Running yarn from a container

Image from https://yarnpkg.com/en/

Within a continuous integration / continuous delivery system one of the hardest problems to deal with as the system and teams grow is build time dependency management. At first, one version of a few dependencies is totally manageable in traditional ways. yum install or apk add is the perfect solution for a single team or project.

Over time as the CI system begins to grow and support more and more teams this solution starts to show weaknesses. Anyone who has had to install rvm, nvm, or any of the other environment based tool version managers within a CI system knows the pain of getting things working right in a stable way. In addition, distributing the configuration for these tools can be just as challenging, especially when dealing with multiple versions of a tool.

One way to help address these complications (in addition to a few others) is to contain your tooling in Docker containers. This gives a few advantages over traditional package managers:

• The software is entirely contained within Docker and can be installed or removed without fuss by any job that needs it
• Containers can (and should) be built in house as to be reproducibly built at a minimum and fully audited and tracked within the change management tool if required
• Multiple versions and configurations can exist side by side without any interference
• Containers can be shared between teams and shell wrappers distributed via Homebrew

How can we run yarn in a container for a local project?

TL;DR: Docker volumes

The way the process works is similar to other Docker workflows:

• We build a Docker image that has whatever tool we need
• Then any configuration (no secrets!) are layered in to the image
• When run the container, we mount our current working directory into the container
• The command is executed in the running container, but acting on the local directory that is volume mounted
• The command finishes and all output is captured in our local directory
• The container exits and since we’ve used --rm, is completely gone

This gives us the effects of the tool without modifying other parts of the filesystem as one does in normal software installation. One issue that this process has as stated is that the command to run the container can be a bit unwieldy. In order to simplify things in this regard we must sacrifice the ‘no changes to the filesystem’ benefit by shipping a shell wrapper. Nonetheless, shipping a shell script or allowing others to create their own is a bit easier than full package / config management.

Anyways, now that we have an idea of how it will work, let’s take a look at how it does work.

Step 1: Build a Docker image for yarn

The first item we need is a Docker image that contains our tooling. In this case we’re going to install yarn, a common dependency manager for Node, similar to npm. Since Yarn depends upon Node, we can create a container that has the specific version of each that is needed by the team using it. In this case we will install the latest Node and Yarn packages, but pinning them to other versions would be a fairly simple task.

Let’s take a look at our Dockerfile here:

# We're using alpine for simplicity. We could make it smaller
# by downloading the tarball and adding to scratch with a multi-stage
# Docker build
FROM alpine

# Install yarn which should pull in node as a dependency
RUN apk add --update yarn

# We will configure the cache to use our volume mounted workspace
RUN yarn config set cache-folder /workspace/.yarn-cache

# Using an entrypoint allows us to pass in any args we need
ENTRYPOINT ["yarn"]

This super simple Dockerfile will give us a container that has yarn as an entrypoint. We can now build the image using a command like so:

docker build -t technolog/run-yarn .

This will give us the container named technolog/run-yarn that we can test with a command like this:

docker run --rm -ti technolog/run-yarn --version
# 1.3.2

Excellent, yarn works! However, in this configuration we have no way to have it operate on a local package.json or node_modules. Everything is still in the container. We will fix that by using a Docker volume mount.

Step 2: Volume mount the current directory into the Docker container

If we were to just run the container with the example above, nothing is going to happen outside of the container. What we need to do is make the current directory accessible inside the container with a Docker volume. This is a simple task and looks something like:

docker run --rm -ti -v "$(pwd):/workspace" technolog/run-yarn init
# yarn init v1.3.2
# question name: left-left-pad
# question version (1.0.0): 10.0.0
# question description: A left pad for the left pad
# question entry point (index.js):
# question repository url: https://github.com/technolo-g/left-left-pad
# question author: Not me!
# question license (MIT):
# question private: no
# success Saved package.json
# Done in 76.15s.
cat ./package.json
# {
#   "name": "left-left-pad",
#   "version": "10.0.0",
#   "description": "A left pad for the left pad",
#   "main": "index.js",
#   "repository": "https://github.com/technolo-g/left-left-pad",
#   "author": "Not me!",
#   "license": "MIT"
# }

Here we can see that the file was created locally (./) and contains all of the info we provided to yarn running in the Docker container. Pretty neat! One thing you may notice is that the Docker command is growing a bit. This exact command (or the one you create) doesn’t roll off of the fingers and so can be hard to have everyone typing the same thing. There are a few solutions to this minor issue and one of them is using bash aliases like so:

alias yarny="docker run --rm -ti -v \"\$(pwd):/workspace\" technolog/run-yarn"
which yarny
# yarny: aliased to docker run --rm -ti -v "$(pwd):/workspace" technolog/run-yarn
yarny --version
# 1.3.2

If we are using this command in a lot of places and especially within the build system, a slightly more robust wrapper may be required. Sometimes dealing with the shell and it’s intricacies is best left to ZSH developers and a script is a more unambiguous approach. What I mean by that is a script that encapsulates the Docker command and is then installed on the user or machine’s path. Let’s take a look at one for yarn:

#!/bin/bash -el
# -e Makes sure we exit on failures
# -l Gives us a login shell

# Define a function that replaces our command
yarn() {
    # docker run
    # Remove the container when it exits
    # Mount ./ into the container at /workspace
    # Allow interactive shell
    # Provision a TTY
    # Specify the container and pass any args
  docker run \
    --rm \
    --volume "$(pwd):/workspace" \
    --interactive \
    --tty \
    technolog/run-yarn $@
}

# Run the function, passing in all args
yarn "$@"

Now if we make this file executable and run it, we should have a fully working yarn installation within a container:

chmod +x yarn
./yarn --version
# 1.3.2

Step 3: Distribute the software

The final step is getting these commands to be available. The beauty of this solution is that there are many ways to distribute this command. The script can live in a variety of places, depending on your needs:

• In the code repo itself for a smaller team
• Included and added to the path of the build runner
• Distributed locally with Homebrew
• Kept in a separate repo that is added to the path of builds

It depends on your environment, but I prefer to make the scripts available by keeping them all in one repo, cloning that repo, and adding it to the path on a build. This allows the scripts to change with the build and versions to be pinned via git tags if needed. Every team can include the scripts they need and use the version that works for them if they have to pin.

Step 4: ….

Run the build with whatever tools are required

Step 5: Cleanup

Now that we’re done with the tools, let’s wipe them out completely. We will do that using Docker’s prune command:

docker rm -fv $(docker ps -qa) || echo "INFO: No containers to remove"
docker system prune --all --force --volumes

This will kill any running containers and then prune (delete):

• Any stopped containers
• Any unused networks
• Any unused images
• Any build cache
• Any dangling images

Pretty much anything we would worry about interfering with the next build. If there are containers (such as the drone itself) that must be kept alive, the command is a bit different, but more or less the same.

Enhancements

build.sh

Building the Docker image repeatably and consistently is key to this whole approach. Changing how the container works depending on who builds it will lead to the same pitfalls of bad dependency management: mainly broken builds.

Here is an example build.sh that I would use for the above container:

#!/bin/bash -el

# If PUSH is set to true, push the image
push="$PUSH"

# Set our image name here for consistency
image="technolog/run-yarn"

# Run the build, adding any passed in params like --no-cache
docker build "$@" -t "$image" $(dirname -- "$0")
if [ "$push" == "true" ]; then
  docker push "$image"
fi

Versioning

Once teams begin using this framework, you’ll find each develops a set of version requirements that may not match all the rest of the teams. When you find yourself in this scenario, it is time to begin versioning the images as well. While :latest should probably always point at the newest version, it’s also reasonable to create :vX.X tags as well so teams can pin to specific versions if desired.

In order to do this, you can add a Docker build argument or environment variable to install a specific version of a piece of software and use that version to tag the image as well. I am going to leave this as an exercise for the user, but the steps would be:

• Read the version in build.sh
• Pass that version as a build arg to docker build
• In the Dockerfile, read that ARG and install a specific version of the software

This becomes a bit more complex when sharing between teams and requiring different versions of both node and yarn, but it can be managed with a smart versioning scheme.

Disclaimer!

This methodology does not encourage just pulling random images from Docker hub and running them! You must always use your good judgement when deciding what software to run in your environment. As you see here, we have used the trusted Alpine Docker image and then installed yarn from trusted Alpine packages ourselves. We did not rely on a random Docker image found on the hub, nor did we install extra software that was not required or executed untrusted commands (curl | sudo bash). This means as long as we trust Alpine, we should be able to trust this image, within reason. As my Mum would say: Downloading unknown or unsigned binaries from the Internet will kill you!

Conclusion

This is a powerful and flexible technique for managing build time dependencies within your continuous integration / continuous delivery system. It is a bit overkill if you have a single dependency and can change it without affecting anything unintended. However, if you, like me, run many versions of software to support many teams’ builds, I think you’ll find this to be a pretty simple and potentially elegant solution.