What is the clickhouse-io skill for Claude Code?

It is a specialized capability that provides Claude with domain-specific patterns for ClickHouse database design, high-performance query writing, and data engineering best practices.

How does it handle data ingestion?

The skill provides patterns for bulk and streaming inserts, helping developers avoid the common mistake of small, frequent writes that can degrade ClickHouse performance.

How does this skill improve query performance?

It guides the user in using ClickHouse-specific functions, proper indexing, and effective filtering techniques to ensure queries run efficiently on large datasets.

Does it support materialized views?

Yes, it includes specific patterns for creating and querying materialized views for real-time data aggregation and performance monitoring.

ClickHouse Performance & Analytics

Name: ClickHouse Performance & Analytics
Author: affaan-m

byaffaan-m

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34,604

データベース管理

Provides high-performance ClickHouse patterns for analytical workloads, query optimization, and efficient data engineering.

概要

This skill equips Claude with specialized knowledge for ClickHouse, a powerful column-oriented OLAP database designed for fast analytical processing. It provides implementation patterns for various MergeTree engines, materialized views, and real-time aggregations, while offering specific guidance on efficient batch insertion and complex analytical queries like cohort and funnel analysis. It is an essential resource for developers building data-intensive applications that require sub-second responses on massive datasets.

主な機能

34,604 GitHub stars
Advanced MergeTree engine table design and partitioning patterns
Optimized SQL query patterns for high-performance filtering and indexing
Pre-built analytics logic for cohort, funnel, and retention analysis
Materialized view templates for real-time pre-aggregation and state management
Batch and streaming data insertion strategies for high-throughput ingestion

ユースケース

Building robust ETL pipelines to sync application data into an OLAP environment
Designing highly compressed, partitioned tables for time-series and event data
Optimizing slow analytical queries using ClickHouse-specific aggregation functions

概要

主な機能

34,604 GitHub stars
Advanced MergeTree engine table design and partitioning patterns
Optimized SQL query patterns for high-performance filtering and indexing
Pre-built analytics logic for cohort, funnel, and retention analysis
Materialized view templates for real-time pre-aggregation and state management
Batch and streaming data insertion strategies for high-throughput ingestion

ユースケース

Building robust ETL pipelines to sync application data into an OLAP environment
Designing highly compressed, partitioned tables for time-series and event data
Optimizing slow analytical queries using ClickHouse-specific aggregation functions