Category: Computing

As an experiment, I have a CodeCommit repository that has a combination of CloudFormation Templates, and some static web content, checked in to two separate prefixes or folders: /Templates/ and /Website/.

What I am trying to do is, upon any commit to the repo, determine if the Website prefix needs an update, or the Templates have to trigger CFN Stack Update.

Starting with the most basic piece, I want the web content to go via CodePipeline, and unpack into an S3 Bucket, against which there is a CloudFront distribution pointing (with an Origin Access Identity already in place).

By default, an S3 unpack expects the entire repo to unpack into S3, but I want to only have a particular sub folder, so I’ve implemented a “repackage” step as a Lambda function in the pipeline, which grabs the Original Artifact the pipeline has, unpacks it, and then create as new Artifact containing just the folder /Website/ and below. Turned out to be around 50 lines of code in Python:

import json
import boto3
import os
import zipfile

def lambda_handler(event, context):
    if (event["CodePipeline.job"]["data"]["inputArtifacts"][0]["location"]["type"] != "S3"):
        return { 'statusCode': 500, 'body': json.dumps('Not on S3') }
    dl_filename = event["CodePipeline.job"]["data"]["inputArtifacts"][0]["location"]["s3Location"]["objectKey"].split('/')[-1]

    s3client = boto3.client('s3',
      aws_access_key_id=event["CodePipeline.job"]["data"]["artifactCredentials"]["accessKeyId"], 
      aws_secret_access_key=event["CodePipeline.job"]["data"]["artifactCredentials"]["secretAccessKey"], 
      aws_session_token=event["CodePipeline.job"]["data"]["artifactCredentials"]["sessionToken"]
      )
    with open("/tmp/" + dl_filename, 'wb') as data:
        s3client.download_fileobj(
            event["CodePipeline.job"]["data"]["inputArtifacts"][0]["location"]["s3Location"]["bucketName"], 
            event["CodePipeline.job"]["data"]["inputArtifacts"][0]["location"]["s3Location"]["objectKey"], 
            data)
    with zipfile.ZipFile("/tmp/" + dl_filename, 'r') as zip:
        zip.extractall('/tmp/')
        zip.close()
    ul_filename = event["CodePipeline.job"]["data"]["outputArtifacts"][0]["location"]["s3Location"]["objectKey"].split('/')[-1]
    zipf = zipfile.ZipFile("/tmp/" + ul_filename, 'w', zipfile.ZIP_DEFLATED)
    os.chdir('/tmp/Website/')
    for root, dirs, files in os.walk('.'):
        for file in files:
            zipf.write(os.path.join(root, file))
    zipf.close()
    #WARNING: CodePipeline artifacts may ave a default BucketPolicy requiring an explict KMS key. Remove that SSE requirement, turn on dfault encryption for the bucket.
    s3response=s3client.upload_file(
        "/tmp/" + ul_filename,
        event["CodePipeline.job"]["data"]["outputArtifacts"][0]["location"]["s3Location"]["bucketName"],
        event["CodePipeline.job"]["data"]["outputArtifacts"][0]["location"]["s3Location"]["objectKey"],
        ExtraArgs={"ServerSideEncryption": "AES256"}
        )
    client_cp = boto3.client('codepipeline')
    response_cp = client_cp.put_job_success_result(jobId=event["CodePipeline.job"][ "id"])
    return {
        'statusCode': 200,
        'body': json.dumps('Done repacking.')
    }

This runs reasonably quickly, and means I am not unpacking the entire CodeCommit repo into my CloudFront distribution.

I’ve been tracking my blog posts and other “contributions” to the AWS developer community since 2017 when the program was originally called the AWS Cloud Warrior Program. This morphed into the Partner Ambassador program, for the top engineering talent in the partner community, and then became a global program.

You can find the ambassadors here. At the time of writing (Nov 1 2021), three are 227 people listed: 114 in APAC, 43 in Europe, 9 in LATAM, and 46 in North America.

I submitted some 28 items to the program in 2021 (until mid-October 2021), from Blog Posts to Case Studies, Open Source work, Event Hosting, and Certification Subject Matter Expert contributions.

This was enough to land me in the #2 position for 2021, as shared during the online Global Ambassador Summit recently – shown in this slide:

And while I sit here with 9 (of 11) AWS certifications (more set to launch during re:Invent), I don’t yet hold the coveted Gold Jacket for holding all available cert (which looks as loud and proud as you can imagine; I think I saw something similar in The Hangover movie).

Arjen and Ian are both amazing engineers; I am honoured to be considered amongst them in this program.

Sharing ideas and solutions has been core to my work in the technology field since I was at University when I first discovered open-source and then became a Debian Linux Developer. Indeed, as developers (and Sys Admins, these days deemed as DevOps Engineers) become more senior, sharing and mentoring becomes more of the job.

Occasionally I get feedback from people that my posts have helped them save time and find a solution quickly, or avoid problems. Often I find myself reading back my own posts years later, thanking younger-me for putting some notes online.

But as with most in this industry, we stand on the shoulders of others; the only right thing to do is to support those coming after us.

November 2021: Note there is a new way to do this natively within CloudFront, and it wont cost you a Lambda@Edge invocation.

For a long time, I’ve been using Lambda@Edge to inject various HTTP security-related headers to help browsers improve the security model of the content that they fetch and render.

I’ve been doing this as I have been using S3 as the origin (accessed via a CloudFront Origin Access Identity). S3 itself cannot add/inject many of the common security headers when it passes

These Functions execute when the origin returns the content to the CloudFront regional edge; the returned content then gets cached with the injected headers included.

The end result is getting a good rating on securityheaders.com, hardenize.com, and other public security evaluation services.

An alternate in the Lanbda@Edge execution lifecycle is to trigger on Viewer Response; in which case the cached version doesn’t have the headers injected, and every viewer request triggers the code execution. Clearly, if every viewer has the same set of headers, there’s no need to execute each view response and pay for the additional Lambda@Edge executions.

Now there’s a new option – CloudFront Functions (AWS blog post). Written entirely in JavaScript, it executes only at Viewer Request, or Viewer Response. There is no Origin Request or Origin Response option. It also executes at the CloudFront Edge, not the Regional Edge.

Thie example injects a number of headers, and would need only minor potential customisation on the Content Security Policy (and possibly Permissions Policy) to work for most sites:

function handler(event) {
    var request = event.request;
    var response = event.response;
    response.headers['strict-transport-security']= { value: 'max-age=31536000' };
    response.headers['x-xss-protection']= { value: '1'};
    response.headers['x-content-type-options']= { value: 'nosniff'};
    response.headers['x-frame-options']= { value: 'DENY'};
    response.headers['referrer-policy']= { value: 'strict-origin-when-cross-origin'};
    response.headers['expect-ct']= { value: 'enforce, max-age=86400'};
    response.headers['permissions-policy']= { value: 'geolocation=(self), midi=(), sync-xhr=(self), microphone=(), camera=(), magnetometer=(), gyroscope=(), fullscreen=(), payment=(), autoplay=(self)'};
    response.headers['content-security-policy'] = { value: "default-src: 'self'; img-src 'self' data: ; style-src 'self' 'unsafe-inline' ; frame-ancestors 'none'; form-action 'none'; base-uri 'self'; "};
    return response;
}

You may want to evaluate the cost of both Lambda@Edge and CloudFront Functions. After the first year, Functions is charged at US$0.10 per million functions. As an equivalent, Lambda@Edge for a similar Node.JS function that executes in one millisecond with 128 MB of memory would be US$0.2021 per million requests.

However, given a busy website, you may want to look at the efficiency differences between Viewer Response execution for CloudFront Functions, and Origin Response and the caching for Lambda@Edge (multiplied by the number of Edge Cache locations (13), and the cache retention rate).

If you have only a few unique URLs, and content that can be cached for a long period, and large volumes of requests, then Lambda@Edge may result in near free execution.

If we were using Lambda@Edge on ViewerResponse, and not caching the object with headers injected, then CloudFront Functions would be cheaper; or if the content being sent was dynamic from the origin and not suitable to be cached, in which case we wouldn’t get the efficiency savings of fewer executions.

Even if we are using Origin Response with Lambda@Edge, we can’t determine the cache expiry of the Lambda@Edge cached responses (we can influence it); the cached objects could expire and re-execute every day, so the Lambda@Edge costs could go up 30x (which would only make CloudFront functions 126 times more expensive). YMMV. TIMTOWTDI.