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
   */
  @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：

    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：

    @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：

  /**
   * 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。