hadoop作业运行部分源码

一、客户端

Map-Reduce的过程首先是由客户端提交一个任务开始的。

提交任务主要是通过JobClient.runJob(JobConf)静态函数实现的：

public static RunningJob runJob(JobConf job) throws IOException {

  //首先生成一个JobClient对象

  JobClient jc = new JobClient(job);

  ……

  //调用submitJob来提交一个任务

  running = jc.submitJob(job);

  JobID jobId = running.getID();

  ……

  while (true) {

     //while循环中不断得到此任务的状态，并打印到客户端console中

  }

  return running;

}

其中JobClient的submitJob函数实现如下：

public RunningJob submitJob(JobConf job) throws FileNotFoundException,

                                InvalidJobConfException, IOException {

  //从JobTracker得到当前任务的id

  JobID jobId = jobSubmitClient.getNewJobId();

  //准备将任务运行所需要的要素写入HDFS：

  //任务运行程序所在的jar封装成job.jar

  //任务所要处理的input split信息写入job.split

  //任务运行的配置项汇总写入job.xml

  Path submitJobDir = new Path(getSystemDir(), jobId.toString());

  Path submitJarFile = new Path(submitJobDir, "job.jar");

  Path submitSplitFile = new Path(submitJobDir, "job.split");

  //此处将-libjars命令行指定的jar上传至HDFS

  configureCommandLineOptions(job, submitJobDir, submitJarFile);

  Path submitJobFile = new Path(submitJobDir, "job.xml");

  ……

  //通过input format的格式获得相应的input split，默认类型为FileSplit

  InputSplit[] splits =

    job.getInputFormat().getSplits(job, job.getNumMapTasks());

 

  // 生成一个写入流，将input split得信息写入job.split文件

  FSDataOutputStream out = FileSystem.create(fs,

      submitSplitFile, new FsPermission(JOB_FILE_PERMISSION));

  try {

    //写入job.split文件的信息包括：split文件头，split文件版本号，split的个数，接着依次写入每一个input split的信息。

    //对于每一个input split写入：split类型名(默认FileSplit)，split的大小，split的内容(对于FileSplit，写入文件名，此split 在文件中的起始位置)，split的location信息(即在那个DataNode上)。

    writeSplitsFile(splits, out);

  } finally {

    out.close();

  }

  job.set("mapred.job.split.file", submitSplitFile.toString());

  //根据split的个数设定map task的个数

  job.setNumMapTasks(splits.length);

  // 写入job的配置信息入job.xml文件      

  out = FileSystem.create(fs, submitJobFile,

      new FsPermission(JOB_FILE_PERMISSION));

  try {

    job.writeXml(out);

  } finally {

    out.close();

  }

  //真正的调用JobTracker来提交任务

  JobStatus status = jobSubmitClient.submitJob(jobId);

  ……

}

 

二、JobTracker

JobTracker作为一个单独的JVM运行，其运行的main函数主要调用有下面两部分：

• 调用静态函数startTracker(new JobConf())创建一个JobTracker对象
• 调用JobTracker.offerService()函数提供服务

在JobTracker的构造函数中，会生成一个taskScheduler成员变量，来进行Job的调度，默认为JobQueueTaskScheduler，也即按照FIFO的方式调度任务。

在offerService函数中，则调用taskScheduler.start()，在这个函数中，为JobTracker(也即taskScheduler的taskTrackerManager)注册了两个Listener：

• JobQueueJobInProgressListener jobQueueJobInProgressListener用于监控job的运行状态
• EagerTaskInitializationListener eagerTaskInitializationListener用于对Job进行初始化

EagerTaskInitializationListener中有一个线程JobInitThread，不断得到jobInitQueue中的JobInProgress对象，调用JobInProgress对象的initTasks函数对任务进行初始化操作。

在上一节中，客户端调用了JobTracker.submitJob函数，此函数首先生成一个JobInProgress对象，然后调用addJob函数，其中有如下的逻辑：

synchronized (jobs) {

  synchronized (taskScheduler) {

    jobs.put(job.getProfile().getJobID(), job);

    //对JobTracker的每一个listener都调用jobAdded函数

    for (JobInProgressListener listener : jobInProgressListeners) {

      listener.jobAdded(job);

    }

  }

}

 

EagerTaskInitializationListener的jobAdded函数就是向jobInitQueue中添加一个JobInProgress对象，于是自然触发了此Job的初始化操作，由JobInProgress得initTasks函数完成：

 

public synchronized void initTasks() throws IOException {

  ……

  //从HDFS中读取job.split文件从而生成input splits

  String jobFile = profile.getJobFile();

  Path sysDir = new Path(this.jobtracker.getSystemDir());

  FileSystem fs = sysDir.getFileSystem(conf);

  DataInputStream splitFile =

    fs.open(new Path(conf.get("mapred.job.split.file")));

  JobClient.RawSplit[] splits;

  try {

    splits = JobClient.readSplitFile(splitFile);

  } finally {

    splitFile.close();

  }

  //map task的个数就是input split的个数

  numMapTasks = splits.length;

  //为每个map tasks生成一个TaskInProgress来处理一个input split

  maps = new TaskInProgress[numMapTasks];

  for(int i=0; i < numMapTasks; ++i) {

    inputLength += splits[i].getDataLength();

    maps[i] = new TaskInProgress(jobId, jobFile,

                                 splits[i],

                                 jobtracker, conf, this, i);

  }

  //对于map task，将其放入nonRunningMapCache，是一个Map<Node, List<TaskInProgress>>，也即对于map task来讲，其将会被分配到其input split所在的Node上。nonRunningMapCache将在JobTracker向TaskTracker分配map task的时候使用。

  if (numMapTasks > 0) {
    nonRunningMapCache = createCache(splits, maxLevel);
  }

 

  //创建reduce task

  this.reduces = new TaskInProgress[numReduceTasks];

  for (int i = 0; i < numReduceTasks; i++) {

    reduces[i] = new TaskInProgress(jobId, jobFile,

                                    numMapTasks, i,

                                    jobtracker, conf, this);

    //reduce task放入nonRunningReduces，其将在JobTracker向TaskTracker分配reduce task的时候使用。

    nonRunningReduces.add(reduces[i]);

  }

 

  //创建两个cleanup task，一个用来清理map，一个用来清理reduce.

  cleanup = new TaskInProgress[2];

  cleanup[0] = new TaskInProgress(jobId, jobFile, splits[0],

          jobtracker, conf, this, numMapTasks);

  cleanup[0].setJobCleanupTask();

  cleanup[1] = new TaskInProgress(jobId, jobFile, numMapTasks,

                     numReduceTasks, jobtracker, conf, this);

  cleanup[1].setJobCleanupTask();

  //创建两个初始化 task，一个初始化map，一个初始化reduce.

  setup = new TaskInProgress[2];

  setup[0] = new TaskInProgress(jobId, jobFile, splits[0],

          jobtracker, conf, this, numMapTasks + 1 );

  setup[0].setJobSetupTask();

  setup[1] = new TaskInProgress(jobId, jobFile, numMapTasks,

                     numReduceTasks + 1, jobtracker, conf, this);

  setup[1].setJobSetupTask();

  tasksInited.set(true);//初始化完毕

  ……

}

 

三、TaskTracker

TaskTracker也是作为一个单独的JVM来运行的，在其main函数中，主要是调用了new TaskTracker(conf).run()，其中run函数主要调用了：

 

State offerService() throws Exception {

  long lastHeartbeat = 0;

  //TaskTracker进行是一直存在的

  while (running && !shuttingDown) {

      ……

      long now = System.currentTimeMillis();

      //每隔一段时间就向JobTracker发送heartbeat

      long waitTime = heartbeatInterval - (now - lastHeartbeat);

      if (waitTime > 0) {

        synchronized(finishedCount) {

          if (finishedCount[0] == 0) {

            finishedCount.wait(waitTime);

          }

          finishedCount[0] = 0;

        }

      }

      ……

      //发送Heartbeat到JobTracker，得到response

      HeartbeatResponse heartbeatResponse = transmitHeartBeat(now);

      ……

     //从Response中得到此TaskTracker需要做的事情

      TaskTrackerAction[] actions = heartbeatResponse.getActions();

      ……

      if (actions != null){

        for(TaskTrackerAction action: actions) {

          if (action instanceof LaunchTaskAction) {

            //如果是运行一个新的Task，则将Action添加到任务队列中

            addToTaskQueue((LaunchTaskAction)action);

          } else if (action instanceof CommitTaskAction) {

            CommitTaskAction commitAction = (CommitTaskAction)action;

            if (!commitResponses.contains(commitAction.getTaskID())) {

              commitResponses.add(commitAction.getTaskID());

            }

          } else {

            tasksToCleanup.put(action);

          }

        }

      }

  }

  return State.NORMAL;

}

其中transmitHeartBeat主要逻辑如下：

 

private HeartbeatResponse transmitHeartBeat(long now) throws IOException {

  //每隔一段时间，在heartbeat中要返回给JobTracker一些统计信息

  boolean sendCounters;

  if (now > (previousUpdate + COUNTER_UPDATE_INTERVAL)) {

    sendCounters = true;

    previousUpdate = now;

  }

  else {

    sendCounters = false;

  }

  ……

  //报告给JobTracker，此TaskTracker的当前状态

  if (status == null) {

    synchronized (this) {

      status = new TaskTrackerStatus(taskTrackerName, localHostname,

                                     httpPort,

                                     cloneAndResetRunningTaskStatuses(

                                       sendCounters),

                                     failures,

                                     maxCurrentMapTasks,

                                     maxCurrentReduceTasks);

    }

  }

  ……

  //当满足下面的条件的时候，此TaskTracker请求JobTracker为其分配一个新的Task来运行：

  //当前TaskTracker正在运行的map task的个数小于可以运行的map task的最大个数

  //当前TaskTracker正在运行的reduce task的个数小于可以运行的reduce task的最大个数

  boolean askForNewTask;

  long localMinSpaceStart;

  synchronized (this) {

    askForNewTask = (status.countMapTasks() < maxCurrentMapTasks ||

                     status.countReduceTasks() < maxCurrentReduceTasks) &&

                    acceptNewTasks;

    localMinSpaceStart = minSpaceStart;

  }

  ……

  //向JobTracker发送heartbeat，这是一个RPC调用

  HeartbeatResponse heartbeatResponse = jobClient.heartbeat(status,

                                                            justStarted, askForNewTask,

                                                            heartbeatResponseId);

  ……

  return heartbeatResponse;

}

 

四、JobTracker

当 JobTracker被RPC调用来发送heartbeat的时候，JobTracker的heartbeat(TaskTrackerStatus status,boolean initialContact, boolean acceptNewTasks, short responseId)函数被调用：

 

public synchronized HeartbeatResponse heartbeat(TaskTrackerStatus status,

                                                boolean initialContact, boolean acceptNewTasks, short responseId)

  throws IOException {

  ……

  String trackerName = status.getTrackerName();

  ……

  short newResponseId = (short)(responseId + 1);

  ……

  HeartbeatResponse response = new HeartbeatResponse(newResponseId, null);

  List<TaskTrackerAction> actions = new ArrayList<TaskTrackerAction>();

  //如果TaskTracker向JobTracker请求一个task运行

  if (acceptNewTasks) {

    TaskTrackerStatus taskTrackerStatus = getTaskTracker(trackerName);

    if (taskTrackerStatus == null) {

      LOG.warn("Unknown task tracker polling; ignoring: " + trackerName);

    } else {

      //setup和cleanup的task优先级最高

      List<Task> tasks = getSetupAndCleanupTasks(taskTrackerStatus);

      if (tasks == null ) {

        //任务调度器分配任务

        tasks = taskScheduler.assignTasks(taskTrackerStatus);

      }

      if (tasks != null) {

        for (Task task : tasks) {

          //将任务放入actions列表，返回给TaskTracker

          expireLaunchingTasks.addNewTask(task.getTaskID());

          actions.add(new LaunchTaskAction(task));

        }

      }

    }

  }

  ……

  int nextInterval = getNextHeartbeatInterval();

  response.setHeartbeatInterval(nextInterval);

  response.setActions(

                      actions.toArray(new TaskTrackerAction[actions.size()]));

  ……

  return response;

}

默认的任务调度器为JobQueueTaskScheduler，其assignTasks如下：

 

public synchronized List<Task> assignTasks(TaskTrackerStatus taskTracker)

    throws IOException {

  ClusterStatus clusterStatus = taskTrackerManager.getClusterStatus();

  int numTaskTrackers = clusterStatus.getTaskTrackers();

  Collection<JobInProgress> jobQueue =　jobQueueJobInProgressListener.getJobQueue();

  int maxCurrentMapTasks = taskTracker.getMaxMapTasks();

  int maxCurrentReduceTasks = taskTracker.getMaxReduceTasks();

  int numMaps = taskTracker.countMapTasks();

  int numReduces = taskTracker.countReduceTasks();

  //计算剩余的map和reduce的工作量：remaining

  int remainingReduceLoad = 0;

  int remainingMapLoad = 0;

  synchronized (jobQueue) {

    for (JobInProgress job : jobQueue) {

      if (job.getStatus().getRunState() == JobStatus.RUNNING) {

        int totalMapTasks = job.desiredMaps();

        int totalReduceTasks = job.desiredReduces();

        remainingMapLoad += (totalMapTasks - job.finishedMaps());

        remainingReduceLoad += (totalReduceTasks - job.finishedReduces());

      }

    }

  }

  //计算平均每个TaskTracker应有的工作量，remaining/numTaskTrackers是剩余的工作量除以TaskTracker的个数。

  int maxMapLoad = 0;

  int maxReduceLoad = 0;

  if (numTaskTrackers > 0) {

    maxMapLoad = Math.min(maxCurrentMapTasks,

                          (int) Math.ceil((double) remainingMapLoad /

                                          numTaskTrackers));

    maxReduceLoad = Math.min(maxCurrentReduceTasks,

                             (int) Math.ceil((double) remainingReduceLoad

                                             / numTaskTrackers));

  }

  ……

 

  //map优先于reduce，当TaskTracker上运行的map task数目小于平均的工作量，则向其分配map task

  if (numMaps < maxMapLoad) {

    int totalNeededMaps = 0;

    synchronized (jobQueue) {

      for (JobInProgress job : jobQueue) {

        if (job.getStatus().getRunState() != JobStatus.RUNNING) {

          continue;

        }

        Task t = job.obtainNewMapTask(taskTracker, numTaskTrackers,

            taskTrackerManager.getNumberOfUniqueHosts());

        if (t != null) {

          return Collections.singletonList(t);

        }

        ……

      }

    }

  }

  //分配完map task，再分配reduce task

  if (numReduces < maxReduceLoad) {

    int totalNeededReduces = 0;

    synchronized (jobQueue) {

      for (JobInProgress job : jobQueue) {

        if (job.getStatus().getRunState() != JobStatus.RUNNING ||

            job.numReduceTasks == 0) {

          continue;

        }

        Task t = job.obtainNewReduceTask(taskTracker, numTaskTrackers,

            taskTrackerManager.getNumberOfUniqueHosts());

        if (t != null) {

          return Collections.singletonList(t);

        }

        ……

      }

    }

  }

  return null;

}

从 上面的代码中我们可以知道，JobInProgress的obtainNewMapTask是用来分配map task的，其主要调用findNewMapTask，根据TaskTracker所在的Node从nonRunningMapCache中查找 TaskInProgress。JobInProgress的obtainNewReduceTask是用来分配reduce task的，其主要调用findNewReduceTask，从nonRunningReduces查找TaskInProgress。

 

五、TaskTracker

在 向JobTracker发送heartbeat后，返回的reponse中有分配好的任务LaunchTaskAction，将其加入队列，调用 addToTaskQueue，如果是map task则放入mapLancher(类型为TaskLauncher)，如果是reduce task则放入reduceLancher(类型为TaskLauncher)：

private void addToTaskQueue(LaunchTaskAction action) {

  if (action.getTask().isMapTask()) {

    mapLauncher.addToTaskQueue(action);

  } else {

    reduceLauncher.addToTaskQueue(action);

  }

}

TaskLauncher 是一个线程，其run函数从上面放入的queue中取出一个TaskInProgress，然后调用 startNewTask(TaskInProgress tip)来启动一个task，其又主要调用了localizeJob(TaskInProgress tip)：

 

private void localizeJob(TaskInProgress tip) throws IOException {

  //首先要做的一件事情是有关Task的文件从HDFS拷贝的TaskTracker的本地文件系统中：job.split，job.xml以及job.jar

  Path localJarFile = null;

  Task t = tip.getTask();

  JobID jobId = t.getJobID();

  Path jobFile = new Path(t.getJobFile());

  ……

  Path localJobFile = lDirAlloc.getLocalPathForWrite(

                                  getLocalJobDir(jobId.toString())

                                  + Path.SEPARATOR + "job.xml",

                                  jobFileSize, fConf);

  RunningJob rjob = addTaskToJob(jobId, tip);

  synchronized (rjob) {

    if (!rjob.localized) {

      FileSystem localFs = FileSystem.getLocal(fConf);

      Path jobDir = localJobFile.getParent();

      ……

      //将job.split拷贝到本地

      systemFS.copyToLocalFile(jobFile, localJobFile);

      JobConf localJobConf = new JobConf(localJobFile);

      Path workDir = lDirAlloc.getLocalPathForWrite(

                       (getLocalJobDir(jobId.toString())

                       + Path.SEPARATOR + "work"), fConf);

      if (!localFs.mkdirs(workDir)) {

        throw new IOException("Mkdirs failed to create "

                    + workDir.toString());

      }

      System.setProperty("job.local.dir", workDir.toString());

      localJobConf.set("job.local.dir", workDir.toString());

      // copy Jar file to the local FS and unjar it.

      String jarFile = localJobConf.getJar();

      long jarFileSize = -1;

      if (jarFile != null) {

        Path jarFilePath = new Path(jarFile);

        localJarFile = new Path(lDirAlloc.getLocalPathForWrite(

                                   getLocalJobDir(jobId.toString())

                                   + Path.SEPARATOR + "jars",

                                   5 * jarFileSize, fConf), "job.jar");

        if (!localFs.mkdirs(localJarFile.getParent())) {

          throw new IOException("Mkdirs failed to create jars directory ");

        }

        //将job.jar拷贝到本地

        systemFS.copyToLocalFile(jarFilePath, localJarFile);

        localJobConf.setJar(localJarFile.toString());

       //将job得configuration写成job.xml

        OutputStream out = localFs.create(localJobFile);

        try {

          localJobConf.writeXml(out);

        } finally {

          out.close();

        }

        // 解压缩job.jar

        RunJar.unJar(new File(localJarFile.toString()),

                     new File(localJarFile.getParent().toString()));

      }

      rjob.localized = true;

      rjob.jobConf = localJobConf;

    }

  }

  //真正的启动此Task

  launchTaskForJob(tip, new JobConf(rjob.jobConf));

}

当所有的task运行所需要的资源都拷贝到本地后，则调用launchTaskForJob，其又调用TaskInProgress的launchTask函数：

public synchronized void launchTask() throws IOException {

    ……

    //创建task运行目录

    localizeTask(task);

    if (this.taskStatus.getRunState() == TaskStatus.State.UNASSIGNED) {

      this.taskStatus.setRunState(TaskStatus.State.RUNNING);

    }

    //创建并启动TaskRunner，对于MapTask，创建的是MapTaskRunner，对于ReduceTask，创建的是ReduceTaskRunner

    this.runner = task.createRunner(TaskTracker.this, this);

    this.runner.start();

    this.taskStatus.setStartTime(System.currentTimeMillis());

}

TaskRunner是一个线程，其run函数如下：

 

public final void run() {

    ……

    TaskAttemptID taskid = t.getTaskID();

    LocalDirAllocator lDirAlloc = new LocalDirAllocator("mapred.local.dir");

    File jobCacheDir = null;

    if (conf.getJar() != null) {

      jobCacheDir = new File(

                        new Path(conf.getJar()).getParent().toString());

    }

    File workDir = new File(lDirAlloc.getLocalPathToRead(

                              TaskTracker.getLocalTaskDir(

                                t.getJobID().toString(),

                                t.getTaskID().toString(),

                                t.isTaskCleanupTask())

                              + Path.SEPARATOR + MRConstants.WORKDIR,

                              conf). toString());

    FileSystem fileSystem;

    Path localPath;

    ……

    //拼写classpath

    String baseDir;

    String sep = System.getProperty("path.separator");

    StringBuffer classPath = new StringBuffer();

    // start with same classpath as parent process

    classPath.append(System.getProperty("java.class.path"));

    classPath.append(sep);

    if (!workDir.mkdirs()) {

      if (!workDir.isDirectory()) {

        LOG.fatal("Mkdirs failed to create " + workDir.toString());

      }

    }

    String jar = conf.getJar();

    if (jar != null) {      

      // if jar exists, it into workDir

      File[] libs = new File(jobCacheDir, "lib").listFiles();

      if (libs != null) {

        for (int i = 0; i < libs.length; i++) {

          classPath.append(sep);            // add libs from jar to classpath

          classPath.append(libs[i]);

        }

      }

      classPath.append(sep);

      classPath.append(new File(jobCacheDir, "classes"));

      classPath.append(sep);

      classPath.append(jobCacheDir);

    }

    ……

    classPath.append(sep);

    classPath.append(workDir);

    //拼写命令行java及其参数

    Vector<String> vargs = new Vector<String>(8);

    File jvm =

      new File(new File(System.getProperty("java.home"), "bin"), "java");

    vargs.add(jvm.toString());

    String javaOpts = conf.get("mapred.child.java.opts", "-Xmx200m");

    javaOpts = javaOpts.replace("@taskid@", taskid.toString());

    String [] javaOptsSplit = javaOpts.split(" ");

    String libraryPath = System.getProperty("java.library.path");

    if (libraryPath == null) {

      libraryPath = workDir.getAbsolutePath();

    } else {

      libraryPath += sep + workDir;

    }

    boolean hasUserLDPath = false;

    for(int i=0; i<javaOptsSplit.length ;i++) {

      if(javaOptsSplit[i].startsWith("-Djava.library.path=")) {

        javaOptsSplit[i] += sep + libraryPath;

        hasUserLDPath = true;

        break;

      }

    }

    if(!hasUserLDPath) {

      vargs.add("-Djava.library.path=" + libraryPath);

    }

    for (int i = 0; i < javaOptsSplit.length; i++) {

      vargs.add(javaOptsSplit[i]);

    }

    //添加Child进程的临时文件夹

    String tmp = conf.get("mapred.child.tmp", "./tmp");

    Path tmpDir = new Path(tmp);

    if (!tmpDir.isAbsolute()) {

      tmpDir = new Path(workDir.toString(), tmp);

    }

    FileSystem localFs = FileSystem.getLocal(conf);

    if (!localFs.mkdirs(tmpDir) && !localFs.getFileStatus(tmpDir).isDir()) {

      throw new IOException("Mkdirs failed to create " + tmpDir.toString());

    }

    vargs.add("-Djava.io.tmpdir=" + tmpDir.toString());

    // Add classpath.

    vargs.add("-classpath");

    vargs.add(classPath.toString());

    //log文件夹

    long logSize = TaskLog.getTaskLogLength(conf);

    vargs.add("-Dhadoop.log.dir=" +

        new File(System.getProperty("hadoop.log.dir")

        ).getAbsolutePath());

    vargs.add("-Dhadoop.root.logger=INFO,TLA");

    vargs.add("-Dhadoop.tasklog.taskid=" + taskid);

    vargs.add("-Dhadoop.tasklog.totalLogFileSize=" + logSize);

    // 运行map task和reduce task的子进程的main class是Child

    vargs.add(Child.class.getName());  // main of Child

    ……

    //运行子进程

    jvmManager.launchJvm(this,

        jvmManager.constructJvmEnv(setup,vargs,stdout,stderr,logSize,

            workDir, env, pidFile, conf));

}

 

六、Child

真正的map task和reduce task都是在Child进程中运行的，Child的main函数的主要逻辑如下：

 

while (true) {

  //从TaskTracker通过网络通信得到JvmTask对象

  JvmTask myTask = umbilical.getTask(jvmId);

  ……

  idleLoopCount = 0;

  task = myTask.getTask();

  taskid = task.getTaskID();

  isCleanup = task.isTaskCleanupTask();

  JobConf job = new JobConf(task.getJobFile());

  TaskRunner.setupWorkDir(job);

  numTasksToExecute = job.getNumTasksToExecutePerJvm();

  task.setConf(job);

  defaultConf.addResource(new Path(task.getJobFile()));

  ……

  //运行task

  task.run(job, umbilical);             // run the task

  if (numTasksToExecute > 0 && ++numTasksExecuted == numTasksToExecute) {

    break;

  }

}

6.1、MapTask

如果task是MapTask，则其run函数如下：

 

public void run(final JobConf job, final TaskUmbilicalProtocol umbilical)

  throws IOException {

  //用于同TaskTracker进行通信，汇报运行状况

  final Reporter reporter = getReporter(umbilical);

  startCommunicationThread(umbilical);

  initialize(job, reporter);

  ……

  //map task的输出

  int numReduceTasks = conf.getNumReduceTasks();

  MapOutputCollector collector = null;

  if (numReduceTasks > 0) {

    collector = new MapOutputBuffer(umbilical, job, reporter);

  } else {

    collector = new DirectMapOutputCollector(umbilical, job, reporter);

  }

  //读取input split，按照其中的信息，生成RecordReader来读取数据

instantiatedSplit = (InputSplit)

      ReflectionUtils.newInstance(job.getClassByName(splitClass), job);

  DataInputBuffer splitBuffer = new DataInputBuffer();

  splitBuffer.reset(split.getBytes(), 0, split.getLength());

  instantiatedSplit.readFields(splitBuffer);

  if (instantiatedSplit instanceof FileSplit) {

    FileSplit fileSplit = (FileSplit) instantiatedSplit;

    job.set("map.input.file", fileSplit.getPath().toString());

    job.setLong("map.input.start", fileSplit.getStart());

    job.setLong("map.input.length", fileSplit.getLength());

  }

  RecordReader rawIn =                  // open input

    job.getInputFormat().getRecordReader(instantiatedSplit, job, reporter);

  RecordReader in = isSkipping() ?

      new SkippingRecordReader(rawIn, getCounters(), umbilical) :

      new TrackedRecordReader(rawIn, getCounters());

  job.setBoolean("mapred.skip.on", isSkipping());

  //对于map task，生成一个MapRunnable，默认是MapRunner

  MapRunnable runner =

    ReflectionUtils.newInstance(job.getMapRunnerClass(), job);

  try {

    //MapRunner的run函数就是依次读取RecordReader中的数据，然后调用Mapper的map函数进行处理。

    runner.run(in, collector, reporter);     

    collector.flush();

  } finally {

    in.close();                               // close input

    collector.close();

  }

  done(umbilical);

}

MapRunner的run函数就是依次读取RecordReader中的数据，然后调用Mapper的map函数进行处理：

public void run(RecordReader<K1, V1> input, OutputCollector<K2, V2> output,

                Reporter reporter)

  throws IOException {

  try {

    K1 key = input.createKey();

    V1 value = input.createValue();

    while (input.next(key, value)) {

      mapper.map(key, value, output, reporter);

      if(incrProcCount) {

        reporter.incrCounter(SkipBadRecords.COUNTER_GROUP,

            SkipBadRecords.COUNTER_MAP_PROCESSED_RECORDS, 1);

      }

    }

  } finally {

    mapper.close();

  }

}

结果集全部收集到MapOutputBuffer中，其collect函数如下：

 

public synchronized void collect(K key, V value)

    throws IOException {

  reporter.progress();

  ……

  //从此处看，此buffer是一个ring的数据结构

  final int kvnext = (kvindex + 1) % kvoffsets.length;

  spillLock.lock();

  try {

    boolean kvfull;

    do {

      //在ring中，如果下一个空闲位置接上起始位置的话，则表示满了

      kvfull = kvnext == kvstart;

      //在ring中计算是否需要将buffer写入硬盘的阈值

      final boolean kvsoftlimit = ((kvnext > kvend)

          ? kvnext - kvend > softRecordLimit

          : kvend - kvnext <= kvoffsets.length - softRecordLimit);

      //如果到达阈值，则开始将buffer写入硬盘，写成spill文件。

      //startSpill主要是notify一个背后线程SpillThread的run()函数，开始调用sortAndSpill()开始排序，合并，写入硬盘

      if (kvstart == kvend && kvsoftlimit) {

        startSpill();

      }

      //如果buffer满了，则只能等待写入完毕

      if (kvfull) {

          while (kvstart != kvend) {

            reporter.progress();

            spillDone.await();

          }

      }

    } while (kvfull);

  } finally {

    spillLock.unlock();

  }

  try {

    //如果buffer不满，则将key, value写入buffer

    int keystart = bufindex;

    keySerializer.serialize(key);

    final int valstart = bufindex;

    valSerializer.serialize(value);

    int valend = bb.markRecord();

    //调用设定的partitioner，根据key, value取得partition id

    final int partition = partitioner.getPartition(key, value, partitions);

    mapOutputRecordCounter.increment(1);

    mapOutputByteCounter.increment(valend >= keystart

        ? valend - keystart

        : (bufvoid - keystart) + valend);

    //将parition id以及key, value在buffer中的偏移量写入索引数组

    int ind = kvindex * ACCTSIZE;

    kvoffsets[kvindex] = ind;

    kvindices[ind + PARTITION] = partition;

    kvindices[ind + KEYSTART] = keystart;

    kvindices[ind + VALSTART] = valstart;

    kvindex = kvnext;

  } catch (MapBufferTooSmallException e) {

    LOG.info("Record too large for in-memory buffer: " + e.getMessage());

    spillSingleRecord(key, value);

    mapOutputRecordCounter.increment(1);

    return;

  }

}

内存buffer的格式如下：

(见几位hadoop大侠的分析http://blog.csdn.net/HEYUTAO007/archive/2010/07/10/5725379.aspx 以及http://caibinbupt.iteye.com/ )

kvoffsets是为了写入内存前排序使用的。

从上面可知，内存buffer写入硬盘spill文件的函数为sortAndSpill：

 

 

private void sortAndSpill() throws IOException {

  ……

  FSDataOutputStream out = null;

  FSDataOutputStream indexOut = null;

  IFileOutputStream indexChecksumOut = null;

  //创建硬盘上的spill文件

  Path filename = mapOutputFile.getSpillFileForWrite(getTaskID(),

                                  numSpills, size);

  out = rfs.create(filename);

  ……

  final int endPosition = (kvend > kvstart)

    ? kvend

    : kvoffsets.length + kvend;

  //按照partition的顺序对buffer中的数据进行排序

  sorter.sort(MapOutputBuffer.this, kvstart, endPosition, reporter);

  int spindex = kvstart;

  InMemValBytes value = new InMemValBytes();

  //依次一个一个parition的写入文件

  for (int i = 0; i < partitions; ++i) {

    IFile.Writer<K, V> writer = null;

    long segmentStart = out.getPos();

    writer = new Writer<K, V>(job, out, keyClass, valClass, codec);

    //如果combiner为空，则直接写入文件

    if (null == combinerClass) {

        ……

        writer.append(key, value);

        ++spindex;

     }

     else {

        ……

        //如果combiner不为空，则先combine，调用combiner.reduce(…)函数后再写入文件

        combineAndSpill(kvIter, combineInputCounter);

     }

  }

  ……

}

当map阶段结束的时候，MapOutputBuffer的flush函数会被调用，其也会调用sortAndSpill将buffer中的写入文件，然后再调用mergeParts来合并写入在硬盘上的多个spill:

 

private void mergeParts() throws IOException {

    ……

    //对于每一个partition

    for (int parts = 0; parts < partitions; parts++){

      //create the segments to be merged

      List<Segment<K, V>> segmentList =

        new ArrayList<Segment<K, V>>(numSpills);

      TaskAttemptID mapId = getTaskID();

       //依次从各个spill文件中收集属于当前partition的段

      for(int i = 0; i < numSpills; i++) {

        final IndexRecord indexRecord =

          getIndexInformation(mapId, i, parts);

        long segmentOffset = indexRecord.startOffset;

        long segmentLength = indexRecord.partLength;

        Segment<K, V> s =

          new Segment<K, V>(job, rfs, filename[i], segmentOffset,

                            segmentLength, codec, true);

        segmentList.add(i, s);

      }

      //将属于同一个partition的段merge到一起

      RawKeyValueIterator kvIter =

        Merger.merge(job, rfs,

                     keyClass, valClass,

                     segmentList, job.getInt("io.sort.factor", 100),

                     new Path(getTaskID().toString()),

                     job.getOutputKeyComparator(), reporter);

      //写入合并后的段到文件

      long segmentStart = finalOut.getPos();

      Writer<K, V> writer =

          new Writer<K, V>(job, finalOut, keyClass, valClass, codec);

      if (null == combinerClass || numSpills < minSpillsForCombine) {

        Merger.writeFile(kvIter, writer, reporter, job);

      } else {

        combineCollector.setWriter(writer);

        combineAndSpill(kvIter, combineInputCounter);

      }

      ……

    }

}

6.2、ReduceTask

ReduceTask的run函数如下：

public void run(JobConf job, final TaskUmbilicalProtocol umbilical)

  throws IOException {

  job.setBoolean("mapred.skip.on", isSkipping());

  //对于reduce，则包含三个步骤：拷贝，排序，Reduce

  if (isMapOrReduce()) {

    copyPhase = getProgress().addPhase("copy");

    sortPhase  = getProgress().addPhase("sort");

    reducePhase = getProgress().addPhase("reduce");

  }

  startCommunicationThread(umbilical);

  final Reporter reporter = getReporter(umbilical);

  initialize(job, reporter);

  //copy阶段，主要使用ReduceCopier的fetchOutputs函数获得map的输出。创建多个线程MapOutputCopier，其中copyOutput进行拷贝。

  boolean isLocal = "local".equals(job.get("mapred.job.tracker", "local"));

  if (!isLocal) {

    reduceCopier = new ReduceCopier(umbilical, job);

    if (!reduceCopier.fetchOutputs()) {

        ……

    }

  }

  copyPhase.complete();

  //sort阶段，将得到的map输出合并，直到文件数小于io.sort.factor时停止，返回一个Iterator用于访问key-value

  setPhase(TaskStatus.Phase.SORT);

  statusUpdate(umbilical);

  final FileSystem rfs = FileSystem.getLocal(job).getRaw();

  RawKeyValueIterator rIter = isLocal

    ? Merger.merge(job, rfs, job.getMapOutputKeyClass(),

        job.getMapOutputValueClass(), codec, getMapFiles(rfs, true),

        !conf.getKeepFailedTaskFiles(), job.getInt("io.sort.factor", 100),

        new Path(getTaskID().toString()), job.getOutputKeyComparator(),

        reporter)

    : reduceCopier.createKVIterator(job, rfs, reporter);

  mapOutputFilesOnDisk.clear();

  sortPhase.complete();

  //reduce阶段

  setPhase(TaskStatus.Phase.REDUCE);

  ……

  Reducer reducer = ReflectionUtils.newInstance(job.getReducerClass(), job);

  Class keyClass = job.getMapOutputKeyClass();

  Class valClass = job.getMapOutputValueClass();

  ReduceValuesIterator values = isSkipping() ?

     new SkippingReduceValuesIterator(rIter,

          job.getOutputValueGroupingComparator(), keyClass, valClass,

          job, reporter, umbilical) :

      new ReduceValuesIterator(rIter,

      job.getOutputValueGroupingComparator(), keyClass, valClass,

      job, reporter);

  //逐个读出key-value list，然后调用Reducer的reduce函数

  while (values.more()) {

    reduceInputKeyCounter.increment(1);

    reducer.reduce(values.getKey(), values, collector, reporter);

    values.nextKey();

    values.informReduceProgress();

  }

  reducer.close();

  out.close(reporter);

  done(umbilical);

}

 

七、总结

Map-Reduce的过程总结如下图：

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