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clickhouse-io

Provide ClickHouse analytics patterns for scalable OLAP workloads with MergeTree engines.

Updated Apr 13, 2026
Search Tags:#analytics#partitioning#etl#clickhouse#data-ingestion#materialized-views#olap
One-click install
npx skills add https://github.com/sakamoto-family-smile/agent_monorepo --skill clickhouse-io-sakamoto-family-smile
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clickhouse-io
Source: https://github.com/sakamoto-family-smile/agent_monorepo/tree/main/.claude/skills/ecc/clickhouse-io
Command: npx skills add https://github.com/sakamoto-family-smile/agent_monorepo --skill clickhouse-io-sakamoto-family-smile

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ClickHouse analytics patterns and best practices for building scalable OLAP workloads, including schema design, query optimization, and real-time analytics.

Core Features & Use Cases

  • Table design patterns: MergeTree family (MergeTree, ReplacingMergeTree, AggregatingMergeTree) with partitioning, ordering, and data types for performance and deduplication.
  • Query optimization patterns: Efficient filtering, window functions, and materialized views for real-time and historical analytics.
  • Ingestion and pipelines: ETL and CDC-style data movement patterns to feed ClickHouse and keep analytics up-to-date.

Quick Start

Create a MergeTree-based table with partitioning and a materialized view to generate hourly aggregates, then run a sample analytics query.

Frequently Asked Questions about clickhouse-io

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design MergeTree tables in ClickHouse for scalable OLAP workloads?▼

Design MergeTree tables in ClickHouse using optimal ordering keys, partitioning strategies, and appropriate data types. This pattern ensures high query performance and scalable OLAP workloads across large datasets.

What is the best way to deduplicate data ingested into ClickHouse?▼

Deduplicate data in ClickHouse using the ReplacingMergeTree engine. This pattern automatically removes duplicate rows during background merges, maintaining clean datasets for analytics without external deduplication logic.

How do I build materialized views for real-time analytics in ClickHouse?▼

Build materialized views in ClickHouse to pre-aggregate data and generate hourly summaries. This pattern automatically updates aggregates upon ingestion, enabling fast real-time analytics without scanning full historical datasets.

Can I use ClickHouse for ETL and CDC pipelines to keep analytics updated?▼

ClickHouse supports robust ETL and CDC data ingestion pipelines. These patterns move data efficiently into ClickHouse, ensuring analytics tables remain continuously updated with source system changes for real-time workloads.

When should I use AggregatingMergeTree over standard MergeTree in ClickHouse?▼

Use AggregatingMergeTree in ClickHouse when pre-aggregation is required for specific queries. This engine stores intermediate aggregate states, reducing storage and accelerating queries compared to standard MergeTree for pre-computed analytics.

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