Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/15389#discussion_r82890280
--- 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 --
The reason that cause the skew should be large batch size, I think we could
decrease the batch size to 10, then call repartition in JVM.
My worry is that _to_java_object_rdd() could be expensive, maybe we should
have some benchmark for that.
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