Docker Full Circle: Continuous Integration (CI) with Cypress

By: Benjamin Martin

You’re at work and your team has adopted Docker for development. That’s great! Everyone is using your custom Docker image to manage dependencies and any other environment requirements. You’ve nearly made it look just like your production environment. High five!

Fast forward to the end of the sprint and you’re all clamouring to get in your features, but you have yet to hand off your work to QA… again. It always ends up that the last day or two of the sprint, you hand off all of your tickets to QA and just wait for your ticket to be closed off. If you are a QA and you’re reading this, I apologize for having contributed to this trend.

But what if I told you there’s a better way? Hopefully, the empathy you have for your QA on your team would encourage you to keep reading. And if you don’t have a QA, then I implore you to continue.

Note: This article is a continuation of my series about Docker. You can read it here: Custom Docker Images for Development

CI: Continuous Integration

Continuous integration is the concept of regularly committing code to the main branch of your app’s repository as opposed to having numerous long lived branches where large features are built and only merged once these large features are considered complete.

However, making many small isolated code check-in (commits) to the mainline (develop or master branch) of your application can be nerve racking if you don’t have the proper checks and balances in place to ensure that you don’t accidentally break your app, (or, if you do, that you catch these breaking changes early and fast).

In this article, we’ll look at building a continuous integration pipeline that will automatically ensure our code passes code linting, unit tests, build step, and even end-to-end (E2E) tests. This pipeline will use CircleCI to monitor your code in GitHub. When it detects an update, it will pull your code and then use Docker to execute validation steps that we configure. If everything passes, our production Docker image will be published to Docker Hub (our Docker image repository) or to a private docker repository.

Prerequisites

We’ll be covering a lot of topics to setup the entire pipeline, including Docker, GitHub, CircleCI, Cypress, and Docker Hub. Below I outline some basic setup and knowledge that will make this easier to follow and understand.

Docker

In this article, we’re going to look at CI pipelines with CircleCI and we’re going to use a modified version of the Docker image from the previous article about Custom Docker Images for Development. Some knowledge of Docker is required. However, instead of using Docker for development, we will build a “production ready” Docker image to run E2E tests against.

Cypress

Cypress is the E2E test runner that we will configure in our CI pipeline. We won’t discuss writing E2E tests in this article. We will only modify the default Cypress config that VueCLI sets up our project with. We will also add an E2E step in our CircleCI configuration. No previous Cypress knowledge is required.

VueCLI

Our app is the default app created by VueCLI (@vue/cli). You can run vue create docker-demo to create a new Vue Project.

GitHub

The CI pipeline will connect to your GitHub repository. You will need a GitHub account. Create your repository in GitHub and push your project.

Connecting CircleCI to GitHub

You will need to configure CircleCI to connect to your GitHub account. There is great documentation on that to get you started. It would be beneficial if you have some understanding of CircleCI and how YAML is structured. We will be going over some more intermediate techniques using CircleCI at the end of this article.

Docker Hub

At the end of the article, we will connect CircleCI with Docker Hub and then publish the Docker image to a remote repository as the final step of our CI pipeline. This is to ensure that the same image we tested is being persisted. We use the same image for deploying to QA/production. This way, we avoid rebuilding this image on a different machine which could introduce some variance in the final built asset. Ensure you have an account with Docker Hub. Otherwise, you can publish to your Docker repository of choice.

Configuring Docker for “Production”

Our app is the default app created by VueCLI. We selected Cypress as the E2E testing library, but we’ll need to change a little bit about how it works by default to be more efficient with our Docker container management. But, before we setup Cypress, let’s look at the Dockerfile responsible for building the "production ready" version of our app.

Create a Dockerfile in your project's root folder and use this content:

Note: CI=true is used to suppress an exorbitant amount of verbose console outputs during the cypress installation

You should notice a few things different from our development Dockerfile.dev. Please use this Dockerfile.dev in your project. It was slightly updated from the previous blog post to also use multistage builds which are described below. It will be used in our CI pipeline to run unit tests.

Multistage Builds

This time, we have 2 FROM statements which are aliased with the as keyword. This allows us to use what are called "Multistage Builds".

Note: Docker’s multistage builds requires Docker v17.05 or later. More documentation can be found here.

In a nutshell, it enables us to slim down our final images, and keeps all the logic to do so in a single Dockerfile. Here are some changes to note:

1. Each new FROM line defines a new stage.
2. Name you stages (as ....). This is how you will reference them later in your Dockerfile
3. You can use --from in the COPY command to pull built assets from earlier stages.
4. --from can also target completely different images (either local or from a linked repo)
5. Running docker build will build the entire Dockerfile (down to the last stage defined) by default
6. You can stop at a specific stage with docker build --target stageName

The builder stage has all of our dependencies, including development dependencies. This is because we require the vue-cli-service (which is a development dependency) to build our final dist/ folder. At the end, we just need an http-server to host our static files, because we don't have a node server running. The prodBuild stage then only needs to copy the dist/ folder from our builder stage and setup the http-server.

Now that we have that setup, let’s also setup our Cypress server.

Cypress in Docker

We’ve taken all this time to optimize our Docker image for our production application. In order to keep our production image clean, we don’t want to include test utils or other libraries that are only used for testing in our production image. Instead, we will isolate Cypress into its own Docker image.

With that in mind, create a new folder in the root of your project named cypress/. In that folder, create a file package.json, cypress.json, and Dockerfile.cypress.

Cypress Dependencies :: package.json

Here we can see what the actual dependencies for running a stand-alone Cypress instance looks like. We have our tests from our main project, and we install Cypress in this Docker image.

Cypress Configuration :: cypress.json

Our cypress configuration only points to the plugins which are the defaults your VueCLI generated project will come with.

Note: If you used VueCLI to build your project, and you selected Cypress for E2E tests in the CLI, you will have @vue/cli-plugin-e2e-cypress as a dependency in your main package.json.

This package is no longer necessary, as we will be running our Cypress tests from a separate container.
This makes it faster to install development dependencies in our main Dockerfile (in the root of the project) since we don't need Cypress in our development image either.

Run the following two commands in the root of your project
npm uninstall --save-dev @vue/cli-plugin-e2e-cypress
npm install --save-dev eslint-plugin-cypress

Cypress Docker Image :: Dockerfile.cypress

Here’s the content for the Dockerfile.cypress

Similar to what we’ve done with our app’s Dockerfile, we first copy our package.json, install dependencies and move those dependencies to our working directory. We don't copy in our tests, because we will eventually be running these tests in a CI environment, and that environment will be pulling our code repository, so instead we will use a volume to mount the tests into the container. This will be managed with docker-compose.

Orchestrating Multiple Containers with Docker Compose

Using Docker Compose is a great way to convert most of the docker command line arguments into configuration in your YML file. Additionally, we can create multiple containers and even alias them and reference them in the configuration for other containers. Let's make a docker-compose.yml file in the root of our project using the following configuration:

In our docker-compose.yml file, we specify two services that will run together; web and cypress. Web will be running the production version of our app on a simple HTTP server, and cypress will be running our Cypress tests.

Note: If you used VueCLI to install cypress as the e2e provider when you setup your project, you will need to comment out the two lines in /tests/e2e/plugins/index.js which set the screenshotsFolder and the videosFolder. We will instead be using the docker-compose.yml file to set these folders, allowing us to easily use environment variables in the paths depending on the environment variable TAG.

Inputs / Environment Vars for Production

As you can see in our docker-compose.yml file, we expect several environment variables to be set in order for this file to work properly. The IMAGE_NAME and TAG are required for the web service to start up. We can supply these inline when we run our docker-compose up command.

The important thing to note here is that web relies on having an existing image to run. We don't want to rebuild our docker image when we run compose. The advantage of using docker is to make sure we use the exact same copy in our tests as we use in production. Therefore, if you want to test your docker-compose locally, you need to first build the prod image as shown in the following section.

Building Production

Run the following command in the root of your project to build your production image. docker build -t YOUR_DOCKER_HUB_ACCOUNT/docker_demo:latest -f Dockerfile .

After running that, run: IMAGE_NAME=docker_demo TAG=latest docker-compose build cypress

followed by:

IMAGE_NAME=docker_demo TAG=latest docker-compose up --abort-on-container-exit

Assuming that you have all passing tests, it should run, pass and then close and remove the Docker container for you.

If you look into your project, there will be a video of the test runner in the project root’s /results/latest/videos folder. Open it up and see the glorious success!

Setting up CI Pipeline

Note: If you haven’t already, as per the prerequisites section above, create a repo for your code in GitHub, push it up, and connect your account to CircleCI.

Using NPM to run our Commands

In order to make our CircleCI config cleaner and to allow our developers to easily run our various docker commands in the terminal, we will alias them to npm run ... commands. Here are the custom scripts we will add to our package.json.

Note: The comments are only for logical separation to make it easier to read.

Important: The name in your package.json must match your $IMAGE_NAME environment variable we use below. I used docker_demo in these examples, so the name in my package.json is also docker_demo

CircleCI Configuration

Create a new folder in your project root named .circleci/ and create a file within it called config.yml with the following content:

In our config.yml file above, we define 3 top level categories of commands, jobs and workflows.

Commands, Jobs and Workflows

Commands are reusable snippets of configuration that can be reused throughout our CI pipeline definition. They can even accept parameters.

Here are the two commands we defined above:

• setupenv will setup our environment by pulling the code that is being run through the pipeline, and setting the relevant environment variables to be used by our package.json commands.
• cache-app helps us cache the built image to a tar file and preserve it in CircleCI to be used in a later step.

Jobs are a series of steps that can be leveraged in our workflows (explained below). In our code above, we define the following jobs:

• unit-test run unit tests, and ensure they're all passing
• build ensure the build doesn't fail
• build-and-cache same as above, but will cache the prodBuild to be used in the acceptance-test
• acceptance-test runs our e2e tests in cypress
• publish pushes our docker images to Docker Hub that successfully pass all the above steps

Workflows are a sequence of jobs that can be filtered and run against your code depending on how the filters are configured. In this project, we have only one workflow that will run sequentially. Some of the jobs in the workflow also have a requires property which specifies which jobs are required to have passed successfully before the job can run.

Basically, we have two main flows, one that will be applied to the branches: develop and master and the second flow which is applied to all other branches.

• develop and master will first execute unit-test followed by build-and-cache, then acceptance-test and finally publish. If any of these jobs fail, the workflow will halt and the build will have failed. CircleCI will then send a notification to people subscribed to the project.
• All other branches will only have unit-test and build run against the code. This is a cost savings measure since we pay for runtime used on CircleCI. This allows for fast feedback and also speeds up the continuous integration pipeline since any subsequent steps could be run in parallel.

Anchors

YAML allows declaring a node as an anchor. This means this node will be referred to somewhere later in the YAML. We use this feature of YAML to avoid repeating the first 3 lines in each of the jobs that we define. You can read more about anchors here.

Cache and Artifacts

These are two cool features that we leverage in our CircleCI config: artifacts (used to preserve the screenshots and videos that Cypress will generate for us to view in the CircleCI webapp) and cache which we use to preserve some assets between jobs. This way we can leverage our built docker images between the build-and-cache and acceptance-test without having to publish it to a remote repository as an intermediary step.

Publishing

In our final step, we publish the image to a docker repository. In our case, we’re using docker hub.

Here are the environment variables we need to set in CircleCI:

• DOCKER_LOGIN :: Your Docker Hub username
• DOCKER_PWD :: Your Docker Hub password

These need to be set as project specific environment variables inside of CircleCI’s project settings. This is covered in their documentation found here.

Enhancements

All of this is to say, this is only one way to accomplish the goal of testing our code automatically as new code is pushed to our repository. Here are a few ways this could be improved.

• Change from publishing to Docker Hub to AWS ECR in order to have private Docker repositories
• Use the same builder for ci and prod. The initial step for running unit tests installs development dependencies. So does the builder in the prod build Dockerfile. We could avoid installing dependencies twice by leveraging a single image that is built from the builder stage for both the unit-tested version and the prod builder version.

If you have your own ideas on how to improve this CI pipeline, reach out to me on Twitter @martindevnow and share your thoughts!