Hadoop-2.7.3源码分析：MapReduce作业提交源码跟踪

1、提交Job

MapReduce程序框架请参考
http://blog.csdn.net/chengyuqiang/article/details/72804007

@Override    public int run(String[] args) throws Exception {        //读取配置文件        Configuration conf = getConf();         //设置参数        conf.set("fs.defaultFS", "hdfs://192.168.11.81:9000");        //自定义key value 之间的分隔符（默认为tab）        conf.set("mapreduce.input.keyvaluelinerecordreader.key.value.separator", ",");        // Job表示一个MapReduce任务,构造器第二个参数为Job的名称。        Job job = Job.getInstance(conf, "MapReduceTemplate");        job.setJarByClass(MapReduceTemplate.class);//主类        Path in = new Path(args[0]);//输入路径        Path out = new Path(args[1]);//输出路径        FileSystem hdfs = out.getFileSystem(conf);        if (hdfs.isDirectory(out)) {//如果输出路径存在就删除            hdfs.delete(out, true);        }        FileInputFormat.setInputPaths(job, in);//文件输入        FileOutputFormat.setOutputPath(job, out);//文件输出        job.setMapperClass(MapTemplate.class); //设置自定义Mapper        job.setReducerClass(ReduceTemplate.class); //设置自定义Reducer        job.setInputFormatClass(KeyValueTextInputFormat.class);//文件输入格式        job.setOutputFormatClass(TextOutputFormat.class);//文件输出格式        job.setOutputKeyClass(Text.class);//设置作业输出值 Key 的类         job.setOutputValueClass(Text.class);//设置作业输出值 Value 的类         return job.waitForCompletion(true)?0:1;//等待作业完成退出    } 

2、进入Job.waitForCompletion()方法

在判断状态state可以提交Job后，执行submit()方法。monitorAndPrintJob()方法会不断的刷新获取job运行的进度信息，并打印。boolean参数verbose为true表明要打印运行进度，为false就只是等待job运行结束，不打印运行日志。

 /**   * 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 {    //当state为define时，则提交    if (state == JobState.DEFINE) {      submit(); //提交过程在submit()方法中    }    //verbose是waitForCompletion(verbose)传进来的true，表示执行monitorAndPrintJob()    //检测并打印Job相关信息    if (verbose) {      monitorAndPrintJob();    } else {      // get the completion poll interval from the client.      //从客户端获得完成轮询时间间隔      int completionPollIntervalMillis =         Job.getCompletionPollInterval(cluster.getConf());      while (!isComplete()) {        try {          Thread.sleep(completionPollIntervalMillis);        } catch (InterruptedException ie) {        }      }    }    //返回一个boolean值，表示作业是否成功提交    return isSuccessful();  }

3、进入submit()方法

submit方法首先是确保当前的Job的状态是处于DEFINE，否则不能提交Job。然后启用新的API，即org.apache.hadoop.mapreduce下的Mapper和Reducer。Connect()方法会产生一个Client实例，用来和ResourceManager通信。其实submit()方法里关键的两处代码,一处是调用connect()方法，另一处是获取一个JobSubmitter类的实例，调用该对象的submitJobInternal方法来提交任务。

  /**   * Submit the job to the cluster and return immediately.   * @throws IOException   */  public void submit()          throws IOException, InterruptedException, ClassNotFoundException {    //再次检查作业的状态    ensureState(JobState.DEFINE);    //两套API，这里使用新API    setUseNewAPI();    connect();    //初始化工作，为cluster赋值，Client即是提交器,分为本体提交器和Yarn提交器，由配置文件决定    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);//提交      }    });    state = JobState.RUNNING;    LOG.info("The url to track the job: " + getTrackingURL());   }

4、首先看connect()方法

MapReduce作业提交时连接集群是通过Job类的connect()方法实现的，它实际上是构造集群Cluster实例cluster

   private synchronized void connect()          throws IOException, InterruptedException, ClassNotFoundException {    if (cluster == null) {//若cluster空，则构造Cluster实例      //cluster是连接MapReduce集群的工具，提供了远程获取MapReduce集群的方法      cluster =         ugi.doAs(new PrivilegedExceptionAction<Cluster>() {                   public Cluster run()                          throws IOException, InterruptedException,                                  ClassNotFoundException {                     return new Cluster(getConfiguration());                   }                 });    }  }

5、进入return的Cluster(getConfiguration())构造方法

来到了Cluster类。先来看下Cluster类的成员信息。

package org.apache.hadoop.mapreduce;import .../** * Provides a way to access information about the map/reduce cluster. */@[e-mail] class Cluster {  @InterfaceStability.Evolving  public static enum JobTrackerStatus {INITIALIZING, RUNNING};  private ClientProtocolProvider clientProtocolProvider;  //客户端通信协议提供者  private ClientProtocol client;        //客户端通信协议实例  private UserGroupInformation ugi;     //用户组信息  private Configuration conf;           //配置信息  private FileSystem fs = null;         //文件系统实例  private Path sysDir = null;           //系统路径  private Path stagingAreaDir = null;   //作业资源存放路径  private Path jobHistoryDir = null;    //作业历史路径  private static final Log LOG = LogFactory.getLog(Cluster.class);  //日志  //客户端通信协议提供者加载器  private static ServiceLoader<ClientProtocolProvider> frameworkLoader =      ServiceLoader.load(ClientProtocolProvider.class);  static {    ConfigUtil.loadResources();  }  public Cluster(Configuration conf) throws IOException {    this(null, conf);  }  public Cluster(InetSocketAddress jobTrackAddr, Configuration conf)       throws IOException {    this.conf = conf;  //设置配置信息    this.ugi = UserGroupInformation.getCurrentUser(); //获取当前用户    initialize(jobTrackAddr, conf);  //完成初始化  }  ...}

Cluster最重要的两个成员变量是客户端通信协议提供者ClientProtocolProvider实例clientProtocolProvider和客户端通信协议ClientProtocol实例client，而后者是依托前者的create()方法生成的。 Cluster类提供了两个构造函数。

6、进入initialize()方法

  private void initialize(InetSocketAddress jobTrackAddr, Configuration conf)      throws IOException {    synchronized (frameworkLoader) {      //依次取出每个ClientProtocolProvider，通过其create()方法构造ClientProtocol实例      for (ClientProtocolProvider provider : frameworkLoader) {        LOG.debug("Trying ClientProtocolProvider : " + provider.getClass().getName());        ClientProtocol clientProtocol = null;         try {          //如果配置文件没有配置YARN信息，则构建LocalRunner,MR任务本地运行          //如果配置文件有配置YARN信息，则构建YarnRunner,MR任务在YARN集群上运行          if (jobTrackAddr == null) {            clientProtocol = provider.create(conf);          } else {            clientProtocol = provider.create(jobTrackAddr, conf);          }          //设置成员变量clientProtocolProvider和client，并退出循环          if (clientProtocol != null) {            clientProtocolProvider = provider;            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);        }      }    }    //如果clientProtocolProvider或client空，抛出IOException    if (null == clientProtocolProvider || null == client) {      throw new IOException(          "Cannot initialize Cluster. Please check your configuration for "              + MRConfig.FRAMEWORK_NAME              + " and the correspond server addresses.");    }  }

7、ClientProtocolProvider实现类LocalClientProtocolProvider

上面create()方法时提到了两种ClientProtocolProvider实现类。
MapReduce中，ClientProtocolProvider抽象类的实现共有YarnClientProtocolProvider、LocalClientProtocolProvider两种，前者为Yarn模式，而后者为Local模式。

我们先看下看下Local模式，LocalClientProtocolProvider的create()方法，代码如下

package org.apache.hadoop.mapred;import java.io.IOException;import java.net.InetSocketAddress;import org.apache.hadoop.classification.InterfaceAudience;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.mapreduce.MRConfig;import org.apache.hadoop.mapreduce.protocol.ClientProtocol;import org.apache.hadoop.mapreduce.protocol.ClientProtocolProvider;@InterfaceAudience.Privatepublic class LocalClientProtocolProvider extends ClientProtocolProvider {  @Override  public ClientProtocol create(Configuration conf) throws IOException {    //两个常量："mapreduce.framework.name","local"    String framework =        conf.get(MRConfig.FRAMEWORK_NAME, MRConfig.LOCAL_FRAMEWORK_NAME);    //若framework是local，则返回LocalJobRunner，并且设置Map任务数1;否则返回null    if (!MRConfig.LOCAL_FRAMEWORK_NAME.equals(framework)) {      return null;    }    conf.setInt(JobContext.NUM_MAPS, 1);    return new LocalJobRunner(conf);  }  @Override  public ClientProtocol create(InetSocketAddress addr, Configuration conf) {    return null; // LocalJobRunner doesn't use a socket  }  @Override  public void close(ClientProtocol clientProtocol) {    // no clean up required  }}

由上可知，MapReduce需要看参数mapreduce.framework.name确定连接模式，但默认是Local模式的。

8、ClientProtocolProvider实现类YarnClientProtocolProvider

再来看Yarn模式，看下YarnClientProtocolProvider的create()方法

package org.apache.hadoop.mapred;import java.io.IOException;import java.net.InetSocketAddress;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.mapreduce.MRConfig;import org.apache.hadoop.mapreduce.protocol.ClientProtocol;import org.apache.hadoop.mapreduce.protocol.ClientProtocolProvider;public class YarnClientProtocolProvider extends ClientProtocolProvider {  @Override  public ClientProtocol create(Configuration conf) throws IOException {    //若参数mapreduce.framework.name配置为Yarn，则构造一个YARNRunner实例并返回，否则返回null    if (MRConfig.YARN_FRAMEWORK_NAME.equals(conf.get(MRConfig.FRAMEWORK_NAME))) {      return new YARNRunner(conf);    }    return null;  }  @Override  public ClientProtocol create(InetSocketAddress addr, Configuration conf)      throws IOException {    return create(conf);  }  @Override  public void close(ClientProtocol clientProtocol) throws IOException {    if (clientProtocol instanceof YARNRunner) {      ((YARNRunner)clientProtocol).close();    }  }}

到了这里，我们就能够知道一个很重要的信息，Cluster中客户端通信协议ClientProtocol实例，要么是Yarn模式下的YARNRunner，要么就是Local模式下的LocalJobRunner。

9、YARNRunner

以Yarn模式来分析MapReduce集群连接，看下YARNRunner的实现。
最重要的一个变量就是ResourceManager的代理ResourceMgrDelegate类型的resMgrDelegate实例，Yarn模式下整个MapReduce客户端就是由它负责与Yarn集群进行通信，完成诸如作业提交、作业状态查询等过程，通过它获取集群的信息，其内部有一个YarnClient实例YarnClient，负责与Yarn进行通信，还有ApplicationId、ApplicationSubmissionContext等与特定应用程序相关的成员变量。以后有时间还要详细介绍这个对象。另外一个比较重要的变量就是客户端缓存ClientCache实例clientCache。

package org.apache.hadoop.mapred;import .../** * This class enables the current JobClient (0.22 hadoop) to run on YARN. */@SuppressWarnings("unchecked")public class YARNRunner implements ClientProtocol {  private static final Log LOG = LogFactory.getLog(YARNRunner.class);  //记录工厂RecordFactory实例  private final RecordFactory recordFactory = RecordFactoryProvider.getRecordFactory(null);  private ResourceMgrDelegate resMgrDelegate;   //ResourceManager代理实例对象  private ClientCache clientCache;              //客户端缓存实例  private Configuration conf;                   //配置信息  private final FileContext defaultFileContext; //文件上下文实例  /**   * Yarn runner incapsulates the client interface of   * yarn   * @param conf the configuration object for the client   */  //先构造ResourceManager代理ResourceMgrDelegate实例，再调用两个参数的YARNRunner构造器  public YARNRunner(Configuration conf) {   this(conf, new ResourceMgrDelegate(new YarnConfiguration(conf)));  }  /**   * Similar to {@link #YARNRunner(Configuration)} but allowing injecting   * {@link ResourceMgrDelegate}. Enables mocking and testing.   * @param conf the configuration object for the client   * @param resMgrDelegate the resourcemanager client handle.   */  //先构造客户端缓存ClientCache实例，再调用三个参数的YARNRunner构造器  public YARNRunner(Configuration conf, ResourceMgrDelegate resMgrDelegate) {   this(conf, resMgrDelegate, new ClientCache(conf, resMgrDelegate));  }  /**   * Similar to {@link YARNRunner#YARNRunner(Configuration, ResourceMgrDelegate)}   * but allowing injecting {@link ClientCache}. Enable mocking and testing.   * @param conf the configuration object   * @param resMgrDelegate the resource manager delegate   * @param clientCache the client cache object.   */  public YARNRunner(Configuration conf, ResourceMgrDelegate resMgrDelegate,      ClientCache clientCache) {    this.conf = conf;  //成员变量赋值    try {      this.resMgrDelegate = resMgrDelegate;      this.clientCache = clientCache;      //获取文件上下文defaultFileContext实例      this.defaultFileContext = FileContext.getFileContext(this.conf);    } catch (UnsupportedFileSystemException ufe) {      throw new RuntimeException("Error in instantiating YarnClient", ufe);    }  }  ...}

YARNRunner一共提供了三个构造函数，而我们之前说的WordCount作业提交时，其内部调用的是YARNRunner带有一个参数的构造函数，它会先构造ResourceManager代理ResourceMgrDelegate实例，然后再调用两个参数的构造函数，继而构造客户端缓存ClientCache实例，然后再调用三个参数的构造函数，而最终的构造函数只是进行简单的类成员变量赋值，然后通过FileContext的静态getFileContext()方法获取文件山下文FileContext实例defaultFileContext。

10、connect()方法总结

MapReduce作业提交时连接集群是通过Job的connect()方法实现的，它实际上是构造集群Cluster实例cluster。Cluster为连接MapReduce集群的一种工具，提供了一种获取MapReduce集群信息的方法。在Cluster内部，有一个与集群进行通信的客户端通信协议ClientProtocol实例client，它由ClientProtocolProvider的静态create()方法构造，而Hadoop2.x中提供了两种模式的ClientProtocol，分别为Yarn模式的YARNRunner和Local模式的LocalJobRunner，Cluster实际上是由它们负责与集群进行通信的，而Yarn模式下，ClientProtocol实例YARNRunner对象内部有一个ResourceManager代理ResourceMgrDelegate实例resMgrDelegate，Yarn模式下整个MapReduce客户端就是由它负责与Yarn集群进行通信，完成诸如作业提交、作业状态查询等过程，通过它获取集群的信息。

11、submitJobInternal()方法

再次回到（3、进入submit()方法）submit()方法，上面已经介绍了connect()方法，下面开始介绍另一个重要的的方法submitJobInternal()。

该方法隶属于JobSubmitter类，顾名思义，该类是MapReduce中作业提交者，而实际上JobSubmitter除了构造方法外，对外提供的唯一一个非private成员变量或方法就是submitJobInternal()方法，它是提交Job的内部方法，实现了提交Job的所有业务逻辑。

JobSubmitter的类一共有四个类成员变量，分别为：
1. 文件系统FileSystem实例jtFs：用于操作作业运行需要的各种文件等；
2. 客户端通信协议ClientProtocol实例submitClient：用于与集群交互，完成作业提交、作业状态查询等，上文已经介绍过了。
3. 提交作业的主机名submitHostName；
4. 提交作业的主机地址submitHostAddress。

@[e-mail] JobSubmitter {  protected static final Log LOG = LogFactory.getLog(JobSubmitter.class);  private static final String SHUFFLE_KEYGEN_ALGORITHM = "HmacSHA1";  private static final int SHUFFLE_KEY_LENGTH = 64;  private FileSystem jtFs;    //文件系统FileSystem实例  private ClientProtocol submitClient;  //客户端通信协议ClientProtocol实例  private String submitHostName;        //提交作业的主机名  private String submitHostAddress;     //提交作业的主机  //构造器  JobSubmitter(FileSystem submitFs, ClientProtocol submitClient)   throws IOException {    this.submitClient = submitClient;    this.jtFs = submitFs;  } ...} 

接下里是最重要的代码——JobSubmitter唯一的对外核心功能方法submitJobInternal()，它被用于提交作业至集群

  /**   * Internal method for submitting jobs to the system.   *    * <p>The job submission process involves:   * <ol>   *   <li>   *   Checking the input and output specifications of the job.   *   </li>   *   <li>   *   Computing the {@link InputSplit}s for the job.   *   </li>   *   <li>   *   Setup the requisite accounting information for the    *   {@link DistributedCache} of the job, if necessary.   *   </li>   *   <li>   *   Copying the job's jar and configuration to the map-reduce system   *   directory on the distributed file-system.    *   </li>   *   <li>   *   Submitting the job to the <code>JobTracker</code> and optionally   *   monitoring it's status.   *   </li>   * </ol></p>   * @param job the configuration to submit   * @param cluster the handle to the Cluster   * @throws ClassNotFoundException   * @throws InterruptedException   * @throws IOException   */  JobStatus submitJobInternal(Job job, Cluster cluster)   throws ClassNotFoundException, InterruptedException, IOException {    //validate the jobs output specs  验证作业输出规格    //检查作业输出路径是否配置并且是否存在。正确情况是已经配置且不存在    //输出路径的配置参数为mapreduce.output.fileoutputformat.outputdir    checkSpecs(job);    //获取配置信息    Configuration conf = job.getConfiguration();    //添加应用框架路径到分布式缓存中    addMRFrameworkToDistributedCache(conf);    //通过静态方法getStagingDir()获取作业执行时相关资源的存放路径    //参数未配置时默认是/tmp/hadoop-yarn/staging/提交作业用户名/.staging    Path jobStagingArea = JobSubmissionFiles.getStagingDir(cluster, conf);    //configure the command line options correctly on the submitting dfs    //在提交dfs上正确配置命令行选项    InetAddress ip = InetAddress.getLocalHost();//获取当前主机IP    if (ip != null) {//记录提交作业的主机IP、主机名，并且设置配置信息conf      submitHostAddress = ip.getHostAddress();      submitHostName = ip.getHostName();      conf.set(MRJobConfig.JOB_SUBMITHOST,submitHostName);      conf.set(MRJobConfig.JOB_SUBMITHOSTADDR,submitHostAddress);    }    JobID jobId = submitClient.getNewJobID();//生成作业ID,即是jobID    job.setJobID(jobId);//将jobID设置入job    //构造提交作业路径,jobStagingArea后接/jobID    Path submitJobDir = new Path(jobStagingArea, jobId.toString());    JobStatus status = null;    try {//设置一些作业参数      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  获得路径的授权令牌      TokenCache.obtainTokensForNamenodes(job.getCredentials(),          new Path[] { submitJobDir }, conf);      //获取秘钥和令牌，并将它们存储到令牌缓存TokenCache中      populateTokenCache(conf, job.getCredentials());      // generate a secret to authenticate shuffle transfers 生成一个秘密来验证洗牌转移      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());      }      if (CryptoUtils.isEncryptedSpillEnabled(conf)) {        conf.setInt(MRJobConfig.MR_AM_MAX_ATTEMPTS, 1);        LOG.warn("Max job attempts set to 1 since encrypted intermediate" +                "data spill is enabled");      }      //复制并配置相关文件      copyAndConfigureFiles(job, submitJobDir);      //获取配置文件路径      Path submitJobFile = JobSubmissionFiles.getJobConfPath(submitJobDir);      // Create the splits for the job 创建作业的拆分      LOG.debug("Creating splits at " + jtFs.makeQualified(submitJobDir));      //调用writeSplits()方法，写分片数据文件job.splits和分片元数据文件job.splitmetainfo,计算map任务数      int maps = writeSplits(job, submitJobDir);      //设置map数      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.获取作业队列名queue，取参数mapreduce.job.queuename,默认值为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);//清除缓存的令牌      //根据参数确定是否需要追踪令牌ID      if (conf.getBoolean(          MRJobConfig.JOB_TOKEN_TRACKING_IDS_ENABLED,          MRJobConfig.DEFAULT_JOB_TOKEN_TRACKING_IDS_ENABLED)) {        // Add HDFS tracking ids 通过job获取令牌id,并且缓存到trackingIds列表中        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 设置保留信息，如果它存在      ReservationId reservationId = job.getReservationId();      if (reservationId != null) {        conf.set(MRJobConfig.RESERVATION_ID, reservationId.toString());      }      // Write job file to submit dir 写作业文件提交目录      writeConf(conf, submitJobFile);      //      // Now, actually submit the job (using the submit name)      // 现在，实际提交作业（使用提交名称）      printTokens(jobId, job.getCredentials());      //通过客户端通信协议ClientProtocol实例submitClient的submitJob()方法提交作业      //并获取作业状态实例status。由上下文可知，此处的submitClient是YARNRunner或LocalJobRunner      status = submitClient.submitJob(jobId, submitJobDir.toString(), job.getCredentials());      if (status != null) {//作业状态不空，直接返回，否则抛出IOException        return status;      } else {        throw new IOException("Could not launch job");      }    } finally {//抛出无法加载作业的IOException前，调用文件系统FileSystem实例jtFs的delete()方法，               //删除作业提交的相关资源目录或者文件submitJobDir      if (status == null) {        LOG.info("Cleaning up the staging area " + submitJobDir);        if (jtFs != null && submitJobDir != null)          jtFs.delete(submitJobDir, true);      }    }  }

至此，MapReduce的Job提交的大体过程就分析完毕！