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From "Dmytro Shkvyra (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-6613) OOM during reading big messages from Kafka
Date Thu, 18 May 2017 10:53:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-6613?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16015569#comment-16015569
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Dmytro Shkvyra commented on FLINK-6613:
---------------------------------------

Hi [~dernasherbrezon], first of all root cause of this issue is using ParallelGC. OOM is normal
behavior for JVM with ParallelGC if application create too much objects (please explore ParallelGC
algoritm). 
-XX:-UseGCOverheadLimit just hide problem with lack of memory.
{quote}
3) If you recommend G1, then default startup scripts should be changed.
{quote}
We don't need change startup scripts. You can {{export JVM_ARGS="$JVM_ARGS -XX:+UseG1GC"}},
you also can pass other JVM options (except memory size options)
JobManager and TaskManager use the same options from {{JVM_ARGS}}

> OOM during reading big messages from Kafka
> ------------------------------------------
>
>                 Key: FLINK-6613
>                 URL: https://issues.apache.org/jira/browse/FLINK-6613
>             Project: Flink
>          Issue Type: Bug
>          Components: Kafka Connector
>    Affects Versions: 1.2.0
>            Reporter: Andrey
>
> Steps to reproduce:
> 1) Setup Task manager with 2G heap size
> 2) Setup job that reads messages from Kafka 10 (i.e. FlinkKafkaConsumer010)
> 3) Send 3300 messages each 635Kb. So total size is ~2G
> 4) OOM in task manager.
> According to heap dump:
> 1) KafkaConsumerThread read messages with total size ~1G.
> 2) Pass them to the next operator using org.apache.flink.streaming.connectors.kafka.internal.Handover
> 3) Then began to read another batch of messages. 
> 4) Task manager was able to read next batch of ~500Mb messages until OOM.
> Expected:
> 1) Either have constraint like "number of messages in-flight" OR
> 2) Read next batch of messages only when previous batch processed OR
> 3) Any other option which will solve OOM.



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