TY  - JOUR
A1  - Boissier, Martin
T1  - Robust and budget-constrained encoding configurations for in-memory database systems
JF  - Proceedings of the VLDB Endowment
N2  - Data encoding has been applied to database systems for decades as it mitigates bandwidth bottlenecks and reduces storage requirements. But even in the presence of these advantages, most in-memory database systems use data encoding only conservatively as the negative impact on runtime performance can be severe. Real-world systems with large parts being infrequently accessed and cost efficiency constraints in cloud environments require solutions that automatically and efficiently select encoding techniques, including heavy-weight compression. In this paper, we introduce workload-driven approaches to automaticaly determine memory budget-constrained encoding configurations using greedy heuristics and linear programming. We show for TPC-H, TPC-DS, and the Join Order Benchmark that optimized encoding configurations can reduce the main memory footprint significantly without a loss in runtime performance over state-of-the-art dictionary encoding. To yield robust selections, we extend the linear programming-based approach to incorporate query runtime constraints and mitigate unexpected performance regressions.
KW  - General Earth and Planetary Sciences
KW  - Water Science and Technology
KW  - Geography, Planning and Development
Y1  - 2021
U6  - https://doi.org/10.14778/3503585.3503588
SN  - 2150-8097
VL  - 15
IS  - 4
SP  - 780
EP  - 793
PB  - Association for Computing Machinery (ACM)
CY  - [New York]
ER  -