Optimizing LinkedIn Publishing with Server-Side Image Caching

When publishing content to LinkedIn, large image sizes can lead to 413 Request Entity Too Large errors, especially when dealing with base64 encoded images. This post discusses how we addressed this issue in the devlog-ist/landing project by implementing server-side caching of images.

The Problem: Large Image Payloads

Directly embedding base64 encoded images into API requests, particularly for social media publishing, results in significant payload sizes. These payloads can easily exceed the limits imposed by web servers like Nginx, leading to failed requests and a poor user experience. In our case, the image data was being sent with the LinkedIn publish request.

The Solution: Server-Side Caching

To mitigate this, we shifted to a server-side caching strategy. Instead of transmitting the entire base64 image with each publish request, we now:

1. Cache the image on the server during the image generation process.
2. Assign a unique key to the cached image.
3. Send only this key in the publish request to LinkedIn.
4. Retrieve the image using the key when needed.

This approach drastically reduces the request payload size, preventing 413 errors and improving the reliability of the publishing process.

Here's a simplified example of how the caching mechanism might look:

# Image caching service (illustrative)
class ImageCache:
    cache = {}
    def store(self, image_data):
        key = generate_unique_key()
        self.cache[key] = image_data
        return key

    def retrieve(self, key):
        return self.cache.get(key)

# Usage example
image_cache = ImageCache()
image_data = generate_image()
image_key = image_cache.store(image_data)

linkedin_payload = {
    "image_key": image_key,
    "content": "Check out this image!"
}

# Later, retrieve the image using the key when needed
retrieved_image = image_cache.retrieve(image_key)

Benefits

• Reduced Payload Size: Smaller requests prevent 413 errors.
• Improved Reliability: More robust publishing process.
• Enhanced Performance: Faster request processing due to smaller data transfer.

Actionable Takeaway

If you're encountering payload size issues with image data in your API requests, consider implementing server-side caching. This approach can significantly reduce the amount of data transmitted, leading to a more stable and efficient system.