一、概要描述
在上篇博文描述了TaskTracker启动一个独立的java进程来执行Map或Reduce任务。在本篇和下篇博文中我们会关注启动的那个入口是org.apache.hadoop.mapred.Child的这个Java进程是如何执行用户定义的map或Reduce任务的。
接上篇文章，TaskRunner线程执行中，会构造一个java –D** Child address port tasked这样第一个java命令，单独启动一个java进程。在Child的main函数中通过TaskUmbilicalProtocol协议，从TaskTracker获得需要执行的Task，并调用Task的run方法来执行，而Task的run方法会通过java反射机制构造Mapper，InputFormat，mapperContext，然后调用构造的mapper的run方法执行mapper操作。
二、 流程描述
1.Child类根据前面输入的三个参数，即tasktracher的地址、端口、taskid。通过TaskUmbilicalProtocol协议，从TaskTracker获得需要执行的Task，在Child的main函数中调用执行。
2. 在Chilld中，执行Task的run方法。Task 的run方法。是真正执行用户定义的map或者reduce任务的入口，通过TaskUmbilicalProtocol向tasktracker上报执行进度。
3. 在MapTask的run中执行runMapper方法来调用mapper定义的方法。
4. 在runNewMapper方法中构造mapper实例和mapper执行的配置信息。并执行mapper.run方法来调用到用户定义的mapper的方法。
5. mapper的run方法中，从输入数据中逐一取出调用map方法来处理每一条数据
6. mapper的map方法是真正用户定义的处理数据的类。也是用户唯一需要定义的方法。
三、代码详细
1. Child的main方法每个task进程都会被在单独的进程中执行，这个方法就是这些进程的入口方法。观察下载在这个方法中做了哪些事情？
1)从传入的参数中获得tasktracker的地址、从传入的参数中获得tasktracker的地址
2) 根据获取的taskTracker的地址和端口通过RPC方式和tasktracker通信，umbilical是作为tasktracker的代理来执行操作。
3) 根据JvmId从taskTracker查询获取到JvmTask
4) 执行任务
public static void main(String[] args) throws Throwable {
LOG.debug("Child starting");
JobConf defaultConf = new JobConf();
//从传入的参数中获得taskTracker的地址
String host = args[0];
//从传入的参数中获得taskTracker的响应请求的端口。
int port = Integer.parseInt(args[1]);
InetSocketAddress address = new InetSocketAddress(host, port);
final TaskAttemptID firstTaskid = TaskAttemptID.forName(args[2]);
final int SLEEP_LONGER_COUNT = 5;
int jvmIdInt = Integer.parseInt(args[3]);
JVMId jvmId = new JVMId(firstTaskid.getJobID(),firstTaskid.isMap(),jvmIdInt);
//通过RPC方式和tasktracker通信，umbilical是作为tasktracker的代理来执行操作。
TaskUmbilicalProtocol umbilical =
(TaskUmbilicalProtocol)RPC.getProxy(TaskUmbilicalProtocol.class,
TaskUmbilicalProtocol.versionID,
address,
defaultConf);
int numTasksToExecute = -1; //-1 signifies "no limit"
int numTasksExecuted = 0;
//for the memory management, a PID file is written and the PID file
//is written once per JVM. We simply symlink the file on a per task
//basis later (see below). Long term, we should change the Memory
//manager to use JVMId instead of TaskAttemptId
Path srcPidPath = null;
Path dstPidPath = null;
int idleLoopCount = 0;
Task task = null;
try {
while (true) {
taskid = null;
//根据JvmId从taskTracker查询获取到JvmTask
JvmTask myTask = umbilical.getTask(jvmId);
if (myTask.shouldDie()) {
break;
} else {
if (myTask.getTask() == null) {
taskid = null;
if (++idleLoopCount >= SLEEP_LONGER_COUNT) {
//we sleep for a bigger interval when we don't receive
//tasks for a while
Thread.sleep(1500);
} else {
Thread.sleep(500);
}
continue;
}
}
idleLoopCount = 0;
task = myTask.getTask();
taskid = task.getTaskID();
isCleanup = task.isTaskCleanupTask();
// reset the statistics for the task
FileSystem.clearStatistics();
TaskLog.syncLogs(firstTaskid, taskid, isCleanup);
JobConf job = new JobConf(task.getJobFile());
if (job.getBoolean("task.memory.mgmt.enabled", false)) {
if (srcPidPath == null) {
srcPidPath = new Path(task.getPidFile());
}
//since the JVM is running multiple tasks potentially, we need
//to do symlink stuff only for the subsequent tasks
if (!taskid.equals(firstTaskid)) {
dstPidPath = new Path(task.getPidFile());
FileUtil.symLink(srcPidPath.toUri().getPath(),
dstPidPath.toUri().getPath());
}
}
//setupWorkDir actually sets up the symlinks for the distributed
//cache. After a task exits we wipe the workdir clean, and hence
//the symlinks have to be rebuilt.
TaskRunner.setupWorkDir(job);
numTasksToExecute = job.getNumTasksToExecutePerJvm();
assert(numTasksToExecute != 0);
TaskLog.cleanup(job.getInt("mapred.userlog.retain.hours", 24));
task.setConf(job);
defaultConf.addResource(new Path(task.getJobFile()));
// use job-specified working directory
FileSystem.get(job).setWorkingDirectory(job.getWorkingDirectory());
try {
//执行任务
task.run(job, umbilical); // run the task
} finally {
TaskLog.syncLogs(firstTaskid, taskid, isCleanup);
if (!taskid.equals(firstTaskid) &&
job.getBoolean("task.memory.mgmt.enabled", false)) {
// delete the pid-file's symlink
new File(dstPidPath.toUri().getPath()).delete();
}
}
if (numTasksToExecute > 0 && ++numTasksExecuted == numTasksToExecute) {
break;
}
}
} catch (FSError e) {
LOG.fatal("FSError from child", e);
umbilical.fsError(taskid, e.getMessage());
} catch (Throwable throwable) {
LOG.warn("Error running child", throwable);
try {
if (task != null) {
// do cleanup for the task
task.taskCleanup(umbilical);
}
} catch (Throwable th) {
LOG.info("Error cleaning up" + th);
}
// Report back any failures, for diagnostic purposes
ByteArrayOutputStream baos = new ByteArrayOutputStream();
throwable.printStackTrace(new PrintStream(baos));
if (taskid != null) {
umbilical.reportDiagnosticInfo(taskid, baos.toString());
}
} finally {
RPC.stopProxy(umbilical);
}
}
2. TaskTracker 的getTask方法。TaskTracker实现了TaskUmbilicalProtocol接扣。getTask是该接口定义的一个方法。是子进程Child调用的根据jvmId获取task。
public synchronized JvmTask getTask(JVMId jvmId)
throws IOException {
TaskInProgress tip = jvmManager.getTaskForJvm(jvmId);
if (tip == null) {
return new JvmTask(null, false);
}
if (tasks.get(tip.getTask().getTaskID()) != null) { //is task still present
LOG.info("JVM with ID: " + jvmId + " given task: " +
tip.getTask().getTaskID());
return new JvmTask(tip.getTask(), false);
} else {
LOG.info("Killing JVM with ID: " + jvmId + " since scheduled task: " +
tip.getTask().getTaskID() + " is " + tip.taskStatus.getRunState());
return new JvmTask(null, true);
}
3.Task 的run方法。因为map和reduce的执行逻辑大不相同，先看下MapTask中该方法的实现。是真正执行用户定义的map或者reduce任务的入口，通过TaskUmbilicalProtocol向tasktracker上报执行进度。开启线程向TaskTracker上报进度，根据task的不同动作要求执行不同的方法，如jobClean，jobsetup，taskCleanup。对于部分的了解可以产看taskTracker获取Task文章中的 JobTracker的 heartbeat方法处的详细解释。
public void run(final JobConf job, final TaskUmbilicalProtocol umbilical)
throws IOException, ClassNotFoundException, InterruptedException { // 开启线程向TaskTracker上报进度
TaskReporter reporter = new TaskReporter(getProgress(), umbilical);
reporter.startCommunicationThread();
boolean useNewApi = job.getUseNewMapper();
initialize(job, getJobID(), reporter, useNewApi); // 根据task的不同动作要求执行不同的方法，如jobClean，jobsetup，taskCleanup
if (jobCleanup) {
runJobCleanupTask(umbilical, reporter);
return;
}
if (jobSetup) {
runJobSetupTask(umbilical, reporter);
return;
}
if (taskCleanup) {
runTaskCleanupTask(umbilical, reporter);
return;
} if (useNewApi) {
runNewMapper(job, split, umbilical, reporter);
} else {
runOldMapper(job, split, umbilical, reporter);
}
done(umbilical, reporter);
}
4. TaskReporter的run方法。定时向父进程TaskTracker上报状态和进度。
public void run() {
final int MAX_RETRIES = 3;
int remainingRetries = MAX_RETRIES;
// get current flag value and reset it as well
boolean sendProgress = resetProgressFlag();
while (!taskDone.get()) {
try {
boolean taskFound = true; // whether TT knows about this task
// sleep for a bit
try {
Thread.sleep(PROGRESS_INTERVAL);
}
break;
}
if (sendProgress) {
// we need to send progress update
updateCounters();
taskStatus.statusUpdate(taskProgress.get(),
taskProgress.toString(),
counters);
taskFound = umbilical.statusUpdate(taskId, taskStatus);
taskStatus.clearStatus();
}
else {
// send ping
taskFound = umbilical.ping(taskId);
}
// if Task Tracker is not aware of our task ID (probably because it died and
// came back up), kill ourselves
if (!taskFound) {
LOG.warn("Parent died. Exiting "+taskId);
System.exit(66);
}
sendProgress = resetProgressFlag();
remainingRetries = MAX_RETRIES;
}
catch (Throwable t) {
}
}
}
}
5. Task 的Initialize方法初始化后续要执行的几个重要变量。包括JobContext OutputFormat OutputCommitter等，这些都是后续执行中要用到的属性实例。
public void initialize(JobConf job, JobID id,
Reporter reporter,
boolean useNewApi) throws IOException,
ClassNotFoundException,
InterruptedException {
jobContext = new JobContext(job, id, reporter);
taskContext = new TaskAttemptContext(job, taskId, reporter);
if (getState() == TaskStatus.State.UNASSIGNED) {
setState(TaskStatus.State.RUNNING);
}
if (useNewApi) {
LOG.debug("using new api for output committer");
outputFormat =
ReflectionUtils.newInstance(taskContext.getOutputFormatClass(), job);
committer = outputFormat.getOutputCommitter(taskContext);
} else {
committer = conf.getOutputCommitter();
}
Path outputPath = FileOutputFormat.getOutputPath(conf);
if (outputPath != null) {
if ((committer instanceof FileOutputCommitter)) {
FileOutputFormat.setWorkOutputPath(conf,
((FileOutputCommitter)committer).getTempTaskOutputPath(taskContext));
} else {
FileOutputFormat.setWorkOutputPath(conf, outputPath);
}
}
committer.setupTask(taskContext);
}
6. Task的 runJobCleanupTask方法。即如果在Task是jobCleanup，则调用OutputCommitter删除输出文件
protected void runJobCleanupTask(TaskUmbilicalProtocol umbilical,
TaskReporter reporter
) throws IOException, InterruptedException {
// set phase for this task
setPhase(TaskStatus.Phase.CLEANUP);
getProgress().setStatus("cleanup");
statusUpdate(umbilical);
// do the cleanup
committer.cleanupJob(jobContext);
done(umbilical, reporter);
}
7.Task的runJobSetupTask。如果Task是setupTask，则调用OutputCommitter，如创建Task要执行的根目录。
protected void runJobSetupTask(TaskUmbilicalProtocol umbilical,
TaskReporter reporter
) throws IOException, InterruptedException {
// do the setup
getProgress().setStatus("setup");
committer.setupJob(jobContext);
done(umbilical, reporter);
}
8. Task的runTaskCleanupTask。如果Task是taskCleanup，则调用taskCleanup 方法。最终OutputCommitter方法删除task的工作目录。
protected void runTaskCleanupTask(TaskUmbilicalProtocol umbilical,
TaskReporter reporter)
throws IOException, InterruptedException {
taskCleanup(umbilical);
done(umbilical, reporter);
}
9.MapTask的runNewMapper方法是我们要重点关注的方法，是真正执行用户定义的map的方法。
1)根据传入的jobconf构造一个context，包含了job相关的所有配置信息，如后面用到的mapper、inputformat等。
2)根据配置的mapper类创建一个Mapper实例
3)根据配置的inputformat创建一个InputFormat实例。
4)重新够构建InputSplit
5)创建RecordReader，其实使用的是适配器模式适配了inputFormat的Reader。
6)构造输出RecordWriter。当没有Reducer时，output是配置的outputFormat的RecordWriter，即直接写输出。如果ruducer数量不为0，则构造一个NewOutputCollector
7)构造Mapper.Context，封装了刚才配置的所有信息，在map执行时候时候使用。
8)调用mapper的run方法来执行map动作。
@SuppressWarnings("unchecked")
private <INKEY,INVALUE,OUTKEY,OUTVALUE>
void runNewMapper(final JobConf job,
final BytesWritable rawSplit,
final TaskUmbilicalProtocol umbilical,
TaskReporter reporter
) throws IOException, ClassNotFoundException,
InterruptedException {
// 1. 根据传入的jobconf构造一个context，包含了job相关的所有配置信息，如后面用到的mapper、inputformat等。
org.apache.hadoop.mapreduce.TaskAttemptContext taskContext =
new org.apache.hadoop.mapreduce.TaskAttemptContext(job, getTaskID());
// 2. 根据配置的mapper类创建一个Mapper实例
org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE> mapper =
(org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE>)
ReflectionUtils.newInstance(taskContext.getMapperClass(), job);
// 根据配置的input format创建一个InputFormat实例。
org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE> inputFormat =
(org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE>)
ReflectionUtils.newInstance(taskContext.getInputFormatClass(), job);
// 4.重新够构建InputSplit
org.apache.hadoop.mapreduce.InputSplit split = null;
DataInputBuffer splitBuffer = new DataInputBuffer();
splitBuffer.reset(rawSplit.getBytes(), 0, rawSplit.getLength());
SerializationFactory factory = new SerializationFactory(job);
Deserializer<? extends org.apache.hadoop.mapreduce.InputSplit>
deserializer =
(Deserializer<? extends org.apache.hadoop.mapreduce.InputSplit>)
factory.getDeserializer(job.getClassByName(splitClass));
deserializer.open(splitBuffer);
split = deserializer.deserialize(null);
//5. 创建RecordReader，其实使用的是适配器模式适配了inputFormat的Reader。
org.apache.hadoop.mapreduce.RecordReader<INKEY,INVALUE> input =
new NewTrackingRecordReader<INKEY,INVALUE>
(inputFormat.createRecordReader(split, taskContext), reporter);
job.setBoolean("mapred.skip.on", isSkipping());
org.apache.hadoop.mapreduce.RecordWriter output = null;
org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE>.Context
mapperContext = null;
try {
Constructor<org.apache.hadoop.mapreduce.Mapper.Context> contextConstructor =
org.apache.hadoop.mapreduce.Mapper.Context.class.getConstructor
(new Class[]{org.apache.hadoop.mapreduce.Mapper.class,
Configuration.class,
org.apache.hadoop.mapreduce.TaskAttemptID.class,
org.apache.hadoop.mapreduce.RecordReader.class,
org.apache.hadoop.mapreduce.RecordWriter.class,
org.apache.hadoop.mapreduce.OutputCommitter.class,
org.apache.hadoop.mapreduce.StatusReporter.class,
org.apache.hadoop.mapreduce.InputSplit.class});
//6. 构造输出RecordWriter。当没有Reducer时，output是配置的outputFormat的RecordWriter，即直接写输出。如果ruducer数量不为0，则构造一个NewOutputCollector
if (job.getNumReduceTasks() == 0) {
output = outputFormat.getRecordWriter(taskContext);
} else {
output = new NewOutputCollector(job, umbilical, reporter);
}
//7.构造Mapper.Context，封装了刚才配置的所有信息，在map执行时候时候使用。
mapperContext = contextConstructor.newInstance(mapper, job, getTaskID(),
input, output, committer,
reporter, split);
input.initialize(split, mapperContext);
//8. 调用mapper的run方法来执行map动作。
mapper.run(mapperContext);
input.close();
output.close(mapperContext);
} catch (NoSuchMethodException e) {
throw new IOException("Can't find Context constructor", e);
} catch (InstantiationException e) {
throw new IOException("Can't create Context", e);
} catch (InvocationTargetException e) {
throw new IOException("Can't invoke Context constructor", e);
} catch (IllegalAccessException e) {
throw new IOException("Can't invoke Context constructor", e);
}
}
10.Mapper的run方法。即对每一个输入的记录执行map方法。一般不会改变，就是拿出输入记录逐条执行map方法。除非要改变记录的执行方式，（如MultithreadedMapper需要多线程来执行），一般该方法不用override。
public void run(Context context) throws IOException, InterruptedException {
setup(context);
while (context.nextKeyValue()) {
map(context.getCurrentKey(), context.getCurrentValue(), context);
}
cleanup(context);
}
}
11.Mapper的map方法。即对每一个输入的记录执行map方法。这个只是默然的map执行方法，把输入不变的输出即可。用户定义的mapper就是override这个方法来按照自己定义的逻辑来处理数据。
protected void map(KEYIN key, VALUEIN value,
Context context) throws IOException, InterruptedException {
context.write((KEYOUT) key, (VALUEOUT) value);
}
完。
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