hadoop源代码分析（二）从wordCount开始，剖析mapreduce的运行机制

在上一篇文章中，只是简单介绍了Mapreduce作业，从执行hadoop jar test.jar 的shell命令，到是如何被加载并找到主类的。那么，从这个文章开始，研究从mapreduce的main方法开始，如何一步步提交、运行mapreduce作业的，此处会涉及到yarn相关知识。

编写的mapreduce程序的main方法如下，(map，reduce阶段代码很简单，就不贴上浪费CSDN的空间了)：

public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
        Job job = Job.getInstance(conf,Test.class.getSimpleName());
        job.setMapperClass(Mapper.class);
        job.setJarByClass(Test.class);
        job.setOutputKeyClass(NullWritable.class);
        job.setOutputValueClass(Text.class);
        FileInputFormat.setInputPaths(job, new Path("hdfs://192.168.80.100:9000/datawarehouse/src/userinfo/2016-02-20"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs://192.168.80.100:9000/datawarehouse/dw/userinfo/"+new SimpleDateFormat("yyyy-MM-dd").format(new Date())));
        System.out.println("--------end---------");

	//上面的只是一些简单配置，从这里开始作业提交流程~~~咱们的分析，也是从这里开始！
        job.waitForCompletion(true);
	}

1、进入waitForCompletion方法：

public boolean waitForCompletion(boolean verbose) throws IOException, InterruptedException,
                                           ClassNotFoundException {
    //在Job内部，有一枚举类public static enum JobState {DEFINE, RUNNING};作业的jobstate有以上两种
    if (state == JobState.DEFINE) {
    //进入submit()方法，提交作业
      submit();
    }
    if (verbose) {
    //作业进入循环监控
      monitorAndPrintJob();
    } else {
    
      //如果false一直循环现成睡眠，睡眠时间取决于mapreduce.client.completion.pollinterval配置，默认为5000
      /**
   * 循环一直持续到!isComplete()状态，即：作业的状态不为：SUCCEEDED，FAILED，KILLED
   * 除此之外，JobStatus.State枚举类内部，还有RUNNING，PREP，以上几种状态，即yarn的几种job运行状态
   */
      int completionPollIntervalMillis = 
        Job.getCompletionPollInterval(cluster.getConf());
      while (!isComplete()) {
        try {
          Thread.sleep(completionPollIntervalMillis);
        } catch (InterruptedException ie) {
        }
      }
    }
    return isSuccessful();
  }

2、上述方法中，调用了submit();方法，下面进入此方法： 

public void submit() 
         throws IOException, InterruptedException, ClassNotFoundException {
    ensureState(JobState.DEFINE);
    /**
   	* 这个方法与主线关系不大具体实现不准备在这里贴出详解，方法内部做了以下事：
  	* 如未做配置，则调用新MR的API
   	* 
   	*/
    setUseNewAPI();
    //链接Resourcemanager，初始化Cluster对象
    /**
   	* 前提，mapred-site.xml中你配置了mapreduce.framework.name选项为"YARN"
  	* Cluster对象的构造方法，参数InetSocketAddress，命名还是jobTrackAddr，但是调用构造方法的时候传入的NULL，这里看着有些别扭。
   	* 
   	*/
    connect();
    final JobSubmitter submitter = 
        getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
    status = ugi.doAs(new PrivilegedExceptionAction() {
      public JobStatus run() throws IOException, InterruptedException, 
      ClassNotFoundException {
      //提交作业，代码在下面贴出
        return submitter.submitJobInternal(Job.this, cluster);
      }
    });
    state = JobState.RUNNING;
    LOG.info("The url to track the job: " + getTrackingURL());
   }

3、进入submitJobInternal方法

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

    //对输出目录，检查
    checkSpecs(job);

    Configuration conf = job.getConfiguration();
    addMRFrameworkToDistributedCache(conf);
		//中专目录。应该是job所需jarxml等文件的父目录，在此基础上，根据
    Path jobStagingArea = JobSubmissionFiles.getStagingDir(cluster, conf);
    //configure the command line options correctly on the submitting dfs
    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 {
    //根据上面代码，做一些基本配置如mapreduce.job.dir
      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 {
			//获得加密key长度
          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;
      //初始化加密使用加密算法Hmac_SHA1和keylen
          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());
      }
			//将所有的资源文件拷贝到资源中转目录中
      copyAndConfigureFiles(job, submitJobDir); 

      //获取job文件job.xml
      Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);
      
      // Create the splits for the job
      LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
      //根据inputsplit数量,决定map数量，同时会引发一些列操作，这里比较重要，需在下一篇独拿出来说明，这里之后你就会明白怎么控制map的数量，单单设置mapreduce.job.maps是不行的
      int maps = writeSplits(job, submitJobDir);
      //因为mapreduce.job.maps的设置，在源代码中conf.setInt(MRJobConfig.NUM_MAPS, maps);直接设置，并未查找配置文件
      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.
      //作业提交到的队列，mapred-site.xm中配置mapreduce.job.queuename，默认default
      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 trackingIds = new ArrayList();
        for (Token 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());
      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);

      }
    }
  }

4、暂时先回到第一个代码片中，作业提交后，进入循环监控方法monitorAndPrintJob()

public boolean monitorAndPrintJob() 
      throws IOException, InterruptedException {
    String lastReport = null;
    Job.TaskStatusFilter filter;
    Configuration clientConf = getConfiguration();
    filter = Job.getTaskOutputFilter(clientConf);
    JobID jobId = getJobID();
    LOG.info("Running job: " + jobId);
    int eventCounter = 0;
    boolean profiling = getProfileEnabled();
    IntegerRanges mapRanges = getProfileTaskRange(true);
    IntegerRanges reduceRanges = getProfileTaskRange(false);
    int progMonitorPollIntervalMillis = 
      Job.getProgressPollInterval(clientConf);
    /* make sure to report full progress after the job is done */
    boolean reportedAfterCompletion = false;
    boolean reportedUberMode = false;
    //如果作业status为RUNNING，PREP，则继续循环，循环时间由mapreduce.client.progressmonitor.pollinterval控制，默认1000
    while (!isComplete() || !reportedAfterCompletion) {
      if (isComplete()) {
        reportedAfterCompletion = true;
      } else {
        Thread.sleep(progMonitorPollIntervalMillis);
      }
      if (status.getState() == JobStatus.State.PREP) {
        continue;
      }      
      if (!reportedUberMode) {
        reportedUberMode = true;
        //循环打印是否开isUber模式，在作业的控制台输出，我们都能看到这个
        LOG.info("Job " + jobId + " running in uber mode : " + isUber());
      }      
      //打印map,reduce任务的进行百分比,这里是一个有意思的地方
      String report = 
        (" map " + StringUtils.formatPercent(mapProgress(), 0)+
            " reduce " + 
            StringUtils.formatPercent(reduceProgress(), 0));
      if (!report.equals(lastReport)) {
        LOG.info(report);
        lastReport = report;
      }

      TaskCompletionEvent[] events = 
        getTaskCompletionEvents(eventCounter, 10); 
      eventCounter += events.length;
      printTaskEvents(events, filter, profiling, mapRanges, reduceRanges);
    }
    boolean success = isSuccessful();
    //结束或者失败,打印出信息
    if (success) {
      LOG.info("Job " + jobId + " completed successfully");
    } else {
      LOG.info("Job " + jobId + " failed with state " + status.getState() + 
          " due to: " + status.getFailureInfo());
    }
    Counters counters = getCounters();
    if (counters != null) {
      LOG.info(counters.toString());
    }
    return success;
  }

以上就是初步的一些分析,还有一些详细的地方,只是做出了说明解释,并未贴出代码,如果涉及到的都要贴出,篇幅太大,顶不住,下一篇准备做一个旁支,详细解释splits生成即map数量,如果可能的话,分析出map任务的本地化算法、host机器的选择。如果花的时间较长，可能这部分会延后，但是肯定会写出来。

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