GitHub user redsanket opened a pull request:
https://github.com/apache/spark/pull/18940
YSPARK-734 Change CacheLoader to limit entries based on memory footprint
Right now the spark shuffle service has a cache for index files. It is
based on a # of files cached (spark.shuffle.service.index.cache.entries). This
can cause issues if people have a lot of reducers because the size of each
entry can fluctuate based on the # of reducers.
We saw an issues with a job that had 170000 reducers and it caused NM with
spark shuffle service to use 700-800MB or memory in NM by itself.
We should change this cache to be memory based and only allow a certain
memory size used. When I say memory based I mean the cache should have a limit
of say 100MB.
https://issues.apache.org/jira/browse/SPARK-21501
Manual Testing with 170000 reducers has been performed with cache loaded up
to max 100MB default limit, with each shuffle index file of size 1.3MB.
Eviction takes place as soon as the total cache size reaches the 100MB limit
and the objects will be ready for garbage collection there by avoiding NM to
crash. No notable difference in runtime has been observed.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/redsanket/spark SPARK-21501
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/18940.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #18940
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commit f23a4c79b69fd1f8a77162da34b8821cb0cc1352
Author: Sanket Chintapalli <schin...@yahoo-inc.com>
Date: 2017-07-27T14:59:40Z
YSPARK-734 Change CacheLoader to limit entries based on memory footprint
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