An Adaptive Column Compression Family for Self-Driving Databases
Abstract
Modern in-memory databases are typically used for high-performance workloads, therefore they have to be optimized for small memory footprint and high query speed at the same time. Data compression has the potential to reduce memory requirements but often reduces query speed too. In this paper we propose a novel, adaptive compressor that offers a new trade-off point of these dimensions, achieving better compression than LZ4 while reaching query speeds close to the fastest existing segment encoders. We evaluate our compressor both with synthetic data in isolation and on the TPC-H and Join Order Benchmarks, integrated into a modern relational column store, Hyrise.
Keywords
Cite
@article{arxiv.2209.02334,
title = {An Adaptive Column Compression Family for Self-Driving Databases},
author = {Marcell Fehér and Daniel E. Lucani and Ioannis Chatzigeorgiou},
journal= {arXiv preprint arXiv:2209.02334},
year = {2022}
}
Comments
Appeared in the Thirteenth International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures (ADMS'22), a workshop of VLDB22