Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/2267#discussion_r17101578
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
---
@@ -396,6 +396,26 @@ class PairDStreamFunctions[K, V](self: DStream[(K,V)])
/**
* Return a new "state" DStream where the state for each key is updated
by applying
+ * the given function on the previous state of the key and the new
values of the key.
+ * org.apache.spark.Partitioner is used to control the partitioning of
each RDD.
+ * @param updateFunc State update function. If `this` function returns
None, then
+ * corresponding state key-value pair will be
eliminated.
+ * @param partitioner Partitioner for controlling the partitioning of
each RDD in the new
+ * DStream.
+ * @tparam S State type
+ */
+ def updateStateByKey[S: ClassTag](
+ updateFunc: (Time, K, Seq[V], Option[S]) => Option[S],
+ partitioner: Partitioner
+ ): DStream[(K, S)] = {
+ val newUpdateFunc = (time: Time, iterator: Iterator[(K, Seq[V],
Option[S])]) => {
--- End diff --
The existing method could call the new method rather than duplicate the
logic. If the user supplies a function with Seq[V], Option[S] as args, that can
be made into a function that also accepts and does nothing with Time.
What's the use case for this though?
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