Скачать или смотреть Resolving the PENDING State of Celery Tasks in a Flask/RabbitMQ App

Struggling with Celery tasks stuck in the `PENDING` state in your Flask/RabbitMQ app? This guide provides a detailed solution and troubleshooting tips to get your task running smoothly!
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Resolving the PENDING State of Celery Tasks in a Flask/RabbitMQ App: A Comprehensive Guide

If you are developing a Flask application with Celery for background task processing using RabbitMQ as the message broker, you might encounter an issue where the Celery task remains stuck in a PENDING state. This can be perplexing, especially when the Docker output shows that everything seems to be running smoothly. In this post, we'll explore why your Celery tasks may not be executing as expected and how to resolve it effectively.

Understanding the Problem

You have set up your Flask application, RabbitMQ, and Celery in Docker, but when you hit the API endpoint, the tasks do not complete, and you see them stuck in the PENDING status. Despite no apparent errors in the Docker logs, there are a few common reasons that may lead to this issue:

Queue Name Mismatch: Celery uses the task's default queue name unless specified otherwise. If there's a mismatch between the queue the task is sent to and what's being consumed, tasks can get stalled.

Worker Not Listening: Ensure that your Celery worker is correctly set up to process tasks from the designated queue.

Configuration Issues: Misconfigured Celery settings can also lead to persistent PENDING states.

Solution Steps

After conducting thorough research and assessment, I found the culprit. The solution lies primarily in adjusting the queue name for the task, which is a straightforward fix. Here’s how to do it:

Step 1: Modify the Task Decorator

Adjust the task declaration in your workerA.py file to explicitly specify the queue that it should listen to. Modify your add_nums function as follows:

[[See Video to Reveal this Text or Code Snippet]]

By adding queue='workerA', we ensure that the task is sent to the correct queue in RabbitMQ, allowing the worker to pick it up for execution.

Step 2: Verify the Celery Worker Configuration

Make sure that your Celery worker is correctly configured to listen to the right queue. In your docker-compose.yml, you have specified:

[[See Video to Reveal this Text or Code Snippet]]

The -Q workerA ensures that the worker listens to the workerA queue, where your tasks will now be routed.

Step 3: Start All Services

Once the modifications have been made, restart your Docker containers to apply the new configurations. You can do this with the command:

[[See Video to Reveal this Text or Code Snippet]]

Testing the Solution

Now that you have adjusted the task decorator and ensured correct worker configuration, it’s time to test the API endpoint again. Run the following command to trigger the task:

[[See Video to Reveal this Text or Code Snippet]]

You should see a response containing the result of the addition in JSON format.

Conclusion

In summary, when encountering Celery tasks stuck in a PENDING state within your Flask/RabbitMQ application, start by checking the task's queue configuration and ensure that the worker is set up correctly. By properly defining the task queue and verifying worker settings, you can get your tasks running smoothly in no time!

If you continue to experience issues, consider checking your RabbitMQ management interface (usually available at http://localhost:15672) to monitor queues and see if tasks are being enqueued correctly.

Feel free to share your experiences or ask questions in the comments below!