pyspark.RDD.cogroup

RDD.cogroup(other: pyspark.rdd.RDD[Tuple[K, U]], numPartitions: Optional[int] = None) → pyspark.rdd.RDD[Tuple[K, Tuple[pyspark.resultiterable.ResultIterable[V], pyspark.resultiterable.ResultIterable[U]]]]

For each key k in self or other, return a resulting RDD that contains a tuple with the list of values for that key in self as well as other.

Examples

>>> x = sc.parallelize([("a", 1), ("b", 4)])
>>> y = sc.parallelize([("a", 2)])
>>> [(x, tuple(map(list, y))) for x, y in sorted(list(x.cogroup(y).collect()))]
[('a', ([1], [2])), ('b', ([4], []))]

pyspark.RDD.coalesce pyspark.RDD.collect