Self-selecting, self-tuning, incrementally optimized indexes
Proceedings of the 13th International Conference on Extending Database Technology
DYFRAM: dynamic fragmentation and replica management in distributed database systems
Distributed and Parallel Databases
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In this paper we explore database segmentation in the context of a column-store DBMS targeted at a scientific database. We present a novel hardware- and scheme-oblivious segmentation algorithm, which learns and adapts to the workload immediately. The approach taken is to capitalize on (intermediate) query results, such that future queries benefit from a more appropriate data layout. The algorithm is implemented as an extension of a complete DBMS and evaluated against a real-life workload. It demonstrates significant performance gains without DBA assistance.