Hadoop MapReduce之ReduceTask任务执行（四）：排序与合并
Segment的存放有两个地方：内存和磁盘，合并是优先合并内存中的Segment，以便清理出内存供reduce使用，当然也不会全部将内存中的数据刷新的磁盘中，因为这些数据还是要传递给reduce函数的，所以留在内存中的这部分数据会直接作为reduce输入。reduce的输入缓存由参数mapred.job.reduce.input.buffer.percent控制，默认为0，如果reduce端的IO比较繁忙可以调大这个值，以减少IO操作。排序阶段的代码如下：
ReduceTask.java 2421行
/** * Create a RawKeyValueIterator from copied map outputs. All copying * threads have exited, so all of the map outputs are available either in * memory or on disk. We also know that no merges are in progress, so * synchronization is more lax, here. * * The iterator returned must satisfy the following constraints: * 1. Fewer than io.sort.factor files may be sources * 2. No more than maxInMemReduce bytes of map outputs may be resident * in memory when the reduce begins * * If we must perform an intermediate merge to satisfy (1), then we can * keep the excluded outputs from (2) in memory and include them in the * first merge pass. If not, then said outputs must be written to disk * first. */ @SuppressWarnings("unchecked") private RawKeyValueIterator createKVIterator( JobConf job, FileSystem fs, Reporter reporter) throws IOException { // merge config params Class<k> keyClass = (Class<k>)job.getMapOutputKeyClass(); Class<v> valueClass = (Class<v>)job.getMapOutputValueClass(); boolean keepInputs = job.getKeepFailedTaskFiles(); final Path tmpDir = new Path(getTaskID().toString()); final RawComparator<k> comparator = (RawComparator<k>)job.getOutputKeyComparator(); // segments required to vacate memory List<Segment<k v="">> memDiskSegments = new ArrayList<Segment<k v="">>(); long inMemToDiskBytes = 0; if (mapOutputsFilesInMemory.size() > 0) { TaskID mapId = mapOutputsFilesInMemory.get(0).mapId; //获得内存Segment inMemToDiskBytes = createInMemorySegments(memDiskSegments, maxInMemReduce); final int numMemDiskSegments = memDiskSegments.size(); //检查是否需要将内存Segment刷新到磁盘中 if (numMemDiskSegments > 0 && ioSortFactor > mapOutputFilesOnDisk.size()) { // 合并Segment并刷新到磁盘中 final Path outputPath = mapOutputFile.getInputFileForWrite(mapId, inMemToDiskBytes); final RawKeyValueIterator rIter = Merger.merge(job, fs, keyClass, valueClass, memDiskSegments, numMemDiskSegments, tmpDir, comparator, reporter, spilledRecordsCounter, null); final Writer writer = new Writer(job, fs, outputPath, keyClass, valueClass, codec, null); try { Merger.writeFile(rIter, writer, reporter, job); addToMapOutputFilesOnDisk(fs.getFileStatus(outputPath)); } catch (Exception e) { if (null != outputPath) { fs.delete(outputPath, true); } throw new IOException("Final merge failed", e); } finally { if (null != writer) { writer.close(); } } LOG.info("Merged " + numMemDiskSegments + " segments, " + inMemToDiskBytes + " bytes to disk to satisfy " + "reduce memory limit"); inMemToDiskBytes = 0; memDiskSegments.clear(); } else if (inMemToDiskBytes != 0) { LOG.info("Keeping " + numMemDiskSegments + " segments, " + inMemToDiskBytes + " bytes in memory for " + "intermediate, on-disk merge"); } } // 处理磁盘Segment，方式为把所有的磁盘Segment放入diskSegments集合中，在merge操作的时候会放入merge队列中 List<Segment<k v="">> diskSegments = new ArrayList<Segment<k v="">>(); long onDiskBytes = inMemToDiskBytes; Path[] onDisk = getMapFiles(fs, false); for (Path file : onDisk) { onDiskBytes += fs.getFileStatus(file).getLen(); diskSegments.add(new Segment<k v="">(job, fs, file, codec, keepInputs)); } LOG.info("Merging " + onDisk.length + " files, " + onDiskBytes + " bytes from disk"); Collections.sort(diskSegments, new Comparator<Segment<k v="">>() { public int compare(Segment<k v=""> o1, Segment<k v=""> o2) { if (o1.getLength() == o2.getLength()) { return 0; } return o1.getLength() < o2.getLength() ? -1 : 1; } }); // 将内存中的Segment和磁盘中的Segment合并在一起 List<Segment<k v="">> finalSegments = new ArrayList<Segment<k v="">>(); long inMemBytes = createInMemorySegments(finalSegments, 0); LOG.info("Merging " + finalSegments.size() + " segments, " + inMemBytes + " bytes from memory into reduce"); if (0 != onDiskBytes) { final int numInMemSegments = memDiskSegments.size(); //将内存Segment合并至磁盘Segment diskSegments.addAll(0, memDiskSegments); memDiskSegments.clear(); RawKeyValueIterator diskMerge = Merger.merge( job, fs, keyClass, valueClass, codec, diskSegments, ioSortFactor, numInMemSegments, tmpDir, comparator, reporter, false, spilledRecordsCounter, null); diskSegments.clear(); //如果最后一次内存Segment合并为0，那么直接返回磁盘Segment集合 if (0 == finalSegments.size()) { return diskMerge; } finalSegments.add(new Segment<k v="">( new RawKVIteratorReader(diskMerge, onDiskBytes), true)); } return Merger.merge(job, fs, keyClass, valueClass, finalSegments, finalSegments.size(), tmpDir, comparator, reporter, spilledRecordsCounter, null); } </k></k></k></k></k></k></k></k></k></k></k></k></k></v></v></k></k>