What ClickHouse table engines does this skill cover?

It provides specialized patterns for ClickHouse's most powerful engines, including MergeTree for general analytics, ReplacingMergeTree for deduplication, and AggregatingMergeTree for pre-aggregation.

Does it support data ingestion patterns?

Absolutely. It covers both batch bulk insert strategies and streaming ingestion patterns to ensure data is loaded efficiently without overwhelming the database.

How does it handle real-time aggregations?

It utilizes ClickHouse-specific State functions and Materialized Views to perform background pre-aggregations, significantly reducing query latency for large datasets.

Can this skill help with slow analytical queries?

Yes, it includes best practices for efficient filtering, leveraging primary keys, and performance monitoring using the system.query_log to identify and fix bottlenecks.

Is this skill suitable for time-series and user behavior analysis?

Yes, the skill includes ready-to-use patterns for common analytics tasks such as daily active users (DAU), retention analysis, and conversion funnels.

ClickHouse Analytics & I/O

Name: ClickHouse Analytics & I/O
Author: ysyecust

byysyecust

데이터베이스 관리

Optimizes high-performance analytical workloads by implementing ClickHouse-specific database patterns and query best practices.

소개

This skill provides specialized guidance for ClickHouse, a column-oriented DBMS designed for real-time online analytical processing (OLAP). It equips Claude with the patterns needed to design efficient MergeTree tables, implement complex analytical queries, manage materialized views, and optimize bulk data ingestion. Whether you are building data pipelines, performing cohort analysis, or monitoring system performance, this skill ensures your ClickHouse implementation follows high-performance standards for large-scale data engineering and real-time analytics.

주요 기능

Optimized table design patterns including MergeTree, ReplacingMergeTree, and AggregatingMergeTree
Advanced analytical query patterns for retention, funnel, and cohort analysis
High-performance query optimization and indexed filtering strategies
Real-time aggregation implementation using Materialized Views
Efficient bulk and streaming data insertion patterns for Node.js and TypeScript
0 GitHub stars

사용 사례

Optimizing slow analytical queries to improve dashboard and reporting performance
Implementing real-time monitoring and table statistics tracking for database health
Designing high-throughput data engineering pipelines for large-scale datasets

소개

주요 기능

Optimized table design patterns including MergeTree, ReplacingMergeTree, and AggregatingMergeTree
Advanced analytical query patterns for retention, funnel, and cohort analysis
High-performance query optimization and indexed filtering strategies
Real-time aggregation implementation using Materialized Views
Efficient bulk and streaming data insertion patterns for Node.js and TypeScript
0 GitHub stars

사용 사례

Optimizing slow analytical queries to improve dashboard and reporting performance
Implementing real-time monitoring and table statistics tracking for database health
Designing high-throughput data engineering pipelines for large-scale datasets