Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/2538#discussion_r18203052
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
---
@@ -0,0 +1,261 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.api.python
+
+import java.util.{ArrayList => JArrayList, List => JList}
+import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
+import scala.collection.mutable
+
+import org.apache.spark.api.java._
+import org.apache.spark.api.python._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{Interval, Duration, Time}
+import org.apache.spark.streaming.dstream._
+import org.apache.spark.streaming.api.java._
+
+
+/**
+ * Interface for Python callback function with three arguments
+ */
+trait PythonRDDFunction {
+ def call(time: Long, rdds: JList[_]): JavaRDD[Array[Byte]]
+}
+
+/**
+ * Wrapper for PythonRDDFunction
+ */
+private[python] class RDDFunction(pfunc: PythonRDDFunction)
+ extends function.Function2[JList[JavaRDD[_]], Time,
JavaRDD[Array[Byte]]] with Serializable {
+
+ def wrapRDD(rdd: Option[RDD[_]]): JavaRDD[_] = {
+ if (rdd.isDefined) {
+ JavaRDD.fromRDD(rdd.get)
+ } else {
+ null
+ }
+ }
+
+ def some(jrdd: JavaRDD[Array[Byte]]): Option[RDD[Array[Byte]]] = {
+ if (jrdd != null) {
+ Some(jrdd.rdd)
+ } else {
+ None
+ }
+ }
+
+ def apply(rdd: Option[RDD[_]], time: Time): Option[RDD[Array[Byte]]] = {
+ some(pfunc.call(time.milliseconds, List(wrapRDD(rdd)).asJava))
+ }
+
+ def apply(rdd: Option[RDD[_]], rdd2: Option[RDD[_]], time: Time):
Option[RDD[Array[Byte]]] = {
+ some(pfunc.call(time.milliseconds, List(wrapRDD(rdd),
wrapRDD(rdd2)).asJava))
+ }
+
+ // for JFunction2
+ def call(rdds: JList[JavaRDD[_]], time: Time): JavaRDD[Array[Byte]] = {
+ pfunc.call(time.milliseconds, rdds)
+ }
+}
+
+private[python]
+abstract class PythonDStream(parent: DStream[_]) extends
DStream[Array[Byte]] (parent.ssc) {
+
+ override def dependencies = List(parent)
+
+ override def slideDuration: Duration = parent.slideDuration
+
+ val asJavaDStream = JavaDStream.fromDStream(this)
+}
+
+private[spark] object PythonDStream {
+
+ // helper function for DStream.foreachRDD(),
+ // cannot be `foreachRDD`, it will confusing py4j
+ def callForeachRDD(jdstream: JavaDStream[Array[Byte]], pyfunc:
PythonRDDFunction){
+ val func = new RDDFunction(pyfunc)
+ jdstream.dstream.foreachRDD((rdd, time) => func(Some(rdd), time))
+ }
+
+ // helper function for ssc.transform()
+ def callTransform(ssc: JavaStreamingContext, jdsteams:
JList[JavaDStream[_]],
+ pyfunc: PythonRDDFunction)
+ :JavaDStream[Array[Byte]] = {
+ val func = new RDDFunction(pyfunc)
+ ssc.transform(jdsteams, func)
+ }
+
+ // convert list of RDD into queue of RDDs, for ssc.queueStream()
+ def toRDDQueue(rdds: JArrayList[JavaRDD[Array[Byte]]]):
java.util.Queue[JavaRDD[Array[Byte]]] = {
+ val queue = new java.util.LinkedList[JavaRDD[Array[Byte]]]
+ rdds.forall(queue.add(_))
+ queue
+ }
+}
+
+/**
+ * Transformed DStream in Python.
+ *
+ * If the result RDD is PythonRDD, then it will cache it as an template
for future use,
+ * this can reduce the Python callbacks.
+ */
+private[spark]
+class PythonTransformedDStream (parent: DStream[_], pfunc:
PythonRDDFunction,
+ var reuse: Boolean = false)
+ extends PythonDStream(parent) {
+
+ val func = new RDDFunction(pfunc)
+ var lastResult: PythonRDD = _
+
+ override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
+ val rdd1 = parent.getOrCompute(validTime)
+ if (rdd1.isEmpty) {
+ return None
+ }
+ if (reuse && lastResult != null) {
+ Some(lastResult.copyTo(rdd1.get))
+ } else {
+ val r = func(rdd1, validTime)
+ if (reuse && r.isDefined && lastResult == null) {
+ r.get match {
+ case rdd: PythonRDD =>
+ if (rdd.parent(0) == rdd1) {
+ // only one PythonRDD
+ lastResult = rdd
+ } else {
+ // may have multiple stages
+ reuse = false
+ }
+ }
+ }
+ r
+ }
+ }
+}
+
+/**
+ * Transformed from two DStreams in Python.
+ */
+private[spark]
+class PythonTransformed2DStream(parent: DStream[_], parent2: DStream[_],
+ pfunc: PythonRDDFunction)
+ extends DStream[Array[Byte]] (parent.ssc) {
+
+ val func = new RDDFunction(pfunc)
+
+ override def slideDuration: Duration = parent.slideDuration
+
+ override def dependencies = List(parent, parent2)
+
+ override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
+ func(parent.getOrCompute(validTime), parent2.getOrCompute(validTime),
validTime)
+ }
+
+ val asJavaDStream = JavaDStream.fromDStream(this)
+}
+
+/**
+ * similar to StateDStream
+ */
+private[spark]
+class PythonStateDStream(parent: DStream[Array[Byte]], preduceFunc:
PythonRDDFunction)
+ extends PythonDStream(parent) {
+
+ val reduceFunc = new RDDFunction(preduceFunc)
+
+ super.persist(StorageLevel.MEMORY_ONLY)
+ override val mustCheckpoint = true
+
+ override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
+ val lastState = getOrCompute(validTime - slideDuration)
+ val rdd = parent.getOrCompute(validTime)
+ if (rdd.isDefined) {
+ reduceFunc(lastState, rdd, validTime)
+ } else {
+ lastState
+ }
+ }
+}
+
+/**
+ * similar to ReducedWindowedDStream
+ */
+private[spark]
+class PythonReducedWindowedDStream(parent: DStream[Array[Byte]],
--- End diff --
it's called in Python, should private[spark] also work?
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