[
https://issues.apache.org/jira/browse/SPARK-2282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14071669#comment-14071669
]
Ken Carlile commented on SPARK-2282:
------------------------------------
Well, something didn't work quite right.. our copy of 1.0.1 is the prebuilt
copy for Hadoop 1/CDH3. So I did a git init in that directory, then did a git
pull https://github.com/apache/spark/ +refs/pull/1503/head
Well, that didn't work...
I don't expect you to solve my git noob problems, so I'll work with someone
here to figure it out.
> PySpark crashes if too many tasks complete quickly
> --------------------------------------------------
>
> Key: SPARK-2282
> URL: https://issues.apache.org/jira/browse/SPARK-2282
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 0.9.1, 1.0.0, 1.0.1
> Reporter: Aaron Davidson
> Assignee: Aaron Davidson
> Fix For: 0.9.2, 1.0.0, 1.0.1
>
>
> Upon every task completion, PythonAccumulatorParam constructs a new socket to
> the Accumulator server running inside the pyspark daemon. This can cause a
> buildup of used ephemeral ports from sockets in the TIME_WAIT termination
> stage, which will cause the SparkContext to crash if too many tasks complete
> too quickly. We ran into this bug with 17k tasks completing in 15 seconds.
> This bug can be fixed outside of Spark by ensuring these properties are set
> (on a linux server);
> echo "1" > /proc/sys/net/ipv4/tcp_tw_reuse
> echo "1" > /proc/sys/net/ipv4/tcp_tw_recycle
> or by adding the SO_REUSEADDR option to the Socket creation within Spark.
--
This message was sent by Atlassian JIRA
(v6.2#6252)
