cid 2074c0a8794de08a22a0481f053759d1d2e2c4b06bf667b9e6e4c2caf743f1d5 · original · via openalex
Title: ClickHouse - Lightning Fast Analytics for Everyone Authors: Robert Schulze; Tom Schreiber; Ilya Yatsishin; Ryadh Dahimene; Alexey Milovidov Venue: Proceedings of the VLDB Endowment Year: 2024 Canonical: doi:10.14778/3685800.3685802 URL: https://doi.org/10.14778/3685800.3685802 Collector: openalex OA: false ABSTRACT Over the past several decades, the amount of data being stored and analyzed has increased exponentially. Businesses across industries and sectors have begun relying on this data to improve products, evaluate performance, and make business-critical decisions. However, as data volumes have increasingly become internet-scale, businesses have needed to manage historical and new data in a cost-effective and scalable manner, while analyzing it using a high number of concurrent queries and an expectation of real-time latencies (e.g. less than one second, depending on the use case). This paper presents an overview of ClickHouse, a popular open-source OLAP database designed for high-performance analytics over petabyte-scale data sets with high ingestion rates. Its storage layer combines a data format based on traditional log-structured merge (LSM) trees with novel techniques for continuous transformation (e.g. aggregation, archiving) of historical data in the background. Queries are written in a convenient SQL dialect and processed by a state-of-the-art vectorized query execution engine with optional code compilation. ClickHouse makes aggressive use of pruning techniques to avoid evaluating irrelevant data in queries. Other data management systems can be integrated at the table function, table engine, or database engine level. Real-world benchmarks demonstrate that ClickHouse is amongst the fastest analytical databases on the market.