Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/15389#discussion_r82929167
--- 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 Thank you! I do a simple benchmark as above with decreasing the
batch size, I don't see an improvement in running time. I.e.,
import time
num_partitions = 20000
a = sc.parallelize(range(int(1e6)), 2)
start = time.time()
l = a.repartition(num_partitions).glom().map(len).collect()
end = time.time()
print(end - start)
Before: 419.447577953
_to_java_object_rdd(): 421.916361094
decreasing the batch size: 423.712255955
Maybe it depends how is expensive actually converting to java object case
by case. Is it generally faster than _to_java_object_rdd()? I would open a
followup for this change.
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