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https://issues.apache.org/jira/browse/SPARK-5395?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14293843#comment-14293843
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Sven Krasser commented on SPARK-5395:
-------------------------------------
I've definitely seen this behavior when adding additional reduceByKey
operations that could execute in parallel (around 2 workers per such
operation), but in my small test script it's a single reduceByKey operation
followed by a single coalesce statement. I'm running it right now, and it
already spiked up to over 30 Python workers per executor, so there must be
something else going on on top of this.
> Large number of Python workers causing resource depletion
> ---------------------------------------------------------
>
> Key: SPARK-5395
> URL: https://issues.apache.org/jira/browse/SPARK-5395
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.2.0
> Environment: AWS ElasticMapReduce
> Reporter: Sven Krasser
>
> During job execution a large number of Python worker accumulates eventually
> causing YARN to kill containers for being over their memory allocation (in
> the case below that is about 8G for executors plus 6G for overhead per
> container).
> In this instance, at the time of killing the container 97 pyspark.daemon
> processes had accumulated.
> {noformat}
> 2015-01-23 15:36:53,654 INFO [Reporter] yarn.YarnAllocationHandler
> (Logging.scala:logInfo(59)) - Container marked as failed:
> container_1421692415636_0052_01_000030. Exit status: 143. Diagnostics:
> Container [pid=35211,containerID=container_1421692415636_0052_01_000030] is
> running beyond physical memory limits. Current usage: 14.9 GB of 14.5 GB
> physical memory used; 41.3 GB of 72.5 GB virtual memory used. Killing
> container.
> Dump of the process-tree for container_1421692415636_0052_01_000030 :
> |- PID PPID PGRPID SESSID CMD_NAME USER_MODE_TIME(MILLIS) SYSTEM_TIME(MILLIS)
> VMEM_USAGE(BYTES) RSSMEM_USAGE(PAGES) FULL_CMD_LINE
> |- 54101 36625 36625 35211 (python) 78 1 332730368 16834 python -m
> pyspark.daemon
> |- 52140 36625 36625 35211 (python) 58 1 332730368 16837 python -m
> pyspark.daemon
> |- 36625 35228 36625 35211 (python) 65 604 331685888 17694 python -m
> pyspark.daemon
> [...]
> {noformat}
> The configuration used uses 64 containers with 2 cores each.
> Full output here: https://gist.github.com/skrasser/e3e2ee8dede5ef6b082c
> Mailinglist discussion:
> https://www.mail-archive.com/user@spark.apache.org/msg20102.html
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