Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/11122#discussion_r53099977
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala
---
@@ -498,10 +502,36 @@ class PairDStreamFunctions[K, V](self: DStream[(K,
V)])
updateFunc: (Iterator[(K, Seq[V], Option[S])]) => Iterator[(K, S)],
partitioner: Partitioner,
rememberPartitioner: Boolean,
- initialRDD: RDD[(K, S)]
- ): DStream[(K, S)] = ssc.withScope {
- new StateDStream(self, ssc.sc.clean(updateFunc), partitioner,
- rememberPartitioner, Some(initialRDD))
+ initialRDD: RDD[(K, S)]): DStream[(K, S)] = ssc.withScope {
+ val cleanedFunc = ssc.sc.clean(updateFunc)
+ val newUpdateFunc = (_: Time, it: Iterator[(K, Seq[V], Option[S])]) =>
{
+ cleanedFunc(it)
+ }
+ new StateDStream(self, newUpdateFunc, partitioner,
+ rememberPartitioner, Some(initialRDD))
+ }
+
+ /**
+ * 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,
+ rememberPartitioner: Boolean,
+ initialRDD: Option[RDD[(K, S)]] = None): DStream[(K, S)] =
ssc.withScope {
+ val cleanedFunc = ssc.sc.clean(updateFunc)
+ val newUpdateFunc = (time: Time, iterator: Iterator[(K, Seq[V],
Option[S])]) => {
+ iterator.flatMap(t => cleanedFunc(time, t._1, t._2, t._3).map(s =>
(t._1, s)))
+ }
+ new StateDStream(self, newUpdateFunc, partitioner,
--- End diff --
nit: can be fit into one line
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