Optimizing Heavy Image Processing in a Django Application with Celery and Redis
I recently switched to I'm refactoring my project and After trying multiple solutions online, I still can't figure this out... I'm currently working with performance optimization in my Django application when processing images. The application uses Celery with Redis as a message broker to handle heavy image processing tasks in the background, but I notice important delays when users upload images, leading to timeouts. The processing tasks involve resizing and applying filters using Pillow, and they can take anywhere from a few seconds to over a minute, depending on the image size. I have tried breaking down the tasks into smaller subtasks, but even then, the overall throughput is not satisfactory. Here’s a minimal example of my Celery task: ```python from celery import shared_task from PIL import Image import os @shared_task(bind=True) def process_image(self, image_path): try: # Simulate heavy processing img = Image.open(image_path) img = img.resize((800, 800)) img.save(image_path) return 'Success' except Exception as e: self.retry(exc=e, countdown=60, max_retries=3) ``` I’m also using Django’s runserver in development mode, which I suspect might be causing additional delays due to its lack of optimizations. When I run the tasks, I’m noticing a backlog in the Celery queue, and my Redis server shows heavy memory usage, which might indicate that tasks are piling up. I've configured Celery with a concurrency setting of 4, but the performance still seems lacking. Additionally, I receive occasional `The operation timed out.` errors when testing with larger images. What strategies can I use to enhance the performance of image processing in this setup? Should I consider implementing a more efficient worker strategy or perhaps optimize the image processing logic further? Any advice would be greatly appreciated! Thanks for taking the time to read this! I'd really appreciate any guidance on this.