Brief update from Matt Bajor / Unauthorized

Yarn Image of Moonrock Mountain by Matt Bajor

Hi everyone! As you may have noticed, there has not been much movement here on CICD 4 Life. I’ve been working on various other projects, including a venture into building and touring large scale art. While I am still working in DevOps (at Workday), my general direction involves moving towards art and sculpture.

However, until we get there I intend to keep updating here and queue up a few new posts on Kubernetes, GKE, and Jenkins.

Until then please check out my new website Winterworks: Fine Art Firepits and Dichroic Sculpture

Running yarn from a container

Yarn Image from

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

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:
# 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": "",
#   "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.


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 that I would use for the above container:

#!/bin/bash -el

# If PUSH is set to true, push the image

# Set our image name here for consistency

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


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
  • 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.


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!


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.

NOTE: If you intend on using techniques such as these and allowing such wide open functionality in Jenkins, I recommend that you run your entire Jenkins build system without outbound internet access by default. Allow access from the build system network segment only to approved endpoints while dropping the rest of the traffic. This will allow you to use potentially dangerous, but extremely powerful scripts while maintaining a high level of security in the system.

API Calls

Jenkinsfile calling Docker API

Making HTTP calls in a Jenkinsfile can prove tricky as everything has to remain serializable else Jenkins cannot keep track of the state when needing to restart a pipeline. If you’ve ever received Jenkins’ error in your console out, you know what I mean. There are not too many clear-cut examples of remote API calls from a Jenkinsfile so I’ve created an example Jenkinsfile that will talk to the Docker Hub API using GET and POST to perform authenticated API calls.

Explicit Versioning

The Docker image build process leaves a good amount to be desired when it comes to versioning. Most workflows depend on the :latest tag which is very ambiguous and can lead to problems being swallowed within your build system. In order to maintain a higher level of determinism and auditability, you may consider creating your own versioning scheme for Docker images.

For instance, a version of 2017.2.1929 #<year>-<week>-<build #> can express much more information than a simple latest. Having this information available for audits or tracking down when a failure was introduced can be invaluable, but there is no built-in way to do Docker versioning in Jenkins. One must rely on an external system (such as Docker Hub or their internal registry) to keep track of versions and use this system of record when promoting builds.

Versioning Scheme

This versioning scheme we are using is not based on Semver1, but it does encode within it the information we need to keep versions in lock and also will always increase in value. Even if the build number is reset, the date + week will keep the versions from ever being lower that the day previously. Version your artifacts however works for your release, but please make sure of these two things:

  • The version string never duplicates
  • The version number never decreases

Interacting with the Docker Hub API in a Jenkinsfile

For this example we are going to connect to the Docker Hub REST API in order to retrieve some tags and promote a build to RC. This type of workflow would be implemented in a release job in which a previously built Docker image is being promoted to a release candidate. The steps we take in the Jenkinsfile are:

Docker Registry

  • Provision a node
    • Stage 1
      • Make an HTTP POST request to the Docker Hub to get an auth token
      • Use the token to fetch the list of tags on an image
      • Filter through those tags to find a tag for the given build #
    • Stage 2
      • Promote (pull, tag, and push) the tag found previously as ${version}-rc
      • Push that tag to latest to make it generally available

This is a fairly complex looking Jenkinsfile as it stands, but these functions can be pulled out into a shared library2 to simplify the Jenkinsfile. We’ll talk about that in another post.


 NOTE: This Jenkinsfile has the following pre-requisites:
   - SECRET (id: docker-hub-user-pass): Username / Password secret containing your
     Docker Hub username and password.
   - ENVIRONMENT: Docker commands should work meaning DOCKER_HOST is set or there
     is access to the socket.
import groovy.json.JsonSlurperClassic; // Required for parseJSON()

// These vars would most likely be set as parameters
imageName = "technolog/serviceone"
build = "103"

// Begin our Scripted Pipeline definition by provisioning a node
node() {

    // First stage sets up version info
    stage('Get Docker Tag from Build Number') {

        // Expose our user/pass credential as vars
        withCredentials([usernamePassword(credentialsId: 'docker-hub-user-pass', passwordVariable: 'pass', usernameVariable: 'user')]) {
            // Generate our auth token
            token = getAuthTokenDockerHub(user, pass)

        // Use our auth token to get the tag
        tag = getTagFromDockerHub(imageName, build, token)

    // Example second stage tags version as -release and pushes to latest
    stage('Promote build to RC') {
        // Enclose in try/catch for cleanup
        try {
            // Define our versions
            def versionImg = "${imageName}:${tag}"
            def latestImg = "${imageName}:latest"

            // Login with our Docker credentials
            withCredentials([usernamePassword(credentialsId: 'docker-hub-user-pass', passwordVariable: 'pass', usernameVariable: 'user')]) {
                sh "docker login -u${user} -p${pass}"

            // Pull, tag, + push the RC
            sh "docker pull ${versionImg}"
            sh "docker tag ${versionImg} ${versionImg}-rc"
            sh "docker push ${versionImg}-rc"

            // Push the RC to latest as well
            sh "docker tag ${versionImg} ${latestImg}"
            sh "docker push ${latestImg}"
        } catch(err) {
            // Display errors and set status to failure
            echo "FAILURE: Caught error: ${err}"
            currentBuild.result = "FAILURE"
        } finally {
            // Finally perform cleanup
            sh 'docker system prune -af'

// NOTE: Everything below here could be put into a shared library

// GET Example
// Get a tag from Docker Hub for a given build number
def getTagFromDockerHub(imgName, build, authToken) {

    // Generate our URL. Auth is required for private repos
    def url = new URL("${imgName}/tags")
    def parsedJSON = parseJSON(url.getText(requestProperties:["Authorization":"JWT ${authToken}"]))

    // We want to find the tag associated with a build
    // EX: 2017.2.103 or 2016.33.23945
    def regexp = "^\\d{4}.\\d{1,2}.${build}\$"

    // Iterate over the tags and return the one we want
    for (result in parsedJSON.results) {
        if ( {

// POST Example
// Get an Authentication token from Docker Hub
def getAuthTokenDockerHub(user, pass) {

    // Define our URL and make the connection
    def url = new URL("")
    def conn = url.openConnection()
    // Set the connection verb and headers
    conn.setRequestProperty("Content-Type", "application/json")
    // Required to send the request body of our POST
    conn.doOutput = true

    // Create our JSON Authentication string
    def authString = "{\"username\": \"${user}\", \"password\": \"${pass}\"}"

    // Send our request
    def writer = new OutputStreamWriter(conn.outputStream)

    // Parse and return the token
    def result = parseJSON(conn.content.text)
    return result.token


// Contain our JsonSlurper in a function to maintain CPS
def parseJSON(json) {
    return new groovy.json.JsonSlurperClassic().parseText(json)

Script Security

Due to the nature of this type of script, there is definitely a lot of trust assumed when allowing something like this to run. If you follow the process we are doing in Modern Jenkins nothing is getting into the build system without peer review and nobody but administrators have access to run scripts like this. With the environment locked down, it can be safe to use something of this nature.

Security Alert

Jenkins has two ways in which Jenkinsfiles (and Groovy in general) can be run: sandboxed or un-sandboxed. After reading Do not disable the Groovy Sandbox by rtyler (@agentdero on Twitter), I will never disable sandbox again. What we are going to do instead is whitelist all of the required signatures automatically with Groovy. The script we are going to use is adapted from my friend Brandon Fryslie and will basically pre-authorize all of the required methods that the pipeline will use to make the API calls.

Pre-authorizing Jenkins Signatures with Groovy

URL: http://localhost:8080/script

import org.jenkinsci.plugins.scriptsecurity.scripts.ScriptApproval

println("INFO: Whitelisting requirements for Jenkinsfile API Calls")

// Create a list of the required signatures
def requiredSigs = [
    'method groovy.json.JsonSlurperClassic parseText java.lang.String',
    'method flush',
    'method write java.lang.String',
    'method java.lang.AutoCloseable close',
    'method setRequestMethod java.lang.String',
    'method openConnection',
    'method connect',
    'method getContent',
    'method getOutputStream',
    'method setDoOutput boolean',
    'method setRequestProperty java.lang.String java.lang.String',
    'new groovy.json.JsonSlurperClassic',
    'staticMethod org.codehaus.groovy.runtime.DefaultGroovyMethods findAll java.lang.String java.lang.String',
    'staticMethod org.codehaus.groovy.runtime.DefaultGroovyMethods getText',
    'staticMethod org.codehaus.groovy.runtime.DefaultGroovyMethods getText java.util.Map',

    // Signatures already approved which may have introduced a security vulnerability (recommend clearing):
    'method openConnection',

// Get a handle on our approval object
approver = ScriptApproval.get()

// Aprove each of them
requiredSigs.each {

println("INFO: Jenkinsfile API calls signatures approved")

After running this script, you can browse to Manage Jenkins -> In-process Script Approval and see that there is a list of pre-whitelisted signatures that will allow our Jenkinsfile to make the necessary calls to interact with the Docker Hub API. You’ll notice there is one method in there that they mark in red as you can see the potential security issues with it. openConnection can be an extremely dangerous method to allow in an unrestricted environment so be careful and make sure you have things locked down in other ways.