Does this skill work with FastAPI?

Yes, it includes specific integration patterns and boilerplate for FastAPI, including dependency injection for command and query buses.

How does this skill handle data consistency?

The patterns focus on eventual consistency, using event-driven projections to update read models following successful command execution on the write side.

When should I apply CQRS patterns to my project?

CQRS is best suited for complex, read-heavy systems where the read model significantly differs from the write model, or when you need to scale read and write operations independently.

Can I use these patterns for simple CRUD applications?

While possible, CQRS is generally considered an anti-pattern for simple CRUD apps due to the added complexity of managing separate models and eventual consistency.

CQRS Design Patterns

Name: CQRS Design Patterns
Author: yonatangross

byyonatangross

•

API開発

Implements Command Query Responsibility Segregation (CQRS) to optimize data access by separating read and write operations into distinct models.

The CQRS Patterns skill provides a comprehensive architectural framework for building high-performance, scalable systems where data modification and data retrieval requirements differ. By separating commands (writes) from queries (reads), this skill enables developers to optimize read-heavy workloads, implement event sourcing, and create denormalized read models tailored for specific UI needs. It includes production-ready implementation patterns for FastAPI and Python, featuring robust command handlers, domain-driven aggregates, and automated event projections that ensure eventual consistency while maximizing system throughput.

主な機能

01Domain-driven design (DDD) aggregate patterns for consistent write operations

02Integrated guidance on anti-patterns to prevent architectural leakage

03Production-ready Python and FastAPI implementation patterns with Pydantic

04Automated event-driven projection logic for creating denormalized read views

05Separate Command and Query models for specialized performance optimization

0669 GitHub stars

ユースケース

01Distributed systems where read and write operations must scale independently

02Event-sourced applications needing comprehensive audit trails and historical data tracking

03High-traffic e-commerce platforms requiring complex, read-heavy dashboard views

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit cqrs-patterns

For use in Claude.ai and ChatGPT

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