[jira] [Resolved] (SPARK-25258) Upgrade kryo package to version 4.0.2

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-25258?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-25258.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 2.4.0

Issue resolved by pull request 22179
[https://github.com/apache/spark/pull/22179]

> Upgrade kryo package to version 4.0.2
> -------------------------------------
>
>                 Key: SPARK-25258
>                 URL: https://issues.apache.org/jira/browse/SPARK-25258
>             Project: Spark
>          Issue Type: Wish
>          Components: Spark Core
>    Affects Versions: 2.1.0, 2.3.1
>            Reporter: liupengcheng
>            Priority: Major
>             Fix For: 2.4.0
>
>
> Recently, we encountered a kryo performance issue in spark2.1.0, and the issue affect all kryo below 4.0.2, so it seems that all spark version might encounter this issue.
> Issue description:
> In shuffle write phase or some spilling operation, spark will use kryo serializer to serialize data if `spark.serializer` is set to `KryoSerializer`, however, when data contains some extremely large records, kryoSerializer's MapReferenceResolver would be expand, and it's `reset` method will take a long time to reset all items in writtenObjects table to null.
> com.esotericsoftware.kryo.util.MapReferenceResolver
> {code:java}
> public void reset () {
>  readObjects.clear();
>  writtenObjects.clear();
> }
> public void clear () {
>  K[] keyTable = this.keyTable;
>  for (int i = capacity + stashSize; i-- > 0;)
>   keyTable[i] = null;
>  size = 0;
>  stashSize = 0;
> }
> {code}
> I checked the kryo project in github, and this issue seems fixed in 4.0.2+
> [https://github.com/EsotericSoftware/kryo/commit/77935c696ee4976963aa5c6ac53d53d9b40b8bdd#diff-215fa9846e1e4e54bbeede0500de1e28]
>  
> I was wondering if we can make spark kryo package upgrade to 4.0.2+ to fix this problem.



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