Github user viirya commented on a diff in the pull request: https://github.com/apache/spark/pull/15389#discussion_r82930615 --- 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 -- @davies The followup is at #15445. Can you take a look? Thanks!
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