Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/1791#discussion_r15916792
--- Diff: python/pyspark/rdd.py ---
@@ -1684,11 +1812,57 @@ def zip(self, other):
>>> x.zip(y).collect()
[(0, 1000), (1, 1001), (2, 1002), (3, 1003), (4, 1004)]
"""
+ if self.getNumPartitions() != other.getNumPartitions():
+ raise ValueError("the number of partitions dose not match"
+ " with each other")
+
pairRDD = self._jrdd.zip(other._jrdd)
deserializer = PairDeserializer(self._jrdd_deserializer,
other._jrdd_deserializer)
return RDD(pairRDD, self.ctx, deserializer)
+ def zipPartitions(self, other, f, preservesPartitioning=False):
+ """
+ Zip this RDD's partitions with one (or more) RDD(s) and return a
+ new RDD by applying a function to the zipped partitions.
+
+ Not implemented.
+ """
+ raise NotImplementedError
+
+ def zipWithIndex(self):
+ """
+ Zips this RDD with its element indices.
--- End diff --
The Scala documentation is much more descriptive about what this method
does:
```scala
/**
* 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 is similar to Scala's zipWithIndex but it uses Long instead of
Int as the index type.
* This method needs to trigger a spark job when this RDD contains more
than one partitions.
*/
def zipWithIndex(): RDD[(T, Long)] = new ZippedWithIndexRDD(this)
```
The Python documentation should explain these subtleties, too.
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