Unexpected High Memory Usage in Django with Celery Tasks and Redis
I've spent hours debugging this and I'm trying to implement I've been researching this but I'm experiencing unexpectedly high memory usage in my Django application that utilizes Celery for background tasks and Redis as the broker... Whenever I trigger a large batch of tasks, the memory consumption spikes significantly, and it seems to lead to slower response times for user requests. This is particularly noticeable when executing tasks that process images. I have set up Celery with Redis following this basic configuration: ```python # settings.py CELERY_BROKER_URL = 'redis://localhost:6379/0' CELERY_ACCEPT_CONTENT = ['json'] CELERY_TASK_SERIALIZER = 'json' ``` Each task is designed to load an image, apply transformations, and save it back to the filesystem. Here’s a simplified version of the task: ```python @shared_task def process_image(image_path): from PIL import Image img = Image.open(image_path) img = img.resize((800, 800)) # resize the image img.save(image_path) ``` I’ve also tried optimizing the memory usage by using `img.close()` after saving, but it hasn’t made a significant difference. The applications run on a Django 4.0.3 environment with Celery 5.2.1 and Redis 6.2.5. Additionally, I've monitored the Redis dashboard and noticed that the memory footprint of the Redis server also increases during execution. When I run the `ps aux` command during high load, I see that the Python processes tied to Celery are consuming a lot of memory. I tried increasing the `CELERYD_CONCURRENCY` to 10, hoping to manage tasks in parallel more efficiently, but it only worsens the situation. Is there a best practice to manage memory more effectively in this setup? Should I be considering different serialization formats or revising how I handle image processing in Celery tasks? Any insights or recommendations would be greatly appreciated! I'm working in a Windows 10 environment. What's the correct way to implement this? I recently upgraded to Python stable. What's the correct way to implement this? For context: I'm using Python on Ubuntu 20.04. What am I doing wrong?