• Auto Build
  • Auto Build using a Dockerfile
  • Auto Build using Cloud Native Buildpacks
    • Mount volumes into the build container
  • Moving from Herokuish to Cloud Native Buildpacks
• Auto Test
  • Currently supported languages
• Auto Code Quality
• Auto SAST
• Auto Secret Detection
• Auto Dependency Scanning
• Auto Container Scanning
• Auto Review Apps
• Auto DAST
  • Overriding the DAST target
  • Skipping Auto DAST
• Auto Browser Performance Testing
• Auto Load Performance Testing
• Auto Deploy
  • GitLab deploy tokens
  • Kubernetes 1.16+
  • Migrations
  • Upgrade auto-deploy-app Chart
  • Workers
  • Running commands in the container
• Auto Code Intelligence

Stages of Auto DevOps

The following sections describe the stages of Auto DevOps. Read them carefully to understand how each one works.

Auto Build

Auto Build creates a build of the application using an existing Dockerfile or Heroku buildpacks. The resulting Docker image is pushed to the Container Registry, and tagged with the commit SHA or tag.

Auto Build using a Dockerfile

If a project’s repository contains a Dockerfile at its root, Auto Build uses docker build to create a Docker image.

If you’re also using Auto Review Apps and Auto Deploy, and you choose to provide your own Dockerfile, you must either:

• Expose your application to port 5000, as the default Helm chart assumes this port is available.
• Override the default values by customizing the Auto Deploy Helm chart.

Auto Build using Cloud Native Buildpacks

Auto Build builds an application using a project’s Dockerfile if present. If no Dockerfile is present, Auto Build builds your application using Cloud Native Buildpacks to detect and build the application into a Docker image. The feature uses the pack command. The default builder is heroku/buildpacks:18 but a different builder can be selected using the CI/CD variable AUTO_DEVOPS_BUILD_IMAGE_CNB_BUILDER.

Each buildpack requires your project’s repository to contain certain files for Auto Build to build your application successfully. The structure is specific to the builder and buildpacks you have selected. For example, when using the Heroku builder (the default), your application’s root directory must contain the appropriate file for your application’s language:

• For Python projects, a Pipfile or requirements.txt file.
• For Ruby projects, a Gemfile or Gemfile.lock file.

For the requirements of other languages and frameworks, read the Heroku buildpacks documentation.

Mount volumes into the build container

The variable BUILDPACK_VOLUMES can be used to pass volume mount definitions to the pack command. The mounts are passed to pack build using --volume arguments. Each volume definition can include any of the capabilities provided by build pack such as the host path, the target path, whether the volume is writable, and one or more volume options.

Use a pipe | character to pass multiple volumes. Each item from the list is passed to build back using a separate --volume argument.

In this example, three volumes are mounted in the container as /etc/foo, /opt/foo, and /var/opt/foo:

buildjob:
  variables:
    BUILDPACK_VOLUMES: /mnt/1:/etc/foo:ro|/mnt/2:/opt/foo:ro|/mnt/3:/var/opt/foo:rw

Read more about defining volumes in the pack build documentation.

Moving from Herokuish to Cloud Native Buildpacks

Builds using Cloud Native Buildpacks support the same options as builds using Herokuish, with the following caveats:

• The buildpack must be a Cloud Native Buildpack. A Heroku buildpack can be converted to a Cloud Native Buildpack using Heroku’s cnb-shim.
• BUILDPACK_URL must be in a format supported by pack.
• The /bin/herokuish command is not present in the built image, and prefixing commands with /bin/herokuish procfile exec is no longer required (nor possible). Instead, custom commands should be prefixed with /cnb/lifecycle/launcher to receive the correct execution environment.

Auto Test

Auto Test runs the appropriate tests for your application using Herokuish and Heroku buildpacks by analyzing your project to detect the language and framework. Several languages and frameworks are detected automatically, but if your language is not detected, you may be able to create a custom buildpack. Check the currently supported languages.

Auto Test uses tests you already have in your application. If there are no tests, it’s up to you to add them.

Currently supported languages

Not all buildpacks support Auto Test yet, as it’s a relatively new enhancement. All of Heroku’s officially supported languages support Auto Test. The languages supported by Heroku’s Herokuish buildpacks all support Auto Test, but notably the multi-buildpack does not.

The supported buildpacks are:

- heroku-buildpack-multi
- heroku-buildpack-ruby
- heroku-buildpack-nodejs
- heroku-buildpack-clojure
- heroku-buildpack-python
- heroku-buildpack-java
- heroku-buildpack-gradle
- heroku-buildpack-scala
- heroku-buildpack-play
- heroku-buildpack-php
- heroku-buildpack-go
- buildpack-nginx

If your application needs a buildpack that is not in the above list, you might want to use a custom buildpack.

Auto Code Quality

Auto Code Quality uses the Code Quality image to run static analysis and other code checks on the current code. After creating the report, it’s uploaded as an artifact which you can later download and check out. The merge request widget also displays any differences between the source and target branches.

Auto SAST

Static Application Security Testing (SAST) runs static analysis on the current code, and checks for potential security issues. The Auto SAST stage requires GitLab Runner 11.5 or above.

After creating the report, it’s uploaded as an artifact which you can later download and check out. The merge request widget also displays any security warnings on Ultimate licenses.

For more information, see Static Application Security Testing (SAST).

Auto Secret Detection

Secret Detection uses the Secret Detection Docker image to run Secret Detection on the current code, and checks for leaked secrets.

After creating the report, it’s uploaded as an artifact which you can later download and evaluate. The merge request widget also displays any security warnings on Ultimate licenses.

For more information, see Secret Detection.

Auto Dependency Scanning

Dependency Scanning runs analysis on the project’s dependencies and checks for potential security issues. The Auto Dependency Scanning stage is skipped on licenses other than Ultimate.

After creating the report, it’s uploaded as an artifact which you can later download and check out. The merge request widget displays any security warnings detected,

For more information, see Dependency Scanning.

Auto Container Scanning

Vulnerability static analysis for containers uses Trivy to check for potential security issues in Docker images. The Auto Container Scanning stage is skipped on licenses other than Ultimate.

After creating the report, it’s uploaded as an artifact which you can later download and check out. The merge request displays any detected security issues.

For more information, see Container Scanning.

Auto Review Apps

This is an optional step, since many projects don’t have a Kubernetes cluster available. If the requirements are not met, the job is silently skipped.

Review apps are temporary application environments based on the branch’s code so developers, designers, QA, product managers, and other reviewers can actually see and interact with code changes as part of the review process. Auto Review Apps create a Review App for each branch.

Auto Review Apps deploy your application to your Kubernetes cluster only. If no cluster is available, no deployment occurs.

The Review App has a unique URL based on a combination of the project ID, the branch or tag name, a unique number, and the Auto DevOps base domain, such as 13083-review-project-branch-123456.example.com. The merge request widget displays a link to the Review App for easy discovery. When the branch or tag is deleted, such as after merging a merge request, the Review App is also deleted.

Review apps are deployed using the auto-deploy-app chart with Helm, which you can customize. The application deploys into the Kubernetes namespace for the environment.

Local Tiller is used. Previous versions of GitLab had a Tiller installed in the project namespace.

Auto DAST

Dynamic Application Security Testing (DAST) uses the popular open source tool OWASP ZAProxy to analyze the current code and check for potential security issues. The Auto DAST stage is skipped on licenses other than Ultimate.

• On your default branch, DAST scans an application deployed specifically for that purpose unless you override the target branch. The app is deleted after DAST has run.
• On feature branches, DAST scans the review app.

After the DAST scan completes, any security warnings are displayed on the Security Dashboard and the merge request widget.

For more information, see Dynamic Application Security Testing (DAST).

Overriding the DAST target

To use a custom target instead of the auto-deployed review apps, set a DAST_WEBSITE CI/CD variable to the URL for DAST to scan.

Skipping Auto DAST

You can skip DAST jobs:

• On all branches by setting the DAST_DISABLED CI/CD variable to "true".
• Only on the default branch by setting the DAST_DISABLED_FOR_DEFAULT_BRANCH variable to "true".
• Only on feature branches by setting REVIEW_DISABLED variable to "true". This also skips the Review App.

Auto Browser Performance Testing

Auto Browser Performance Testing measures the browser performance of a web page with the Sitespeed.io container, creates a JSON report including the overall performance score for each page, and uploads the report as an artifact. By default, it tests the root page of your Review and Production environments. If you want to test additional URLs, add the paths to a file named .gitlab-urls.txt in the root directory, one file per line. For example:

/
/features
/direction

Any browser performance differences between the source and target branches are also shown in the merge request widget.

Auto Load Performance Testing

Auto Load Performance Testing measures the server performance of an application with the k6 container, creates a JSON report including several key result metrics, and uploads the report as an artifact.

Some initial setup is required. A k6 test needs to be written that’s tailored to your specific application. The test also needs to be configured so it can pick up the environment’s dynamic URL via a CI/CD variable.

Any load performance test result differences between the source and target branches are also shown in the merge request widget.

Auto Deploy

You have the choice to deploy to Amazon Elastic Compute Cloud (Amazon EC2) in addition to a Kubernetes cluster.

Auto Deploy is an optional step for Auto DevOps. If the requirements are not met, the job is skipped.

After a branch or merge request is merged into the project’s default branch, Auto Deploy deploys the application to a production environment in the Kubernetes cluster, with a namespace based on the project name and unique project ID, such as project-4321.

Auto Deploy does not include deployments to staging or canary environments by default, but the Auto DevOps template contains job definitions for these tasks if you want to enable them.

You can use CI/CD variables to automatically scale your pod replicas, and to apply custom arguments to the Auto DevOps helm upgrade commands. This is an easy way to customize the Auto Deploy Helm chart.

Helm uses the auto-deploy-app chart to deploy the application into the Kubernetes namespace for the environment.

Local Tiller is used. Previous versions of GitLab had a Tiller installed in the project namespace.

GitLab deploy tokens

GitLab Deploy Tokens are created for internal and private projects when Auto DevOps is enabled, and the Auto DevOps settings are saved. You can use a Deploy Token for permanent access to the registry. After you manually revoke the GitLab Deploy Token, it isn’t automatically created.

If the GitLab Deploy Token can’t be found, CI_REGISTRY_PASSWORD is used.

Kubernetes 1.16+

In Kubernetes 1.16 and later, a number of APIs were removed, including support for Deployment in the extensions/v1beta1 version.

To use Auto Deploy on a Kubernetes 1.16+ cluster:

1. If you are deploying your application for the first time in GitLab 13.0 or later, no configuration should be required.

2. If you have an in-cluster PostgreSQL database installed with AUTO_DEVOPS_POSTGRES_CHANNEL set to 1, follow the guide to upgrade PostgreSQL.

Migrations

You can configure database initialization and migrations for PostgreSQL to run within the application pod by setting the project CI/CD variables DB_INITIALIZE and DB_MIGRATE.

If present, DB_INITIALIZE is run as a shell command within an application pod as a Helm post-install hook. As some applications can’t run without a successful database initialization step, GitLab deploys the first release without the application deployment, and only the database initialization step. After the database initialization completes, GitLab deploys a second release with the application deployment as standard.

A post-install hook means that if any deploy succeeds, DB_INITIALIZE isn’t processed thereafter.

If present, DB_MIGRATE is run as a shell command within an application pod as a Helm pre-upgrade hook.

For example, in a Rails application in an image built with Cloud Native Buildpacks:

• DB_INITIALIZE can be set to RAILS_ENV=production /cnb/lifecycle/launcher bin/rails db:setup
• DB_MIGRATE can be set to RAILS_ENV=production /cnb/lifecycle/launcher bin/rails db:migrate

Unless your repository contains a Dockerfile, your image is built with Cloud Native Buildpacks, and you must prefix commands run in these images with /cnb/lifecycle/launcher to replicate the environment where your application runs.

Upgrade auto-deploy-app Chart

You can upgrade the auto-deploy-app chart by following the upgrade guide.

Workers

Some web applications must run extra deployments for “worker processes”. For example, Rails applications commonly use separate worker processes to run background tasks like sending emails.

The default Helm chart used in Auto Deploy has support for running worker processes.

To run a worker, you must ensure the worker can respond to the standard health checks, which expect a successful HTTP response on port 5000. For Sidekiq, you can use the sidekiq_alive gem.

To work with Sidekiq, you must also ensure your deployments have access to a Redis instance. Auto DevOps doesn’t deploy this instance for you, so you must:

• Maintain your own Redis instance.
• Set a CI/CD variable K8S_SECRET_REDIS_URL, which is the URL of this instance, to ensure it’s passed into your deployments.

After configuring your worker to respond to health checks, run a Sidekiq worker for your Rails application. You can enable workers by setting the following in the .gitlab/auto-deploy-values.yaml file:

workers:
  sidekiq:
    replicaCount: 1
    command:
      - /cnb/lifecycle/launcher
      - sidekiq
    preStopCommand:
      - /cnb/lifecycle/launcher
      - sidekiqctl
      - quiet
    terminationGracePeriodSeconds: 60

Running commands in the container

Unless your repository contains a custom Dockerfile, applications built with Auto Build might require commands to be wrapped as follows:

/cnb/lifecycle/launcher $COMMAND

Some of the reasons you may need to wrap commands:

• Attaching using kubectl exec.
• Using the GitLab Web Terminal.

For example, to start a Rails console from the application root directory, run:

/cnb/lifecycle/launcher procfile exec bin/rails c

Auto Code Intelligence

GitLab code intelligence adds code navigation features common to interactive development environments (IDE), including type signatures, symbol documentation, and go-to definition. It’s powered by LSIF and available for Auto DevOps projects using Go language only. GitLab plans to add support for more languages as more LSIF indexers become available. You can follow the code intelligence epic for updates.

This stage is enabled by default. You can disable it by adding the CODE_INTELLIGENCE_DISABLED CI/CD variable. Read more about disabling Auto DevOps jobs.