In the last couple of years, increasingly more people begin to stream data into HBase in near time, and
use high level queries (e.g., Hive) to analyze the data in HBase directly. While HBase already has very effective MapReduce integration with its good scanning performance, query processing using MapReduce on HBase still has significant gaps compared to HDFS: ~3x space overheads and 3~5x performance overheads according to our measurement.
We propose to implement a document store on HBase, which can greatly improve query processing on HBase (by leveraging the relational model and read-mostly access patterns). According to our prototype, it can reduce space usage by up-to ~3x and speedup query processing by up-to ~1.8x.
|1.||Extend co-processor framework to provide observers for filter operations||Open||Unassigned|