HBase-1.2.4LruBlockCache实现分析（一）

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：

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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：

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 // 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   */   @Override   public 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 trouble       if (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1     long 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：

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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：

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/**  * 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：

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@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：

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@Override  public 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：

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/**  * 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bytesToFree      long 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、memory      BlockBucket 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 emptied          bytesFreed = 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 1:2          long 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-bucket            bytesFreed = 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-bucket            bytesFreed = 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 blocks            bytesFreed = 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。