How we made ClickStack 5x faster for ClickHouse observability

ClickStack's journey to general availability involved significant schema optimizations to handle large observability workloads while maintaining performance. The team focused on redesigning primary keys, implementing text indexes, query rewrites, and leveraging ClickHouse features to address slow query issues and optimize for common query patterns. These efforts included benchmarking with ClickCannon to simulate realistic workloads and evaluate resource requirements effectively. Changes such as modifying the primary key strategy, adopting text indexes for better granule pruning, and introducing materialized views improved query efficiencies and reduced latency by more than fivefold. The optimizations also integrated features like alias columns for dynamic attributes, enhancing the overall user experience by making queries more efficient and metadata-driven features more responsive. The result was a robust, scalable schema that not only improved ingestion, storage, and operational efficiency but also aligned with native ClickHouse capabilities, ensuring adaptability to future database enhancements.

No tracked trend matches for this post yet.