GitHub user tgravescs opened a pull request:
https://github.com/apache/spark/pull/13443
[SPARK-15671] performance regression CoalesceRDD.pickBin with large #â¦
I was running a 15TB join job with 202000 partitions. It looks like the
changes I made to CoalesceRDD in pickBin() are really slow with that large of
partitions. The array filter with that many elements just takes to long.
It took about an hour for it to pickBins for all the partitions.
original change:
https://github.com/apache/spark/commit/83ee92f60345f016a390d61a82f1d924f64ddf90
Just reverting the pickBin code back to get currpreflocs fixes the issue
After reverting the pickBin code the coalesce takes about 10 seconds so for
now it makes sense to revert those changes and we can look at further
optimizations later.
Tested this via RDDSuite unit test and manually testing the very large job.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/tgravescs/spark SPARK-15671
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/13443.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 #13443
----
commit f630b64a590bd85e88e02f8896fffd253a43bed5
Author: Thomas Graves <[email protected]>
Date: 2016-06-01T15:35:04Z
[SPARK-15671] performance regression CoalesceRDD.pickBin with large #
partitions
----
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
