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 add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, which can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

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 add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, which can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

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 add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, which can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

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 add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, which can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently on one or more worker nodes. Tasks can execute asyn- fact. UUID Every task has an UUID (Universally Unique Identifier), which is the task id used to query task status and return value. Retries Tasks can be retried if they fail, with configurable maximum been executed, and even retrieve the results in order. Progress bars, anyone? Made for Web You can query status and results via URLs, enabling the ability to poll task status using Ajax. Error e-mails

distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently on one or more worker nodes. Tasks can execute asynchronously fact. UUID Every task has an UUID (Universally Unique Identifier), which is the task id used to query task status and return value. Retries Tasks can be retried if they fail, with configurable maximum been executed, and even retrieve the results in order. Progress bars, anyone? Made for Web You can query status and results via URLs, enabling the ability to poll task status using Ajax. Error e-mails Can

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 add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

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 add >>> add.delay(4, 4) The task has now been processed another method called apply_async(): >>> add.apply_async((2, 2)) The latter enables you to specify execution options like the time to run (countdown), the queue it should be sent to, and so on: >>> add.apply_async((2 apply_async methods return an AsyncResult instance, that can be used to keep track of the tasks execution state. But for this you need to enable a result backend so that the state can be stored somewhere

distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently on one or more worker nodes using multiprocessing fact. UUID Every task has an UUID (Universally Unique Identifier), which is the task id used to query task status and return value. Retries Tasks can be retried if they fail, with configurable maximum been executed, and even retrieve the results in order. Progress bars, anyone? Made for Web You can query status and results via URLs, enabling the ability to poll task status using Ajax. Error Emails Can

distributed message passing. It is focused on real-time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently on one or more worker nodes using multiprocessing fact. UUID Every task has an UUID (Universally Unique Identifier), which is the task id used to query task status and return value. Retries Tasks can be retried if they fail, with configurable maximum been executed, and even retrieve the results in order. Progress bars, anyone? Made for Web You can query status and results via URLs, enabling the ability to poll task status using Ajax. Error E-mails