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

Table Design Patterns

🧠 Knowledge Modules (Fractal Skills)

1. MergeTree Engine (Most Common)

2. ReplacingMergeTree (Deduplication)

3. AggregatingMergeTree (Pre-aggregation)

4. Efficient Filtering

5. Aggregations

6. Window Functions

7. Bulk Insert (Recommended)

8. Streaming Insert

9. Real-time Aggregations

10. Query Performance

11. Table Statistics

12. Time Series Analysis

13. Funnel Analysis

14. Cohort Analysis

15. ETL Pattern

16. Change Data Capture (CDC)

17. 1. Partitioning Strategy

18. 2. Ordering Key

19. 3. Data Types

20. 4. Avoid

21. 5. Monitoring