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()
调用分区组件Partitionner计算当前这组key-value的分区号
③将当前key-value收集到MapOutPutBuffer中
如果超过溢写的阀值，在后台启动溢写线程，来进行溢写！
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！
排序并不在内存中移动key-value，而是记录排序后key-value的有序索引！
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
如果没有定义Combiner，直接溢写！
如果定义了Combiner，使用CombinerRunner.conbine（）对key-value处理后再次溢写！
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！
在合并为一个总的文件前，会执行归并排序，保证合并后的文件，各个分区也是有序的！
如果定义了Conbiner，Conbiner会再次运行（前提是溢写的文件个数大于3）！
否则，就直接溢写！
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：
①每次溢写前，如果指定了Combiner，会运行
②将多个溢写片段，进行合并为一个最终的文件时，也会运行Combiner，前提是片段数>=3
ReduceTask:
③reduceTask在运行时，需要启动shuffle进程拷贝MapTask产生的数据！
数据在copy后，进入shuffle工作的内存，在内存中进行merge和sort！
数据过多，内部不够，将部分数据溢写在磁盘！
如果有溢写的过程，那么combiner会再次运行！
①一定会运行，②，③需要条件！