Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/2092#discussion_r16633434
--- Diff: python/pyspark/rdd.py ---
@@ -1715,6 +1715,52 @@ def batch_as(rdd, batchSize):
other._jrdd_deserializer)
return RDD(pairRDD, self.ctx, deserializer)
+ def zipWithIndex(self):
+ """
+ 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)]
+ """
+ starts = [0]
+ if self.getNumPartitions() > 1:
+ nums = self.mapPartitions(lambda it: [sum(1 for i in
it)]).collect()
+ for i in range(len(nums) - 1):
+ starts.append(starts[-1] + nums[i])
+
+ def func(k, it):
+ return enumerate(it, starts[k])
+
+ return self.mapPartitionsWithIndex(func)
+
+ def zipWithUniqueId(self):
+ """
+ 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()
--- End diff --
Here, it might be better to use three partitions (or some other value) so
that there's a gap in the ids.
---
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