Task运行过程分析2

16-08-15 来源：[db:作者]

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Child类包含一个入口主方法main，在运行的时候需要传递对应的参数，来运行MapTask和ReduceTask，通过命令行输入如下5个参数：
host：表示TaskTracker节点的主机名称
port：表示TaskTracker节点RPc端口号
taskID：表示启动的Task对应的TaskAttemptID，标识一个Task的一个运行实例
log location：表示该Task运行实例对应的日志文件的路径
JVM ID：表示该Task实例对应的JVMId信息，包括JobID、Task类型（MapTask/ReduceTask）、JVM编号（标识该JVM实例对应的id）
代码如下：

  public static void main(String[] args) throws Throwable {
    LOG.debug("Child starting");

    final JobConf defaultConf = new JobConf();
    String host = args[0];
    int port = Integer.parseInt(args[1]);
    final InetSocketAddress address = NetUtils.makeSocketAddr(host, port);
    final TaskAttemptID firstTaskid = TaskAttemptID.forName(args[2]);
    final String logLocation = args[3];
    final int SLEEP_LONGER_COUNT = 5;
    int jvmIdInt = Integer.parseInt(args[4]);
    JVMId jvmId = new JVMId(firstTaskid.getJobID(),firstTaskid.isMap(),jvmIdInt);
    String prefix = firstTaskid.isMap() ? "MapTask" : "ReduceTask";

    cwd = System.getenv().get(TaskRunner.HADOOP_WORK_DIR);
    if (cwd == null) {
      throw new IOException("Environment variable " + 
                             TaskRunner.HADOOP_WORK_DIR + " is not set");
    }

    // file name is passed thru env
    String jobTokenFile = 
      System.getenv().get(UserGroupInformation.HADOOP_TOKEN_FILE_LOCATION);
    Credentials credentials = 
      TokenCache.loadTokens(jobTokenFile, defaultConf);
    LOG.debug("loading token. # keys =" +credentials.numberOfSecretKeys() + 
        "; from file=" + jobTokenFile);

    Token jt = TokenCache.getJobToken(credentials);
    SecurityUtil.setTokenService(jt, address);
    UserGroupInformation current = UserGroupInformation.getCurrentUser();
    current.addToken(jt);

    UserGroupInformation taskOwner 
     = UserGroupInformation.createRemoteUser(firstTaskid.getJobID().toString());
    taskOwner.addToken(jt);

    // Set the credentials
    defaultConf.setCredentials(credentials);

    final TaskUmbilicalProtocol umbilical = 
      taskOwner.doAs(new PrivilegedExceptionAction() {
        @Override
        public TaskUmbilicalProtocol run() throws Exception {
          return (TaskUmbilicalProtocol)RPC.getProxy(TaskUmbilicalProtocol.class,
              TaskUmbilicalProtocol.versionID,
              address,
              defaultConf);
        }
    });

    int numTasksToExecute = -1; //-1 signifies "no limit"
    int numTasksExecuted = 0;
    Runtime.getRuntime().addShutdownHook(new Thread() {
      public void run() {
        try {
          if (taskid != null) {
            TaskLog.syncLogs
              (logLocation, taskid, isCleanup, currentJobSegmented);
          }
        } catch (Throwable throwable) {
        }
      }
    });
    Thread t = new Thread() {
      public void run() {
        //every so often wake up and syncLogs so that we can track
        //logs of the currently running task
        while (true) {
          try {
            Thread.sleep(5000);
            if (taskid != null) {
              TaskLog.syncLogs
                (logLocation, taskid, isCleanup, currentJobSegmented);
            }
          } catch (InterruptedException ie) {
          } catch (IOException iee) {
            LOG.error("Error in syncLogs: " + iee);
            System.exit(-1);
          }
        }
      }
    };
    t.setName("Thread for syncLogs");
    t.setDaemon(true);
    t.start();

    String pid = "";
    if (!Shell.WINDOWS) {
      pid = System.getenv().get("JVM_PID");
    }
    JvmContext context = new JvmContext(jvmId, pid);
    int idleLoopCount = 0;
    Task task = null;

    UserGroupInformation childUGI = null;

    final JvmContext jvmContext = context;
    try {
      while (true) {
        taskid = null;
        currentJobSegmented = true;

        JvmTask myTask = umbilical.getTask(context);
        if (myTask.shouldDie()) {
          break;
        } else {
          if (myTask.getTask() == null) {
            taskid = null;
            currentJobSegmented = true;

            if (++idleLoopCount >= SLEEP_LONGER_COUNT) {
              //we sleep for a bigger interval when we don't receive
              //tasks for a while
              Thread.sleep(1500);
            } else {
              Thread.sleep(500);
            }
            continue;
          }
        }
        idleLoopCount = 0;
        task = myTask.getTask();
        task.setJvmContext(jvmContext);
        taskid = task.getTaskID();

        // Create the JobConf and determine if this job gets segmented task logs
        final JobConf job = new JobConf(task.getJobFile());
        currentJobSegmented = logIsSegmented(job);

        isCleanup = task.isTaskCleanupTask();
        // reset the statistics for the task
        FileSystem.clearStatistics();

        // Set credentials
        job.setCredentials(defaultConf.getCredentials());
        //forcefully turn off caching for localfs. All cached FileSystems
        //are closed during the JVM shutdown. We do certain
        //localfs operations in the shutdown hook, and we don't
        //want the localfs to be "closed"
        job.setBoolean("fs.file.impl.disable.cache", false);

        // set the jobTokenFile into task
        task.setJobTokenSecret(JobTokenSecretManager.
            createSecretKey(jt.getPassword()));

        // setup the child's mapred-local-dir. The child is now sandboxed and
        // can only see files down and under attemtdir only.
        TaskRunner.setupChildMapredLocalDirs(task, job);

        // setup the child's attempt directories
        localizeTask(task, job, logLocation);

        //setupWorkDir actually sets up the symlinks for the distributed
        //cache. After a task exits we wipe the workdir clean, and hence
        //the symlinks have to be rebuilt.
        TaskRunner.setupWorkDir(job, new File(cwd));

        //create the index file so that the log files 
        //are viewable immediately
        TaskLog.syncLogs
          (logLocation, taskid, isCleanup, logIsSegmented(job));

        numTasksToExecute = job.getNumTasksToExecutePerJvm();
        assert(numTasksToExecute != 0);

        task.setConf(job);

        // Initiate Java VM metrics
        initMetrics(prefix, jvmId.toString(), job.getSessionId());

        LOG.debug("Creating remote user to execute task: " + job.get("user.name"));
        childUGI = UserGroupInformation.createRemoteUser(job.get("user.name"));
        // Add tokens to new user so that it may execute its task correctly.
        for(Token token : UserGroupInformation.getCurrentUser().getTokens()) {
          childUGI.addToken(token);
        }

        // Create a final reference to the task for the doAs block
        final Task taskFinal = task;
        childUGI.doAs(new PrivilegedExceptionAction

Child类的核心代码框架如下：

public static void main(String args[]){
    //创建RPC Client，启动日志同步线程
    ...
    while(true){//不断询问TaskTracker，以获取新任务
        JvmTask myTask = umbilical.getTask(context) ;//获取新任务
        if(myTask.shouldDie()){//JVM所属作业不存在或者被杀死
            break ;
        }else{
            if(myTask.getTask() == null){//暂时没有新任务
                //等待一段时间继续询问TaskTracker
                ...
                continue ;
            }
        }
        //有新任务,进行任务本地化
        ...
        taskFinal.run(job,umbilical) ;//启动该任务
        ...
        //如果JVM复用次数达到上限数目，则直接退出
        if(numTasksToExecute > 0 && ++numTaskExecuted == numTasksToExecute){
            break ;
        }
    }
}

然后会调用org.apache.hadoop.mapred.Task.java中的run方法，Task是一个抽象类，两个子类分别是MapTask和ReduceTask。先关注MapTask的run方法。

  @Override
  public void run(final JobConf job, final TaskUmbilicalProtocol umbilical) 
    throws IOException, ClassNotFoundException, InterruptedException {
    this.umbilical = umbilical;

    // start thread that will handle communication with parent
    TaskReporter reporter = new TaskReporter(getProgress(), umbilical,
        jvmContext);
    reporter.startCommunicationThread();
    boolean useNewApi = job.getUseNewMapper();
    initialize(job, getJobID(), reporter, useNewApi);

    // check if it is a cleanupJobTask
    if (jobCleanup) {
      runJobCleanupTask(umbilical, reporter);
      return;
    }
    if (jobSetup) {
      runJobSetupTask(umbilical, reporter);
      return;
    }
    if (taskCleanup) {
      runTaskCleanupTask(umbilical, reporter);
      return;
    }

    if (useNewApi) {
      runNewMapper(job, splitMetaInfo, umbilical, reporter);
    } else {
      runOldMapper(job, splitMetaInfo, umbilical, reporter);
    }
    done(umbilical, reporter);
  }

runJobCleanupTask()：清理Job对应的相关目录和文件
runJobSetupTask()：创建Job运行所需要的相关目录和文件
runTaskCleanupTask()：清理一个Task对应的工作目录下与Task相关的目录或文件
runNewMapper()/runOldMapper()：调用用户编写的MapReduce程序中的Mapper中的处理逻辑

在runNewMapper（）中，有如下代码

    // make a mapper
    org.apache.hadoop.mapreduce.Mapper mapper =
      (org.apache.hadoop.mapreduce.Mapper)
        ReflectionUtils.newInstance(taskContext.getMapperClass(), job);

通过反射，执行用户编写的mapper函数。。。。

2、Map Task的输入从哪里来
这首先要了解两个文件job.split和job.splitmetainfo
a）org.apache.hadoop.mapreduce.Job.java中的submit（）提交job之后，会调用org.apache.hadoop.mapred.JobClient中的submitJobInternal（）；
b）在submitJobInternal函数中会给job创建分片

int maps = writeSplits(job,submitJobDir) ;

在该函数中会调用writeNewSplits() ;
c）在org.apache.hadoop.mapred.JobClient的writeNewSplits函数中，通过反射获得InputFormat对象，会调用该对象中的getSplits()方法来进行分片，从而得到InputSplit[]数组，然后会通过JobSplitWriter.createSplitFiles方法将数组内容写出，writeNewSplits方法返回的是分片数目，决定了会创建多少个map task
d）在org.apache.hadoop.mapreduce.split.JobSplitWriter.java的JobSplitWriter.createSplitFiles方法中，会打开一个输出流out，输出文件名是（{jobSubmitDir}/job.splitInfo）,其中调用writeNewSplits()来完成写出操作，与此同时，该函数会返回一个SplitMetaInfo的数组。
e）最后调用org.apache.hadoop.mapreduce.split.JobSplitWriter.java的writeJobSplitMetaInfo（）方法，将上一步中的SplitMetaInfo数组写入到另一个文件中，文件名是（${jobSubmitDir}/job.splitInfo）

上述步骤中最终会输出两个文件job.split和job.splitmetainfo
如下图所示，job.split存储了所有划分出来的InputSplit，而每个InputSplit记录如下信息：
1、该Split的类型(ClassName, mostly org.apache.hadoop.mapreduce.lib.input.FileSplit)
2、该Split所属文件的路径(FilePath)
3、该Split在所属文件中的起始位置(FileOffset)
4、该Split的字节长度(Length)
job.splitmetainfo存储了有关InputSplit的元数据：
1、该Split在哪些Node上是local data(Location)
2、该Split对应的InputSplit在job.split文件中的位置(SplitFileOffset)
3、该Split的字节长度(Length, the same as that in job.split)
这里写图片描述
到此为之，将分片信息记录完成，写入到HDFS中相应的文件中。
对于job.splitmetainfo、job.split文件使用，splitmetainfo只是保存了split的元数据信息，如果想读取数据，还是要用到job.split
调度器调用JobTracker.initJob()函数对新作业进行初始化，会调用org.apache.hadoop.mapred.JobInProgress.java中的initTasks（）方法

  //
    // read input splits and create a map per a split
    //
    TaskSplitMetaInfo[] splits = createSplits(jobId);

在createSplits函数中会通过SplitMetaInfoReader.readSplitMetaInfo函数从job.splitmetainfo文件中读出相应的信息，首先会先验证META_SPLIT_VERSION和读取numSplits，然后会依次读取出每个splitMetaInfo，根据splitMetaInfo再从job.split中读取相应数据，构建出TaskSplitIndex对象，然后得到TaskSplitMetaInfo对象，最后返回TaskSplitMetaInfo[]数组
TaskSplitMetaInfo：用于保存InputSplit元信息的数据结构，包括以下三项内容：

private TaskSplitIndex splitIndex ;//保存读取job.split文件中的split内容
private long inputDataLength ; //InputSplit的数据长度
private String[] locations ; //InputSplit所在的host列表

这些信息是在作业初始化时，JobTracker从文件job.splitmetainfo中获取。其中，host列表信息是任务调度器判断任务是否具有本地性的最重要因素，而splitIndex信息保存了新任务需处理的数据位置信息在文件job.split中的索引，TaskTracker（从JobTracker端）收到该信息后，便可以从job.split文件中读取InputSplit信息，进而运行一个任务。
TaskSplitIndex：JobTracker向TaskTracker分配新任务时，TaskSplitIndex用于指定新任务待处理数据位置信息在文件job.split中的索引，主要包括两项内容：

private String splitLocation ;//job.split文件的位置（目录）
private long startOffset ; //InputSplit信息在job.split文件中的位置

在org.apache.hadoop.mapred.MapTask.java中的runNewMapper()/runOldMapper()方法最终会调用用户编写的MapReduce程序中的Mapper中的处理逻辑，它们所传的参数中都含有TaskSplitIndex对象，也就可以找到job.split文件读取数据。。。

对于job.split、job.splitmetainfo、TaskSplitMetaInfo、TaskSplitIndex在生成它们时保存的信息
job.split内容

FileSplit={file=hdfs://server1:9000/user/admin/in/yellow.txt,length=67108864,start=0}

FileSplit={file=hdfs://server1:9000/user/admin/in/yellow.txt,length=67108864,start= 67108864}

FileSplit={file=hdfs://server1:9000/user/admin/in/yellow.txt,length= 66782272,start=134217728}

job.splitmetainfo内容

JobSplit$SplitMetaInfo={data-size : 67108864，start-offset: 7，locations:[server3, server2]

}

JobSplit$SplitMetaInfo={data-size : 67108864，start-offset: 116，locations:[server3, server2]

}

JobSplit$SplitMetaInfo={data-size : 66782272，start-offset: 225，locations:[server3, server2]

对比job.split和job.splitmetainfo内容:

job.splitmetainfo的data-size即job.split的length，

job.splitmetainfo的locations即所对应的job.split的数据所在块的主机列表，

job.splitmetainfo的start-offset意思是job.split中某条FileSplit记录的起始地址。

TaskSplitMetaInfo、TaskSplitIndex的内容

TaskSplitMetaInfo[0]={inputDataLength=67108864,locations=[server3,server2], splitIndex=JobSplit$TaskSplitIndex{splitLocation="hdfs://server1:9000/tmp/hadoop-admin/mapred/staging/admin/.staging/job_201404200521_0001/job.split", startOffset=7}

}

TaskSplitMetaInfo[1]={inputDataLength=67108864,locations=[server3,server2], splitIndex=JobSplit$TaskSplitIndex{splitLocation="hdfs://server1:9000/tmp/hadoop-admin/mapred/staging/admin/.staging/job_201404200521_0001/job.split", startOffset=116}

}

TaskSplitMetaInfo[2]={inputDataLength=66782272, locations=[server3,server2], splitIndex=JobSplit$TaskSplitIndex{splitLocation="hdfs://server1:9000/tmp/hadoop-admin/mapred/staging/admin/.staging/job_201404200521_0001/job.split", startOffset=225}

好了，现在我们已经知道map函数如何输入了，接下来再思考一个问题，在org.apache.hadoop.mapred.JobClient.java中的getSplits方法中会得到分片的主机列表，如果一个分片在一个block内，直接返回该block的主机列表即可，但是存在一种情况，该分片跨越多个block，这个时候如何返回主机列表呢？

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