clickhouse-io

Provide ClickHouse schema and query optimization patterns for analytical workloads.

6|Updated Feb 25, 2026
One-click install
npx skills add https://github.com/archibate/archibate-skills --skill clickhouse-io-archibate
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: clickhouse-io
Source: https://github.com/archibate/archibate-skills/tree/main/old-skills/minor-skills/clickhouse-io
Command: npx skills add https://github.com/archibate/archibate-skills --skill clickhouse-io-archibate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ClickHouse-io provides clear patterns and practical guidance to design ClickHouse table schemas, optimize analytical queries, and build reliable ingestion pipelines so teams can run high-performance OLAP workloads without guesswork.

Core Features & Use Cases

  • Schema & Engine Guidance: Recommendations for MergeTree variants, partitioning, ordering keys, and appropriate data types to maximize query performance and storage efficiency.
  • Query Optimization: Techniques for partition pruning, using ClickHouse aggregation functions, window functions, projections, and identifying slow queries.
  • Ingestion & CDC: Best practices for batch and streaming inserts, Kafka integration, ETL pipelines, and change-data-capture flows for migrating from PostgreSQL/MySQL.
  • Operational Patterns: Using materialized views for pre-aggregation, monitoring system tables for query and table health, and common diagnostics for slow queries and table sizing.

Quick Start

Ask the skill to analyze a sample workload and produce a recommended MergeTree CREATE TABLE statement with partitioning, ORDER BY keys, and an ingestion approach.

Frequently Asked Questions about clickhouse-io

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

FAQPage Schema
How do I design a MergeTree table schema in ClickHouse for high-performance OLAP?

Optimize ClickHouse queries using partition pruning, aggregation functions, projections, and window functions. Diagnose slow queries by monitoring system tables to identify table health and sizing issues.

What's the best way to ingest CDC data into ClickHouse from PostgreSQL or MySQL?

Use ClickHouse materialized views for pre-aggregating data. This operational pattern accelerates real-time dashboard queries by computing aggregations automatically during ingestion.

How do I optimize slow ClickHouse queries using materialized views and projections?

Optimize ClickHouse queries using partition pruning, aggregation functions, projections, and window functions. Diagnose slow queries by monitoring system tables to identify table health and sizing issues.

What's the best way to ingest CDC data into ClickHouse from PostgreSQL or MySQL?

Build reliable CDC pipelines into ClickHouse using batch and streaming inserts, Kafka integration, and ETL flows. This supports smooth migrations from PostgreSQL or MySQL to OLAP workloads.

When should I use materialized views for pre-aggregation in ClickHouse?

Use ClickHouse materialized views for pre-aggregating data. This operational pattern accelerates real-time dashboard queries by computing aggregations automatically during ingestion.

How do I monitor ClickHouse system metrics to identify slow queries?

Monitor ClickHouse system tables to diagnose slow queries and assess table health. Querying system metrics reveals operational bottlenecks and sizing issues for analytical workloads.