Modern CI/CD solutions usually work with a file in your code repository that defines the steps that need to be executed. The CI/CD solution will read this file and then execute the appropriate scripts to build, test or deploy your application. Since each tool defines its own configuration format let’s compare them to see similarities and differences.

## Travis CI

I will start with Travis CI, because it’s the first one I have used and one many small open source projects on GitHub use. For Travis CI you have to put a file named .travis.yml into the root directory of your GitHub repository. When you connect Travis to your repository it will monitor the repository for changes and execute the build or test.

Travis’ configuration file is a YAML file. The first entry you have to set is the selection of the language of your program. This selection will influence the behaviour of some of the other options in the file. For example Travis will assume some default commands based on the language selection.

The Travis configuration file is built around the Travis Job Lifecycle. The job lifecycle defines that there is an install phase and a script phase and optionally a deploy phase (plus additional phases before and after these steps). Depending on the language and tools you use you possibly might not have to define anything apart from the language of the program. For example, if you choose language: rust Travis will define default values for both the install phase (cargo build --verbose) as well as for the script phase (cargo build --verbose; cargo test --verbose). If you build your Rust project with these commands you can use a minimal Travis configuration file of:

language: rust


Multiple different versions or variants in Travis are tested with so called test matrices. Test matrices can either be defined explicitly in a YAML map named matrix or implicitly when you set multiple values for options like python (=Python version) in a Python build.

### Example

language: python

matrix:
include:
- python: 3.6
env:
- TOXENV=py36

install: pip install tox
script: tox


## Gitlab CI

The self-hosted Git solution Gitlab also comes with a CI/CD tool. It is also configured with a YAML configuration file, called .gitlab-ci.yml. The Gitlab CI configuration has a lot of options, we will only look at the most important ones here to understand the general workflow with Gitlab CI.

Gitlab’s continuous integration is based on jobs. A job basically is one thing you want to do, for example build your program, perform your unit tests with a specific Linux distribution and so on. When you create a .gitlab-ci.yml you will add a collection of jobs to it. Each job then has a key called script which defines the command(s) the job should execute. In all cases I have seen these commands are plain bash commands, but I can imagine that theoretically it’s possible to get these commands interpreted by other interpreters than shell.

The most common use case of Gitlab CI is to have the jobs execute within Docker containers. With an option image in your job configuration you can define which Docker image should be used to start the container. You can also start additional containers with the services option, e.g. for databases.

It is possible to define conditions for the job execution. E.g. oftentimes you want to execute a job only for the master branch or only for tags (like building and releasing versions). Its also possible to define a job to be run only on scheduled times, but the times have to be defined in the Gitlab GUI.

The execution order of multiple jobs is defined with stages. You can assign each job to a stage and jobs within the same stage are executed in parallel. The order of the stages defines the order of execution. First, all jobs from the first stage are executed in parallel, then all jobs from the second stage and so on. Common stages are test, build and deploy.

artifacts and dependencies are two job options that are used to provide artifacts from one pipeline job to the next. E.g. your build-linux job could store the binary to the artifact storage and the release job can upload it to a public FTP server.

### Example

stages:
- test

test36:
stage: test
image: python:3.6-stretch
before_script:
- python -V
- pip install virtualenv
- virtualenv venv
- source venv/bin/activate
- pip install tox
script:
- TOXENV=py36 tox


## Jenkins pipeline

Jenkins started as a build platform on which you define your workflows in a graphical user interface independent from your code repository, but more recently it also received a workflow to perform builds with a Jenkinsfile in your repository.

Jenkins is built around a plugin system. You usually have a plugin to execute shell commands on Linux or batch commands on windows, but also more advanced plugins for specific compilers or communication with cloud services.

A Jenkinsfile can be written in two different flavours, either in declarative or in scripted style. Both styles build upon a list of stages that get executed (examples for stages are build or deploy) and each stage consists of a list of steps that will be executed to complete this stage. Each step is a call to one of the plugins, which can be a shell command, but also a different plugin. With the git plugin you’d write git url: 'git://example.com/project.git', branch: 'master' instead of git clone git://example.com/project.git && git checkout master.

It’s possible to execute each stage with a different agent. Agents are the executors of the commands and could for example be Linux or Windows servers (you would probably want to test a Windows release on a Windows machine).

As with Gitlab there is a when directive to limit the execution of a stage to certain situations and there is a post directive for scripts that should execute after build success, failure or always.

### Example

Since I do not use Jenkins myself this example does not make use of any specific plugins:

pipeline {
/* agent can be defined either globally for all stages
* or for each stage individually
*/
agent any

stages {
stage('Test') {
steps {
sh 'tox'
}
}
}
}


## CircleCI

CircleCI’s configuration file is a mixture between Gitlab and Jenkins. It’s a YAML file and the central components are Workflows and Jobs. Workflows are optional if you have a job named build. Otherwise, you have to define a workflow.

Similar to Gitlab a job in CircleCI consists of multiple steps that get executed in order. Probably most of the times people will use the run step to execute a command, but there are also other step types available like checkout or store_artifacts. The when conditional in CircleCI is also implemented as a step that takes a condition as its argument and a list of steps that should be executed if the condition is met.

Similar to Jenkins, CircleCI allows you to create re-usable components that can be used as steps (called Orbs). There are official Orbs and Third-Party Orbs. They are used in the configuration file just like any other step, only with the name of the orb as the step name.

Each job is run inside a specific executor like docker or machine (=VM).

The order of job execution is defined by workflows. A workflow contains a list of jobs that should be executed with optional requirement definitions between jobs. Jobs that require another job to run before will be delayed until that job has finished execution. Workflows can be triggered by a push to the repository or based on a cron schedule. The cron schedule is written directly into the CircleCI configuration file (unlike Gitlab).

### Example

Since I do not use CircleCI myself this is a simplfied and adjusted example from the documentation:

version: 2
jobs:
test:
docker:
- image: python:3.6-stretch
steps:
- checkout
- run: |
pip install tox
tox
workflows:
version: 2
test:
jobs:
- test

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