Posted by Edd Mann on Jun 15, 2018

Mince Pie Challenge: Setting up the Serverless Framework with Docker, Webpack and Babel

Now that we have spent some time working out how the API is going to look, we can move on to building it! We will start off by configuring the initial API project, setting up a Dockerized Serverless Framework with Webpack and Babel support. If you are keen to see how the finished configuration looks, you can access it within the API repository.

Serverless Framework via Docker

So as to make the project easy-to-setup on many different development environments, we will employ Docker to manage the Serverless Framework and Node dependencies. We will take advantage of a pre-built Node 8 image and add the ability to run the Serverless Framework within it. For allowing us to support future Node dependencies that require glibc and container exploration via a Bash shell, we will also include these into our image definition.

FROM node:8-alpine
RUN apk --no-cache add bash ca-certificates wget
RUN wget -q -O /etc/apk/keys/ && \
    wget && \
    apk add glibc-2.27-r0.apk && \
    rm -f glibc-2.27-r0.apk
RUN yarn global add serverless@1.27.3
WORKDIR /opt/app

When the container is initiated we wish to supply the AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) required by Serverless using a given .env file. This allows us to easily control what credentials are being used to deploy and maintain the application. We will also allow the container to access the code-base and provide a separate volume for Node dependencies to be persisted. Doing this allows us to isolate working dependencies targeted at the container architecture and dependencies installed for the host machine.

version: '3'

    build: .
    env_file: .env
      - .:/opt/app
      - app-modules:/opt/app/node_modules


Finally, to make the process of executing the common-place actions on our host machine seamless, we shall define a simple Makefile which outlines these capabilities in a single place.

  docker-compose run --rm serverless npm install

  docker-compose run --rm serverless sls webpack -v

  docker-compose run --rm serverless sls deploy -v

  docker-compose run --rm serverless bash

Serverless with Babel and Webpack

It should be noted that it is not strictly necessary to require Babel and Webpack when working with Lambda and the Serverless Framework. However, the ability to use all the latest JavaScript features, along with Webpack’s module bundling (inc. tree-shaking) are invaluable additions. We will start off by defining and installing the explicit development dependencies that we will need. Within Lambda, the aws-sdk dependency comes pre-baked into the runtime, so we only care about including this during development.

  "name": "mince-pie-challenge-api",
  "version": "1.0.0",
  "private": true,
  "devDependencies": {
    "aws-sdk": "^2.268.1",
    "babel-core": "^6.26.3",
    "babel-loader": "^7.1.5",
    "babel-plugin-transform-object-rest-spread": "^6.26.0",
    "babel-preset-env": "^1.7.0",
    "serverless-webpack": "^5.1.5",
    "webpack": "^4.15.1",
    "webpack-node-externals": "^1.7.2"
  "dependencies": {}

From here we can make install and see the Docker module volume in action. You will notice that a node_modules directory has been created within the code-base but it is empty on your host machine?! However, if you make shell into the container you will notice that the container specific dependencies are present. This allows you to optionally install the dependencies on your host machine, without conflicting with the container.

Now lets setup the initial serverless.yml, webpack.config.js and .babelrc configurations.

service: mince-pie-challenge-api

  name: aws
  runtime: nodejs8.10
  stage: dev
  region: eu-west-1
    GREETING: "Hello"
  iamRoleStatements: ${file(./roles.yml)}

    webpackConfig: ./webpack.config.js
        - aws-sdk

  - serverless-webpack

  individually: true

resources: ${file(./resources.yml)}

functions: ${file(./functions.yml)}

You can see that we have decided to break up the roles, resources and functions into separate YAML files. This allows us to manage these definitions in an easier manor, preventing us from creating one huge file. We have specified that we wish to use the latest Lambda node runtime (at the time of writing), with a single global environment variable set (this will be used in a following demo). So as to take full advantage of Webpack we have specified that we wish each function to be packaged individually when deployed. This allows us to prune any unused dependencies on a per-function basis.

const path = require('path');
const slsw = require('serverless-webpack');
const nodeExternals = require('webpack-node-externals');

module.exports = {
  entry: slsw.lib.entries,
  target: 'node',
  mode: slsw.lib.webpack.isLocal ? 'development' : 'production',
  optimization: { minimize: false },
  performance: { hints: false },
  devtool: 'nosources-source-map',
  externals: [nodeExternals()],
  module: {
    rules: [
        test: /\.js$/,
        exclude: /node_modules/,
        use: [{ loader: 'babel-loader' }],
  output: {
    libraryTarget: 'commonjs2',
    path: path.join(__dirname, '.webpack'),
    filename: '[name].js',
    sourceMapFilename: '[file].map',

We supply Webpack with a simple configuration, specifying that we are targeting Node (allowing us to ignore packaging built-in modules) and that wish to use Babel to process our JavaScript files. We also define how we wish each function to be output (with their associated source mapping), and which mode to run in based on a serverless-offline flag.

  "comments": false,
  "presets": [["env", { "targets": { "node": "8.10" } }]],
  "plugins": ["transform-object-rest-spread"]

We let Babel know that we wish to target the specific version of Node that is present within the Lambda runtime. This allows Babel to only include pollyfilled support for features that are used and do not natively exist in this version. One such feature that is not present within the env preset at this time is Object rest spread, so we explicitly include this plugin.

This is all that is required to begin developing Lambda functions using the Serverless Framework, with access to the latest JavaScript features. 🎉

The ‘Hello World’ Example

Before we delve into developing the API, lets get ourselves accustom to building and deploying a trivial HTTP-based Lambda function. We will create a function which is invoked by an API Gateway endpoint and returns a greeting based on the supplied name. The environment variable that we specified in the serverless.yml file will make sense now.

We will start off by defining the function within our functions.yml file, and store the implementation itself within a src directory.

  handler: src/hello.handler
    - http:
        path: /hello/{name}
        method: get
                name: true

With this simple definition Serverless is able to create all the AWS-specific infrastructure required for us to get the endpoint up and running. We define a single handler which is invoked via HTTP and requires a specified name to be included with the request.

const { GREETING } = process.env;

export const handler = async event => {
  const { name } = event.pathParameters || {};
  return {
    statusCode: 200,
    body: JSON.stringify({ greeting: `${GREETING} ${name}` }),

Once API Gateway has passed the request to our Lambda function we will use the supplied name to return a 200 OK response with the associated greeting.

Now that we have some actual functionality we can interact with, we can go about deploying this application to AWS. Simply executing make deploy will instruct our Dockerized Serverless Framework to package up and deploy the function, along with the required resources. If you are interested in a more in-depth look into this process I have created several different videos on the topic. Once this has completed, an API Gateway endpoint will be presented that you can then visit to test out the service. You can see this module installation and deployment process in-action within the following screencast.

With the Serverless Framework configured and a sample function now deployed, we can begin the process of implementing our API. Join me in the next post were I will discuss the process of configuring Amazon Cognito, the authentication service that will be used within our API.

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