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In-memory columnar databases have become mainstream over the last decade andhave vastly improved the fast processing of large volumes of data throughmulti-core parallelism and in-memory compression thereby eliminating the usualbottlenecks associated with disk-based databases. For scenarios, where the datavolume grows into terabytes and petabytes, keeping all the data in memory isexorbitantly expensive. Hence, the data is compressed efficiently usingdifferent algorithms to exploit the multi-core parallelization technologies forquery processing. Several compression methods are studied for compressing thecolumn array, post Dictionary Encoding. In this paper, we will present twonovel optimizations in compression techniques - Block Size Optimized ClusterEncoding and Block Size Optimized Indirect Encoding - which perform better thantheir predecessors. In the end, we also propose heuristics to choose the bestencoding amongst common compression schemes.

Optimizations and Heuristics to improve Compression in Columnar Database Systems