update-contrib作者： clickhouse

Run ClickHouse SQL directly in Python — no server needed. Query local files, remote databases, and cloud storage with full ClickHouse SQL power.

DataStore is a lazy, ClickHouse-backed pandas replacement . Your existing pandas code works unchanged — but operations compile to optimized SQL and execute only when results are needed (e.g., print() , len() , iteration).

MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system…

28 ClickHouse best practices rules organized by schema design, query optimization, and data ingestion strategy. Covers three critical areas: primary key and data type selection (immutable design decisions), JOIN and query optimization, and insert batching with mutation avoidance Includes 28 rules prioritized by impact, with schema design and query optimization rules marked CRITICAL due to ClickHouse's columnar storage and sparse index mechanics Provides structured review procedures for...

Use when a user wants to deploy ClickHouse to the cloud, go to production, use ClickHouse Cloud, host a managed ClickHouse service, or migrate from a local…

Use when a user wants to build an application with ClickHouse, set up a local ClickHouse development environment, install ClickHouse, create a local server,…

Guides users through setting up the ClickHouse MCP server connection bundled with this plugin. Use when the user first installs the plugin or has trouble…

Reference: https://clickhouse.com/docs/integrations/javascript