An Adaptive Column Compression Family for Self-Driving Databases

Marcell Fehér, Daniel Enrique Lucani Rötter, Ioannis Chatzigeorgiou

Research output: Contribution to conferencePaperResearchpeer-review

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 compres- sor 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.
Original languageEnglish
Publication date26 Jul 2022
Number of pages11
Publication statusAccepted/In press - 26 Jul 2022
EventThirteenth International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures: A workshop of the 48th International Conference on Very Large Databases (VLDB) - Sydney, Australia
Duration: 5 Sept 20225 Sept 2022
https://adms-conf.org/

Workshop

WorkshopThirteenth International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures
Country/TerritoryAustralia
CitySydney
Period05/09/202205/09/2022
Internet address

Fingerprint

Dive into the research topics of 'An Adaptive Column Compression Family for Self-Driving Databases'. Together they form a unique fingerprint.

Cite this