reduce
fun <K, V> <Error class: unknown class><<Error class: unknown class><K, V>>.reduceByKey(numPartitions: Int = dstream().ssc().sc().defaultParallelism(), reduceFunc: (V, V) -> V): <Error class: unknown class><<Error class: unknown class><K, V>>
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Return a new DStream by applying reduceByKey to each RDD. The values for each key are merged using the supplied reduce function. Hash partitioning is used to generate the RDDs with numPartitions partitions.
fun <K, V> <Error class: unknown class><<Error class: unknown class><K, V>>.reduceByKey(partitioner: <Error class: unknown class>, reduceFunc: (V, V) -> V): <Error class: unknown class><<Error class: unknown class><K, V>>
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Return a new DStream by applying reduceByKey to each RDD. The values for each key are merged using the supplied reduce function. org.apache.spark.Partitioner is used to control the partitioning of each RDD.