AWS lambda offers a great computing ability that makes it perfect for small and on-demand jobs, although it is usually used to handle different events raised by AWS services in an event-driven manner, when we often return response body as JSON.
Another way to use lambda is to return binary data, in order to make that possible we need to use Functions URL and return the data as Base64 format, in this article we will explore how to read images from AWS S3, then process and serve those images as dynamic binary images using lambda and leverage the AWS CloudFront to cache the result.
Walking through the project
During this article we are going to use terraform to create a lambda function that runs on top of Nodejs runtime(make sure to install terraform", the lambda function will read an s3 image specified in the url path along the two params( width and height), then we will use the sharp package to resize the image then return the newly resized image.
Setting up lambda
Before we go further we need first to create a trust policy that allows Lambda to assume the role, then we give our function permission to read images stored on the s3 bucket, in addition to basic permissions to work with CloudWatch logs.
and here is the terraform code that declares the basic execution role for lambda, including the S3 read permission.
resource "aws_iam_role" "lambda_role" {
name = "th_gen_lambda_function_Role"
assume_role_policy = <<POLICY
{
"Version": "2012-10-17",
"Statement": [
{
"Action": "sts:AssumeRole",
"Principal": {
"Service": "lambda.amazonaws.com"
},
"Effect": "Allow",
"Sid": "AllowLambdaToAssumeRole"
}
]
}
POLICY
}
resource "aws_iam_policy" "iam_policy_for_lambda" {
name = "aws_iam_policy_for_terraform_aws_lambda_role"
path = "/"
description = "AWS IAM Policy for managing aws lambda role"
policy = <<POLICY
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "BasicLambdaLogsPolicy",
"Action": [
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents"
],
"Resource": "arn:aws:logs:*:*:*",
"Effect": "Allow"
},
{
"Sid": "AllowS3Read",
"Action": [
"s3:GetObject"
],
"Effect": "Allow",
"Resource": [
"arn:aws:s3:::samyouaret-thumbnail-pictures/*"
]
}
]
}
POLICY
}
resource "aws_iam_role_policy_attachment" "attach_iam_policy_to_iam_role" {
role = aws_iam_role.lambda_role.name
policy_arn = aws_iam_policy.iam_policy_for_lambda.arn
}
Creating Lambda function
After we declare the role necessary for lambda, we will create our lambda function.
resource "aws_lambda_function" "lambda_generator" {
function_name = "test_th_gen"
filename = "lambda_function.zip"
handler = "index.handler"
source_code_hash = filebase64sha256("lambda_function.zip")
role = aws_iam_role.lambda_role.arn
runtime = "nodejs16.x"
environment {
variables = {
BUCKET = "samyouaret-thumbnail-pictures"
}
}
}
let's explain the config used to create the function
- function_name: the name of the function.
- filename: the zipped file that is used to deploy lambda.
- index.handler: the entry file(index.js) and the target function that handles the request(handler).
- source_code_hash: this is important to calculate the hash of our zipped file so terraform can decide whether to redeploy the function if the code change( so we get a new hash).
-
runtime: it specifies
Nodejs.16xas the target runtime. - role: assign the previously created role to lambda.
Enabling Lambda function url
Enabling function URL is a straightforward task using terraform we declare the aws_lambda_function_url resource.
resource "aws_lambda_function_url" "lambda_url" {
function_name = aws_lambda_function.lambda_generator.function_name
authorization_type = "NONE"
}
Creating s3 bucket
We are going to create a public s3 bucket for this setup.
resource "aws_s3_bucket" "s3_bucket" {
bucket = "samyouaret-thumbnail-pictures"
tags = {
"project" = "thumbnail-gen"
}
}
resource "aws_s3_bucket_public_access_block" "bucket_public_access" {
bucket = aws_s3_bucket.s3_bucket.id
block_public_acls = false
block_public_policy = false
ignore_public_acls = false
restrict_public_buckets = false
}
Our Lambda code
After we successfully created our resources let's write the lambda function that handles the code, we going to init the project using yarn(you can use npm it is just a preference).
yarn init -y
to resize the image we will use the sharp package.
yarn add sharp
ultimately to read images from s3 we need to install S3 AWS client sdk
yarn add @aws-sdk/client-s3
Then we need to import your packages to get started
const sharp = require('sharp');
const { GetObjectCommand, S3Client } = require('@aws-sdk/client-s3');
Using the latest AWS s3 client, that implements Nodejs streams we are going create a simple function that reads images and then returns the image mime type and its content as a Nodejs buffer.
async function getImage(bucket,imageKey) {
const s3Client = new S3Client();
return new Promise(async (resolve,reject)=>{
try {
let getCommand = new GetObjectCommand({
Bucket: bucket,
Key: imageKey
});
let response = await s3Client.send(getCommand);
let chunks = [];
response.Body.on('data',(chunk)=> chunks.push(chunk));
response.Body.once('end',async ()=>resolve({
body: Buffer.concat(chunks),
contentType: response.ContentType
}));
} catch (error) {
reject(error);
}
});
}
Now let's create a function to resize the image with sharp package.
async function resizeImage(imageBuffer,options) {
return sharp(imageBuffer).resize(options).toBuffer();
}
Creating the lambda handler function
Our function will read the pathname and width and height params specified in the function URL when it is invoked, use the pathname as the key to reading the s3 image, and pass the params and image content to sharp to resize it.
async function handler(event, context) {
let imageKey = event.rawPath.replace('/','');
let image = await getImage(process.env.BUCKET,imageKey);
let body = await resizeImage(image.body, {
width: parseInt(event.queryStringParameters.w),
height: parseInt(event.queryStringParameters.h),
});
return {
statusCode: 200,
headers: { "Content-Type": image.contentType },
body: body.toString('base64'),
isBase64Encoded: true
}
}
Now the important part is to specify to content type to image content type eg. image/png, more importantly, is to set the property isBase64Encoded to true so Lambda knows that the body is base64 encoded.
Using the nodejs buffer.toString('base64') will encode the sharp result(buffer) as base64. To call the function we simply call the function as follows
https://function-url/image-key?w=300&h=3000
to package the function in a zip file
zip lambda_function.zip index.js node_modules yarn.lock package.json -r
One thing to do before we can use terraform is to export AWS credentials
export AWS_SECRET_ACCESS_KEY= AWS_ACCESS_KEY_ID=
Plan the terraform setup, if that satisfies you then apply the config.
terraform plan
Then run apply the changes
terraform apply
Adding Cloudfront Cache layer
Let's add CloudFront to the cache of the result and reduce the computing resource and the cost.
We are going to create a CloudFront distribution, and the origin will be the function URL, we will also create
- A cache policy so the cache key will depend on width and height parameters.
- An Origin request policy so we can forward the width and height parameters to the Lambda function.
For the purpose of testing, we use a TTL of 60s.
resource "aws_cloudfront_cache_policy" "thumnail_generator_cache_policy" {
name = "thumnail_generator_cache_policy"
min_ttl = 60
default_ttl = 60
max_ttl = 60
parameters_in_cache_key_and_forwarded_to_origin {
cookies_config {
cookie_behavior = "none"
cookies {
items = []
}
}
headers_config {
header_behavior = "none"
headers {
items = []
}
}
query_strings_config {
query_string_behavior = "whitelist"
query_strings {
items = ["w", "h"]
}
}
}
}
resource "aws_cloudfront_origin_request_policy" "forward_resize_params" {
name = "forward_resize_params"
comment = "forward resize params to origin"
query_strings_config {
query_string_behavior = "whitelist"
query_strings {
items = ["w", "h"]
}
}
headers_config {
header_behavior = "none"
}
cookies_config {
cookie_behavior = "none"
}
}
resource "aws_cloudfront_distribution" "lambda_distribution" {
origin {
domain_name = "${aws_lambda_function_url.lambda_url.url_id}.lambda-url.us-east-1.on.aws"
origin_id = "${aws_lambda_function_url.lambda_url.url_id}.lambda-url.us-east-1.on.aws"
custom_origin_config {
http_port = "80"
https_port = "443"
origin_protocol_policy = "https-only"
origin_ssl_protocols = ["TLSv1.2"]
}
}
depends_on = [
aws_cloudfront_cache_policy.thumnail_generator_cache_policy,
aws_cloudfront_origin_request_policy.forward_resize_params,
]
enabled = true
default_cache_behavior {
allowed_methods = ["DELETE", "GET", "HEAD", "OPTIONS", "PATCH", "POST", "PUT"]
cached_methods = ["GET", "HEAD"]
target_origin_id = "${aws_lambda_function_url.lambda_url.url_id}.lambda-url.us-east-1.on.aws"
viewer_protocol_policy = "allow-all"
cache_policy_id = aws_cloudfront_cache_policy.thumnail_generator_cache_policy.id
origin_request_policy_id = aws_cloudfront_origin_request_policy.forward_resize_params.id
}
restrictions {
geo_restriction {
restriction_type = "none"
locations = []
}
}
viewer_certificate {
cloudfront_default_certificate = true
}
}
Now again, Plan the Terraform setup, if that satisfies you then apply the config.
terraform plan
Then run apply the changes
terraform apply
Cleaning up Our infrastructure
Finally, let's clean the infrastructure we've created by running the terraform destroy command.
terraform destroy
Conclusion
In this article, we explored how we can use AWS lambda to serve binary content, and how we can leverage CloudFront to enhance our solution.
Although we were able to serve binary content with lambda, I found it inefficient when the image size was medium/large(it took almost 1.3s on average), probably due to the process of encoding and decoding the content from and to base64.
Top comments (0)