execute the task in the current process, so that no message is sent: >>> add(2, 2) 4 These three methods - delay(), apply_async(), and applying (__call__), make up the Celery calling API, which is also invocation will be given a unique identifier (an UUID) – this is the task id. The delay and apply_async methods return an AsyncResult instance, which can be used to keep track of the tasks execution state. But instead – so to check whether the task succeeded or failed, you’ll have to use the corresponding methods on the result instance: >>> res.failed() True >>> res.successful() False So how does it know if

execute the task in the current process, so that no message is sent: >>> add(2, 2) 4 These three methods - delay(), apply_async(), and applying (__call__), make up the Celery calling API, which is also invocation will be given a unique identifier (an UUID) – this is the task id. The delay and apply_async methods return an AsyncResult instance, which can be used to keep track of the tasks execution state. But instead – so to check whether the task succeeded or failed, you’ll have to use the corresponding methods on the result instance: >>> res.failed() True >>> res.successful() False So how does it know if

execute the task in the current process, so that no message is sent: >>> add(2, 2) 4 These three methods - delay(), apply_async(), and applying (__call__), make up the Celery calling API, which is also invocation will be given a unique identifier (an UUID) – this is the task id. The delay and apply_async methods return an AsyncResult instance, which can be used to keep track of the tasks execution state. But instead – so to check whether the task succeeded or failed, you’ll have to use the corresponding methods on the result instance: >>> res.failed() True >>> res.successful() False So how does it know if

execute the task in the current process, so that no message is sent: >>> add(2, 2) 4 These three methods - delay(), apply_async(), and applying (__call__), make up the Celery calling API, which is also invocation will be given a unique identifier (an UUID) – this is the task id. The delay and apply_async methods return an AsyncResult instance, which can be used to keep track of the tasks execution state. But instead – so to check whether the task succeeded or failed, you’ll have to use the corresponding methods on the result instance: >>> res.failed() True >>> res.successful() False So how does it know if

execute the task in the current process, so that no message is sent: >>> add(2, 2) 4 These three methods - delay(), apply_async(), and applying (__call__), make up the Celery calling API, which is also invocation will be given a unique identifier (an UUID) – this is the task id. The delay and apply_async methods return an AsyncResult instance, which can be used to keep track of the tasks execution state. But instead – so to check whether the task succeeded or failed, you’ll have to use the corresponding methods on the result instance: >>> res.failed() True >>> res.successful() False So how does it know if

execute the task in the current process, so that no message is sent: >>> add(2, 2) 4 These three methods - delay(), apply_async(), and applying (__call__), represents the Celery calling API, that’s also invocation will be given a unique identifier (an UUID), this is the task id. The delay and apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But instead, and so to check whether the task succeeded or failed you’ll have to use the corresponding methods on the result instance: >>> res.failed() True >>> res.successful() False So how does it know if

execute the task in the current process, so that no message is sent: >>> add(2, 2) 4 These three methods - delay(), apply_async(), and applying (__call__), represents the Celery calling API, that’s also invocation will be given a unique identifier (an UUID), this is the task id. The delay and apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But instead, and so to check whether the task succeeded or failed you’ll have to use the corresponding methods on the result instance: >>> res.failed() True >>> res.successful() False So how does it know if

execute the task in the current process, so that no message is sent: >>> add(2, 2) 4 These three methods - delay(), apply_async(), and applying (__call__), represents the Celery calling API, that’s also invocation will be given a unique identifier (an UUID), this is the task id. The delay and apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But instead, and so to check whether the task succeeded or failed you’ll have to use the corresponding methods on the result instance: >>> res.failed() True >>> res.successful() False So how does it know if

are optional. userid and password are also optional, but needed if your MongoDB server requires authentication. Results If you also want to store the state and return values of tasks in MongoDB, you should are optional. userid and password are also optional, but needed if your CouchDB server requires authentication. Results Storing task state and results in CouchDB is currently not supported. Limitations does not currently support: Remote control commands (celery control, celery inspect, broadcast) Authentication This document describes the current stable version of Celery (3.1). For development docs, go

are optional. userid and password are also optional, but needed if your MongoDB server requires authentication. Results If you also want to store the state and return values of tasks in MongoDB, you should are optional. userid and password are also optional, but needed if your CouchDB server requires authentication. Results Storing task state and results in CouchDB is currently not supported. Limitations currently support: • Remote control commands (celery control, celery inspect, broadcast) • Authentication Using IronMQ Installation For IronMQ support, you’ll need the [iron_celery](http://github