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Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/97#issuecomment-37505082
BTW as mentioned above please use PriorityQueue here instead of copying
their heap. It's just a lot of work to copy the heap.. we can take the
performance hit instead.
Github user ScrapCodes commented on the pull request:
https://github.com/apache/spark/pull/97#issuecomment-37505304
PriorityQueue in a way is just a wrapper over heapq and allows for blocking
for put and get(AFAIU). We would need maxheapq to retain the top N smallest
elements. One
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Github user ScrapCodes commented on the pull request:
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Hey Matei,
Got rid of copying `heapq.py` and all the license stuff. But resorted to
using internal API of heapq though. It should be simpler.
I
Github user mateiz commented on the pull request:
https://github.com/apache/spark/pull/97#issuecomment-37479507
takeOrdered should always return the smallest elements according to the
ordering, so it's not the same as top. For example takeOrdered(2) on [1,2,3,4]
should return [1,2].
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Github user ScrapCodes commented on the pull request:
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Jenkins, test this please.
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GitHub user ScrapCodes opened a pull request:
https://github.com/apache/spark/pull/97
Spark 1162 Implemented takeOrdered in pyspark.
Since python does not have a library for max heap and usual tricks like
inverting values etc.. does not work for all cases. So best thing I could
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Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/97#discussion_r10406957
--- Diff: python/pyspark/maxheapq.py ---
@@ -0,0 +1,115 @@
+# -*- coding: latin-1 -*-
+
+Heap queue algorithm (a.k.a. priority queue).
+
+#
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