[{"data":1,"prerenderedAt":820},["ShallowReactive",2],{"/en-us/blog/integrating-gitlab-com-with-atlassian-jira-cloud":3,"navigation-en-us":42,"banner-en-us":453,"footer-en-us":463,"blog-post-authors-en-us-Tye Davis":702,"blog-related-posts-en-us-integrating-gitlab-com-with-atlassian-jira-cloud":716,"blog-promotions-en-us":757,"next-steps-en-us":810},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":20,"description":17,"extension":25,"externalUrl":26,"featured":14,"heroImage":19,"isFeatured":14,"meta":27,"navigation":28,"path":29,"publishedDate":20,"rawbody":30,"seo":31,"slug":13,"stem":35,"tagSlugs":36,"tags":40,"template":15,"updatedDate":26,"__hash__":41},"blogPosts/en-us/blog/integrating-gitlab-com-with-atlassian-jira-cloud.yml","How to integrate GitLab.com with Jira Cloud",[7],"tye-davis",[9],"Tye Davis","By moving to the cloud engineering teams can accelerate innovation and scale resources across an organization. The ease of access and reduced infrastructure costs that comes with moving to the cloud is a direct result of using a platform that easily integrates your data and keeps it secure yet accessible. Gitlab.com, the cloud (SAAS) platform for GitLab, modernizes data platforms to leverage new applications and advances end-to-end software delivery. GitLab partners with other best-in-class cloud companies so your teams can use tools that best align with your team's DevOps ecosystem. Application development requires speed and iteration, making seamless collaboration a necessity to deliver real business value. GitLab embraces connecting all phases of the software development lifecycle (SDLC) in a DevOps ecosystem that fuels visibility, collaboration, and velocity.\n\n## How to use GitLab with Atlassian's Jira\n\nWe know that many companies have been using Jira for project management, and have existing data and business processes built into their instance. For some of these customers, this means it can be difficult and cost-prohibitive to move off of Jira. We believe that people (and tools) work better when they're all in one place, so to serve these customers, we built a seamless integration between GitLab and Jira. By using the [GitLab for Jira app in the Atlassian Marketplace](https://marketplace.atlassian.com/apps/1221011/gitlab-com-for-jira-cloud), you can integrate GitLab.com and Jira Cloud harmoniously.\n\nHere's a short list of what you can do when integrating GitLab with Jira:\n\n* One GitLab project integrates with all the Jira projects in a single Jira instance.\n* Quickly navigate to Jira issues from GitLab.\n* Detect and link to Jira issues from GitLab commits and merge requests.\n* Log GitLab events in the associated Jira issue.\n* Automatically close (also called \"transition\") Jira issues with GitLab commits and merge requests.\n\n## How to configure the integration\n\nThere are two methods for configuring the integration. The [Jira DVCS connector](https://docs.gitlab.com/integration/jira/dvcs/), and the method we describe in this blog post. The DVCS connector updates data only once per hour, while our method syncs data in real time. We recommend using our method for this reason, but if you are not using both of these environments then use the Jira DVCS connector instead.\n\n- First, go to Jira Settings > Apps > Find new apps, then search for GitLab.\n- Next, click GitLab for Jira, then click \"Get it now\". Or, go the [App in the marketplace](https://marketplace.atlassian.com/apps/1221011/gitlab-for-jira), directly.\n\n![Arrow pointing to \"get it now button\" on GitLab on Atlassian Marketplace App](https://about.gitlab.com/images/blogimages/atlassianjira/gitlabonatlassianmarketplace.png)\nClick the yellow button to download the app.\n\n\n- Third, after installing, click \"Get started to go to the configurations\" page. This page is always available under Jira Settings > Apps > Manage apps.\n\n![GitLab on Atlassian Marketplace App](https://about.gitlab.com/images/blogimages/atlassianjira/manageappsjira.png)\nClick the \"Get started button\".\n\n\n- Fourth, in Namespace, enter the group or personal namespace, and then click \"Link namespace to Jira\". The user that is setting up GitLab for Jira must have Maintainer access to the GitLab namespace. Note: The GitLab user only needs access when adding a new namespace. For syncing with Jira, we do not depend on the user’s token.\n\n![GitLab for Jira Configuration](https://about.gitlab.com/images/blogimages/atlassianjira/gitlabforjiraintegration.png)\nAdd a namespace.\n\n\nAfter a namespace is added, all of the future commits, branches, and merge requests within all projects under that namespace will be synced to Jira. At the moment, past data cannot be synced.\n\nFor more information, see [the documentation](https://docs.gitlab.com/integration/jira/#usage).\n\n### How to troubleshoot GitLab for Jira\n\nThe GitLab for Jira App uses an iframe to add namespaces on the settings page. Some browsers block cross-site cookies which can lead to a message saying that the user needs to log on to GitLab.com even though the user is already logged in: \"You need to sign in or sign up before continuing.\"\n\nIn this situation, we recommend using [Firefox](https://www.mozilla.org/en-US/firefox/), [Google Chrome](https://www.google.com/chrome/index.html) or enabling cross-site cookies in your browser.\n\n### What are the limitations of GitLab for Jira?\n\nThis integration is currently not supported on GitLab instances under a [relative URL](https://docs.gitlab.com/omnibus/settings/configuration/#configuring-a-relative-url-for-gitlab) (for example, http://yourcompanyname.com/gitlab).\n\n## How to use GitLab for Jira\n\nAfter the integrating GitLab and Jira, you can:\n\n- Refer to any Jira issue by its ID in GitLab branch names, commit messages, and merge request titles.\n\n- Using commit messages in GitLab, you can move Jira issues along that Jira projects defined transitions.\n\n![GitLab for Jira Setup](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot.png)\nIn this image, you can see that this Jira issue has four stages: Backlog, selected for development, in progress, and done.\n\n\n- As referenced in the base GitLab-Jira integration, when you reference an issue in a comment on a merge request and commit, e.g., PROJECT-7, the basic integration adds a comment in Jira issue. Also, by commenting in a Jira transition (putting a # first), this will move a Jira issue to the desired transition. Below is an example using the built-in GitLab Web IDE (this can be done in your Web IDE of choice as well).\n\n![View of Jira Transitions](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot2.png)\nThere are multiple Jira transition options.\n\n\n- Now, the user can see linked branches, commits, and merge requests in Jira issues (merge requests are called \"pull requests\" in Jira issues).\nJira issue IDs must be formatted in UPPERCASE for the integration to work.\n\n![View branches, commits and merge requests in your jira issue](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot4.png)\nView branches, commits, and merge requests in your Jira issue.\n\n\n- Click the links to see your GitLab repository data.\n\n![Deep Dive into your GitLab commits](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot5.png)\nHow to take a look at your GitLab commits.\n\n\n![Deep Dive into your GitLab branches](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot6.png)\nTake a deep Dive into your GitLab merge requests.\n\n\nFor more information on using Jira Smart Commits to track time against an issue, specify an issue transition, or add a custom comment, see the Atlassian page using [Smart Commits](https://support.atlassian.com/jira-cloud-administration/docs/enable-smart-commits/).\n\n## Watch and learn\n\nMore of a video person? For a walkthrough of the integration with GitLab for Jira, watch and learn how to configure GitLab Jira Integration using Marketplace App.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/SwR-g1s1zTo\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nGitLab helps teams ship software faster with technology integration options, such as the integration with Jira, that automate tasks, provide visibility into development progress and the greater end-to-end software lifecycle. We recognize that many companies use Jira for Agile project management and our seamless integration brings Jira together with GitLab.\n\nCover image by [Mikołaj Idziak](https://unsplash.com/@mikidz) on [Unsplash](https://unsplash.com/photos/nwjRmbXbLgw).\n","engineering",{"slug":13,"featured":14,"template":15},"integrating-gitlab-com-with-atlassian-jira-cloud",false,"BlogPost",{"title":5,"description":17,"authors":18,"heroImage":19,"date":20,"body":10,"category":11,"tags":21},"Check out how to use the GitLab App on the Atlassian Marketplace to connect your merge requests, branches, and commits to a Jira issue.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667260/Blog/Hero%20Images/twopeasinapod.jpg","2021-03-25",[22,23,24],"cloud native","DevOps","CD","yml",null,{},true,"/en-us/blog/integrating-gitlab-com-with-atlassian-jira-cloud","seo:\n title: How to integrate GitLab.com with Jira Cloud\n description: >-\n Check out how to use the GitLab App on the Atlassian Marketplace to connect\n your merge requests, branches, and commits to a Jira issue.\n ogTitle: How to integrate GitLab.com with Jira Cloud\n ogDescription: >-\n Check out how to use the GitLab App on the Atlassian Marketplace to connect\n your merge requests, branches, and commits to a Jira issue.\n noIndex: false\n ogImage: >-\n https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667260/Blog/Hero%20Images/twopeasinapod.jpg\n ogUrl: >-\n https://about.gitlab.com/blog/integrating-gitlab-com-with-atlassian-jira-cloud\n ogSiteName: https://about.gitlab.com\n ogType: article\n canonicalUrls: >-\n https://about.gitlab.com/blog/integrating-gitlab-com-with-atlassian-jira-cloud\ncontent:\n title: How to integrate GitLab.com with Jira Cloud\n description: >-\n Check out how to use the GitLab App on the Atlassian Marketplace to connect\n your merge requests, branches, and commits to a Jira issue.\n authors:\n - Tye Davis\n heroImage: >-\n https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667260/Blog/Hero%20Images/twopeasinapod.jpg\n date: '2021-03-25'\n body: >\n By moving to the cloud engineering teams can accelerate innovation and scale\n resources across an organization. The ease of access and reduced\n infrastructure costs that comes with moving to the cloud is a direct result\n of using a platform that easily integrates your data and keeps it secure yet\n accessible. Gitlab.com, the cloud (SAAS) platform for GitLab, modernizes\n data platforms to leverage new applications and advances end-to-end software\n delivery. GitLab partners with other best-in-class cloud companies so your\n teams can use tools that best align with your team's DevOps ecosystem.\n Application development requires speed and iteration, making seamless\n collaboration a necessity to deliver real business value. GitLab embraces\n connecting all phases of the software development lifecycle (SDLC) in a\n DevOps ecosystem that fuels visibility, collaboration, and velocity.\n\n\n ## How to use GitLab with Atlassian's Jira\n\n\n We know that many companies have been using Jira for project management, and\n have existing data and business processes built into their instance. For\n some of these customers, this means it can be difficult and cost-prohibitive\n to move off of Jira. We believe that people (and tools) work better when\n they're all in one place, so to serve these customers, we built a seamless\n integration between GitLab and Jira. By using the [GitLab for Jira app in\n the Atlassian\n Marketplace](https://marketplace.atlassian.com/apps/1221011/gitlab-com-for-jira-cloud),\n you can integrate GitLab.com and Jira Cloud harmoniously.\n\n\n Here's a short list of what you can do when integrating GitLab with Jira:\n\n\n * One GitLab project integrates with all the Jira projects in a single Jira\n instance.\n\n * Quickly navigate to Jira issues from GitLab.\n\n * Detect and link to Jira issues from GitLab commits and merge requests.\n\n * Log GitLab events in the associated Jira issue.\n\n * Automatically close (also called \"transition\") Jira issues with GitLab\n commits and merge requests.\n\n\n ## How to configure the integration\n\n\n There are two methods for configuring the integration. The [Jira DVCS\n connector](https://docs.gitlab.com/integration/jira/dvcs/), and the\n method we describe in this blog post. The DVCS connector updates data only\n once per hour, while our method syncs data in real time. We recommend using\n our method for this reason, but if you are not using both of these\n environments then use the Jira DVCS connector instead.\n\n\n - First, go to Jira Settings > Apps > Find new apps, then search for GitLab.\n\n - Next, click GitLab for Jira, then click \"Get it now\". Or, go the [App in\n the\n marketplace](https://marketplace.atlassian.com/apps/1221011/gitlab-for-jira),\n directly.\n\n\n ![Arrow pointing to \"get it now button\" on GitLab on Atlassian Marketplace\n App](https://about.gitlab.com/images/blogimages/atlassianjira/gitlabonatlassianmarketplace.png)\n\n Click the yellow button to download the app.\n\n\n\n - Third, after installing, click \"Get started to go to the configurations\"\n page. This page is always available under Jira Settings > Apps > Manage\n apps.\n\n\n ![GitLab on Atlassian Marketplace\n App](https://about.gitlab.com/images/blogimages/atlassianjira/manageappsjira.png)\n\n Click the \"Get started button\".\n\n\n\n - Fourth, in Namespace, enter the group or personal namespace, and then\n click \"Link namespace to Jira\". The user that is setting up GitLab for Jira\n must have Maintainer access to the GitLab namespace. Note: The GitLab user\n only needs access when adding a new namespace. For syncing with Jira, we do\n not depend on the user’s token.\n\n\n ![GitLab for Jira\n Configuration](https://about.gitlab.com/images/blogimages/atlassianjira/gitlabforjiraintegration.png)\n\n Add a namespace.\n\n\n\n After a namespace is added, all of the future commits, branches, and merge\n requests within all projects under that namespace will be synced to Jira. At\n the moment, past data cannot be synced.\n\n\n For more information, see [the\n documentation](https://docs.gitlab.com/integration/jira/#usage).\n\n\n ### How to troubleshoot GitLab for Jira\n\n\n The GitLab for Jira App uses an iframe to add namespaces on the settings\n page. Some browsers block cross-site cookies which can lead to a message\n saying that the user needs to log on to GitLab.com even though the user is\n already logged in: \"You need to sign in or sign up before continuing.\"\n\n\n In this situation, we recommend using\n [Firefox](https://www.mozilla.org/en-US/firefox/), [Google\n Chrome](https://www.google.com/chrome/index.html) or enabling cross-site\n cookies in your browser.\n\n\n ### What are the limitations of GitLab for Jira?\n\n\n This integration is currently not supported on GitLab instances under a\n [relative\n URL](https://docs.gitlab.com/omnibus/settings/configuration/#configuring-a-relative-url-for-gitlab)\n (for example, http://yourcompanyname.com/gitlab).\n\n\n ## How to use GitLab for Jira\n\n\n After the integrating GitLab and Jira, you can:\n\n\n - Refer to any Jira issue by its ID in GitLab branch names, commit messages,\n and merge request titles.\n\n\n - Using commit messages in GitLab, you can move Jira issues along that Jira\n projects defined transitions.\n\n\n ![GitLab for Jira\n Setup](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot.png)\n\n In this image, you can see that this Jira issue has four stages: Backlog,\n selected for development, in progress, and done.\n\n\n\n - As referenced in the base GitLab-Jira integration, when you reference an\n issue in a comment on a merge request and commit, e.g., PROJECT-7, the basic\n integration adds a comment in Jira issue. Also, by commenting in a Jira\n transition (putting a # first), this will move a Jira issue to the desired\n transition. Below is an example using the built-in GitLab Web IDE (this can\n be done in your Web IDE of choice as well).\n\n\n ![View of Jira\n Transitions](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot2.png)\n\n There are multiple Jira transition options.\n\n\n\n - Now, the user can see linked branches, commits, and merge requests in Jira\n issues (merge requests are called \"pull requests\" in Jira issues).\n\n Jira issue IDs must be formatted in UPPERCASE for the integration to work.\n\n\n ![View branches, commits and merge requests in your jira\n issue](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot4.png)\n\n View branches, commits, and merge requests in your Jira issue.\n\n\n\n - Click the links to see your GitLab repository data.\n\n\n ![Deep Dive into your GitLab\n commits](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot5.png)\n\n How to take a look at your GitLab commits.\n\n\n\n ![Deep Dive into your GitLab\n branches](https://about.gitlab.com/images/blogimages/atlassianjira/jiraissuescreenshot6.png)\n\n Take a deep Dive into your GitLab merge requests.\n\n\n\n For more information on using Jira Smart Commits to track time against an\n issue, specify an issue transition, or add a custom comment, see the\n Atlassian page using [Smart\n Commits](https://support.atlassian.com/jira-cloud-administration/docs/enable-smart-commits/).\n\n\n ## Watch and learn\n\n\n More of a video person? For a walkthrough of the integration with GitLab for\n Jira, watch and learn how to configure GitLab Jira Integration using\n Marketplace App.\n\n\n \u003C!-- blank line -->\n\n \u003Cfigure class=\"video_container\">\n \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/SwR-g1s1zTo\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n \u003C/figure>\n\n \u003C!-- blank line -->\n\n\n GitLab helps teams ship software faster with technology integration options,\n such as the integration with Jira, that automate tasks, provide visibility\n into development progress and the greater end-to-end software lifecycle. 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Building a successful DevOps platform at enterprise scale **should include** understanding pipeline performance, job execution patterns, and quantifiable operational insights — especially for organizations running GitLab self-managed instances.\n\nTo help GitLab customers maximize their platform investments, we developed the GitLab CI/CD Observability solution as part of our Platform Excellence program, which transforms raw pipeline metrics into actionable operational insights.\n\nA leading financial services organization partnered with GitLab's customer success architect to gain visibility into their GitLab self-managed deployment. Together, we implemented a containerized observability solution combining the open-source gitlab-ci-pipelines-exporter with enterprise-grade Prometheus and Grafana infrastructure.\n\nIn this article, you'll learn the challenges they faced managing pipelines at scale and how GitLab CI/CD Observability addressed them with a practical, end-to-end implementation.\n\n## The challenge: Measuring CI/CD performance\nBefore implementing any observability solution, define your measurement landscape:\n* **What metrics matter?** Pipeline duration, job success rates, queue times, runner utilization\n* **Who needs visibility?** Developers, DevOps engineers, platform teams, leadership\n* **What decisions will this drive?** Infrastructure investment, bottleneck remediation, capacity planning\n\n## Solution architecture: A full set of dashboards for observability\nOnce deployed, the observability stack provides a set of Grafana dashboards that give real-time and historical visibility into your CI/CD platform. A typical deployment includes:\n* **Pipeline Overview Dashboard:** A top-level view showing total pipeline runs, success/failure rates over time (as stacked bar or time-series charts), and average pipeline duration trends. Panels use color-coded status indicators (green for success, red for failure, amber for cancelled) so platform teams can spot degradation at a glance.\n* **Job Performance Dashboard:** Drill-down panels showing individual job duration distributions (histogram), the top 10 slowest jobs by average duration, and job failure heatmaps by project and stage. This is where teams identify specific bottleneck jobs worth optimizing.\n* **Runner & Infrastructure Dashboard:** Combines Node Exporter host metrics (CPU, memory, disk) with pipeline queue-time data to correlate infrastructure saturation with pipeline wait times. Useful for capacity planning decisions such as scaling runner pools or upgrading instance sizes.\n* **Deployment Frequency Dashboard:** Tracks deployment count and deployment duration over time per environment, aligned with DORA metrics. Helps engineering leadership assess delivery throughput and environment drift (commits behind main).\n\nEach dashboard is provisioned automatically via Grafana's file-based provisioning, so it deploys consistently across environments. The dashboards can be further customized with Grafana variables to filter by project, ref/branch, or time range.\n\n![Solution architecture](https://res.cloudinary.com/about-gitlab-com/image/upload/v1777382608/Blog/Imported/blog-building-ci-cd-observability-stack-for-gitlab-self-managed/image1.png)\n\nThe solution requires two exporters:\n* **Pipeline Exporter:** Collects CI/CD metrics via GitLab API (pipeline duration, job status, deployments)\n* **Node Exporter:** Collects host-level metrics (CPU, memory, disk) for infrastructure correlation\n\n**Prerequisites:**\n* GitLab Self-Managed Version 18.1+\n* **Container orchestration platform:** A Kubernetes cluster (recommended for enterprise deployments) or a container runtime such as Docker/Podman for smaller scale or proof-of-concept environments. The primary deployment guide below targets Kubernetes; a Docker Compose alternative is provided in the appendix for local testing and evaluation\n* GitLab Personal Access Token (**read_api** scope)\n\n## Kubernetes deployment (recommended)\nFor enterprise environments, deploy each component as a separate Deployment within a dedicated namespace. This approach integrates with existing cluster infrastructure, secrets management, and network policies.\n\n### 1. Create namespace and secret\n```bash\nkubectl create namespace gitlab-observability\n\n# Create the GitLab token secret (see Secrets Management section below\n# for enterprise-grade approaches using external secret operators)\nkubectl create secret generic gitlab-token \\\n --from-literal=token=glpat-xxxxxxxxxxxx \\\n -n gitlab-observability\n```\n\n\n### 2. Deploy the Pipeline Exporter\n```yaml\n# exporter-deployment.yaml\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: gitlab-ci-pipelines-exporter\n namespace: gitlab-observability\nspec:\n replicas: 1\n selector:\n matchLabels:\n app: gitlab-ci-pipelines-exporter\n template:\n metadata:\n labels:\n app: gitlab-ci-pipelines-exporter\n spec:\n containers:\n - name: exporter\n image: mvisonneau/gitlab-ci-pipelines-exporter:latest\n ports:\n - containerPort: 8080\n env:\n - name: GCPE_GITLAB_TOKEN\n valueFrom:\n secretKeyRef:\n name: gitlab-token\n key: token\n - name: GCPE_CONFIG\n value: /etc/gcpe/config.yml\n volumeMounts:\n - name: config\n mountPath: /etc/gcpe\n volumes:\n - name: config\n configMap:\n name: gcpe-config\n---\napiVersion: v1\nkind: Service\nmetadata:\n name: gitlab-ci-pipelines-exporter\n namespace: gitlab-observability\nspec:\n selector:\n app: gitlab-ci-pipelines-exporter\n ports:\n - port: 8080\n targetPort: 8080\n```\n\n### 3. Deploy Node Exporter (DaemonSet)\n```yaml\n# node-exporter-daemonset.yaml\napiVersion: apps/v1\nkind: DaemonSet\nmetadata:\n name: node-exporter\n namespace: gitlab-observability\nspec:\n selector:\n matchLabels:\n app: node-exporter\n template:\n metadata:\n labels:\n app: node-exporter\n spec:\n containers:\n - name: node-exporter\n image: prom/node-exporter:latest\n ports:\n - containerPort: 9100\n---\napiVersion: v1\nkind: Service\nmetadata:\n name: node-exporter\n namespace: gitlab-observability\nspec:\n selector:\n app: node-exporter\n ports:\n - port: 9100\n targetPort: 9100\n```\n\n### 4. Deploy Prometheus\n```yaml\n# prometheus-deployment.yaml\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: prometheus\n namespace: gitlab-observability\nspec:\n replicas: 1\n selector:\n matchLabels:\n app: prometheus\n template:\n metadata:\n labels:\n app: prometheus\n spec:\n containers:\n - name: prometheus\n image: prom/prometheus:latest\n ports:\n - containerPort: 9090\n volumeMounts:\n - name: config\n mountPath: /etc/prometheus\n volumes:\n - name: config\n configMap:\n name: prometheus-config\n---\napiVersion: v1\nkind: Service\nmetadata:\n name: prometheus\n namespace: gitlab-observability\nspec:\n selector:\n app: prometheus\n ports:\n - port: 9090\n targetPort: 9090\n```\n\n### 5. Deploy Grafana\nThe Grafana deployment below starts with authentication disabled (`GF_AUTH_ANONYMOUS_ENABLED: true`) for initial setup convenience.\n\n**This setting allows anyone with network access to view all dashboards without logging in.** For production deployments, remove this variable or set it to false and configure a proper authentication provider (LDAP, SAML/SSO, or OAuth) to restrict access to authorized users.\n```yaml\n# grafana-deployment.yaml\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: grafana\n namespace: gitlab-observability\nspec:\n replicas: 1\n selector:\n matchLabels:\n app: grafana\n template:\n metadata:\n labels:\n app: grafana\n spec:\n containers:\n - name: grafana\n image: grafana/grafana:10.0.0\n ports:\n - containerPort: 3000\n env:\n # REMOVE or set to 'false' for production.\n # When 'true', any user with network access can\n # view dashboards without authentication.\n - name: GF_AUTH_ANONYMOUS_ENABLED\n value: 'true'\n volumeMounts:\n - name: dashboards-provider\n mountPath: /etc/grafana/provisioning/dashboards\n - name: datasources\n mountPath: /etc/grafana/provisioning/datasources\n - name: dashboards\n mountPath: /var/lib/grafana/dashboards\n volumes:\n - name: dashboards-provider\n configMap:\n name: grafana-dashboards-provider\n - name: datasources\n configMap:\n name: grafana-datasources\n - name: dashboards\n configMap:\n name: grafana-dashboards\n---\napiVersion: v1\nkind: Service\nmetadata:\n name: grafana\n namespace: gitlab-observability\nspec:\n selector:\n app: grafana\n ports:\n - port: 3000\n targetPort: 3000\n```\n\n### 6. Set network policy\nRestrict inter-pod traffic to only the required communication paths:\n```yaml\n# network-policy.yaml\napiVersion: networking.k8s.io/v1\nkind: NetworkPolicy\nmetadata:\n name: observability-policy\n namespace: gitlab-observability\nspec:\n podSelector: {}\n policyTypes:\n - Ingress\n ingress:\n # Prometheus scrapes exporter and node-exporter\n - from:\n - podSelector:\n matchLabels:\n app: prometheus\n ports:\n - port: 8080\n - port: 9100\n # Grafana queries Prometheus\n - from:\n - podSelector:\n matchLabels:\n app: grafana\n ports:\n - port: 9090\n```\n\n### 7. Validate\n```bash\nkubectl get pods -n gitlab-observability\nkubectl port-forward svc/grafana 3000:3000 -n gitlab-observability\ncurl http://localhost:3000/api/health\n```\n\n## Configuration reference\n### Exporter configuration\n```yaml\n# gitlab-ci-pipelines-exporter.yml (ConfigMap: gcpe-config)\nlog:\n level: info\ngitlab:\n url: https://gitlab.your-domain.com\n maximum_requests_per_second: 10\nproject_defaults:\n pull:\n pipeline:\n jobs:\n enabled: true\nwildcards:\n - owner:\n name: your-group-name\n kind: group\n archived: false\n```\n\n### Prometheus configuration\n```yaml\n# prometheus.yml (ConfigMap: prometheus-config)\nglobal:\n scrape_interval: 15s\nscrape_configs:\n - job_name: 'gitlab-ci-pipelines-exporter'\n static_configs:\n - targets: ['gitlab-ci-pipelines-exporter:8080']\n - job_name: 'node-exporter'\n static_configs:\n - targets: ['node-exporter:9100']\n```\n\n### Grafana data sources\n```yaml\n# datasources.yml (ConfigMap: grafana-datasources)\napiVersion: 1\ndatasources:\n - name: Prometheus\n type: prometheus\n access: proxy\n url: http://prometheus:9090\n isDefault: true\n# dashboards.yml (ConfigMap: grafana-dashboards-provider)\napiVersion: 1\nproviders:\n - name: 'default'\n folder: 'GitLab CI/CD'\n type: file\n options:\n path: /var/lib/grafana/dashboards\n```\n\n## Key metrics\n### Pipeline Exporter metrics\n| Metric | Description |\n| :---- | :---- |\n| `gitlab_ci_pipeline_duration_seconds` | Pipeline execution time |\n| `gitlab_ci_pipeline_status` | Pipeline success/failure by project |\n| `gitlab_ci_pipeline_job_duration_seconds` | Individual job execution time |\n| `gitlab_ci_pipeline_job_status` | Job success/failure status |\n| `gitlab_ci_pipeline_job_artifact_size_bytes` | Artifact storage consumption |\n| `gitlab_ci_pipeline_coverage` | Code coverage percentage |\n| `gitlab_ci_environment_deployment_count` | Deployment frequency |\n| `gitlab_ci_environment_deployment_duration_seconds` | Deployment execution time |\n| `gitlab_ci_environment_behind_commits_count` | Environment drift from main |\n\n### Node Exporter metrics\n| Metric | Description |\n| :---- | :---- |\n| `node_cpu_seconds_total` | CPU utilization |\n| `node_memory_MemAvailable_bytes` | Available memory |\n| `node_filesystem_avail_bytes` | Disk space available |\n| `node_load1` | 1-minute load average |\n\n## Troubleshooting\n### Air-gapped Grafana plugin installation\nFor offline environments, install plugins manually. Example for Kubernetes:\n```bash\n# Copy plugin zip into the Grafana pod\nkubectl cp grafana-polystat-panel-2.1.16.zip \\\n gitlab-observability/grafana-\u003Cpod-id>:/tmp/\n# Extract plugin\nkubectl exec -it -n gitlab-observability deploy/grafana -- \\\n sh -c \"unzip /tmp/grafana-polystat-panel-2.1.16.zip -d /var/lib/grafana/plugins/\"\n# Restart Grafana pod\nkubectl rollout restart deployment/grafana -n gitlab-observability\n# Verify installation\nkubectl exec -it -n gitlab-observability deploy/grafana -- \\\n ls -al /var/lib/grafana/plugins/\n```\n\n## Enterprise considerations\nFor regulated industries, ensure:\n* **Token security:** Store GitLab Personal Access Tokens in a dedicated secrets manager rather than hardcoded in ConfigMaps. Enforce token rotation policies and limit scope to **read\\_api** only.\n* **Network segmentation:** Deploy behind a reverse proxy with TLS termination. In Kubernetes, use an Ingress controller with automated certificate provisioning.\n* **Authentication:** Configure Grafana with your organization's identity provider (SAML, LDAP, or OAuth/OIDC) to enforce role-based access control on dashboards.\n\n## Why GitLab?\nGitLab's API-first design enables custom observability solutions that complement native capabilities like Value Stream Analytics and DORA metrics. The open architecture allows organizations to integrate proven open-source tooling — like the gitlab-ci-pipelines-exporter — directly with their existing enterprise infrastructure, without disrupting established workflows.\n\nAs your observability maturity grows, GitLab's built-in Observability capabilities provide a natural next step — offering deeper, integrated visibility without additional tooling. Learn more about what's available natively in the platform for [GitLab Observability](https://docs.gitlab.com/operations/observability/observability/).\n",[111,727,728],"product","tutorial",{"featured":14,"template":15,"slug":730},"how-to-build-ci-cd-observability-at-scale",{"content":732,"config":743},{"body":733,"title":734,"description":735,"authors":736,"heroImage":738,"date":739,"category":11,"tags":740},"Most CI/CD tools can run a build and ship a deployment. Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n - trigger\n\ntrigger-services:\n stage: trigger\n trigger:\n include:\n - local: '.gitlab/ci/api-service.yml'\n - local: '.gitlab/ci/web-service.yml'\n - local: '.gitlab/ci/worker-service.yml'\n strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n - build\n - test\n\nbuild-api:\n stage: build\n script:\n - echo \"Building API service\"\n\ntest-api:\n stage: test\n needs: [build-api]\n script:\n - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/api/jobs/#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n - build\n - test\n - trigger-backend\n\nbuild-frontend:\n stage: build\n script:\n - echo \"Building frontend and generating API contract...\"\n - mkdir -p dist\n - |\n echo '{\n \"api_version\": \"v2\",\n \"breaking_changes\": false\n }' > dist/api-contract.json\n - cat dist/api-contract.json\n artifacts:\n paths:\n - dist/api-contract.json\n expire_in: 1 hour\n\ntest-frontend:\n stage: test\n script:\n - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n stage: trigger-backend\n trigger:\n project: my-org/backend-service\n branch: main\n strategy: depend\n rules:\n - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n - build\n - test\n\nbuild-backend:\n stage: build\n script:\n - echo \"All backend tests passed!\"\n\nintegration-test:\n stage: test\n rules:\n - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n script:\n - echo \"Fetching API contract from frontend...\"\n - |\n curl --silent --fail \\\n --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n --output api-contract.json \\\n \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n - cat api-contract.json\n - |\n if grep -q '\"breaking_changes\": true' api-contract.json; then\n echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n exit 1\n fi\n echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n - generate\n - trigger-environments\n\ngenerate-config:\n stage: generate\n script:\n - |\n # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n # Default to dev, staging, prod if not set.\n ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n for ENV in $ENVIRONMENTS; do\n cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n stages:\n - deploy\n - verify\n deploy-${ENV}:\n stage: deploy\n script:\n - echo \"Deploying to ${ENV} environment\"\n verify-${ENV}:\n stage: verify\n script:\n - echo \"Running smoke tests on ${ENV}\"\n EOF\n done\n artifacts:\n paths:\n - \"*.yml\"\n exclude:\n - \".gitlab-ci.yml\"\n\n.trigger-template:\n stage: trigger-environments\n trigger:\n strategy: depend\n\ntrigger-dev:\n extends: .trigger-template\n trigger:\n include:\n - artifact: dev-pipeline.yml\n job: generate-config\n\ntrigger-staging:\n extends: .trigger-template\n needs: [trigger-dev]\n trigger:\n include:\n - artifact: staging-pipeline.yml\n job: generate-config\n\ntrigger-prod:\n extends: .trigger-template\n needs: [trigger-staging]\n trigger:\n include:\n - artifact: prod-pipeline.yml\n job: generate-config\n when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n* [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n* [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n* [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n rules:\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n when: never\n - if: $CI_COMMIT_BRANCH\n - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n - fast-checks\n - expensive-tests\n - deploy\n\nlint-code:\n stage: fast-checks\n script:\n - echo \"Running linter\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"push\"\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n stage: fast-checks\n script:\n - echo \"Running unit tests\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"push\"\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n stage: expensive-tests\n script:\n - echo \"Running integration tests (15 min)\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n stage: expensive-tests\n script:\n - echo \"Running E2E tests (30 min)\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n stage: expensive-tests\n script:\n - echo \"Running full nightly suite (2 hours)\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n stage: deploy\n script:\n - echo \"Deploying to production\"\n rules:\n - if: $CI_COMMIT_BRANCH == \"main\"\n when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n inputs:\n stage:\n default: deploy\n environment:\n default: production\n---\ndeploy-job:\n stage: $[[ inputs.stage ]]\n script:\n - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n - echo \"Deploy URL: $DEPLOY_URL\"\n environment:\n name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n APP_NAME: \"my-awesome-app\"\n DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n - component: gitlab.com/my-org/component-library/build@v1.0.6\n - component: gitlab.com/my-org/component-library/test@v1.0.6\n - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n inputs:\n environment: staging\n\nstages:\n - build\n - test\n - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n* A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n* A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n* A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n* [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n* [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n* [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n* [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n* [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n* [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n* [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n* [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n* [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n* [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n* [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[737],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[111,741,728,742],"DevOps platform","features",{"featured":28,"template":15,"slug":744},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":746,"config":755},{"title":747,"description":748,"authors":749,"heroImage":751,"date":752,"body":753,"category":11,"tags":754},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[750],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n 1. Do I have this cached? → Return it\n 2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│ CI/CD Pipeline │\n│ │ │\n│ ▼ │\n│ gitlab.com/virtual_registries/container/\u003Cid>/image │\n└─────────────────────────────────────────────────────────┘\n │\n ▼\n┌─────────────────────────────────────────────────────────┐\n│ Container Virtual Registry │\n│ │\n│ Upstream 1: Docker Hub ────────────────┐ │\n│ Upstream 2: dhi.io (Hardened) ────────┐│ │\n│ Upstream 3: MCR ─────────────────────┐││ │\n│ Upstream 4: Quay.io ────────────────┐│││ │\n│ ││││ │\n│ ┌─────────────────┴┴┴┴──┐ │\n│ │ Cache │ │\n│ │ (manifests + layers) │ │\n│ └───────────────────────┘ │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficult to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n group_id=\"785414\", # Your top-level group ID\n name=\"platform-images\",\n description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n registry_id=registry['id'],\n url=\"https://registry-1.docker.io\",\n name=\"Docker Hub\",\n cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n registry_id=registry['id'],\n url=\"https://dhi.io\",\n name=\"Docker Hardened Images\",\n username=\"your-docker-username\",\n password=\"your-docker-access-token\",\n cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n registry_id=registry['id'],\n url=\"https://mcr.microsoft.com\",\n name=\"Microsoft Container Registry\",\n cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n registry_id=registry['id'],\n url=\"https://quay.io\",\n name=\"Quay.io\",\n cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n \nbuild:\n image: docker:24\n services:\n - docker:24-dind\n before_script:\n # Authenticate to GitLab (which handles upstream auth for you)\n - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n script:\n # All of these go through your single virtual registry\n \n # Official Docker Hub images (use library/ prefix)\n - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n \n # Docker Hardened Images from dhi.io (no prefix needed)\n - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n \n # .NET from MCR\n - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n entries = client.list_cache_entries(upstream['id'])\n print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n registry_id=registry['id'],\n url=\"https://dhi.io\",\n name=\"Docker Hardened Images\",\n username=\"your-username\",\n password=\"your-token\",\n cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[728,727,742],{"featured":14,"template":15,"slug":756},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"promotions":758},[759,773,784,796],{"id":760,"categories":761,"header":763,"text":764,"button":765,"image":770},"ai-modernization",[762],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":766,"config":767},"Get your AI maturity score",{"href":768,"dataGaName":769,"dataGaLocation":246},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":774,"categories":775,"header":776,"text":764,"button":777,"image":781},"devops-modernization",[727,571],"Are you just managing tools or shipping innovation?",{"text":778,"config":779},"Get your DevOps maturity score",{"href":780,"dataGaName":769,"dataGaLocation":246},"/assessments/devops-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":785,"categories":786,"header":788,"text":764,"button":789,"image":793},"security-modernization",[787],"security","Are you trading speed for security?",{"text":790,"config":791},"Get your security maturity score",{"href":792,"dataGaName":769,"dataGaLocation":246},"/assessments/security-modernization-assessment/",{"config":794},{"src":795},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":797,"paths":798,"header":801,"text":802,"button":803,"image":808},"github-azure-migration",[799,800],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":804,"config":805},"See how GitLab compares to GitHub",{"href":806,"dataGaName":807,"dataGaLocation":246},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":809},{"src":783},{"header":811,"blurb":812,"button":813,"secondaryButton":818},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":814,"config":815},"Get your free trial",{"href":816,"dataGaName":53,"dataGaLocation":817},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":509,"config":819},{"href":57,"dataGaName":58,"dataGaLocation":817},1777493577854]