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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-20920) ForkJoinPool pools are leaked when writing hive tables with many partitions
Date Tue, 06 Jun 2017 10:35:18 GMT

     [ https://issues.apache.org/jira/browse/SPARK-20920?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-20920:
------------------------------------

    Assignee:     (was: Apache Spark)

> ForkJoinPool pools are leaked when writing hive tables with many partitions
> ---------------------------------------------------------------------------
>
>                 Key: SPARK-20920
>                 URL: https://issues.apache.org/jira/browse/SPARK-20920
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.1.1
>            Reporter: Rares Mirica
>
> This bug is loosely related to SPARK-17396
> In this case it happens when writing to a hive table with many, many, partitions (my
table is partitioned by hour and stores data it gets from kafka in a spark streaming application):
> df.repartition()
>   .write
>   .format("orc")
>   .option("path", s"$tablesStoragePath/$tableName")
>   .mode(SaveMode.Append)
>   .partitionBy("dt", "hh")
>   .saveAsTable(tableName)
> As this table grows beyond a certain size, ForkJoinPool pools start leaking. Upon examination
(with a debugger) I found that the caller is AlterTableRecoverPartitionsCommand and the problem
happens when `evalTaskSupport` is used (line 555). I have tried setting a very large threshold
via `spark.rdd.parallelListingThreshold` and the problem went away.
> My assumption is that the problem happens in this case and not in the one in SPARK-17396
due to the fact that AlterTableRecoverPartitionsCommand is a case class while UnionRDD is
an object so multiple instances are not possible, therefore no leak.
> Regards,
> Rares



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