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Posted to commits@samza.apache.org by "Xinyu Liu (JIRA)" <ji...@apache.org> on 2018/03/22 17:02:00 UTC

[jira] [Updated] (SAMZA-1627) Watermark broadcast enhancements

     [ https://issues.apache.org/jira/browse/SAMZA-1627?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xinyu Liu updated SAMZA-1627:
-----------------------------
    Description: 
Currently each upstream task needs to broadcast to every single partition of intermediate streams in order to aggregate watermarks in the consumers. It's O(n^2). For 256 tasks, 256-partition intermediate stream this can easily result in 64k msg/s if we send watermark every second. To illustrate:

T1     T2    T3

 |   \   /\ |  /\ /\ |

P1      P2    P3

 

A better way to do this is to have only one downstream consumer doing the aggregation, and then broadcast to all the partitions. This is safe as we can do a simple proof: if P1 received watermark of t from all T1, T2, and T3, all the messages before t have been published to (P1, P2, P3) already (might not be consumed yet). So P1 can safely broadcast the watermark t to P2 and P3. To illustrate:

T1     T2     T3

    \      |       /

         P1

       /    \

      P2  P3

This reduced the full message count from O(n^2) to O(n). The cost is that this might introduce a few milliseconds delay since we need to exchange the message twice. The benefit clearly wins. In practice, the aggregate consumer can be decided from the (topic.hash() % total partitions) to spread the aggregation if we have multiple intermediate streams.

  was:
Currently each upstream task needs to broadcast to every single partition of intermediate streams in order to aggregate watermarks in the consumers. It's O(n^2). For 256 tasks, 256-partition intermediate stream this can easily result in 64k msg/s if we send watermark every second. To illustrate:

T1     T2    T3

 |   \   /\ |  /\ /\ |

P1      P2    P3

 

A better way to do this is to have only one downstream consumer doing the aggregation, and then broadcast to all the partitions. This is safe as we can do a simple proof: if P1 received watermark of t from all T1, T2, and T3, all the messages before t have been published to (P1, P2, P3) already (might not be consumed yet). So P1 can safely broadcast the watermark t to P2 and P3. To illustrate:

T1     T2     T3

    \      |       /

         P1

       /    \

      P2  P3

This reduced the full message count from O(n^2) to O(n). In practice, the aggregate consumer can be decided from the (topic.hash() % total partitions) to spread the aggregation if we have multiple intermediate streams.


> Watermark broadcast enhancements
> --------------------------------
>
>                 Key: SAMZA-1627
>                 URL: https://issues.apache.org/jira/browse/SAMZA-1627
>             Project: Samza
>          Issue Type: Bug
>            Reporter: Xinyu Liu
>            Assignee: Xinyu Liu
>            Priority: Major
>
> Currently each upstream task needs to broadcast to every single partition of intermediate streams in order to aggregate watermarks in the consumers. It's O(n^2). For 256 tasks, 256-partition intermediate stream this can easily result in 64k msg/s if we send watermark every second. To illustrate:
> T1     T2    T3
>  |   \   /\ |  /\ /\ |
> P1      P2    P3
>  
> A better way to do this is to have only one downstream consumer doing the aggregation, and then broadcast to all the partitions. This is safe as we can do a simple proof: if P1 received watermark of t from all T1, T2, and T3, all the messages before t have been published to (P1, P2, P3) already (might not be consumed yet). So P1 can safely broadcast the watermark t to P2 and P3. To illustrate:
> T1     T2     T3
>     \      |       /
>          P1
>        /    \
>       P2  P3
> This reduced the full message count from O(n^2) to O(n). The cost is that this might introduce a few milliseconds delay since we need to exchange the message twice. The benefit clearly wins. In practice, the aggregate consumer can be decided from the (topic.hash() % total partitions) to spread the aggregation if we have multiple intermediate streams.



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