clickhouse-io

Optimize ClickHouse schemas and queries for analytical workloads.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/Maelwalser/claude-config --skill clickhouse-io-maelwalser
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clickhouse-io
Source: https://github.com/Maelwalser/claude-config/tree/main/skills/clickhouse-io
Command: npx skills add https://github.com/Maelwalser/claude-config --skill clickhouse-io-maelwalser

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides practical guidance to design ClickHouse schemas and queries that deliver fast analytical performance while minimizing storage and compute costs.

Core Features & Use Cases

  • Schema & Engine Selection: Advice on choosing MergeTree variants, partitioning granularity, and ORDER BY keys for efficient reads and compression.
  • Query Optimization & Aggregation: Patterns for efficient filters, aggregations, window functions, quantiles, and avoiding anti-patterns that slow OLAP queries.
  • Ingestion & Real-time Aggregation: Recommendations for bulk versus streaming inserts, Kafka integration, materialized views, and AggregatingMergeTree pre-aggregations.
  • Use Case: Migrate an event analytics workload from PostgreSQL to ClickHouse by redesigning tables, adding materialized views for real-time dashboards, and implementing batch/CDC ingestion pipelines.

Quick Start

Ask the skill to review a ClickHouse table schema and recommend MergeTree engine, partitioning, ORDER BY keys, and any materialized views to optimize a given query workload.

Frequently Asked Questions about clickhouse-io

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

FAQPage Schema
How do I optimize ClickHouse table schemas for fast analytical queries?▼

Optimize ClickHouse schemas by selecting the right MergeTree engine variant, defining appropriate partitioning granularity, and designing ORDER BY keys to maximize read efficiency and data compression for analytical workloads.

When should I use materialized views in ClickHouse for real-time dashboards?▼

Use materialized views and AggregatingMergeTree pre-aggregations in ClickHouse to process streaming data and maintain real-time dashboard metrics, reducing query latency by pre-computing aggregations as data arrives.

What is the best way to ingest bulk vs streaming data into ClickHouse?▼

The best data ingestion pattern for ClickHouse depends on your pipeline: use bulk inserts for large batch loads and streaming ingestion with Kafka integration for continuous real-time event data delivery.

How do I choose the right MergeTree engine and ORDER BY keys for my ClickHouse workload?▼

Choose MergeTree engine variants and ORDER BY keys by analyzing your query workload's filter conditions and aggregation patterns to ensure optimal data locality, compression, and scan performance.

Can I migrate an event analytics workload from PostgreSQL to ClickHouse?▼

You can migrate event analytics from PostgreSQL to ClickHouse by redesigning tables for OLAP, adding materialized views for dashboards, and implementing batch or CDC ingestion pipelines for continuous data sync.

Why does my ClickHouse OLAP query run slowly and how can I fix it?▼

Slow ClickHouse OLAP queries often stem from schema anti-patterns, suboptimal ORDER BY keys, or inefficient aggregation strategies, which can be fixed by adding projections, adjusting partitioning, and rewriting query filters.