ForkJoinPool pools are leaked when writing hive tables with many partitions

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