Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/9143#discussion_r44236366
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/util/BatchedWriteAheadLog.scala
---
@@ -0,0 +1,221 @@
+/*
+ * 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.util
+
+import java.nio.ByteBuffer
+import java.util.concurrent.LinkedBlockingQueue
+import java.util.{Iterator => JIterator}
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.{Await, Promise}
+import scala.concurrent.duration._
+import scala.util.control.NonFatal
+
+import org.apache.spark.{SparkConf, SparkException, Logging}
+import org.apache.spark.util.Utils
+
+/**
+ * A wrapper for a WriteAheadLog that batches records before writing data.
Handles aggregation
+ * during writes, and de-aggregation in the `readAll` method. The end
consumer has to handle
+ * de-aggregation after the `read` method. In addition, the
`WriteAheadLogRecordHandle` returned
+ * after the write will contain the batch of records rather than
individual records.
+ *
+ * When writing a batch of records, the `time` passed to the `wrappedLog`
will be the timestamp
+ * of the latest record in the batch. This is very important in achieving
correctness. Consider the
+ * following example:
+ * We receive records with timestamps 1, 3, 5, 7. We use "log-1" as the
filename. Once we receive
+ * a clean up request for timestamp 3, we would clean up the file "log-1",
and lose data regarding
+ * 5 and 7.
+ *
+ * In addition, notice that the write method is still a blocking call.
This will ensure that a
+ * receiver will not be able to submit multiple `AddBlock` calls,
jeopardizing the ordering of data.
+ *
+ * All other methods of the WriteAheadLog interface will be passed on to
the wrapped WriteAheadLog.
+ */
+private[util] class BatchedWriteAheadLog(val wrappedLog: WriteAheadLog,
conf: SparkConf)
+ extends WriteAheadLog with Logging {
+
+ import BatchedWriteAheadLog._
+
+ private val walWriteQueue = new LinkedBlockingQueue[Record]()
+
+ // Whether the writer thread is active
+ @volatile private var active: Boolean = true
+ private val buffer = new ArrayBuffer[Record]()
+
+ private val batchedWriterThread = startBatchedWriterThread()
+
+ /**
+ * Write a byte buffer to the log file. This method adds the byteBuffer
to a queue and blocks
+ * until the record is properly written by the parent.
+ */
+ override def write(byteBuffer: ByteBuffer, time: Long):
WriteAheadLogRecordHandle = {
+ val promise = Promise[WriteAheadLogRecordHandle]()
+ val putSuccessfully = synchronized {
+ if (active) {
+ walWriteQueue.offer(Record(byteBuffer, time, promise))
+ true
+ } else {
+ false
+ }
+ }
+ if (putSuccessfully) {
+ Await.result(promise.future,
WriteAheadLogUtils.getBatchingTimeout(conf).milliseconds)
+ } else {
+ throw new SparkException("close() was called on BatchedWriteAheadLog
before " +
+ s"write request with time $time could be fulfilled.")
+ }
+ }
+
+ /**
+ * This method is not supported as the resulting ByteBuffer would
actually require de-aggregation.
+ * This method is primarily used in testing, and to ensure that it is
not used in production,
+ * we throw an UnsupportedOperationException.
+ */
+ override def read(segment: WriteAheadLogRecordHandle): ByteBuffer = {
+ throw new UnsupportedOperationException("read() is not supported for
BatchedWriteAheadLog " +
+ "as the data may require de-aggregation.")
+ }
+
+ /**
+ * Read all the existing logs from the log directory. The output of the
wrapped WriteAheadLog
+ * will be de-aggregated.
+ */
+ override def readAll(): JIterator[ByteBuffer] = {
+ wrappedLog.readAll().asScala.flatMap(deaggregate).asJava
+ }
+
+ /**
+ * Delete the log files that are older than the threshold time.
+ *
+ * This method is handled by the parent WriteAheadLog.
+ */
+ override def clean(threshTime: Long, waitForCompletion: Boolean): Unit =
{
+ wrappedLog.clean(threshTime, waitForCompletion)
+ }
+
+
+ /**
+ * Stop the batched writer thread, fulfill promises with failures and
close the wrapped WAL.
+ */
+ override def close(): Unit = {
+ logInfo(s"BatchedWriteAheadLog shutting down at time:
${System.currentTimeMillis()}.")
+ synchronized {
+ active = false
+ }
+ batchedWriterThread.interrupt()
+ batchedWriterThread.join()
+ while (!walWriteQueue.isEmpty) {
+ val Record(_, time, promise) = walWriteQueue.poll()
+ promise.failure(new SparkException("close() was called on
BatchedWriteAheadLog before " +
+ s"write request with time $time could be fulfilled."))
+ }
+ wrappedLog.close()
+ }
+
+ /** Start the actual log writer on a separate thread. */
+ private def startBatchedWriterThread(): Thread = {
+ val thread = new Thread(new Runnable {
+ override def run(): Unit = {
+ while (active) {
+ try {
+ flushRecords()
+ } catch {
+ case NonFatal(e) =>
+ logWarning("Encountered exception in Batched Writer
Thread.", e)
+ }
+ }
+ logInfo("Batched WAL Writer thread exiting.")
+ }
+ }, "Batched WAL Writer")
--- End diff --
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