Hi Tom,
I believe a workaround is to set `spark.dynamicAllocation.initialExecutors`
to 0. As others have mentioned, from Spark 1.5.2 onwards this should no
longer be necessary.
-Andrew
2015-11-09 8:19 GMT-08:00 Jonathan Kelly :
> Tom,
>
> You might be hitting https://issues.apache.org/jira/brow
Tom,
You might be hitting https://issues.apache.org/jira/browse/SPARK-10790,
which was introduced in Spark 1.5.0 and fixed in 1.5.2. Spark 1.5.2 just
passed release candidate voting, so it should be tagged, released and
announced soon. If you are able to build from source yourself and run with
tha
Did you go through
http://spark.apache.org/docs/latest/job-scheduling.html#configuration-and-setup
for yarn, i guess you will have to copy the spark-1.5.1-yarn-shuffle.jar to
the classpath of all nodemanagers in your cluster.
Thanks
Best Regards
On Fri, Oct 30, 2015 at 7:41 PM, Tom Stewart <
stew
https://issues.apache.org/jira/browse/SPARK-10790
Changed to add minExecutors < initialExecutors < maxExecutors and that
works.
spark-shell --conf spark.dynamicAllocation.enabled=true --conf
spark.shuffle.service.enabled=true --conf
spark.dynamicAllocation.minExecutors=2 --conf
spark.dynamicAlloc
