圆形缓冲区-MapReduce中的

本文主要是介绍圆形缓冲区-MapReduce中的，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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4byteprivate IntBuffer kvmeta; // metadata overlay on backing storeint kvstart; // marks origin of spill metadataint kvend; // marks end of spill metadataint 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/serializationint bufstart; // marks beginning of spillint bufend; // marks beginning of collectableint bufmark; // marks end of recordint bufindex; // marks end of collectedint 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 bufferprivate 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 acctprivate 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 acctprivate 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 {...//MAPSORTSPILL_PERCENT = mapreduce.map.sort.spill.percent// map 端buffer所占的百分比//sanity checksfinal float spillper =job.getFloat(JobContext.MAPSORTSPILL_PERCENT, (float)0.8);//IOSORTMB = "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.IOSORTMB, 100);// 所有的spill index 在内存所占的大小的阈值indexCacheMemoryLimit = job.getInt(JobContext.INDEXCACHEMEMORY_LIMIT,INDEXCACHEMEMORYLIMITDEFAULT);...// 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IOSORTMB的单位是MB，左移20位将单位转化为byteint 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4bytekvmeta = 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sortmb*spillper）// 更加精确的是kvbuffer.length*spillersoftLimit = (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 serializationcomparator = job.getOutputKeyComparator();keyClass = (Class )job.getMapOutputKeyClass();valClass = (Class )job.getMapOutputValueClass();serializationFactory = new SerializationFactory(job);keySerializer = serializationFactory.getSerializer(keyClass);// 将bb作为key序列化写入的outputkeySerializer.open(bb);valSerializer = serializationFactory.getSerializer(valClass);// 将bb作为value序列化写入的outputvalSerializer.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combinerminSpillsForCombine = job.getInt(JobContext.MAP COMBINEMIN_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bytefinal 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.collectpublic 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// reachedspillLock.lock();try {do {// 首次spill时，spillInProgress是falseif (!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" bytesfinal int bUsed = distanceTo(kvbidx, bufindex);final boolean bufsoftlimit = bUsed >= softLimit;if ((kvbend METASIZE) % kvbuffer.length !=equator - (equator % METASIZE)) {// spill finished, reclaim spaceresetSpill();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 pcntstartSpill();final int avgRec = (int)(mapOutputByteCounter.getCounter() /mapOutputRecordCounter.getCounter());// leave at least half the split buffer for serialization data// ensure that kvindex >= bufindexfinal 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 limitbufferRemaining = Math.min(// metadata maxdistanceTo(bufend, newPos),Math.min(// serialization maxdistanceTo(newPos, serBound),// soft limitsoftLimit)) - 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 bufferint keystart = bufindex;// 将key序列化写入kvbuffer中，并移动bufindexkeySerializer.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 contiguousbb.shiftBufferedKey();keystart = 0;}// serialize value bytes into bufferfinal 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 writtenint valend = bb.markRecord();

    mapOutputRecordCounter.increment(1);mapOutputByteCounter.increment(distanceTo(keystart, valend, bufvoid));

    // write accounting infokvmeta.put(kvindex   PARTITION, partition);kvmeta.put(kvindex   KEYSTART, keystart);kvmeta.put(kvindex   VALSTART, valstart);kvmeta.put(kvindex   VALLEN, distanceTo(valstart, valend));// advance kvindexkvindex = (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 == 0bufferRemaining -= 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 necessaryboolean blockwrite = false;spillLock.lock();try {do {checkSpillException();

        final int kvbidx = 4 * kvindex;final int kvbend = 4 * kvend;// ser distance to key indexfinal int distkvi = distanceTo(bufindex, kvbidx);// ser distance to spill end indexfinal 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 writeblockwrite = 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 failresetSpill(); // resetSpill doesn't move bufindex, kvindexbufferRemaining = Math.min(distkvi - 2 * METASIZE,softLimit - distanceTo(kvbidx, bufindex)) - len;continue;}// we have records we can spill; only spill if blockedif (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 equatorsetEquator(bufmark);} else {// We have no buffered records, and this record is too large// to write into kvbuffer. We must spill it directly from// collectfinal 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 spilltry {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 writeif (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.shiftBufferedKeyprotected void shiftBufferedKey() throws IOException {// spillLock unnecessary; both kvend and kvindex are currentint 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 limitbufferRemaining = Math.min(// metadata maxdistanceTo(bufend, newPos),Math.min(// serialization maxdistanceTo(newPos, serBound),// soft limitsoftLimit)) - 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 partitionsfinal 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 writer = null;try {long segmentStart = out.getPos();FSDataOutputStream partitionOut = CryptoUtils.wrapIfNecessary(job, out);writer = new Writer (job, partitionOut, keyClass, valClass, codec,spilledRecordsCounter);// 往磁盘写数据时先判断是否有combinerif (combinerRunner == null) {// spill directlyDataInputBuffer 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 writerwriter.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，

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 overflowkvstart = 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。

声明：本号所有文章除特殊注明，都为原创，公众号读者拥有优先阅读权，未经作者本人允许不得转载，否则追究侵权责任。

关注我的公众号，后台回复【JAVAPDF】获取200页面试题！5万人关注的大数据成神之路，不来了解一下吗？5万人关注的大数据成神之路，真的不来了解一下吗？5万人关注的大数据成神之路，确定真的不来了解一下吗？