Clickhouse Io
This one covers ClickHouse table design and query optimization for OLAP workloads. You get patterns for choosing the right MergeTree engine variant (standard, ReplacingMergeTree for deduplication, AggregatingMergeTree for pre-aggregations), along with query examples that show the difference between fast and slow approaches. The materialized views section is solid for real-time dashboards, and there are practical templates for time series analysis, retention cohorts, and funnel queries. The bulk insert patterns matter because ClickHouse really punishes row-by-row inserts. Use this when you're moving analytics off Postgres or building something that needs to crunch millions of rows without melting your infrastructure.
- skills.sh installs
- 5.7K
- GitHub stars
- 229.1K
- last updated
- Jul 14, 2026
Install
$ npx -y skills add affaan-m/everything-claude-code --skill clickhouse-io --agent claude-codeInstalls into .claude/skills of the current project.
Files
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.
Key Features:
- Column-oriented storage
- Data compression
- Parallel query execution
- Distributed queries
- Real-time analytics
Table Design Patterns
MergeTree Engine (Most Common)
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- Repository
- github.com/affaan-m/everything-claude-code
- Category
- Databases
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