Does it include code for data ingestion?

Yes, it provides optimized TypeScript examples for both bulk inserts and streaming data pipelines to ensure high-throughput ingestion without overloading the database.

Can I use this for real-time reporting?

Absolutely. It includes patterns for Materialized Views, which allow you to maintain real-time aggregated statistics as data is inserted.

How does this skill improve query performance?

It provides best practices for partition strategies, sorting keys, and utilizing ClickHouse-specific aggregate functions like quantiles and uniqMerge to reduce CPU and memory usage.

Which ClickHouse engines are supported by this skill?

The skill covers essential engines including MergeTree for general purposes, ReplacingMergeTree for deduplication, and AggregatingMergeTree for pre-calculated metrics.

ClickHouse Analytics & Data Engineering

Name: ClickHouse Analytics & Data Engineering
Author: xu-xiang

byxu-xiang

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Database Management

Optimizes ClickHouse database performance with specialized table patterns, query optimization, and real-time analytical workflows.

This skill provides Claude with comprehensive expertise in ClickHouse, a high-performance columnar database for OLAP. It includes standardized patterns for table engine selection (MergeTree, AggregatingMergeTree), advanced query optimization techniques, and efficient data ingestion strategies like batch and stream processing. It is particularly useful for engineers building real-time analytics, time-series monitoring, or complex data pipelines where query speed and data compression are critical. By following these patterns, users can avoid common pitfalls like frequent small inserts or inefficient join operations while leveraging powerful features like Materialized Views for instant aggregations.

Key Features

01Efficient batch and streaming data ingestion patterns for Node.js/TypeScript

02Real-time data aggregation blueprints using Materialized Views

03Production-ready table design patterns for various MergeTree engines

0461 GitHub stars

05Query optimization strategies for high-cardinality filtering and quantiles

06Pre-built analytical query templates for funnel, cohort, and retention analysis

Use Cases

01Building high-performance real-time analytics dashboards and monitoring systems

02Optimizing slow analytical queries on massive datasets to sub-second latency

03Designing scalable ETL and CDC pipelines for large-scale data warehousing

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add xu-xiang/everything-claude-code-zh clickhouse-io

For use in Claude.ai and ChatGPT

Download Skill