In this project, you’ll build a simple Go server application and Dockerise it to run within a container. You’ll write tests that run against the container, and then build GitHub actions to automate continuous testing and release of the application. You’ll then run it in the cloud by pushing the container image to Amazon AWS Elastic Container Repository, and then launch it in Amazon AWS Elastic Container Service using the user interface.

⚠️ This project requires you to have access to an Amazon AWS account, with permissions to configure ECS, ECR, Fargate, IAM, and Elastic Load Balancing. Ask on CYF Slack for help with that.

⚠️ You need a GitHub repo to complete this because we’ll be using GitHub Actions. If you are not already working in your own fork of the immersive-go-course repository, now would be a good time!

Learning objectives:

• Set up Docker locally
• Containerise an application using Docker syntax
• Run applications locally using Docker
• Push container images to a repository (ECR)
• Deploy images to Elastic Cloud resources using Elastic Container Service
• Destroy cloud resources to mitigate cost

Timebox: 2 days

Project

This project will require us to pull together information from several different guides to get an application developed locally to run in the cloud. This project will give you experience of building and deploying a real applications. Our focus here is not on the Go code, but on the infrastructure around it.

Motivation

We’re going to build a simple Go server, and then containerise it.

In this project, we’ll use the terms containerise and dockerise interchangeably to mean the same thing: making an application run in a container using Docker.

A container is a kind of sandboxed process on a computer that is isolated from all other processes, unless specifically allowed.

Containers have an isolated filesystem. This filesystem is provided by a container image. Since the image contains the container’s filesystem, it must contain everything needed to run an application: all dependencies, configurations, scripts, binaries, & other files or data. The image also contains other configuration for the container, such as environment variables, a default command to run, and other metadata.

By combining images and containers, we can package whole applications in a way that is transferrable (we can create them completely separately from running them) and highly reproducible. Both of these are very important in a production environment.

The isolation and security allows us to run many containers simultaneously on a host computer. Containers are lightweight and contain everything needed to run the application, so we do not need to rely on what is currently installed on the host (and don’t need to worry that it may change between deployments). We can easily share containers while we work, and be sure that everyone we share with gets the same container that works in the same way.

This makes containers & images a very desirable way to build and deploy applications. It’s the way we’ll work in most of the future projects.

To summarize, a container:

• is a runnable instance of an image. We can create, start, stop, move, or delete containers
• can be run on local machines, virtual machines or deployed to the cloud
• is portable (can be run on any OS)
• is isolated from other containers and runs its own software, binaries, and configurations

For even more on containers, you can read Julia Evan’s fantastic guide to What even is a container?.

Background

Docker

Docker is an open platform for developing, deploying, and running applications, based around containers and images. Docker provides tooling and a platform to manage the lifecycle of our containers:

• We develop the application and its supporting components using containers
• The container becomes the unit for distributing and testing the application.
• When ready, we deploy the application onto a production environment that will handle real workloads.

This works the same whether the production environment is a local data center, a cloud provider, or a hybrid of the two. Read this guide to get an overview of Docker.

To build hands-on familiarity with Docker, complete parts 1, 2 and 3 of this tutorial, after which you should know about:

• Running applications with docker: docker run -dp 80:80 docker/getting-started
• Containers and images: process & filesystem isolation
• Dockerfile: a text-based script of instructions that is used to create a container image
• Starting, managing processes and images: docker ps and docker rm -fs

Next work through the Go Docker tutorial, after which you should know about:

• Dockerising a go application
• Starting and stopping containers
• Volumes & networking between docker containers
• Basics of docker-compose and CockroachDB
• GitHub actions for pushing the image to Docker Hub

Spend some time on these steps, and feel free to complete other tutorials too. It’s very important to grasp the core ideas of containers, images and docker:

• Docker is a set of tools for managing containers and images
• Images are frozen file systems that hold everything a container needs to run
• Containers are the running application, based on an image

Cloud hosting in AWS

We’re going to host our application in the cloud, specifically in Amazon Web Services (AWS). AWS is a large suite of products for running technology in the cloud.

The set of AWS products we’re going to interact with directly are:

• Elastic Container Repository (ECR): store images that can later be run as containers
• Elastic Container Service (ECS): run containers, including all the infrastructure needed to make them accessible to the internet
• Identity & Access Management (IAM): manage security, identity and access within AWS

To get familiar with ECS, run through the AWS tutorial, after which you should know about:

• Container & task: like a blueprint for our application
• Service & load balancing: launches and maintains copies of the task definition in a cluster, and directs real traffic
• Cluster: compute resources used to run the service & load balancing

Building & Dockerising server

The rest of this project will cover putting this all together to run an application that we’ve written on ECS. The steps will be:

• Build a simple Go server
• Dockerise it to run locally within a container
• Write tests that run against the docker container
• Build GitHub actions to automate CI/CD
• Push the image to ECR
• Launch it in ECS using the UI

Make sure to commit code as you, work with clear commit messages. We are going to work in a tight loop with GitHub — pushing code and testing it — so good Git hygiene is important.

Server

Write a simple server in Go. It should have this behaviour when you go run it:

> curl localhost:8090
Hello, world.

> curl localhost:8090/ping
pong

Make sure that the port is configurable with an environment variable HTTP_PORT, and defaults to port 80.

Dockerise

Write a Dockerfile for the application.

It should build & run like this:

> docker build . -t docker-cloud
[+] Building 22.6s (15/15) FINISHED
...

> docker run -dp 8090:80 docker-cloud
306cf309f3970d5380cd07c3a54aead7ee8cf4f6726b752fecaec39e40da69f5

> curl localhost:8090
Hello, world.

> curl localhost:8090/ping
pong

Optionally, make this a bit more complete and reproducible by including a multi-stage build.

Tests

Write some simple tests for your server. For writing tests, use dockertest.

The principle of dockertest is to test against real running services, end to end, in containers. The advantage is that these services can be destroyed after testing and recreated from scratch, so that the tests are highly reproducible.

Following docs here and example here, write some tests.

Make sure to COPY go.sum ./ in the “build” portion of your Dockerfile. Read this guide to understand why.

CI/CD

Over time, software engineers have developed a set good practices for building software that is used in production for real workloads. One key component of this is called Continuous Integration & Continuous Deployment, referred to as CI/CD. We’re going to focus on the “CI” component of this, which means:

• Working with version control (Git)
• Using automated checks on the code added to Git (e.g. running tests)
• Automating the steps required to “build” a version of the application that could be deployed to production (e.g. building a Docker image and pushing it to a shared location)

This automation runs in the cloud — not on developer laptops — so that it is highly reproducible and the same for whoever writes the the code. Each step in the flow depends on the last one. The idea is that, should the tests fail, the code will not be built or pushed to the repository, so it’s harder to push buggy code to production. In a CI/CD world, code is never pushed to production from a laptop.

Github Action: running tests

The system we’ll use for CI testing and image creation is GitHub Actions.

Follow this guide on GitHub to get a GitHub action testing the code. We’ll be adding a single file to the .github/workflows directory of the repo.

Pay attention to the following:

# Make sure that the working directory for the tests is correct
defaults:
  run:
    working-directory: docker-cloud

# Use the right go version file and dependency path
# Note that these are *not* subject to the working-directory defaults supplied above
- name: Set up Go
  uses: actions/setup-go@v3
  with:
    go-version-file: "docker-cloud/go.mod"
    cache-dependency-path: "docker-cloud/go.sum"
    cache: true

Github Action: publish image

Next, we’re going build an action that creates and pushes the image to AWS Elastic Container Registry.

The end result will work something like this, including the steps that follow to deploy the container to ECS:

1. We (the developers) push code to GitHub
2. GitHub triggers Actions that run against our code
3. After running tests, an Action creates and publishes a Docker image to the Elastic Container Registry, identified by the commit ID
4. Via the ECS UI, we will deploy this image as a container (which will also create a task, service, cluster and load balancer, amongst other things)
5. Finally, our server will be able to receive HTTP requests from anyone on the internet!

We’ve already managed steps 1 and 2.

Step three requires us to do the following:

• We need a repository on ECR where we’ll publish our images. A repository is a collection of images. Ours will be public. We’ll create this via the ECR user interface.
• We need to authorise GitHub to push images to our repository. We don’t want anyone to be able to push images. Just us. We’ll do this by creating an Identify Provider, two Policies, and a Role, plus making use of some standard AWS GitHub actions.

Here are the steps: figuring out the gaps is up to you. The guide here may be helpful to you, as well as this guide on creating policies.

1. Create a repository. Make sure it’s public.
   Make a note of the URI of the registry — something like public.ecr.aws/w0k4j6h5/immersive-go-course/docker-cloud. The alias (w0k4j6h5) and name immersive-go-course/docker-cloud will be useful later.
2. Via the IAM dashboard (use the AWS navigation to find this):
   1. Add a new OpenID Connect Identify Provider
      • Provider URL: https://token.actions.githubusercontent.com (Don’t forget to click Get Thumbprint)
      • Audience: sts.amazonaws.com
   2. Create two Policies, which will allow the Role to push Docker images:
      1. Call the first Policy GetAuthorizationToken, and set it up to:
         • GetAuthorizationToken against the Elastic Container Registry Public service
         • GetServiceBearerToken against the STS service
      2. Call the second Policy AllowPush, and set it up with the following against the Elastic Container Registry Public service:
         • Read BatchCheckLayerAvailability
         • Write CompleteLayerUpload, InitiateLayerUpload, PutImage, UploadLayerPart
   3. Create a Role called GitHubActionECRPublicPushImage:
      • Chose Web Identity, select the Identity provider you created in the previous step, and its audience
      • Assign the GetAuthorizationToken and AllowPush policies to the Role
   4. After you’ve created the role, edit it. In the Trust Relationships tab, update the "Condition" block so that it references your repository:

      "Condition": {
          "StringEquals": {
              "token.actions.githubusercontent.com:aud": "sts.amazonaws.com"
          },
          "StringLike": {
              "token.actions.githubusercontent.com:sub": "repo:[YOUR_GITHUB_USERNAME]/[YOUR_GITHUB_REPO]:*"
          }
      }
      

We’re now ready to create the GitHub Actions. We’re going to use two actions, in order:

1. aws-actions/configure-aws-credentials — this sets up environment variables so that subsequent actions can authenticate with AWS
2. aws-actions/amazon-ecr-login — this authenticates the Docker client (docker commands) with the AWS container registry

A couple of things that we shouldn’t miss…

Make sure that the action has a write token:

permissions:
  id-token: write

And that we have correctly copied the Amazon Resource Name (ARN) for the Role:

- name: configure aws credentials
  uses: aws-actions/configure-aws-credentials@v1
  with:
    role-to-assume: arn:aws:iam::1234567890:role/your-role-arn

Finally, we can push the image to ECR:

- name: Build, tag, and push docker image to Amazon ECR Public
  env:
    REGISTRY: ${{ steps.login-ecr-public.outputs.registry }}
    REGISTRY_ALIAS: YOUR_ALIAS
    REPOSITORY: YOUR_REPOSITORY
    IMAGE_TAG: ${{ github.sha }}
  run: |
    docker build -t $REGISTRY/$REGISTRY_ALIAS/$REPOSITORY:$IMAGE_TAG .
    docker push $REGISTRY/$REGISTRY_ALIAS/$REPOSITORY:$IMAGE_TAG    

The docker push command uses the URL of the registry (public.ecr.aws) to figure out which credentials to use, so we don’t have to explicitly refer to the AWS configuration & login steps.

Once this action is written, we can add commit it and push to GitHub to test it out. The logs from the GitHub action will look something like this:

If it’s worked, our public repository with a published image will show up on the Amazon console.

Running on ECS

Lastly, run the server on Amazon Elastic Container Service, enabling step 4 and 5 in the architecture diagram. This guide (from step 4) will give us most of what we need. Here’s some things to watch out for:

• We need to set up a custom container definition
• The Image field will use the URL from the ECR repository, under Image URI
• We need to add a port mapping, using whichever port the service runs under inside the container (likely 80)
• Edit the Task definition name doesn’t need to change, but it’s good to give it a recognisable, meaningful name. Read more about task definitions here
• Make sure to create an Application Load Balancer

Follow the guide to find the running URL for the server. If it’s working, visiting /ping should give back pong. Nice!

Deploying changes

Deploying once is great, but in reality code will change over time. Let’s make some changes and get them deployed using our GitHub Actions CI process:

• Change the server code (pong to Hello! for example)
• Commit & use the full CI workflow to test and publish a version of the Docker image to ECR
• Follow this guide to create an updated task definition which points to the new image
• Follow this guide for updating a service to release the changes. We only need to change the task definition to refer to the new revision, with the new image!

And… done! Well done, this was a steep learning curve.