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[SPARK-2871] [PySpark] add zipWithIndex() and zipWithUniqueId() #2092
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QA tests have started for PR 2092 at commit
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QA tests have finished for PR 2092 at commit
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are you going to add tests for these? |
I think doc tests should be enough. |
fair enough +1 lgtm |
more than one partitions. | ||
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>>> sc.parallelize(range(4), 2).zipWithIndex().collect() | ||
[(0, 0), (1, 1), (2, 2), (3, 3)] |
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This isn't the best example because it's not clear which element is the item and which element is its index. In the Scala API, this is clear from the method's return type. Maybe we should update the documentation to explicitly state that the second element is the id (like the Scala API).
I think this implementation has things backwards w.r.t. the Scala one:
>>> sc.parallelize(['a', 'b', 'c', 'd'], 2).zipWithIndex().collect()
[(0, 'a'), (1, 'b'), (2, 'c'), (3, 'd')]
versus
scala> sc.parallelize(Seq('a', 'b', 'c', 'd')).zipWithIndex().collect()
res0: Array[(Char, Long)] = Array((a,0), (b,1), (c,2), (d,3))
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I will change it.
Jenkins, test this please. |
QA tests have started for PR 2092 at commit
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QA tests have finished for PR 2092 at commit
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LGTM, so I've merged this into |
I also merged this into |
RDD.zipWithIndex() Zips this RDD with its element indices. The ordering is first based on the partition index and then the ordering of items within each partition. So the first item in the first partition gets index 0, and the last item in the last partition receives the largest index. This method needs to trigger a spark job when this RDD contains more than one partitions. >>> sc.parallelize(range(4), 2).zipWithIndex().collect() [(0, 0), (1, 1), (2, 2), (3, 3)] RDD.zipWithUniqueId() Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method won't trigger a spark job, which is different from L{zipWithIndex} >>> sc.parallelize(range(4), 2).zipWithUniqueId().collect() [(0, 0), (2, 1), (1, 2), (3, 3)] Author: Davies Liu <davies.liu@gmail.com> Closes #2092 from davies/zipWith and squashes the following commits: cebe5bf [Davies Liu] improve test cases, reverse the order of index 0d2a128 [Davies Liu] add zipWithIndex() and zipWithUniqueId() (cherry picked from commit fb0db77) Signed-off-by: Josh Rosen <joshrosen@apache.org>
RDD.zipWithIndex() Zips this RDD with its element indices. The ordering is first based on the partition index and then the ordering of items within each partition. So the first item in the first partition gets index 0, and the last item in the last partition receives the largest index. This method needs to trigger a spark job when this RDD contains more than one partitions. >>> sc.parallelize(range(4), 2).zipWithIndex().collect() [(0, 0), (1, 1), (2, 2), (3, 3)] RDD.zipWithUniqueId() Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method won't trigger a spark job, which is different from L{zipWithIndex} >>> sc.parallelize(range(4), 2).zipWithUniqueId().collect() [(0, 0), (2, 1), (1, 2), (3, 3)] Author: Davies Liu <davies.liu@gmail.com> Closes apache#2092 from davies/zipWith and squashes the following commits: cebe5bf [Davies Liu] improve test cases, reverse the order of index 0d2a128 [Davies Liu] add zipWithIndex() and zipWithUniqueId()
RDD.zipWithIndex()
RDD.zipWithUniqueId()