❌ IMPORTANT This project is in its early stages. While it is functional, it may not cover all edge cases or have comprehensive documentation. Use it at your own risk and feel free to contribute! We are working fast to make it production-ready.
This project aims to use Django's new Task back-end to expose AWS and Azure native services to manage long-running tasks asynchronously. This project is useful for tasks that must run outside the request-response cycle, like sending emails, processing images, or running machine learning models. It leverages the power of AWS and Azure native services. You can choose between the two cloud providers (more on this in the installation and setup sections). With the reliance on cloud native services, the project provides a scalable and reliable solution for managing long-running tasks.
Installation depends on which cloud provider you want to use. You can install the package using PIP for all cloud providers as follows:
For AWS:
pip install django-tasks-cloud[aws]For Azure:
pip install django-tasks-cloud[azure]For support for both AWS and Azure:
pip install django-tasks-cloud[aws,azure]Note
There is a package with very similar name called django-cloud-tasks. Ensrue you install django-tasks-cloud. Also, running the above command will install the core package without any cloud provider-specific dependencies. You must install the cloud provider-specific dependencies separately as mentioned below.
Once installed, add it to your Django INSTALLED_APPS in settings.py:
INSTALLED_APPS = [
...
"django_cloud_task.base",
# Either or Both
"django_tasks_cloud.aws", # For AWS
"django_tasks_cloud.azure", # For Azure
...
]If you want to work with the nightly builds, please install it from Test PyPI as follows:
pip install -i https://test.pypi.org/simple/ django-tasks-cloudThe CD pipeline automatically publishes to Test PyPI on every commit to the main branch. Production releases are published to the official PyPI repository on every tagged release.
TASKS = {
"default": {
"BACKEND": "cloud_tasks_aws.backends.SNSTopicBackend",
"QUEUES": ["email-ingestor"],
"OPTIONS": {
"AWS_DEFAULT_TOPIC_NAME": "testing",
"AWS_SNS_ARN_PREFIX": "arn:aws:sns:<region>:<account-number>:",
"AWS_REGION": "ap-south-1",
},
},
}Each entry in QUEUES corresponds to an SNS topic that you have created in your AWS account. The AWS_DEFAULT_TOPIC_NAME is the default topic that will be used if no specific topic is mentioned when creating a task.
TASKS = {
"default": {
"BACKEND": "cloud_tasks_aws.backends.SQSBackend",
"QUEUES": ["email-ingestor"],
"OPTIONS": {
"AWS_DEFAULT_QUEUE_NAME": "testing",
"AWS_REGION": "ap-south-1",
},
},
}Each entry in QUEUES corresponds to an SQS queue that you have created in your AWS account. The AWS_DEFAULT_QUEUE_NAME is the default queue that will be used if no specific queue is mentioned when creating a task.
In this setup, EventBridge Scheduler is used to schedule tasks that will be sent to SQS queues. Since SQS and SNS doesn't support per-message scheduling natively, EventBridge Scheduler acts as an intermediary to handle the scheduling.
TASKS = {
"eventbridge_scheduler": {
"BACKEND": "cloud_tasks_aws.backends.EventBridgeSchedulerBackend",
"QUEUES": ["email-ingestor"],
"OPTIONS": {
"AWS_DEFAULT_SQS_QUEUE_NAME": "testing",
"EVENTBRIDGE_SCHEDULER_ROLE_ARN": "",
"AWS_REGION": "ap-south-1",
},
},
}Each name in the QUEUES list corresponds to an SQS queue that you have created in your AWS account. EventBridge Scheduler, once the time is due, will send the task to the specified SQS queue. The AWS_DEFAULT_SQS_QUEUE_NAME is the default queue that will be used if no specific queue is mentioned when creating a task.
With this back-end, you can invoke AWS Lambda functions asynchronously. This is useful for tasks that can be handled by serverless functions.
TASKS = {
"default": {
"BACKEND": "cloud_tasks_aws.backends.LambdaAsyncInvocationBackend",
"QUEUES": ["email-ingestor"],
"OPTIONS": {
"AWS_DEFAULT_LAMBDA_FUNCTION_NAME": "testing",
"AWS_REGION": "ap-south-1",
},
},
}Each name in the QUEUES list corresponds to a Lambda function that you have created in your AWS account. The AWS_DEFAULT_LAMBDA_FUNCTION_NAME is the default Lambda function that will be used if no specific function is mentioned when creating a task.
TASKS = {
"default": {
"BACKEND": "django_cloud_tasks_azure.backends.service_bus.ServiceBusQueueBackend",
"QUEUES": ["default"],
"OPTIONS": {
"SERVICEBUS_DEFAULT_QUEUE_NAME": "testing",
"SERVICEBUS_USE_CONNECTION_STRING": True,
"SERVICEBUS_CONNECTION_STRING": "",
"SERVICEBUS_NAMESPACE_FQDN": "",
"SERVICEBUS_CREDENTIAL_LOADER": "this.that.custom_credential_loader_function",
},
},
}For a simple setup, you can use the SERVICEBUS_USE_CONNECTION_STRING option set to True and provide the SERVICEBUS_CONNECTION_STRING. This connection string can be obtained from the Azure portal.
If you don't wanna hardcode credentials, you must provide the SERVICEBUS_NAMESPACE_FQDN and provide SERVICEBUS_CREDENTIAL_LOADER. If you don't provide a custom credential loader, the default will use DefaultAzureCredential from the azure-identity package.
To use a custom credential class from a suite of Azure credential classes, set the SERVICEBUS_CREDENTIAL_LOADER option to a method of the following function signature:
def custom_credential_loader() -> TokenCredential: ...Mention it's full module path in the SERVICEBUS_CREDENTIAL_LOADER setting. The function must return an instance of a class that inherits from azure.core.credentials.TokenCredential. A function wrapper is required because some TokenCredential classes require parameters for initialization. To know more about the available credential classes, refer to the Azure Identity documentation.
Important
QUEUES is a list of queue names that you want to use. Each queue name in the list corresponds to a queue that you have created in your Azure Service Bus namespace.
TASKS = {
"default": {
"BACKEND": "django_cloud_tasks_azure.backends.service_bus.ServiceBusTopicBackend",
"QUEUES": ["default"],
"OPTIONS": {
"SERVICEBUS_DEFAULT_TOPIC_NAME": "testing",
"SERVICEBUS_USE_CONNECTION_STRING": True,
"SERVICEBUS_CONNECTION_STRING": "",
"SERVICEBUS_NAMESPACE_FQDN": "",
"SERVICEBUS_CREDENTIAL_LOADER": "this.that.custom_credential_loader_function",
},
},
}The configuration is similar to the Service Bus Queue backend. The main difference is that you need to provide the SERVICEBUS_DEFAULT_TOPIC_NAME instead of the queue name.
TASKS = {
"sa_queue": {
"BACKEND": "cloud_tasks_azure.backends.sa_queue.StorageAccountQueueBackend",
"QUEUES": ["email-ingestor"],
"OPTIONS": {
"STORAGE_ACCOUNT_QUEUE_DEFAULT_QUEUE_NAME": "testing",
"STORAGE_ACCOUNT_USE_CONNECTION_STRING": True,
"STORAGE_ACCOUNT_CONNECTION_STRING": "",
"STORAGE_ACCOUNT_URL": "",
"STORAGE_ACCOUNT_CREDENTIAL_LOADER": "this.that.custom_credential_loader_function",
},
},
}The configuration is similar to the Service Bus Queue backend. The main difference is that you need to provide the STORAGE_ACCOUNT_QUEUE_DEFAULT_QUEUE_NAME instead of the queue name.
For all back-ends, the payload sent to the cloud provider will be a JSON object with the following structure:
{
"task_name": "function_name",
"args": [/* positional arguments */],
"kwargs": { /* keyword arguments */ },
}The task name will be the name of the function you decorated with @task. The args and kwargs will contain the positional and keyword arguments passed to the task when it was called. So the function you define is merely a signature. You can either keep it a signature, or implement it and use the same codebase in the runner that processes the tasks.
Once you enqueue a task, you'll immediately receive a TaskResult object. You can use this to later track the status of the task. However, note that the actual execution and result tracking of the task is outside the scope of this package. While implementing the remote worker, you must write logic to call back your Django application at a particular endpoint to update the task status and result. Note that database persistence is still a work in progress and will be added in future releases.
Contributions are welcome! Please read the CONTRIBUTING.md file for more information on how to contribute to this project.
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