[{"data":1,"prerenderedAt":818},["ShallowReactive",2],{"/en-us/blog/insights":3,"navigation-en-us":40,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Sara Kassabian":700,"blog-related-posts-en-us-insights":714,"blog-promotions-en-us":755,"next-steps-en-us":808},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/insights.yml","Insights",[7],"sara-kassabian",null,"engineering",{"slug":11,"featured":12,"template":13},"insights",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"We're dogfooding a tool to help visualize high-level trends in GitLab projects","How our easy to configure Insights technology takes data from issues and merge requests to build visually appealing charts.",[18],"Sara Kassabian","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749681053/Blog/Hero%20Images/birdseyeview.jpg","2020-01-30","Our policy at GitLab is to [dogfood everything](https://handbook.gitlab.com/handbook/engineering/development/principles/#dogfooding) – meaning we aren't going to introduce a new product or feature to our [DevOps platform](/solutions/devops-platform/) before our engineering team tests it out. Sometimes though, the development process happens in reverse: The product and engineering teams need a specific tool or functionality to help us run GitLab better and discover a tool that has the capacity to solve many different customer use cases.\n\n[Insights](https://docs.gitlab.com/ee/user/project/insights/), which is available to [GitLab Ultimate](/pricing/ultimate/) users, is an example of such a tool. Insights is a flexible feature of GitLab that allows our users to visualize different trends in workflows, bugs, merge request (MR) throughput, and issue activity that is based upon the underlying labeling system of a group. In this blog post, we'll go in-depth on how and why we built this tool, how we use the tool at GitLab, and explain how to configure Insights for your own projects.\n\n\n- [Why we built Insights](#why-we-built-insights)\n- [Labels powers Insights](#why-label-hygiene-matters)\n- [How to configure Insights](#configuring-your-insights-dashboard)\n- [How GitLab uses Insights](#how-we-are-dogfooding-insights)\n- [Implementing Insights in your instance](#implementing-insights-for-your-team)\n\n[Kyle Wiebers](/company/team/#kwiebers), quality engineering manager on Engineering Productivity, gives an overview of how we use Insights at GitLab in the GitLab Unfiltered video embedded below. Watch the video and read the rest of the post to learn all about this exciting new tool we're dogfooding at GitLab.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/kKnQzS9qorc\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Why we built Insights\n\nThe [Engineering Productivity team](https://handbook.gitlab.com/handbook/product/product-processes/) at GitLab first built Insights to provide an overview of trends in the issue tracker, but soon realized that this technology can be applied in different ways that were beneficial to our needs, and the needs of our users.\n\n\"The initial thing was we were interested in when the bugs were being raised: Were they being raised around release time or were they being raised the middle of a phase?\" says [Mark Fletcher](/company/team/#markglenfletcher), backend engineer on Engineering Productivity. \"Because we did have bugs being created just after release, which led to regressions, which led to patch fixes. So we were just interested in exploring those kinds of trends.\"\n\nTo capture this trend data the Quality Engineering team created the [quality dashboard](https://quality-dashboard.gitlap.com/groups/gitlab-org), which was essentially the first iteration of Insights for GitLab. While the quality dashboard showed trends in bugs being raised per release cycle, it also showed how much work was being accomplished over the same period.\n\n\"And that's where the scope really changed from looking at issues that are bugs to merge requests and being able to have generic rules based on labels that we can use to align with our workflow,\" says Kyle.\n\n## Why label hygiene matters\n\nThe Engineering Productivity team soon realized that a lot of the different trends they were aiming to capture with Insights were powered by [labels](https://docs.gitlab.com/ee/user/project/labels.html#overview). Labels allow a GitLab user to categorize epics, issues, and merge requests with descriptive titles such as \"bug\" or \"feature request\" and quickly filter based upon category. The label filtering system works inside the [issue tracker](https://gitlab.com/gitlab-org/gitlab/-/issues/?sort=created_date&state=opened&first_page_size=100), and all throughout GitLab, and is a core part of the underlying configuration of Insights.\n\nA good example of an Insights dashboard that is configured by labels and the metadata that underlies issues and merge requests (such as creation date) is the [MR throughputs dashboard](https://gitlab.com/groups/gitlab-org/-/insights/#/throughputs).\n\n![Merge request throughputs for group](https://about.gitlab.com/images/blogimages/merge_throughputs_group.png){: .shadow.medium.center}\nA screenshot of the chart for merge request throughouts at the group level.\n\n\nThe MR throughputs dashboard captures how many MRs are completed during a given week or month to measure our organization's overall performance. It is part of our workflow to assign labels to MRs that help distinguish the type of MR being worked on: feature, bug, community contribution, security, or backstage. This dashboard is configured as a stacked bar chart, which makes it easy to visualize MR throughput by type so we can see the type of work being created over a fixed period of time. The chart is also divided into weekly or monthly views, which helps us see both short- and long-term trends.\n\n\"So, we can look at short-term trends and longer-term trends to see: Are we delivering more work? Are we hitting a bottleneck? Are we plateauing? And that allows us to dive a little bit deeper and take corrective action,\" says Kyle.\n\n### Labels help simplify the configuration of dashboards\n\nIf you look to the lefthand sidebar of the MR throughputs dashboard, you'll notice that the dashboard is configured at the Gitlab-org group level. The group level of GitLab-org contains all of the projects within GitLab-org and therefore captures all of the MR throughput data across all projects.\n\nThe project level is a level below the group level and looks at a specific project contained within a larger group, such as the GitLab project in the GitLab-org group.\n\n![Merge request throughputs for project](https://about.gitlab.com/images/blogimages/mr_throughputs_product.png){: .shadow.medium.center}\nA screenshot of the chart for merge request throughoutputs at the project level.\n\n\nAny Insights dashboard, including the MR throughputs dashboard, can be filtered at the group level or the project level, but the configuration remains the same regardless of how the dashboard is filtered.\n\n\"So everything that's contained within a group, and in our case, it would be the GitLab-org group, you can also have this on a project level,\" says Kyle. \"So if you want to look at Insights on a project, you can configure the same thing on a project. Just for our use case, it made sense to look at MR throughputs across multiple projects versus one specific project.\"\n\nBut in the end, it all comes back to labels. We don't have to configure the Insights dashboard differently for groups and projects because all of our labels at GitLab are set up at the group level and then propagate down to the project level.\n\nOne of the characteristics of Insights that makes it such a valuable feature is that the configuration is so flexible. While most customers will use the same labeling system across groups and projects as GitLab does, it is possible to configure the charts separately at the project and group level.\n\n\"The scope [of Insights] changed from looking at issues that are bugs to merge requests and being able to have generic rules based on labels that we can use to align with our workflow,\" says Kyle. \"Then that flexibility allows any customers to leverage the same feature based on their own specific workflow or labeling practices.\"\n\nA user can use Insights on a group or project regardless of the underlying labeling system. They just need to configure the dashboard according to their workflow.\n\n## Configuring your Insights dashboard\n\nThere are numerous Insights dashboards that are available out of the box or that can be [easily configured](https://docs.gitlab.com/ee/user/project/insights/#configure-your-insights) based on a user's labeling workflow.\n\nAll of the Insights dashboards within GitLab are [driven by a YAML file](https://gitlab.com/gitlab-org/quality/insights-config/-/blob/master/.gitlab/insights.yml). The configuration for each chart includes configuration parameters: title, type, and query.\n\nThe query section defines the type of issues and/or merge requests from the issue tracker that will be included in the chart. The [parameters for which labels are contained in the chart](https://docs.gitlab.com/ee/user/project/insights/#queryfilter_labels) fall under the query section as well.\n\n\"The Insights configuration is actually stored in [one of your project's repositories]. So, it can be changed just like you do any of your code. It can be [version-controlled](/topics/version-control/) so you can see changes over time. That gives you a lot of value to just ensure that there's very clear traceability into why was this dashboard changed, and when was it changed,\" says Kyle.\n\nHere is the configuration that underlies the [MR throughputs dashboard](https://gitlab.com/groups/gitlab-org/-/insights/#/throughputs) we looked at extensively in the section above.\n\n```text\nthroughputs:\n  title: Merge Request Throughputs (product only projects)\n  charts:\n    - title: Throughputs per Week\n      type: stacked-bar\n      query:\n        issuable_type: merge_request\n        issuable_state: merged\n        collection_labels:\n          - Community contribution\n          - security\n          - bug\n          - feature\n          - backstage\n        group_by: week\n        period_limit: 12\n    - title: Throughputs per Month\n      type: stacked-bar\n      query:\n        issuable_type: merge_request\n        issuable_state: merged\n        collection_labels:\n          - Community contribution\n          - security\n          - bug\n          - feature\n          - backstage\n        group_by: month\n        period_limit: 24\n\n```\n\n\nExplore the [Insights YAML file for GitLab](https://gitlab.com/gitlab-org/gitlab-insights/blob/master/.gitlab/insights.yml) to see how we set up some of our other charts.\n\n## How we are dogfooding Insights\n\nInsights is most effective at monitoring high-level trends, as well as measuring performance against a specific measurable objective with the aim of taking corrective action. At GitLab, we've been using our Insights technology in different ways to visualize our overall performance or to answer specific questions.\n\nOur Support and Quality Engineering teams at GitLab currently use Insights, but in different ways. By dogfooding the technology here at GitLab, we've found numerous use cases for Insights that could be valuable to our customers.\n\n### How our Support team uses Insights\n\nThe Support team uses Insights both as an out of the box issue tracking dashboard and as a customized dashboard made possible using automation.\n\n#### Bugs SLO chart\n\nThe [Bugs SLO dashboard](https://gitlab.com/gitlab-org/gitlab/insights/#/bugsPastSLO) was created so the Support department and engineering leaders can identify bugs overdue from SLO.\n\n![Support team Bugs SLO chart](https://about.gitlab.com/images/blogimages/bugs_slo.png){: .shadow.medium.center}\nA chart specially configured for the Support team to show how many bugs missed the SLO each month.\n\n\nThe Bugs SLO chart is configured in the GitLab-org group but lives in the GitLab project. The chart pulls open issues pertaining to bugs and customer bugs, that are labeled `missed-SLO` and groups them by month. We also have a [labeling system for categorizing based on priority](https://docs.gitlab.com/ee/development/labels/index.html#priority-labels) – P1 bugs are top priority, P2 bugs are second priority.\n\n\"This really allows us to, again, look at the trends: Are we improving? Are we getting worse? Do we need to look a little bit deeper here and do a corrective action to help address any problems that we see within the trends that Insights provides?\" says Kyle.\n\n#### Configuration of SLO chart\n\nHere is a peek at what happens inside the YAML file to configure the bugs SLO chart.\n\n```yaml\nbugsPastSLO:\n  title: Bugs Past SLO\n  charts:\n    - title: Open bugs past priority SLO by creation month\n      type: stacked-bar\n      query:\n        issuable_type: issue\n        issuable_state: opened\n        filter_labels:\n          - bug\n          - missed-SLO\n        collection_labels:\n          - P1\n          - P2\n        group_by: month\n        period_limit: 24\n    - title: Open customer bugs past priority SLO by creation month\n      type: stacked-bar\n      query:\n        issuable_type: issue\n        issuable_state: opened\n        filter_labels:\n          - bug\n          - missed-SLO\n          - customer\n        collection_labels:\n          - P1\n          - P2\n        group_by: month\n        period_limit: 24\n\n```\n\n\n#### Triage helps ensure good label hygiene\n\nFor the Bugs SLO chart, we use the [GitLab triage project](https://gitlab.com/gitlab-org/gitlab-triage) to [automatically apply the `missed-SLO` label to open issues with priority labels that miss the SLO target](https://handbook.gitlab.com/handbook/engineering/infrastructure/engineering-productivity/triage-operations/#missed-slo). We use automation here because the GitLab project is so massive, it would not be feasible to manually apply this label based upon the missed SLO target rules. Insights is flexible enough that either manual labeling or automation can be used on any dashboard.\n\n### Support issue tracker\n\nThe Support team used one of our out of the box dashboards to [see how many Support issues are open and closed per month](https://gitlab.com/gitlab-com/support-forum/insights/#/issues) with the [GitLab.com Support Tracker project](https://gitlab.com/gitlab-com/support-forum), which looks at support issues raised by GitLab.com users that don't go through the Support team.\n\n![Support issue tracker](https://about.gitlab.com/images/blogimages/support_issue_tracker.png){: .shadow.medium.center}\nThe Support team also uses one of our out of the box dashboards that tracks the number of issues open and closed each month.\n\n\n\"This shows that [the dashboard] is quite useful out of the box to just see some visualizations without doing any configuration,\" says Mark. \"These were the charts that we thought would give the most value to a team or to a project without doing any config whatsoever.\"\n\n## How our Quality Engineering team uses Insights\n\nThe Quality Engineering team uses Insights to look at opportunities to remedy gaps in a specific project in our EE, as well as to visualize flaky tests on GitLab based on reported issues.\n\n### Enterprise Edition testcases chart\n\nOne of our more specific use cases is the Enterprise testcases chart. The Quality Engineering department is working to close the gap in testcases in the GitLab Enterprise. The team [configured a chart](https://gitlab.com/gitlab-org/quality/testcases/insights/#/eeTestcasesCharts) within the [testcases project](https://gitlab.com/gitlab-org/quality/testcases/tree/master) to help visualize how many open and closed test gaps there are, separated by GitLab product area, and GitLab product tier.\n\n![EE testcases chart](https://about.gitlab.com/images/blogimages/EE_testcases.png){: .shadow.medium.center}\nQuality Engineering configured this chart to visualize gaps in testcases on GitLab Enterprise.\n\n\n\"Looking at this chart, we may say, ‘Maybe we should have a few people focus on the gaps in verify because it has the most open testcases at the current point',\" says Kyle.\n\n#### Configuration of EE testcases chart\n\nThe EE testcases chart is not something that is available out of the box, but the [configuration for the chart](https://gitlab.com/gitlab-org/quality/testcases/blob/master/.gitlab/insights.yml) is pretty simple nonetheless.\n\n```text\neeTestcasesCharts:\n  title: 'Charts for EE Testcases'\n  charts:\n    - title: Open testcases (backlog) by stage\n      type: bar\n      query:\n        issuable_type: issue\n        issuable_state: opened\n        filter_labels:\n          - \"Quality:EE test gaps\"\n        collection_labels:\n          - \"devops::configure\"\n          - \"devops::create\"\n          - \"devops::protect\"\n          - \"devops::enablement\"\n          - \"devops::growth\"\n          - \"devops::manage\"\n          - \"devops::monitor\"\n          - \"devops::package\"\n          - \"devops::plan\"\n          - \"devops::release\"\n          - \"devops::secure\"\n          - \"devops::verify\"\n\n```\n\n\nThe configuration shows that this is a bar chart that is looking at open issues with the filter `Quality:EE test gaps`. The collection labels are what broke the bars out into different columns. While it is possible to illustrate the data in very intricate ways, the underlying schema to configure the chart is actually quite simple, mirroring the process of searching the issue tracker by filtering based on labels.\n\n![Issue tracker](https://about.gitlab.com/images/blogimages/issue_tracker_EE.png){: .shadow.medium.center}\nThe issues represented in the EE testcases chart can be searched for by label using the issue tracker in the testcases project.\n\n\nOpening the issue tracker for the testcases project, you can search by `Quality:EE test gaps` label, select open issues, to see the actual issues represented by the Insights chart.\n\nThe key takeaway: If your team has good label hygiene and a logical workflow, building charts based on Insights should not be particularly challenging.\n\n### End-to-end transient failures\n\nThe Quality Engineering team monitors how often we have reports of flaky tests in our pipeline by looking at the number of issues created that fit the label schema.\n\n![End-to-end transient failure chart](https://about.gitlab.com/images/blogimages/end_to_end_chart.png){: .shadow.medium.center}\nA second chart configured for Quality Engineering is the end-to-end transient failure chart, which looks at flaky tests.\n\n\nSimilar to many of our other charts, this is a stacked bar graph that looks at both open and closed issues on a weekly basis, and the underlying configuration is as you might expect.\n\n```yaml\ntransientFailures:\n  title: End to end transient failures\n  charts:\n    - title: Opened transient failures per week\n      type: stacked-bar\n      query:\n        issuable_type: issue\n        issuable_state: opened\n        filter_labels:\n          - \"Quality\"\n          - \"QA\"\n          - \"bug\"\n        collection_labels:\n          - \"found:gitlab.com\"\n          - \"found:canary.gitlab.com\"\n          - \"found:staging.gitlab.com\"\n          - \"found:staging-orchestrated\"\n          - \"found:dev.gitlab.com\"\n          - \"found:nightly\"\n          - \"found:in MR\"\n        group_by: week\n        period_limit: 24\n    - title: Closed transient failures per week\n      type: stacked-bar\n      query:\n        issuable_type: issue\n        issuable_state: closed\n        filter_labels:\n          - \"Quality\"\n          - \"QA\"\n          - \"bug\"\n        collection_labels:\n          - \"found:gitlab.com\"\n          - \"found:canary.gitlab.com\"\n          - \"found:staging.gitlab.com\"\n          - \"found:staging-orchestrated\"\n          - \"found:dev.gitlab.com\"\n          - \"found:nightly\"\n          - \"found:in MR\"\n        group_by: week\n        period_limit: 24\n\n```\n\n\n## Implementing Insights for your team\n\nIf your team is often pulling data from GitLab through an API or CSV export, and then building charts based on issues and merge request data, then Insights will make your life a lot easier!\n\nSome questions to think about before implementing Insights include: How would you want to categorize the work being done and the issues that are being created? How do you want to monitor the open/close rates on your issues? Also, how do you plan on using labels?\n\nInsights users really need to define their workflows and have a clear idea about how they're using labels. We recommend having some sort of [automated mechanism to ensure good label hygiene](https://handbook.gitlab.com/handbook/engineering/infrastructure/engineering-productivity/triage-operations/#triage-automation). [GitLab Triage](https://gitlab.com/gitlab-org/gitlab-triage) is our open source project that automates labeling of issues on our giant GitLab project and is a good candidate for any organization that has a large backlog of issues.\n\nWe recommend users [read up more on the issues workflow](https://docs.gitlab.com/ee/development/contributing/issue_workflow.html) to learn more about how to use labels and the issue tracker, which is valuable background knowledge to improve your use of Insights.\n\nWe've been dogfooding Insights for a time to help iron out any wrinkles in the implementation or application of this technology, but we also want to hear your ideas of how we can make improvements to Insights. [Create an issue in the GitLab project issue tracker](https://gitlab.com/gitlab-org/gitlab/issues?scope=all&utf8=%E2%9C%93&state=opened&label_name[]=insights) with the Insights label to share your feedback with us!\n\nCover photo by [Aaron Burden](https://unsplash.com/@aaronburden) on [Unsplash](https://unsplash.com/photos/Qy-CBKUg_X8).\n",[23,24,25],"features","DevOps","inside GitLab","yml",{},true,"/en-us/blog/insights",{"title":31,"description":16,"ogTitle":31,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":32,"ogSiteName":33,"ogType":34,"canonicalUrls":32},"GitLab: New Tool to Visualize High-Level Project 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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/ee/api/jobs.html#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.",[721],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[109,725,726,23],"DevOps platform","tutorial",{"featured":28,"template":13,"slug":728},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":730,"config":740},{"title":731,"description":732,"authors":733,"heroImage":735,"date":736,"body":737,"category":9,"tags":738},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[734],"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/)",[726,739,23],"product",{"featured":12,"template":13,"slug":741},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":743,"config":753},{"title":744,"description":745,"authors":746,"heroImage":748,"date":749,"category":9,"tags":750,"body":752},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[747],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[262,622,751],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":754,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":756},[757,771,782,794],{"id":758,"categories":759,"header":761,"text":762,"button":763,"image":768},"ai-modernization",[760],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":764,"config":765},"Get your AI maturity score",{"href":766,"dataGaName":767,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":772,"categories":773,"header":774,"text":762,"button":775,"image":779},"devops-modernization",[739,568],"Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":767,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":786,"text":762,"button":787,"image":791},"security-modernization",[785],"security","Are you trading speed for security?",{"text":788,"config":789},"Get your security maturity score",{"href":790,"dataGaName":767,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":792},{"src":793},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":795,"paths":796,"header":799,"text":800,"button":801,"image":806},"github-azure-migration",[797,798],"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":802,"config":803},"See how GitLab compares to GitHub",{"href":804,"dataGaName":805,"dataGaLocation":244},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":807},{"src":781},{"header":809,"blurb":810,"button":811,"secondaryButton":816},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":812,"config":813},"Get your free trial",{"href":814,"dataGaName":51,"dataGaLocation":815},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":817},{"href":55,"dataGaName":56,"dataGaLocation":815},1776442959845]