[jira] [Created] (SPARK-20920) ForkJoinPool pools are leaked when writing hive tables with many partitions

["Rares Mirica (JIRA)" <ji...@apache.org>]

Rares Mirica created SPARK-20920:
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             Summary: 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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