HBase BlockCache

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;
}




otected 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。