How Sentry queries unstructured data in ClickHouse 62x faster

Sentry processes billions of unstructured data spans from its users to measure product performance, encompassing diverse operations such as webpage loads and phone calls. To manage this data efficiently, Sentry utilizes the open-source database ClickHouse, originally designed for structured data, which poses challenges due to the lack of a fixed schema in user-provided spans. Initially, attempts to store spans with numerous columns in ClickHouse failed due to excessive memory allocation. By leveraging ClickHouse's Map type, Sentry created a schema with minimal columns, but performance issues arose due to the need to process large files. Inspired by hash tables, Sentry developed a third schema version that distributes data into multiple buckets, significantly enhancing query performance by reducing the data scanned per query. This innovative application of hash tables improved operations by up to 62 times, facilitating the development of new Sentry features such as custom dashboards and tracing while demonstrating the practical value of traditional computer science concepts in modern applications.

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