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An adaptive column compression family for self-driving databases

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

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An adaptive column compression family for self-driving databases. / Fehér, Marcell; Lucani, Daniel; Chatzigeorgiou, Ioannis.
2022. Paper presented at 13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, Sydney, Australia.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Harvard

Fehér, M, Lucani, D & Chatzigeorgiou, I 2022, 'An adaptive column compression family for self-driving databases', Paper presented at 13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, Sydney, Australia, 5/09/22. <https://adms-conf.org/2022-camera-ready/ADMS22_feher.pdf>

APA

Fehér, M., Lucani, D., & Chatzigeorgiou, I. (2022). An adaptive column compression family for self-driving databases. Paper presented at 13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, Sydney, Australia. https://adms-conf.org/2022-camera-ready/ADMS22_feher.pdf

Vancouver

Fehér M, Lucani D, Chatzigeorgiou I. An adaptive column compression family for self-driving databases. 2022. Paper presented at 13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, Sydney, Australia.

Author

Fehér, Marcell ; Lucani, Daniel ; Chatzigeorgiou, Ioannis. / An adaptive column compression family for self-driving databases. Paper presented at 13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, Sydney, Australia.11 p.

Bibtex

@conference{c0a955bf1b664af9a2fdb97c6e0c0a4f,
title = "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.",
author = "Marcell Feh{\'e}r and Daniel Lucani and Ioannis Chatzigeorgiou",
year = "2022",
month = sep,
day = "7",
language = "English",
note = "13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS ; Conference date: 05-09-2022",
url = "https://adms-conf.org/",

}

RIS

TY - CONF

T1 - An adaptive column compression family for self-driving databases

AU - Fehér, Marcell

AU - Lucani, Daniel

AU - Chatzigeorgiou, Ioannis

PY - 2022/9/7

Y1 - 2022/9/7

N2 - 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.

AB - 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.

M3 - Conference paper

T2 - 13th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures

Y2 - 5 September 2022

ER -