MapReduce源码解读之MapTask-output
写在前面
我们知道MapTask执行完之后，会调用context.write()方法将map的输出结果写入到磁盘中，然后Reducer从磁盘中拉取结果，那么在源码层面，这一过程到底是怎么执行的呢？前面我们分析了MapTas类的input部分代码，现在我们来分析MapTask类的run()方法中关于output的部分代码，看看shuffle过程是怎么执行的。
注：本人使用的版本为Hadoop2.6.5，所以源码分析也是基于Hadoop2.6.5，如果对源码感兴趣的话可以点击这里下载源码
1. 入口：MapTask 类
@Override
/**
*为了保证内容的完整性，我们仍从MapTask类开始分析
*/
public void run(final JobConf job, final TaskUmbilicalProtocol umbilical)
throws IOException, ClassNotFoundException, InterruptedException {
this.umbilical = umbilical;
if (isMapTask()) {
// 从conf中获取ReduceTask的数量，如果没有ReduceTask，那么map阶段占100%
if (conf.getNumReduceTasks() == 0) {
mapPhase = getProgress().addPhase("map", 1.0f);
} else {
// 如果有ReduceTask，Map会多一个排序阶段，map阶段占67%，排序阶段占33%
mapPhase = getProgress().addPhase("map", 0.667f);
sortPhase = getProgress().addPhase("sort", 0.333f);
}
}
TaskReporter reporter = startReporter(umbilical);
boolean useNewApi = job.getUseNewMapper();
initialize(job, getJobID(), reporter, useNewApi);
// 检查和清理JobTask
if (jobCleanup) {
runJobCleanupTask(umbilical, reporter);
return;
}
if (jobSetup) {
runJobSetupTask(umbilical, reporter);
return;
}
if (taskCleanup) {
runTaskCleanupTask(umbilical, reporter);
return;
}
if (useNewApi) {
//我们使用的是新API，直接进入runNewMapper()方法
runNewMapper(job, splitMetaInfo, umbilical, reporter);
} else {
runOldMapper(job, splitMetaInfo, umbilical, reporter);
}
done(umbilical, reporter);
}
2.进入runNewMapper()方法
private <INKEY,INVALUE,OUTKEY,OUTVALUE>
void runNewMapper(final JobConf job,
final TaskSplitIndex splitIndex,
final TaskUmbilicalProtocol umbilical,
TaskReporter reporter
) throws IOException, ClassNotFoundException,
InterruptedException {
// 创建MapTask的上下文,注意，job里面已经有从HDFS中下载的各种配置信息
org.apache.hadoop.mapreduce.TaskAttemptContext taskContext =
new org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl(job,
getTaskID(),
reporter);
// 创建一个mapper，这个mapper就是用户手写的类的实例，taskContext会从job中获取用户设定的MapperClass，然后传给mapper
org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE> mapper =
(org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE>)
//这里通过反射来创建类，反射的类就是用户手写的Mapper类
ReflectionUtils.newInstance(taskContext.getMapperClass(), job);
// 创建一个输入格式化类实例inputformat
org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE> inputFormat =
(org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE>)
//这里通过反射来创建一个InputFormatClass实例，
//注意，这个getInputFormatClass()方法在客户端提交作业时就出现过，默认是TextInputFormat
ReflectionUtils.newInstance(taskContext.getInputFormatClass(), job);
/** 重新构建Inputsplit实例split，为什么要重新构建呢？因为原split中包含了所有MapTask应该要执行的文件路径，起始位置，偏移量。
而单个MapTask只需要关心自己执行哪一部分就行，所以需要重新构建应该split来存放自己要执行的split信息**/
org.apache.hadoop.mapreduce.InputSplit split = null;
split = getSplitDetails(new Path(splitIndex.getSplitLocation()),
splitIndex.getStartOffset());
LOG.info("Processing split: " + split);
//这里使用split,inputFormat, reporter, taskContext构造出了一个RecordReader实例input
org.apache.hadoop.mapreduce.RecordReader<INKEY,INVALUE> input =
new NewTrackingRecordReader<INKEY,INVALUE>
(split, inputFormat, reporter, taskContext);
job.setBoolean(JobContext.SKIP_RECORDS, isSkipping());
//创建一个output
org.apache.hadoop.mapreduce.RecordWriter output = null;
// 对output进行赋值,如果没有ReduceTask，直接输出，无需排序
if (job.getNumReduceTasks() == 0) {
output =
new NewDirectOutputCollector(taskContext, job, umbilical, reporter);
} else {
//如果ReduceTask不为0，执行这个方法，我们假设有ReduceTask
//进入NewOutputCollector类
output = new NewOutputCollector(taskContext, job, umbilical, reporter);
}
3. 进入NewOutputCollector类
private class NewOutputCollector<K,V>
extends org.apache.hadoop.mapreduce.RecordWriter<K,V> {
private final MapOutputCollector<K,V> collector;
private final org.apache.hadoop.mapreduce.Partitioner<K,V> partitioner;
private final int partitions;
@SuppressWarnings("unchecked")
NewOutputCollector(org.apache.hadoop.mapreduce.JobContext jobContext,
JobConf job,
TaskUmbilicalProtocol umbilical,
TaskReporter reporter
) throws IOException, ClassNotFoundException {
//collector涉及到溢写等过程，分完区才会执行溢写，所以先放着待会分析
collector = createSortingCollector(job, reporter);
//这里可以看到，partitions的数量就是ReduceTasks的数量
partitions = jobContext.getNumReduceTasks();
//如果partition>1，则通过反射创建分区器，我们进入getPartitionerClass()方法获取分区器
if (partitions > 1) {
partitioner = (org.apache.hadoop.mapreduce.Partitioner<K,V>)
ReflectionUtils.newInstance(jobContext.getPartitionerClass(), job);
} else {
// 如果partition的数量为1，意味着所有的map输出都会分到一个分区器里面
//所以会创建一个分区器Partitioner，Partitioner是一个接口类，所以要实现它的方法getPartition()
partitioner = new org.apache.hadoop.mapreduce.Partitioner<K,V>() {
@Override
//getPartition()返回的就是分区号，因为只有一个分区，所以这里直接返回0号分区
public int getPartition(K key, V value, int numPartitions) {
return partitions - 1;
}
};
}
}
3. 进入getPartitionerClass()方法
/**
*这个方法的实现类是JobContextImpl
*/
public Class<? extends Partitioner<?,?>> getPartitionerClass()
throws ClassNotFoundException {
return (Class<? extends Partitioner<?,?>>)
//这里我们可以看到，如果用户没有传入Partitioner，默认会使用HashPartitioner
//进入HasPartitioner类
conf.getClass(PARTITIONER_CLASS_ATTR, HashPartitioner.class);
}
4. 进入HashPartitioner类
public class HashPartitioner<K, V> extends Partitioner<K, V> {
/** Use {@link Object#hashCode()} to partition. */
public int getPartition(K key, V value,
int numReduceTasks) {
//这里直接对numReduceTasks取模，key.hashCode() & Integer.MAX_VALUE是为了返回一个正整数
//hashCode()是一个稳定算法，相同的key返回的hashCode一定相等，也就一定会返回相同的分区号
//假设numReduceTasks为3，那么取模的结果就只能是0,1，2，这样就返回了这条记录的分区号
//现在key,value,partitioner都准备好了，我们进入用户实现的map()方法
return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
}
}
5. 进入用户实现的map()方法
/**
*这个方法就是我们写的map方法，这里调用了context.write()方法
*/
//key 是每一行字符串自己第一个字节面向源文件的偏移量
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
//这里调用了context.write()方法,我们进入context.write()方法
context.write(word, one);
}
}
6.进入context.write()方法
@Override
/**
*这个方法的实现类是NewOutputCollector
*/
public void write(K key, V value) throws IOException, InterruptedException {
//这里我们发现之前创建的collector在这里调用collect方法将数据写入缓冲区
//现在我们来分析一下这个collector究竟是什么，回到NewOutputCollector()方法
collector.collect(key, value,
partitioner.getPartition(key, value, partitions));
}
7. 回到NewOutputCollector()方法
/**
*这是NewOutputCollector的构造方法
*/
NewOutputCollector(org.apache.hadoop.mapreduce.JobContext jobContext,
JobConf job,
TaskUmbilicalProtocol umbilical,
TaskReporter reporter
) throws IOException, ClassNotFoundException {
//这里通过createSortingCollector()创建了一个collector，进入createSortingCollector()方法
collector = createSortingCollector(job, reporter);
partitions = jobContext.getNumReduceTasks();
8.进入createSortingCollector()方法
private <KEY, VALUE> MapOutputCollector<KEY, VALUE>
createSortingCollector(JobConf job, TaskReporter reporter)
throws IOException, ClassNotFoundException {
//这里从job中获取并创建上下文信息
MapOutputCollector.Context context =
new MapOutputCollector.Context(this, job, reporter);
//这里获取用户设置的MAP_OUTPUT_COLLECTOR_CLASS_ATTR，如果没有，默认为MapOutputBuffer
Class<?>[] collectorClasses = job.getClasses(
JobContext.MAP_OUTPUT_COLLECTOR_CLASS_ATTR, MapOutputBuffer.class);
int remainingCollectors = collectorClasses.length;
//clazz在这里被创建
for (Class clazz : collectorClasses) {
try {
if (!MapOutputCollector.class.isAssignableFrom(clazz)) {
throw new IOException("Invalid output collector class: " + clazz.getName() +
" (does not implement MapOutputCollector)");
}
//clazz创建出一个MapOutputCollector的子类
Class<? extends MapOutputCollector> subclazz =
clazz.asSubclass(MapOutputCollector.class);
LOG.debug("Trying map output collector class: " + subclazz.getName());
//调用反射subclazz来实例化一个collector
MapOutputCollector<KEY, VALUE> collector =
ReflectionUtils.newInstance(subclazz, job);
//到这里collector开始初始化，进入init()方法
collector.init(context);
LOG.info("Map output collector class = " + collector.getClass().getName());
return collector;
} catch (Exception e) {
String msg = "Unable to initialize MapOutputCollector " + clazz.getName();
if (--remainingCollectors > 0) {
msg += " (" + remainingCollectors + " more collector(s) to try)";
}
LOG.warn(msg, e);
}
}
throw new IOException("Unable to initialize any output collector");
}
9. 进入init()方法（最重要）
public void init(MapOutputCollector.Context context
) throws IOException, ClassNotFoundException {
job = context.getJobConf();
reporter = context.getReporter();
mapTask = context.getMapTask();
mapOutputFile = mapTask.getMapOutputFile();
sortPhase = mapTask.getSortPhase();
spilledRecordsCounter = reporter.getCounter(TaskCounter.SPILLED_RECORDS);
partitions = job.getNumReduceTasks();
rfs = ((LocalFileSystem)FileSystem.getLocal(job)).getRaw();
//一个浮点数，默认为0.8，其实就是溢写的阈值
final float spillper =
job.getFloat(JobContext.MAP_SORT_SPILL_PERCENT, (float)0.8);
//设置一个缓冲区sortmb，默认大小为100MB
final int sortmb = job.getInt(JobContext.IO_SORT_MB, 100);
indexCacheMemoryLimit = job.getInt(JobContext.INDEX_CACHE_MEMORY_LIMIT,
INDEX_CACHE_MEMORY_LIMIT_DEFAULT);
if (spillper > (float)1.0 || spillper <= (float)0.0) {
throw new IOException("Invalid \"" + JobContext.MAP_SORT_SPILL_PERCENT +
"\": " + spillper);
}
if ((sortmb & 0x7FF) != sortmb) {
throw new IOException(
"Invalid \"" + JobContext.IO_SORT_MB + "\": " + sortmb);
}
//通过反射来创建一个排序类，如果用户没有设置，默认为快排
sorter = ReflectionUtils.newInstance(job.getClass("map.sort.class",
QuickSort.class, IndexedSorter.class), job);
// buffers and accounting
int maxMemUsage = sortmb << 20;
maxMemUsage -= maxMemUsage % METASIZE;
kvbuffer = new byte[maxMemUsage];
bufvoid = kvbuffer.length;
kvmeta = ByteBuffer.wrap(kvbuffer)
.order(ByteOrder.nativeOrder())
.asIntBuffer();
setEquator(0);
bufstart = bufend = bufindex = equator;
kvstart = kvend = kvindex;
maxRec = kvmeta.capacity() / NMETA;
//这里可以看到，缓冲区数据的限制容量为80%，也就是说，如果写入缓冲区的数据大小超过了缓冲区容量的80%，就会发生溢写
softLimit = (int)(kvbuffer.length * spillper);
bufferRemaining = softLimit;
LOG.info(JobContext.IO_SORT_MB + ": " + sortmb);
LOG.info("soft limit at " + softLimit);
LOG.info("bufstart = " + bufstart + "; bufvoid = " + bufvoid);
LOG.info("kvstart = " + kvstart + "; length = " + maxRec);
// 创建了一个比较器，这个比较器会优先被定义为用户定义的排序比较器，如果用户未定义，则定义为key自身的比较器
comparator = job.getOutputKeyComparator();
keyClass = (Class<K>)job.getMapOutputKeyClass();
valClass = (Class<V>)job.getMapOutputValueClass();
//先将参数进行序列化，然后才能进行比较
serializationFactory = new SerializationFactory(job);
keySerializer = serializationFactory.getSerializer(keyClass);
keySerializer.open(bb);
valSerializer = serializationFactory.getSerializer(valClass);
valSerializer.open(bb);
// output counters
mapOutputByteCounter = reporter.getCounter(TaskCounter.MAP_OUTPUT_BYTES);
mapOutputRecordCounter =
reporter.getCounter(TaskCounter.MAP_OUTPUT_RECORDS);
fileOutputByteCounter = reporter
.getCounter(TaskCounter.MAP_OUTPUT_MATERIALIZED_BYTES);
// compression
if (job.getCompressMapOutput()) {
Class<? extends CompressionCodec> codecClass =
job.getMapOutputCompressorClass(DefaultCodec.class);
codec = ReflectionUtils.newInstance(codecClass, job);
} else {
codec = null;
}
// 这里创建了一个combiner，这个combiner其实就是在map端进行一次Reduce，为了减少shuffle过程中产生的I/O量
//假设一个分区里有10w条(hell0,1)的记录，我们做单词统计时就可以使用combiner将10w条压缩成1条(hello,10w)
final Counters.Counter combineInputCounter =
reporter.getCounter(TaskCounter.COMBINE_INPUT_RECORDS);
combinerRunner = CombinerRunner.create(job, getTaskID(),
combineInputCounter,
reporter, null);
if (combinerRunner != null) {
final Counters.Counter combineOutputCounter =
reporter.getCounter(TaskCounter.COMBINE_OUTPUT_RECORDS);
combineCollector= new CombineOutputCollector<K,V>(combineOutputCounter, reporter, job);
} else {
combineCollector = null;
}
spillInProgress = false;
minSpillsForCombine = job.getInt(JobContext.MAP_COMBINE_MIN_SPILLS, 3);
spillThread.setDaemon(true);
spillThread.setName("SpillThread");
spillLock.lock();
try {
//这里启动了一个溢写线程，我们进入这个溢写线程spillThread
spillThread.start();
while (!spillThreadRunning) {
spillDone.await();
}
} catch (InterruptedException e) {
throw new IOException("Spill thread failed to initialize", e);
} finally {
spillLock.unlock();
}
if (sortSpillException != null) {
throw new IOException("Spill thread failed to initialize",
sortSpillException);
}
}
10. 进入溢写线程spillThread
protected class SpillThread extends Thread {
@Override
public void run() {
spillLock.lock();
spillThreadRunning = true;
try {
while (true) {
spillDone.signal();
while (!spillInProgress) {
spillReady.await();
}
try {
spillLock.unlock();
//这里执行二次排序过程，先排序再溢写，进入sortAndSpill()
sortAndSpill();
} catch (Throwable t) {
sortSpillException = t;
} finally {
spillLock.lock();
if (bufend < bufstart) {
bufvoid = kvbuffer.length;
}
kvstart = kvend;
bufstart = bufend;
spillInProgress = false;
}
}
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
} finally {
spillLock.unlock();
spillThreadRunning = false;
}
}
}
11. 进入sortAndSpill()
注： 这里发生了溢写，详细过程可以参考MapReduce shuffle过程详解，我认为这篇文章写得非常详细，值得推荐。
private void sortAndSpill() throws IOException, ClassNotFoundException,
InterruptedException {
//approximate the length of the output file to be the length of the
//buffer + header lengths for the partitions
final long size = distanceTo(bufstart, bufend, bufvoid) +
partitions * APPROX_HEADER_LENGTH;
FSDataOutputStream out = null;
try {
// 创建一个溢写文件
final SpillRecord spillRec = new SpillRecord(partitions);
final Path filename =
mapOutputFile.getSpillFileForWrite(numSpills, size);
out = rfs.create(filename);
//NMETA值为16，mstart是记录起始数
final int mstart = kvend / NMETA;
//kvbuffer是一个环形缓冲区，当end>kvbuffer.size的时候，kvend就从kvbuffer[0]开始存
//所以在这里需要对kvstart和kvend做判断
final int mend = 1 + // kvend is a valid record
(kvstart >= kvend
? kvstart
: kvmeta.capacity() + kvstart) / NMETA;
//开始对记录排序
sorter.sort(MapOutputBuffer.this, mstart, mend, reporter);
int spindex = mstart;
final IndexRecord rec = new IndexRecord();
final InMemValBytes value = new InMemValBytes();
//开始按分区一条一条地写入磁盘
for (int i = 0; i < partitions; ++i) {
IFile.Writer<K, V> writer = null;
try {
long segmentStart = out.getPos();
FSDataOutputStream partitionOut = CryptoUtils.wrapIfNecessary(job, out);
writer = new Writer<K, V>(job, partitionOut, keyClass, valClass, codec,
spilledRecordsCounter);
if (combinerRunner == null) {
// 如果用户没有定义combiner，那么直接发生溢写
DataInputBuffer key = new DataInputBuffer();
while (spindex < mend &&
kvmeta.get(offsetFor(spindex % maxRec) + PARTITION) == i) {
final int kvoff = offsetFor(spindex % maxRec);
int keystart = kvmeta.get(kvoff + KEYSTART);
int valstart = kvmeta.get(kvoff + VALSTART);
key.reset(kvbuffer, keystart, valstart - keystart);
getVBytesForOffset(kvoff, value);
writer.append(key, value);
++spindex;
}
} else {
//有combiner，先进行combiner
int spstart = spindex;
while (spindex < mend &&
kvmeta.get(offsetFor(spindex % maxRec)
+ PARTITION) == i) {
++spindex;
}
if (spstart != spindex) {
combineCollector.setWriter(writer);
RawKeyValueIterator kvIter =
new MRResultIterator(spstart, spindex);
combinerRunner.combine(kvIter, combineCollector);
}
}
// close the writer
writer.close();
// record offsets
rec.startOffset = segmentStart;
rec.rawLength = writer.getRawLength() + CryptoUtils.cryptoPadding(job);
rec.partLength = writer.getCompressedLength() + CryptoUtils.cryptoPadding(job);
spillRec.putIndex(rec, i);
writer = null;
} finally {
if (null != writer) writer.close();
}
}
if (totalIndexCacheMemory >= indexCacheMemoryLimit) {
// create spill index file
Path indexFilename =
mapOutputFile.getSpillIndexFileForWrite(numSpills, partitions
* MAP_OUTPUT_INDEX_RECORD_LENGTH);
spillRec.writeToFile(indexFilename, job);
} else {
indexCacheList.add(spillRec);
totalIndexCacheMemory +=
spillRec.size() * MAP_OUTPUT_INDEX_RECORD_LENGTH;
}
LOG.info("Finished spill " + numSpills);
++numSpills;
} finally {
if (out != null) out.close();
}
}
至此，output的工作已经完成。溢写完成之后，剩下的工作交给ReduceTask。