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From "The Data Lorax (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (KAFKA-3775) Throttle maximum number of tasks assigned to a single KafkaStreams
Date Fri, 03 Jun 2016 07:12:59 GMT

    [ https://issues.apache.org/jira/browse/KAFKA-3775?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15313773#comment-15313773
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The Data Lorax commented on KAFKA-3775:
---------------------------------------

I wonder how surah a design would maintain the level of resiliency currently offered? At the
moment, in a running multi-process cluster the other processes pick up the slack if one of
them should fail. With the purposed design some partitions would remain with out a consumer.
This seems like a fundamental switch away from Kafka's current model.

Could you also elaborate on why settings such as 'max.poll.records' don't help stop your initial
instance going pop? Maybe there are other alternative solutions here... 

> Throttle maximum number of tasks assigned to a single KafkaStreams
> ------------------------------------------------------------------
>
>                 Key: KAFKA-3775
>                 URL: https://issues.apache.org/jira/browse/KAFKA-3775
>             Project: Kafka
>          Issue Type: Improvement
>          Components: streams
>    Affects Versions: 0.10.0.0
>            Reporter: Yuto Kawamura
>            Assignee: Yuto Kawamura
>             Fix For: 0.10.1.0
>
>
> As of today, if I start a Kafka Streams app on a single machine which consists of single
KafkaStreams instance, that instance gets all partitions of the target topic assigned.
> As we're using it to process topics which has huge number of partitions and message traffic,
it is a problem that we don't have a way of throttling the maximum amount of partitions assigned
to a single instance.
> In fact, when we started a Kafka Streams app which consumes a topic which has more than
10MB/sec traffic of each partition we saw that all partitions assigned to the first instance
and soon the app dead by OOM.
> I know that there's some workarounds considerable here. for example:
> - Start multiple instances at once so the partitions distributed evenly.
>   => Maybe works. but as Kafka Streams is a library but not an execution framework,
there's no predefined procedure of starting Kafka Streams apps so some users might wanna take
an option to start the first single instance and check if it works as expected with lesster
number of partitions(I want :p)
> - Adjust config parameters such as {{buffered.records.per.partition}}, {{max.partition.fetch.bytes}}
and {{max.poll.records}} to reduce the heap pressure.
>   => Maybe works. but still have two problems IMO:
>   - Still leads traffic explosion with high throughput processing as it accepts all incoming
messages from hundreads of partitions.
>   - In the first place, by the distributed system principle, it's wired that users don't
have a away to control maximum "partitions" assigned to a single shard(an instance of KafkaStreams
here). Users should be allowed to provide the maximum amount of partitions that is considered
as possible to be processed with single instance(or host).
> Here, I'd like to introduce a new configuration parameter {{max.tasks.assigned}}, which
limits the number of tasks(a notion of partition) assigned to the processId(which is the notion
of single KafkaStreams instance).
> At the same time we need to change StreamPartitionAssignor(TaskAssignor) to tolerate
the incomplete assignment. That is, Kafka Streams should continue working for the part of
partitions even there are some partitions left unassigned, in order to satisfy this> "user
may want to take an option to start the first single instance and check if it works as expected
with lesster number of partitions(I want :p)".
> I've implemented the rough POC for this. PTAL and if it make sense I will continue sophisticating
it.



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