Hadoop学习Map-Reduce的执行过程源码详细解析

本帖最后由阿飞于 2014-11-5 23:18 编辑

问题导读

1.客户端提交任务，需要做哪些准备？
2.JobTracker中main函数主要调用有哪两部分组成？
3.TaskTracker做了哪些工作？
4.map task和reduce task都是哪个进程中进行的？

一、客户端
Map-Reduce的过程首先是由客户端提交一个任务开始的。
提交任务主要是通过JobClient.runJob(JobConf)静态函数实现的：

public static RunningJob runJob(JobConf job) throws IOException {
//首先生成一个JobClient对象
JobClient jc = new JobClient(job);
……
//调用submitJob来提交一个任务
running = jc.submitJob(job);
JobID jobId = running.getID();
……
while (true) {
//while循环中不断得到此任务的状态，并打印到客户端console中
}
return running;
}

其中JobClient的submitJob函数实现如下：

public RunningJob submitJob(JobConf job) throws FileNotFoundException,

                              InvalidJobConfException, IOException {

  //从JobTracker得到当前任务的id

  JobID jobId = jobSubmitClient.getNewJobId();

  //准备将任务运行所需要的要素写入HDFS：

  //任务运行程序所在的jar封装成job.jar

  //任务所要处理的input split信息写入job.split

  //任务运行的配置项汇总写入job.xml

  Path submitJobDir = new Path(getSystemDir(), jobId.toString());

  Path submitJarFile = new Path(submitJobDir, "job.jar");

  Path submitSplitFile = new Path(submitJobDir, "job.split");

  //此处将-libjars命令行指定的jar上传至HDFS

  configureCommandLineOptions(job, submitJobDir, submitJarFile);

  Path submitJobFile = new Path(submitJobDir, "job.xml");

  ……

  //通过input format的格式获得相应的input split，默认类型为FileSplit

  InputSplit[] splits =

job.getInputFormat().getSplits(job, job.getNumMapTasks());

  // 生成一个写入流，将input split得信息写入job.split文件

  FSDataOutputStream out = FileSystem.create(fs,

   submitSplitFile, new FsPermission(JOB_FILE_PERMISSION));

  try {

//写入job.split文件的信息包括：split文件头，split文件版本号，split的个数，接着依次写入每一个input split的信息。

//对于每一个input split写入：split类型名(默认FileSplit)，split的大小，split的内容(对于FileSplit，写入文件名，此split 在文件中的起始位置)，split的location信息(即在那个DataNode上)。

writeSplitsFile(splits, out);

  } finally {

out.close();

  }

  job.set("mapred.job.split.file", submitSplitFile.toString());

  //根据split的个数设定map task的个数

  job.setNumMapTasks(splits.length);

  // 写入job的配置信息入job.xml文件

  out = FileSystem.create(fs, submitJobFile,

   new FsPermission(JOB_FILE_PERMISSION));

  try {

job.writeXml(out);

  } finally {

out.close();

  }

  //真正的调用JobTracker来提交任务

  JobStatus status = jobSubmitClient.submitJob(jobId);

  ……

}

二、JobTracker

JobTracker作为一个单独的JVM运行，其运行的main函数主要调用有下面两部分：

调用静态函数startTracker(new JobConf())创建一个JobTracker对象
调用JobTracker.offerService()函数提供服务

在JobTracker的构造函数中，会生成一个taskScheduler成员变量，来进行Job的调度，默认为JobQueueTaskScheduler，也即按照FIFO的方式调度任务。
在offerService函数中，则调用taskScheduler.start()，在这个函数中，为JobTracker(也即taskScheduler的taskTrackerManager)注册了两个Listener：

JobQueueJobInProgressListener jobQueueJobInProgressListener用于监控job的运行状态
EagerTaskInitializationListener eagerTaskInitializationListener用于对Job进行初始化

EagerTaskInitializationListener中有一个线程JobInitThread，不断得到jobInitQueue中的JobInProgress对象，调用JobInProgress对象的initTasks函数对任务进行初始化操作。

在上一节中，客户端调用了JobTracker.submitJob函数，此函数首先生成一个JobInProgress对象，然后调用addJob函数，其中有如下的逻辑：

synchronized (jobs) {
  synchronized (taskScheduler) {
    jobs.put(job.getProfile().getJobID(), job);
    //对JobTracker的每一个listener都调用jobAdded函数
    for (JobInProgressListener listener : jobInProgressListeners) {
      listener.jobAdded(job);
    }
  }
}
复制代码

EagerTaskInitializationListener的jobAdded函数就是向jobInitQueue中添加一个JobInProgress对象，于是自然触发了此Job的初始化操作，由JobInProgress得initTasks函数完成：

public synchronized void initTasks() throws IOException {
……
//从HDFS中读取job.split文件从而生成input splits
String jobFile = profile.getJobFile();
Path sysDir = new Path(this.jobtracker.getSystemDir());
FileSystem fs = sysDir.getFileSystem(conf);
DataInputStream splitFile =
fs.open(new Path(conf.get("mapred.job.split.file")));
JobClient.RawSplit[] splits;
try {
splits = JobClient.readSplitFile(splitFile);
} finally {
splitFile.close();
}
//map task的个数就是input split的个数
numMapTasks = splits.length;
//为每个map tasks生成一个TaskInProgress来处理一个input split
maps = new TaskInProgress[numMapTasks];
for(int i=0; i < numMapTasks; ++i) {
inputLength += splits.getDataLength();
maps = new TaskInProgress(jobId, jobFile,
splits,
jobtracker, conf, this, i);
}
//对于map task，将其放入nonRunningMapCache，是一个Map<Node, List<TaskInProgress>>，也即对于map task来讲，其将会被分配到其input split所在的Node上。nonRunningMapCache将在JobTracker向TaskTracker分配map task的时候使用。
if (numMapTasks > 0) {
nonRunningMapCache = createCache(splits, maxLevel);
}

//创建reduce task
this.reduces = new TaskInProgress[numReduceTasks];
for (int i = 0; i < numReduceTasks; i++) {
reduces = new TaskInProgress(jobId, jobFile,
numMapTasks, i,
jobtracker, conf, this);
//reduce task放入nonRunningReduces，其将在JobTracker向TaskTracker分配reduce task的时候使用。
nonRunningReduces.add(reduces);
}

//创建两个cleanup task，一个用来清理map，一个用来清理reduce.
cleanup = new TaskInProgress[2];
cleanup[0] = new TaskInProgress(jobId, jobFile, splits[0],
jobtracker, conf, this, numMapTasks);
cleanup[0].setJobCleanupTask();
cleanup[1] = new TaskInProgress(jobId, jobFile, numMapTasks,
numReduceTasks, jobtracker, conf, this);
cleanup[1].setJobCleanupTask();
//创建两个初始化 task，一个初始化map，一个初始化reduce.
setup = new TaskInProgress[2];
setup[0] = new TaskInProgress(jobId, jobFile, splits[0],
jobtracker, conf, this, numMapTasks + 1 );
setup[0].setJobSetupTask();
setup[1] = new TaskInProgress(jobId, jobFile, numMapTasks,
numReduceTasks + 1, jobtracker, conf, this);
setup[1].setJobSetupTask();
tasksInited.set(true);//初始化完毕
……
}

三、TaskTracker

TaskTracker也是作为一个单独的JVM来运行的，在其main函数中，主要是调用了new TaskTracker(conf).run()，其中run函数主要调用了：

State offerService() throws Exception {
long lastHeartbeat = 0;
//TaskTracker进行是一直存在的
while (running && !shuttingDown) {
……
long now = System.currentTimeMillis();
//每隔一段时间就向JobTracker发送heartbeat
long waitTime = heartbeatInterval - (now - lastHeartbeat);
if (waitTime > 0) {
synchronized(finishedCount) {
if (finishedCount[0] == 0) {
finishedCount.wait(waitTime);
}
finishedCount[0] = 0;
}
}
……
//发送Heartbeat到JobTracker，得到response
HeartbeatResponse heartbeatResponse = transmitHeartBeat(now);
……
//从Response中得到此TaskTracker需要做的事情
TaskTrackerAction[] actions = heartbeatResponse.getActions();
……
if (actions != null){
for(TaskTrackerAction action: actions) {
if (action instanceof LaunchTaskAction) {
//如果是运行一个新的Task，则将Action添加到任务队列中
addToTaskQueue((LaunchTaskAction)action);
} else if (action instanceof CommitTaskAction) {
CommitTaskAction commitAction = (CommitTaskAction)action;
if (!commitResponses.contains(commitAction.getTaskID())) {
commitResponses.add(commitAction.getTaskID());
}
} else {
tasksToCleanup.put(action);
}
}
}
}
return State.NORMAL;
}

其中transmitHeartBeat主要逻辑如下：

private HeartbeatResponse transmitHeartBeat(long now) throws IOException {
//每隔一段时间，在heartbeat中要返回给JobTracker一些统计信息
boolean sendCounters;
if (now > (previousUpdate + COUNTER_UPDATE_INTERVAL)) {
sendCounters = true;
previousUpdate = now;
}
else {
sendCounters = false;
}
……
//报告给JobTracker，此TaskTracker的当前状态
if (status == null) {
synchronized (this) {
status = new TaskTrackerStatus(taskTrackerName, localHostname,
httpPort,
cloneAndResetRunningTaskStatuses(
sendCounters),
failures,
maxCurrentMapTasks,
maxCurrentReduceTasks);
}
}
……
//当满足下面的条件的时候，此TaskTracker请求JobTracker为其分配一个新的Task来运行：
//当前TaskTracker正在运行的map task的个数小于可以运行的map task的最大个数
//当前TaskTracker正在运行的reduce task的个数小于可以运行的reduce task的最大个数
boolean askForNewTask;
long localMinSpaceStart;
synchronized (this) {
askForNewTask = (status.countMapTasks() < maxCurrentMapTasks ||
status.countReduceTasks() < maxCurrentReduceTasks) &&
acceptNewTasks;
localMinSpaceStart = minSpaceStart;
}
……
//向JobTracker发送heartbeat，这是一个RPC调用
HeartbeatResponse heartbeatResponse = jobClient.heartbeat(status,
justStarted, askForNewTask,
heartbeatResponseId);
……
return heartbeatResponse;
}

四、JobTracker

当 JobTracker被RPC调用来发送heartbeat的时候，JobTracker的heartbeat(TaskTrackerStatus status,boolean initialContact, boolean acceptNewTasks, short responseId)函数被调用：

public synchronized HeartbeatResponse heartbeat(TaskTrackerStatus status,
boolean initialContact, boolean acceptNewTasks, short responseId)
throws IOException {
……
String trackerName = status.getTrackerName();
……
short newResponseId = (short)(responseId + 1);
……
HeartbeatResponse response = new HeartbeatResponse(newResponseId, null);
List<TaskTrackerAction> actions = new ArrayList<TaskTrackerAction>();
//如果TaskTracker向JobTracker请求一个task运行
if (acceptNewTasks) {
TaskTrackerStatus taskTrackerStatus = getTaskTracker(trackerName);
if (taskTrackerStatus == null) {
LOG.warn("Unknown task tracker polling; ignoring: " + trackerName);
} else {
//setup和cleanup的task优先级最高
List<Task> tasks = getSetupAndCleanupTasks(taskTrackerStatus);
if (tasks == null ) {
//任务调度器分配任务
tasks = taskScheduler.assignTasks(taskTrackerStatus);
}
if (tasks != null) {
for (Task task : tasks) {
//将任务放入actions列表，返回给TaskTracker
expireLaunchingTasks.addNewTask(task.getTaskID());
actions.add(new LaunchTaskAction(task));
}
}
}
}
……
int nextInterval = getNextHeartbeatInterval();
response.setHeartbeatInterval(nextInterval);
response.setActions(
actions.toArray(new TaskTrackerAction[actions.size()]));
……
return response;
}

默认的任务调度器为JobQueueTaskScheduler，其assignTasks如下：

public synchronized List<Task> assignTasks(TaskTrackerStatus taskTracker)
throws IOException {
ClusterStatus clusterStatus = taskTrackerManager.getClusterStatus();
int numTaskTrackers = clusterStatus.getTaskTrackers();
Collection<JobInProgress> jobQueue =　jobQueueJobInProgressListener.getJobQueue();
int maxCurrentMapTasks = taskTracker.getMaxMapTasks();
int maxCurrentReduceTasks = taskTracker.getMaxReduceTasks();
int numMaps = taskTracker.countMapTasks();
int numReduces = taskTracker.countReduceTasks();
//计算剩余的map和reduce的工作量：remaining
int remainingReduceLoad = 0;
int remainingMapLoad = 0;
synchronized (jobQueue) {
for (JobInProgress job : jobQueue) {
if (job.getStatus().getRunState() == JobStatus.RUNNING) {
int totalMapTasks = job.desiredMaps();
int totalReduceTasks = job.desiredReduces();
remainingMapLoad += (totalMapTasks - job.finishedMaps());
remainingReduceLoad += (totalReduceTasks - job.finishedReduces());
}
}
}
//计算平均每个TaskTracker应有的工作量，remaining/numTaskTrackers是剩余的工作量除以TaskTracker的个数。
int maxMapLoad = 0;
int maxReduceLoad = 0;
if (numTaskTrackers > 0) {
maxMapLoad = Math.min(maxCurrentMapTasks,
(int) Math.ceil((double) remainingMapLoad /
numTaskTrackers));
maxReduceLoad = Math.min(maxCurrentReduceTasks,
(int) Math.ceil((double) remainingReduceLoad
/ numTaskTrackers));
}
……

//map优先于reduce，当TaskTracker上运行的map task数目小于平均的工作量，则向其分配map task
if (numMaps < maxMapLoad) {
int totalNeededMaps = 0;
synchronized (jobQueue) {
for (JobInProgress job : jobQueue) {
if (job.getStatus().getRunState() != JobStatus.RUNNING) {
continue;
}
Task t = job.obtainNewMapTask(taskTracker, numTaskTrackers,
taskTrackerManager.getNumberOfUniqueHosts());
if (t != null) {
return Collections.singletonList(t);
}
……
}
}
}
//分配完map task，再分配reduce task
if (numReduces < maxReduceLoad) {
int totalNeededReduces = 0;
synchronized (jobQueue) {
for (JobInProgress job : jobQueue) {
if (job.getStatus().getRunState() != JobStatus.RUNNING ||
job.numReduceTasks == 0) {
continue;
}
Task t = job.obtainNewReduceTask(taskTracker, numTaskTrackers,
taskTrackerManager.getNumberOfUniqueHosts());
if (t != null) {
return Collections.singletonList(t);
}
……
}
}
}
return null;
}

从上面的代码中我们可以知道，JobInProgress的obtainNewMapTask是用来分配map task的，其主要调用findNewMapTask，根据TaskTracker所在的Node从nonRunningMapCache中查找 TaskInProgress。JobInProgress的obtainNewReduceTask是用来分配reduce task的，其主要调用findNewReduceTask，从nonRunningReduces查找TaskInProgress。

五、TaskTracker

在向JobTracker发送heartbeat后，返回的reponse中有分配好的任务LaunchTaskAction，将其加入队列，调用 addToTaskQueue，如果是map task则放入mapLancher(类型为TaskLauncher)，如果是reduce task则放入reduceLancher(类型为TaskLauncher)：

private void addToTaskQueue(LaunchTaskAction action) {
  if (action.getTask().isMapTask()) {
    mapLauncher.addToTaskQueue(action);
  } else {
    reduceLauncher.addToTaskQueue(action);
  }
}
复制代码

TaskLauncher 是一个线程，其run函数从上面放入的queue中取出一个TaskInProgress，然后调用 startNewTask(TaskInProgress tip)来启动一个task，其又主要调用了localizeJob(TaskInProgress tip)：

private void localizeJob(TaskInProgress tip) throws IOException {
//首先要做的一件事情是有关Task的文件从HDFS拷贝的TaskTracker的本地文件系统中：job.split，job.xml以及job.jar
Path localJarFile = null;
Task t = tip.getTask();
JobID jobId = t.getJobID();
Path jobFile = new Path(t.getJobFile());
……
Path localJobFile = lDirAlloc.getLocalPathForWrite(
getLocalJobDir(jobId.toString())
+ Path.SEPARATOR + "job.xml",
jobFileSize, fConf);
RunningJob rjob = addTaskToJob(jobId, tip);
synchronized (rjob) {
if (!rjob.localized) {
FileSystem localFs = FileSystem.getLocal(fConf);
Path jobDir = localJobFile.getParent();
……
//将job.split拷贝到本地
systemFS.copyToLocalFile(jobFile, localJobFile);
JobConf localJobConf = new JobConf(localJobFile);
Path workDir = lDirAlloc.getLocalPathForWrite(
(getLocalJobDir(jobId.toString())
+ Path.SEPARATOR + "work"), fConf);
if (!localFs.mkdirs(workDir)) {
throw new IOException("Mkdirs failed to create "
+ workDir.toString());
}
System.setProperty("job.local.dir", workDir.toString());
localJobConf.set("job.local.dir", workDir.toString());
// copy Jar file to the local FS and unjar it.
String jarFile = localJobConf.getJar();
long jarFileSize = -1;
if (jarFile != null) {
Path jarFilePath = new Path(jarFile);
localJarFile = new Path(lDirAlloc.getLocalPathForWrite(
getLocalJobDir(jobId.toString())
+ Path.SEPARATOR + "jars",
5 * jarFileSize, fConf), "job.jar");
if (!localFs.mkdirs(localJarFile.getParent())) {
throw new IOException("Mkdirs failed to create jars directory ");
}
//将job.jar拷贝到本地
systemFS.copyToLocalFile(jarFilePath, localJarFile);
localJobConf.setJar(localJarFile.toString());
//将job得configuration写成job.xml
OutputStream out = localFs.create(localJobFile);
try {
localJobConf.writeXml(out);
} finally {
out.close();
}
// 解压缩job.jar
RunJar.unJar(new File(localJarFile.toString()),
new File(localJarFile.getParent().toString()));
}
rjob.localized = true;
rjob.jobConf = localJobConf;
}
}
//真正的启动此Task
launchTaskForJob(tip, new JobConf(rjob.jobConf));
}

当所有的task运行所需要的资源都拷贝到本地后，则调用launchTaskForJob，其又调用TaskInProgress的launchTask函数：

public synchronized void launchTask() throws IOException {
……
//创建task运行目录
localizeTask(task);
if (this.taskStatus.getRunState() == TaskStatus.State.UNASSIGNED) {
this.taskStatus.setRunState(TaskStatus.State.RUNNING);
}
//创建并启动TaskRunner，对于MapTask，创建的是MapTaskRunner，对于ReduceTask，创建的是ReduceTaskRunner
this.runner = task.createRunner(TaskTracker.this, this);
this.runner.start();
this.taskStatus.setStartTime(System.currentTimeMillis());
}

TaskRunner是一个线程，其run函数如下：

public final void run() {

……

TaskAttemptID taskid = t.getTaskID();

LocalDirAllocator lDirAlloc = new LocalDirAllocator("mapred.local.dir");

File jobCacheDir = null;

if (conf.getJar() != null) {

   jobCacheDir = new File(

                     new Path(conf.getJar()).getParent().toString());

}

File workDir = new File(lDirAlloc.getLocalPathToRead(

                           TaskTracker.getLocalTaskDir(

                              t.getJobID().toString(),

                              t.getTaskID().toString(),

                              t.isTaskCleanupTask())

                           + Path.SEPARATOR + MRConstants.WORKDIR,

                           conf). toString());

FileSystem fileSystem;

Path localPath;

……

//拼写classpath

String baseDir;

String sep = System.getProperty("path.separator");

StringBuffer classPath = new StringBuffer();

// start with same classpath as parent process

classPath.append(System.getProperty("java.class.path"));

classPath.append(sep);

if (!workDir.mkdirs()) {

   if (!workDir.isDirectory()) {

      LOG.fatal("Mkdirs failed to create " + workDir.toString());

   }

}

String jar = conf.getJar();

if (jar != null) {

   // if jar exists, it into workDir

   File[] libs = new File(jobCacheDir, "lib").listFiles();

   if (libs != null) {

      for (int i = 0; i < libs.length; i++) {

      classPath.append(sep);          // add libs from jar to classpath

      classPath.append(libs);

      }

   }

   classPath.append(sep);

   classPath.append(new File(jobCacheDir, "classes"));

   classPath.append(sep);

   classPath.append(jobCacheDir);

}

……

classPath.append(sep);

classPath.append(workDir);

//拼写命令行java及其参数

Vector<String> vargs = new Vector<String>(8);

File jvm =

   new File(new File(System.getProperty("java.home"), "bin"), "java");

vargs.add(jvm.toString());

String javaOpts = conf.get("mapred.child.java.opts", "-Xmx200m");

javaOpts = javaOpts.replace("@taskid@", taskid.toString());

String [] javaOptsSplit = javaOpts.split(" ");

String libraryPath = System.getProperty("java.library.path");

if (libraryPath == null) {

   libraryPath = workDir.getAbsolutePath();

} else {

   libraryPath += sep + workDir;

}

boolean haSUSErLDPath = false;

for(int i=0; i<javaOptsSplit.length ;i++) {

   if(javaOptsSplit.startsWith("-Djava.library.path=")) {

      javaOptsSplit += sep + libraryPath;

      hasUserLDPath = true;

      break;

   }

}

if(!hasUserLDPath) {

   vargs.add("-Djava.library.path=" + libraryPath);

}

for (int i = 0; i < javaOptsSplit.length; i++) {

   vargs.add(javaOptsSplit);

}

//添加Child进程的临时文件夹

String tmp = conf.get("mapred.child.tmp", "./tmp");

Path tmpDir = new Path(tmp);

if (!tmpDir.isAbsolute()) {

   tmpDir = new Path(workDir.toString(), tmp);

}

FileSystem localFs = FileSystem.getLocal(conf);

if (!localFs.mkdirs(tmpDir) && !localFs.getFileStatus(tmpDir).isDir()) {

   throw new IOException("Mkdirs failed to create " + tmpDir.toString());

}

vargs.add("-Djava.io.tmpdir=" + tmpDir.toString());

// Add classpath.

vargs.add("-classpath");

vargs.add(classPath.toString());

//log文件夹

long logSize = TaskLog.getTaskLogLength(conf);

vargs.add("-DHadoop.log.dir=" +

      new File(System.getProperty("hadoop.log.dir")

      ).getAbsolutePath());

vargs.add("-Dhadoop.root.logger=INFO,TLA");

vargs.add("-Dhadoop.tasklog.taskid=" + taskid);

vargs.add("-Dhadoop.tasklog.totalLogFileSize=" + logSize);

// 运行map task和reduce task的子进程的main class是Child

vargs.add(Child.class.getName());  // main of Child

……

//运行子进程

jvmManager.launchJvm(this,

      jvmManager.constructJvmEnv(setup,vargs,stdout,stderr,logSize,

         workDir, env, pidFile, conf));

}

六、Child

真正的map task和reduce task都是在Child进程中运行的，Child的main函数的主要逻辑如下：

while (true) {
  //从TaskTracker通过网络通信得到JvmTask对象
  JvmTask myTask = umbilical.getTask(jvmId);
  ……
  idleLoopCount = 0;
  task = myTask.getTask();
  taskid = task.getTaskID();
  isCleanup = task.isTaskCleanupTask();
  JobConf job = new JobConf(task.getJobFile());
  TaskRunner.setupWorkDir(job);
  numTasksToExecute = job.getNumTasksToExecutePerJvm();
  task.setConf(job);
  defaultConf.addResource(new Path(task.getJobFile()));
  ……
  //运行task
  task.run(job, umbilical);          // run the task
  if (numTasksToExecute > 0 && ++numTasksExecuted == numTasksToExecute) {
break;
  }
}

6.1、MapTask
如果task是MapTask，则其run函数如下：

public void run(final JobConf job, final TaskUmbilicalProtocol umbilical)
throws IOException {
//用于同TaskTracker进行通信，汇报运行状况
final Reporter reporter = getReporter(umbilical);
startCommunicationThread(umbilical);
initialize(job, reporter);
……
//map task的输出
int numReduceTasks = conf.getNumReduceTasks();
MapOutputCollector collector = null;
if (numReduceTasks > 0) {
collector = new MapOutputBuffer(umbilical, job, reporter);
} else {
collector = new DirectMapOutputCollector(umbilical, job, reporter);
}
//读取input split，按照其中的信息，生成RecordReader来读取数据
instantiatedSplit = (InputSplit)
ReflectionUtils.newInstance(job.getClassByName(splitClass), job);
DataInputBuffer splitBuffer = new DataInputBuffer();
splitBuffer.reset(split.getBytes(), 0, split.getLength());
instantiatedSplit.readFields(splitBuffer);
if (instantiatedSplit instanceof FileSplit) {
FileSplit fileSplit = (FileSplit) instantiatedSplit;
job.set("map.input.file", fileSplit.getPath().toString());
job.setLong("map.input.start", fileSplit.getStart());
job.setLong("map.input.length", fileSplit.getLength());
}
RecordReader rawIn = // open input
job.getInputFormat().getRecordReader(instantiatedSplit, job, reporter);
RecordReader in = isSkipping() ?
new SkippingRecordReader(rawIn, getCounters(), umbilical) :
new TrackedRecordReader(rawIn, getCounters());
job.setBoolean("mapred.skip.on", isSkipping());
//对于map task，生成一个MapRunnable，默认是MapRunner
MapRunnable runner =
ReflectionUtils.newInstance(job.getMapRunnerClass(), job);
try {
//MapRunner的run函数就是依次读取RecordReader中的数据，然后调用Mapper的map函数进行处理。
runner.run(in, collector, reporter);
collector.flush();
} finally {
in.close(); // close input
collector.close();
}
done(umbilical);
}

MapRunner的run函数就是依次读取RecordReader中的数据，然后调用Mapper的map函数进行处理：

public void run(RecordReader<K1, V1> input, OutputCollector<K2, V2> output,
                Reporter reporter)
  throws IOException {
  try {
    K1 key = input.createKey();
    V1 value = input.createValue();
    while (input.next(key, value)) {
      mapper.map(key, value, output, reporter);
      if(incrProcCount) {
        reporter.incrCounter(SkipBadRecords.COUNTER_GROUP,
            SkipBadRecords.COUNTER_MAP_PROCESSED_RECORDS, 1);
      }
    }
  } finally {
    mapper.close();
  }
}
复制代码

结果集全部收集到MapOutputBuffer中，其collect函数如下：

public synchronized void collect(K key, V value)
throws IOException {
reporter.progress();
……
//从此处看，此buffer是一个ring的数据结构
final int kvnext = (kvindex + 1) % kvoffsets.length;
spillLock.lock();
try {
boolean kvfull;
do {
//在ring中，如果下一个空闲位置接上起始位置的话，则表示满了
kvfull = kvnext == kvstart;
//在ring中计算是否需要将buffer写入硬盘的阈值
final boolean kvsoftlimit = ((kvnext > kvend)
? kvnext - kvend > softRecordLimit
: kvend - kvnext <= kvoffsets.length - softRecordLimit);
//如果到达阈值，则开始将buffer写入硬盘，写成spill文件。
//startSpill主要是notify一个背后线程SpillThread的run()函数，开始调用sortAndSpill()开始排序，合并，写入硬盘
if (kvstart == kvend && kvsoftlimit) {
startSpill();
}
//如果buffer满了，则只能等待写入完毕
if (kvfull) {
while (kvstart != kvend) {
reporter.progress();
spillDone.await();
}
}
} while (kvfull);
} finally {
spillLock.unlock();
}
try {
//如果buffer不满，则将key, value写入buffer
int keystart = bufindex;
keySerializer.serialize(key);
final int valstart = bufindex;
valSerializer.serialize(value);
int valend = bb.markRecord();
//调用设定的partitioner，根据key, value取得partition id
final int partition = partitioner.getPartition(key, value, partitions);
mapOutputRecordCounter.increment(1);
mapOutputByteCounter.increment(valend >= keystart
? valend - keystart
: (bufvoid - keystart) + valend);
//将parition id以及key, value在buffer中的偏移量写入索引数组
int ind = kvindex * ACCTSIZE;
kvoffsets[kvindex] = ind;
kvindices[ind + PARTITION] = partition;
kvindices[ind + KEYSTART] = keystart;
kvindices[ind + VALSTART] = valstart;
kvindex = kvnext;
} catch (MapBufferTooSmallException e) {
LOG.info("Record too large for in-memory buffer: " + e.getMessage());
spillSingleRecord(key, value);
mapOutputRecordCounter.increment(1);
return;
}
}

内存buffer的格式如下：
(见几位Hadoop大侠的分析http://blog.csdn.net/HEYUTAO007/archive/2010/07/10/5725379.aspx 以及http://caibinbupt.javaeye.com/)

kvoffsets是为了写入内存前排序使用的。
从上面可知，内存buffer写入硬盘spill文件的函数为sortAndSpill：

private void sortAndSpill() throws IOException {
  ……
  FSDataOutputStream out = null;
  FSDataOutputStream indexOut = null;
  IFileOutputStream indexChecksumOut = null;
  //创建硬盘上的spill文件
  Path filename = mapOutputFile.getSpillFileForWrite(getTaskID(),
                                  numSpills, size);
  out = rfs.create(filename);
  ……
  final int endPosition = (kvend > kvstart)
    ? kvend
    : kvoffsets.length + kvend;
  //按照partition的顺序对buffer中的数据进行排序
  sorter.sort(MapOutputBuffer.this, kvstart, endPosition, reporter);
  int spindex = kvstart;
  InMemValBytes value = new InMemValBytes();
  //依次一个一个parition的写入文件
  for (int i = 0; i < partitions; ++i) {
    IFile.Writer<K, V> writer = null;
    long segmentStart = out.getPos();
    writer = new Writer<K, V>(job, out, keyClass, valClass, codec);
    //如果combiner为空，则直接写入文件
    if (null == combinerClass) {
        ……
        writer.append(key, value);
        ++spindex;
     }
     else {
        ……
        //如果combiner不为空，则先combine，调用combiner.reduce(…)函数后再写入文件
        combineAndSpill(kvIter, combineInputCounter);
     }
  }
  ……
}
复制代码

当map阶段结束的时候，MapOutputBuffer的flush函数会被调用，其也会调用sortAndSpill将buffer中的写入文件，然后再调用mergeParts来合并写入在硬盘上的多个spill:

private void mergeParts() throws IOException {
    ……
    //对于每一个partition
    for (int parts = 0; parts < partitions; parts++){
      //create the segments to be merged
      List<Segment<K, V>> segmentList =
        new ArrayList<Segment<K, V>>(numSpills);
      TaskAttemptID mapId = getTaskID();
       //依次从各个spill文件中收集属于当前partition的段
      for(int i = 0; i < numSpills; i++) {
        final IndexRecord indexRecord =
          getIndexInformation(mapId, i, parts);
        long segmentOffset = indexRecord.startOffset;
        long segmentLength = indexRecord.partLength;
        Segment<K, V> s =
          new Segment<K, V>(job, rfs, filename[i], segmentOffset,
                            segmentLength, codec, true);
        segmentList.add(i, s);
      }
      //将属于同一个partition的段merge到一起
      RawKeyValueIterator kvIter =
        Merger.merge(job, rfs,
                     keyClass, valClass,
                     segmentList, job.getInt("io.sort.factor", 100),
                     new Path(getTaskID().toString()),
                     job.getOutputKeyComparator(), reporter);
      //写入合并后的段到文件
      long segmentStart = finalOut.getPos();
      Writer<K, V> writer =
          new Writer<K, V>(job, finalOut, keyClass, valClass, codec);
      if (null == combinerClass || numSpills < minSpillsForCombine) {
        Merger.writeFile(kvIter, writer, reporter, job);
      } else {
        combineCollector.setWriter(writer);
        combineAndSpill(kvIter, combineInputCounter);
      }
      ……
    }
}
复制代码

6.2、ReduceTask
ReduceTask的run函数如下：

public void run(JobConf job, final TaskUmbilicalProtocol umbilical)
  throws IOException {
  job.setBoolean("mapred.skip.on", isSkipping());
  //对于reduce，则包含三个步骤：拷贝，排序，Reduce
  if (isMapOrReduce()) {
copyPhase = getProgress().addPhase("copy");
sortPhase  = getProgress().addPhase("sort");
reducePhase = getProgress().addPhase("reduce");
  }
  startCommunicationThread(umbilical);
  final Reporter reporter = getReporter(umbilical);
  initialize(job, reporter);
  //copy阶段，主要使用ReduceCopier的fetchOutputs函数获得map的输出。创建多个线程MapOutputCopier，其中copyOutput进行拷贝。
  boolean isLocal = "local".equals(job.get("mapred.job.tracker", "local"));
  if (!isLocal) {
reduceCopier = new ReduceCopier(umbilical, job);
if (!reduceCopier.fetchOutputs()) {
      ……
}
  }
  copyPhase.complete();
  //sort阶段，将得到的map输出合并，直到文件数小于io.sort.factor时停止，返回一个Iterator用于访问key-value
  setPhase(TaskStatus.Phase.SORT);
  statusUpdate(umbilical);
  final FileSystem rfs = FileSystem.getLocal(job).getRaw();
  RawKeyValueIterator rIter = isLocal
? Merger.merge(job, rfs, job.getMapOutputKeyClass(),
      job.getMapOutputValueClass(), codec, getMapFiles(rfs, true),
      !conf.getKeepFailedTaskFiles(), job.getInt("io.sort.factor", 100),
      new Path(getTaskID().toString()), job.getOutputKeyComparator(),
      reporter)
: reduceCopier.createKVIterator(job, rfs, reporter);
  mapOutputFilesOnDisk.clear();
  sortPhase.complete();
  //reduce阶段
  setPhase(TaskStatus.Phase.REDUCE);
  ……
  Reducer reducer = ReflectionUtils.newInstance(job.getReducerClass(), job);
  Class keyClass = job.getMapOutputKeyClass();
  Class valClass = job.getMapOutputValueClass();
  ReduceValuesIterator values = isSkipping() ?
   new SkippingReduceValuesIterator(rIter,
      job.getOutputValueGroupingComparator(), keyClass, valClass,
      job, reporter, umbilical) :
   new ReduceValuesIterator(rIter,
   job.getOutputValueGroupingComparator(), keyClass, valClass,
   job, reporter);
  //逐个读出key-value list，然后调用Reducer的reduce函数
  while (values.more()) {
reduceInputKeyCounter.increment(1);
reducer.reduce(values.getKey(), values, collector, reporter);
values.nextKey();
values.informReduceProgress();
  }
  reducer.close();
  out.close(reporter);
  done(umbilical);
}

xinren · 发表于 2014-11-6 09:23:32

很值得学习。。。。。

hery · 发表于 2014-11-6 11:07:24

写点不错哦。。。。

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