HBase中缓存的优先级

ava代码  

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

// Scan entire map putting into appropriate buckets
for(CachedBlock cachedBlock : map.values()) {
  switch(cachedBlock.getPriority()) {
    case SINGLE: {
      bucketSingle.add(cachedBlock);
      break;
    }
    case MULTI: {
      bucketMulti.add(cachedBlock);
      break;
    }
    case MEMORY: {
      bucketMemory.add(cachedBlock);
      break;
    }
  }
}

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--;
}

    hbase内部的blockcache分三个队列：single、multi以及memory，分别占用25%，50%,25%的大小。这涉及到family属性中的in-memory选项，默认是false。
    设为false的话，第一次访问到该数据时，会将它写入single队列，否则写入memory队列。当再次访问该数据并且在single中读到了该数据时，single会升级为multi
    这三个队列其实是在共用blockcache的资源，区别是在LRU淘汰数据时，single会优先淘汰，其次为multi，最后为memory。

    所以结论有两点：
    1 同一个family不会占用全部的blockcache资源
    2 当某些family特别重要时，可以将它的in-memory设为true，单独使用一个缓存队列，保证cache的优先使用