pyspark.RDD.cogroup¶
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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], []))]