Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/9256#discussion_r43231944
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/dstream/TrackeStateDStream.scala
---
@@ -0,0 +1,192 @@
+/*
+ * 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.dstream
+
+import java.io.{IOException, ObjectOutputStream}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
+
+import org.apache.spark._
+import org.apache.spark.rdd.{EmptyRDD, RDD}
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming._
+import org.apache.spark.streaming.util.StateMap
+import org.apache.spark.util.Utils
+
+private[streaming] case class TrackStateRDDRecord[K: ClassTag, S:
ClassTag, T: ClassTag](
+ stateMap: StateMap[K, S], emittedRecords: Seq[T])
+
+
+private[streaming] class TrackStateRDDPartition(
+ idx: Int,
+ @transient private var prevStateRDD: RDD[_],
+ @transient private var partitionedDataRDD: RDD[_]) extends Partition {
+
+ private[dstream] var previousSessionRDDPartition: Partition = null
+ private[dstream] var partitionedDataRDDPartition: Partition = null
+
+ override def index: Int = idx
+ override def hashCode(): Int = idx
+
+ @throws(classOf[IOException])
+ private def writeObject(oos: ObjectOutputStream): Unit =
Utils.tryOrIOException {
+ // Update the reference to parent split at the time of task
serialization
+ previousSessionRDDPartition = prevStateRDD.partitions(index)
+ partitionedDataRDDPartition = partitionedDataRDD.partitions(index)
+ oos.defaultWriteObject()
+ }
+}
+
+private[streaming] class TrackStateRDD[K: ClassTag, V: ClassTag, S:
ClassTag, T: ClassTag](
+ _sc: SparkContext,
+ private var prevStateRDD: RDD[TrackStateRDDRecord[K, S, T]],
+ private var partitionedDataRDD: RDD[(K, V)],
+ trackingFunction: (K, Option[V], State[S]) => Option[T],
+ currentTime: Long, timeoutThresholdTime: Option[Long]
+ ) extends RDD[TrackStateRDDRecord[K, S, T]](
+ _sc,
+ List(
+ new OneToOneDependency[TrackStateRDDRecord[K, S, T]](prevStateRDD),
+ new OneToOneDependency(partitionedDataRDD))
+ ) {
+
+ @volatile private var doFullScan = false
+
+ require(partitionedDataRDD.partitioner.nonEmpty)
+ require(partitionedDataRDD.partitioner == prevStateRDD.partitioner)
+
+ override val partitioner = prevStateRDD.partitioner
+
+ override def checkpoint(): Unit = {
+ super.checkpoint()
+ doFullScan = true
+ }
+
+ override def compute(
+ partition: Partition, context: TaskContext):
Iterator[TrackStateRDDRecord[K, S, T]] = {
+
+ val stateRDDPartition = partition.asInstanceOf[TrackStateRDDPartition]
+ val prevStateRDDIterator = prevStateRDD.iterator(
+ stateRDDPartition.previousSessionRDDPartition, context)
+ val dataIterator = partitionedDataRDD.iterator(
+ stateRDDPartition.partitionedDataRDDPartition, context)
+ if (!prevStateRDDIterator.hasNext) {
+ throw new SparkException(s"Could not find state map in previous
state RDD")
+ }
+
+ val newStateMap = prevStateRDDIterator.next().stateMap.copy()
+ val emittedRecords = new ArrayBuffer[T]
+
+ val wrappedState = new StateImpl[S]()
+
+ dataIterator.foreach { case (key, value) =>
+ wrappedState.wrap(newStateMap.get(key))
+ val emittedRecord = trackingFunction(key, Some(value), wrappedState)
+ if (wrappedState.isRemoved) {
+ newStateMap.remove(key)
+ } else if (wrappedState.isUpdated) {
+ newStateMap.put(key, wrappedState.get(), currentTime)
+ }
+ emittedRecords ++= emittedRecord
--- End diff --
The comparison to `HadoopRDD` does not apply here. When cached, the records
of `HadoopRDD`'s partitions is grabbed through the iterator, unrolled and
stored in MemoryStore. There each record is a (key, value) pair, like (String,
String). So the unrolled data is stored in the MemoryStore as a
ArrayBuffer[(String, String)] (or equivalent), which can be iterated over any
number of times.
In our case, the record type is (StateMap, seq-of-emitted-records), which
is not the same as a simple (string, string) record of a `HadoopRDD`s. So if
that is persisted, it will be store in MemoryStore a ArrayBuffer[(StateMap,
seq-of-emitted-records)]. Now if this sequence of emitted records is an
iterator, then it will be ArrayBuffer[(StateMap, Iterator[T]]. You can access
the record of tuple (map, iterator) multiple times, but you can unroll the
iterator *inside* the record **only once**. Subsequent accesses of the
ArrayBuffer will lead to a (StateMap, empty-iterator), which violates the RDD
principle.
---
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