Continuation of our series on how to build a CloudFront distribution that serves your S3 Media, performs on-demand image transformation and even authenticates incoming requests.
*This is part III of the series of posts where we are building a CloudFront distribution that serves your S3 Media, performs on-demand image transformation and authenticates incoming requests.
If you missed part II, you can check it out here!
Creating the origin-response lambda
So a bit of recap from the last parts: We created a CloudFront distribution that is backed by an S3 bucket that contains our media. We then added dynamic origin selection using an origin-request Lambda@Edge to send requests to Cloudinary if the user provides transformations parameters, so we can have on-demand image manipulation on our distribution.
Now, there is a bit of inconsistency going on: S3 and Cloudinary will provide different error responses if something unexpected occurs. While this is not a big deal, we want to streamline the error responses that we return to our users. We can achieve this by adding an origin-response Lambda@Edge!
Let's create a new
origin-response.ts file with the following content:
In the code above, if the status code returned is anything above or equal to 400 (therefore either a client-side or a server-side) we rewrite the response that we will return to CloudFront. Since this is an origin-response lambda, whatever we return here will be cached by CloudFront. We can control how it will be cached by setting the cache-control response header: we just set it to no-cache as default, but then set a five-minute TTL for 404 in order to prevent a lot of requests to a non-existing file to go through. If the response is not an error, we just return the response we had in the first place, and nothing is changed.
Now let's build a lambda with this code and attach it to CloudFront in a similar way we did with the origin response:
First we add a new entrypoint in our
Then, create the lambda on Terraform:
And lastly we attach it to CloudFront, using the l
ambda_function_association inside the
Now you can compile your code using rollup and plan-and-apply the terraform changes or leave it to do all of this by the end of this article.
Creating the viewer request lambda
We now want to prevent any unauthenticated request from accessing our media, so we add some authentication to it! For now, we will just check the token provided against a hardcoded secret, but this could just be an HTTP call to your backend to check its authenticity.
Let's create a new
viewer-request.ts file with the following content:
If the authentication fails, we just return a 403. Now if the user is authenticated, we let the request pass along, but first we move the token query parameter to a header. We do this because the token query parameter should not interfere in our routing between S3 and Cloudinary, but we cannot just remove it, as we need it in order to use it in our Cloudinary fetch.
We need to modify our origin request to reflect this authentication. We get the token from headers and then remove the header to prevent it from being sent downstream to our origins. Then, we use the token when creating the media URL. We also need to encode our media URL as it now contains a ?, otherwise it can be interpreted as a query param of the origin request instead of the media URL. In the end, our handler will look like this:
In the viewer-request code, we are just checking if the token is equal to MY_SECRET, but you can (and should) have a different authentication mechanism here, like sending the secret to an auth endpoint to verify it, or even maybe just inject a JWT secret to the lambda and verify the token using it. You can inject env vars the same way we did with the CLOUDINARY_ID.
A couple of notes here though before you proceed with the authentication method of your choice:
First, Lambda@Edge cannot import third-party modules as there is no package.json or node_modules there to use. However, Rollup will solve this issue for us: you can install and import any modules as you like in your .ts file, and Rollup will include their source code in your .js bundled file!
Second, if you go with a remote authentication (like sending an HTTP request somewhere), please bear in mind that this will increase latency significantly depending on where your user is requesting it. Remember, viewer-request lambdas will run every time, even if the response is cached on a PoP nearby. The Lambda@Edge will be running on a PoP close to the user, but this becomes ineffective if it has to go to a faraway datastore anyway to check the authentication token. The workaround for this is to also have your authentication endpoint backed by a CDN so that the authentication check of your lambda requests is also cached around the world.
For now, let's just test it using the hardcoded secret. Same drill as usual now. Add another entry point to our
Create the lambda on Terraform:
And once again, we attach it to CloudFront:
Now, same as we did in the previous part, compile your code by running:
terraform plan and
terraform apply. After everything is done and applied, not only error responses should be normalized, but you now will need to pass the
token=MY_SECRET as a query parameter in order to access your media!
All good! We now have normalized error response and authentication! In the next and last steps, we will add logging and monitoring metrics to our lambdas. See you in the next article!
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