Skill UI

This guide provides comprehensive patterns and best practices for working with ClickHouse, a high-performance OLAP database. It covers advanced topics such as MergeTree engine design, efficient query optimization (filtering, aggregations, window functions), bulk/streaming data ingestion, materialized view creation, and performance monitoring techniques, ensuring optimal performance for large-scale analytical workloads.

Guides ClickHouse database architects through schema designs, query optimization, ingestion strategies, and monitoring techniques tailored for high-performance analytical workloads.

A comprehensive guide to reducing expenses on ClickHouse Cloud. Learn advanced techniques for storage optimization (compression, TTL), compute resource tuning (scaling, thread limits), and query efficiency improvements. Use this when managing data budgets or analyzing high ClickHouse billing charges.

This tool collects comprehensive diagnostic data from ClickHouse system tables. It gathers crucial metrics, query logs, merge statuses, and disk usage information. It is essential for troubleshooting performance bottlenecks, preparing detailed support tickets, or investigating complex database infrastructure issues.

A comprehensive guide on deploying applications connected to ClickHouse Cloud across various modern cloud environments, including Vercel, Fly.io, and Google Cloud Run. This guide covers best practices for connection pooling, secure secret management, implementing health checks, and ensuring graceful shutdowns in serverless and containerized setups.

This guide details how to configure and manage resource constraints in ClickHouse, addressing server-side limits, user quotas, and connection management. Use it when encountering "too many simultaneous queries," optimizing resource usage, or implementing robust rate limiting strategies in applications connecting to ClickHouse. Covers SQL quota setup and client-side pooling techniques.

A comprehensive guide and implementation template for building robust data ingestion pipelines into ClickHouse. Supports multiple sources including HTTP webhooks (with batching), Kafka streams, S3, and direct SQL inserts. Demonstrates best practices for data handling, such as deduplication using ReplacingMergeTree and monitoring query logs, ensuring reliable, scalable data analytics.