Image delivery with AWS Lambda ⚡️
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Serverless Image Rendering

Fast image delivering system using Sharp, AWS Lambda and Serverless framework.


Demo Lambda function available here

Medium article available here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.


This project requires NodeJS (at least version 6) and NPM. Node and NPM are really easy to install. To make sure you have them available on your machine, try running the following command.

$ node --version

$ npm --version

You may also want to install Serverless globally on your local machine for accessing the CLI functionality and directly interact with your deployed Lambda function.

To install Serverless globally on your machine, run the following command

$ npm i -g serverless

Please, read the official Serverless documentation to know more about the installation process.

If you want to deploy your code to AWS, then you will also need an AWS account and set-up your provider credentials

Optional requisites

This project is making use of Serverless and Docker. They are both optionals however Docker will be required for deploying your local code to Lambda if you are not running this project from a Linux machine. Read the Docker section to know more about.


Start with cloning this repo on your local machine:

$ git clone
$ cd serverless-image-rendering

Then install all the Node dependencies usin npm or Yarn

$ npm install
# Or using Yarn for a faster installation
$ yarn

Images Bucket

This project is making use of a pre-existing S3 bucket from where fetching the images to be processed and deliver to the client. If you don't have an S3 already, create one from your AWS dashboard and continue reading the Enviroment configuration section.


Enviroment configuration

This project contains a serverless.sample.yml file. You need to manually renamed it to serverless.yml. In order for your application to be correctly deployed to AWS, you will need to replace the BUCKET name under the environment section according to your S3 bucket name previously created.


For your local development you will need a .env file containing your S3 bucket name. Rename the .env.sample file in this project to be .env first, then replace the your-s3-bucket-name placeholder with the correct name of your S3 Bucket.

Serving the app

$ npm run dev

This will run NodeJS against local.js using express. This file will be only used during your local development and the real Lambda function handler is instead sitting inside handler.js so any change apported to local.js that you want to deploy in your Lambda function will require a correspondent change made inside your handler.js file as well.

Resizing your images

While ruinng your local app, open a browser to http://localhost:3000/resize-image/status

You should be able to see a JSON information to prove that your app is actually up and runing.

You can then replace the /status endpoint with your image information.


Query strinng name Type Required Description
f String Yes The complete image name uploaded to your S3 bucke (eg. placeholder.jpg)
w Number No The image width
h Number No The image height
q Number No The image quality (between 1-100)
t String No The image type (default is webp) Available values are [webp, jpeg and png]

Note. If the Type is different from your original image type, it will automatically be converted into the nnew format.



Assuminng that you have an image called placeholder.png on your S3 bucket:

Unit teststing

$ npm test

This is using Jest framework. If you want to keep running Jest in the background and watch for file change while developing use the following npm command instead:

$ npm run test:watch


  1. Install serverless globally (optional)
  2. Make sure you have correctly set up your AWS credentials
  3. Run Make package
  4. run npm run deploy


The Make package command will run npm install from inside a Docker container to reproduce the same envirnment configuration present in AWS Lambda.


Note. The following commands will require Serverless to be globally installed on your machine. Please read the Prerequisites section for more information.

When your Lambda function has been deployed, you will be able to easily log all the events directly inside your local terminal.

$ sls logs -f {function-name} -t

If you don't remember what's your function name, you can easily retrieve all your Lambda ingormation running the following command from inside your project's directory.

$ sls info


  1. Fork it!
  2. Create your feature branch: git checkout -b my-new-feature
  3. Add your changes: git add .
  4. Commit your changes: git commit -am 'Add some feature'
  5. Push to the branch: git push origin my-new-feature
  6. Submit a pull request 😎

Built With

  • VSCode
  • Docker
  • dotENV
  • Serverless
  • Love


MIT License © Andrea SonnY