Hi,
I was able to resolve the issue with increasing the timeout and reducing
the number of executors and increasing number of cores per executor.
The issue is resolved but I am still not sure why reducing the number of
executors and increasing number of cores per executor fixed issues related
to
Hi,
I am upgrading my jobs to Spark 1.6 and am running into shuffle issues. I
have tried all options and now am falling back to legacy memory model but
still running into same issue.
I have set spark.shuffle.blockTransferService to nio.
16/10/12 06:00:10 INFO MapOutputTrackerMaster: Size of
