Contents of the BlockCache
In HBase, a block is a single unit of I/O. The block cache keeps data blocks resident in the memory after they are read.
BlockCachecorrectly, you need to understand what HBase places into it.
- Your data: Each time a Get or Scan operation occurs, the result is added to the BlockCache if it was not already cached there. If you use the BucketCache, data blocks are always cached in the BucketCache.
- Row keys: When a value is loaded into the cache, its row key is also cached. This is one reason to make your row keys as small as possible. A larger row key takes up more space in the cache.
- hbase:meta: The
hbase:metacatalog table keeps track of which RegionServer is serving which regions. It can consume several megabytes of cache if you have a large number of regions, and has
in-memoryaccess priority, which means HBase attempts to keep it in the cache as long as possible.
- Indexes of HFiles: HBase stores its data in HDFS in a format called HFile. These HFiles contain indexes which allow HBase to seek for data within them without needing to open the entire HFile. The size of an index is a factor of the block size, the size of your row keys, and the amount of data you are storing. For big data sets, the size can exceed 1 GB per RegionServer, although the entire index is unlikely to be in the cache at the same time. If you use the BucketCache, indexes are always cached on-heap.
- Bloom filters: If you use Bloom filters, they are stored in the BlockCache. If you use the BucketCache, Bloom filters are always cached on-heap.
The sum of the sizes of these objects is highly dependent on your usage patterns and the characteristics of your data. For this reason, the HBase Web UI and Cloudera Manager each expose several metrics to help you size and tune the BlockCache.