专栏首页波波烤鸭Hadoop之MapReduce04【客户端源码分析】

Hadoop之MapReduce04【客户端源码分析】

  本文是基于hadoop2.6.5的源码分析。

客户端源码分析

启动的客户端代码

public static void main(String[] args) throws Exception {
	// 创建配置文件对象
	Configuration conf = new Configuration(true);
	
	// 获取Job对象
	Job job = Job.getInstance(conf);
	// 设置相关类
	job.setJarByClass(WcTest.class);
	
	// 指定 Map阶段和Reduce阶段的处理类
	job.setMapperClass(MyMapperTask.class);
	job.setReducerClass(MyReducerTask.class);
	
	// 指定Map阶段的输出类型
	job.setMapOutputKeyClass(Text.class);
	job.setMapOutputValueClass(IntWritable.class);
	
	// 指定job的原始文件的输入输出路径 通过参数传入
	FileInputFormat.setInputPaths(job, new Path(args[0]));
	FileOutputFormat.setOutputPath(job, new Path(args[1]));
	// 提交任务,并等待响应
	job.waitForCompletion(true);
}

1.Configuration 对象

  Configuration 用来存储相关的配置文件。在该类中有一段static代码块

2.Job对象的获取

  我们来看下Job对象的实例化过程。

// 获取Job对象
Job job = Job.getInstance(conf);

进入getInstance(conf)方法。

  public static Job getInstance(Configuration conf) throws IOException {
    // create with a null Cluster
    JobConf jobConf = new JobConf(conf);
    return new Job(jobConf);
  }

Job类中同样有static代码块。

进入loadResources方法

3.waitForCompletion

  该方法的执行过程比较复杂,我们慢慢来分析,首先来看下简化的时序图

3.1waitForCompletion

public boolean waitForCompletion(boolean verbose
                                   ) throws IOException, InterruptedException,
                                            ClassNotFoundException {
    // 判断任务的状态,如果是DEFINE就提交
    if (state == JobState.DEFINE) {
      submit();
    }
    if (verbose) {
      // 监听并且输出任务信息
      monitorAndPrintJob();
    } else {
      // get the completion poll interval from the client.
      int completionPollIntervalMillis = 
        Job.getCompletionPollInterval(cluster.getConf());
      while (!isComplete()) {
        try {
          // 间隔判断是否执行完成
          Thread.sleep(completionPollIntervalMillis);
        } catch (InterruptedException ie) {
        }
      }
    }
    return isSuccessful();
  }

3.2submit

  进入submit方法查看

  public void submit() 
         throws IOException, InterruptedException, ClassNotFoundException {
    // 再次确认任务状态
    ensureState(JobState.DEFINE);
    // 默认使用new APIs
    setUseNewAPI();
    // 初始化cluster对象
    connect();
    // 根据初始化得到的cluster对象生成JobSubmitter对象
    final JobSubmitter submitter = 
        getJobSubmitter(cluster.getFileSystem(), cluster.getClient());
    // 
    status = ugi.doAs(new PrivilegedExceptionAction<JobStatus>() {
      public JobStatus run() throws IOException, InterruptedException, 
      ClassNotFoundException {
      	// 进入 submitJobInternal 方法查看
        return submitter.submitJobInternal(Job.this, cluster);
      }
    });
    //将job的状态设置为RUNNING
    state = JobState.RUNNING;
    LOG.info("The url to track the job: " + getTrackingURL());
   }

3.3 submitJobInternal

/**
*
* 检查job的输入输出规范
* 计算job的InputSplit
* 如果需要的话,设置需要的核算信息对于job的分布式缓存
* 复制job的jar和配置文件到分布式文件系统的系统目录
* 提交作业执行以及监控它的状态
*/
 JobStatus submitJobInternal(Job job, Cluster cluster) 
  throws ClassNotFoundException, InterruptedException, IOException {

    //检查job的输出空间 
    checkSpecs(job);
	
    Configuration conf = job.getConfiguration();
    // 将MapReduce框架加入分布式缓存中
    addMRFrameworkToDistributedCache(conf);
	// 初始化job的工作根目录并返回path路径
    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);
    }
    //  为job分配一个名字
    JobID jobId = submitClient.getNewJobID();
    job.setJobID(jobId);
    // 获得job的提交路径,也就是在jobStagingArea目录下建一个以jobId为文件名的目录
    Path submitJobDir = new Path(jobStagingArea, jobId.toString());
    JobStatus status = null;
    // 进行一系列的配置
    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 {
          keyGen = KeyGenerator.getInstance(SHUFFLE_KEYGEN_ALGORITHM);
          keyGen.init(SHUFFLE_KEY_LENGTH);
        } catch (NoSuchAlgorithmException e) {
          throw new IOException("Error generating shuffle secret key", e);
        }
        SecretKey shuffleKey = keyGen.generateKey();
        TokenCache.setShuffleSecretKey(shuffleKey.getEncoded(),
            job.getCredentials());
      }
	  // 这个方法实现文件上传	
      copyAndConfigureFiles(job, submitJobDir);
      Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);
      
      // Create the splits for the job
      LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));
      // 方法内部会根据我们之前的设置,选择使用new-api还是old-api分别进行分片操作
      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()]));
      }

      // 提交规划文件 job.split wc.jar ...
      writeConf(conf, submitJobFile);
      
      //
      // Now, actually submit the job (using the submit name)
      // 提交任务
      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);

      }
    }
  }

3.4writeSplits

  private int writeSplits(org.apache.hadoop.mapreduce.JobContext job,
      Path jobSubmitDir) throws IOException,
      InterruptedException, ClassNotFoundException {
    JobConf jConf = (JobConf)job.getConfiguration();
    int maps;
    if (jConf.getUseNewMapper()) {
     //进入
      maps = writeNewSplits(job, jobSubmitDir);
    } else {
      maps = writeOldSplits(jConf, jobSubmitDir);
    }
    return maps;
  }

3.5writeNewSplits

  int writeNewSplits(JobContext job, Path jobSubmitDir) throws IOException,
      InterruptedException, ClassNotFoundException {
    Configuration conf = job.getConfiguration();
    // 根据我们设置的inputFormat.class通过反射获得inputFormat对象
    InputFormat<?, ?> input =
      ReflectionUtils.newInstance(job.getInputFormatClass(), conf);
	// 获取分片信息
    List<InputSplit> splits = input.getSplits(job);
    T[] array = (T[]) splits.toArray(new InputSplit[splits.size()]);

    // sort the splits into order based on size, so that the biggest
    // go first
    Arrays.sort(array, new SplitComparator());
    // 将分片的信息写入到jobSubmitDir --job.split文件中
    JobSplitWriter.createSplitFiles(jobSubmitDir, conf, 
        jobSubmitDir.getFileSystem(conf), array);
    return array.length;
  }

3.6 getSplits

  public List<InputSplit> getSplits(JobContext job) throws IOException {
    Stopwatch sw = new Stopwatch().start();
    // 最小值
    long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job));
    // 最大值
    long maxSize = getMaxSplitSize(job);

    // generate splits
    List<InputSplit> splits = new ArrayList<InputSplit>();
    List<FileStatus> files = listStatus(job);
    for (FileStatus file: files) {
      Path path = file.getPath();
      long length = file.getLen();
      if (length != 0) {
        BlockLocation[] blkLocations;
        if (file instanceof LocatedFileStatus) {
          blkLocations = ((LocatedFileStatus) file).getBlockLocations();
        } else {
          FileSystem fs = path.getFileSystem(job.getConfiguration());
          blkLocations = fs.getFileBlockLocations(file, 0, length);
        }
        if (isSplitable(job, path)) {
          // 获取block大小
          long blockSize = file.getBlockSize();
          // 获取splitSize大小
          long splitSize = computeSplitSize(blockSize, minSize, maxSize);

          long bytesRemaining = length;
          while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
            int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
            splits.add(makeSplit(path, length-bytesRemaining, splitSize,
                        blkLocations[blkIndex].getHosts(),
                        blkLocations[blkIndex].getCachedHosts()));
            bytesRemaining -= splitSize;
          }

          if (bytesRemaining != 0) {
            int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
            splits.add(makeSplit(path, length-bytesRemaining, bytesRemaining,
                       blkLocations[blkIndex].getHosts(),
                       blkLocations[blkIndex].getCachedHosts()));
          }
        } else { // not splitable
          splits.add(makeSplit(path, 0, length, blkLocations[0].getHosts(),
                      blkLocations[0].getCachedHosts()));
        }
      } else { 
        //Create empty hosts array for zero length files
        splits.add(makeSplit(path, 0, length, new String[0]));
      }
    }
    // Save the number of input files for metrics/loadgen
    job.getConfiguration().setLong(NUM_INPUT_FILES, files.size());
    sw.stop();
    if (LOG.isDebugEnabled()) {
      LOG.debug("Total # of splits generated by getSplits: " + splits.size()
          + ", TimeTaken: " + sw.elapsedMillis());
    }
    return splits;
  }

3.7computeSplitSize

protected long computeSplitSize(long blockSize, long minSize,
                                long maxSize) {
  return Math.max(minSize, Math.min(maxSize, blockSize));
}

3.8 submitJobInternal

回到 submitJobInternal方法中

	// 提交规划文件 job.split wc.jar ...
      writeConf(conf, submitJobFile);
      
      //
      // Now, actually submit the job (using the submit name)
      // 提交任务
      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)
        	// 删除规划文件
          jtFs.delete(submitJobDir, true);

      }
    }

至此整理流程代码看完~ 详细的可以多看下源码

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