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Part 2: Run Adobe Target NodeJS SDK for Experimentation and Personalization on Edge Platforms (AWS Lambda@Edge)

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13-09-2021

Author: Artur Ciocanu (@arturc85303583)

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This is the second part of our series that covers Adobe Target NodeJS SDK with On-Device Decisioning capabilities and how to run it in a serverless/edge compute environment. In this second part, we will be covering AWS Lambda and specifically AWS Lambda@Edge.

This blog is Part 2 in a three-part series that will cover how anyone could use Adobe Target NodeJS SDK to run experimentation and personalization on an edge compute platform. The parts are:

  • Part 1: Akamai Edge Workers and Adobe Target NodeJS SDK
  • Part 2: AWS Lambda@Edge and Adobe Target NodeJS SDK
  • Part 3: Cloudflare Workers and Adobe Target NodeJS SDKStep by step guide

As mentioned in our previous blog, we use Terraform heavily at Adobe Target. In this article, we will show how you can leverage Terraform and Adobe Target NodeJS SDK to create an AWS Lambda@Edge.

AWS Lambda@Edge is a great technology if you intend to run some piece of logic in 200+ points of presence provided by AWS CloudFront. However, it is not trivial to set up, especially if we want to set it up in a secure way. That's why we will be using Terraform to bootstrap all the infrastructure elements.

Prerequisites

Before we begin there are a few prerequisites:

  • AWS account: You will need a valid AWS account and credentials. Terraform relies on these credentials.
  • Terraform: We will use it to create all the required AWS resources. Please check the official Hashicorp documentation on how to install Terraform on your particular OS. In this article, we will be showing examples using Mac OS X.
  • NodeJS: We will use NodeJS to get the Adobe Target NodeJS SDK dependency as well as using NPM to package the JavaScript and prepare it for AWS Lambda.

Creating the origin S3 bucket resources

In order to use AWS Lambda@Edge we need to create a CloudFront distribution. At the same time, a CloudFront distribution requires an "origin". We don't really need an "origin", because we will use our own code to build an HTTP response. However to make AWS happy we will create a dummy S3 bucket. Here is the Terraform code to create a simple S3 bucket:

resource "aws_s3_bucket" "s3_bucket" {
  bucket = var.bucket_name
}

It is recommended to always keep S3 bucket private, so to make sure CloudFront can access our S3 bucket we need to create an Origin Access Identity. Here is the Terraform code to do it:

resource "aws_cloudfront_origin_access_identity" 
"origin_access_identity" {
}

Once we have the S3 bucket and Origin Access Identity we can combine the two and create the S3 bucket policy. Here is the Terraform code to do it:

data "aws_iam_policy_document" "s3_policy" {
  statement {
    actions   = ["s3:GetObject"]
    resources = ["${aws_s3_bucket.s3_bucket.arn}/*"]
    principals {
      type        = "AWS"
      identifiers = [aws_cloudfront_origin_access_identity.origin_access_identity.iam_arn]
    }
  }
}
resource "aws_s3_bucket_policy" "s3_bucket_policy" {
  bucket = aws_s3_bucket.s3_bucket.id
  policy = data.aws_iam_policy_document.s3_policy.json
}

Note: Here we have used Terraform data to create a policy document. We could have also used a JSON document and embedded into bucket policy, without a data element.

Creating the AWS Lambda function

Once we have everything in place from “origin” perspective, the next step is to create the AWS Lambda function that will be referenced by CloudFront distribution. Here is the Terraform code to do it:

resource "aws_lambda_function" "main" {
  function_name    = var.function_name
  description      = var.function_description
  filename         = var.filename
  source_code_hash = filebase64sha256(var.filename)
  handler          = var.handler
  runtime          = var.runtime
  role             = aws_iam_role.execution_role.arn
  timeout          = var.timeout
  memory_size      = var.memory_size
  publish          = true
}

Note: This is a bare-bones function, for production use cases you’ll want to make sure that function errors and logs are forwarded to AWS CloudWatch.

Looking at the Terraform code for AWS Lambda function we can se that there is a filename, handler and runtime fields. Let's see why we need these fields:

  • filename: This is the path to the ZIP archive containing the AWS Lambda function source code.
  • handler: This is a reference to a NodeJS exported function. Usually, it is something like index.handler, assuming that the main file from the ZIP archive is index.js and it exports a function named handler.
  • runtime: This is the NodeJS runtime, we recommend using the latest NodeJS LTS version which is nodejs12.x.

Having all the Terraform code related to AWS Lambda function out of the way, let's see how we can use Adobe Target NodeJS SDK to power the Lambda function.

In order to use Adobe Target NodeJS SDK we need to download it from NPM, we can use the following command:

$ npm i /target-nodejs-sdk -P

Once we have the Adobe Target NodeJS SDK dependency, we need to create the AWS Lambda function handler. Here is the sample code:

const TargetClient = require("@adobe/target-nodejs-sdk");
const RULES = require("./rules.json");
const createTargetClient = () => {
  return new Promise(resolve => {
    const result = TargetClient.create({
      client: "<client code>",
      organizationId: "<IMS organization ID>",
      logger: console,
      decisioningMethod: "on-device",
      artifactPayload: RULES,
      events: {
        clientReady: () => resolve(result)
      }
    });
  });
};
const getRequestBody = event => {
  const request = event.Records[0].cf.request;
  const body = Buffer.from(request.body.data, "base64").toString();
  return JSON.parse(body);
};
const buildResponse = body => {
  return {
    status: "200",
    statusDescription: "OK",
    headers: {
      "content-type": [{
        key: "Content-Type",
        value: "application/json"
      }]
    },
    body: JSON.stringify(body)
  }
};
const buildSuccessResponse = response => {
  return buildResponse(response);
};
const buildErrorResponse = error => {
  const response = {
    message: "Something went wrong.",
    error
  };
  return buildResponse(response);
};
const targetClientPromise = createTargetClient();
exports.handler = (event, context, callback) => {
  // extremely important otherwise execution hangs
  context.callbackWaitsForEmptyEventLoop = false; 
  const request = getRequestBody(event);
  
  targetClientPromise
  .then(client => client.getOffers({request}))
  .then(deliveryResponse => {
    console.log("Response", deliveryResponse);
    
    callback(null, buildSuccessResponse(deliveryResponse.response));
  })
  .catch(error => {
    console.log("Error", error);
    
    callback(null, buildErrorResponse(error));
  });
};

Note: The RULES constant references the On-Device Decisioning artifact rules.json file. This file can be downloaded from https://assets.adobetarget.com/<client code>/production/v1/rules.json. This file will be available only after you have enabled On-Device Decisioning for your Adobe Target account.

There is one thing worth mentioning, in the context of AWS Lambda function, Adobe Target NodeJS SDK has been created and tested in a server-side context and it has a few "background processes" like polling for On-Device Decisioning artifact updates, etc, so in order to make sure that AWS Lambda function does not hang and timeouts, we have to use:

context.callbackWaitsForEmptyEventLoop = false;

For more details around context.callbackWaitsForEmptyEventLoop please check the official Amazon documentation, which can be found here.

We have the sample AWS Lambda function handler and we have the On-Device Decisioning artifact aka rules.json. To be able to use this code we need to package it in a ZIP archive. On a UNIX based system this can be done using:

$ zip -r function.zip .

Creating the CloudFront distribution

To connect all the dots, we need to create the CloudFront distribution. Here is the Terraform code to do it:

resource "aws_cloudfront_distribution" "cloudfront_distribution" {
  enabled         = true
  is_ipv6_enabled = true
  origin {
    s3_origin_config {
      origin_access_identity = aws_cloudfront_origin_access_identity.origin_access_identity.cloudfront_access_identity_path
    }
    
    domain_name = aws_s3_bucket.s3_bucket.bucket_domain_name
    origin_id   = var.bucket_name
  }
  
  restrictions {
    geo_restriction {
      restriction_type = "none"
    }
  }
  
  default_cache_behavior {
    target_origin_id = var.bucket_name
    allowed_methods = ["HEAD", "DELETE", "POST", "GET", "OPTIONS", "PUT", "PATCH"]
    cached_methods  = ["GET", "HEAD"]
    
    lambda_function_association {
      event_type   = "viewer-request"
      lambda_arn   = aws_lambda_function.main.qualified_arn
      include_body = true
    }
    
    forwarded_values {
      query_string = false
      cookies {
        forward = "none"
      }
    }
    viewer_protocol_policy = "redirect-to-https"
    min_ttl                = 0
    default_ttl            = 7200
    max_ttl                = 86400
  }
  
  viewer_certificate {
    cloudfront_default_certificate = true
  }
}

There is a lot of boilerplate, but the most interesting pieces are:

  • origin: Here we connect S3 bucket and Origin Access Identity
  • default_cache_behavior: Here we have to make sure allowed_methods is set to ["HEAD", "DELETE", "POST", "GET", "OPTIONS", "PUT", "PATCH"] otherwise, we won't b able to process POST requests
  • lambda_function_association: Here we reference AWS Lambda function and ensure that we respond to viewer-request event type, which means that AWS Lambda will generate the response without "origin" being involved.

Testing it out

If everything was set up properly, then you should have a CloudFront distribution domain name. Using the domain name you could run a simple cURL command to check that everything is looking good. Here is a sample:

curl --location --request POST 'dpqwfa2gsmjjr.cloudfront.net/v1/personalization' \
--header 'Content-Type: application/json' \
--data-raw '{      
  "execute": {
    "pageLoad": {}
  }
}

This will simulate a “pageLoad” request aka “Target global mbox” call. The output would look something like this:

{
    "status": 200,
    "requestId": "63575665f53944a1af93337ebcd68a47",
    "id": {
        "tntId": "459b761e8c90453885ec68a845b3d0da.37_0"
    },
    "client": "targettesting",
    "execute": {
        "pageLoad": {
            "options": [
                {
                    "type": "html",
                    "content": "<div>Srsly, who dis?</div>"
                },
                {
                    "type": "html",
                    "content": "<div>mouse</div>"
                }
            ]
        }
    }
}

Closing thoughts

By looking at the sheer amount of Terraform code one might ask:

  • Why even bother?
  • Why should I spend so much time and energy trying to deploy Adobe Target NodeJS SDK on AWS Lambda@Edge?

Here are a few benefits:

  • Isolation: From a security point of view, sometimes it is quite complicated to add yet another third-party dependency such as Adobe Target NodeJS SDK to your codebase. While deploying a similar code to AWS Lambda is pretty easy and everything is well isolated.
  • Decoupling: If your codebase depends on Adobe Target NodeJS SDK and there is a bug or security issue, sometimes it might be difficult to have a release, while with AWS Lambda being a serverless platform, this is trivial and less dangerous.
  • Flexibility: In the provided sample, we are returning a Target Delivery API response, but nothing stops you from adding custom logic and transformation to have a custom JSON output. Also, you could build custom REST APIs on top of AWS Lambda@Edge that is tailored to your domain.
  • Performance: Not everyone is Amazon or Google or Adobe and even if you have a presence in multiple geographic locations you can't beat CloudFront with its 200+ points of presence. By using AWS Lambda@Edge and Adobe Target NodeJS SDK you get low latency and a lot of flexibility.

Follow the Adobe Experience Platform Community Blog for more developer stories and resources, and check out Adobe Developers on Twitter for the latest news and developer products. Sign up here for future Adobe Experience Platform Meetup.

Previous Blogs

Part 1: Run Adobe Target NodeJS SDK for Experimentation and Personalization on Edge Platforms (Akamai Edge Workers)

Resources

  1. Source code — https://github.com/artur-ciocanu/odd-lambda-edge
  2. Adobe Target — https://business.adobe.com/products/target/adobe-target.html
  3. Adobe Experience Platform — https://business.adobe.com/products/experience-platform/adobe-experience-platform.html
  4. Adobe Target NodeJS SDK — https://adobetarget-sdks.gitbook.io/docs/sdk-reference-guides/nodejs-sdk
  5. Terraform — https://www.terraform.io/
  6. AWS Terraform provider — https://registry.terraform.io/providers/hashicorp/aws/latest/docs
  7. AWS Lambda@Edge — https://aws.amazon.com/lambda/edge/
  8. AWS CloudFront- https://aws.amazon.com/cloudfront/
  9. Adobe Target NodeJS SDK-https://github.com/adobe/target-nodejs-sdk
  10. Documentation — AWS Lambda context object in Node.js- https://docs.aws.amazon.com/lambda/latest/dg/nodejs-context.html

 

 

Originally published: May 20, 2021