Cost Optimization: Battle-Tested Strategies

This guide is part of a collection of findings gained from community meetups. For more real world solutions and insights you can browse by specific problem. Want to learn about creative use cases for ClickHouse? Check out the Creative Use Cases community insights guide.

The Partition Deletion vs TTL Discovery

Hard-learned lesson from production: TTL mutations are resource-intensive and slow down everything.

"Don't try to mutate data if there isn't a world where you absolutely need to... when you mutate data ClickHouse creates a new version of the data and then it merges it with the existing data... it's resource intensive... significantly significant performance impact"

Better strategy: Delete entire partitions instead of TTL row-by-row deletion.

Storage Hot Spots Analysis

Find your biggest storage consumers: Identify which columns and patterns drive your storage costs.

Cost-Driven Retention Analysis

Real production strategy: "Once we get this kind of deletion signal... we do the row based deletion... we know what needs to be deleted and keep on tracking"

The key insight: Instead of deleting entire rows, strategically drop the expensive columns first while preserving the essential data structure for longer periods. This can save "several terabytes" as Displayce discovered.