Hadoop 2.0 代码：Client端代码简要分析

1.概览

  以下主要叙述Hadoop如何将用户写好的MR程序，以Job的形式提交

  主要涉及的四个java类文件：

hadoop-mapreduce-client-core下的包org.apache.hadoop.mapreduce：

       Job.java、JobSubmitter.java

hadoop-mapreduce-client-jobclient下的包org.apache.hadoop.mapred：

       YARNRunner.java、ResourceMgrDelegate.java

2.代码分析与执行逻辑过程

1).客户运行写好类下下面的程序，这里省去map和reduce的函数的实现：

Job job = new Job(new Configuration());
job.setJarByClass(MyJob.class);
   
// Specify various job-specific parameters     
job.setJobName("myjob");
    
job.setInputPath(new Path("in"));
job.setOutputPath(new Path("out"));

job.setMapperClass(MyJob.MyMapper.class);
job.setReducerClass(MyJob.MyReducer.class);

// Submit the job, then poll for progress until the job is complete
job.waitForCompletion(true);

2).客户提交的客户程序调用了Job中的waitForCompletion()函数

/**
  * Submit the job to the cluster and wait for it to finish.
  * @param verbose print the progress to the user
  * @return true if the job succeeded
  * @throws IOException thrown if the communication with the 
  *         <code>JobTracker</code> is lost
  */

public boolean waitForCompletion(boolean verbose
                                   ) throws IOException, InterruptedException,
                                            ClassNotFoundException {
    if (state == JobState.DEFINE) {
      submit();
    }
    if (verbose) {
      monitorAndPrintJob();
    } else {
      // get the completion poll interval from the client.
      int completionPollIntervalMillis = 
        Job.getCompletionPollInterval(cluster.getConf());
      while (!isComplete()) {
        try {
          Thread.sleep(completionPollIntervalMillis);
        } catch (InterruptedException ie) {
        }
      }
    }
    return isSuccessful();
  }

Job如果已经初始化好，立即调用submit()函数,之后调用monitorAndPrintJob()检查Job和Task的运行状况，或者自身进入循环，以一定的时间间隔轮询检查所提交的Job是是否执行完成。如果执行完成，跳出循环，调用isSuccessful()函数返回执行后的状态。

2).waitForCompletion()函数调用submit(),进入submit()函数

/**
   * Submit the job to the cluster and return immediately.
   * @throws IOException
   */
  public void submit() 
         throws IOException, InterruptedException, ClassNotFoundException {
    ensureState(JobState.DEFINE);
    setUseNewAPI();
    connect();
    final JobSubmitter submitter = 
        getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
    status = ugi.doAs(new PrivilegedExceptionAction<JobStatus>() {
      public JobStatus run() throws IOException, InterruptedException, 
      ClassNotFoundException {
        return submitter.submitJobInternal(Job.this, cluster);
      }
    });
    state = JobState.RUNNING;
    LOG.info("The url to track the job: " + getTrackingURL());
   }

submit函数主要先调用connect()来获取需的调用协议(ClientProtocol)信息，连接信息，最后写入Cluster对象中，之后调用JobSubmitter类下的submitJobInternal()函数，获取其返回的状态设置JobStatus为Running，最后直接退出。

3).进入JobSubmitter类下的submitJobInternal()函数

 /**
   * Internal method for submitting jobs to the system.
   */
  JobStatus submitJobInternal(Job job, Cluster cluster) 
  throws ClassNotFoundException, InterruptedException, IOException {

    //validate the jobs output specs 
    checkSpecs(job);
    
    Path jobStagingArea = JobSubmissionFiles.getStagingDir(cluster, 
                                                     job.getConfiguration());
    //configure the command line options correctly on the submitting dfs
    Configuration conf = job.getConfiguration();
    InetAddress ip = InetAddress.getLocalHost();
    if (ip != null) {
      submitHostAddress = ip.getHostAddress();
      submitHostName = ip.getHostName();
      conf.set(MRJobConfig.JOB_SUBMITHOST,submitHostName);
      conf.set(MRJobConfig.JOB_SUBMITHOSTADDR,submitHostAddress);
    }
    JobID jobId = submitClient.getNewJobID();
    job.setJobID(jobId);
    Path submitJobDir = new Path(jobStagingArea, jobId.toString());
    JobStatus status = null;
    try {
      conf.set("hadoop.http.filter.initializers", 
          "org.apache.hadoop.yarn.server.webproxy.amfilter.AmFilterInitializer");
      conf.set(MRJobConfig.MAPREDUCE_JOB_DIR, submitJobDir.toString());
      LOG.debug("Configuring job " + jobId + " with " + submitJobDir 
          + " as the submit dir");
      // get delegation token for the dir
      TokenCache.obtainTokensForNamenodes(job.getCredentials(),
          new Path[] { submitJobDir }, conf);
      
      populateTokenCache(conf, job.getCredentials());

      copyAndConfigureFiles(job, submitJobDir);
      Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);
      
      // Create the splits for the job
      LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
      int maps = writeSplits(job, submitJobDir);
      conf.setInt(MRJobConfig.NUM_MAPS, maps);
      LOG.info("number of splits:" + maps);

      // write "queue admins of the queue to which job is being submitted"
      // to job file.
      String queue = conf.get(MRJobConfig.QUEUE_NAME,
          JobConf.DEFAULT_QUEUE_NAME);
      AccessControlList acl = submitClient.getQueueAdmins(queue);
      conf.set(toFullPropertyName(queue,
          QueueACL.ADMINISTER_JOBS.getAclName()), acl.getAclString());

      // removing jobtoken referrals before copying the jobconf to HDFS
      // as the tasks don't need this setting, actually they may break
      // because of it if present as the referral will point to a
      // different job.
      TokenCache.cleanUpTokenReferral(conf);

      // Write job file to submit dir
      writeConf(conf, submitJobFile);
      
      //
      // Now, actually submit the job (using the submit name)
      //
      printTokens(jobId, job.getCredentials());
      status = submitClient.submitJob(
          jobId, submitJobDir.toString(), job.getCredentials());
      if (status != null) {
        return status;
      } else {
        throw new IOException("Could not launch job");
      }
    } finally {
      if (status == null) {
        LOG.info("Cleaning up the staging area " + submitJobDir);
        if (jtFs != null && submitJobDir != null)
          jtFs.delete(submitJobDir, true);

      }
    }
  }

Submit主要进行如下操作

• 检查Job的输入输出是各项参数，获取配置信息和远程主机的地址，生成JobID，确定所需工作目录(也是MRAppMaster.java所在目录)，执行期间设置必要的信息
• 拷贝所需要的Jar文件和配置文件信息到HDFS系统上的指定工作目录，以便各个节点调用使用
• 计算并获数去输入分片(Input Split)的数目，以确定map的个数
• 调用YARNRunner类下的submitJob()函数，提交Job，传出相应的所需参数(例如 JobID等)。
• 等待submit()执行返回Job执行状态，最后删除相应的工作目录。

4).YARNRunner类下的submitJob()函数

@Override
  public JobStatus submitJob(JobID jobId, String jobSubmitDir, Credentials ts)
  throws IOException, InterruptedException {
    
    /* check if we have a hsproxy, if not, no need */
    MRClientProtocol hsProxy = clientCache.getInitializedHSProxy();
    if (hsProxy != null) {
      // JobClient will set this flag if getDelegationToken is called, if so, get
      // the delegation tokens for the HistoryServer also.
      if (conf.getBoolean(JobClient.HS_DELEGATION_TOKEN_REQUIRED, 
          DEFAULT_HS_DELEGATION_TOKEN_REQUIRED)) {
        Token hsDT = getDelegationTokenFromHS(hsProxy, new Text( 
                conf.get(JobClient.HS_DELEGATION_TOKEN_RENEWER)));
        ts.addToken(hsDT.getService(), hsDT);
      }
    }

    // Upload only in security mode: TODO
    Path applicationTokensFile =
        new Path(jobSubmitDir, MRJobConfig.APPLICATION_TOKENS_FILE);
    try {
      ts.writeTokenStorageFile(applicationTokensFile, conf);
    } catch (IOException e) {
      throw new YarnException(e);
    }

    // Construct necessary information to start the MR AM
    ApplicationSubmissionContext appContext =
      createApplicationSubmissionContext(conf, jobSubmitDir, ts);

    // Submit to ResourceManager
    ApplicationId applicationId = resMgrDelegate.submitApplication(appContext);

    ApplicationReport appMaster = resMgrDelegate
        .getApplicationReport(applicationId);
    String diagnostics =
        (appMaster == null ?
            "application report is null" : appMaster.getDiagnostics());
    if (appMaster == null || appMaster.getYarnApplicationState() == YarnApplicationState.FAILED
        || appMaster.getYarnApplicationState() == YarnApplicationState.KILLED) {
      throw new IOException("Failed to run job : " +
        diagnostics);
    }
    return clientCache.getClient(jobId).getJobStatus(jobId);
  }

• 设置必要的配置信息，初始化Application上下文信息，其中上下文信息中包括MRAppMaster所需要的资源，执行MRAppMaster的命令得等。
• 然后调用ResourceMgrDelegate的submitApplication()方法，同时传入Application上下文信息，提交Job到ResourceManager，函数执行最后返回已生成的ApplicationId(实际生成JobID的时候ApplicationId就已经生成)。
• 最后返回Job此时的状态，函数退出。

5).ResourceMgrDelegate类下的submitApplication()函数

public ApplicationId submitApplication(
      ApplicationSubmissionContext appContext) 
  throws IOException {
    appContext.setApplicationId(applicationId);
    SubmitApplicationRequest request = 
        recordFactory.newRecordInstance(SubmitApplicationRequest.class);
    request.setApplicationSubmissionContext(appContext);
    applicationsManager.submitApplication(request);
    LOG.info("Submitted application " + applicationId + " to ResourceManager" +
            " at " + rmAddress);
    return applicationId;
  }

这个函数很简单

• 设置Application上下文中的ApplicationId，
• 将Application上下文信息设置到要请求的request信息当中去
• 最后用Hadoop RPC远程调用ResourcesManager端的ClientRMService类下的submitApplication()方法，提交已经设置好的包含有Application上下文信息请求信息到ResourcesManager端。