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[SPARK-5224] [PySpark] improve performance of parallelize list/ndarray #4024

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[SPARK-5224] [PySpark] improve performance of parallelize list/ndarray #4024

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2 changes: 1 addition & 1 deletion python/pyspark/context.py
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Original file line number Diff line number Diff line change
Expand Up @@ -319,7 +319,7 @@ def f(split, iterator):
# Make sure we distribute data evenly if it's smaller than self.batchSize
if "__len__" not in dir(c):
c = list(c) # Make it a list so we can compute its length
batchSize = max(1, min(len(c) // numSlices, self._batchSize))
batchSize = max(1, min(len(c) // numSlices, self._batchSize or 1024))
serializer = BatchedSerializer(self._unbatched_serializer, batchSize)
serializer.dump_stream(c, tempFile)
tempFile.close()
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4 changes: 4 additions & 0 deletions python/pyspark/serializers.py
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Expand Up @@ -181,6 +181,10 @@ def __init__(self, serializer, batchSize=UNLIMITED_BATCH_SIZE):
def _batched(self, iterator):
if self.batchSize == self.UNLIMITED_BATCH_SIZE:
yield list(iterator)
elif hasattr(iterator, "__len__") and hasattr(iterator, "__getslice__"):
n = len(iterator)
for i in xrange(0, n, self.batchSize):
yield iterator[i: i + self.batchSize]
else:
items = []
count = 0
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