这篇文章来自一个读者在面试过程中的一个问题，Hadoop在shuffle过程中使用了一个数据结构-环形缓冲区。
环形队列是在实际编程极为有用的数据结构，它是一个首尾相连的FIFO的数据结构，采用数组的线性空间,数据组织简单。能很快知道队列是否满为空。能以很快速度的来存取数据。 因为有简单高效的原因，甚至在硬件都实现了环形队列。
环形队列广泛用于网络数据收发，和不同程序间数据交换（比如内核与应用程序大量交换数据，从硬件接收大量数据）均使用了环形队列。 环形缓冲区数据结构
Map过程中环形缓冲区是指数据被map处理之后会先放入内存，内存中的这片区域就是环形缓冲区。
环形缓冲区是在MapTask.MapOutputBuffer中定义的，相关的属性如下：
// k/v accounting // 存放meta数据的IntBuffer，都是int entry，占4byte private IntBuffer kvmeta; // metadata overlay on backing store int kvstart; // marks origin of spill metadata int kvend; // marks end of spill metadata int kvindex; // marks end of fully serialized records // 分割meta和key value内容的标识 // meta数据和key value内容都存放在同一个环形缓冲区，所以需要分隔开 int equator; // marks origin of meta/serialization int bufstart; // marks beginning of spill int bufend; // marks beginning of collectable int bufmark; // marks end of record int bufindex; // marks end of collected int bufvoid; // marks the point where we should stop // reading at the end of the buffer // 存放key value的byte数组，单位是byte，注意与kvmeta区分 byte[] kvbuffer; // main output buffer private final byte[] b0 = new byte[0];
// key value在kvbuffer中的地址存放在偏移kvindex的距离 private static final int VALSTART = 0; // val offset in acct private static final int KEYSTART = 1; // key offset in acct // partition信息存在kvmeta中偏移kvindex的距离 private static final int PARTITION = 2; // partition offset in acct private static final int VALLEN = 3; // length of value // 一对key value的meta数据在kvmeta中占用的个数 private static final int NMETA = 4; // num meta ints // 一对key value的meta数据在kvmeta中占用的byte数 private static final int METASIZE = NMETA * 4; // size in bytes
环形缓冲区其实是一个数组，数组中存放着key、value的序列化数据和key、value的元数据信息，key/value的元数据存储的格式是int类型，每个key/value对应一个元数据，元数据由4个int组成，第一个int存放value的起始位置，第二个存放key的起始位置，第三个存放partition，最后一个存放value的长度。
key/value序列化的数据和元数据在环形缓冲区中的存储是由equator分隔的，key/value按照索引递增的方向存储，meta则按照索引递减的方向存储，将其数组抽象为一个环形结构之后，以equator为界，key/value顺时针存储，meta逆时针存储。
初始化
环形缓冲区的结构在MapOutputBuffer.init中创建。
public void init(MapOutputCollector.Context context ) throws IOException, ClassNotFoundException { ... //MAP_SORT_SPILL_PERCENT = mapreduce.map.sort.spill.percent // map 端buffer所占的百分比 //sanity checks final float spillper = job.getFloat(JobContext.MAP_SORT_SPILL_PERCENT, (float)0.8); //IO_SORT_MB = "mapreduce.task.io.sort.mb" // map 端buffer大小 // mapreduce.task.io.sort.mb * mapreduce.map.sort.spill.percent 最好是16的整数倍 final int sortmb = job.getInt(JobContext.IO_SORT_MB, 100); // 所有的spill index 在内存所占的大小的阈值 indexCacheMemoryLimit = job.getInt(JobContext.INDEX_CACHE_MEMORY_LIMIT, INDEX_CACHE_MEMORY_LIMIT_DEFAULT); ... // 排序的实现类，可以自己实现。这里用的是改写的快排 sorter = ReflectionUtils.newInstance(job.getClass("map.sort.class", QuickSort.class, IndexedSorter.class), job); // buffers and accounting // 上面IO_SORT_MB的单位是MB，左移20位将单位转化为byte int maxMemUsage = sortmb << 20; // METASIZE是元数据的长度，元数据有4个int单元，分别为 // VALSTART、KEYSTART、PARTITION、VALLEN，而int为4个byte， // 所以METASIZE长度为16。下面是计算buffer中最多有多少byte来存元数据 maxMemUsage -= maxMemUsage % METASIZE; // 元数据数组 以byte为单位 kvbuffer = new byte[maxMemUsage]; bufvoid = kvbuffer.length; // 将kvbuffer转化为int型的kvmeta 以int为单位，也就是4byte kvmeta = ByteBuffer.wrap(kvbuffer) .order(ByteOrder.nativeOrder()) .asIntBuffer(); // 设置buf和kvmeta的分界线 setEquator(0); bufstart = bufend = bufindex = equator; kvstart = kvend = kvindex; // kvmeta中存放元数据实体的最大个数 maxRec = kvmeta.capacity() / NMETA; // buffer spill时的阈值（不单单是sortmbspillper） // 更加精确的是kvbuffer.lengthspiller softLimit = (int)(kvbuffer.length * spillper); // 此变量较为重要，作为spill的动态衡量标准 bufferRemaining = softLimit; ... // k/v serialization comparator = job.getOutputKeyComparator(); keyClass = (Class<k>)job.getMapOutputKeyClass(); valClass = (Class<v>)job.getMapOutputValueClass(); serializationFactory = new SerializationFactory(job); keySerializer = serializationFactory.getSerializer(keyClass); // 将bb作为key序列化写入的output keySerializer.open(bb); valSerializer = serializationFactory.getSerializer(valClass); // 将bb作为value序列化写入的output valSerializer.open(bb); ... // combiner ... spillInProgress = false; // 最后一次merge时，在有combiner的情况下，超过此阈值才执行combiner minSpillsForCombine = job.getInt(JobContext.MAP_COMBINE_MIN_SPILLS, 3); spillThread.setDaemon(true); spillThread.setName("SpillThread"); spillLock.lock(); try { 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); } }
init是对环形缓冲区进行初始化构造，由mapreduce.task.io.sort.mb决定map中环形缓冲区的大小sortmb，默认是100M。
此缓冲区也用于存放meta，一个meta占用METASIZE(16byte)，则其中用于存放数据的大小是maxMemUsage -= sortmb << 20 % METASIZE(由此可知最好设置sortmb转换为byte之后是16的整数倍)，然后用maxMemUsage初始化kvbuffer字节数组和kvmeta整形数组，最后设置数组的一些标识信息。利用setEquator(0)设置kvbuffer和kvmeta的分界线，初始化的时候以0为分界线，kvindex为aligned - METASIZE + kvbuffer.length，其位置在环形数组中相当于按照逆时针方向减去METASIZE，由kvindex设置kvstart = kvend = kvindex，由equator设置bufstart = bufend = bufindex = equator，还得设置bufvoid = kvbuffer.length，bufvoid用于标识用于存放数据的最大位置。
为了提高效率，当buffer占用达到阈值之后，会进行spill，这个阈值是由bufferRemaining进行检查的，bufferRemaining由softLimit = (int)(kvbuffer.length * spillper); bufferRemaining = softLimit;进行初始化赋值，这里需要注意的是softLimit并不是sortmbspillper，而是kvbuffer.length * spillper，当sortmb << 20是16的整数倍时，才可以认为softLimit是sortmbspillper。
下面是setEquator的代码
// setEquator(0)的代码如下 private void setEquator(int pos) { equator = pos; // set index prior to first entry, aligned at meta boundary // 第一个 entry的末尾位置，即元数据和kv数据的分界线 单位是byte final int aligned = pos - (pos % METASIZE); // Cast one of the operands to long to avoid integer overflow // 元数据中存放数据的起始位置 kvindex = (int) (((long)aligned - METASIZE + kvbuffer.length) % kvbuffer.length) / 4; LOG.info("(EQUATOR) " + pos + " kvi " + kvindex + "(" + (kvindex * 4) + ")"); }
buffer初始化之后的抽象数据结构如下图所示： 环形缓冲区数据结构图
环形缓冲区数据结构图
写入buffer
Map通过NewOutputCollector.write方法调用collector.collect向buffer中写入数据，数据写入之前已在NewOutputCollector.write中对要写入的数据进行逐条分区，下面看下collect
// MapOutputBuffer.collect public synchronized void collect(K key, V value, final int partition ) throws IOException { ... // 新数据collect时，先将剩余的空间减去元数据的长度，之后进行判断 bufferRemaining -= METASIZE; if (bufferRemaining <= 0) { // start spill if the thread is not running and the soft limit has been // reached spillLock.lock(); try { do { // 首次spill时，spillInProgress是false if (!spillInProgress) { // 得到kvindex的byte位置 final int kvbidx = 4 * kvindex; // 得到kvend的byte位置 final int kvbend = 4 * kvend; // serialized, unspilled bytes always lie between kvindex and // bufindex, crossing the equator. Note that any void space // created by a reset must be included in "used" bytes final int bUsed = distanceTo(kvbidx, bufindex); final boolean bufsoftlimit = bUsed >= softLimit; if ((kvbend + METASIZE) % kvbuffer.length != equator - (equator % METASIZE)) { // spill finished, reclaim space resetSpill(); bufferRemaining = Math.min( distanceTo(bufindex, kvbidx) - 2 * METASIZE, softLimit - bUsed) - METASIZE; continue; } else if (bufsoftlimit && kvindex != kvend) { // spill records, if any collected; check latter, as it may // be possible for metadata alignment to hit spill pcnt startSpill(); final int avgRec = (int) (mapOutputByteCounter.getCounter() / mapOutputRecordCounter.getCounter()); // leave at least half the split buffer for serialization data // ensure that kvindex >= bufindex final int distkvi = distanceTo(bufindex, kvbidx); final int newPos = (bufindex + Math.max(2 * METASIZE - 1, Math.min(distkvi / 2, distkvi / (METASIZE + avgRec) * METASIZE))) % kvbuffer.length; setEquator(newPos); bufmark = bufindex = newPos; final int serBound = 4 * kvend; // bytes remaining before the lock must be held and limits // checked is the minimum of three arcs: the metadata space, the // serialization space, and the soft limit bufferRemaining = Math.min( // metadata max distanceTo(bufend, newPos), Math.min( // serialization max distanceTo(newPos, serBound), // soft limit softLimit)) - 2 * METASIZE; } } } while (false); } finally { spillLock.unlock(); } } // 将key value 及元数据信息写入缓冲区 try { // serialize key bytes into buffer int keystart = bufindex; // 将key序列化写入kvbuffer中，并移动bufindex keySerializer.serialize(key); // key所占空间被bufvoid分隔，则移动key， // 将其值放在连续的空间中便于sort时key的对比 if (bufindex < keystart) { // wrapped the key; must make contiguous bb.shiftBufferedKey(); keystart = 0; } // serialize value bytes into buffer final int valstart = bufindex; valSerializer.serialize(value); // It's possible for records to have zero length, i.e. the serializer // will perform no writes. To ensure that the boundary conditions are // checked and that the kvindex invariant is maintained, perform a // zero-length write into the buffer. The logic monitoring this could be // moved into collect, but this is cleaner and inexpensive. For now, it // is acceptable. bb.write(b0, 0, 0);
// the record must be marked after the preceding write, as the metadata
// for this record are not yet written
int valend = bb.markRecord();
mapOutputRecordCounter.increment(1);
mapOutputByteCounter.increment(
distanceTo(keystart, valend, bufvoid));
// write accounting info
kvmeta.put(kvindex + PARTITION, partition);
kvmeta.put(kvindex + KEYSTART, keystart);
kvmeta.put(kvindex + VALSTART, valstart);
kvmeta.put(kvindex + VALLEN, distanceTo(valstart, valend));
// advance kvindex
kvindex = (kvindex - NMETA + kvmeta.capacity()) % kvmeta.capacity();
} catch (MapBufferTooSmallException e) { LOG.info("Record too large for in-memory buffer: " + e.getMessage()); spillSingleRecord(key, value, partition); mapOutputRecordCounter.increment(1); return; } } 每次写入数据时，执行bufferRemaining -= METASIZE之后，检查bufferRemaining，
如果大于0，直接将key/value序列化对和对应的meta写入buffer中，key/value是序列化之后写入的，key/value经过一些列的方法调用Serializer.serialize(key/value) -> WritableSerializer.serialize(key/value) -> BytesWritable.write(dataOut) -> DataOutputStream.write(bytes, 0, size) -> MapOutputBuffer.Buffer.write(b, off, len)，最后由MapOutputBuffer.Buffer.write(b, off, len)将数据写入kvbuffer中，write方法如下：
public void write(byte b[], int off, int len) throws IOException { // must always verify the invariant that at least METASIZE bytes are // available beyond kvindex, even when len == 0 bufferRemaining -= len; if (bufferRemaining <= 0) { // writing these bytes could exhaust available buffer space or fill // the buffer to soft limit. check if spill or blocking are necessary boolean blockwrite = false; spillLock.lock(); try { do { checkSpillException();
final int kvbidx = 4 * kvindex;
final int kvbend = 4 * kvend;
// ser distance to key index
final int distkvi = distanceTo(bufindex, kvbidx);
// ser distance to spill end index
final int distkve = distanceTo(bufindex, kvbend);
// if kvindex is closer than kvend, then a spill is neither in
// progress nor complete and reset since the lock was held. The
// write should block only if there is insufficient space to
// complete the current write, write the metadata for this record,
// and write the metadata for the next record. If kvend is closer,
// then the write should block if there is too little space for
// either the metadata or the current write. Note that collect
// ensures its metadata requirement with a zero-length write
blockwrite = distkvi <= distkve
? distkvi <= len + 2 * METASIZE
: distkve <= len || distanceTo(bufend, kvbidx) < 2 * METASIZE;
if (!spillInProgress) {
if (blockwrite) {
if ((kvbend + METASIZE) % kvbuffer.length !=
equator - (equator % METASIZE)) {
// spill finished, reclaim space
// need to use meta exclusively; zero-len rec & 100% spill
// pcnt would fail
resetSpill(); // resetSpill doesn't move bufindex, kvindex
bufferRemaining = Math.min(
distkvi - 2 * METASIZE,
softLimit - distanceTo(kvbidx, bufindex)) - len;
continue;
}
// we have records we can spill; only spill if blocked
if (kvindex != kvend) {
startSpill();
// Blocked on this write, waiting for the spill just
// initiated to finish. Instead of repositioning the marker
// and copying the partial record, we set the record start
// to be the new equator
setEquator(bufmark);
} else {
// We have no buffered records, and this record is too large
// to write into kvbuffer. We must spill it directly from
// collect
final int size = distanceTo(bufstart, bufindex) + len;
setEquator(0);
bufstart = bufend = bufindex = equator;
kvstart = kvend = kvindex;
bufvoid = kvbuffer.length;
throw new MapBufferTooSmallException(size + " bytes");
}
}
}
if (blockwrite) {
// wait for spill
try {
while (spillInProgress) {
reporter.progress();
spillDone.await();
}
} catch (InterruptedException e) {
throw new IOException(
"Buffer interrupted while waiting for the writer", e);
}
}
} while (blockwrite);
} finally {
spillLock.unlock();
}
} // here, we know that we have sufficient space to write if (bufindex + len > bufvoid) { final int gaplen = bufvoid - bufindex; System.arraycopy(b, off, kvbuffer, bufindex, gaplen); len -= gaplen; off += gaplen; bufindex = 0; } System.arraycopy(b, off, kvbuffer, bufindex, len); bufindex += len; } write方法将key/value写入kvbuffer中，如果bufindex+len超过了bufvoid，则将写入的内容分开存储，将一部分写入bufindex和bufvoid之间，然后重置bufindex，将剩余的部分写入，这里不区分key和value，写入key之后会在collect中判断bufindex < keystart，当bufindex小时，则key被分开存储，执行bb.shiftBufferedKey()，value则直接写入，不用判断是否被分开存储，key不能分开存储是因为要对key进行排序。
这里需要注意的是要写入的数据太长，并且kvinde==kvend，则抛出MapBufferTooSmallException异常，在collect中捕获，将此数据直接spill到磁盘spillSingleRecord，也就是当单条记录过长时，不写buffer，直接写入磁盘。
下面看下bb.shiftBufferedKey()代码
// BlockingBuffer.shiftBufferedKey protected void shiftBufferedKey() throws IOException { // spillLock unnecessary; both kvend and kvindex are current int headbytelen = bufvoid - bufmark; bufvoid = bufmark; final int kvbidx = 4 * kvindex; final int kvbend = 4 * kvend; final int avail = Math.min(distanceTo(0, kvbidx), distanceTo(0, kvbend)); if (bufindex + headbytelen < avail) { System.arraycopy(kvbuffer, 0, kvbuffer, headbytelen, bufindex); System.arraycopy(kvbuffer, bufvoid, kvbuffer, 0, headbytelen); bufindex += headbytelen; bufferRemaining -= kvbuffer.length - bufvoid; } else { byte[] keytmp = new byte[bufindex]; System.arraycopy(kvbuffer, 0, keytmp, 0, bufindex); bufindex = 0; out.write(kvbuffer, bufmark, headbytelen); out.write(keytmp); } } shiftBufferedKey时，判断首部是否有足够的空间存放key，有没有足够的空间，则先将首部的部分key写入keytmp中，然后分两次写入，再次调用Buffer.write，如果有足够的空间，分两次copy，先将首部的部分key复制到headbytelen的位置，然后将末尾的部分key复制到首部，移动bufindex，重置bufferRemaining的值。
key/value写入之后，继续写入元数据信息并重置kvindex的值。
spill
一次写入buffer结束，当写入数据比较多，bufferRemaining小于等于0时，准备进行spill，首次spill，spillInProgress为false，此时查看bUsed = distanceTo(kvbidx, bufindex)，此时bUsed >= softLimit 并且 (kvbend + METASIZE) % kvbuffer.length == equator - (equator % METASIZE)，则进行spill，调用startSpill
private void startSpill() { // 元数据的边界赋值 kvend = (kvindex + NMETA) % kvmeta.capacity(); // key/value的边界赋值 bufend = bufmark; // 设置spill运行标识 spillInProgress = true; ... // 利用重入锁，对spill线程进行唤醒 spillReady.signal(); } startSpill唤醒spill线程之后，进程spill操作，但此时map向buffer的写入操作并没有阻塞，需要重新边界equator和bufferRemaining的值，先来看下equator和bufferRemaining值的设定：
// 根据已经写入的kv得出每个record的平均长度 final int avgRec = (int) (mapOutputByteCounter.getCounter() / mapOutputRecordCounter.getCounter()); // leave at least half the split buffer for serialization data // ensure that kvindex >= bufindex // 得到空余空间的大小 final int distkvi = distanceTo(bufindex, kvbidx); // 得出新equator的位置 final int newPos = (bufindex + Math.max(2 * METASIZE - 1, Math.min(distkvi / 2, distkvi / (METASIZE + avgRec) * METASIZE))) % kvbuffer.length; setEquator(newPos); bufmark = bufindex = newPos; final int serBound = 4 * kvend; // bytes remaining before the lock must be held and limits // checked is the minimum of three arcs: the metadata space, the // serialization space, and the soft limit bufferRemaining = Math.min( // metadata max distanceTo(bufend, newPos), Math.min( // serialization max distanceTo(newPos, serBound), // soft limit softLimit)) - 2 * METASIZE;
因为equator是kvbuffer和kvmeta的分界线，为了更多的空间存储kv，则最多拿出distkvi的一半来存储meta，并且利用avgRec估算distkvi能存放多少个record和meta对，根据record和meta对的个数估算meta所占空间的大小，从distkvi/2和meta所占空间的大小中取最小值，又因为distkvi中最少得存放一个meta，所占空间为METASIZE，在选取kvindex时需要求aligned，aligned最多为METASIZE-1，总和上述因素，最终选取equator为(bufindex + Math.max(2 * METASIZE - 1, Math.min(distkvi / 2, distkvi / (METASIZE + avgRec) * METASIZE)))。equator选取之后，设置bufmark = bufindex = newPos和kvindex，但此时并不设置bufstart、bufend和kvstart、kvend，因为这几个值要用来表示spill数据的边界。
spill之后，可用的空间减少了，则控制spill的bufferRemaining也应该重新设置，bufferRemaining取三个值的最小值减去2METASIZE，三个值分别是meta可用占用的空间distanceTo(bufend, newPos),kv可用空间distanceTo(newPos, serBound)和softLimit。这里为什么要减去2METASIZE，一个是spill之前kvend到kvindex的距离，另一个是当时的kvindex空间？？？？此时，已有一个record要写入buffer，需要从bufferRemaining中减去当前record的元数据占用的空间，即减去METASIZE，另一个METASIZE是在计算equator时，没有包括kvindex到kvend(spill之前)的这段METASIZE，所以要减去这个METASIZE。
接下来解析下SpillThread线程，查看其run方法：
public void run() { spillLock.lock(); spillThreadRunning = true; try { while (true) { spillDone.signal(); // 判断是否在spill，false则挂起SpillThread线程，等待唤醒 while (!spillInProgress) { spillReady.await(); } try { spillLock.unlock(); // 唤醒之后，进行排序和溢写到磁盘 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; } }
run中主要是sortAndSpill，
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 { // create spill file // 用来存储index文件 final SpillRecord spillRec = new SpillRecord(partitions); // 创建写入磁盘的spill文件 final Path filename = mapOutputFile.getSpillFileForWrite(numSpills, size); // 打开文件流 out = rfs.create(filename); // kvend/4 是截止到当前位置能存放多少个元数据实体 final int mstart = kvend / NMETA; // kvstart 处能存放多少个元数据实体 // 元数据则在mstart和mend之间，(mstart - mend)则是元数据的个数 final int mend = 1 + // kvend is a valid record (kvstart >= kvend ? kvstart : kvmeta.capacity() + kvstart) / NMETA; // 排序 只对元数据进行排序,只调整元数据在kvmeta中的顺序 // 排序规则是MapOutputBuffer.compare， // 先对partition进行排序其次对key值排序 sorter.sort(MapOutputBuffer.this, mstart, mend, reporter); int spindex = mstart; // 创建rec，用于存放该分区在数据文件中的信息 final IndexRecord rec = new IndexRecord(); final InMemValBytes value = new InMemValBytes(); for (int i = 0; i < partitions; ++i) { // 临时文件是IFile格式的 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); // 往磁盘写数据时先判断是否有combiner if (combinerRunner == null) { // spill directly DataInputBuffer key = new DataInputBuffer(); // 写入相同partition的数据 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 { int spstart = spindex; while (spindex < mend && kvmeta.get(offsetFor(spindex % maxRec) + PARTITION) == i) { ++spindex; } // Note: we would like to avoid the combiner if we've fewer // than some threshold of records for a partition if (spstart != spindex) { combineCollector.setWriter(writer); RawKeyValueIterator kvIter = new MRResultIterator(spstart, spindex); combinerRunner.combine(kvIter, combineCollector); } }
// close the writer
writer.close();
// record offsets
// 记录当前partition i的信息写入索文件rec中
rec.startOffset = segmentStart;
rec.rawLength = writer.getRawLength() + CryptoUtils.cryptoPadding(job);
rec.partLength = writer.getCompressedLength() + CryptoUtils.cryptoPadding(job);
// spillRec中存放了spill中partition的信息，便于后续堆排序时，取出partition相关的数据进行排序
spillRec.putIndex(rec, i);
writer = null;
} finally {
if (null != writer) writer.close();
}
}
// 判断内存中的index文件是否超出阈值，超出则将index文件写入磁盘
// 当超出阈值时只是把当前index和之后的index写入磁盘
if (totalIndexCacheMemory >= indexCacheMemoryLimit) {
// create spill index file
// 创建index文件
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(); } } sortAndSpill中，有mstart和mend得到一共有多少条record需要spill到磁盘，调用sorter.sort对meta进行排序，先对partition进行排序，然后按key排序，排序的结果只调整meta的顺序。
排序之后，判断是否有combiner，没有则直接将record写入磁盘，写入时是一个partition一个IndexRecord，如果有combiner，则将该partition的record写入kvIter，然后调用combinerRunner.combine执行combiner。
写入磁盘之后，将spillx.out对应的spillRec放入内存indexCacheList.add(spillRec)，如果所占内存totalIndexCacheMemory超过了indexCacheMemoryLimit，则创建index文件，将此次及以后的spillRec写入index文件存入磁盘。
最后spill次数递增。sortAndSpill结束之后，回到run方法中，执行finally中的代码，对kvstart和bufstart赋值，kvstart = kvend，bufstart = bufend，设置spillInProgress的状态为false。
在spill的同时，map往buffer的写操作并没有停止，依然在调用collect，再次回到collect方法中，
// MapOutputBuffer.collect public synchronized void collect(K key, V value, final int partition ) throws IOException { ... // 新数据collect时，先将剩余的空间减去元数据的长度，之后进行判断 bufferRemaining -= METASIZE; if (bufferRemaining <= 0) { // start spill if the thread is not running and the soft limit has been // reached spillLock.lock(); try { do { // 首次spill时，spillInProgress是false if (!spillInProgress) { // 得到kvindex的byte位置 final int kvbidx = 4 * kvindex; // 得到kvend的byte位置 final int kvbend = 4 * kvend; // serialized, unspilled bytes always lie between kvindex and // bufindex, crossing the equator. Note that any void space // created by a reset must be included in "used" bytes final int bUsed = distanceTo(kvbidx, bufindex); final boolean bufsoftlimit = bUsed >= softLimit; if ((kvbend + METASIZE) % kvbuffer.length != equator - (equator % METASIZE)) { // spill finished, reclaim space resetSpill(); bufferRemaining = Math.min( distanceTo(bufindex, kvbidx) - 2 * METASIZE, softLimit - bUsed) - METASIZE; continue; } else if (bufsoftlimit && kvindex != kvend) { ... } } } while (false); } finally { spillLock.unlock(); } } ... } 有新的record需要写入buffer时，判断bufferRemaining -= METASIZE，此时的bufferRemaining是在开始spill时被重置过的(此时的bufferRemaining应该比初始的softLimit要小)，当bufferRemaining小于等最后一个METASIZE是当前record进入collect之后bufferRemaining减去的那个METASIZE。
于0时，进入if，此时spillInProgress的状态为false，进入if (!spillInProgress)，startSpill时对kvend和bufend进行了重置，则此时(kvbend + METASIZE) % kvbuffer.length != equator - (equator % METASIZE)，调用resetSpill()，将kvstart、kvend和bufstart、bufend设置为上次startSpill时的位置。此时buffer已将一部分内容写入磁盘，有大量空余的空间，则对bufferRemaining进行重置，此次不spill。
bufferRemaining取值为Math.min(distanceTo(bufindex, kvbidx) - 2 * METASIZE, softLimit - bUsed) - METASIZE
private void resetSpill() { final int e = equator; bufstart = bufend = e; final int aligned = e - (e % METASIZE); // set start/end to point to first meta record // Cast one of the operands to long to avoid integer overflow kvstart = kvend = (int) (((long)aligned - METASIZE + kvbuffer.length) % kvbuffer.length) / 4; LOG.info("(RESET) equator " + e + " kv " + kvstart + "(" + (kvstart * 4) + ")" + " kvi " + kvindex + "(" + (kvindex * 4) + ")"); }
当bufferRemaining再次小于等于0时，进行spill，这以后就都是套路了。环形缓冲区分析到此结束。
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来源：oschina
链接：https://my.oschina.net/wangzhiwubigdata/blog/3164649