> Runtime changes for broadcast would be almost none (just exposing existing
> features) and for multicast they shouldn't be huge as well. However maybe
> we should consider those two things together at the API level, so that we
> do not make wrong decisions when just looking at the simpler/more narrow
> broadcast support?
>
> Piotrek
>
> [1]
> https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/state/broadcast_state.html
> <
> https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/state/broadcast_state.html
> >
> [2] https://github.com/apache/flink/pull/7713 <
> https://github.com/apache/flink/pull/7713>
>
> > On 26 Aug 2019, at 09:35, Kurt Young  wrote:
> >
> > From SQL's perspective, distributed cross join is a valid feature but not
> > very
> > urgent. Actually this discuss reminds me about another useful feature
> > (sorry
> > for the distraction):
> >
> > when doing broadcast in batch shuffle mode, we can make each producer
> only
> > write one copy of the output data, but not for every consumer. Broadcast
> > join
> > is much more useful, and this is a very important optimization. Not sure
> if
> > we
> > have already consider this.
> >
> > Best,
> > Kurt
> >
> >
> > On Mon, Aug 26, 2019 at 12:16 PM Guowei Ma  wrote:
> >
> >> Thanks Yun for bringing up this discussion and very thanks for all the
> deep
> >> thoughts!
> >>
> >> For now, I think this discussion contains two scenarios: one if for
> >> iteration library support and the other is for SQL join support. I think
> >> both of the two scenarios are useful but they seem to have different
> best
> >> suitable solutions. For making the discussion more clear, I would
> suggest
> >> to split the discussion into two threads.
> >>
> >> And I agree with Piotr that it is very tricky that a keyed stream
> received
> >> a "broadcast element". So we may add some new interfaces, which could
> >> broadcast or process some special "broadcast event". In that way
> "broadcast
> >> event" will not be sent with the normal process.
> >>
> >> Best,
> >> Guowei
> >>
> >>
> >> SHI Xiaogang  于2019年8月26日周一 上午9:27写道：
> >>
> >>> Hi all,
> >>>
> >>> I also think that multicasting is a necessity in Flink, but more
> details
> >>> are needed to be considered.
> >>>
> >>> Currently network is tightly coupled with states in Flink to achieve
> >>> automatic scaling. We can only access keyed states in keyed streams and
> >>> operator states in all streams.
> >>> In the concrete example of theta-joins implemented with mutlticasting,
> >> the
> >>> following questions exist:
> >>>
> >>>   - In which type of states will the data be stored? Do we need another
> >>>   type of states which is coupled with multicasting streams?
> >>>   - How to ensure the consistency between network and states when jobs
> >>>   scale out or scale in?
> >>>
> >>> Regards,
> >>> Xiaogang
> >>>
> >>> Xingcan Cui  于2019年8月25日周日 上午10:03写道：
> >>>
>  Hi all,
> 
>  Sorry for joining this thread late. Basically, I think enabling
> >> multicast
>  pattern could be the right direction, but more detailed implementation
>  policies need to be discussed.
> 
>  Two years ago, I filed an issue [1] about the multicast API. However,
> >> due
>  to some reasons, it was laid aside. After that, when I tried to
> >>> cherry-pick
>  the change for experimental use, I found the return type of
>  `selectChannels()` method had changed from `int[]` to `int`, which
> >> makes
>  the old implementation not work anymore.
> 
>  From my side, the multicast has always been used for theta-join. As
> far
> >>> as
>  I know, it’s an essential requirement for some sophisticated joining
>  algorithms. Until now, the Flink non-equi joins can still only be
> >>> executed
>  single-threaded. If we'd like to make some improvements on this, we
> >>> should
>  first take some measures to support multicast pattern.
> 
>  Best,
>  Xingcan
> 
>  [1] 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Piotr Nowojski]

Hi,

Xiaogang, those things worry me the most.

1. Regarding the broadcasting, doesn’t the BroadcastState [1] cover our issues? 
Can not we construct a job graph, where one operator has two outputs, one keyed 
another broadcasted, which are wired together back to the 
KeyedBroadcastProcessFunction or BroadcastProcessFunction? 

2. Multicast on keyed streams, might be done by iterating over all of the keys. 
However I have a feeling that might not be the feature which distributed 
cross/theta joins would want, since they would probably need a guarantee to 
have only a single key per operator instance.

Kurt, by broadcast optimisation do you mean [2]?

I’m not sure if we should split the discussion yet. Most of the changes 
required by either multicast or broadcast will be in the API/state layers. 
Runtime changes for broadcast would be almost none (just exposing existing 
features) and for multicast they shouldn't be huge as well. However maybe we 
should consider those two things together at the API level, so that we do not 
make wrong decisions when just looking at the simpler/more narrow broadcast 
support?

Piotrek

[1] 
https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/state/broadcast_state.html
 

[2] https://github.com/apache/flink/pull/7713 


> On 26 Aug 2019, at 09:35, Kurt Young  wrote:
> 
> From SQL's perspective, distributed cross join is a valid feature but not
> very
> urgent. Actually this discuss reminds me about another useful feature
> (sorry
> for the distraction):
> 
> when doing broadcast in batch shuffle mode, we can make each producer only
> write one copy of the output data, but not for every consumer. Broadcast
> join
> is much more useful, and this is a very important optimization. Not sure if
> we
> have already consider this.
> 
> Best,
> Kurt
> 
> 
> On Mon, Aug 26, 2019 at 12:16 PM Guowei Ma  wrote:
> 
>> Thanks Yun for bringing up this discussion and very thanks for all the deep
>> thoughts!
>> 
>> For now, I think this discussion contains two scenarios: one if for
>> iteration library support and the other is for SQL join support. I think
>> both of the two scenarios are useful but they seem to have different best
>> suitable solutions. For making the discussion more clear, I would suggest
>> to split the discussion into two threads.
>> 
>> And I agree with Piotr that it is very tricky that a keyed stream received
>> a "broadcast element". So we may add some new interfaces, which could
>> broadcast or process some special "broadcast event". In that way "broadcast
>> event" will not be sent with the normal process.
>> 
>> Best,
>> Guowei
>> 
>> 
>> SHI Xiaogang  于2019年8月26日周一 上午9:27写道：
>> 
>>> Hi all,
>>> 
>>> I also think that multicasting is a necessity in Flink, but more details
>>> are needed to be considered.
>>> 
>>> Currently network is tightly coupled with states in Flink to achieve
>>> automatic scaling. We can only access keyed states in keyed streams and
>>> operator states in all streams.
>>> In the concrete example of theta-joins implemented with mutlticasting,
>> the
>>> following questions exist:
>>> 
>>>   - In which type of states will the data be stored? Do we need another
>>>   type of states which is coupled with multicasting streams?
>>>   - How to ensure the consistency between network and states when jobs
>>>   scale out or scale in?
>>> 
>>> Regards,
>>> Xiaogang
>>> 
>>> Xingcan Cui  于2019年8月25日周日 上午10:03写道：
>>> 
 Hi all,
 
 Sorry for joining this thread late. Basically, I think enabling
>> multicast
 pattern could be the right direction, but more detailed implementation
 policies need to be discussed.
 
 Two years ago, I filed an issue [1] about the multicast API. However,
>> due
 to some reasons, it was laid aside. After that, when I tried to
>>> cherry-pick
 the change for experimental use, I found the return type of
 `selectChannels()` method had changed from `int[]` to `int`, which
>> makes
 the old implementation not work anymore.
 
 From my side, the multicast has always been used for theta-join. As far
>>> as
 I know, it’s an essential requirement for some sophisticated joining
 algorithms. Until now, the Flink non-equi joins can still only be
>>> executed
 single-threaded. If we'd like to make some improvements on this, we
>>> should
 first take some measures to support multicast pattern.
 
 Best,
 Xingcan
 
 [1] https://issues.apache.org/jira/browse/FLINK-6936
 
> On Aug 24, 2019, at 5:54 AM, Zhu Zhu  wrote:
> 
> Hi Piotr,
> 
> Thanks for the explanation.
> Agreed that the broadcastEmit(record) is a better choice for
>>> broadcasting
> for the iterations.
> As broadcasting for the iterations is the first motivation, let's
>>> support
> it first.
> 
> 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Kurt Young]

>From SQL's perspective, distributed cross join is a valid feature but not
very
urgent. Actually this discuss reminds me about another useful feature
(sorry
for the distraction):

when doing broadcast in batch shuffle mode, we can make each producer only
write one copy of the output data, but not for every consumer. Broadcast
join
is much more useful, and this is a very important optimization. Not sure if
we
have already consider this.

Best,
Kurt


On Mon, Aug 26, 2019 at 12:16 PM Guowei Ma  wrote:

> Thanks Yun for bringing up this discussion and very thanks for all the deep
> thoughts!
>
> For now, I think this discussion contains two scenarios: one if for
> iteration library support and the other is for SQL join support. I think
> both of the two scenarios are useful but they seem to have different best
> suitable solutions. For making the discussion more clear, I would suggest
> to split the discussion into two threads.
>
> And I agree with Piotr that it is very tricky that a keyed stream received
> a "broadcast element". So we may add some new interfaces, which could
> broadcast or process some special "broadcast event". In that way "broadcast
> event" will not be sent with the normal process.
>
> Best,
> Guowei
>
>
> SHI Xiaogang  于2019年8月26日周一 上午9:27写道：
>
> > Hi all,
> >
> > I also think that multicasting is a necessity in Flink, but more details
> > are needed to be considered.
> >
> > Currently network is tightly coupled with states in Flink to achieve
> > automatic scaling. We can only access keyed states in keyed streams and
> > operator states in all streams.
> > In the concrete example of theta-joins implemented with mutlticasting,
> the
> > following questions exist:
> >
> >- In which type of states will the data be stored? Do we need another
> >type of states which is coupled with multicasting streams?
> >- How to ensure the consistency between network and states when jobs
> >scale out or scale in?
> >
> > Regards,
> > Xiaogang
> >
> > Xingcan Cui  于2019年8月25日周日 上午10:03写道：
> >
> > > Hi all,
> > >
> > > Sorry for joining this thread late. Basically, I think enabling
> multicast
> > > pattern could be the right direction, but more detailed implementation
> > > policies need to be discussed.
> > >
> > > Two years ago, I filed an issue [1] about the multicast API. However,
> due
> > > to some reasons, it was laid aside. After that, when I tried to
> > cherry-pick
> > > the change for experimental use, I found the return type of
> > > `selectChannels()` method had changed from `int[]` to `int`, which
> makes
> > > the old implementation not work anymore.
> > >
> > > From my side, the multicast has always been used for theta-join. As far
> > as
> > > I know, it’s an essential requirement for some sophisticated joining
> > > algorithms. Until now, the Flink non-equi joins can still only be
> > executed
> > > single-threaded. If we'd like to make some improvements on this, we
> > should
> > > first take some measures to support multicast pattern.
> > >
> > > Best,
> > > Xingcan
> > >
> > > [1] https://issues.apache.org/jira/browse/FLINK-6936
> > >
> > > > On Aug 24, 2019, at 5:54 AM, Zhu Zhu  wrote:
> > > >
> > > > Hi Piotr,
> > > >
> > > > Thanks for the explanation.
> > > > Agreed that the broadcastEmit(record) is a better choice for
> > broadcasting
> > > > for the iterations.
> > > > As broadcasting for the iterations is the first motivation, let's
> > support
> > > > it first.
> > > >
> > > > Thanks,
> > > > Zhu Zhu
> > > >
> > > > Yun Gao  于2019年8月23日周五 下午11:56写道：
> > > >
> > > >> Hi Piotr,
> > > >>
> > > >>  Very thanks for the suggestions!
> > > >>
> > > >> Totally agree with that we could first focus on the broadcast
> > > >> scenarios and exposing the broadcastEmit method first considering
> the
> > > >> semantics and performance.
> > > >>
> > > >> For the keyed stream, I also agree with that broadcasting keyed
> > > >> records to all the tasks may be confused considering the semantics
> of
> > > keyed
> > > >> partitioner. However, in the iteration case supporting broadcast
> over
> > > keyed
> > > >> partitioner should be required since users may create any subgraph
> for
> > > the
> > > >> iteration body, including the operators with key. I think a possible
> > > >> solution to this issue is to introduce another data type for
> > > >> 'broadcastEmit'. For example, for an operator Operator, it may
> > > broadcast
> > > >> emit another type E instead of T, and the transmitting E will bypass
> > the
> > > >> partitioner and setting keyed context. This should result in the
> > design
> > > to
> > > >> introduce customized operator event (option 1 in the document). The
> > > cost of
> > > >> this method is that we need to introduce a new type of StreamElement
> > and
> > > >> new interface for this type, but it should be suitable for both
> keyed
> > or
> > > >> non-keyed partitioner.
> > > >>
> > > >> Best,
> > > >> Yun
> > > >>
> > > >>
> > > >>
> > > >> 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Guowei Ma]

Thanks Yun for bringing up this discussion and very thanks for all the deep
thoughts!

For now, I think this discussion contains two scenarios: one if for
iteration library support and the other is for SQL join support. I think
both of the two scenarios are useful but they seem to have different best
suitable solutions. For making the discussion more clear, I would suggest
to split the discussion into two threads.

And I agree with Piotr that it is very tricky that a keyed stream received
a "broadcast element". So we may add some new interfaces, which could
broadcast or process some special "broadcast event". In that way "broadcast
event" will not be sent with the normal process.

Best,
Guowei


SHI Xiaogang  于2019年8月26日周一 上午9:27写道：

> Hi all,
>
> I also think that multicasting is a necessity in Flink, but more details
> are needed to be considered.
>
> Currently network is tightly coupled with states in Flink to achieve
> automatic scaling. We can only access keyed states in keyed streams and
> operator states in all streams.
> In the concrete example of theta-joins implemented with mutlticasting, the
> following questions exist:
>
>- In which type of states will the data be stored? Do we need another
>type of states which is coupled with multicasting streams?
>- How to ensure the consistency between network and states when jobs
>scale out or scale in?
>
> Regards,
> Xiaogang
>
> Xingcan Cui  于2019年8月25日周日 上午10:03写道：
>
> > Hi all,
> >
> > Sorry for joining this thread late. Basically, I think enabling multicast
> > pattern could be the right direction, but more detailed implementation
> > policies need to be discussed.
> >
> > Two years ago, I filed an issue [1] about the multicast API. However, due
> > to some reasons, it was laid aside. After that, when I tried to
> cherry-pick
> > the change for experimental use, I found the return type of
> > `selectChannels()` method had changed from `int[]` to `int`, which makes
> > the old implementation not work anymore.
> >
> > From my side, the multicast has always been used for theta-join. As far
> as
> > I know, it’s an essential requirement for some sophisticated joining
> > algorithms. Until now, the Flink non-equi joins can still only be
> executed
> > single-threaded. If we'd like to make some improvements on this, we
> should
> > first take some measures to support multicast pattern.
> >
> > Best,
> > Xingcan
> >
> > [1] https://issues.apache.org/jira/browse/FLINK-6936
> >
> > > On Aug 24, 2019, at 5:54 AM, Zhu Zhu  wrote:
> > >
> > > Hi Piotr,
> > >
> > > Thanks for the explanation.
> > > Agreed that the broadcastEmit(record) is a better choice for
> broadcasting
> > > for the iterations.
> > > As broadcasting for the iterations is the first motivation, let's
> support
> > > it first.
> > >
> > > Thanks,
> > > Zhu Zhu
> > >
> > > Yun Gao  于2019年8月23日周五 下午11:56写道：
> > >
> > >> Hi Piotr,
> > >>
> > >>  Very thanks for the suggestions!
> > >>
> > >> Totally agree with that we could first focus on the broadcast
> > >> scenarios and exposing the broadcastEmit method first considering the
> > >> semantics and performance.
> > >>
> > >> For the keyed stream, I also agree with that broadcasting keyed
> > >> records to all the tasks may be confused considering the semantics of
> > keyed
> > >> partitioner. However, in the iteration case supporting broadcast over
> > keyed
> > >> partitioner should be required since users may create any subgraph for
> > the
> > >> iteration body, including the operators with key. I think a possible
> > >> solution to this issue is to introduce another data type for
> > >> 'broadcastEmit'. For example, for an operator Operator, it may
> > broadcast
> > >> emit another type E instead of T, and the transmitting E will bypass
> the
> > >> partitioner and setting keyed context. This should result in the
> design
> > to
> > >> introduce customized operator event (option 1 in the document). The
> > cost of
> > >> this method is that we need to introduce a new type of StreamElement
> and
> > >> new interface for this type, but it should be suitable for both keyed
> or
> > >> non-keyed partitioner.
> > >>
> > >> Best,
> > >> Yun
> > >>
> > >>
> > >>
> > >> --
> > >> From:Piotr Nowojski 
> > >> Send Time:2019 Aug. 23 (Fri.) 22:29
> > >> To:Zhu Zhu 
> > >> Cc:dev ; Yun Gao 
> > >> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication
> > Pattern
> > >>
> > >> Hi,
> > >>
> > >> If the primary motivation is broadcasting (for the iterations) and we
> > have
> > >> no immediate need for multicast (cross join), I would prefer to first
> > >> expose broadcast via the DataStream API and only later, once we
> finally
> > >> need it, support multicast. As I wrote, multicast would be more
> > challenging
> > >> to implement, with more complicated runtime and API. And re-using
> > multicast
> > >> just to support broadcast doesn’t have much 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[SHI Xiaogang]

Hi all,

I also think that multicasting is a necessity in Flink, but more details
are needed to be considered.

Currently network is tightly coupled with states in Flink to achieve
automatic scaling. We can only access keyed states in keyed streams and
operator states in all streams.
In the concrete example of theta-joins implemented with mutlticasting, the
following questions exist:

   - In which type of states will the data be stored? Do we need another
   type of states which is coupled with multicasting streams?
   - How to ensure the consistency between network and states when jobs
   scale out or scale in?

Regards,
Xiaogang

Xingcan Cui  于2019年8月25日周日 上午10:03写道：

> Hi all,
>
> Sorry for joining this thread late. Basically, I think enabling multicast
> pattern could be the right direction, but more detailed implementation
> policies need to be discussed.
>
> Two years ago, I filed an issue [1] about the multicast API. However, due
> to some reasons, it was laid aside. After that, when I tried to cherry-pick
> the change for experimental use, I found the return type of
> `selectChannels()` method had changed from `int[]` to `int`, which makes
> the old implementation not work anymore.
>
> From my side, the multicast has always been used for theta-join. As far as
> I know, it’s an essential requirement for some sophisticated joining
> algorithms. Until now, the Flink non-equi joins can still only be executed
> single-threaded. If we'd like to make some improvements on this, we should
> first take some measures to support multicast pattern.
>
> Best,
> Xingcan
>
> [1] https://issues.apache.org/jira/browse/FLINK-6936
>
> > On Aug 24, 2019, at 5:54 AM, Zhu Zhu  wrote:
> >
> > Hi Piotr,
> >
> > Thanks for the explanation.
> > Agreed that the broadcastEmit(record) is a better choice for broadcasting
> > for the iterations.
> > As broadcasting for the iterations is the first motivation, let's support
> > it first.
> >
> > Thanks,
> > Zhu Zhu
> >
> > Yun Gao  于2019年8月23日周五 下午11:56写道：
> >
> >> Hi Piotr,
> >>
> >>  Very thanks for the suggestions!
> >>
> >> Totally agree with that we could first focus on the broadcast
> >> scenarios and exposing the broadcastEmit method first considering the
> >> semantics and performance.
> >>
> >> For the keyed stream, I also agree with that broadcasting keyed
> >> records to all the tasks may be confused considering the semantics of
> keyed
> >> partitioner. However, in the iteration case supporting broadcast over
> keyed
> >> partitioner should be required since users may create any subgraph for
> the
> >> iteration body, including the operators with key. I think a possible
> >> solution to this issue is to introduce another data type for
> >> 'broadcastEmit'. For example, for an operator Operator, it may
> broadcast
> >> emit another type E instead of T, and the transmitting E will bypass the
> >> partitioner and setting keyed context. This should result in the design
> to
> >> introduce customized operator event (option 1 in the document). The
> cost of
> >> this method is that we need to introduce a new type of StreamElement and
> >> new interface for this type, but it should be suitable for both keyed or
> >> non-keyed partitioner.
> >>
> >> Best,
> >> Yun
> >>
> >>
> >>
> >> --
> >> From:Piotr Nowojski 
> >> Send Time:2019 Aug. 23 (Fri.) 22:29
> >> To:Zhu Zhu 
> >> Cc:dev ; Yun Gao 
> >> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication
> Pattern
> >>
> >> Hi,
> >>
> >> If the primary motivation is broadcasting (for the iterations) and we
> have
> >> no immediate need for multicast (cross join), I would prefer to first
> >> expose broadcast via the DataStream API and only later, once we finally
> >> need it, support multicast. As I wrote, multicast would be more
> challenging
> >> to implement, with more complicated runtime and API. And re-using
> multicast
> >> just to support broadcast doesn’t have much sense:
> >>
> >> 1. It’s a bit obfuscated. It’s easier to understand
> >> collectBroadcast(record) or broadcastEmit(record) compared to some
> >> multicast channel selector that just happens to return all of the
> channels.
> >> 2. There are performance benefits of explicitly calling
> >> `RecordWriter#broadcastEmit`.
> >>
> >>
> >> On a different note, what would be the semantic of such broadcast emit
> on
> >> KeyedStream? Would it be supported? Or would we limit support only to
> the
> >> non-keyed streams?
> >>
> >> Piotrek
> >>
> >>> On 23 Aug 2019, at 12:48, Zhu Zhu  wrote:
> >>>
> >>> Thanks Piotr,
> >>>
> >>> Users asked for this feature sometimes ago when they migrating batch
> >> jobs to Flink(Blink).
> >>> It's not very urgent as they have taken some workarounds to solve
> >> it.(like partitioning data set to different job vertices)
> >>> So it's fine to not make it top priority.
> >>>
> >>> Anyway, as a commonly known scenario, I think users can benefit from
> 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Zhu Zhu]

Hi Piotr,

Thanks for the explanation.
Agreed that the broadcastEmit(record) is a better choice for broadcasting
for the iterations.
As broadcasting for the iterations is the first motivation, let's support
it first.

Thanks,
Zhu Zhu

Yun Gao  于2019年8月23日周五 下午11:56写道：

>  Hi Piotr,
>
>   Very thanks for the suggestions!
>
>  Totally agree with that we could first focus on the broadcast
> scenarios and exposing the broadcastEmit method first considering the
> semantics and performance.
>
>  For the keyed stream, I also agree with that broadcasting keyed
> records to all the tasks may be confused considering the semantics of keyed
> partitioner. However, in the iteration case supporting broadcast over keyed
> partitioner should be required since users may create any subgraph for the
> iteration body, including the operators with key. I think a possible
> solution to this issue is to introduce another data type for
> 'broadcastEmit'. For example, for an operator Operator, it may broadcast
> emit another type E instead of T, and the transmitting E will bypass the
> partitioner and setting keyed context. This should result in the design to
> introduce customized operator event (option 1 in the document). The cost of
> this method is that we need to introduce a new type of StreamElement and
> new interface for this type, but it should be suitable for both keyed or
> non-keyed partitioner.
>
> Best,
> Yun
>
>
>
> --
> From:Piotr Nowojski 
> Send Time:2019 Aug. 23 (Fri.) 22:29
> To:Zhu Zhu 
> Cc:dev ; Yun Gao 
> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
>
> Hi,
>
> If the primary motivation is broadcasting (for the iterations) and we have
> no immediate need for multicast (cross join), I would prefer to first
> expose broadcast via the DataStream API and only later, once we finally
> need it, support multicast. As I wrote, multicast would be more challenging
> to implement, with more complicated runtime and API. And re-using multicast
> just to support broadcast doesn’t have much sense:
>
> 1. It’s a bit obfuscated. It’s easier to understand
> collectBroadcast(record) or broadcastEmit(record) compared to some
> multicast channel selector that just happens to return all of the channels.
> 2. There are performance benefits of explicitly calling
> `RecordWriter#broadcastEmit`.
>
>
> On a different note, what would be the semantic of such broadcast emit on
> KeyedStream? Would it be supported? Or would we limit support only to the
> non-keyed streams?
>
> Piotrek
>
> > On 23 Aug 2019, at 12:48, Zhu Zhu  wrote:
> >
> > Thanks Piotr,
> >
> > Users asked for this feature sometimes ago when they migrating batch
> jobs to Flink(Blink).
> > It's not very urgent as they have taken some workarounds to solve
> it.(like partitioning data set to different job vertices)
> > So it's fine to not make it top priority.
> >
> > Anyway, as a commonly known scenario, I think users can benefit from
> cross join sooner or later.
> >
> > Thanks,
> > Zhu Zhu
> >
> > Piotr Nowojski mailto:pi...@ververica.com>>
> 于2019年8月23日周五 下午6:19写道：
> > Hi,
> >
> > Thanks for the answers :) Ok I understand the full picture now. +1 from
> my side on solving this issue somehow. But before we start discussing how
> to solve it one last control question:
> >
> > I guess this multicast is intended to be used in blink planner, right?
> Assuming that we implement the multicast support now, when would it be used
> by the blink? I would like to avoid a scenario, where we implement an
> unused feature and we keep maintaining it for a long period of time.
> >
> > Piotrek
> >
> > PS, try to include motivating examples, including concrete ones in the
> proposals/design docs, for example in the very first paragraph. Especially
> if it’s a commonly known feature like cross join :)
> >
> > > On 23 Aug 2019, at 11:38, Yun Gao 
> wrote:
> > >
> > > Hi Piotr,
> > >
> > >Thanks a lot for sharing the thoughts!
> > >
> > >For the iteration, agree with that multicasting is not
> necessary. Exploring the broadcast interface to Output of the operators in
> some way should also solve this issue, and I think it should be even more
> convenient to have the broadcast method for the iteration.
> > >
> > >Also thanks Zhu Zhu for the cross join case!
> > >  Best,
> > >   Yun
> > >
> > >
> > >
> > > --
> > > From:Zhu Zhu mailto:reed...@gmail.com>>
> > > Send Time:2019 Aug. 23 (Fri.) 17:25
> > > To:dev mailto:dev@flink.apache.org>>
> > > Cc:Yun Gao mailto:yungao...@aliyun.com>>
> > > Subject:Re: [DISCUSS] Enhance Support for Multicast Communication
> Pattern
> > >
> > > Hi Piotr,
> > >
> > > Yes you are right it's a distributed cross join requirement.
> > > Broadcast join can help with cross join cases. But users cannot use it
> if the data set to join is too large to fit into one 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Yun Gao]

 Hi Piotr,

  Very thanks for the suggestions!  

 Totally agree with that we could first focus on the broadcast scenarios 
and exposing the broadcastEmit method first considering the semantics and 
performance. 

 For the keyed stream, I also agree with that broadcasting keyed records to 
all the tasks may be confused considering the semantics of keyed partitioner. 
However, in the iteration case supporting broadcast over keyed partitioner 
should be required since users may create any subgraph for the iteration body, 
including the operators with key. I think a possible solution to this issue is 
to introduce another data type for 'broadcastEmit'. For example, for an 
operator Operator, it may broadcast emit another type E instead of T, and 
the transmitting E will bypass the partitioner and setting keyed context. This 
should result in the design to introduce customized operator event (option 1 in 
the document). The cost of this method is that we need to introduce a new type 
of StreamElement and new interface for this type, but it should be suitable for 
both keyed or non-keyed partitioner.

Best,
Yun 



--
From:Piotr Nowojski 
Send Time:2019 Aug. 23 (Fri.) 22:29
To:Zhu Zhu 
Cc:dev ; Yun Gao 
Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

Hi,

If the primary motivation is broadcasting (for the iterations) and we have no 
immediate need for multicast (cross join), I would prefer to first expose 
broadcast via the DataStream API and only later, once we finally need it, 
support multicast. As I wrote, multicast would be more challenging to 
implement, with more complicated runtime and API. And re-using multicast just 
to support broadcast doesn’t have much sense:

1. It’s a bit obfuscated. It’s easier to understand collectBroadcast(record) or 
broadcastEmit(record) compared to some multicast channel selector that just 
happens to return all of the channels.
2. There are performance benefits of explicitly calling 
`RecordWriter#broadcastEmit`.


On a different note, what would be the semantic of such broadcast emit on 
KeyedStream? Would it be supported? Or would we limit support only to the 
non-keyed streams?

Piotrek

> On 23 Aug 2019, at 12:48, Zhu Zhu  wrote:
> 
> Thanks Piotr,
> 
> Users asked for this feature sometimes ago when they migrating batch jobs to 
> Flink(Blink). 
> It's not very urgent as they have taken some workarounds to solve it.(like 
> partitioning data set to different job vertices)
> So it's fine to not make it top priority.
> 
> Anyway, as a commonly known scenario, I think users can benefit from cross 
> join sooner or later.
> 
> Thanks,
> Zhu Zhu
> 
> Piotr Nowojski mailto:pi...@ververica.com>> 
> 于2019年8月23日周五 下午6:19写道：
> Hi,
> 
> Thanks for the answers :) Ok I understand the full picture now. +1 from my 
> side on solving this issue somehow. But before we start discussing how to 
> solve it one last control question:
> 
> I guess this multicast is intended to be used in blink planner, right? 
> Assuming that we implement the multicast support now, when would it be used 
> by the blink? I would like to avoid a scenario, where we implement an unused 
> feature and we keep maintaining it for a long period of time.
> 
> Piotrek
> 
> PS, try to include motivating examples, including concrete ones in the 
> proposals/design docs, for example in the very first paragraph. Especially if 
> it’s a commonly known feature like cross join :)
> 
> > On 23 Aug 2019, at 11:38, Yun Gao  wrote:
> > 
> > Hi Piotr,
> > 
> >Thanks a lot for sharing the thoughts! 
> > 
> >For the iteration, agree with that multicasting is not necessary. 
> > Exploring the broadcast interface to Output of the operators in some way 
> > should also solve this issue, and I think it should be even more convenient 
> > to have the broadcast method for the iteration. 
> > 
> >Also thanks Zhu Zhu for the cross join case!
> >  Best, 
> >   Yun
> > 
> > 
> > 
> > --
> > From:Zhu Zhu mailto:reed...@gmail.com>>
> > Send Time:2019 Aug. 23 (Fri.) 17:25
> > To:dev mailto:dev@flink.apache.org>>
> > Cc:Yun Gao mailto:yungao...@aliyun.com>>
> > Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
> > 
> > Hi Piotr,
> > 
> > Yes you are right it's a distributed cross join requirement.
> > Broadcast join can help with cross join cases. But users cannot use it if 
> > the data set to join is too large to fit into one subtask.
> > 
> > Sorry for left some details behind.
> > 
> > Thanks,
> > Zhu Zhu
> > Piotr Nowojski mailto:pi...@ververica.com>> 
> > 于2019年8月23日周五 下午4:57写道：
> > Hi Yun and Zhu Zhu,
> > 
> > Thanks for the more detailed example Zhu Zhu.
> > 
> > As far as I understand for the iterations example we do not need 
> > multicasting. Regarding the Join example, I don’t fully understand it. The 
> > 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Piotr Nowojski]

Hi,

If the primary motivation is broadcasting (for the iterations) and we have no 
immediate need for multicast (cross join), I would prefer to first expose 
broadcast via the DataStream API and only later, once we finally need it, 
support multicast. As I wrote, multicast would be more challenging to 
implement, with more complicated runtime and API. And re-using multicast just 
to support broadcast doesn’t have much sense:

1. It’s a bit obfuscated. It’s easier to understand collectBroadcast(record) or 
broadcastEmit(record) compared to some multicast channel selector that just 
happens to return all of the channels.
2. There are performance benefits of explicitly calling 
`RecordWriter#broadcastEmit`.


On a different note, what would be the semantic of such broadcast emit on 
KeyedStream? Would it be supported? Or would we limit support only to the 
non-keyed streams?

Piotrek

> On 23 Aug 2019, at 12:48, Zhu Zhu  wrote:
> 
> Thanks Piotr,
> 
> Users asked for this feature sometimes ago when they migrating batch jobs to 
> Flink(Blink). 
> It's not very urgent as they have taken some workarounds to solve it.(like 
> partitioning data set to different job vertices)
> So it's fine to not make it top priority.
> 
> Anyway, as a commonly known scenario, I think users can benefit from cross 
> join sooner or later.
> 
> Thanks,
> Zhu Zhu
> 
> Piotr Nowojski mailto:pi...@ververica.com>> 
> 于2019年8月23日周五 下午6:19写道：
> Hi,
> 
> Thanks for the answers :) Ok I understand the full picture now. +1 from my 
> side on solving this issue somehow. But before we start discussing how to 
> solve it one last control question:
> 
> I guess this multicast is intended to be used in blink planner, right? 
> Assuming that we implement the multicast support now, when would it be used 
> by the blink? I would like to avoid a scenario, where we implement an unused 
> feature and we keep maintaining it for a long period of time.
> 
> Piotrek
> 
> PS, try to include motivating examples, including concrete ones in the 
> proposals/design docs, for example in the very first paragraph. Especially if 
> it’s a commonly known feature like cross join :)
> 
> > On 23 Aug 2019, at 11:38, Yun Gao  wrote:
> > 
> > Hi Piotr,
> > 
> >Thanks a lot for sharing the thoughts! 
> > 
> >For the iteration, agree with that multicasting is not necessary. 
> > Exploring the broadcast interface to Output of the operators in some way 
> > should also solve this issue, and I think it should be even more convenient 
> > to have the broadcast method for the iteration. 
> > 
> >Also thanks Zhu Zhu for the cross join case!
> >  Best, 
> >   Yun
> > 
> > 
> > 
> > --
> > From:Zhu Zhu mailto:reed...@gmail.com>>
> > Send Time:2019 Aug. 23 (Fri.) 17:25
> > To:dev mailto:dev@flink.apache.org>>
> > Cc:Yun Gao mailto:yungao...@aliyun.com>>
> > Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
> > 
> > Hi Piotr,
> > 
> > Yes you are right it's a distributed cross join requirement.
> > Broadcast join can help with cross join cases. But users cannot use it if 
> > the data set to join is too large to fit into one subtask.
> > 
> > Sorry for left some details behind.
> > 
> > Thanks,
> > Zhu Zhu
> > Piotr Nowojski mailto:pi...@ververica.com>> 
> > 于2019年8月23日周五 下午4:57写道：
> > Hi Yun and Zhu Zhu,
> > 
> > Thanks for the more detailed example Zhu Zhu.
> > 
> > As far as I understand for the iterations example we do not need 
> > multicasting. Regarding the Join example, I don’t fully understand it. The 
> > example that Zhu Zhu presented has a drawback of sending both tables to 
> > multiple nodes. What’s the benefit of using broadcast join over a hash join 
> > in such case? As far as I know, the biggest benefit of using broadcast join 
> > instead of hash join is that we can avoid sending the larger table over the 
> > network, because we can perform the join locally. In this example we are 
> > sending both of the tables to multiple nodes, which should defeat the 
> > purpose.
> > 
> > Is it about implementing cross join or near cross joins in a distributed 
> > fashion? 
> > 
> >> if we introduce a new MulticastRecordWriter
> > 
> > That’s one of the solutions. It might have a drawback of 3 class 
> > virtualisation problem (We have RecordWriter and BroadcastRecordWriter 
> > already). With up to two implementations, JVM is able to devirtualise the 
> > calls.
> > 
> > Previously I was also thinking about just providing two different 
> > ChannelSelector interfaces. One with `int[]` and `SingleChannelSelector` 
> > with plain `int` and based on that, RecordWriter could perform some magic 
> > (worst case scenario `instaceof` checks).
> > 
> > Another solution might be to change `ChannelSelector` interface into an 
> > iterator.
> > 
> > But let's discuss the details after we agree on implementing this.
> > 
> > Piotrek
> > 
> >> On 23 Aug 2019, at 10:20, 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Zhu Zhu]

Thanks Piotr,

Users asked for this feature sometimes ago when they migrating batch jobs
to Flink(Blink).
It's not very urgent as they have taken some workarounds to solve it.(like
partitioning data set to different job vertices)
So it's fine to not make it top priority.

Anyway, as a commonly known scenario, I think users can benefit from cross
join sooner or later.

Thanks,
Zhu Zhu

Piotr Nowojski  于2019年8月23日周五 下午6:19写道：

> Hi,
>
> Thanks for the answers :) Ok I understand the full picture now. +1 from my
> side on solving this issue somehow. But before we start discussing how to
> solve it one last control question:
>
> I guess this multicast is intended to be used in blink planner, right?
> Assuming that we implement the multicast support now, when would it be used
> by the blink? I would like to avoid a scenario, where we implement an
> unused feature and we keep maintaining it for a long period of time.
>
> Piotrek
>
> PS, try to include motivating examples, including concrete ones in the
> proposals/design docs, for example in the very first paragraph. Especially
> if it’s a commonly known feature like cross join :)
>
> > On 23 Aug 2019, at 11:38, Yun Gao  wrote:
> >
> > Hi Piotr,
> >
> >Thanks a lot for sharing the thoughts!
> >
> >For the iteration, agree with that multicasting is not necessary.
> Exploring the broadcast interface to Output of the operators in some way
> should also solve this issue, and I think it should be even more convenient
> to have the broadcast method for the iteration.
> >
> >Also thanks Zhu Zhu for the cross join case!
> >  Best,
> >   Yun
> >
> >
> >
> > --
> > From:Zhu Zhu 
> > Send Time:2019 Aug. 23 (Fri.) 17:25
> > To:dev 
> > Cc:Yun Gao 
> > Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
> >
> > Hi Piotr,
> >
> > Yes you are right it's a distributed cross join requirement.
> > Broadcast join can help with cross join cases. But users cannot use it
> if the data set to join is too large to fit into one subtask.
> >
> > Sorry for left some details behind.
> >
> > Thanks,
> > Zhu Zhu
> > Piotr Nowojski  于2019年8月23日周五 下午4:57写道：
> > Hi Yun and Zhu Zhu,
> >
> > Thanks for the more detailed example Zhu Zhu.
> >
> > As far as I understand for the iterations example we do not need
> multicasting. Regarding the Join example, I don’t fully understand it. The
> example that Zhu Zhu presented has a drawback of sending both tables to
> multiple nodes. What’s the benefit of using broadcast join over a hash join
> in such case? As far as I know, the biggest benefit of using broadcast join
> instead of hash join is that we can avoid sending the larger table over the
> network, because we can perform the join locally. In this example we are
> sending both of the tables to multiple nodes, which should defeat the
> purpose.
> >
> > Is it about implementing cross join or near cross joins in a distributed
> fashion?
> >
> >> if we introduce a new MulticastRecordWriter
> >
> > That’s one of the solutions. It might have a drawback of 3 class
> virtualisation problem (We have RecordWriter and BroadcastRecordWriter
> already). With up to two implementations, JVM is able to devirtualise the
> calls.
> >
> > Previously I was also thinking about just providing two different
> ChannelSelector interfaces. One with `int[]` and `SingleChannelSelector`
> with plain `int` and based on that, RecordWriter could perform some magic
> (worst case scenario `instaceof` checks).
> >
> > Another solution might be to change `ChannelSelector` interface into an
> iterator.
> >
> > But let's discuss the details after we agree on implementing this.
> >
> > Piotrek
> >
> >> On 23 Aug 2019, at 10:20, Yun Gao  wrote:
> >>
> >>   Hi Piotr,
> >>
> >>Thanks a lot for the suggestions!
> >>
> >>The core motivation of this discussion is to implement a new
> iteration library on the DataStream, and it requires to insert special
> records in the stream to notify the progress of the iteration. The
> mechanism of such records is very similar to the current Watermark, and we
> meet the problem of sending normal records according to the partition
> (Rebalance, etc..) and also be able to broadcast the inserted progress
> records to all the connected records. I have read the notes in the google
> doc and I totally agree with that exploring the broadcast interface in
> RecordWriter in some way is able to solve this issue.
> >>
> >>   Regarding to `int[] ChannelSelector#selectChannels()`, I'm
> wondering if we introduce a new MulticastRecordWriter and left the current
> RecordWriter untouched, could we avoid the performance degradation ? Since
> with such a modification the normal RecordWriter does not need to iterate
> the return array by ChannelSelector, and the only difference will be
> returning an array instead of an integer, and accessing the first element
> of the returned array instead 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Piotr Nowojski]

Hi,

Thanks for the answers :) Ok I understand the full picture now. +1 from my side 
on solving this issue somehow. But before we start discussing how to solve it 
one last control question:

I guess this multicast is intended to be used in blink planner, right? Assuming 
that we implement the multicast support now, when would it be used by the 
blink? I would like to avoid a scenario, where we implement an unused feature 
and we keep maintaining it for a long period of time.

Piotrek

PS, try to include motivating examples, including concrete ones in the 
proposals/design docs, for example in the very first paragraph. Especially if 
it’s a commonly known feature like cross join :)

> On 23 Aug 2019, at 11:38, Yun Gao  wrote:
> 
> Hi Piotr,
> 
>Thanks a lot for sharing the thoughts! 
> 
>For the iteration, agree with that multicasting is not necessary. 
> Exploring the broadcast interface to Output of the operators in some way 
> should also solve this issue, and I think it should be even more convenient 
> to have the broadcast method for the iteration. 
> 
>Also thanks Zhu Zhu for the cross join case!
>  Best, 
>   Yun
> 
> 
> 
> --
> From:Zhu Zhu 
> Send Time:2019 Aug. 23 (Fri.) 17:25
> To:dev 
> Cc:Yun Gao 
> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
> 
> Hi Piotr,
> 
> Yes you are right it's a distributed cross join requirement.
> Broadcast join can help with cross join cases. But users cannot use it if the 
> data set to join is too large to fit into one subtask.
> 
> Sorry for left some details behind.
> 
> Thanks,
> Zhu Zhu
> Piotr Nowojski  于2019年8月23日周五 下午4:57写道：
> Hi Yun and Zhu Zhu,
> 
> Thanks for the more detailed example Zhu Zhu.
> 
> As far as I understand for the iterations example we do not need 
> multicasting. Regarding the Join example, I don’t fully understand it. The 
> example that Zhu Zhu presented has a drawback of sending both tables to 
> multiple nodes. What’s the benefit of using broadcast join over a hash join 
> in such case? As far as I know, the biggest benefit of using broadcast join 
> instead of hash join is that we can avoid sending the larger table over the 
> network, because we can perform the join locally. In this example we are 
> sending both of the tables to multiple nodes, which should defeat the purpose.
> 
> Is it about implementing cross join or near cross joins in a distributed 
> fashion? 
> 
>> if we introduce a new MulticastRecordWriter
> 
> That’s one of the solutions. It might have a drawback of 3 class 
> virtualisation problem (We have RecordWriter and BroadcastRecordWriter 
> already). With up to two implementations, JVM is able to devirtualise the 
> calls.
> 
> Previously I was also thinking about just providing two different 
> ChannelSelector interfaces. One with `int[]` and `SingleChannelSelector` with 
> plain `int` and based on that, RecordWriter could perform some magic (worst 
> case scenario `instaceof` checks).
> 
> Another solution might be to change `ChannelSelector` interface into an 
> iterator.
> 
> But let's discuss the details after we agree on implementing this.
> 
> Piotrek
> 
>> On 23 Aug 2019, at 10:20, Yun Gao  wrote:
>> 
>>   Hi Piotr,
>> 
>>Thanks a lot for the suggestions!
>> 
>>The core motivation of this discussion is to implement a new 
>> iteration library on the DataStream, and it requires to insert special 
>> records in the stream to notify the progress of the iteration. The mechanism 
>> of such records is very similar to the current Watermark, and we meet the 
>> problem of sending normal records according to the partition (Rebalance, 
>> etc..) and also be able to broadcast the inserted progress records to all 
>> the connected records. I have read the notes in the google doc and I totally 
>> agree with that exploring the broadcast interface in RecordWriter in some 
>> way is able to solve this issue. 
>> 
>>   Regarding to `int[] ChannelSelector#selectChannels()`, I'm wondering 
>> if we introduce a new MulticastRecordWriter and left the current 
>> RecordWriter untouched, could we avoid the performance degradation ? Since 
>> with such a modification the normal RecordWriter does not need to iterate 
>> the return array by ChannelSelector, and the only difference will be 
>> returning an array instead of an integer, and accessing the first element of 
>> the returned array instead of reading the integer directly.
>> 
>> Best,
>> Yun
>> 
>> --
>> From:Piotr Nowojski 
>> Send Time:2019 Aug. 23 (Fri.) 15:20
>> To:dev 
>> Cc:Yun Gao 
>> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
>> 
>> Hi,
>> 
>> Yun:
>> 
>> Thanks for proposing the idea. I have checked the document and left couple 
>> of questions there, but it might be better to answer them here.
>> 
>> What is the exact 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Yun Gao]

 Hi Piotr,

Thanks a lot for sharing the thoughts! 

For the iteration, agree with that multicasting is not necessary. 
Exploring the broadcast interface to Output of the operators in some way should 
also solve this issue, and I think it should be even more convenient to have 
the broadcast method for the iteration. 

Also thanks Zhu Zhu for the cross join case!
  Best, 
   Yun



--
From:Zhu Zhu 
Send Time:2019 Aug. 23 (Fri.) 17:25
To:dev 
Cc:Yun Gao 
Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

Hi Piotr,

Yes you are right it's a distributed cross join requirement.
Broadcast join can help with cross join cases. But users cannot use it if the 
data set to join is too large to fit into one subtask.

Sorry for left some details behind.

Thanks,
Zhu Zhu
Piotr Nowojski  于2019年8月23日周五 下午4:57写道：
Hi Yun and Zhu Zhu,

 Thanks for the more detailed example Zhu Zhu.

 As far as I understand for the iterations example we do not need multicasting. 
Regarding the Join example, I don’t fully understand it. The example that Zhu 
Zhu presented has a drawback of sending both tables to multiple nodes. What’s 
the benefit of using broadcast join over a hash join in such case? As far as I 
know, the biggest benefit of using broadcast join instead of hash join is that 
we can avoid sending the larger table over the network, because we can perform 
the join locally. In this example we are sending both of the tables to multiple 
nodes, which should defeat the purpose.

 Is it about implementing cross join or near cross joins in a distributed 
fashion? 

 > if we introduce a new MulticastRecordWriter

 That’s one of the solutions. It might have a drawback of 3 class 
virtualisation problem (We have RecordWriter and BroadcastRecordWriter 
already). With up to two implementations, JVM is able to devirtualise the calls.

 Previously I was also thinking about just providing two different 
ChannelSelector interfaces. One with `int[]` and `SingleChannelSelector` with 
plain `int` and based on that, RecordWriter could perform some magic (worst 
case scenario `instaceof` checks).

 Another solution might be to change `ChannelSelector` interface into an 
iterator.

 But let's discuss the details after we agree on implementing this.

 Piotrek

 > On 23 Aug 2019, at 10:20, Yun Gao  wrote:
 > 
 >Hi Piotr,
 > 
 > Thanks a lot for the suggestions!
 > 
 > The core motivation of this discussion is to implement a new 
 > iteration library on the DataStream, and it requires to insert special 
 > records in the stream to notify the progress of the iteration. The mechanism 
 > of such records is very similar to the current Watermark, and we meet the 
 > problem of sending normal records according to the partition (Rebalance, 
 > etc..) and also be able to broadcast the inserted progress records to all 
 > the connected records. I have read the notes in the google doc and I totally 
 > agree with that exploring the broadcast interface in RecordWriter in some 
 > way is able to solve this issue. 
 > 
 >Regarding to `int[] ChannelSelector#selectChannels()`, I'm wondering 
 > if we introduce a new MulticastRecordWriter and left the current 
 > RecordWriter untouched, could we avoid the performance degradation ? Since 
 > with such a modification the normal RecordWriter does not need to iterate 
 > the return array by ChannelSelector, and the only difference will be 
 > returning an array instead of an integer, and accessing the first element of 
 > the returned array instead of reading the integer directly.
 > 
 > Best,
 > Yun
 > 
 > --
 > From:Piotr Nowojski 
 > Send Time:2019 Aug. 23 (Fri.) 15:20
 > To:dev 
 > Cc:Yun Gao 
 > Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
 > 
 > Hi,
 > 
 > Yun:
 > 
 > Thanks for proposing the idea. I have checked the document and left couple 
 > of questions there, but it might be better to answer them here.
 > 
 > What is the exact motivation and what problems do you want to solve? We have 
 > dropped multicast support from the network stack [1] for two reasons:
 > 1. Performance 
 > 2. Code simplicity 
 > 
 > The proposal to re introduce `int[] ChannelSelector#selectChannels()` would 
 > revert those changes. At that time we were thinking about a way how to keep 
 > the multicast support on the network level, while keeping the performance 
 > and simplicity for non multicast cases and there are ways to achieve that. 
 > However they would add extra complexity to Flink, which it would be better 
 > to avoid.
 > 
 > On the other hand, supporting dual pattern: standard partitioning or 
 > broadcasting is easy to do, as LatencyMarkers are doing exactly that. It 
 > would be just a matter of exposing this to the user in some way. So before 
 > we go any further, can you describe 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Zhu Zhu]

Hi Piotr,

Yes you are right it's a distributed cross join requirement.
Broadcast join can help with cross join cases. But users cannot use it if
the data set to join is too large to fit into one subtask.

Sorry for left some details behind.

Thanks,
Zhu Zhu

Piotr Nowojski  于2019年8月23日周五 下午4:57写道：

> Hi Yun and Zhu Zhu,
>
> Thanks for the more detailed example Zhu Zhu.
>
> As far as I understand for the iterations example we do not need
> multicasting. Regarding the Join example, I don’t fully understand it. The
> example that Zhu Zhu presented has a drawback of sending both tables to
> multiple nodes. What’s the benefit of using broadcast join over a hash join
> in such case? As far as I know, the biggest benefit of using broadcast join
> instead of hash join is that we can avoid sending the larger table over the
> network, because we can perform the join locally. In this example we are
> sending both of the tables to multiple nodes, which should defeat the
> purpose.
>
> Is it about implementing cross join or near cross joins in a distributed
> fashion?
>
> > if we introduce a new MulticastRecordWriter
>
> That’s one of the solutions. It might have a drawback of 3 class
> virtualisation problem (We have RecordWriter and BroadcastRecordWriter
> already). With up to two implementations, JVM is able to devirtualise the
> calls.
>
> Previously I was also thinking about just providing two different
> ChannelSelector interfaces. One with `int[]` and `SingleChannelSelector`
> with plain `int` and based on that, RecordWriter could perform some magic
> (worst case scenario `instaceof` checks).
>
> Another solution might be to change `ChannelSelector` interface into an
> iterator.
>
> But let's discuss the details after we agree on implementing this.
>
> Piotrek
>
> > On 23 Aug 2019, at 10:20, Yun Gao  wrote:
> >
> >Hi Piotr,
> >
> > Thanks a lot for the suggestions!
> >
> > The core motivation of this discussion is to implement a new
> iteration library on the DataStream, and it requires to insert special
> records in the stream to notify the progress of the iteration. The
> mechanism of such records is very similar to the current Watermark, and we
> meet the problem of sending normal records according to the partition
> (Rebalance, etc..) and also be able to broadcast the inserted progress
> records to all the connected records. I have read the notes in the google
> doc and I totally agree with that exploring the broadcast interface in
> RecordWriter in some way is able to solve this issue.
> >
> >Regarding to `int[] ChannelSelector#selectChannels()`, I'm
> wondering if we introduce a new MulticastRecordWriter and left the current
> RecordWriter untouched, could we avoid the performance degradation ? Since
> with such a modification the normal RecordWriter does not need to iterate
> the return array by ChannelSelector, and the only difference will be
> returning an array instead of an integer, and accessing the first element
> of the returned array instead of reading the integer directly.
> >
> > Best,
> > Yun
> >
> > --
> > From:Piotr Nowojski 
> > Send Time:2019 Aug. 23 (Fri.) 15:20
> > To:dev 
> > Cc:Yun Gao 
> > Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
> >
> > Hi,
> >
> > Yun:
> >
> > Thanks for proposing the idea. I have checked the document and left
> couple of questions there, but it might be better to answer them here.
> >
> > What is the exact motivation and what problems do you want to solve? We
> have dropped multicast support from the network stack [1] for two reasons:
> > 1. Performance
> > 2. Code simplicity
> >
> > The proposal to re introduce `int[] ChannelSelector#selectChannels()`
> would revert those changes. At that time we were thinking about a way how
> to keep the multicast support on the network level, while keeping the
> performance and simplicity for non multicast cases and there are ways to
> achieve that. However they would add extra complexity to Flink, which it
> would be better to avoid.
> >
> > On the other hand, supporting dual pattern: standard partitioning or
> broadcasting is easy to do, as LatencyMarkers are doing exactly that. It
> would be just a matter of exposing this to the user in some way. So before
> we go any further, can you describe your use cases/motivation? Isn’t mix of
> standard partitioning and broadcasting enough? Do we need multicasting?
> >
> > Zhu:
> >
> > Could you rephrase your example? I didn’t quite understand it.
> >
> > Piotrek
> >
> > [1] https://issues.apache.org/jira/browse/FLINK-10662 <
> https://issues.apache.org/jira/browse/FLINK-10662>
> >
> > On 23 Aug 2019, at 09:17, Zhu Zhu  reed...@gmail.com>> wrote:
> >
> > Thanks Yun for starting this discussion.
> > I think the multicasting can be very helpful in certain cases.
> >
> > I have received requirements from users that they want to do broadcast
> > join, while the 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Piotr Nowojski]

Hi Yun and Zhu Zhu,

Thanks for the more detailed example Zhu Zhu.

As far as I understand for the iterations example we do not need multicasting. 
Regarding the Join example, I don’t fully understand it. The example that Zhu 
Zhu presented has a drawback of sending both tables to multiple nodes. What’s 
the benefit of using broadcast join over a hash join in such case? As far as I 
know, the biggest benefit of using broadcast join instead of hash join is that 
we can avoid sending the larger table over the network, because we can perform 
the join locally. In this example we are sending both of the tables to multiple 
nodes, which should defeat the purpose.

Is it about implementing cross join or near cross joins in a distributed 
fashion? 

> if we introduce a new MulticastRecordWriter

That’s one of the solutions. It might have a drawback of 3 class virtualisation 
problem (We have RecordWriter and BroadcastRecordWriter already). With up to 
two implementations, JVM is able to devirtualise the calls.

Previously I was also thinking about just providing two different 
ChannelSelector interfaces. One with `int[]` and `SingleChannelSelector` with 
plain `int` and based on that, RecordWriter could perform some magic (worst 
case scenario `instaceof` checks).

Another solution might be to change `ChannelSelector` interface into an 
iterator.

But let's discuss the details after we agree on implementing this.

Piotrek

> On 23 Aug 2019, at 10:20, Yun Gao  wrote:
> 
>Hi Piotr,
> 
> Thanks a lot for the suggestions!
> 
> The core motivation of this discussion is to implement a new 
> iteration library on the DataStream, and it requires to insert special 
> records in the stream to notify the progress of the iteration. The mechanism 
> of such records is very similar to the current Watermark, and we meet the 
> problem of sending normal records according to the partition (Rebalance, 
> etc..) and also be able to broadcast the inserted progress records to all the 
> connected records. I have read the notes in the google doc and I totally 
> agree with that exploring the broadcast interface in RecordWriter in some way 
> is able to solve this issue. 
> 
>Regarding to `int[] ChannelSelector#selectChannels()`, I'm wondering 
> if we introduce a new MulticastRecordWriter and left the current RecordWriter 
> untouched, could we avoid the performance degradation ? Since with such a 
> modification the normal RecordWriter does not need to iterate the return 
> array by ChannelSelector, and the only difference will be returning an array 
> instead of an integer, and accessing the first element of the returned array 
> instead of reading the integer directly.
> 
> Best,
> Yun
> 
> --
> From:Piotr Nowojski 
> Send Time:2019 Aug. 23 (Fri.) 15:20
> To:dev 
> Cc:Yun Gao 
> Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern
> 
> Hi,
> 
> Yun:
> 
> Thanks for proposing the idea. I have checked the document and left couple of 
> questions there, but it might be better to answer them here.
> 
> What is the exact motivation and what problems do you want to solve? We have 
> dropped multicast support from the network stack [1] for two reasons:
> 1. Performance 
> 2. Code simplicity 
> 
> The proposal to re introduce `int[] ChannelSelector#selectChannels()` would 
> revert those changes. At that time we were thinking about a way how to keep 
> the multicast support on the network level, while keeping the performance and 
> simplicity for non multicast cases and there are ways to achieve that. 
> However they would add extra complexity to Flink, which it would be better to 
> avoid.
> 
> On the other hand, supporting dual pattern: standard partitioning or 
> broadcasting is easy to do, as LatencyMarkers are doing exactly that. It 
> would be just a matter of exposing this to the user in some way. So before we 
> go any further, can you describe your use cases/motivation? Isn’t mix of 
> standard partitioning and broadcasting enough? Do we need multicasting?
> 
> Zhu:
> 
> Could you rephrase your example? I didn’t quite understand it.
> 
> Piotrek
> 
> [1] https://issues.apache.org/jira/browse/FLINK-10662 
> 
> 
> On 23 Aug 2019, at 09:17, Zhu Zhu  > wrote:
> 
> Thanks Yun for starting this discussion.
> I think the multicasting can be very helpful in certain cases.
> 
> I have received requirements from users that they want to do broadcast
> join, while the data set to broadcast is too large to fit in one task.
> Thus the requirement turned out to be to support cartesian product of 2
> data set(one of which can be infinite stream).
> For example, A(parallelism=2) broadcast join B(parallelism=2) in JobVertex
> C.
> The idea to is have 4 C subtasks to deal with different combinations of A/B
> partitions, like C1(A1,B1), C2(A1,B2), C3(A2,B1), 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Yun Gao]

   Hi Piotr,

Thanks a lot for the suggestions!

The core motivation of this discussion is to implement a new iteration 
library on the DataStream, and it requires to insert special records in the 
stream to notify the progress of the iteration. The mechanism of such records 
is very similar to the current Watermark, and we meet the problem of sending 
normal records according to the partition (Rebalance, etc..) and also be able 
to broadcast the inserted progress records to all the connected records. I have 
read the notes in the google doc and I totally agree with that exploring the 
broadcast interface in RecordWriter in some way is able to solve this issue. 

   Regarding to `int[] ChannelSelector#selectChannels()`, I'm wondering if 
we introduce a new MulticastRecordWriter and left the current RecordWriter 
untouched, could we avoid the performance degradation ? Since with such a 
modification the normal RecordWriter does not need to iterate the return array 
by ChannelSelector, and the only difference will be returning an array instead 
of an integer, and accessing the first element of the returned array instead of 
reading the integer directly.

Best,
Yun


--
From:Piotr Nowojski 
Send Time:2019 Aug. 23 (Fri.) 15:20
To:dev 
Cc:Yun Gao 
Subject:Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

Hi,

Yun:

Thanks for proposing the idea. I have checked the document and left couple of 
questions there, but it might be better to answer them here.

What is the exact motivation and what problems do you want to solve? We have 
dropped multicast support from the network stack [1] for two reasons:
1. Performance 
2. Code simplicity 

The proposal to re introduce `int[] ChannelSelector#selectChannels()` would 
revert those changes. At that time we were thinking about a way how to keep the 
multicast support on the network level, while keeping the performance and 
simplicity for non multicast cases and there are ways to achieve that. However 
they would add extra complexity to Flink, which it would be better to avoid.

On the other hand, supporting dual pattern: standard partitioning or 
broadcasting is easy to do, as LatencyMarkers are doing exactly that. It would 
be just a matter of exposing this to the user in some way. So before we go any 
further, can you describe your use cases/motivation? Isn’t mix of standard 
partitioning and broadcasting enough? Do we need multicasting?

Zhu:

Could you rephrase your example? I didn’t quite understand it.

Piotrek

[1] https://issues.apache.org/jira/browse/FLINK-10662


On 23 Aug 2019, at 09:17, Zhu Zhu  wrote:
Thanks Yun for starting this discussion.
I think the multicasting can be very helpful in certain cases.

I have received requirements from users that they want to do broadcast
join, while the data set to broadcast is too large to fit in one task.
Thus the requirement turned out to be to support cartesian product of 2
data set(one of which can be infinite stream).
For example, A(parallelism=2) broadcast join B(parallelism=2) in JobVertex
C.
The idea to is have 4 C subtasks to deal with different combinations of A/B
partitions, like C1(A1,B1), C2(A1,B2), C3(A2,B1), C4(A2,B2).
This requires one record to be sent to multiple downstream subtasks, but
not to all subtasks.

With current interface this is not supported, as one record can only be
sent to one subtask, or to all subtasks of a JobVertex.
And the user had to split the broadcast data set manually to several
different JobVertices, which is hard to maintain and extend.

Thanks,
Zhu Zhu

Yun Gao  于2019年8月22日周四 下午8:42写道：

Hi everyone,
  In some scenarios we met a requirement that some operators want to
send records to theirs downstream operators with an multicast communication
pattern. In detail, for some records, the operators want to send them
according to the partitioner (for example, Rebalance), and for some other
records, the operators want to send them to all the connected operators and
tasks. Such a communication pattern could be viewed as a kind of multicast:
it does not broadcast every record, but some record will indeed be sent to
multiple downstream operators.

However, we found that this kind of communication pattern seems could not
be implemented rightly if the operators have multiple consumers with
different parallelism, using the customized partitioner. To solve the above
problem, we propose to enhance the support for such kind of irregular
communication pattern. We think there may be two options:

 1. Support a kind of customized operator events, which share much
similarity with Watermark, and these events can be broadcasted to the
downstream operators separately.
 2. Let the channel selector supports multicast, and also add the
separate RecordWriter implementation to avoid impacting the performance of
the channel selector that does not need multicast.

The problem and options are 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Zhu Zhu]

Hi Piotr,

The case is about a broadcast join:
A--\
 +--(join)--> C
B--/

Usually we can broadcast A(the result that JobVertex A produces) to all
subtasks of C.
But in this case the size of A is too large to fit in one subtask of C.
Thus we have to partition A to (A_0, A_1, A_2, ..., A_m-1).
The throughput of B is too large to deal in one subtask as well. And we
partition B into (B_0, B_1, B_2, ..., B_n-1).

Now if we want to join A and B, the basic idea is to set parallelism of C
to be m*n, and subtask C_kn+l should deal with the join work of (A_k, B_l).
To achieve this,
each record in partition A_k should to sent to *n* downstream subtasks:
{C_kn, C_kn+1, C_kn+2, ..., C_kn+n-1}
each record in partition B_l should to sent to *m* downstream
subtasks:  {C_l, C_n+l, C_2n+l, ..., C_(m-1)n+l}

This is different from current single-cast or broad-cast way.
That's why I think multi-cast can help with this case.

Thanks,
Zhu Zhu

Piotr Nowojski  于2019年8月23日周五 下午3:20写道：

> Hi,
>
> Yun:
>
> Thanks for proposing the idea. I have checked the document and left couple
> of questions there, but it might be better to answer them here.
>
> What is the exact motivation and what problems do you want to solve? We
> have dropped multicast support from the network stack [1] for two reasons:
> 1. Performance
> 2. Code simplicity
>
> The proposal to re introduce `int[] ChannelSelector#selectChannels()`
> would revert those changes. At that time we were thinking about a way how
> to keep the multicast support on the network level, while keeping the
> performance and simplicity for non multicast cases and there are ways to
> achieve that. However they would add extra complexity to Flink, which it
> would be better to avoid.
>
> On the other hand, supporting dual pattern: standard partitioning or
> broadcasting is easy to do, as LatencyMarkers are doing exactly that. It
> would be just a matter of exposing this to the user in some way. So before
> we go any further, can you describe your use cases/motivation? Isn’t mix of
> standard partitioning and broadcasting enough? Do we need multicasting?
>
> Zhu:
>
> Could you rephrase your example? I didn’t quite understand it.
>
> Piotrek
>
> [1] https://issues.apache.org/jira/browse/FLINK-10662 <
> https://issues.apache.org/jira/browse/FLINK-10662>
>
> > On 23 Aug 2019, at 09:17, Zhu Zhu  wrote:
> >
> > Thanks Yun for starting this discussion.
> > I think the multicasting can be very helpful in certain cases.
> >
> > I have received requirements from users that they want to do broadcast
> > join, while the data set to broadcast is too large to fit in one task.
> > Thus the requirement turned out to be to support cartesian product of 2
> > data set(one of which can be infinite stream).
> > For example, A(parallelism=2) broadcast join B(parallelism=2) in
> JobVertex
> > C.
> > The idea to is have 4 C subtasks to deal with different combinations of
> A/B
> > partitions, like C1(A1,B1), C2(A1,B2), C3(A2,B1), C4(A2,B2).
> > This requires one record to be sent to multiple downstream subtasks, but
> > not to all subtasks.
> >
> > With current interface this is not supported, as one record can only be
> > sent to one subtask, or to all subtasks of a JobVertex.
> > And the user had to split the broadcast data set manually to several
> > different JobVertices, which is hard to maintain and extend.
> >
> > Thanks,
> > Zhu Zhu
> >
> > Yun Gao  于2019年8月22日周四 下午8:42写道：
> >
> >> Hi everyone,
> >>  In some scenarios we met a requirement that some operators want to
> >> send records to theirs downstream operators with an multicast
> communication
> >> pattern. In detail, for some records, the operators want to send them
> >> according to the partitioner (for example, Rebalance), and for some
> other
> >> records, the operators want to send them to all the connected operators
> and
> >> tasks. Such a communication pattern could be viewed as a kind of
> multicast:
> >> it does not broadcast every record, but some record will indeed be sent
> to
> >> multiple downstream operators.
> >>
> >> However, we found that this kind of communication pattern seems could
> not
> >> be implemented rightly if the operators have multiple consumers with
> >> different parallelism, using the customized partitioner. To solve the
> above
> >> problem, we propose to enhance the support for such kind of irregular
> >> communication pattern. We think there may be two options:
> >>
> >> 1. Support a kind of customized operator events, which share much
> >> similarity with Watermark, and these events can be broadcasted to the
> >> downstream operators separately.
> >> 2. Let the channel selector supports multicast, and also add the
> >> separate RecordWriter implementation to avoid impacting the performance
> of
> >> the channel selector that does not need multicast.
> >>
> >> The problem and options are detailed in
> >>
> 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Piotr Nowojski]

Hi,

Yun:

Thanks for proposing the idea. I have checked the document and left couple of 
questions there, but it might be better to answer them here.

What is the exact motivation and what problems do you want to solve? We have 
dropped multicast support from the network stack [1] for two reasons:
1. Performance 
2. Code simplicity 

The proposal to re introduce `int[] ChannelSelector#selectChannels()` would 
revert those changes. At that time we were thinking about a way how to keep the 
multicast support on the network level, while keeping the performance and 
simplicity for non multicast cases and there are ways to achieve that. However 
they would add extra complexity to Flink, which it would be better to avoid.

On the other hand, supporting dual pattern: standard partitioning or 
broadcasting is easy to do, as LatencyMarkers are doing exactly that. It would 
be just a matter of exposing this to the user in some way. So before we go any 
further, can you describe your use cases/motivation? Isn’t mix of standard 
partitioning and broadcasting enough? Do we need multicasting?

Zhu:

Could you rephrase your example? I didn’t quite understand it.

Piotrek

[1] https://issues.apache.org/jira/browse/FLINK-10662 


> On 23 Aug 2019, at 09:17, Zhu Zhu  wrote:
> 
> Thanks Yun for starting this discussion.
> I think the multicasting can be very helpful in certain cases.
> 
> I have received requirements from users that they want to do broadcast
> join, while the data set to broadcast is too large to fit in one task.
> Thus the requirement turned out to be to support cartesian product of 2
> data set(one of which can be infinite stream).
> For example, A(parallelism=2) broadcast join B(parallelism=2) in JobVertex
> C.
> The idea to is have 4 C subtasks to deal with different combinations of A/B
> partitions, like C1(A1,B1), C2(A1,B2), C3(A2,B1), C4(A2,B2).
> This requires one record to be sent to multiple downstream subtasks, but
> not to all subtasks.
> 
> With current interface this is not supported, as one record can only be
> sent to one subtask, or to all subtasks of a JobVertex.
> And the user had to split the broadcast data set manually to several
> different JobVertices, which is hard to maintain and extend.
> 
> Thanks,
> Zhu Zhu
> 
> Yun Gao  于2019年8月22日周四 下午8:42写道：
> 
>> Hi everyone,
>>  In some scenarios we met a requirement that some operators want to
>> send records to theirs downstream operators with an multicast communication
>> pattern. In detail, for some records, the operators want to send them
>> according to the partitioner (for example, Rebalance), and for some other
>> records, the operators want to send them to all the connected operators and
>> tasks. Such a communication pattern could be viewed as a kind of multicast:
>> it does not broadcast every record, but some record will indeed be sent to
>> multiple downstream operators.
>> 
>> However, we found that this kind of communication pattern seems could not
>> be implemented rightly if the operators have multiple consumers with
>> different parallelism, using the customized partitioner. To solve the above
>> problem, we propose to enhance the support for such kind of irregular
>> communication pattern. We think there may be two options:
>> 
>> 1. Support a kind of customized operator events, which share much
>> similarity with Watermark, and these events can be broadcasted to the
>> downstream operators separately.
>> 2. Let the channel selector supports multicast, and also add the
>> separate RecordWriter implementation to avoid impacting the performance of
>> the channel selector that does not need multicast.
>> 
>> The problem and options are detailed in
>> https://docs.google.com/document/d/1npi5c_SeP68KuT2lNdKd8G7toGR_lxQCGOnZm_hVMks/edit?usp=sharing
>> 
>> We are also wondering if there are other methods to implement this
>> requirement with or without changing Runtime. Very thanks for any feedbacks
>> !
>> 
>> 
>> Best,
>> Yun
>> 
>> 

Re: [DISCUSS] Enhance Support for Multicast Communication Pattern

[Zhu Zhu]

Thanks Yun for starting this discussion.
I think the multicasting can be very helpful in certain cases.

I have received requirements from users that they want to do broadcast
join, while the data set to broadcast is too large to fit in one task.
Thus the requirement turned out to be to support cartesian product of 2
data set(one of which can be infinite stream).
For example, A(parallelism=2) broadcast join B(parallelism=2) in JobVertex
C.
The idea to is have 4 C subtasks to deal with different combinations of A/B
partitions, like C1(A1,B1), C2(A1,B2), C3(A2,B1), C4(A2,B2).
This requires one record to be sent to multiple downstream subtasks, but
not to all subtasks.

With current interface this is not supported, as one record can only be
sent to one subtask, or to all subtasks of a JobVertex.
And the user had to split the broadcast data set manually to several
different JobVertices, which is hard to maintain and extend.

Thanks,
Zhu Zhu

Yun Gao  于2019年8月22日周四 下午8:42写道：

> Hi everyone,
>   In some scenarios we met a requirement that some operators want to
> send records to theirs downstream operators with an multicast communication
> pattern. In detail, for some records, the operators want to send them
> according to the partitioner (for example, Rebalance), and for some other
> records, the operators want to send them to all the connected operators and
> tasks. Such a communication pattern could be viewed as a kind of multicast:
> it does not broadcast every record, but some record will indeed be sent to
> multiple downstream operators.
>
> However, we found that this kind of communication pattern seems could not
> be implemented rightly if the operators have multiple consumers with
> different parallelism, using the customized partitioner. To solve the above
> problem, we propose to enhance the support for such kind of irregular
> communication pattern. We think there may be two options:
>
>  1. Support a kind of customized operator events, which share much
> similarity with Watermark, and these events can be broadcasted to the
> downstream operators separately.
>  2. Let the channel selector supports multicast, and also add the
> separate RecordWriter implementation to avoid impacting the performance of
> the channel selector that does not need multicast.
>
> The problem and options are detailed in
> https://docs.google.com/document/d/1npi5c_SeP68KuT2lNdKd8G7toGR_lxQCGOnZm_hVMks/edit?usp=sharing
>
> We are also wondering if there are other methods to implement this
> requirement with or without changing Runtime. Very thanks for any feedbacks
> !
>
>
> Best,
> Yun
>
>