Skills · SkillBackend & APIsData & AIReviewed

clickhouse-io

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads. Use when writing ClickHouse schemas or queries, or when an analytical query is too slow.

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Kind: Skill
Works with: Claude Code, Codex
Languages: SQL
Source: affaan-m/ECC @ d3b8a3e90890
Path: skills/clickhouse-io/SKILL.md
License: MIT
Jobs: Documentation

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# 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)

```sql
CREATE TABLE markets_analytics (
    date Date,
    market_id String,
    market_name String,
    volume UInt64,
    trades UInt32,
    unique_traders UInt32,
    avg_trade_size Float64,
    created_at DateTime
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(date)
…