Task queues are celery root recipes indian as a mechanism to distribute work across threads or machines. A task queue’s input is a unit of work called a task. Dedicated worker processes constantly monitor task queues for new work to perform.

Celery communicates via messages, usually using a broker to mediate between clients and workers. To initiate a task the client adds a message to the queue, the broker then delivers that message to a worker. A Celery system can consist of multiple workers and brokers, giving way to high availability and horizontal scaling. Celery is written in Python, but the protocol can be implemented in any language. In addition to Python there’s node-celery and node-celery-ts for Node. Celery is a project with minimal funding, so we don’t support Microsoft Windows.

Please don’t open any issues related to that platform. Celery requires a message transport to send and receive messages. The RabbitMQ and Redis broker transports are feature complete, but there’s also support for a myriad of other experimental solutions, including using SQLite for local development. Celery can run on a single machine, on multiple machines, or even across data centers. If this is the first time you’re trying to use Celery, or if you haven’t kept up with development in the 3. Celery is easy to use and maintain, and it doesn’t need configuration files.