ClickHouse
ClickHouse is a recurring reference point for analytical database design: data modeling, sparse or skipping indexes, precomputation, and execution-time pruning. Most quantitative gains in this corpus come from vendor material or benchmark-specific reports, so they illustrate mechanisms rather than general performance guarantees.
Recurring themes
- Schema and primary-key order are presented as central to reducing read volume; partitioning, data types, batching, projections, and materialized views are workload-specific levers. [src]
- Top-N and release examples illustrate granule-level data skipping, dynamic thresholds, limit pushdown, grouping shortcuts, and query-cache controls. [src] [src]
- The pgrust comparison frames ClickHouse against a different execution model, helping distinguish columnar/vectorized analytics from PostgreSQL’s traditional row-at-a-time executor. [src]