spark BatchedWriteAheadLog 源码

spark BatchedWriteAheadLog 代码

文件路径：/streaming/src/main/scala/org/apache/spark/streaming/util/BatchedWriteAheadLog.scala

/*
 * 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.{Iterator => JIterator}
import java.util.concurrent.LinkedBlockingQueue
import java.util.concurrent.atomic.AtomicBoolean

import scala.collection.JavaConverters._
import scala.collection.mutable.ArrayBuffer
import scala.concurrent.Promise
import scala.concurrent.duration._
import scala.util.control.NonFatal

import org.apache.spark.SparkConf
import org.apache.spark.internal.Logging
import org.apache.spark.network.util.JavaUtils
import org.apache.spark.util.{ThreadUtils, 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.
 *
 * This means the caller can assume the same write semantics as any other WriteAheadLog
 * implementation despite the batching in the background - when the write() returns, the data is
 * written to the WAL and is durable. To take advantage of the batching, the caller can write from
 * multiple threads, each of which will stay blocked until the corresponding data has been written.
 *
 * 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
  private val active: AtomicBoolean = new AtomicBoolean(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.get()) {
        walWriteQueue.offer(Record(byteBuffer, time, promise))
        true
      } else {
        false
      }
    }
    if (putSuccessfully) {
      ThreadUtils.awaitResult(
        promise.future, WriteAheadLogUtils.getBatchingTimeout(conf).milliseconds)
    } else {
      throw new IllegalStateException("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()}.")
    if (!active.getAndSet(false)) return
    batchedWriterThread.interrupt()
    batchedWriterThread.join()
    while (!walWriteQueue.isEmpty) {
      val Record(_, time, promise) = walWriteQueue.poll()
      promise.failure(new IllegalStateException("close() was called on BatchedWriteAheadLog " +
        s"before 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(() => {
      while (active.get()) {
        try {
          flushRecords()
        } catch {
          case NonFatal(e) =>
            logWarning("Encountered exception in Batched Writer Thread.", e)
        }
      }
      logInfo("BatchedWriteAheadLog Writer thread exiting.")
    }, "BatchedWriteAheadLog Writer")
    thread.setDaemon(true)
    thread.start()
    thread
  }

  /** Write all the records in the buffer to the write ahead log. */
  private def flushRecords(): Unit = {
    try {
      buffer += walWriteQueue.take()
      val numBatched = walWriteQueue.drainTo(buffer.asJava) + 1
      logDebug(s"Received $numBatched records from queue")
    } catch {
      case _: InterruptedException =>
        logWarning("BatchedWriteAheadLog Writer queue interrupted.")
    }
    try {
      var segment: WriteAheadLogRecordHandle = null
      if (buffer.nonEmpty) {
        logDebug(s"Batched ${buffer.length} records for Write Ahead Log write")
        // threads may not be able to add items in order by time
        val sortedByTime = buffer.sortBy(_.time)
        // We take the latest record for the timestamp. Please refer to the class Javadoc for
        // detailed explanation
        val time = sortedByTime.last.time
        segment = wrappedLog.write(aggregate(sortedByTime.toSeq), time)
      }
      buffer.foreach(_.promise.success(segment))
    } catch {
      case e: InterruptedException =>
        logWarning("BatchedWriteAheadLog Writer queue interrupted.", e)
        buffer.foreach(_.promise.failure(e))
      case NonFatal(e) =>
        logWarning(s"BatchedWriteAheadLog Writer failed to write $buffer", e)
        buffer.foreach(_.promise.failure(e))
    } finally {
      buffer.clear()
    }
  }

  /** Method for querying the queue length. Should only be used in tests. */
  private def getQueueLength(): Int = walWriteQueue.size()
}

/** Static methods for aggregating and de-aggregating records. */
private[util] object BatchedWriteAheadLog {

  /**
   * Wrapper class for representing the records that we will write to the WriteAheadLog. Coupled
   * with the timestamp for the write request of the record, and the promise that will block the
   * write request, while a separate thread is actually performing the write.
   */
  case class Record(data: ByteBuffer, time: Long, promise: Promise[WriteAheadLogRecordHandle])

  /** Aggregate multiple serialized ReceivedBlockTrackerLogEvents in a single ByteBuffer. */
  def aggregate(records: Seq[Record]): ByteBuffer = {
    ByteBuffer.wrap(Utils.serialize[Array[Array[Byte]]](
      records.map(record => JavaUtils.bufferToArray(record.data)).toArray))
  }

  /**
   * De-aggregate serialized ReceivedBlockTrackerLogEvents in a single ByteBuffer.
   * A stream may not have used batching initially, but started using it after a restart. This
   * method therefore needs to be backwards compatible.
   */
  def deaggregate(buffer: ByteBuffer): Array[ByteBuffer] = {
    val prevPosition = buffer.position()
    try {
      Utils.deserialize[Array[Array[Byte]]](JavaUtils.bufferToArray(buffer)).map(ByteBuffer.wrap)
    } catch {
      case _: ClassCastException => // users may restart a stream with batching enabled
        // Restore `position` so that the user can read `buffer` later
        buffer.position(prevPosition)
        Array(buffer)
    }
  }
}

相关信息

spark 源码目录

相关文章

spark FileBasedWriteAheadLog 源码

spark FileBasedWriteAheadLogRandomReader 源码

spark FileBasedWriteAheadLogReader 源码

spark FileBasedWriteAheadLogSegment 源码

spark FileBasedWriteAheadLogWriter 源码

spark HdfsUtils 源码

spark RateLimitedOutputStream 源码

spark RawTextHelper 源码

spark RawTextSender 源码

spark RecurringTimer 源码