clickhouse-best-practices-ts-py

What problem does it solve?

Aligns MooseStack data modeling with ClickHouse best practices to reduce schema drift, optimize query performance, and simplify complex data pipelines. It provides inline, action-oriented guidance that helps engineers make correct, production-ready decisions without performing exhaustive rule reviews upfront.

Core Features & Use Cases

• Inline guidance for core design choices such as ORDER BY keys, data types, partitioning, and materialized views to accelerate authoring and reduce costly migrations.
• Scenario-driven guidance focused on schema design, join strategies, and insert/maintenance patterns for analytics workloads.
• Use Case: While prototyping a new analytics feature, apply 1–2 relevant rules directly in the code path to validate an efficient schema and fast query plans without performing a full audit.

Quick Start

Pick the 1–2 rules directly relevant to your immediate decision and apply inline guidance while continuing to write code; request a formal review only if you explicitly ask for it.