1. 收集阶段
在Mapper中，调用context.write(key,value)实际是调用代理NewOutPutCollector的wirte方法
public void write(KEYOUT key, VALUEOUT value
) throws IOException, InterruptedException {
output.write(key, value);
}
实际调用的是MapOutPutBuffer的collect（），在进行收集前，调用partitioner来计算每个key-value的分区号
@Override
public void write(K key, V value) throws IOException, InterruptedException {
collector.collect(key, value,
partitioner.getPartition(key, value, partitions));
}
2. NewOutPutCollector对象的创建
@SuppressWarnings("unchecked")
NewOutputCollector(org.apache.hadoop.mapreduce.JobContext jobContext,
JobConf job,
TaskUmbilicalProtocol umbilical,
TaskReporter reporter
) throws IOException, ClassNotFoundException {
// 创建实际用来收集key-value的缓存区对象
collector = createSortingCollector(job, reporter);
// 获取总的分区个数
partitions = jobContext.getNumReduceTasks();
if (partitions > 1) {
partitioner = (org.apache.hadoop.mapreduce.Partitioner<K,V>)
ReflectionUtils.newInstance(jobContext.getPartitionerClass(), job);
} else {
// 默认情况，直接创建一个匿名内部类，所有的key-value都分配到0号分区
partitioner = new org.apache.hadoop.mapreduce.Partitioner<K,V>() {
@Override
public int getPartition(K key, V value, int numPartitions) {
return partitions - 1;
}
};
}
}
3. 创建环形缓冲区对象
@SuppressWarnings("unchecked")
private <KEY, VALUE> MapOutputCollector<KEY, VALUE>
createSortingCollector(JobConf job, TaskReporter reporter)
throws IOException, ClassNotFoundException {
MapOutputCollector.Context context =
new MapOutputCollector.Context(this, job, reporter);
// 从当前Job的配置中，获取mapreduce.job.map.output.collector.class，如果没有设置，使用MapOutputBuffer.class
Class<?>[] collectorClasses = job.getClasses(
JobContext.MAP_OUTPUT_COLLECTOR_CLASS_ATTR, MapOutputBuffer.class);
int remainingCollectors = collectorClasses.length;
Exception lastException = null;
for (Class clazz : collectorClasses) {
try {
if (!MapOutputCollector.class.isAssignableFrom(clazz)) {
throw new IOException("Invalid output collector class: " + clazz.getName() +
" (does not implement MapOutputCollector)");
}
Class<? extends MapOutputCollector> subclazz =
clazz.asSubclass(MapOutputCollector.class);
LOG.debug("Trying map output collector class: " + subclazz.getName());
// 创建缓冲区对象
MapOutputCollector<KEY, VALUE> collector =
ReflectionUtils.newInstance(subclazz, job);
// 创建完缓冲区对象后，执行初始化
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)";
}
lastException = e;
LOG.warn(msg, e);
}
}
throw new IOException("Initialization of all the collectors failed. " +
"Error in last collector was :" + lastException.getMessage(), lastException);
}
3. MapOutPutBuffer的初始化 环形缓冲区对象
@SuppressWarnings("unchecked")
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);
// 获取分区总个数，取决于ReduceTask的数量
partitions = job.getNumReduceTasks();
rfs = ((LocalFileSystem)FileSystem.getLocal(job)).getRaw();
//sanity checks
// 从当前配置中，获取mapreduce.map.sort.spill.percent，如果没有设置，就是0.8
final float spillper =
job.getFloat(JobContext.MAP_SORT_SPILL_PERCENT, (float)0.8);
// 获取mapreduce.task.io.sort.mb，如果没设置，就是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);
}
// 在溢写前，对key-value排序，采用的排序器，使用快速排序，只排索引
sorter = ReflectionUtils.newInstance(job.getClass("map.sort.class",
QuickSort.class, IndexedSorter.class), job);
// buffers and accounting
int maxMemUsage = sortmb << 20;
maxMemUsage -= maxMemUsage % METASIZE;
// 存放key-value
kvbuffer = new byte[maxMemUsage];
bufvoid = kvbuffer.length;
// 存储key-value的属性信息，分区号，索引等
kvmeta = ByteBuffer.wrap(kvbuffer)
.order(ByteOrder.nativeOrder())
.asIntBuffer();
setEquator(0);
bufstart = bufend = bufindex = equator;
kvstart = kvend = kvindex;
maxRec = kvmeta.capacity() / NMETA;
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);
// k/v serialization
// 获取快速排序的Key的比较器，排序只按照key进行排序！
comparator = job.getOutputKeyComparator();
// 获取key-value的序列化器
keyClass = (Class<K>)job.getMapOutputKeyClass();
valClass = (Class<V>)job.getMapOutputValueClass();
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);
// 溢写到磁盘，可以使用一个压缩格式！ 获取指定的压缩编解码器
// compression
if (job.getCompressMapOutput()) {
Class<? extends CompressionCodec> codecClass =
job.getMapOutputCompressorClass(DefaultCodec.class);
codec = ReflectionUtils.newInstance(codecClass, job);
} else {
codec = null;
}
// 获取Combiner组件
// combiner
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);
// 设置溢写线程在后台运行，溢写是在后台运行另外一个溢写线程！和收集是两个线程！
spillThread.setDaemon(true);
spillThread.setName("SpillThread");
spillLock.lock();
try {
// 启动线程
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);
}
}
4. Paritionner的获取
从配置中读取mapreduce.job.partitioner.class，如果没有指定，采用HashPartitioner.class
如果reduceTask > 1， 还没有设置分区组件，使用HashPartitioner
@SuppressWarnings("unchecked")
public Class<? extends Partitioner<?,?>> getPartitionerClass()
throws ClassNotFoundException {
return (Class<? extends Partitioner<?,?>>)
conf.getClass(PARTITIONER_CLASS_ATTR, HashPartitioner.class);
}
public class HashPartitioner<K, V> extends Partitioner<K, V> {
/** Use {@link Object#hashCode()} to partition. **/
public int getPartition(K key, V value,
int numReduceTasks) {
return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
}
}
分区号的限制：0 <= 分区号 < 总的分区数(reduceTask的个数)
if (partition < 0 || partition >= partitions) {
throw new IOException("Illegal partition for " + key + " (" +
partition + ")");
}
5.MapTask shuffle的流程
①在map()调用context.write()
②调用MapoutPutBuffer的collect()
③将当前key-value收集到MapOutPutBuffer中
④溢写前，先根据分区号，将相同分区号的key-value，采用快速排序算法，进行排序！
⑤ 开始溢写，按照排序后有序的索引，将文件写入到一个临时的溢写文件中
⑥多次溢写后，每次溢写都会产生一个临时文件
⑦最后，执行一次flush()，将剩余的key-value进行溢写
⑧MergeParts: 将多次溢写的结果，保存为一个总的文件！
⑨最终保证生成一个最终的文件，这个文件根据总区号，分为若干部分，每个部分的key-value都已经排好序，等待ReduceTask来拷贝相应分区的数据
6. Combiner
combiner其实就是Reducer类型：
Class<? extends Reducer<K,V,K,V>> cls =
(Class<? extends Reducer<K,V,K,V>>) job.getCombinerClass();
Combiner的运行时机：
MapTask：
ReduceTask:
③reduceTask在运行时，需要启动shuffle进程拷贝MapTask产生的数据！
①一定会运行，②，③需要条件！
总结
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