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:

Auto Build using Heroku buildpacks

Auto Build builds an application using a project’s Dockerfile if present. If no Dockerfile is present, it uses Herokuish and Heroku buildpacks to detect and build the application into a Docker image.

Each buildpack requires your project’s repository to contain certain files for Auto Build to build your application successfully. For example, 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.

Tip: If Auto Build fails despite the project meeting the buildpack requirements, set a project variable TRACE=true to enable verbose logging, which may help you troubleshoot.

Auto Build using Cloud Native Buildpacks (beta)

Introduced in GitLab 12.10.

Auto Build supports building your application using Cloud Native Buildpacks through the pack command. To use Cloud Native Buildpacks, set the CI variable AUTO_DEVOPS_BUILD_IMAGE_CNB_ENABLED to a non-empty value. The default builder is heroku/buildpacks:18 but a different builder can be selected using the CI variable AUTO_DEVOPS_BUILD_IMAGE_CNB_BUILDER.

Cloud Native Buildpacks (CNBs) are an evolution of Heroku buildpacks, and will eventually supersede Herokuish-based builds within Auto DevOps. For more information, see this issue.

Builds using Cloud Native Buildpacks support the same options as builds using Heroku buildpacks, 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 form supported by pack.
  • The /bin/herokuish command is not present in the resulting image, and prefixing commands with /bin/herokuish procfile exec is no longer required (nor possible).
Note: Auto Test still uses Herokuish, as test suite detection is not yet part of the Cloud Native Buildpack specification. For more information, see this issue.

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

Note that 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

Introduced in GitLab Ultimate 10.3.

Static Application Security Testing (SAST) uses the SAST Docker image to run static analysis on the current code, and checks for potential security issues. The Auto SAST stage will be skipped on licenses other than Ultimate, and 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.

To learn more about how SAST works, see the documentation.

Auto Dependency Scanning

Introduced in GitLab Ultimate 10.7.

Dependency Scanning uses the Dependency Scanning Docker image to run analysis on the project dependencies and check for potential security issues. The Auto Dependency Scanning stage is skipped on licenses other than Ultimate and 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 displays any security warnings detected,

To learn more about Dependency Scanning, see the documentation.

Auto License Compliance

Introduced in GitLab Ultimate 11.0.

License Compliance uses the License Compliance Docker image to search the project dependencies for their license. The Auto License Compliance 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 licenses.

To learn more about License Compliance, see the documentation.

Auto Container Scanning

Introduced in GitLab 10.4.

Vulnerability Static Analysis for containers uses Clair to check for potential security issues on 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.

To learn more about Container Scanning, see the documentation.

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.

Since GitLab 11.4, local Tiller is used. Previous versions of GitLab had a Tiller installed in the project namespace.

Caution: Your apps should not be manipulated outside of Helm (using Kubernetes directly). This can cause confusion with Helm not detecting the change and subsequent deploys with Auto DevOps can undo your changes. Also, if you change something and want to undo it by deploying again, Helm may not detect that anything changed in the first place, and thus not realize that it needs to re-apply the old configuration.

Auto DAST

Introduced in GitLab Ultimate 10.4.

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.

To learn more about Dynamic Application Security Testing, see the documentation.

Overriding the DAST target

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

Danger: If DAST Full Scan is enabled, GitLab strongly advises not to set DAST_WEBSITE to any staging or production environment. DAST Full Scan actively attacks the target, which can take down your application and lead to data loss or corruption.

Disabling Auto DAST

You can disable DAST:

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

Auto Browser Performance Testing

Introduced in GitLab Premium 10.4.

Auto Browser Performance Testing measures the 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 performance differences between the source and target branches are also shown in the merge request widget.

Auto Deploy

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

After a branch or merge request is merged into the project’s default branch (usually master), 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 environment 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.

Since GitLab 11.4, a local Tiller is used. Previous versions of GitLab had a Tiller installed in the project namespace.

Caution: Your apps should not be manipulated outside of Helm (using Kubernetes directly). This can cause confusion with Helm not detecting the change and subsequent deploys with Auto DevOps can undo your changes. Also, if you change something and want to undo it by deploying again, Helm may not detect that anything changed in the first place, and thus not realize that it needs to re-apply the old configuration.

GitLab deploy tokens

Introduced in GitLab 11.0.

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 won’t be automatically created.

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

Note: CI_REGISTRY_PASSWORD is only valid during deployment. Kubernetes will be able to successfully pull the container image during deployment, but if the image must be pulled again, such as after pod eviction, Kubernetes will fail to do so as it attempts to fetch the image using CI_REGISTRY_PASSWORD.

Kubernetes 1.16+

Version history
  • Introduced in GitLab 12.8.
  • Support for deploying a PostgreSQL version that supports Kubernetes 1.16+ was introduced in GitLab 12.9.
  • Supported out of the box for new deployments as of GitLab 13.0.
Deprecation The default value for the deploymentApiVersion setting was changed from extensions/v1beta to apps/v1 in GitLab 13.0.

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 on GitLab 13.0 or newer, no configuration should be required.

  2. On GitLab 12.10 or older, set the following in the .gitlab/auto-deploy-values.yaml file:

    deploymentApiVersion: apps/v1
    
  3. If you have an in-cluster PostgreSQL database installed with AUTO_DEVOPS_POSTGRES_CHANNEL set to 1, follow the guide to upgrade PostgreSQL.

  4. If you are deploying your application for the first time and are using GitLab 12.9 or 12.10, set AUTO_DEVOPS_POSTGRES_CHANNEL to 2.

Danger: On GitLab 12.9 and 12.10, opting into AUTO_DEVOPS_POSTGRES_CHANNEL version 2 deletes the version 1 PostgreSQL database. Follow the guide to upgrading PostgreSQL to back up and restore your database before opting into version 2 (On GitLab 13.0, an additional variable is required to trigger the database deletion).

Migrations

Introduced in GitLab 11.4

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

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 normal.

Note that a post-install hook means that if any deploy succeeds, DB_INITIALIZE won’t be 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 Herokuish:

  • DB_INITIALIZE can be set to RAILS_ENV=production /bin/herokuish procfile exec bin/rails db:setup
  • DB_MIGRATE can be set to RAILS_ENV=production /bin/herokuish procfile exec bin/rails db:migrate

Unless your repository contains a Dockerfile, your image is built with Herokuish, and you must prefix commands run in these images with /bin/herokuish procfile exec to replicate the environment where your application will run.

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 won’t deploy this instance for you, so you must:

  • Maintain your own Redis instance.
  • Set a CI 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:
    - /bin/herokuish
    - procfile
    - exec
    - sidekiq
    preStopCommand:
    - /bin/herokuish
    - procfile
    - exec
    - sidekiqctl
    - quiet
    terminationGracePeriodSeconds: 60

Network Policy

Introduced in GitLab 12.7.

By default, all Kubernetes pods are non-isolated, and accept traffic to and from any source. You can use NetworkPolicy to restrict connections to and from selected pods, namespaces, and the Internet.

Note: You must use a Kubernetes network plugin that implements support for NetworkPolicy. The default network plugin for Kubernetes (kubenet) does not implement support for it. The Cilium network plugin can be installed as a cluster application to enable support for network policies.

You can enable deployment of a network policy by setting the following in the .gitlab/auto-deploy-values.yaml file:

networkPolicy:
  enabled: true

The default policy deployed by the Auto Deploy pipeline allows traffic within a local namespace, and from the gitlab-managed-apps namespace. All other inbound connections are blocked. Outbound traffic (for example, to the Internet) is not affected by the default policy.

You can also provide a custom policy specification in the .gitlab/auto-deploy-values.yaml file, for example:

networkPolicy:
  enabled: true
  spec:
    podSelector:
      matchLabels:
        app.gitlab.com/env: staging
    ingress:
    - from:
      - podSelector:
          matchLabels: {}
      - namespaceSelector:
          matchLabels:
            app.gitlab.com/managed_by: gitlab

For more information on installing Network Policies, see Install Cilium using GitLab CI/CD.

Web Application Firewall (ModSecurity) customization

Introduced in GitLab 12.8.

Customization on an Ingress or on a deployment base is available for clusters with ModSecurity installed.

To enable ModSecurity with Auto Deploy, you must create a .gitlab/auto-deploy-values.yaml file in your project with the following attributes.

Attribute Description Default
enabled Enables custom configuration for ModSecurity, defaulting to the Core Rule Set false
secRuleEngine Configures the rules engine DetectionOnly
secRules Creates one or more additional rule nil

In the following auto-deploy-values.yaml example, some custom settings are enabled for ModSecurity. Those include setting its engine to process rules instead of only logging them, while adding two specific header-based rules:

ingress:
  modSecurity:
    enabled: true
    secRuleEngine: "On"
    secRules:
      - variable: "REQUEST_HEADERS:User-Agent"
        operator: "printer"
        action: "log,deny,id:'2010',status:403,msg:'printer is an invalid agent'"
      - variable: "REQUEST_HEADERS:Content-Type"
        operator: "text/plain"
        action: "log,deny,id:'2011',status:403,msg:'Text is not supported as content type'"

Running commands in the container

Applications built with Auto Build using Herokuish, the default unless your repository contains a custom Dockerfile, may require commands to be wrapped as follows:

/bin/herokuish procfile exec $COMMAND

Some of the reasons you may need to wrap commands:

  • Attaching using kubectl exec.
  • Using GitLab’s Web Terminal.

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

/bin/herokuish procfile exec bin/rails c

Auto Monitoring

After your application deploys, Auto Monitoring helps you monitor your application’s server and response metrics right out of the box. Auto Monitoring uses Prometheus to retrieve system metrics, such as CPU and memory usage, directly from Kubernetes, and response metrics, such as HTTP error rates, latency, and throughput, from the NGINX server.

The metrics include:

  • Response Metrics: latency, throughput, error rate
  • System Metrics: CPU utilization, memory utilization

GitLab provides some initial alerts for you after you install Prometheus:

  • Ingress status code 500 > 0.1%
  • NGINX status code 500 > 0.1%

To use Auto Monitoring:

  1. Install and configure the requirements.
  2. Enable Auto DevOps, if you haven’t done already.
  3. Navigate to your project’s CI/CD > Pipelines and click Run Pipeline.
  4. After the pipeline finishes successfully, open the monitoring dashboard for a deployed environment to view the metrics of your deployed application. To view the metrics of the whole Kubernetes cluster, navigate to Operations > Metrics.

Auto Metrics