clickhouse-io
ClickHouse table design, query optimization, and analytics patterns for high-performance OLAP workloads.
- Covers three primary MergeTree engine variants: standard MergeTree for time-series data, ReplacingMergeTree for deduplication, and AggregatingMergeTree for pre-aggregated metrics
- Includes query optimization patterns for efficient filtering, aggregations, window functions, and percentile calculations with ClickHouse-specific functions
- Demonstrates data insertion strategies (bulk inserts, streaming), materialized views for real-time aggregations, and performance monitoring via system tables
- Provides ready-to-use patterns for common analytics workflows: time-series analysis, retention, funnels, cohorts, and ETL/CDC pipelines with TypeScript examples
ClickHouse Analytics Patterns
ClickHouse-specific patterns for high-performance analytics and data engineering.
When to Activate
- Designing ClickHouse table schemas (MergeTree engine selection)
- Writing analytical queries (aggregations, window functions, joins)
- Optimizing query performance (partition pruning, projections, materialized views)
- Ingesting large volumes of data (batch inserts, Kafka integration)
- Migrating from PostgreSQL/MySQL to ClickHouse for analytics
- Implementing real-time dashboards or time-series analytics
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.
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