HBase BlockCache 代码分析

1. Cache 读写
调用逻辑：
hmaster.handleCreateTable->HRegion.createHRegion-> HRegion. initialize->initializeRegionInternals->instantiateHStore
->Store.Store->new CacheConfig(conf, family)-> CacheConfig.instantiateBlockCache->new LruBlockCache
传入参数

  /**
   * Configurable constructor.  Use this constructor if not using defaults.
   * @param maxSize maximum size of this cache, in bytes
   * @param blockSize expected average size of blocks, in bytes
   * @param evictionThread whether to run evictions in a bg thread or not
   * @param mapInitialSize initial size of backing ConcurrentHashMap
   * @param mapLoadFactor initial load factor of backing ConcurrentHashMap
   * @param mapConcurrencyLevel initial concurrency factor for backing CHM
   * @param minFactor percentage of total size that eviction will evict until
   * @param acceptableFactor percentage of total size that triggers eviction
   * @param singleFactor percentage of total size for single-access blocks
   * @param multiFactor percentage of total size for multiple-access blocks
   * @param memoryFactor percentage of total size for in-memory blocks
   */
  public LruBlockCache(long maxSize, long blockSize, boolean evictionThread,
      int mapInitialSize, float mapLoadFactor, int mapConcurrencyLevel,
      float minFactor, float acceptableFactor,
      float singleFactor, float multiFactor, float memoryFactor) 

new LruBlockCache时除了设置默认的参数外，还会创建evictionThread并wait和一个定时打印的线程StatisticsThread


当执行HFileReaderV2的readBlock时，会先看判断是否开户了Cache ，如果开启，则使用cache中block

        // Check cache for block. If found return.
        if (cacheConf.isBlockCacheEnabled()) {
          // Try and get the block from the block cache.  If the useLock variable is true then this
          // is the second time through the loop and it should not be counted as a block cache miss.
          HFileBlock cachedBlock = (HFileBlock)
              cacheConf.getBlockCache().getBlock(cacheKey, cacheBlock, useLock);
          if (cachedBlock != null) {
            BlockCategory blockCategory =
                cachedBlock.getBlockType().getCategory();

            getSchemaMetrics().updateOnCacheHit(blockCategory, isCompaction);

            if (cachedBlock.getBlockType() == BlockType.DATA) {
              HFile.dataBlockReadCnt.incrementAndGet();
            }

            validateBlockType(cachedBlock, expectedBlockType);

            // Validate encoding type for encoded blocks. We include encoding
            // type in the cache key, and we expect it to match on a cache hit.
            if (cachedBlock.getBlockType() == BlockType.ENCODED_DATA &&
                cachedBlock.getDataBlockEncoding() !=
                    dataBlockEncoder.getEncodingInCache()) {
              throw new IOException("Cached block under key " + cacheKey + " " +
                  "has wrong encoding: " + cachedBlock.getDataBlockEncoding() +
                  " (expected: " + dataBlockEncoder.getEncodingInCache() + ")");
            }
            return cachedBlock;
          }
          // Carry on, please load.
        }

在getBlock方法中，会更新一些统计数据，重要的时更新

BlockPriority.SINGLE为BlockPriority.MULTI
  public Cacheable getBlock(BlockCacheKey cacheKey, boolean caching, boolean repeat) {
    CachedBlock cb = map.get(cacheKey);
    if(cb == null) {
      if (!repeat) stats.miss(caching);
      return null;
    }
    stats.hit(caching);
    cb.access(count.incrementAndGet());
    return cb.getBuffer();
  }

---------------------
若是第一次读，则将block加入Cache.

        // Cache the block if necessary
        if (cacheBlock && cacheConf.shouldCacheBlockOnRead(
            hfileBlock.getBlockType().getCategory())) {
          cacheConf.getBlockCache().cacheBlock(cacheKey, hfileBlock,
              cacheConf.isInMemory());
        }

2. LRU evict

写入cache时就是将block加入到 一个 ConcurrentHashMap中，并更新Metrics,之后判断if(newSize > acceptableSize() && !evictionInProgress), acceptableSize是初始化时给的值(long)Math.floor(this.maxSize * this.acceptableFactor)，acceptableFactor是一个百分比，是可以配置的："hbase.lru.blockcache.acceptable.factor"(0.85f)， 这里的意思就是判断总Size是不是大于这个值，如果大于并且没有正在执行的eviction线程， 那么就执行evict。

/**
   * Cache the block with the specified name and buffer.
   * <p>
   * It is assumed this will NEVER be called on an already cached block.  If
   * that is done, an exception will be thrown.
   * @param cacheKey block's cache key
   * @param buf block buffer
   * @param inMemory if block is in-memory
   */
  public void cacheBlock(BlockCacheKey cacheKey, Cacheable buf, boolean inMemory) {
    CachedBlock cb = map.get(cacheKey);
    if(cb != null) {
      throw new RuntimeException("Cached an already cached block");
    }
    cb = new CachedBlock(cacheKey, buf, count.incrementAndGet(), inMemory);
    long newSize = updateSizeMetrics(cb, false);
    map.put(cacheKey, cb);
    elements.incrementAndGet();
    if(newSize > acceptableSize() && !evictionInProgress) {
      runEviction();
    }
  }

在evict方法中，
1. 计算总size和需要free的size, minsize = (long)Math.floor(this.maxSize * this.minFactor);其中minFactor是可配置的"hbase.lru.blockcache.min.factor"（0.75f）;

      long currentSize = this.size.get();
      long bytesToFree = currentSize - minSize();

2. 初始化三种BlockBucket：bucketSingle，bucketMulti，bucketMemory并遍历map,按照三种类型分别add进各自的queue（MinMaxPriorityQueue.expectedSize(initialSize).create();）中， 并按照访问的次数逆序。
三种类型的区别是：
    SINGLE对应第一次读的
    MULTI对应多次读
    MEMORY是设定column family中的IN_MEMORY为true的

      // Instantiate priority buckets
      BlockBucket bucketSingle = new BlockBucket(bytesToFree, blockSize,
          singleSize());
      BlockBucket bucketMulti = new BlockBucket(bytesToFree, blockSize,
          multiSize());
      BlockBucket bucketMemory = new BlockBucket(bytesToFree, blockSize,
          memorySize());

其中三种BlockBuckt Size大小分配比例默认是：
  static final float DEFAULT_SINGLE_FACTOR = 0.25f;
  static final float DEFAULT_MULTI_FACTOR = 0.50f;
  static final float DEFAULT_MEMORY_FACTOR = 0.25f;

  private long singleSize() {
    return (long)Math.floor(this.maxSize * this.singleFactor * this.minFactor);
  }
  private long multiSize() {
    return (long)Math.floor(this.maxSize * this.multiFactor * this.minFactor);
  }
  private long memorySize() {
    return (long)Math.floor(this.maxSize * this.memoryFactor * this.minFactor);
  }

并将三种BlockBuckt 加入到优先队列中，按照totalSize - bucketSize排序，,再计算需要free大小，执行free：

PriorityQueue<BlockBucket> bucketQueue =
        new PriorityQueue<BlockBucket>(3);

      bucketQueue.add(bucketSingle);
      bucketQueue.add(bucketMulti);
      bucketQueue.add(bucketMemory);

      int remainingBuckets = 3;
      long bytesFreed = 0;

      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--;
      }

free方法中一个一个取出queue中block，由于是按照访问次数逆序，所以从后面取出就是先取出访问次数少的，将其在map中一个一个remove， 并更新Mertrics.

    public long free(long toFree) {
      CachedBlock cb;
      long freedBytes = 0;
      while ((cb = queue.pollLast()) != null) {
        freedBytes += evictBlock(cb);
        if (freedBytes >= toFree) {
          return freedBytes;
        }
      }
      return freedBytes;
    }




  protected long evictBlock(CachedBlock block) {
    map.remove(block.getCacheKey());
    updateSizeMetrics(block, true);
    elements.decrementAndGet();
    stats.evicted();
    return block.heapSize();
  }

3. HBase LruBlockCache的特点是针对不同的访问次数使用不同的策略，避免频繁的更新的Cache（便如Scan）,这样更加有利于提高读的性能。