ClickHouse Analytics Patterns

ClickHouse-specific patterns for high-performance analytics and data engineering.

Overview

ClickHouse is a column-oriented database management system (DBMS) for online analytical processing (OLAP). It's optimized for fast analytical queries on large datasets.

Key Features:

• Column-oriented storage
• Data compression
• Parallel query execution
• Distributed queries
• Real-time analytics

Correctness and measurement boundaries

The tables/queries are independent schema sketches, not one runnable application. Validate column/aggregate-state types and units together. ReplacingMergeTree deduplicates equal full sorting keys eventually within compatible partitions; including timestamp in ORDER BY does not deduplicate arbitrary repeated event IDs, and background merges are not a read-time correctness guarantee. Predicate textual order alone does not prove index efficiency; inspect plans/read_rows/read_bytes. The retention subquery below groups by activity day, so its per-day MIN is not a true signup cohort; use a verified signup source or windowed user minimum before retention reporting. A simple event-count funnel is not an ordered unique-user/session conversion funnel. Protect query logs and scope DDL/ingestion.