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An adaptive column compression family for self-driving databases

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

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Publication date7/09/2022
Number of pages11
<mark>Original language</mark>English
Event13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures - Sydney, Australia
Duration: 5/09/2022 → …
https://adms-conf.org/

Workshop

Workshop13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures
Abbreviated titleADMS
Country/TerritoryAustralia
CitySydney
Period5/09/22 → …
Internet address

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.