Distributed Task Queue (development branch)
Simple job queues for Python
Real-time monitor and web admin for Celery distributed task queue
a little task queue for python
A multiprocessing distributed task queue for Django
Python task queue using Redis
Mr. Queue - A distributed worker task queue in Python using Redis & gevent
Celery Once allows you to prevent multiple execution and queuing of celery tasks.
A simple distributed queue designed for handling one-off tasks with large sets of tasks
yotaq - Your Own Task Queue for Python
Odoo generic connector framework (jobs queue, asynchronous tasks, channels)
⚙️ Simple task queue for Python
Cloud Pub/Sub Task Queue for Python
Kale is a python task worker library that supports priority queues on Amazon SQS
Z task queue, a redis based async task queue for python. Moved to : http://github.com/easydo-cn/ztq/...
Python task-queues for Amazon SQS
Fast, Safe & Simple Asynchronous Task Queues Written In Pure Python
Retask is a simple task queue implementation written for human beings. It provides generic solution to create and manage task queues....
A database-backed work queue for Django
Web hook task queue.
Minimal example utilizing fastapi and celery with RabbitMQ for task queue, Redis for celery backend and flower for monitoring the celery tasks....
Python Async Task Queue
distributed task queue for asyncio
[UNMAINTAINED] A task queue based on the elegant python RQ but with a django postgresql backend.
A simple task queue implementation to enqeue jobs on local or remote processes.
A task queue for django
A lightweight task queue for Django using RabbitMQ
Asynchronous message queue consumer and scheduler
Broker-less Python Task Queue
PostgreSQL-based Task Queue for Python
Python task queue
AintQ Is Not Task Queue - a Python asyncio task queue on PostgreSQL.
A distributed task queue with asyncio and redis
爬虫监控及可视化 ( Prometheus and Grafana ) Building a crawler with distributed task queues (Celery) and fetching data with a reliable monitor system....
asynchronous task queues using python's multiprocessing library