Github user davies commented on a diff in the pull request:
    --- Diff: python/pyspark/ ---
    @@ -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 --
    Should we have a benchmark to Complicated types (match the assumption that 
serialization is not trivial)?

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