Best Practices in ClickHouse
This section provides the best practices you will want to follow to get the most out of ClickHouse.| Page | Description |
|---|---|
| Choosing a Primary Key | How to select primary keys that maximize query performance and minimize storage overhead. |
| Select Data Types | Choose optimal data types to reduce memory usage, improve compression, and accelerate queries. |
| Use Materialized Views | Leverage materialized views to pre-aggregate data and dramatically speed up analytical queries. |
| Minimize and Optimize JOINs | Best practices for using ClickHouse’s JOIN capabilities efficiently. |
| Choosing a Partitioning Key | Select partitioning strategies that enable efficient data pruning and faster query execution. |
| Selecting an Insert Strategy | Optimize data ingestion throughput and reduce resource consumption with proper insert patterns. |
| Data Skipping Indices | Apply secondary indices strategically to skip irrelevant data blocks and accelerate filtered queries. |
| Avoid Mutations | Design schemas and workflows that eliminate costly UPDATE/DELETE operations for better performance. |
| Avoid OPTIMIZE FINAL | Prevent performance bottlenecks by understanding when OPTIMIZE FINAL hurts more than it helps. |
| Use JSON where appropriate | Balance flexibility and performance when working with semi-structured JSON data in ClickHouse. |