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Date: Fri, 3 Jun 2016 20:16:59 +0000 (UTC)
From: "Jay Kreps (JIRA)" <jira@apache.org>
To: dev@kafka.apache.org
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Subject: [jira] [Commented] (KAFKA-3775) Throttle maximum number of tasks
 assigned to a single KafkaStreams
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archived-at: Fri, 03 Jun 2016 20:17:01 -0000


    [ https://issues.apache.org/jira/browse/KAFKA-3775?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15314721#comment-15314721 ] 

Jay Kreps commented on KAFKA-3775:
----------------------------------

I'd have to agree I think this is a memory management problem I think we should solve that directly rather than having you throttle tasks which is a very roundabout way to control memory and would then lead to unprocessed partitions. I think the problems are
1. Kafka consumer does a poor job of controlling memory usage (known issue, needs to be fixed)
2. We may exacerbate it by giving configs around memory that are per-task when they should likely be global.
3. Maybe others?

> 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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