How to implement asynchronous processing in Django with Celery and Amazon SQS

Introduction

Asynchronous processing is a key technique to improve performance and scalability in modern web applications. In frameworks like Django, it reduces response times by delegating heavy or long-running tasks to background processes. In this article you'll learn how to integrate Celery with Amazon SQS to build a robust asynchronous processing architecture. We'll also cover how to set up a local development environment using Docker and LocalStack so you can develop without depending directly on AWS.

Foundations of asynchronous processing

Asynchronous processing works through a queue system composed of these elements:

• Producer: sends messages to the queue.
• Broker (intermediary): manages the queue (in this case, SQS).
• Worker: consumes messages and executes tasks.
• Message: contains the task information.
• Queue: temporarily stores messages.

It’s important to understand that receiving a message does not automatically remove it. Processing must be explicitly acknowledged to avoid duplications.

In this setup we use:

• Celery as the distributed task system.
• Amazon SQS as the messaging broker.

Running with Docker and a Celery worker

To run the system correctly:

• Define a container for the Django app.
• Define a separate container for the Celery worker.
• Mount AWS credentials into the containers.

With Docker Compose you can:

• Build containers
• Start them in the background
• Tail logs in real time
• Verify tasks are being processed correctly

This separation allows responsibilities to be isolated and lets you scale workers independently.

Recommendations

• Use FIFO queues when the execution order is critical.
• Set VisibilityTimeout larger than the maximum expected task execution time.
• Enable CELERY_TASK_ACKS_LATE to avoid losing messages on failures.
• Separate environments (base, local, production) into different configuration files.
• Use LocalStack for development and testing before deploying to AWS.

Conclusions

• Implementing asynchronous processing with Celery and Amazon SQS in Django significantly improves system scalability and efficiency.
• Celery simplifies sending, receiving, and acknowledging messages, while SQS provides a robust managed broker. Integrating Docker and LocalStack makes the environment more flexible and productive for local development.
• Mastering this architecture is an important step toward modern, resilient backend applications ready to scale in the cloud.

Glossary

Asynchronous processing: Technique that runs tasks in the background without blocking the system’s main response. Broker: Intermediary system that manages message queues between producers and consumers. Worker: Process that consumes messages from a queue and executes associated tasks. FIFO queue: Queue type where messages are processed in the same order they were sent. Visibility Timeout: Time during which a received message remains hidden before it can be processed again.