Show HN: Storing and Analyzing 160 billion Quotes in ClickHouse
ClickHouse is effective for managing large financial datasets, offering fast query execution, efficient compression, and features like data deduplication and date partitioning, while alternatives like KDB and Shakti are also considered.
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The article discusses the use of ClickHouse for storing and analyzing large-scale financial data, specifically focusing on the management of 160 billion quotes. It highlights the importance of efficient data storage for quantitative trading and compares various storage solutions, emphasizing ClickHouse's performance advantages. ClickHouse, developed by Yandex, is noted for its ability to handle append-only data with fast query capabilities. The article outlines the schema design for trades and quotes, detailing features such as the ReplacingMergeTree engine for data deduplication, ZSTD compression for disk space savings, and partitioning by date to optimize query performance. Performance metrics demonstrate ClickHouse's speed, with examples showing rapid counts and average calculations over billions of rows. The author also mentions the integration of Python for further data analysis using the connectorx library. The conclusion asserts that ClickHouse is a strong candidate for financial data analysis due to its exceptional query performance, efficient storage, and flexibility, while also suggesting that alternatives like KDB and Shakti may be considered for specialized applications. Future considerations include benchmarking against other databases and exploring DuckDB.
- ClickHouse is effective for storing and analyzing large financial datasets.
- It offers high performance with fast query execution and efficient data compression.
- The ReplacingMergeTree engine allows for automatic data deduplication.
- Partitioning by date enhances query performance by limiting data scans.
- Alternatives like KDB and Shakti may be suitable for specific use cases.
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