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.