Evaluating Compressed Indexes in DBMS

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In-memory database management systems (DBMSs) are an essential part of real-world applications. The memory footprint is the essential resource in such systems, while the database indexes consume a large portion of the memory and can reach up to 50\% of total memory consumption. B+tree is the most popular default index supported in these systems. There has been intensive work to create compressed B+tree indexes to lower the memory footprint This paper evaluates two state-of-the-art compressed B+tree implementations, a Hybrid Index and Blind Trie implementation called SeqTree. We evaluate these compressed indexing on the system level. We integrated those indexing into HSTORE and evaluated them with TPC-C workload. In our evaluation, SeqTree is more attractive since it consumes 30% less space than the Hybrid Index with the same throughput. We confirmed the potential of compressed B+-tree indexes. We see a 60%-75% reduction in the memory consumption of compressed B+-tree indexes compared to vanilla B+tree for 10%-15% in performance degradation.