Reduce ClickHouse storage costs by choosing the right data types, applying LowCardinality where it matters, and tuning compression codecs. Includes diagnostic queries to audit your schema.

BigData Boutique blog

Schema design in ClickHouse directly impacts storage costs and query performance. Key optimizations include: using the smallest appropriate integer types (e.g., Int8 instead of Int32), applying LowCardinality to string columns with few unique values to enable dictionary encoding, and choosing compression codecs strategically (ZSTD for large rarely-queried columns, LZ4 for hot-path columns). Diagnostic queries against system.parts_columns help identify the largest columns and their compression ratios, while value distribution checks reveal downsizing opportunities. These are foundational choices that compound across billions of rows.

Optimizing Your ClickHouse Schema for Storage Efficiency