Does this skill support scheduled tasks?

Yes, it includes implementation patterns for Celery Beat, allowing you to schedule cron-like periodic jobs and maintenance tasks.

Can I use this for LLM-related background work?

While these patterns work for any async task, it is recommended to use LangGraph for complex LLM workflow persistence, though simple LLM calls can certainly be offloaded to these queues.

When should I choose ARQ over Celery?

ARQ is recommended for simpler, native async Python applications like FastAPI where low complexity is desired. Celery is better for complex enterprise workflows that require task chains, chords, and extensive monitoring.

How are task failures handled in these patterns?

The skill provides specific patterns for resilience, including automatic retries with exponential backoff, jitter, and proper error logging.

Python Background Jobs & Queues

Name: Python Background Jobs & Queues
Author: yonatangross

byyonatangross

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API開発

Implements robust asynchronous task processing and job queues using Celery, ARQ, and Redis for Python backends.

概要

This skill provides production-ready patterns for offloading long-running tasks from Python web applications to background workers. It covers a range of implementations from lightweight async queues with ARQ for FastAPI to complex enterprise workflows using Celery. Developers can use these patterns to implement reliable email notifications, data processing pipelines, and scheduled periodic tasks while adhering to industry best practices for retries, error handling, and worker resource management.

主な機能

Enterprise-grade Celery configurations for complex task workflows
Native ARQ (Async Redis Queue) patterns for FastAPI integration
Job status tracking and progress monitoring implementation guides
Scheduled and periodic task management using Celery Beat
Implementation of robust retry strategies with exponential backoff
29 GitHub stars

ユースケース

Sending transactional emails and processing external webhooks
Offloading heavy data processing and PDF report generation
Running periodic database maintenance and data synchronization tasks

概要

主な機能

Enterprise-grade Celery configurations for complex task workflows
Native ARQ (Async Redis Queue) patterns for FastAPI integration
Job status tracking and progress monitoring implementation guides
Scheduled and periodic task management using Celery Beat
Implementation of robust retry strategies with exponential backoff
29 GitHub stars

ユースケース

Sending transactional emails and processing external webhooks
Offloading heavy data processing and PDF report generation
Running periodic database maintenance and data synchronization tasks