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[SPARK-4398][PySpark] specialize sc.parallelize(xrange) #3264

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[SPARK-4398][PySpark] specialize sc.parallelize(xrange) #3264

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specialize sc.parallelize(xrange)
mengxr Nov 14, 2014
8953c41
follow davies' suggestion
mengxr Nov 14, 2014
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31 changes: 25 additions & 6 deletions python/pyspark/context.py
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Original file line number Diff line number Diff line change
Expand Up @@ -289,12 +289,31 @@ def stop(self):

def parallelize(self, c, numSlices=None):
"""
Distribute a local Python collection to form an RDD.

>>> sc.parallelize(range(5), 5).glom().collect()
[[0], [1], [2], [3], [4]]
"""
numSlices = numSlices or self.defaultParallelism
Distribute a local Python collection to form an RDD. Use xrange if
the input represents a range for performance.

>>> sc.parallelize([0, 2, 3, 4, 6], 5).glom().collect()
[[0], [2], [3], [4], [6]]
>>> sc.parallelize(xrange(0, 6, 2), 5).glom().collect()
[[], [0], [], [2], [4]]
"""
numSlices = int(numSlices) if numSlices is not None else self.defaultParallelism
if isinstance(c, xrange):
size = len(c)
if size == 0:
return self.parallelize([], numSlices)
step = c[1] - c[0] if size > 1 else 1
c1 = xrange(c[0], c[0] + (size + 1) * step, step)

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@davies davies Nov 14, 2014

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How about pre-calculate all the boundaries for all the partitions?

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@mengxr mengxr Nov 14, 2014

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This only serializes an xrange object. If we pre-calculate the boundaries, the cost is O(p).

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@davies davies Nov 14, 2014

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Yes, but the size + 1 is tricky, how about this one:

start = c[0]
def getStart(split):
      return start + size * split / numSlices * step
def f(split, iterator):
      return xrange(getStart(split), getStart(split+1), step)

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@mengxr mengxr Nov 14, 2014

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Yes, this is better!

def getStartIndex(split):
return split * size / numSlices

def f(split, iterator):
startIndex = getStartIndex(split)
endIndex = getStartIndex(split + 1)
return xrange(c1[startIndex], c1[endIndex], step)

return self.parallelize([], numSlices).mapPartitionsWithIndex(f)
# Calling the Java parallelize() method with an ArrayList is too slow,
# because it sends O(n) Py4J commands. As an alternative, serialized
# objects are written to a file and loaded through textFile().
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