Hadoop总结篇之三---一个Job到底被提交到哪去了

我们会定义Job，我们会定义map和reduce程序。那么，这个Job到底是怎么提交的？提交到哪去了？它到底和集群怎么进行交互的呢？

这篇文章将从头讲起。

开发hadoop的程序时，一共有三大块，也就是Driver、map、reduce，在Driver中，我们要定义Configuration，定义Job，在mian方法最后，往往会以这么一段代码结尾：

if (!job.waitForCompletion(true))
			return;

而这句的作用，就是提交了我们的Job。进入代码里（其实就是Job类）我们可以看到具体实现：

public boolean waitForCompletion(boolean verbose
                                   ) throws IOException, InterruptedException,
                                            ClassNotFoundException {
    if (state == JobState.DEFINE) {
//这句是重点，提交。那么从代码里看出这个似乎是异步提交啊，否则后面的监测怎么执行呢？我们拭目以待
      submit();
    }
    if (verbose) {
      monitorAndPrintJob();
    } else {
      // get the completion poll interval from the client.
     //从配置里取得轮训的间隔时间，来分析当前job是否执行完毕
      int completionPollIntervalMillis =
        Job.getCompletionPollInterval(cluster.getConf());
      while (!isComplete()) {
        try {
          Thread.sleep(completionPollIntervalMillis);
        } catch (InterruptedException ie) {
        }
      }
    }
    return isSuccessful();
  }

　　依然在Job.class里，这个方法主要动作有二，一是找到集群，二是讲Job提交到集群

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

　　我们继续往下看，看下提交的时候都做了什么？

JobStatus submitJobInternal(Job job, Cluster cluster)
			throws ClassNotFoundException, InterruptedException, IOException {

		// 检查输出目录合法性（已存在？没指定？），这就是为什么每次提交作业，总是这个 错比较靠前的报出来
		checkSpecs(job);

		Configuration conf = job.getConfiguration();
		// 将框架提交到集群缓存（具体左右还未知？）
		addMRFrameworkToDistributedCache(conf);

		// 获得登录区，用以存放作业执行过程中用到的文件，默认位置/tmp/hadoop-yarn/staging/root/.staging
		// ，可通过yarn.app.mapreduce.am.staging-dir修改
		Path jobStagingArea = JobSubmissionFiles.getStagingDir(cluster, conf);
		// configure the command line options correctly on the submitting dfs
		// 这是获取和设置提交job机器的地址和主机名
		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);
		}
		// 取得当前Job的ID（后面详细关注此处）
		JobID jobId = submitClient.getNewJobID();
		job.setJobID(jobId);
		// 作业提交目录
		Path submitJobDir = new Path(jobStagingArea, jobId.toString());
		JobStatus status = null;
		try {
			conf.set(MRJobConfig.USER_NAME, UserGroupInformation.getCurrentUser().getShortUserName());
			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());

			// generate a secret to authenticate shuffle transfers
			if (TokenCache.getShuffleSecretKey(job.getCredentials()) == null) {
				KeyGenerator keyGen;
				try {

					int keyLen = CryptoUtils.isShuffleEncrypted(conf)
							? conf.getInt(MRJobConfig.MR_ENCRYPTED_INTERMEDIATE_DATA_KEY_SIZE_BITS,
									MRJobConfig.DEFAULT_MR_ENCRYPTED_INTERMEDIATE_DATA_KEY_SIZE_BITS)
							: SHUFFLE_KEY_LENGTH;
					keyGen = KeyGenerator.getInstance(SHUFFLE_KEYGEN_ALGORITHM);
					keyGen.init(keyLen);
				} catch (NoSuchAlgorithmException e) {
					throw new IOException("Error generating shuffle secret key", e);
				}
				SecretKey shuffleKey = keyGen.generateKey();
				TokenCache.setShuffleSecretKey(shuffleKey.getEncoded(), job.getCredentials());
			}
			// 从本地copy文件到hdfs，比如我们提交的wordcount.jar
			copyAndConfigureFiles(job, submitJobDir);

			Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);

			// Create the splits for the job，其实也就是确定了map的数量
			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);

			if (conf.getBoolean(MRJobConfig.JOB_TOKEN_TRACKING_IDS_ENABLED,
					MRJobConfig.DEFAULT_JOB_TOKEN_TRACKING_IDS_ENABLED)) {
				// Add HDFS tracking ids
				ArrayList<String> trackingIds = new ArrayList<String>();
				for (Token<? extends TokenIdentifier> t : job.getCredentials().getAllTokens()) {
					trackingIds.add(t.decodeIdentifier().getTrackingId());
				}
				conf.setStrings(MRJobConfig.JOB_TOKEN_TRACKING_IDS,
						trackingIds.toArray(new String[trackingIds.size()]));
			}

			// Set reservation info if it exists
			ReservationId reservationId = job.getReservationId();
			if (reservationId != null) {
				conf.set(MRJobConfig.RESERVATION_ID, reservationId.toString());
			}
			// Write job file to submit dir
			writeConf(conf, submitJobFile);

			//
			// Now, actually submit the job (using the submit name)
			//
			printTokens(jobId, job.getCredentials());
　　　　　　　　　　　　　　//提交！！！！！！！！
			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);

			}
		}
	}

　　那么这个最终提交用到的submitClient是哪来的？他是怎么定义的？

它是上文提到的，连接集群的时候创建的。这个集群定义了很多信息：客户端信息、用户组信息、文件系统信息，配置信息，历史job目录，系统目录等。其中客户端信息，提供了初始化方法，如下：

public Cluster(InetSocketAddress jobTrackAddr, Configuration conf)
      throws IOException {
    this.conf = conf;
    this.ugi = UserGroupInformation.getCurrentUser();
//初始化是重点
    initialize(jobTrackAddr, conf);
  }

　　具体看下初始化过程：

private void initialize(InetSocketAddress jobTrackAddr, Configuration conf)
      throws IOException {

    synchronized (frameworkLoader) {
      for (ClientProtocolProvider provider : frameworkLoader) {
        LOG.debug("Trying ClientProtocolProvider : "
            + provider.getClass().getName());
　　//根据配置，创建客户端协议提供者
        ClientProtocol clientProtocol = null;
        try {
          if (jobTrackAddr == null) {
　　//提供者返回的是一个具体的协议
            clientProtocol = provider.create(conf);
          } else {
            clientProtocol = provider.create(jobTrackAddr, conf);
          }

          if (clientProtocol != null) {
            clientProtocolProvider = provider;
　　//看到没？协议是什么？协议其实就是个类，里面封装了一些约定好的属性，以及操作这些属性的方法。实例化为对象后，就是一个可用于通信的客户端
            client = clientProtocol;
            LOG.debug("Picked " + provider.getClass().getName()
                + " as the ClientProtocolProvider");
            break;
          }
          else {
            LOG.debug("Cannot pick " + provider.getClass().getName()
                + " as the ClientProtocolProvider - returned null protocol");
          }
        }
        catch (Exception e) {
          LOG.info("Failed to use " + provider.getClass().getName()
              + " due to error: " + e.getMessage());
        }
      }
    }

    if (null == clientProtocolProvider || null == client) {
      throw new IOException(
          "Cannot initialize Cluster. Please check your configuration for "
              + MRConfig.FRAMEWORK_NAME
              + " and the correspond server addresses.");
    }
  }

　　创建客户端协议提供者，用java.util.ServiceLoader,目前包含两个具体实现，LocalClientProtocolProvider（本地作业） YarnClientProtocolProvider（Yarn作业），此处会根据mapreduce.framework.name的配置选择使用哪个创建相应的客户端。

而YarnClientProtocolProvider的本质是创建了一个YarnRunner对象

 public ClientProtocol create(Configuration conf) throws IOException {
    if (MRConfig.YARN_FRAMEWORK_NAME.equals(conf.get(MRConfig.FRAMEWORK_NAME))) {
      return new YARNRunner(conf);
    }
    return null;
  }

　　YarnRunner对象是干什么的？根据注释解释，是让当前JobClient在yarn上运行的。提供一些提交Job啊，杀死Job之类的方法。它实现了ClientProtocol接口，上面讲的提交的最后一步，其实最终就是调用了YarnRunner的submitJob方法。

它里面封装了ResourceMgrDelegate委托，委托的方法正是YarnClient类里的提交方法submitApplication。这样，当前作业（Application）提交过程，走到了YarnClient阶段。

总结：Job目前提交到了YarnClient实例中。那么YarnClient接下来怎么处理呢？