pyspark.RDD.aggregateByKey¶
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RDD.aggregateByKey(zeroValue: U, seqFunc: Callable[[U, V], U], combFunc: Callable[[U, U], U], numPartitions: Optional[int] = None, partitionFunc: Callable[[K], int] = <function portable_hash>) → pyspark.rdd.RDD[Tuple[K, U]][source]¶ Aggregate the values of each key, using given combine functions and a neutral “zero value”. This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U’s, The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.
New in version 1.1.0.
- Parameters
- zeroValueU
the initial value for the accumulated result of each partition
- seqFuncfunction
a function to merge a V into a U
- combFuncfunction
a function to combine two U’s into a single one
- numPartitionsint, optional
the number of partitions in new
RDD- partitionFuncfunction, optional, default portable_hash
function to compute the partition index
- Returns
Examples
>>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 2)]) >>> seqFunc = (lambda x, y: (x[0] + y, x[1] + 1)) >>> combFunc = (lambda x, y: (x[0] + y[0], x[1] + y[1])) >>> sorted(rdd.aggregateByKey((0, 0), seqFunc, combFunc).collect()) [('a', (3, 2)), ('b', (1, 1))]