spark FileBasedWriteAheadLog 源码

spark FileBasedWriteAheadLog 代码

文件路径：/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.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.io.FileNotFoundException
import java.nio.ByteBuffer
import java.util.{Iterator => JIterator}
import java.util.concurrent.RejectedExecutionException

import scala.collection.JavaConverters._
import scala.collection.mutable.ArrayBuffer
import scala.collection.parallel.ExecutionContextTaskSupport
import scala.collection.parallel.immutable.ParVector
import scala.concurrent.{Await, ExecutionContext, Future}

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.Path

import org.apache.spark.SparkConf
import org.apache.spark.internal.Logging
import org.apache.spark.util.{CompletionIterator, ThreadUtils}

/**
 * This class manages write ahead log files.
 *
 *  - Writes records (bytebuffers) to periodically rotating log files.
 *  - Recovers the log files and the reads the recovered records upon failures.
 *  - Cleans up old log files.
 *
 * Uses [[org.apache.spark.streaming.util.FileBasedWriteAheadLogWriter]] to write
 * and [[org.apache.spark.streaming.util.FileBasedWriteAheadLogReader]] to read.
 *
 * @param logDirectory Directory when rotating log files will be created.
 * @param hadoopConf Hadoop configuration for reading/writing log files.
 */
private[streaming] class FileBasedWriteAheadLog(
    conf: SparkConf,
    logDirectory: String,
    hadoopConf: Configuration,
    rollingIntervalSecs: Int,
    maxFailures: Int,
    closeFileAfterWrite: Boolean
  ) extends WriteAheadLog with Logging {

  import FileBasedWriteAheadLog._

  private val pastLogs = new ArrayBuffer[LogInfo]
  private val callerName = getCallerName

  private val threadpoolName = {
    "WriteAheadLogManager" + callerName.map(c => s" for $c").getOrElse("")
  }
  private val forkJoinPool = ThreadUtils.newForkJoinPool(threadpoolName, 20)
  private val executionContext = ExecutionContext.fromExecutorService(forkJoinPool)

  override protected def logName = {
    getClass.getName.stripSuffix("$") +
      callerName.map("_" + _).getOrElse("").replaceAll("[ ]", "_")
  }

  private var currentLogPath: Option[String] = None
  private var currentLogWriter: FileBasedWriteAheadLogWriter = null
  private var currentLogWriterStartTime: Long = -1L
  private var currentLogWriterStopTime: Long = -1L

  initializeOrRecover()

  /**
   * Write a byte buffer to the log file. This method synchronously writes the data in the
   * ByteBuffer to HDFS. When this method returns, the data is guaranteed to have been flushed
   * to HDFS, and will be available for readers to read.
   */
  def write(byteBuffer: ByteBuffer, time: Long): FileBasedWriteAheadLogSegment = synchronized {
    var fileSegment: FileBasedWriteAheadLogSegment = null
    var failures = 0
    var lastException: Exception = null
    var succeeded = false
    while (!succeeded && failures < maxFailures) {
      try {
        fileSegment = getLogWriter(time).write(byteBuffer)
        if (closeFileAfterWrite) {
          resetWriter()
        }
        succeeded = true
      } catch {
        case ex: Exception =>
          lastException = ex
          logWarning("Failed to write to write ahead log")
          resetWriter()
          failures += 1
      }
    }
    if (fileSegment == null) {
      logError(s"Failed to write to write ahead log after $failures failures")
      throw lastException
    }
    fileSegment
  }

  def read(segment: WriteAheadLogRecordHandle): ByteBuffer = {
    val fileSegment = segment.asInstanceOf[FileBasedWriteAheadLogSegment]
    var reader: FileBasedWriteAheadLogRandomReader = null
    var byteBuffer: ByteBuffer = null
    try {
      reader = new FileBasedWriteAheadLogRandomReader(fileSegment.path, hadoopConf)
      byteBuffer = reader.read(fileSegment)
    } finally {
      reader.close()
    }
    byteBuffer
  }

  /**
   * Read all the existing logs from the log directory.
   *
   * Note that this is typically called when the caller is initializing and wants
   * to recover past state from the write ahead logs (that is, before making any writes).
   * If this is called after writes have been made using this manager, then it may not return
   * the latest the records. This does not deal with currently active log files, and
   * hence the implementation is kept simple.
   */
  def readAll(): JIterator[ByteBuffer] = synchronized {
    val logFilesToRead = pastLogs.map{ _.path} ++ currentLogPath
    logInfo("Reading from the logs:\n" + logFilesToRead.mkString("\n"))
    def readFile(file: String): Iterator[ByteBuffer] = {
      logDebug(s"Creating log reader with $file")
      val reader = new FileBasedWriteAheadLogReader(file, hadoopConf)
      CompletionIterator[ByteBuffer, Iterator[ByteBuffer]](reader, () => reader.close())
    }
    if (!closeFileAfterWrite) {
      logFilesToRead.iterator.flatMap(readFile).asJava
    } else {
      // For performance gains, it makes sense to parallelize the recovery if
      // closeFileAfterWrite = true
      seqToParIterator(executionContext, logFilesToRead.toSeq, readFile).asJava
    }
  }

  /**
   * Delete the log files that are older than the threshold time.
   *
   * Its important to note that the threshold time is based on the time stamps used in the log
   * files, which is usually based on the local system time. So if there is coordination necessary
   * between the node calculating the threshTime (say, driver node), and the local system time
   * (say, worker node), the caller has to take account of possible time skew.
   *
   * If waitForCompletion is set to true, this method will return only after old logs have been
   * deleted. This should be set to true only for testing. Else the files will be deleted
   * asynchronously.
   */
  def clean(threshTime: Long, waitForCompletion: Boolean): Unit = {
    val oldLogFiles = synchronized {
      val expiredLogs = pastLogs.filter { _.endTime < threshTime }
      pastLogs --= expiredLogs
      expiredLogs
    }
    logInfo(s"Attempting to clear ${oldLogFiles.size} old log files in $logDirectory " +
      s"older than $threshTime: ${oldLogFiles.map { _.path }.mkString("\n")}")

    def deleteFile(walInfo: LogInfo): Unit = {
      try {
        val path = new Path(walInfo.path)
        val fs = HdfsUtils.getFileSystemForPath(path, hadoopConf)
        fs.delete(path, true)
        logDebug(s"Cleared log file $walInfo")
      } catch {
        case ex: Exception =>
          logWarning(s"Error clearing write ahead log file $walInfo", ex)
      }
      logInfo(s"Cleared log files in $logDirectory older than $threshTime")
    }
    oldLogFiles.foreach { logInfo =>
      if (!executionContext.isShutdown) {
        try {
          val f = Future { deleteFile(logInfo) }(executionContext)
          if (waitForCompletion) {
            import scala.concurrent.duration._
            // scalastyle:off awaitready
            Await.ready(f, 1.second)
            // scalastyle:on awaitready
          }
        } catch {
          case e: RejectedExecutionException =>
            logWarning("Execution context shutdown before deleting old WriteAheadLogs. " +
              "This would not affect recovery correctness.", e)
        }
      }
    }
  }


  /** Stop the manager, close any open log writer */
  def close(): Unit = synchronized {
    if (!executionContext.isShutdown) {
      if (currentLogWriter != null) {
        currentLogWriter.close()
      }
      executionContext.shutdown()
    }
    logInfo("Stopped write ahead log manager")
  }

  /** Get the current log writer while taking care of rotation */
  private def getLogWriter(currentTime: Long): FileBasedWriteAheadLogWriter = synchronized {
    if (currentLogWriter == null || currentTime > currentLogWriterStopTime) {
      resetWriter()
      currentLogPath.foreach {
        pastLogs += LogInfo(currentLogWriterStartTime, currentLogWriterStopTime, _)
      }
      currentLogWriterStartTime = currentTime
      currentLogWriterStopTime = currentTime + (rollingIntervalSecs * 1000L)
      val newLogPath = new Path(logDirectory,
        timeToLogFile(currentLogWriterStartTime, currentLogWriterStopTime))
      currentLogPath = Some(newLogPath.toString)
      currentLogWriter = new FileBasedWriteAheadLogWriter(currentLogPath.get, hadoopConf)
    }
    currentLogWriter
  }

  /** Initialize the log directory or recover existing logs inside the directory */
  private def initializeOrRecover(): Unit = synchronized {
    val logDirectoryPath = new Path(logDirectory)
    val fileSystem = HdfsUtils.getFileSystemForPath(logDirectoryPath, hadoopConf)

    try {
      // If you call listStatus(file) it returns a stat of the file in the array,
      // rather than an array listing all the children.
      // This makes it hard to differentiate listStatus(file) and
      // listStatus(dir-with-one-child) except by examining the name of the returned status,
      // and once you've got symlinks in the mix that differentiation isn't easy.
      // Checking for the path being a directory is one more call to the filesystem, but
      // leads to much clearer code.
      if (fileSystem.getFileStatus(logDirectoryPath).isDirectory) {
        val logFileInfo = logFilesTologInfo(
          fileSystem.listStatus(logDirectoryPath).map { _.getPath })
        pastLogs.clear()
        pastLogs ++= logFileInfo
        logInfo(s"Recovered ${logFileInfo.size} write ahead log files from $logDirectory")
        logDebug(s"Recovered files are:\n${logFileInfo.map(_.path).mkString("\n")}")
      }
    } catch {
      case _: FileNotFoundException =>
        // there is no log directory, hence nothing to recover
    }
  }

  private def resetWriter(): Unit = synchronized {
    if (currentLogWriter != null) {
      currentLogWriter.close()
      currentLogWriter = null
    }
  }
}

private[streaming] object FileBasedWriteAheadLog {

  case class LogInfo(startTime: Long, endTime: Long, path: String)

  val logFileRegex = """log-(\d+)-(\d+)""".r

  def timeToLogFile(startTime: Long, stopTime: Long): String = {
    s"log-$startTime-$stopTime"
  }

  def getCallerName(): Option[String] = {
    val ignoreList = Seq("WriteAheadLog", "Logging", "java.lang", "scala.")
    Thread.currentThread.getStackTrace()
      .map(_.getClassName)
      .find { c => !ignoreList.exists(c.contains) }
      .flatMap(_.split("\\.").lastOption)
      .flatMap(_.split("\\$\\$").headOption)
  }

  /** Convert a sequence of files to a sequence of sorted LogInfo objects */
  def logFilesTologInfo(files: Seq[Path]): Seq[LogInfo] = {
    files.flatMap { file =>
      logFileRegex.findFirstIn(file.getName()) match {
        case Some(logFileRegex(startTimeStr, stopTimeStr)) =>
          val startTime = startTimeStr.toLong
          val stopTime = stopTimeStr.toLong
          Some(LogInfo(startTime, stopTime, file.toString))
        case None | Some(_) =>
          None
      }
    }.sortBy { _.startTime }
  }

  /**
   * This creates an iterator from a parallel collection, by keeping at most `n` objects in memory
   * at any given time, where `n` is at most the max of the size of the thread pool or 8. This is
   * crucial for use cases where we create `FileBasedWriteAheadLogReader`s during parallel recovery.
   * We don't want to open up `k` streams altogether where `k` is the size of the Seq that we want
   * to parallelize.
   */
  def seqToParIterator[I, O](
      executionContext: ExecutionContext,
      source: Seq[I],
      handler: I => Iterator[O]): Iterator[O] = {
    val taskSupport = new ExecutionContextTaskSupport(executionContext)
    val groupSize = taskSupport.parallelismLevel.max(8)

    source.grouped(groupSize).flatMap { group =>
      val parallelCollection = new ParVector(group.toVector)
      parallelCollection.tasksupport = taskSupport
      parallelCollection.map(handler)
    }.flatten
  }
}

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