Model-Driven Integration of Compression Algorithms in Column-Store Database Systems

Modern database systems are very often in the position to store their entire data in main memory. Aside from increased main memory capacities, a further driver for in-memory database systems was the shift to a decomposition storage model in combination with lightweight data compression algorithms. Using both mentioned storage design concepts, large datasets can be held and processed in main memory with a low memory footprint. In recent years, a large corpus of lightweight data compression algorithms has been developed to efficiently support different data characteristics. In this paper, we present our novel model-driven concept to integrate this large and evolving corpus of lightweight data compression algorithms in column-store database systems. Core components of our concept are (i) a unified conceptual model for lightweight compression algorithms, (ii) specifying algorithms as platform-independent model instances, (iii) transforming model instances into low-level system code, and (iv) integrating low-level system code into a storage layer.