Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/2940#discussion_r19515397
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala
---
@@ -0,0 +1,173 @@
+/*
+ * 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.receiver
+
+import java.nio.ByteBuffer
+
+import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.{Await, ExecutionContext, Future}
+import scala.concurrent.duration._
+import scala.language.{existentials, postfixOps}
+
+import WriteAheadLogBasedBlockHandler._
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.{Logging, SparkConf, SparkException}
+import org.apache.spark.storage._
+import org.apache.spark.streaming.util.{Clock, SystemClock,
WriteAheadLogManager}
+import org.apache.spark.util.Utils
+
+private[streaming] sealed trait ReceivedBlock
+private[streaming] case class ArrayBufferBlock(arrayBuffer:
ArrayBuffer[_]) extends ReceivedBlock
+private[streaming] case class IteratorBlock(iterator: Iterator[_]) extends
ReceivedBlock
+private[streaming] case class ByteBufferBlock(byteBuffer: ByteBuffer)
extends ReceivedBlock
+
+
+/** Trait that represents a class that handles the storage of blocks
received by receiver */
+private[streaming] trait ReceivedBlockHandler {
+
+ /** Store a received block with the given block id */
+ def storeBlock(blockId: StreamBlockId, receivedBlock: ReceivedBlock):
Option[Any]
+
+ /** Cleanup old blocks older than the given threshold time */
+ def cleanupOldBlock(threshTime: Long)
+}
+
+/**
+ * Implementation of a
[[org.apache.spark.streaming.receiver.ReceivedBlockHandler]] which
+ * stores the received blocks into a block manager with the specified
storage level.
+ */
+private[streaming] class BlockManagerBasedBlockHandler(
+ blockManager: BlockManager, storageLevel: StorageLevel)
+ extends ReceivedBlockHandler with Logging {
+
+ def storeBlock(blockId: StreamBlockId, receivedBlock: ReceivedBlock):
Option[Any] = {
+ val putResult: Seq[(BlockId, BlockStatus)] = receivedBlock match {
+ case ArrayBufferBlock(arrayBuffer) =>
+ blockManager.putIterator(blockId, arrayBuffer.iterator,
storageLevel, tellMaster = true)
+ case IteratorBlock(iterator) =>
+ blockManager.putIterator(blockId, iterator, storageLevel,
tellMaster = true)
+ case ByteBufferBlock(byteBuffer) =>
+ blockManager.putBytes(blockId, byteBuffer, storageLevel,
tellMaster = true)
+ case o =>
+ throw new SparkException(
+ s"Could not store $blockId to block manager, unexpected block
type ${o.getClass.getName}")
+ }
+ if (!putResult.map { _._1 }.contains(blockId)) {
+ throw new SparkException(
+ s"Could not store $blockId to block manager with storage level
$storageLevel")
+ }
+ None
+ }
+
+ def cleanupOldBlock(threshTime: Long) {
+ // this is not used as blocks inserted into the BlockManager are
cleared by DStream's clearing
+ // of BlockRDDs.
+ }
+}
+
+/**
+ * Implementation of a
[[org.apache.spark.streaming.receiver.ReceivedBlockHandler]] which
+ * stores the received blocks in both, a write ahead log and a block
manager.
+ */
+private[streaming] class WriteAheadLogBasedBlockHandler(
+ blockManager: BlockManager,
+ streamId: Int,
+ storageLevel: StorageLevel,
+ conf: SparkConf,
+ hadoopConf: Configuration,
+ checkpointDir: String,
+ clock: Clock = new SystemClock
+ ) extends ReceivedBlockHandler with Logging {
+
+ private val blockStoreTimeout = conf.getInt(
+ "spark.streaming.receiver.blockStoreTimeout", 30).seconds
+ private val rollingInterval = conf.getInt(
+ "spark.streaming.receiver.writeAheadLog.rollingInterval", 60)
+ private val maxFailures = conf.getInt(
+ "spark.streaming.receiver.writeAheadLog.maxFailures", 3)
+
+ // Manages rolling log files
+ private val logManager = new WriteAheadLogManager(
+ checkpointDirToLogDir(checkpointDir, streamId),
+ hadoopConf, rollingInterval, maxFailures,
+ callerName = this.getClass.getSimpleName,
+ clock = clock
+ )
+
+ // For processing futures used in parallel block storing into block
manager and write ahead log
+ implicit private val executionContext =
ExecutionContext.fromExecutorService(
+ Utils.newDaemonFixedThreadPool(2, this.getClass.getSimpleName))
--- End diff --
There is a good scope of future optimization here. We can always create
multiple WALManagers and write in parallel to multiple WALs. That would improve
performance depending on where the bottle neck is.
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