一、简介

BlockCache是HBase中的一个重要特性，相比于写数据时缓存为Memstore，读数据时的缓存则为BlockCache。
      LruBlockCache是HBase中BlockCache的默认实现，它采用严格的LRU算法来淘汰Block。

二、缓存级别

目前有三种缓存级别，定义在BlockPriority中，如下：

public enum BlockPriority {/*** Accessed a single time (used for scan-resistance)*/SINGLE,/*** Accessed multiple times*/MULTI,/*** Block from in-memory store*/MEMORY
}

1、SINGLE：主要用于scan等，避免大量的这种一次的访问导致缓存替换；

2、MULTI：多次缓存；

3、MEMORY：常驻缓存的，比如meta信息等。

三、缓存实现分析

LruBlockCache缓存的实现在方法cacheBlock()中，实现逻辑如下：

1、首先需要判断需要缓存的数据大小是否超过最大块大小，按照2%的频率记录warn类型log并返回；

2、从缓存map中根据cacheKey尝试获取已缓存数据块cb；

3、如果已经缓存过，比对下内容，如果内容不一样，抛出异常，否则记录warn类型log并返回；

4、获取当前缓存大小currentSize，获取可以接受的缓存大小currentAcceptableSize，计算硬性限制大小hardLimitSize；

5、如果当前大小超过硬性限制，当回收没在执行时，执行回收并返回，否则直接返回；

6、利用cacheKey、数据buf等构造Lru缓存数据块实例cb；

7、将cb放置入map缓存中；

8、元素个数原子性增1；

9、如果新大小超过当前可以接受的大小，且未执行回收过程中，执行内存回收。

详细代码如下，可自行阅读分析：

  // BlockCache implementation/*** Cache the block with the specified name and buffer.* <p>* It is assumed this will NOT be called on an already cached block. In rare cases (HBASE-8547)* this can happen, for which we compare the buffer contents.* @param cacheKey block's cache key* @param buf block buffer* @param inMemory if block is in-memory* @param cacheDataInL1*/@Overridepublic void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory,final boolean cacheDataInL1) {// 首先需要判断需要缓存的数据大小是否超过最大块大小if (buf.heapSize() > maxBlockSize) {// If there are a lot of blocks that are too// big this can make the logs way too noisy.// So we log 2%if (stats.failInsert() % 50 == 0) {LOG.warn("Trying to cache too large a block "+ cacheKey.getHfileName() + " @ "+ cacheKey.getOffset()+ " is " + buf.heapSize()+ " which is larger than " + maxBlockSize);}return;}// 从缓存map中根据cacheKey尝试获取已缓存数据块LruCachedBlock cb = map.get(cacheKey);if (cb != null) {// 如果已经缓存过// compare the contents, if they are not equal, we are in big troubleif (compare(buf, cb.getBuffer()) != 0) {// 比对缓存内容，如果不相等，抛出异常，否则记录warn日志throw new RuntimeException("Cached block contents differ, which should not have happened."+ "cacheKey:" + cacheKey);}String msg = "Cached an already cached block: " + cacheKey + " cb:" + cb.getCacheKey();msg += ". This is harmless and can happen in rare cases (see HBASE-8547)";LOG.warn(msg);return;}// 获取当前缓存大小long currentSize = size.get();// 获取可以接受的缓存大小long currentAcceptableSize = acceptableSize();// 计算硬性限制大小long hardLimitSize = (long) (hardCapacityLimitFactor * currentAcceptableSize);if (currentSize >= hardLimitSize) {// 如果当前大小超过硬性限制，当回收没在执行时，执行回收并返回stats.failInsert();if (LOG.isTraceEnabled()) {LOG.trace("LruBlockCache current size " + StringUtils.byteDesc(currentSize)+ " has exceeded acceptable size " + StringUtils.byteDesc(currentAcceptableSize) + "  too many."+ " the hard limit size is " + StringUtils.byteDesc(hardLimitSize) + ", failed to put cacheKey:"+ cacheKey + " into LruBlockCache.");}if (!evictionInProgress) {// 当回收没在执行时，执行回收并返回runEviction();}return;}// 利用cacheKey、数据buf等构造Lru缓存数据块实例cb = new LruCachedBlock(cacheKey, buf, count.incrementAndGet(), inMemory);long newSize = updateSizeMetrics(cb, false);// 放置入map缓存中map.put(cacheKey, cb);// 元素个数原子性增1long val = elements.incrementAndGet();if (LOG.isTraceEnabled()) {long size = map.size();assertCounterSanity(size, val);}// 如果新大小超过当前可以接受的大小，且未执行回收过程中if (newSize > currentAcceptableSize && !evictionInProgress) {runEviction();// 执行内存回收}}

四、淘汰缓存实现分析

淘汰缓存的实现方式有两种：

1、第一种是在主线程中执行缓存淘汰；

2、第二种是在一个专门的淘汰线程中通过持有对外部类LruBlockCache的弱引用WeakReference来执行缓存淘汰。

应用那种方式，取决于构造函数中的boolean参数evictionThread，如下：

    if(evictionThread) {this.evictionThread = new EvictionThread(this);this.evictionThread.start(); // FindBugs SC_START_IN_CTOR} else {this.evictionThread = null;}

而在执行淘汰缓存的runEviction()方法中，有如下判断：

  /*** Multi-threaded call to run the eviction process.* 多线程调用以执行回收过程*/private void runEviction() {if(evictionThread == null) {// 如果未指定回收线程evict();} else {// 如果执行了回收线程evictionThread.evict();}}

而EvictionThread的evict()实现如下：

    @edu.umd.cs.findbugs.annotations.SuppressWarnings(value="NN_NAKED_NOTIFY",justification="This is what we want")public void evict() {synchronized(this) {this.notifyAll();}}

通过synchronized获取EvictionThread线程的对象锁，然后主线程通过回收线程对象的notifyAll唤醒EvictionThread线程，那么这个线程是何时wait的呢？答案就在其run()方法中，notifyAll()之后，线程run()方法得以继续执行：

    @Overridepublic void run() {enteringRun = true;while (this.go) {synchronized(this) {try {this.wait(1000 * 10/*Don't wait for ever*/);} catch(InterruptedException e) {LOG.warn("Interrupted eviction thread ", e);Thread.currentThread().interrupt();}}LruBlockCache cache = this.cache.get();if (cache == null) break;cache.evict();}}

线程会wait10s，放弃对象锁，在notifyAll()后，继续执行后面的淘汰流程，即：

  /*** Eviction method.*/void evict() {// Ensure only one eviction at a time// 通过可重入互斥锁ReentrantLock确保同一时刻只有一个回收在执行if(!evictionLock.tryLock()) return;try {// 标志位，是否正在进行回收过程evictionInProgress = true;// 当前缓存大小long currentSize = this.size.get();// 计算应该释放的缓冲大小bytesToFreelong bytesToFree = currentSize - minSize();if (LOG.isTraceEnabled()) {LOG.trace("Block cache LRU eviction started; Attempting to free " +StringUtils.byteDesc(bytesToFree) + " of total=" +StringUtils.byteDesc(currentSize));}// 如果需要回收的大小小于等于0，直接返回if(bytesToFree <= 0) return;// Instantiate priority buckets// 实例化优先级队列：single、multi、memoryBlockBucket bucketSingle = new BlockBucket("single", bytesToFree, blockSize,singleSize());BlockBucket bucketMulti = new BlockBucket("multi", bytesToFree, blockSize,multiSize());BlockBucket bucketMemory = new BlockBucket("memory", bytesToFree, blockSize,memorySize());// Scan entire map putting into appropriate buckets// 扫描缓存，分别加入上述三个优先级队列for(LruCachedBlock cachedBlock : map.values()) {switch(cachedBlock.getPriority()) {case SINGLE: {bucketSingle.add(cachedBlock);break;}case MULTI: {bucketMulti.add(cachedBlock);break;}case MEMORY: {bucketMemory.add(cachedBlock);break;}}}long bytesFreed = 0;if (forceInMemory || memoryFactor > 0.999f) {// 如果memoryFactor或者InMemory缓存超过99.9%，long s = bucketSingle.totalSize();long m = bucketMulti.totalSize();if (bytesToFree > (s + m)) {// 如果需要回收的缓存超过则全部回收Single、Multi中的缓存大小和，则全部回收Single、Multi中的缓存，剩余的则从InMemory中回收// this means we need to evict blocks in memory bucket to make room,// so the single and multi buckets will be emptiedbytesFreed = bucketSingle.free(s);bytesFreed += bucketMulti.free(m);if (LOG.isTraceEnabled()) {LOG.trace("freed " + StringUtils.byteDesc(bytesFreed) +" from single and multi buckets");}// 剩余的则从InMemory中回收bytesFreed += bucketMemory.free(bytesToFree - bytesFreed);if (LOG.isTraceEnabled()) {LOG.trace("freed " + StringUtils.byteDesc(bytesFreed) +" total from all three buckets ");}} else {// 否则，不需要从InMemory中回收，按照如下策略回收Single、Multi中的缓存：尝试让single-bucket和multi-bucket的比例为1:2// this means no need to evict block in memory bucket,// and we try best to make the ratio between single-bucket and// multi-bucket is 1:2long bytesRemain = s + m - bytesToFree;if (3 * s <= bytesRemain) {// single-bucket足够小，从multi-bucket中回收// single-bucket is small enough that no eviction happens for it// hence all eviction goes from multi-bucketbytesFreed = bucketMulti.free(bytesToFree);} else if (3 * m <= 2 * bytesRemain) {// multi-bucket足够下，从single-bucket中回收// multi-bucket is small enough that no eviction happens for it// hence all eviction goes from single-bucketbytesFreed = bucketSingle.free(bytesToFree);} else {// single-bucket和multi-bucket中都回收，且尽量满足回收后比例为1:2// both buckets need to evict some blocksbytesFreed = bucketSingle.free(s - bytesRemain / 3);if (bytesFreed < bytesToFree) {bytesFreed += bucketMulti.free(bytesToFree - bytesFreed);}}}} else {// 否则，从三个队列中循环回收PriorityQueue<BlockBucket> bucketQueue =new PriorityQueue<BlockBucket>(3);bucketQueue.add(bucketSingle);bucketQueue.add(bucketMulti);bucketQueue.add(bucketMemory);int remainingBuckets = 3;BlockBucket bucket;while((bucket = bucketQueue.poll()) != null) {long overflow = bucket.overflow();if(overflow > 0) {long bucketBytesToFree = Math.min(overflow,(bytesToFree - bytesFreed) / remainingBuckets);bytesFreed += bucket.free(bucketBytesToFree);}remainingBuckets--;}}if (LOG.isTraceEnabled()) {long single = bucketSingle.totalSize();long multi = bucketMulti.totalSize();long memory = bucketMemory.totalSize();LOG.trace("Block cache LRU eviction completed; " +"freed=" + StringUtils.byteDesc(bytesFreed) + ", " +"total=" + StringUtils.byteDesc(this.size.get()) + ", " +"single=" + StringUtils.byteDesc(single) + ", " +"multi=" + StringUtils.byteDesc(multi) + ", " +"memory=" + StringUtils.byteDesc(memory));}} finally {// 重置标志位，释放锁等stats.evict();evictionInProgress = false;evictionLock.unlock();}}

逻辑比较清晰，如下：

1、通过可重入互斥锁ReentrantLock确保同一时刻只有一个回收在执行；

2、设置标志位evictionInProgress，是否正在进行回收过程为true；

3、获取当前缓存大小currentSize；

4、计算应该释放的缓冲大小bytesToFree：currentSize - minSize()；

5、如果需要回收的大小小于等于0，直接返回；

6、实例化优先级队列：single、multi、memory；

7、扫描缓存，分别加入上述三个优先级队列；

8、如果forceInMemory或者InMemory缓存超过99.9%：

8.1、如果需要回收的缓存超过则全部回收Single、Multi中的缓存大小和，则全部回收Single、Multi中的缓存，剩余的则从InMemory中回收（this means we need to evict blocks in memory bucket to make room,so the single and multi buckets will be emptied）：

8.2、否则，不需要从InMemory中回收，按照如下策略回收Single、Multi中的缓存：尝试让single-bucket和multi-bucket的比例为1:2：

8.2.1、 single-bucket足够小，从multi-bucket中回收；

8.2.2、 multi-bucket足够小，从single-bucket中回收；

8.2.3、single-bucket和multi-bucket中都回收，且尽量满足回收后比例为1:2；

9、否则，从三个队列中循环回收；

10、最后，重置标志位，释放锁等。

四、实例化

参见《HBase-1.2.4 Allow block cache to be external分析》最后。