Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/15389#discussion_r82907726
--- Diff: python/pyspark/rdd.py ---
@@ -2029,7 +2028,15 @@ def coalesce(self, numPartitions, shuffle=False):
>>> sc.parallelize([1, 2, 3, 4, 5], 3).coalesce(1).glom().collect()
[[1, 2, 3, 4, 5]]
"""
- jrdd = self._jrdd.coalesce(numPartitions, shuffle)
+ if shuffle:
+ # In Scala's repartition code, we will distribute elements
evenly across output
+ # partitions. However, the RDD from Python is serialized as a
single binary data,
+ # so the distribution fails and produces highly skewed
partitions. We need to
+ # convert it to a RDD of java object before repartitioning.
+ data_java_rdd =
self._to_java_object_rdd().coalesce(numPartitions, shuffle)
--- End diff --
Hi @davies, actually it seems a simple benchmark was done in
https://github.com/apache/spark/pull/15389#discussion_r82444378
If you worry, then, I'd like to proceed another benchmark with larger data
and then will share when I have some time.
---
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]
