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hadoop-mapreduce中reducetask执行分析
ReduceTask的执行
Reduce处理程序中须要运行三个类型的处理,
1.copy,从各map中copy数据过来
2.sort,对数据进行排序操作。
3.reduce,运行业务逻辑的处理。
ReduceTask的执行也是通过run方法開始,
通过mapreduce.job.reduce.shuffle.consumer.plugin.class配置shuffle的plugin,
默认是Shuffle实现类。实现ShuffleConsumerPlugin接口。
生成Shuffle实例,并运行plugin的init函数进行初始化。
Class<?extendsShuffleConsumerPlugin>clazz =
job.getClass(MRConfig.SHUFFLE_CONSUMER_PLUGIN,Shuffle.class,ShuffleConsumerPlugin.class);
shuffleConsumerPlugin =ReflectionUtils.newInstance(clazz, job);
LOG.info("UsingShuffleConsumerPlugin: " +shuffleConsumerPlugin);
ShuffleConsumerPlugin.ContextshuffleContext =
newShuffleConsumerPlugin.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);
shuffleConsumerPlugin.init(shuffleContext);
运行shuffle的run函数。得到RawKeyValueIterator的实例。
rIter =shuffleConsumerPlugin.run();
Shuffle.run函数定义:
.....................................
inteventsPerReducer = Math.max(MIN_EVENTS_TO_FETCH,
MAX_RPC_OUTSTANDING_EVENTS/ jobConf.getNumReduceTasks());
intmaxEventsToFetch = Math.min(MAX_EVENTS_TO_FETCH,eventsPerReducer);
生成map的完毕状态获取线程,并启动此线程,此线程中从am中获取此job中全部完毕的map的event
通过ShuffleSchedulerImpl实例把全部的map的完毕的map的host,mapid,
等记录到mapLocations容器中。此线程每一秒运行一个获取操作。
//Start the map-completion events fetcher thread
finalEventFetcher<K,V> eventFetcher =
newEventFetcher<K,V>(reduceId,umbilical,scheduler,this,
maxEventsToFetch);
eventFetcher.start();
以下看看EventFetcher.run函数的运行过程:以下代码中我仅仅保留了代码的主体部分。
...................
EventFetcher.run:
publicvoid run() {
intfailures = 0;
........................
intnumNewMaps = getMapCompletionEvents();
..................................
}
......................
}
EventFetcher.getMapCompletionEvents
..................................
MapTaskCompletionEventsUpdateupdate =
umbilical.getMapCompletionEvents(
(org.apache.hadoop.mapred.JobID)reduce.getJobID(),
fromEventIdx,
maxEventsToFetch,
(org.apache.hadoop.mapred.TaskAttemptID)reduce);
events =update.getMapTaskCompletionEvents();
.....................
for(TaskCompletionEvent event : events) {
scheduler.resolve(event);
if(TaskCompletionEvent.Status.SUCCEEDED== event.getTaskStatus()) {
++numNewMaps;
}
}
shecduler是ShuffleShedulerImpl的实例。
ShuffleShedulerImpl.resolve
caseSUCCEEDED:
URI u = getBaseURI(reduceId,event.getTaskTrackerHttp());
addKnownMapOutput(u.getHost() +":"+ u.getPort(),
u.toString(),
event.getTaskAttemptId());
maxMapRuntime= Math.max(maxMapRuntime,event.getTaskRunTime());
break;
.......
ShuffleShedulerImpl.addKnownMapOutput函数:
把mapid与相应的host加入到mapLocations容器中,
MapHost host =mapLocations.get(hostName);
if(host == null){
host = newMapHost(hostName, hostUrl);
mapLocations.put(hostName,host);
}
此时会把host的状设置为PENDING
host.addKnownMap(mapId);
同一时候把host加入到pendingHosts容器中。notify相关的Fetcher文件copy线程。
//Mark the host as pending
if(host.getState() == State.PENDING){
pendingHosts.add(host);
notifyAll();
}
.....................
回到ReduceTask.run函数中,接着向下运行
//Start the map-output fetcher threads
booleanisLocal = localMapFiles!= null;
通过mapreduce.reduce.shuffle.parallelcopies配置的值。默觉得5。生成获取map数据的线程数。
生成Fetcher线程实例,并启动相关的线程。
通过mapreduce.reduce.shuffle.connect.timeout配置连接超时时间。默认180000
通过mapreduce.reduce.shuffle.read.timeout配置读取超时时间。默觉得180000
finalintnumFetchers = isLocal ? 1 :
jobConf.getInt(MRJobConfig.SHUFFLE_PARALLEL_COPIES,5);
Fetcher<K,V>[] fetchers =newFetcher[numFetchers];
if(isLocal) {
fetchers[0] = newLocalFetcher<K, V>(jobConf,reduceId,scheduler,
merger,reporter,metrics,this,reduceTask.getShuffleSecret(),
localMapFiles);
fetchers[0].start();
}else{
for(inti=0; i < numFetchers; ++i) {
fetchers[i] = newFetcher<K,V>(jobConf,reduceId,scheduler,merger,
reporter,metrics,this,
reduceTask.getShuffleSecret());
fetchers[i].start();
}
}
.........................
接下来进行Fetcher线程里面,看看Fetcher.run函数执行流程:
..........................
MapHost host = null;
try{
//If merge is on, block
merger.waitForResource();
从ShuffleScheduler中取出一个MapHost实例,
//Get a host to shuffle from
host = scheduler.getHost();
metrics.threadBusy();
运行shuffle操作。
//Shuffle
copyFromHost(host);
} finally{
if(host != null){
scheduler.freeHost(host);
metrics.threadFree();
}
}
接下来看看ShuffleScheduler中的getHost函数:
........
假设pendingHosts的值没有。先wait住,等待EventFetcher线程去获取数据来notify此wait
while(pendingHosts.isEmpty()){
wait();
}
MapHost host = null;
Iterator<MapHost> iter =pendingHosts.iterator();
从pendingHosts中random出一个MapHost,并返回给调用程序。
intnumToPick = random.nextInt(pendingHosts.size());
for(inti=0; i <= numToPick; ++i) {
host = iter.next();
}
pendingHosts.remove(host);
........................
当得到一个MapHost后,运行copyFromHost来进行数据的copy操作。
此时。一个task的host的url样子基本上是这个样子:
host:port/mapOutput?
job=xxx&reduce=123(当前reduce的partid值)&map=
copyFromHost的代码部分:
.....
List<TaskAttemptID>maps = scheduler.getMapsForHost(host);
.....
Set<TaskAttemptID>remaining = newHashSet<TaskAttemptID>(maps);
.....
此部分完毕后,url样子中map=后面会有非常多个mapid。多个用英文的”,”号分开的。
URLurl = getMapOutputURL(host, maps);
此处依据url打开httpconnection,
假设mapreduce.shuffle.ssl.enabled配置为true时,会打开SSL连接。默觉得false.
openConnection(url);
.....
设置连接超时时间,header,读取超时时间等值。并打开HttpConnection的连接。
// put url hashinto http header
connection.addRequestProperty(
SecureShuffleUtils.HTTP_HEADER_URL_HASH,encHash);
//set the read timeout
connection.setReadTimeout(readTimeout);
//put shuffle version into httpheader
connection.addRequestProperty(ShuffleHeader.HTTP_HEADER_NAME,
ShuffleHeader.DEFAULT_HTTP_HEADER_NAME);
connection.addRequestProperty(ShuffleHeader.HTTP_HEADER_VERSION,
ShuffleHeader.DEFAULT_HTTP_HEADER_VERSION);
connect(connection,connectionTimeout);
.....
运行文件的copy操作。此处是迭代运行。每个读取一个map的文件。
并把remaining中的值去掉一个。直到remaining的值所有读取完毕。
TaskAttemptID[] failedTasks =null;
while(!remaining.isEmpty() && failedTasks == null){
在copyMapOutput函数中。每次读取一个mapid,
依据MergeManagerImpl中的reserve函数,
1.检查map的输出是否超过了mapreduce.reduce.memory.totalbytes配置的大小。
此配置的默认值
是当前Runtime的maxMemory*mapreduce.reduce.shuffle.input.buffer.percent配置的值。
Buffer.percent的默认值为0.90;
假设mapoutput超过了此配置的大小时,生成一个OnDiskMapOutput实例。
2.假设没有超过此大小,生成一个InMemoryMapOutput实例。
failedTasks =copyMapOutput(host, input, remaining);
}
在copyMapOutput函数中首先调用的MergeManagerImpl.reserve函数:
if(!canShuffleToMemory(requestedSize)) {
.....
returnnewOnDiskMapOutput<K,V>(mapId, reduceId,this,requestedSize,
jobConf,mapOutputFile,fetcher, true);
}
.....
if(usedMemory> memoryLimit){
.....,当前使用的memory已经超过了配置的内存使用大小。此时返回null,
把host又一次加入到shuffleScheduler的pendingHosts队列中。
returnnull;
}
returnunconditionalReserve(mapId, requestedSize, true);
生成一个InMemoryMapOutput,并把usedMemory加上此mapoutput的大小。
privatesynchronizedInMemoryMapOutput<K, V> unconditionalReserve(
TaskAttemptID mapId, longrequestedSize, booleanprimaryMapOutput) {
usedMemory+= requestedSize;
returnnewInMemoryMapOutput<K,V>(jobConf,mapId, this,(int)requestedSize,
codec,primaryMapOutput);
}
以下是当usedMemory使用超过了指定的大小后,的处理部分。又一次把host加入到队列中。
例如以下所看到的:copyMapOutput函数
if(mapOutput == null){
LOG.info("fetcher#"+ id + "- MergeManager returned status WAIT ...");
//Notan error but wait to process data.
returnEMPTY_ATTEMPT_ID_ARRAY;
}
此时host中还有没处理完毕的mapoutput,在Fetcher.run中,又一次加入到队列中把此host
if(host != null){
scheduler.freeHost(host);
metrics.threadFree();
}
.........
接下来还是在copyMapOutput函数中,
通过mapoutput也就是merge.reserve函数返回的实例的shuffle函数。
假设mapoutput是InMemoryMapOutput,在调用shuffle时,直接把map输出写入到内存。
假设是OnDiskMapOutput,在调用shuffle时,直接把map的输出写入到local暂时文件里。
....
最后,运行ShuffleScheduler.copySucceeded完毕文件的copy,调用mapout.commit函数。
scheduler.copySucceeded(mapId,host, compressedLength,
endTime -startTime, mapOutput);
并从remaining中移出处理过的mapid,
接下来看看MapOutput.commit函数:
a.InMemoryMapOutput.commit函数:
publicvoidcommit() throwsIOException {
merger.closeInMemoryFile(this);
}
调用MergeManagerImpl.closeInMemoryFile函数:
publicsynchronizedvoidcloseInMemoryFile(InMemoryMapOutput<K,V> mapOutput) {
把此mapOutput实例加入到inMemoryMapOutputs列表中。
inMemoryMapOutputs.add(mapOutput);
LOG.info("closeInMemoryFile-> map-output of size: " +mapOutput.getSize()
+ ",inMemoryMapOutputs.size() -> " +inMemoryMapOutputs.size()
+ ",commitMemory -> " + commitMemory+ ", usedMemory ->"+ usedMemory);
把commitMemory的大小添加当前传入的mapoutput的size大小。
commitMemory+=mapOutput.getSize();
检查是否达到merge的值,
此值是mapreduce.reduce.memory.totalbytes配置
*mapreduce.reduce.shuffle.merge.percent配置的值。
默认是当前Runtime的memory*0.90*0.90
也就是说,仅仅有有新的mapoutput增加,这个检查条件就肯定会达到
//Can hang if mergeThreshold is really low.
if(commitMemory>= mergeThreshold){
.......
把正在进行merge的mapoutput列表加入到一起发起merge操作。
inMemoryMapOutputs.addAll(inMemoryMergedMapOutputs);
inMemoryMergedMapOutputs.clear();
inMemoryMerger.startMerge(inMemoryMapOutputs);
commitMemory= 0L; // Reset commitMemory.
}
假设mapreduce.reduce.merge.memtomem.enabled配置为true,默觉得false
同一时候inMemoryMapOutputs中的mapoutput个数
达到了mapreduce.reduce.merge.memtomem.threshold配置的值,
默认值是mapreduce.task.io.sort.factor配置的值,默觉得100
发起memTomem的merger操作。
if(memToMemMerger!= null){
if(inMemoryMapOutputs.size()>= memToMemMergeOutputsThreshold){
memToMemMerger.startMerge(inMemoryMapOutputs);
}
}
}
MergemanagerImpl.InMemoryMerger.merger函数操作:
在运行inMemoryMerger.startMerge(inMemoryMapOutputs);操作后,会notify此线程,
同一时候运行merger函数:
publicvoidmerge(List<InMemoryMapOutput<K,V>> inputs) throwsIOException {
if(inputs == null|| inputs.size() == 0) {
return;
}
....................
TaskAttemptID mapId =inputs.get(0).getMapId();
TaskID mapTaskId =mapId.getTaskID();
List<Segment<K, V>>inMemorySegments = newArrayList<Segment<K, V>>();
生成InMemoryReader实例,并把传入的容器清空,把生成好后的segment放到到inmemorysegments中。
longmergeOutputSize =
createInMemorySegments(inputs,inMemorySegments,0);
intnoInMemorySegments = inMemorySegments.size();
生成一个输出的文件路径。
Path outputPath =
mapOutputFile.getInputFileForWrite(mapTaskId,
mergeOutputSize).suffix(
Task.MERGED_OUTPUT_PREFIX);
针对输出的暂时文件生成一个Write实例。
Writer<K,V> writer =
newWriter<K,V>(jobConf,rfs,outputPath,
(Class<K>)jobConf.getMapOutputKeyClass(),
(Class<V>)jobConf.getMapOutputValueClass(),
codec,null);
RawKeyValueIterator rIter = null;
CompressAwarePathcompressAwarePath;
try{
LOG.info("Initiatingin-memory merge with " +noInMemorySegments +
"segments...");
此部分与map端的输出没什么差别,得到几个segment的文件的一个iterator,
此部分是一个优先堆,每一次next都会从全部的segment中读取出最小的一个key与value
rIter = Merger.merge(jobConf,rfs,
(Class<K>)jobConf.getMapOutputKeyClass(),
(Class<V>)jobConf.getMapOutputValueClass(),
inMemorySegments,inMemorySegments.size(),
newPath(reduceId.toString()),
(RawComparator<K>)jobConf.getOutputKeyComparator(),
reporter,spilledRecordsCounter,null,null);
假设没有combiner程序,直接写入到文件,否则,假设有combiner,先运行combiner处理。
if(null== combinerClass){
Merger.writeFile(rIter,writer, reporter,jobConf);
} else{
combineCollector.setWriter(writer);
combineAndSpill(rIter,reduceCombineInputCounter);
}
writer.close();
此处与map端的输出不同的地方在这里,这里不写入spillindex文件,
而是生成一个CompressAwarePath,把输出路径,大写和小写入到此实例中。
compressAwarePath = newCompressAwarePath(outputPath,
writer.getRawLength(),writer.getCompressedLength());
LOG.info(reduceId+
"Merge of the " + noInMemorySegments+
"files in-memory complete." +
"Local file is " + outputPath + "of size " +
localFS.getFileStatus(outputPath).getLen());
} catch(IOException e) {
//makesure that we delete the ondiskfile that we created
//earlierwhen we invoked cloneFileAttributes
localFS.delete(outputPath,true);
throwe;
}
此处,把生成的文件加入到onDiskMapOutputs属性中。
并检查此容器中的文件是否达到了mapreduce.task.io.sort.factor配置的值,
假设是,发起disk的merger操作。
//Note the output of the merge
closeOnDiskFile(compressAwarePath);
}
}
上面最后一行的所有定义在以下这里。
publicsynchronizedvoidcloseOnDiskFile(CompressAwarePath file) {
onDiskMapOutputs.add(file);
if(onDiskMapOutputs.size()>= (2 * ioSortFactor- 1)) {
onDiskMerger.startMerge(onDiskMapOutputs);
}
}
b.OnDiskMapOutput.commit函数:
把tmp文件rename到指定的文件夹下,生成一个CompressAwarePath实例。调用上面提到的处理程序。
publicvoidcommit() throwsIOException {
fs.rename(tmpOutputPath,outputPath);
CompressAwarePathcompressAwarePath = newCompressAwarePath(outputPath,
getSize(), this.compressedSize);
merger.closeOnDiskFile(compressAwarePath);
}
MergeManagerImpl.OnDiskMerger.merger函数:
这个函数到如今基本上没有什么能够讲解的东西,注意一点就是,
每merge一个文件后,会把这个merge后的文件路径又一次加入到onDiskMapOutputs容器中。
publicvoidmerge(List<CompressAwarePath> inputs) throwsIOException {
//sanity check
if(inputs == null|| inputs.isEmpty()) {
LOG.info("Noondisk files to merge...");
return;
}
longapproxOutputSize = 0;
intbytesPerSum =
jobConf.getInt("io.bytes.per.checksum",512);
LOG.info("OnDiskMerger:We have " + inputs.size() +
"map outputs on disk. Triggering merge...");
//1. Prepare the list of files to be merged.
for(CompressAwarePath file : inputs) {
approxOutputSize +=localFS.getFileStatus(file).getLen();
}
//add the checksum length
approxOutputSize +=
ChecksumFileSystem.getChecksumLength(approxOutputSize,bytesPerSum);
//2. Start the on-disk merge process
Path outputPath =
localDirAllocator.getLocalPathForWrite(inputs.get(0).toString(),
approxOutputSize,jobConf).suffix(Task.MERGED_OUTPUT_PREFIX);
Writer<K,V> writer =
newWriter<K,V>(jobConf,rfs,outputPath,
(Class<K>)jobConf.getMapOutputKeyClass(),
(Class<V>)jobConf.getMapOutputValueClass(),
codec,null);
RawKeyValueIterator iter = null;
CompressAwarePathcompressAwarePath;
Path tmpDir = newPath(reduceId.toString());
try{
iter = Merger.merge(jobConf,rfs,
(Class<K>)jobConf.getMapOutputKeyClass(),
(Class<V>)jobConf.getMapOutputValueClass(),
codec,inputs.toArray(newPath[inputs.size()]),
true,ioSortFactor,tmpDir,
(RawComparator<K>)jobConf.getOutputKeyComparator(),
reporter,spilledRecordsCounter,null,
mergedMapOutputsCounter,null);
Merger.writeFile(iter,writer, reporter,jobConf);
writer.close();
compressAwarePath = newCompressAwarePath(outputPath,
writer.getRawLength(),writer.getCompressedLength());
} catch(IOException e) {
localFS.delete(outputPath,true);
throwe;
}
closeOnDiskFile(compressAwarePath);
LOG.info(reduceId+
"Finished merging " + inputs.size()+
"map output files on disk of total-size "+
approxOutputSize + "."+
"Local output file is " + outputPath+ " of size "+
localFS.getFileStatus(outputPath).getLen());
}
}
ok,如今map的copy部分运行完毕,回到ShuffleConsumerPlugin的run方法中,
也就是Shuffle的run方法中,接着上面的代码向下分析:
此处等待全部的copy操作完毕。
//Wait for shuffle to complete successfully
while(!scheduler.waitUntilDone(PROGRESS_FREQUENCY)){
reporter.progress();
synchronized(this){
if(throwable!= null){
thrownewShuffleError("error in shuffle in "+ throwingThreadName,
throwable);
}
}
}
假设运行到这一行时。说明全部的mapcopy操作已经完毕,
关闭查找map执行状态的线程与执行copy操作的几个线程。
//Stop the event-fetcher thread
eventFetcher.shutDown();
//Stop the map-output fetcher threads
for(Fetcher<K,V> fetcher : fetchers) {
fetcher.shutDown();
}
//stop the scheduler
scheduler.close();
发am发送状态,通知AM。此时要运行排序操作。
copyPhase.complete();// copy is already complete
taskStatus.setPhase(TaskStatus.Phase.SORT);
reduceTask.statusUpdate(umbilical);
运行最后的merge,事实上在合并全部文件与memory中的数据时。也同一时候会进行排序操作。
//Finish the on-going merges...
RawKeyValueIterator kvIter =null;
try{
kvIter = merger.close();
}catch(Throwable e) {
thrownewShuffleError("Error while doingfinal merge " , e);
}
//Sanity check
synchronized(this){
if(throwable!= null){
thrownewShuffleError("error in shuffle in "+ throwingThreadName,
throwable);
}
}
最后返回这个合并后的iterator实例。
returnkvIter;
Merger也就是MergeManagerImpl.close函数:
publicRawKeyValueIterator close() throwsThrowable {
关闭几个merge的线程。在关闭时会等待现有的merge完毕。
//Wait for on-going merges to complete
if(memToMemMerger!= null){
memToMemMerger.close();
}
inMemoryMerger.close();
onDiskMerger.close();
List<InMemoryMapOutput<K,V>> memory =
newArrayList<InMemoryMapOutput<K, V>>(inMemoryMergedMapOutputs);
inMemoryMergedMapOutputs.clear();
memory.addAll(inMemoryMapOutputs);
inMemoryMapOutputs.clear();
List<CompressAwarePath>disk = newArrayList<CompressAwarePath>(onDiskMapOutputs);
onDiskMapOutputs.clear();
运行终于的merge操作。
returnfinalMerge(jobConf,rfs,memory, disk);
}
最后的一个merge操作
privateRawKeyValueIterator finalMerge(JobConf job, FileSystem fs,
List<InMemoryMapOutput<K,V>>inMemoryMapOutputs,
List<CompressAwarePath>onDiskMapOutputs
)throwsIOException {
LOG.info("finalMergecalled with " +
inMemoryMapOutputs.size() +" in-memory map-outputs and "+
onDiskMapOutputs.size() + "on-disk map-outputs");
finalfloatmaxRedPer =
job.getFloat(MRJobConfig.REDUCE_INPUT_BUFFER_PERCENT,0f);
if(maxRedPer > 1.0 || maxRedPer < 0.0) {
thrownewIOException(MRJobConfig.REDUCE_INPUT_BUFFER_PERCENT+
maxRedPer);
}
得到能够cache到内存的大小,比例通过mapreduce.reduce.input.buffer.percent配置,
intmaxInMemReduce = (int)Math.min(
Runtime.getRuntime().maxMemory()* maxRedPer, Integer.MAX_VALUE);
//merge configparams
Class<K> keyClass =(Class<K>)job.getMapOutputKeyClass();
Class<V> valueClass =(Class<V>)job.getMapOutputValueClass();
booleankeepInputs = job.getKeepFailedTaskFiles();
finalPath tmpDir = newPath(reduceId.toString());
finalRawComparator<K> comparator =
(RawComparator<K>)job.getOutputKeyComparator();
//segments required to vacate memory
List<Segment<K,V>>memDiskSegments = newArrayList<Segment<K,V>>();
longinMemToDiskBytes = 0;
booleanmergePhaseFinished = false;
if(inMemoryMapOutputs.size() > 0) {
TaskID mapId =inMemoryMapOutputs.get(0).getMapId().getTaskID();
这个地方依据可cache到内存的值。把不能cache到内存的部分生成InMemoryReader实例。
并加入到memDiskSegments容器中。
inMemToDiskBytes =createInMemorySegments(inMemoryMapOutputs,
memDiskSegments,
maxInMemReduce);
finalintnumMemDiskSegments = memDiskSegments.size();
把内存中多于部分的mapoutput数据写入到文件里,并把文件路径加入到onDiskMapOutputs容器中。
if(numMemDiskSegments > 0 &&
ioSortFactor> onDiskMapOutputs.size()) {
...........
此部分主要是写入内存中多于的mapoutput到磁盘中去
mergePhaseFinished = true;
//must spill to disk, but can‘t retain in-memfor intermediate merge
finalPath outputPath =
mapOutputFile.getInputFileForWrite(mapId,
inMemToDiskBytes).suffix(
Task.MERGED_OUTPUT_PREFIX);
finalRawKeyValueIterator rIter = Merger.merge(job,fs,
keyClass, valueClass,memDiskSegments, numMemDiskSegments,
tmpDir, comparator,reporter,spilledRecordsCounter,null,
mergePhase);
Writer<K,V> writer = newWriter<K,V>(job, fs, outputPath,
keyClass, valueClass, codec,null);
try{
Merger.writeFile(rIter,writer, reporter,job);
writer.close();
onDiskMapOutputs.add(newCompressAwarePath(outputPath,
writer.getRawLength(),writer.getCompressedLength()));
writer = null;
//add to list of final disk outputs.
} catch(IOException e) {
if(null!= outputPath) {
try{
fs.delete(outputPath,true);
} catch(IOException ie) {
//NOTHING
}
}
throwe;
} finally{
if(null!= writer) {
writer.close();
}
}
LOG.info("Merged" + numMemDiskSegments + "segments, " +
inMemToDiskBytes + "bytes to disk to satisfy " +
"reducememory limit");
inMemToDiskBytes = 0;
memDiskSegments.clear();
} elseif(inMemToDiskBytes != 0) {
LOG.info("Keeping" + numMemDiskSegments + "segments, " +
inMemToDiskBytes + "bytes in memory for " +
"intermediate,on-disk merge");
}
}
//segments on disk
List<Segment<K,V>>diskSegments = newArrayList<Segment<K,V>>();
longonDiskBytes = inMemToDiskBytes;
longrawBytes = inMemToDiskBytes;
生成眼下文件里有的全部的mapoutput路径的onDisk数组
CompressAwarePath[] onDisk =onDiskMapOutputs.toArray(
newCompressAwarePath[onDiskMapOutputs.size()]);
for(CompressAwarePath file : onDisk) {
longfileLength = fs.getFileStatus(file).getLen();
onDiskBytes += fileLength;
rawBytes +=(file.getRawDataLength() > 0) ?
file.getRawDataLength() :fileLength;
LOG.debug("Diskfile: " + file + "Length is " + fileLength);
把如今reduce端接收过来并存储到文件里的mapoutput生成segment并加入到distSegments容器中
diskSegments.add(newSegment<K, V>(job, fs, file, codec,keepInputs,
(file.toString().endsWith(
Task.MERGED_OUTPUT_PREFIX)?
null: mergedMapOutputsCounter),file.getRawDataLength()
));
}
LOG.info("Merging" + onDisk.length+ " files, "+
onDiskBytes + "bytes from disk");
按内容的大小从小到大排序此distSegments容器
Collections.sort(diskSegments,newComparator<Segment<K,V>>() {
publicintcompare(Segment<K, V> o1, Segment<K, V> o2) {
if(o1.getLength() == o2.getLength()) {
return0;
}
returno1.getLength() < o2.getLength() ? -1 : 1;
}
});
把如今memory中全部的mapoutput内容生成segment并加入到finalSegments容器中。
//build final list of segments from merged backed by disk + in-mem
List<Segment<K,V>>finalSegments = newArrayList<Segment<K,V>>();
longinMemBytes = createInMemorySegments(inMemoryMapOutputs,
finalSegments,0);
LOG.info("Merging" + finalSegments.size() + "segments, " +
inMemBytes + "bytes from memory into reduce");
if(0 != onDiskBytes) {
finalintnumInMemSegments = memDiskSegments.size();
diskSegments.addAll(0,memDiskSegments);
memDiskSegments.clear();
//Pass mergePhase only if there is a going to be intermediate
//merges. See comment where mergePhaseFinished is being set
Progress thisPhase =(mergePhaseFinished) ? null: mergePhase;
这个部分是把如今磁盘上的mapoutput生成一个iterator,
RawKeyValueIterator diskMerge =Merger.merge(
job, fs, keyClass, valueClass,codec,diskSegments,
ioSortFactor,numInMemSegments, tmpDir, comparator,
reporter,false,spilledRecordsCounter,null,thisPhase);
diskSegments.clear();
if(0 == finalSegments.size()) {
returndiskMerge;
}
把如今磁盘上的iterator也相同加入到finalSegments容器中。
也就是此时,这个容器中有两个优先堆排序的队列,每next一次,要从内存与磁盘中找出最小的一个kv.
finalSegments.add(newSegment<K,V>(
newRawKVIteratorReader(diskMerge, onDiskBytes), true,rawBytes));
}
returnMerger.merge(job,fs, keyClass, valueClass,
finalSegments,finalSegments.size(), tmpDir,
comparator, reporter,spilledRecordsCounter,null,
null);
}
shuffle部分如今所有运行完毕。又一次加到ReduceTask.run函数中,接着代码向下分析:
rIter =shuffleConsumerPlugin.run();
............
RawComparatorcomparator = job.getOutputValueGroupingComparator();
if(useNewApi) {
runNewReducer(job, umbilical,reporter, rIter, comparator,
keyClass,valueClass);
}else{
runOldReducer........
}
在以上代码中运行runNewReducer主要是运行reduce的run函数。
org.apache.hadoop.mapreduce.TaskAttemptContexttaskContext =
neworg.apache.hadoop.mapreduce.task.TaskAttemptContextImpl(job,
getTaskID(), reporter);
//make a reducer
org.apache.hadoop.mapreduce.Reducer<INKEY,INVALUE,OUTKEY,OUTVALUE>reducer =
(org.apache.hadoop.mapreduce.Reducer<INKEY,INVALUE,OUTKEY,OUTVALUE>)
ReflectionUtils.newInstance(taskContext.getReducerClass(),job);
org.apache.hadoop.mapreduce.RecordWriter<OUTKEY,OUTVALUE>trackedRW =
newNewTrackingRecordWriter<OUTKEY, OUTVALUE>(this,taskContext);
job.setBoolean("mapred.skip.on",isSkipping());
job.setBoolean(JobContext.SKIP_RECORDS,isSkipping());
org.apache.hadoop.mapreduce.Reducer.Context
reducerContext =createReduceContext(reducer, job, getTaskID(),
rIter,reduceInputKeyCounter,
reduceInputValueCounter,
trackedRW,
committer,
reporter,comparator, keyClass,
valueClass);
try{
reducer.run(reducerContext);
}finally{
trackedRW.close(reducerContext);
}
以上代码中创建Reducer执行的Context,并执行reducer.run函数:
createReduceContext函数定义部分代码:
org.apache.hadoop.mapreduce.ReduceContext<INKEY,INVALUE, OUTKEY, OUTVALUE>
reduceContext =
newReduceContextImpl<INKEY, INVALUE, OUTKEY, OUTVALUE>(job,taskId,
rIter,
inputKeyCounter,
inputValueCounter,
output,
committer,
reporter,
comparator,
keyClass,
valueClass);
org.apache.hadoop.mapreduce.Reducer<INKEY,INVALUE,OUTKEY,OUTVALUE>.Context
reducerContext =
newWrappedReducer<INKEY, INVALUE, OUTKEY,OUTVALUE>().getReducerContext(
reduceContext);
ReduceContextImpl主要是运行在RawKeyValueInterator中读取数据的相关操作。
Reducer.run函数:
publicvoid run(Context context) throwsIOException, InterruptedException {
setup(context);
try{
while(context.nextKey()) {
reduce(context.getCurrentKey(),context.getValues(),context);
//If a back up store is used, reset it
Iterator<VALUEIN> iter =context.getValues().iterator();
if(iterinstanceofReduceContext.ValueIterator) {
((ReduceContext.ValueIterator<VALUEIN>)iter).resetBackupStore();
}
}
}finally{
cleanup(context);
}
}
在run函数中通过context.nextkey来得到下一行的数据,这部分主要在ReduceContextImpl中完毕:
nextkey调用nextKeyValue函数:
publicboolean nextKeyValue() throwsIOException, InterruptedException {
if(!hasMore){
key= null;
value= null;
returnfalse;
}
此处用来检查是否是一个key以下的第一个value,假设是第一个value时。此值为false,
也就是说。nextKeyIsSame的值是true时,表示如今next的数据与current的key是一行数据。
否则表示已经进行了换行操作。
firstValue= !nextKeyIsSame;
运行一下RawKeyValueInterator(也就是Merge中的队列),得到当前最小的key
DataInputBuffer nextKey =input.getKey();
把key设置到buffer中,设置到buffer中的目的是为了通过keyDeserializer来读取一个key的值。
currentRawKey.set(nextKey.getData(),nextKey.getPosition(),
nextKey.getLength()- nextKey.getPosition());
buffer.reset(currentRawKey.getBytes(),0, currentRawKey.getLength());
从buffer中读取key的值,并存储到key中,这个地方要注意一下,
以下先看看这部分的定义:
.........................
生成一个key的Deserializer实例,
this.keyDeserializer= serializationFactory.getDeserializer(keyClass);
把buffer当成keyDeserializer的InputStream。
this.keyDeserializer.open(buffer);
Deserializer中运行deserializer函数的定义:
此部分定义能够看出,一个key/value仅仅会生成实例,此部分从性能上考虑主要是为了降低对象的生成。
每次生成一个数据时,都是通过readFields又一次去生成Writable实例中的内容。
因此,非常多同学在reduce中使用value时,会出现数据引用不正确的情况,
由于对象还是同一个对象,但值是最后一个,所以会出现数据不正确的情况
publicWritable deserialize(Writable w) throwsIOException {
Writable writable;
if(w == null){
writable
= (Writable)ReflectionUtils.newInstance(writableClass,getConf());
} else{
writable = w;
}
writable.readFields(dataIn);
returnwritable;
}
.........................
读取key的内容
key= keyDeserializer.deserialize(key);
按key同样的方式,得到当前的value的值。
DataInputBuffer nextVal =input.getValue();
buffer.reset(nextVal.getData(),nextVal.getPosition(), nextVal.getLength()
- nextVal.getPosition());
value= valueDeserializer.deserialize(value);
currentKeyLength= nextKey.getLength() - nextKey.getPosition();
currentValueLength= nextVal.getLength() - nextVal.getPosition();
isMarked的值为false,同一时候backupStore属性为null
if(isMarked){
backupStore.write(nextKey,nextVal);
}
把input运行一次next操作,此处会从全部的文件/memory中找到最小的一个kv.
hasMore= input.next();
if(hasMore) {
比較一下,是否与currentkey是同一个key,假设是表示在同一行中。
也就是key同样。
nextKey = input.getKey();
nextKeyIsSame= comparator.compare(currentRawKey.getBytes(),0,
currentRawKey.getLength(),
nextKey.getData(),
nextKey.getPosition(),
nextKey.getLength()- nextKey.getPosition()
)== 0;
}else{
nextKeyIsSame= false;
}
inputValueCounter.increment(1);
returntrue;
}
接下来是调用reduce函数。此时会通过context.getValues函数把key相应的全部的value传给reduce.
此处的context.getValues例如以下所看到的:
ReduceContextImpl.getValues()
public
Iterable<VALUEIN>getValues() throwsIOException, InterruptedException {
returniterable;
}
以上代码中直接返回的是iterable的实例,此实例在ReduceContextImpl实例生成时生成。
privateValueIterable iterable = newValueIterable();
这个类是ReduceContextImpl中的内部类
protectedclass ValueIterable implementsIterable<VALUEIN> {
privateValueIterator iterator= newValueIterator();
@Override
publicIterator<VALUEIN> iterator() {
returniterator;
}
}
此实例中引用一个ValueIterator类。这也是一个内部类。
每次进行运行时。通过此ValueIterator.next来获取一条数据,
publicVALUEIN next() {
inReset的值默觉得false.也就是说inReset检查内部的代码不会运行,事实上backupStore本身值就是null
假设想使用backupStore,须要运行其内部的make函数。
if(inReset) {
.................里面的代码不分析
}
假设是key以下的第一个value,把firstValue设置为false,由于下一次来时,就不是firstValue了.
返回当前的value
//if this is the first record, we don‘t need to advance
if(firstValue){
firstValue= false;
returnvalue;
}
//if this isn‘t the first record and the next key is different, they
//can‘t advance it here.
if(!nextKeyIsSame){
thrownewNoSuchElementException("iteratepast last value");
}
//otherwise, go to the next key/value pair
try{
这里表示不是第一个value的时候,也就是firstValue的值为false,运行一下nextKeyValue函数。
得到当前的value.返回。
nextKeyValue();
returnvalue;
} catch(IOException ie) {
thrownewRuntimeException("next valueiterator failed", ie);
} catch(InterruptedException ie) {
//this is bad, but we can‘t modify the exception list of java.util
thrownewRuntimeException("next valueiterator interrupted", ie);
}
}
当reduce运行完毕后的输出。跟map端无reduce时的输出一样。
直接输出。
hadoop-mapreduce中reducetask执行分析