An adaptive column compression family for self-driving databases

Fehér, Marcell and Lucani, Daniel and Chatzigeorgiou, Ioannis (2022) An adaptive column compression family for self-driving databases. In: 13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, 2022-09-05.

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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.

Item Type:
Contribution to Conference (Paper)
Journal or Publication Title:
13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures
ID Code:
175609
Deposited By:
Deposited On:
06 Oct 2022 14:45
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Nov 2022 01:10