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 be implemented in any language. In addition to Python there’s node-celery for Node.js, and a PHP client. Language interoperability can also be achieved exposing an HTTP endpoint and having a task that the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import

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 addition to Python there’s node-celery [https://github.com/mher/node-celery] for Node.js, and a PHP client [https://github.com/gjedeer/celery-php]. Language interoperability can also be achieved exposing the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import

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 be implemented in any language. In addition to Python there’s node-celery for Node.js, and a PHP client. Language interoperability can also be achieved exposing an HTTP endpoint and having a task that the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import

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 be implemented in any language. In addition to Python there’s node-celery for Node.js, and a PHP client. Language interoperability can also be achieved exposing an HTTP endpoint and having a task that the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import

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 be implemented in any language. In addition to Python there’s node-celery for Node.js, and a PHP client. Language interoperability can also be achieved exposing an HTTP endpoint and having a task that the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import

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 be implemented in any language. In addition to Python there’s node-celery for Node.js, and a PHP client. Language interoperability can also be achieved exposing an HTTP endpoint and having a task that the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import

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 be implemented in any language. In addition to Python there’s node-celery for Node.js, and a PHP client. Language interoperability can also be achieved exposing an HTTP endpoint and having a task that the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import

via messages, usually using a broker to mediate between clients and workers. To initiate a task, a client adds a message to the queue, which the broker then delivers to a worker. A Celery system can consist language. So far there’s RCelery for the Ruby programming language, node-celery for Node.js and a PHP client. Language interoperability can also be achieved by using webhooks. What do I need? Version Requirements the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Calling Tasks): >>> from tasks import

execute our task, we use the delay() method of the task class. This is a handy shortcut to the apply_async() method which gives greater control of the task execution (see Executing Tasks). >>> from tasks been retried. An integer starting at 0. is_eager Set to True if the task is executed locally in the client, and not by a worker. logfile The file the worker logs to. See Logging. loglevel The current log together, so if you’re using relative imports you should set the name explicitly. For example if the client imports the module “myapp.tasks” as ”.tasks”, and the worker imports the module as “myapp.tasks”

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 addition to Python there’s node-celery [https://github.com/mher/node-celery] for Node.js, and a PHP client [https://github.com/gjedeer/celery-php]. Language interoperability can also be achieved exposing the task To call our task you can use the delay() method. This is a handy shortcut to the apply_async() method that gives greater control of the task execution (see Calling Tasks): >>> from tasks import