When should I use the Postgres queue pattern?

The 'Just Use Postgres' pattern is recommended for moderate throughput (up to 10k msg/s) where simplicity is preferred over managing a dedicated broker like Kafka.

Which message brokers are supported by this skill?

This skill supports RabbitMQ (AMQP), Apache Kafka, Redis Streams, and Postgres-based queues, providing specific implementation patterns for each.

Does this skill handle message serialization?

Yes, it includes patterns for FastStream which provides automatic Pydantic validation and serialization for message payloads.

How does it ensure message delivery reliability?

The skill implements patterns for manual acknowledgments, consumer groups, idempotent producers, and Dead Letter Queues (DLQ) to prevent data loss.

Message Queue Patterns

Name: Message Queue Patterns
Author: yonatangross

byyonatangross

•

API 개발

Implements high-performance asynchronous messaging and event-driven architectures using RabbitMQ, Kafka, and Redis Streams.

This skill provides a comprehensive toolkit for building resilient distributed systems through asynchronous communication. It offers production-ready implementation patterns for major message brokers including Kafka for high-throughput streaming, RabbitMQ for complex routing and task queues, and Redis Streams for real-time processing. By incorporating the FastStream framework and 'Just Use Postgres' patterns, it enables developers to scale from simple task distribution to complex microservices architectures while ensuring reliability through dead-letter handling, manual acknowledgments, and exactly-once semantics.

주요 기능

01Unified messaging API support via FastStream with Pydantic validation

02Reliability patterns including Dead Letter Queues (DLQ), retries, and skip-locked Postgres queues

03Redis Streams integration for lightweight, real-time event processing

04High-throughput Kafka producer and consumer group implementations

05RabbitMQ patterns for persistent task queues and complex exchange routing

0669 GitHub stars

사용 사례

01Building high-volume event streaming pipelines for logs or analytics

02Implementing reliable background job processing and load leveling

03Decoupling microservices to improve system resilience and scalability

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit message-queues

For use in Claude.ai and ChatGPT

Download Skill