Top 10 best practices tips for ClickHouse

Summary

ClickHouse delivers rapid analytical queries across massive datasets, but suboptimal schemas and queries can significantly reduce performance and storage efficiency. Aligning table definitions with ClickHouse storage and execution mechanics resolves these bottlenecks. Key practices include placing low-cardinality columns first in the ORDER BY clause to enable granule pruning, and selecting optimal data types while avoiding Nullable wrappers to cut memory overhead. Developers should treat partitioning as a data management tool for TTL drops or merge engines rather than a generic speed booster, preventing fragmented parts from slowing query execution. Recent improvements like the Analyzer query planner have also made joins viable, though dictionaries remain faster for static lookups.

Takeaways

1. 1

   Setting the sorting key to start with low-cardinality query filter columns allows ClickHouse to prune granules and avoid full table scans.

2. 2

   Partitioning does not inherently speed up queries and can cause substantial query slowdowns due to part fragmentation, unless used for TTL operations or merge-engine part consolidation.

3. 3

   Memory-resident dictionaries accessed via dictGet bypass hash join execution overhead and outperform standard joins on static lookup data.