Streaming : a way to "key by partition id" without redispatching data

[Gwenhael Pasquiers]

Hello,

 

(Flink 1.2.1)

 

For performances reasons I’m trying to reduce the volume of data of my stream as soon as possible by windowing/folding it for 15 minutes before continuing to the rest of the chain that contains keyBys and windows that will transfer data everywhere.

 

Because of the huge volume of data, I want to avoid “moving” the data between partitions as much as possible (not like a naïve KeyBy does). I wanted to create a custom ProcessFunction (using timer and state to fold data for X minutes) in order to fold my data over itself before keying the stream but even ProcessFunction needs a keyed stream…

 

Is there a specific “key” value that would ensure me that my data won’t be moved to another taskmanager (that it’s hashcode will match the partition it is already in) ? I thought about the subtask id but I doubt I’d be that lucky :-)

 

Suggestions

·         Wouldn’t it be useful to be able to do a “partitionnedKeyBy” that would not move data between nodes, for windowing operations that can be parallelized.

o   Something like kafka => partitionnedKeyBy(0) => first folding => keyBy(0) => second folding => ….

·         Finally, aren’t all streams keyed ? Even if they’re keyed by a totally arbitrary partition id until the user chooses its own key, shouldn’t we be able to do a window (not windowAll) or process over any normal Stream’s partition ?

 

B.R.

 

Gwenhaël PASQUIERS

[Gwenhael Pasquiers]

Maybe you don’t need to bother with that question.

 

I’m currently discovering AbstractStreamOperator, OneInputStreamOperator and Triggerable.

 

That should do it :-)

 

From: Gwenhael Pasquiers [mailto:[hidden email]]
Sent: jeudi 9 novembre 2017 18:00
To: '[hidden email]' <[hidden email]>
Subject: Streaming : a way to "key by partition id" without redispatching data

 

Hello,

 

(Flink 1.2.1)

 

For performances reasons I’m trying to reduce the volume of data of my stream as soon as possible by windowing/folding it for 15 minutes before continuing to the rest of the chain that contains keyBys and windows that will transfer data everywhere.

 

Because of the huge volume of data, I want to avoid “moving” the data between partitions as much as possible (not like a naïve KeyBy does). I wanted to create a custom ProcessFunction (using timer and state to fold data for X minutes) in order to fold my data over itself before keying the stream but even ProcessFunction needs a keyed stream…

 

Is there a specific “key” value that would ensure me that my data won’t be moved to another taskmanager (that it’s hashcode will match the partition it is already in) ? I thought about the subtask id but I doubt I’d be that lucky :-)

 

Suggestions

·         Wouldn’t it be useful to be able to do a “partitionnedKeyBy” that would not move data between nodes, for windowing operations that can be parallelized.

o   Something like kafka => partitionnedKeyBy(0) => first folding => keyBy(0) => second folding => ….

·         Finally, aren’t all streams keyed ? Even if they’re keyed by a totally arbitrary partition id until the user chooses its own key, shouldn’t we be able to do a window (not windowAll) or process over any normal Stream’s partition ?

 

B.R.

 

Gwenhaël PASQUIERS

[Gwenhael Pasquiers]

Hello,

 

Finally, even after creating my operator, I still get the error : “Timers can only be used on keyed operators”.

 

Isn’t there any way around this ? A way to “key” my stream without shuffling the data ?

 

From: Gwenhael Pasquiers
Sent: vendredi 10 novembre 2017 11:42
To: Gwenhael Pasquiers <[hidden email]>; '[hidden email]' <[hidden email]>
Subject: RE: Streaming : a way to "key by partition id" without redispatching data

 

Maybe you don’t need to bother with that question.

 

I’m currently discovering AbstractStreamOperator, OneInputStreamOperator and Triggerable.

 

That should do it :-)

 

From: Gwenhael Pasquiers [[hidden email]]
Sent: jeudi 9 novembre 2017 18:00
To: '[hidden email]' <[hidden email]>
Subject: Streaming : a way to "key by partition id" without redispatching data

 

Hello,

 

(Flink 1.2.1)

 

For performances reasons I’m trying to reduce the volume of data of my stream as soon as possible by windowing/folding it for 15 minutes before continuing to the rest of the chain that contains keyBys and windows that will transfer data everywhere.

 

Because of the huge volume of data, I want to avoid “moving” the data between partitions as much as possible (not like a naïve KeyBy does). I wanted to create a custom ProcessFunction (using timer and state to fold data for X minutes) in order to fold my data over itself before keying the stream but even ProcessFunction needs a keyed stream…

 

Is there a specific “key” value that would ensure me that my data won’t be moved to another taskmanager (that it’s hashcode will match the partition it is already in) ? I thought about the subtask id but I doubt I’d be that lucky :-)

 

Suggestions

·         Wouldn’t it be useful to be able to do a “partitionnedKeyBy” that would not move data between nodes, for windowing operations that can be parallelized.

o   Something like kafka => partitionnedKeyBy(0) => first folding => keyBy(0) => second folding => ….

·         Finally, aren’t all streams keyed ? Even if they’re keyed by a totally arbitrary partition id until the user chooses its own key, shouldn’t we be able to do a window (not windowAll) or process over any normal Stream’s partition ?

 

B.R.

 

Gwenhaël PASQUIERS

[Gwenhael Pasquiers]

I think I finally found a way to “simulate” a Timer thanks to the the processWatermark function of the AbstractStreamOperator.

 

Sorry for the monologue.

 

From: Gwenhael Pasquiers [mailto:[hidden email]]
Sent: vendredi 10 novembre 2017 16:02
To: '[hidden email]' <[hidden email]>
Subject: RE: Streaming : a way to "key by partition id" without redispatching data

 

Hello,

 

Finally, even after creating my operator, I still get the error : “Timers can only be used on keyed operators”.

 

Isn’t there any way around this ? A way to “key” my stream without shuffling the data ?

 

From: Gwenhael Pasquiers
Sent: vendredi 10 novembre 2017 11:42
To: Gwenhael Pasquiers <[hidden email]>; '[hidden email]' <[hidden email]>
Subject: RE: Streaming : a way to "key by partition id" without redispatching data

 

Maybe you don’t need to bother with that question.

 

I’m currently discovering AbstractStreamOperator, OneInputStreamOperator and Triggerable.

 

That should do it :-)

 

From: Gwenhael Pasquiers [[hidden email]]
Sent: jeudi 9 novembre 2017 18:00
To: '[hidden email]' <[hidden email]>
Subject: Streaming : a way to "key by partition id" without redispatching data

 

Hello,

 

(Flink 1.2.1)

 

For performances reasons I’m trying to reduce the volume of data of my stream as soon as possible by windowing/folding it for 15 minutes before continuing to the rest of the chain that contains keyBys and windows that will transfer data everywhere.

 

Because of the huge volume of data, I want to avoid “moving” the data between partitions as much as possible (not like a naïve KeyBy does). I wanted to create a custom ProcessFunction (using timer and state to fold data for X minutes) in order to fold my data over itself before keying the stream but even ProcessFunction needs a keyed stream…

 

Is there a specific “key” value that would ensure me that my data won’t be moved to another taskmanager (that it’s hashcode will match the partition it is already in) ? I thought about the subtask id but I doubt I’d be that lucky :-)

 

Suggestions

·         Wouldn’t it be useful to be able to do a “partitionnedKeyBy” that would not move data between nodes, for windowing operations that can be parallelized.

o   Something like kafka => partitionnedKeyBy(0) => first folding => keyBy(0) => second folding => ….

·         Finally, aren’t all streams keyed ? Even if they’re keyed by a totally arbitrary partition id until the user chooses its own key, shouldn’t we be able to do a window (not windowAll) or process over any normal Stream’s partition ?

 

B.R.

 

Gwenhaël PASQUIERS

[Derek VerLee]

I was about to ask this question myself.  I find myself re-keying by the same keys repeatedly.  I think in principle you could always just roll more work into one window operation with a more complex series of maps/folds/windowfunctions or processfunction.  However this doesn't always feel the most clean or convenient, or composible.  It would be great if there was a way to just express that you want to keep the same partitions as the last window, or that the new key is 1-to-1 with the previous one.  Even more generally, if the new key is "based" off the old key in a way that is one to one or one to many, in either case it may not be necessary to send data over the wire, although in the later case, there is a risk of hot-spotting , I suppose.

On 11/10/17 12:01 PM, Gwenhael Pasquiers wrote:

I think I finally found a way to “simulate” a Timer thanks to the the processWatermark function of the AbstractStreamOperator.

 

Sorry for the monologue.

 

From: Gwenhael Pasquiers [[hidden email]]
Sent: vendredi 10 novembre 2017 16:02
To: '[hidden email]' [hidden email]
Subject: RE: Streaming : a way to "key by partition id" without redispatching data

 

Hello,

 

Finally, even after creating my operator, I still get the error : “Timers can only be used on keyed operators”.

 

Isn’t there any way around this ? A way to “key” my stream without shuffling the data ?

 

From: Gwenhael Pasquiers
Sent: vendredi 10 novembre 2017 11:42
To: Gwenhael Pasquiers <[hidden email]>; '[hidden email]' <[hidden email]>
Subject: RE: Streaming : a way to "key by partition id" without redispatching data

 

Maybe you don’t need to bother with that question.

 

I’m currently discovering AbstractStreamOperator, OneInputStreamOperator and Triggerable.

 

That should do it :-)

 

From: Gwenhael Pasquiers [[hidden email]]
Sent: jeudi 9 novembre 2017 18:00
To: '[hidden email]' <[hidden email]>
Subject: Streaming : a way to "key by partition id" without redispatching data

 

Hello,

 

(Flink 1.2.1)

 

For performances reasons I’m trying to reduce the volume of data of my stream as soon as possible by windowing/folding it for 15 minutes before continuing to the rest of the chain that contains keyBys and windows that will transfer data everywhere.

 

Because of the huge volume of data, I want to avoid “moving” the data between partitions as much as possible (not like a naïve KeyBy does). I wanted to create a custom ProcessFunction (using timer and state to fold data for X minutes) in order to fold my data over itself before keying the stream but even ProcessFunction needs a keyed stream…

 

Is there a specific “key” value that would ensure me that my data won’t be moved to another taskmanager (that it’s hashcode will match the partition it is already in) ? I thought about the subtask id but I doubt I’d be that lucky :-)

 

Suggestions

·         Wouldn’t it be useful to be able to do a “partitionnedKeyBy” that would not move data between nodes, for windowing operations that can be parallelized.

o   Something like kafka => partitionnedKeyBy(0) => first folding => keyBy(0) => second folding => ….

·         Finally, aren’t all streams keyed ? Even if they’re keyed by a totally arbitrary partition id until the user chooses its own key, shouldn’t we be able to do a window (not windowAll) or process over any normal Stream’s partition ?

 

B.R.

 

Gwenhaël PASQUIERS

[Gwenhael Pasquiers]

From what I understood, in your case you might solve your issue by using specific key classes instead of Strings.

 

Maybe you could create key classes that have a user-specified hashcode that could take the previous key’s hashcode as a value. That way your data shouldn’t be sent over the wire and stay in the same partition thus on the same taskmanager..

[Nico Kruber]

Hi Gwenhaël,
several functions in Flink require keyed streams because they manage their
internal state by key. These keys, however, should be independent of the
current execution and its parallelism so that checkpoints may be restored to
different levels of parallelism (for re-scaling, see [1]).
Also, different operators, e.g. the source vs. the map, may have a different
number of parallel tasks in which case you'd need to shuffle the data in order
to adapt. The same goes for possible differences in the parallelism of the
Kafka partitions vs. the parallelism you use in Flink.

If, however, all your operators have the same parallelism, doing multiple
keyBy(0) calls in your program will not re-shuffle the data, because of the
deterministic assignment of keys to operators.


Nico

[1] https://flink.apache.org/features/2017/07/04/flink-rescalable-state.html
On Thursday, 9 November 2017 18:00:13 CET Gwenhael Pasquiers wrote:

> Hello,
>
> (Flink 1.2.1)
>
> For performances reasons I'm trying to reduce the volume of data of my
> stream as soon as possible by windowing/folding it for 15 minutes before
> continuing to the rest of the chain that contains keyBys and windows that
> will transfer data everywhere.
>
> Because of the huge volume of data, I want to avoid "moving" the data
> between partitions as much as possible (not like a naïve KeyBy does). I
> wanted to create a custom ProcessFunction (using timer and state to fold
> data for X minutes) in order to fold my data over itself before keying the
> stream but even ProcessFunction needs a keyed stream...
>
> Is there a specific "key" value that would ensure me that my data won't be
> moved to another taskmanager (that it's hashcode will match the partition
> it is already in) ? I thought about the subtask id but I doubt I'd be that
> lucky :-)
>
> Suggestions
>
> ·         Wouldn't it be useful to be able to do a "partitionnedKeyBy" that
> would not move data between nodes, for windowing operations that can be
> parallelized.
>
> o   Something like kafka => partitionnedKeyBy(0) => first folding =>
> keyBy(0) => second folding => ....
>
> ·         Finally, aren't all streams keyed ? Even if they're keyed by a
> totally arbitrary partition id until the user chooses its own key,
> shouldn't we be able to do a window (not windowAll) or process over any
> normal Stream's partition ?
>
> B.R.
>
> Gwenhaël PASQUIERS