大数据:Hadoopreduce阶段

Mapreduce中由于sort的存在,MapTask和ReduceTask直接是工作流的架构。而不是数据流的架构。在MapTask尚未结束,其输出结果尚未排序及合并前,ReduceTask是又有数据输入的,因此即使ReduceTask已经创建也只能睡眠等待MapTask完成。从而可以从MapTask节点获取数据。一个MapTask最终的数据输出是一个合并的spill文件,可以通过Web地址访问。所以reduceTask一般在MapTask快要完成的时候才启动。启动早了浪费container资源。

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ReduceTask是个线程,这个线程运行在YarnChild的Java虚拟机上,我们从ReduceTask.run开始看Reduce阶段。 获取更多大数据视频资料请加QQ群:947967114

public void run(JobConf job, final TaskUmbilicalProtocol umbilical)

throws IOException, InterruptedException, ClassNotFoundException {

job.setBoolean(JobContext.SKIP_RECORDS, isSkipping());

if (isMapOrReduce()) {

/添加reduce过程需要经过的几个阶段。以便通知TaskTracker目前运 行的情况/

copyPhase = getProgress().addPhase("copy");

sortPhase = getProgress().addPhase("sort");

reducePhase = getProgress().addPhase("reduce");

}

// start thread that will handle communication with parent

TaskReporter reporter = startReporter(umbilical);

// 设置并启动reporter进程以便和TaskTracker进行交流

boolean useNewApi = job.getUseNewReducer();

//在job client中初始化job时,默认就是用新的API,详见Job.setUseNewAPI()方法

initialize(job, getJobID(), reporter, useNewApi);

/用来初始化任务,主要是进行一些和任务输出相关的设置,比如创建commiter,设置工作目录等/

// check if it is a cleanupJobTask

/以下4个if语句均是根据任务类型的不同进行相应的操作,这些方 法均是Task类的方法,所以与任务是MapTask还是ReduceTask无关/

if (jobCleanup) {

runJobCleanupTask(umbilical, reporter);

return;//只是为了JobCleanup,做完就停

}

if () {

runJobSetupTask(umbilical, reporter);

return;

//主要是创建工作目录的FileSystem对象

}

if (taskCleanup) {

runTaskCleanupTask(umbilical, reporter);

return;

//设置任务目前所处的阶段为结束阶段,并且删除工作目录

}

下面才是真正要成为reducer

// Initialize the codec

codec = initCodec();

RawKeyValueIterator rIter = null;

ShuffleConsumerPlugin shuffleConsumerPlugin = null;

Class combinerClass = conf.getCombinerClass();

CombineOutputCollector combineCollector =

(null != combinerClass) ?

new CombineOutputCollector(reduceCombineOutputCounter, reporter, conf) : null;

//如果需要就创建combineCollector

Classextends ShuffleConsumerPlugin> clazz =

job.getClass(MRConfig.SHUFFLE_CONSUMER_PLUGIN, Shuffle.class, ShuffleConsumerPlugin.class);

//配置文件找mapreduce.job.reduce.shuffle.consumer.plugin.class默认是shuffle.class

shuffleConsumerPlugin = ReflectionUtils.newInstance(clazz, job);

//创建shuffle类对象

LOG.info("Using ShuffleConsumerPlugin: " + shuffleConsumerPlugin);

ShuffleConsumerPlugin.Context shuffleContext =

new ShuffleConsumerPlugin.Context(getTaskID(), job, FileSystem.getLocal(job), umbilical,

super.lDirAlloc, reporter, codec,

combinerClass, combineCollector,

spilledRecordsCounter, reduceCombineInputCounter,

shuffledMapsCounter,

reduceShuffleBytes, failedShuffleCounter,

mergedMapOutputsCounter,

taskStatus, copyPhase, sortPhase, this,

mapOutputFile, localMapFiles);

//创建context对象,ShuffleConsumerPlugin.Context

shuffleConsumerPlugin.init(shuffleContext);

//这里调用的起始是shuffle的init函数,重点摘要如下。

this.localMapFiles = context.getLocalMapFiles();

scheduler = new ShuffleSchedulerImpl(jobConf, taskStatus, reduceId,

this, copyPhase, context.getShuffledMapsCounter(),

context.getReduceShuffleBytes(), context.getFailedShuffleCounter());

//创建shuffle所需的调度器

merger = createMergeManager(context);

//创建shuffle内部的merge,createMergeManager里面源码:

return new MergeManagerImpl(reduceId, jobConf, context.getLocalFS(),

context.getLocalDirAllocator(), reporter, context.getCodec(),

context.getCombinerClass(), context.getCombineCollector(),

context.getSpilledRecordsCounter(),

context.getReduceCombineInputCounter(),

context.getMergedMapOutputsCounter(), this, context.getMergePhase(),

context.getMapOutputFile());

//创建MergeMnagerImpl对象和Merge线程

rIter = shuffleConsumerPlugin.run();

//从各个Mapper复制其输出文件,并加以合并排序,等待直到完成为止

// free up the data structures

mapOutputFilesOnDisk.clear();

sortPhase.complete();

//排序阶段完成

setPhase(TaskStatus.Phase.REDUCE);

//进入reduce阶段

statusUpdate(umbilical);

Class keyClass = job.getMapOutputKeyClass();

Class valueClass = job.getMapOutputValueClass();

RawComparator comparator = job.getOutputValueGroupingComparator();

//3.Reduce 1.Reduce任务的最后一个阶段。它会准备好Map的 keyClass("mapred.output.key.class""mapred.mapoutput.key.class"),valueClass("mapred.mapoutput.value.class"或"mapred.output.value.class")和 Comparator (“mapred.output.value.groupfn.class”或“mapred.output.key.comparator.class”)

if (useNewApi) {

//2.根据参数useNewAPI判断执行runNewReduce还是runOldReduce。分析润runNewReduce

runNewReducer(job, umbilical, reporter, rIter, comparator,

keyClass, valueClass);

//0.像报告进程书写一些信息,1.获得一个TaskAttemptContext对象。通过这个对象创建reduce、output及用于跟踪的统计output的RecordWrit、最后创建用于收集reduce结果的Context,2.reducer.run(reducerContext)开始执行reduce

} else {//老API

runOldReducer(job, umbilical, reporter, rIter, comparator,

keyClass, valueClass);

}

shuffleConsumerPlugin.close();

done(umbilical, reporter);

}

(1)reduce分为三个阶段(copy就是远程拷贝Map的输出数据、sort就是对所有的数据做排序、reduce做聚集就是我们自己写的reducer),为这三个阶段分别设置Progress,用来和TaskTracker通信报道状态。

(2)上面代码的15-40行和MapReduce的MapTask任务的运行源码级分析中对应部分基本相同,可参考之;

(3)codec = initCodec()这句是检查map的输出是否是压缩的,压缩的则返回压缩codec实例,否则返回null,这里讨论不压缩的;

(4)我们讨论完全分布式的hadoop,即isLocal==false,然后构造一个ReduceCopier对象reduceCopier,并调用reduceCopier.fetchOutputs()方法拷贝各个Mapper的输出,到本地;

(5)然后copy阶段完成,设置接下来的阶段是sort阶段,更新状态信息;

(6)根据isLocal来选择KV迭代器,完全分布式的会使用reduceCopier.createKVIterator(job, rfs, reporter)作为KV迭代器;

(7)sort阶段完成,设置接下来的阶段是reduce阶段,更新状态信息;

(8)然后获取一些配置信息,并根据是否使用新API选择不同的处理方式,这里是新的API,调用runNewReducer(job, umbilical, reporter, rIter, comparator, keyClass, valueClass)会执行reducer;

(9)done(umbilical, reporter)这个方法用于做结束任务的一些清理工作:更新计数器updateCounters();如果任务需要提交,设置Taks状态为COMMIT_PENDING,并利用TaskUmbilicalProtocol,汇报Task完成,等待提交,然后调用commit提交任务;设置任务结束标志位;结束Reporter通信线程;发送最后一次统计报告(通过sendLastUpdate方法);利用TaskUmbilicalProtocol报告结束状态(通过sendDone方法)。

有些人将Reduce Task分为了5个阶段:一、shuffle阶段:也称为Copy阶段,就是从各个MapTask上远程拷贝一片数据,如果大小超过一定阈值就写到磁盘,否则放入内存;二、Merge阶段:在远程拷贝数据的同时,Reduce Task启动了两个后台线程对内存和磁盘上的文件进行合并,防止内存使用过多和磁盘文件过多;三、sort阶段:用户编写的reduce方法的输入数据是按key进行聚集的,需要对copy过来的数据排序,这里用的是归并排序,因为Map Task的结果是有序的;四、Reduce阶段:将每组数据依次交给用户编写的Reduce方法处理;五、write阶段:就是将结果写入HDFS。

上面的5个阶段分的比较细了,代码里分为3个阶段copy、sort、reduce,我们在eclipse运行MR程序时,控制台看到的reduce阶段的百分比就分为3个阶段各占33.3%。

这里的shuffleConsumerPlugin是实现了ShuffleConsumerPlugin的某个类对象。具体可以通过配置文件mapreduce.job.reduce.shuffle.consumer.plugin.class选项设置,默认情况下是使用shuffle。我们在代码中分析过完成shuffleConsumerPlugin.run,通常是shuffle.run,因为有了这个过程Mapper的合成的spill文件才能通过HTTP协议传输到Reducer端。有了数据才能进行runNewReducer或者runOldReducer。可以说shuffle对象就是MapTask的搬运工。而且shuffle的搬运方式不是一遍搬运一遍Reducer处理,而是要把MapTask所有的数据都搬运过来,并且进行合并排序之后才开始提供给对应的Reducer。

一般而言,MapTask和ReduceTask是多对多的关系,假如有M个Mapper有N个Reducer。我们知道N个Reducer对应着N个partition,所以每个Mapper都会被划分成N个Partition,每个Reducer承担着一个Partition部分的操作。这样每一个Reducer从每个不同的Mapper内拿来属于自己的那部分数据,这样每个Reducer就有M份不同Mapper的数据,把M份数据合并在一起就是一个最终完整的Partition,有必要还会进行排序,这时候才成为了Reducer的具体输入数据。这个数据搬运和重组的过程被叫做shuffle过程。shuffle这个过程开销颇大,会占用较大的网络流量,因为涉及到大量数据的传输,shuffle过程也会有延迟,因为M个Mapper的计算有快有慢,但是shuffle要所有的Mapper完成才能开始,Reduce又必须等shuffle完成才能开始,当然这种延迟不是shuffle造成的,如果Reducer不需要全部Partition数据到位并排序,就不用与最慢的Mapper同步,这是排序付出的代价。

所以shuffle在MapReduce框架中起着非常重要的作用。我们先看shuffle的摘要: 获取更多大数据视频资料请加QQ群:947967114

public class Shuffle implements ShuffleConsumerPlugin, ExceptionReporter

private ShuffleConsumerPlugin.Context context;

private TaskAttemptID reduceId;

private JobConf jobConf;

private TaskUmbilicalProtocol umbilical;

private ShuffleSchedulerImpl scheduler;

private MergeManager merger;

private Task reduceTask; //Used for status updates

private Map localMapFiles;

public void init(ShuffleConsumerPlugin.Context context)

public RawKeyValueIterator run() throws IOException, InterruptedException

在ReduceTask.run中看到调用了shuffle.init,在run理创建了ShuffleSchedulerImpl和MergeManagerImpl对象。后面会讲解就是是做什么用的。

之后就是对shuffle.run的调用,shuffle虽然有一个run但是并非是一个线程,只是用了这个名字而已。

我们看:ReduceTask.run->Shuffle.run

public RawKeyValueIterator run() throws IOException, InterruptedException {

int eventsPerReducer = Math.max(MIN_EVENTS_TO_FETCH,

MAX_RPC_OUTSTANDING_EVENTS / jobConf.getNumReduceTasks());

int maxEventsToFetch = Math.min(MAX_EVENTS_TO_FETCH, eventsPerReducer);

// Start the map-completion events fetcher thread

final EventFetcher eventFetcher =

new EventFetcher(reduceId, umbilical, scheduler, this,

maxEventsToFetch);

eventFetcher.start();

//通过查看EventFetcher我们看到他继承了Thread,所以他是一个线程

// Start the map-output fetcher threads

boolean isLocal = localMapFiles != null;

final int numFetchers = isLocal ? 1 :

jobConf.getInt(MRJobConfig.SHUFFLE_PARALLEL_COPIES, 5);

Fetcher[] fetchers = new Fetcher[numFetchers];

//创建了一个线程池

if (isLocal) {

//如果Mapper和Reducer在同一台机器上,就在本地fetche

fetchers[0] = new LocalFetcher(jobConf, reduceId, scheduler,

merger, reporter, metrics, this, reduceTask.getShuffleSecret(),

localMapFiles);

//LocalFetcher是对Fetcher的扩展,也是线程。

fetchers[0].start();//本地Fecher只有一个

} else {

//Mapper集合Reducer不在同一个机器上,需要跨多个节点Fecher

for (int i=0; i < numFetchers; ++i) {

//启动所有的Fecher

fetchers[i] = new Fetcher(jobConf, reduceId, scheduler, merger,

reporter, metrics, this,

reduceTask.getShuffleSecret());

//创建Fecher线程

fetchers[i].start();

//跨节点的Fecher需要好多个,都需要开启

}

}

// Wait for shuffle to complete successfully

while (!scheduler.waitUntilDone(PROGRESS_FREQUENCY)) {

reporter.progress();

//等待所有的Fecher都完成,如果有超时情况就报告进度

synchronized (this) {

if (throwable != null) {

throw new ShuffleError("error in shuffle in " + throwingThreadName,

throwable);

}

}

}

// Stop the event-fetcher thread

eventFetcher.shutDown();

//关闭eventFetcher,代表shuffle操作完成,所有的MapTask的数据都拷贝过来了

// Stop the map-output fetcher threads

for (Fetcher fetcher : fetchers) {

fetcher.shutDown();//关闭所有的fetcher。

}

// stop the scheduler

scheduler.close();

//也不需要shuffle的调度,所以关闭

copyPhase.complete(); // copy is already complete

//文件复制阶段结束

以下就是Reduce阶段的MergeSort了

taskStatus.setPhase(TaskStatus.Phase.SORT);

//完成排序

reduceTask.statusUpdate(umbilical);

//通过umbilical向MRAppMaster汇报,更新状态

// Finish the on-going merges...

RawKeyValueIterator kvIter = null;

try {

kvIter = merger.close();

//合并和排序,完成后返回一个队列kvIter 。

} catch (Throwable e) {

throw new ShuffleError("Error while doing final merge " , e);

}

// Sanity check

synchronized (this) {

if (throwable != null) {

throw new ShuffleError("error in shuffle in " + throwingThreadName,

throwable);

}

}

return kvIter;

}

数据从MapTask转移到ReduceTask就两种方式,一MapTask送,二ReduceTask取,hadoop采用的是第二种方式,就是文件的复制。在Shuffle进入run之前,RduceTask.run调用过他的init函数shuffleConsumerPlugin.init(shuffleContext),在init里创建了scheduler和用于合并排序的merge,进入run后又创建了EventFetcher线程和若干个Fetcher线程。Fetcher的作用就是拿取,向MapTask节点提取数据。但是我们要清楚EventFetcher虽然也是Fetcher,但是提取的是event,不是数据本身。我们可以认为它只是对Fetcher过程的一个事件的控制。

Fetcher线程的数量也不一定,Uber模式下,MapTask和ReduceTask在同一个节点上,并且只有一个MapTask,所以只有一个Fetcher就能够完成,而且这个Fetcher是localFetcher。如果不是Uber模式可能会有很多MapTask并且一般和ReduceTask不在同一个节点。这时Fetcher的数量可以进行配置,默认有5个。数组fetchers就相当于Fetcher的线程池。

创建了EventFetcher和Fetcher线程池后,进入了while循环,但是while循环什么都不做,一直等待,所以实际的操作都是在线程完成的,也就是通过EventFetcher和若干的Fetcher完成。EventFetcher起到了非常关键的枢纽的作用。

我们查看EventFetcher的源代码摘要,我们提取关键的东西:

class EventFetcher extends Thread {

private final TaskAttemptID reduce;

private final TaskUmbilicalProtocol umbilical;

private final ShuffleScheduler scheduler;

private final int maxEventsToFetch;

public void run() {

int failures = 0;

LOG.info(reduce + " Thread started: " + getName());

try {

while (!stopped && !Thread.currentThread().isInterrupted()) {//线程没有被打断

try {

int numNewMaps = getMapCompletionEvents();

//获取Map的完成的事件,接着我们看getMapCompletionEvents源代码:

protected int getMapCompletionEvents()

throws IOException, InterruptedException {

int numNewMaps = 0;

TaskCompletionEvent events[] = null;

do {

MapTaskCompletionEventsUpdate update =

umbilical.getMapCompletionEvents(

(org.apache.hadoop.mapred.JobID)reduce.getJobID(),

fromEventIdx,

maxEventsToFetch,

(org.apache.hadoop.mapred.TaskAttemptID)reduce);

//汇报umbilical从MRAppMaster获取Map完成的时间的报告

events = update.getMapTaskCompletionEvents();

//获取有关具体的MapTask结束运行的情况

LOG.debug("Got " + events.length + " map completion events from " +

fromEventIdx);

assert !update.shouldReset() : "Unexpected legacy state";

//做了一个断言 获取更多大数据视频资料请加QQ群:947967114

// Update the last seen event ID

fromEventIdx += events.length;

// Process the TaskCompletionEvents:

// 1. Save the SUCCEEDED maps in knownOutputs to fetch the outputs.

// 2. Save the OBSOLETE/FAILED/KILLED maps in obsoleteOutputs to stop

// fetching from those maps.

// 3. Remove TIPFAILED maps from neededOutputs since we don't need their

// outputs at all.

for (TaskCompletionEvent event : events) {

//对于获取的每个事件的报告

scheduler.resolve(event);

//这里使用了ShuffleSchedullerImpl.resolve函数,源代码如下:

public void resolve(TaskCompletionEvent event) {

switch (event.getTaskStatus()) {

case SUCCEEDED://如果成功

URI u = getBaseURI(reduceId, event.getTaskTrackerHttp());//获取其URI

addKnownMapOutput(u.getHost() + ":" + u.getPort(),

u.toString(),

event.getTaskAttemptId());

//记录这个MapTask的节点主机记录下来,供Fetcher使用,getBaseURI的源代码:

static URI getBaseURI(TaskAttemptID reduceId, String url) {

StringBuffer baseUrl = new StringBuffer(url);

if (!url.endsWith("/")) {

baseUrl.append("/");

}

baseUrl.append("mapOutput?job=");

baseUrl.append(reduceId.getJobID());

baseUrl.append("&reduce=");

baseUrl.append(reduceId.getTaskID().getId());

baseUrl.append("&map=");

URI u = URI.create(baseUrl.toString());

return u;

获取各种信息,然后添加都URI对象中。

}

回到源代码

maxMapRuntime = Math.max(maxMapRuntime, event.getTaskRunTime());

//最大的尝试时间

break;

case FAILED:

case KILLED:

case OBSOLETE://如果MapTask运行失败

obsoleteMapOutput(event.getTaskAttemptId());//获取TaskId

LOG.info("Ignoring obsolete output of " + event.getTaskStatus() +

" map-task: '" + event.getTaskAttemptId() + "'");//写日志

break;

case TIPFAILED://如果失败

tipFailed(event.getTaskAttemptId().getTaskID());

LOG.info("Ignoring output of failed map TIP: '" +

event.getTaskAttemptId() + "'");//写日志

break;

}

}

回到源代码

if (TaskCompletionEvent.Status.SUCCEEDED == event.getTaskStatus()) {//如果事件成功

++numNewMaps;//增加map数量

}

}

} while (events.length == maxEventsToFetch);

return numNewMaps;

}

回到源代码

failures = 0;

if (numNewMaps > 0) {

LOG.info(reduce + ": " + "Got " + numNewMaps + " new map-outputs");

}

LOG.debug("GetMapEventsThread about to sleep for " + SLEEP_TIME);

if (!Thread.currentThread().isInterrupted()) {

Thread.sleep(SLEEP_TIME);

}

} catch (InterruptedException e) {

LOG.info("EventFetcher is interrupted.. Returning");

return;

} catch (IOException ie) {

LOG.info("Exception in getting events", ie);

// check to see whether to abort

if (++failures >= MAX_RETRIES) {

throw new IOException("too many failures downloading events", ie);//失败数量大于重试的数量

}

// sleep for a bit

if (!Thread.currentThread().isInterrupted()) {

Thread.sleep(RETRY_PERIOD);

}

}

}

} catch (InterruptedException e) {

return;

} catch (Throwable t) {

exceptionReporter.reportException(t);

return;

}

}

MapTask和ReduceTask没有直接的关系,MapTask不知道ReduceTask在哪些节点上,它只是把进度的时间报告给MRAppMaster。ReduceTask通过“脐带”执行getMapCompletionEvents操作想MRAppMaster获取MapTask结束运行的时间报告。有个别的MapTask可能会失败,但是绝大多数都会成功,只要成功的就通过Fetcher去索取输出数据,这个信息就是通过shcheduler完成的也就是ShuffleSchedulerImpl对象,ShuffleSchedulerImpl对象并不多,只是个普通的对象。

fetchers就像线程池,里面有若干线程(默认有5个),这些线程等待EventFetcher的通知,一旦有MapTask完成就前往提取数据。

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大数据 : Hadoop reduce阶段

我们看Fetcher线程类的run方法:

public void run() {

try {

while (!stopped && !Thread.currentThread().isInterrupted()) {

MapHost host = null;

try {

// If merge is on, block

merger.waitForResource();

// Get a host to shuffle from

host = scheduler.getHost();

//从scheduler获取一个已经成功完成的MapTask的节点。

metrics.threadBusy();

//线程变成繁忙状态

// Shuffle

copyFromHost(host);

//开始复制这个节点的数据

} finally {

if (host != null) {//maphost还有运行中的

scheduler.freeHost(host);

//状态设置成空闲状态,等待其完成。

metrics.threadFree();

}

}

}

} catch (InterruptedException ie) {

return;

} catch (Throwable t) {

exceptionReporter.reportException(t);

}

}

这里的重点是copyFromHost获取数据的函数。

protected void copyFromHost(MapHost host) throws IOException {

// reset retryStartTime for a new host

//这是在ReduceTask的节点上运行的

retryStartTime = 0;

// Get completed maps on 'host'

List maps = scheduler.getMapsForHost(host);

//获取目标节点上的MapTask集合。

// Sanity check to catch hosts with only 'OBSOLETE' maps,

// especially at the tail of large jobs

if (maps.size() == 0) {

return;//没有完成的直接返回

}

if(LOG.isDebugEnabled()) {

LOG.debug("Fetcher " + id + " going to fetch from " + host + " for: "

  • maps);

}

// List of maps to be fetched yet

Set remaining = new HashSet(maps);

//已经完成、等待shuffle的MapTask集合。

// Construct the url and connect

DataInputStream input = null;

URL url = getMapOutputURL(host, maps);

//生成MapTask所在节点的URL,下面要看getMapOutputURL源码:

private URL getMapOutputURL(MapHost host, Collection maps

) throws MalformedURLException {

// Get the base url

StringBuffer url = new StringBuffer(host.getBaseUrl());

boolean first = true;

for (TaskAttemptID mapId : maps) {

if (!first) {

url.append(",");

}

url.append(mapId);//在URL后面加上mapid

first = false;

}

LOG.debug("MapOutput URL for " + host + " -> " + url.toString());

//写日志

return new URL(url.toString());

//返回URL

}

回到主代码:

try {

setupConnectionsWithRetry(host, remaining, url);

//和对方主机建立HTTP连接,setupConnectionsWithRetry使用了openConnectionWithRetry函数打开链接。

openConnectionWithRetry(host, remaining, url);

这段源代码有使用了openConnection(url);方式,继续查看。

如下是链接的主要过程:

protected synchronized void openConnection(URL url)

throws IOException {

HttpURLConnection conn = (HttpURLConnection) url.openConnection();

//使用的是HTTPURL进行连接

if (sslShuffle) {//如果是有信任证书的

HttpsURLConnection httpsConn = (HttpsURLConnection) conn;

//强转conn类型

try {

httpsConn.setSSLSocketFactory(sslFactory.createSSLSocketFactory());//添加一个证书socket的工厂

} catch (GeneralSecurityException ex) {

throw new IOException(ex);

}

httpsConn.setHostnameVerifier(sslFactory.getHostnameVerifier());

}

connection = conn;

}

在setupConnectionsWithRetry中继续写到:

setupShuffleConnection(encHash);

//建立了Shuffle链接

connect(connection, connectionTimeout);

// verify that the thread wasn't stopped during calls to connect

if (stopped) {

return;

}

verifyConnection(url, msgToEncode, encHash);

}

//至此连接通过。

if (stopped) {

abortConnect(host, remaining);

//这里边是关闭连接,可以点进去看一下,满足列表和等待的两个条件

return;

}

} catch (IOException ie) {

boolean connectExcpt = ie instanceof ConnectException;

ioErrs.increment(1);

LOG.warn("Failed to connect to " + host + " with " + remaining.size() +

" map outputs", ie);

回到主代码

input = new DataInputStream(connection.getInputStream());

//实例一个输入流对象。

try {

// Loop through available map-outputs and fetch them

// On any error, faildTasks is not null and we exit

// after putting back the remaining maps to the

// yet_to_be_fetched list and marking the failed tasks.

TaskAttemptID[] failedTasks = null;

while (!remaining.isEmpty() && failedTasks == null) {

//如果需要fetcher的列表不空,并且失败的task数量没有

try {

failedTasks = copyMapOutput(host, input, remaining, fetchRetryEnabled);

//复制数据出来copyMapOutput的源代码如下:

try {

ShuffleHeader header = new ShuffleHeader();

header.readFields(input);

mapId = TaskAttemptID.forName(header.mapId);

//获取mapID

compressedLength = header.compressedLength;

decompressedLength = header.uncompressedLength;

forReduce = header.forReduce;

} catch (IllegalArgumentException e) {

badIdErrs.increment(1);

LOG.warn("Invalid map id ", e);

//Don't know which one was bad, so consider all of them as bad

return remaining.toArray(new TaskAttemptID[remaining.size()]);

}

InputStream is = input;

is = CryptoUtils.wrapIfNecessary(jobConf, is, compressedLength);

compressedLength -= CryptoUtils.cryptoPadding(jobConf);

decompressedLength -= CryptoUtils.cryptoPadding(jobConf);

//如果需要解压或解密

// Do some basic sanity verification

if (!verifySanity(compressedLength, decompressedLength, forReduce,

remaining, mapId)) {

return new TaskAttemptID[] {mapId};

}

if(LOG.isDebugEnabled()) {

LOG.debug("header: " + mapId + ", len: " + compressedLength +

", decomp len: " + decompressedLength);

}

try {

mapOutput = merger.reserve(mapId, decompressedLength, id);

//为merge预留一个MapOutput:是内存还是磁盘上。

} catch (IOException ioe) {

// kill this reduce attempt

ioErrs.increment(1);

scheduler.reportLocalError(ioe);

//报告错误

return EMPTY_ATTEMPT_ID_ARRAY;

}

// Check if we can shuffle now ...

if (mapOutput == null) {

LOG.info("fetcher#" + id + " - MergeManager returned status WAIT ...");

//Not an error but wait to process data.

return EMPTY_ATTEMPT_ID_ARRAY;

}

// The codec for lz0,lz4,snappy,bz2,etc. throw java.lang.InternalError

// on decompression failures. Catching and re-throwing as IOException

// to allow fetch failure logic to be processed

try {

// Go!

LOG.info("fetcher#" + id + " about to shuffle output of map "

  • mapOutput.getMapId() + " decomp: " + decompressedLength

  • " len: " + compressedLength + " to " + mapOutput.getDescription());

mapOutput.shuffle(host, is, compressedLength, decompressedLength,

metrics, reporter);

//跨节点把Mapper的文件内容拷贝到reduce的内存或者磁盘上。

} catch (java.lang.InternalError e) {

LOG.warn("Failed to shuffle for fetcher#"+id, e);

throw new IOException(e);

}

// Inform the shuffle scheduler

long endTime = Time.monotonicNow();

// Reset retryStartTime as map task make progress if retried before.

retryStartTime = 0;

scheduler.copySucceeded(mapId, host, compressedLength,

startTime, endTime, mapOutput);

//告诉调度器完成了一个节点的Map输出的文件拷贝。

remaining.remove(mapId);

//这个MapTask的输出已经shuffle完毕

metrics.successFetch();

return null;后面的异常失败信息我们不管。

这里的mapOutput是用来容纳MapTask输出文件的存储空间,根据输出文件的内容大小和内存的情况,可以是内存的Output也可以是DiskOutput。 如果是内存需要预约,因为不止一个Fetcher。我们以InMemoryMapOutput为例。

代码结构;

Fetcher.run-->copyFromHost-->copyMapOutput-->merger.reserve(MergeManagerImpl.reserve)-->InmemoryMapOutput.shuffle

public void shuffle(MapHost host, InputStream input,

long compressedLength, long decompressedLength,

ShuffleClientMetrics metrics,

Reporter reporter) throws IOException {

//跨节点从Mapper拷贝spill文件

IFileInputStream checksumIn =

new IFileInputStream(input, compressedLength, conf);

//校验和的输入流

input = checksumIn;

// Are map-outputs compressed?

if (codec != null) {

//如果涉及到了压缩

decompressor.reset();

//重启解压器

input = codec.createInputStream(input, decompressor);

//加了解压器的输入流

}

try {

IOUtils.readFully(input, memory, 0, memory.length);

//从Mapper方把特定的Partition数据读入Reducer的内存缓冲区。

metrics.inputBytes(memory.length);

reporter.progress();//汇报进度

LOG.info("Read " + memory.length + " bytes from map-output for " +

getMapId());

/**

  • We've gotten the amount of data we were expecting. Verify the

  • decompressor has nothing more to offer. This action also forces the

  • decompressor to read any trailing bytes that weren't critical

  • for decompression, which is necessary to keep the stream

  • in sync.

*/

if (input.read() >= 0 ) {

throw new IOException("Unexpected extra bytes from input stream for " +

getMapId());

}

} catch (IOException ioe) {

// Close the streams

IOUtils.cleanup(LOG, input);

// Re-throw

throw ioe;

} finally {

CodecPool.returnDecompressor(decompressor);

//释放解压器

}

}

从对方把spill文件中属于本partition数据复制过来,回到copyFromHost中,通过scheduler.copySuccessed告知scheduler,并把这个MapTask的ID从remaining集合中删除,进入下一个循环,复制下一个MapTask数据。直到把所有的属于本Partition的数据都复制过来。

以上是Reducer端Fetcher的过程,它向Mapper端发送HTTP GET请求,下载文件。在MapTask就有一个与之对应的Server,这个网络协议的源代码不做深究,课下有兴趣自己研究。 获取更多大数据视频资料请加QQ群:947967114


文章题目:大数据:Hadoopreduce阶段
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