Displaying 1 to 20 from 32 results

flower - Real-time monitor and web admin for Celery distributed task queue

  •    Python

Flower is a web based tool for monitoring and administrating Celery clusters. Flower API enables to manage the cluster via REST API, call tasks and receive task events in real-time via WebSockets.

tornado-celery - Non-blocking Celery client for Tornado

  •    Python

NOTE: Currently callbacks only work with AMQP and Redis backends. To use the Redis backend, you must install tornado-redis.

metta - An information security preparedness tool to do adversarial simulation.

  •    Python

Metta is an information security preparedness tool. This project uses Redis/Celery, python, and vagrant with virtualbox to do adversarial simulation. This allows you to test (mostly) your host based instrumentation but may also allow you to test any network based detection and controls depending on how you set up your vagrants.

Kombu - Messaging Framework for Python

  •    Python

Kombu is a Messaging Framework for Python. The aim of Kombu is to make messaging in Python as easy as possible by providing an idiomatic high-level interface for the AMQ protocol, and also provide proven and tested solutions to common messaging problems. It allows application authors to support several message server solutions by using pluggable transports.

node-celery - Celery client for Node.js

  •    Javascript

Note: When using AMQP as result backend with celery prior to version 3.1.7 the result queue needs to be non durable or it will fail with a: Queue.declare: (406) PRECONDITION_FAILED. For RabbitMQ backends, the entire broker options can be passed as an object that is handed off to AMQP. This allows you to specify parameters such as SSL keyfiles, vhost, and connection timeout among others.

flack - Companion code to my PyCon 2016 "Flask at Scale" tutorial session.

  •    Python

This repository contains the companion code to my PyCon 2016 "Flask At Scale" class. IMPORTANT NOTE: The initial commit in this repository has a version of this application that has a few scalability problems discussed during class. These problems are addressed in subsequent commits.

django-celery-beat - Celery Periodic Tasks backed by the Django ORM

  •    Python

This extension enables you to store the periodic task schedule in the database. The periodic tasks can be managed from the Django Admin interface, where you can create, edit and delete periodic tasks and how often they should run.

enferno - A Flask-based Framework for the Next Decade.

  •    Python

A framework for the next decade, this is a collection of cutting-edge libraries and tools based on Flask framework. If you are prefer to use a SQL compatible backend, please check out the "sql" branch.

celery-director - Simple and rapid framework to build workflows with Celery

  •    Python

Director is a simple and rapid framework used to manage tasks and build workflows using Celery. See how to use Director with the quickstart and guides in the documentation.


  •    Python

This extension enables you to store Celery task results using the Django ORM. It defines a single model (django_celery_results.models.TaskResult) used to store task results, and you can query this database table like any other Django model.

django-celery-email - A Django email backend that uses a celery task for sending the email.

  •    Python

A Django email backend that uses a Celery queue for out-of-band sending of the messages. By default django-celery-email will use Django's builtin SMTP email backend for the actual sending of the mail. If you'd like to use another backend, you may set it in CELERY_EMAIL_BACKEND just like you would normally have set EMAIL_BACKEND before you were using Celery. In fact, the normal installation procedure will most likely be to get your email working using only Django, then change EMAIL_BACKEND to CELERY_EMAIL_BACKEND, and then add the new EMAIL_BACKEND setting from above.

celery-singleton - Seamlessly prevent duplicate executions of celery tasks

  •    Python

Duplicate tasks clogging up your message broker? Do time based rate limits make you feel icky? Look no further! This is a baseclass for celery tasks that ensures only one instance of the task can be queued or running at any given time. Uses the task's name+arguments to determine uniqueness. That's it! Your task is a singleton and calls to do_stuff.delay() will either queue a new task or return an AsyncResult for the currently queued/running instance of the task.

django-celery-inspect - Django reusable app that uses Celery Inspect command to monitor workers/tasks via the Django REST Framework

  •    Python

Django reusable-app that uses Celery Inspect command to monitor workers via the Django REST Framework. The main idea is to be able to monitor celery workers from another external service or server via a REST API and figure out if they are running or not by using celery's own [Inspect API] (http://docs.celeryproject.org/en/latest/userguide/workers.html#inspecting-workers).

full-stack - Full stack, modern web application generator

  •    Python

Generate a back end and front end stack using Python, including interactive API documentation. Copy the contents and use that as password / secret key. And run that again to generate another secure key.

django-pushy - Your push notifications handled at scale.

  •    Python

Your push notifications handled at scale. Python / Django app that provides push notifications functionality with celery. The main purpose of this app is to help you send push notifications to your users at scale. If you have lots of registered device keys, django-pushy will split your keys into smaller groups which run in parallel making the process of sending notifications faster.

node-celery-ts - TypeScript Celery client for Node

  •    TypeScript

node-celery-ts is a Celery client for Node.js written in TypeScript. node-celery-ts supports RabbitMQ and Redis result brokers and RPC (over RabbitMQ) and Redis result backends. node-celery-ts provides higher performance than Celery on PyPy and provides greater feature support than node-celery, including Redis Sentinel and Cluster, RPC result backends, YAML serialization, zlib task compression, and Promise-based interfaces. node-celery-ts uses amqplib and ioredis for RabbitMQ and Redis, respectively. node-celery-ts does not support Amazon SQS or Zookeeper message brokers, nor does it support SQLAlchemy, Memcached, Cassandra, Elasticsearch, IronCache, Couchbase, CouchDB, filesystem, or Consul result backends. RedisBackend and RedisBroker both accept a RedisOptions object, which is an interface that can be extended by the user to allow new creational patterns.

dash-redis-demo - Connect to Redis from Dash

  •    Python

This app demonstrates how to connect to a Redis database from Dash. It works out of the box with the Redis server built in to Dash On Premise but could be adapted to work with other servers such as Heroku Redis or your local Redis server.

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