Benchmarking Specialized Databases for High-frequency Data

29 Pages Posted: 31 Jan 2023

See all articles by Fazl Barez

Fazl Barez

The University of Edinburgh

Paul Bilokon

Department of Computing, Imperial College London; Department of Mathematics, Imperial College London; Thalesians Ltd

Ruijie Xiong

Imperial College London - Department of Computing

Date Written: January 29, 2023

Abstract

This paper presents a benchmarking suite designed for the evaluation and comparison of time series databases for high-frequency data, with a focus on financial applications. The proposed suite comprises of four specialized databases: ClickHouse, InfluxDB, kdb+ and TimescaleDB. The results from the suite demonstrate that kdb+ has the highest performance amongst the tested databases, while also highlighting the strengths and weaknesses of each of the databases. The benchmarking suite was designed to provide an objective measure of the performance of these databases as well as to compare their capabilities for different types of data. This provides valuable insights into the suitability of different time series databases for different use cases and provides benchmarks that can be used to inform system design decisions.

Keywords: databases, high-frequency data, big data, time series

Suggested Citation

Barez, Fazl and Bilokon, Paul and Xiong, Ruijie, Benchmarking Specialized Databases for High-frequency Data (January 29, 2023). Available at SSRN: https://ssrn.com/abstract=4342004 or http://dx.doi.org/10.2139/ssrn.4342004

Fazl Barez (Contact Author)

The University of Edinburgh ( email )

Paul Bilokon

Department of Computing, Imperial College London ( email )

180 Queen's Gate
London, SW7 2AZ
United Kingdom

Department of Mathematics, Imperial College London ( email )

South Kensington Campus
Imperial College
LONDON, SW7 2AZ
United Kingdom

Thalesians Ltd ( email )

HOME PAGE: http://www.thalesians.com

Ruijie Xiong

Imperial College London - Department of Computing ( email )

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