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Apache Spark commented on SPARK-25258: -------------------------------------- User 'wangyum' has created a pull request for this issue: 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 > > 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. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org