[{"data":1,"prerenderedAt":819},["ShallowReactive",2],{"/en-us/blog/moving-to-headless-chrome":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Mike Greiling":700,"blog-related-posts-en-us-moving-to-headless-chrome":714,"blog-promotions-en-us":756,"next-steps-en-us":809},{"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":34,"tagSlugs":35,"__hash__":37},"blogPosts/en-us/blog/moving-to-headless-chrome.yml","Moving To Headless Chrome",[7],"mike-greiling",null,"engineering",{"slug":11,"featured":12,"template":13},"moving-to-headless-chrome",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How GitLab switched to Headless Chrome for testing","A detailed explanation with examples of how GitLab made the switch to headless Chrome.",[18],"Mike Greiling","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749680270/Blog/Hero%20Images/headless-chrome-cover.jpg","2017-12-19","GitLab recently switched from PhantomJS to headless Chrome for both our\nfrontend tests and our RSpec feature tests. In this post we will detail the\nreasons we made this transition, the challenges we faced, and the solutions we\ndeveloped. We hope this will benefit others making the switch.\n\n\u003C!-- more -->\n\nWe now have a truly accurate way to test GitLab within a real, modern browser.\nThe switch has improved our ability to write tests and debug them while running\nthem directly in Chrome. Plus the change forced us to confront and clean up a\nnumber of hacks we had been using in our tests.\n\n## Switching to headless Chrome from PhantomJS: background\n\n[PhantomJS](http://phantomjs.org) has been a part of GitLab's test framework\n[for almost five years](https://gitlab.com/gitlab-org/gitlab-ce/commit/ba25b2dc84cc25e66d6fa1450fee39c9bac002c5).\nIt has been an immensely useful tool for running browser integration tests in a\nheadless environment at a time when few options were available. However, it\nhad some shortcomings:\n\nThe most recent version of PhantomJS (v2.1.1) is compiled with a three-year-old\nversion of [QtWebKit](https://trac.webkit.org/wiki/QtWebKit) (a fork of WebKit\nv538.1 according to the user-agent string). This puts it on par with something\nlike Safari 7 on macOS 10.9. It resembles a real modern browser, but it's not\nquite there. It has a different JavaScript engine, an older rendering engine,\nand a host of missing features and quirks.\n\nAt this time, GitLab supports [the current and previous major\nrelease](https://docs.gitlab.com/ee/install/requirements.html#supported-web-browsers) of\nFirefox, Chrome, Safari, and Microsoft Edge/IE. This puts PhantomJS and its\ncapabilities somewhere near or below our lowest common denominator. Many modern\nbrowser features either [do not work](http://phantomjs.org/supported-web-standards.html),\nor [require vendor prefixes](http://phantomjs.org/tips-and-tricks.html) and\npolyfills that none of our supported browsers require. We could selectively\nadd these polyfills, prefixes, and other workarounds just within our test\nenvironment, but doing so would increase technical debt, cause confusion, and\nmake the tests less representative of a true production environment. In most\ncases we had opted to simply omit them or hack around them (more on this\n[later](#trigger-method)).\n\nHere's a screenshot of the way PhantomJS renders a page from GitLab, followed\nby the same page rendered in Google Chrome:\n\n![Page Rendered by PhantomJS](https://about.gitlab.com/images/blogimages/moving-to-headless-chrome/render-phantomjs.png){: .shadow.center}\n\n![Page Rendered by Google Chrome](https://about.gitlab.com/images/blogimages/moving-to-headless-chrome/render-chrome.png){: .shadow.center}\n\nYou can see in PhantomJS the filter tabs are rendered horizontally, the icons\nin the sidebar render on their own lines, the global search field is\noverflowing off the navbar, etc.\n\nWhile it looks ugly, in most cases we could still use this to run functional\ntests, so long as elements of the page remain visible and clickable, but this\ndisparity with the way GitLab rendered in a real browser did introduce several\nedge cases.\n\n## What is headless Chrome\n\nIn April of this year, [news spread](https://news.ycombinator.com/item?id=14101233)\nthat Chrome 59 would support a [native, cross-platform headless\nmode](https://www.chromestatus.com/features/5678767817097216). It was\npreviously possible to simulate a headless Chrome browser in CI/CD [using\nvirtual frame buffer](https://gist.github.com/addyosmani/5336747), but this\nrequired a lot of memory and extra complexities. A native headless mode is a\ngame changer. It is now possible to run integration tests in a headless\nenvironment on a real, modern web browser that our users actually use!\n\nSoon after this was revealed, Vitaly Slobodin, PhantomJS's chief developer,\nannounced that the project [would no longer be\nmaintained](https://github.com/ariya/phantomjs/issues/15105#issuecomment-322850178):\n\n\u003Cdiv class=\"center\">\n\n\u003Cblockquote class=\"twitter-tweet\" data-cards=\"hidden\" data-lang=\"en\">\u003Cp lang=\"en\" dir=\"ltr\">This is the end - \u003Ca href=\"https://t.co/GVmimAyRB5\">https://t.co/GVmimAyRB5\u003C/a>\u003Ca href=\"https://twitter.com/hashtag/phantomjs?src=hash&ref_src=twsrc%5Etfw\">#phantomjs\u003C/a> 2.5 will not be released. Sorry, guys!\u003C/p>— Vitaly Slobodin (@Vitalliumm) \u003Ca href=\"https://twitter.com/Vitalliumm/status/852450027318464513?ref_src=twsrc%5Etfw\">April 13, 2017\u003C/a>\u003C/blockquote>\n\u003Cscript async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\">\u003C/script>\n\n\u003C/div>\n\nIt became clear that we would need to make the transition away from PhantomJS at\nsome point, so we [opened up an issue](https://gitlab.com/gitlab-org/gitlab-ce/issues/30876),\ndownloaded the Chrome 59 beta, and started looking at options.\n\n### Frontend tests (Karma)\n\nOur frontend test suite utilizes the [Karma](http://karma-runner.github.io/)\ntest runner, and updating this to work with Google Chrome was surprisingly\nsimple ([here's the merge request](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/12036)).\nThe [karma-chrome-launcher](https://github.com/karma-runner/karma-chrome-launcher)\nplugin was very quickly updated to support headless mode starting from\n[version 2.1.0](https://github.com/karma-runner/karma-chrome-launcher/releases/tag/v2.1.0),\nand it was essentially a drop-in replacement for the PhantomJS launcher. Once\nwe [re-built our CI/CD build images](https://gitlab.com/gitlab-org/gitlab-build-images/merge_requests/41)\nto include Google Chrome 59 (and fiddled around with some pesky timeout\nsettings), it worked!  We were also able to remove some rather ugly\nPhantomJS-specific hacks that Jasmine required to spy on some built-in browser\nfunctions.\n\n### Backend feature tests (RSpec + Capybara)\n\nOur feature tests use RSpec and [Capybara](https://github.com/teamcapybara/capybara)\nto perform full end-to-end integration testing of database, backend, and\nfrontend interactions. Before switching to headless Chrome, we had used\n[Poltergeist](https://github.com/teampoltergeist/poltergeist) which is a\nPhantomJS driver for Capybara. It would spin up a PhantomJS browser instance\nand direct it to browse, fill out forms, and click around on pages to verify\nthat everything behaved as it should.\n\nSwitching from PhantomJS to Google Chrome required a change in drivers from\nPoltergeist to Selenium and [ChromeDriver](https://sites.google.com/a/chromium.org/chromedriver/).\nSetting this up was pretty straightforward. You can install ChromeDriver on\nmacOS with `brew install chromedriver` and the process is similar on any given\npackage manager in Linux. After this we added the `selenium-webdriver` gem to\nour test dependencies and configured Capybara like so:\n\n```ruby\nrequire 'selenium-webdriver'\n\nCapybara.register_driver :chrome do |app|\n  options = Selenium::WebDriver::Chrome::Options.new(\n    args: %w[headless disable-gpu no-sandbox]\n  )\n  Capybara::Selenium::Driver.new(app, browser: :chrome, options: options)\nend\n\nCapybara.javascript_driver = :chrome\n```\n\nGoogle says the [`disable-gpu` option is necessary for the time\nbeing](https://developers.google.com/web/updates/2017/04/headless-chrome#cli)\nuntil some bugs are resolved. The `no-sandbox` option also appears to be\nnecessary to get Chrome running inside a Docker container for [GitLab's CI/CD\nenvironment](/topics/ci-cd/). Google provides a [useful guide for working with headless Chrome\nand Selenium](https://developers.google.com/web/updates/2017/04/headless-chrome).\n\nIn our final implementation we changed this to conditionally add the `headless`\noption unless you have `CHROME_HEADLESS=false` in your environment. This makes\nit easy to disable headless mode while debugging or writing tests. It's also\npretty fun to watch tests execute on the browser window in real time:\n\n```shell\nexport CHROME_HEADLESS=false\nbundle exec rspec spec/features/merge_requests/filter_merge_requests_spec.rb\n```\n\n![Tests Executing in Chrome](https://about.gitlab.com/images/blogimages/moving-to-headless-chrome/headlessless-chrome-tests.gif){: .shadow.center}\n\n### What is the differences between Poltergeist and Selenium?\n\nThe process of switching drivers here was not nearly as straightforward as\nit was with the frontend test suite. Dozens of tests started failing as soon\nas we changed our Capybara configuration, and this was due to some major\ndifferences in the way Selenium/ChromeDriver implemented Capybara's driver API\ncompared to Poltergeist/PhantomJS. Here are some of the challenges we ran into:\n\n1.  **JavaScript modals are no longer accepted automatically**\n\n    We often use JavaScript `confirm(\"Are you sure you want to do X?\");` click\n    events when performing a destructive action such as deleting a branch or\n    removing a user from a group. Under Poltergeist a `.click` action would\n    automatically accept modals like `alert()` and `confirm()`, but under\n    Selenium, you now need to wrap these with one of `accept_alert`,\n    `accept_confirm`, or `dismiss_confirm`. e.g.:\n\n    ```ruby\n    # Before\n    page.within('.some-selector') do\n      click_link 'Delete'\n    end\n\n    # After\n    page.within('.some-selector') do\n      accept_confirm { click_link 'Delete' }\n    end\n    ```\n\n1.  **Selenium `Element.visible?` returns false for empty elements**\n\n    If you have an empty `div` or `span` that you want to access in your test,\n    Selenium does not consider these \"visible.\" This is not much of an issue\n    unless you set `Capybara.ignore_hidden_elements = true` as we do in our\n    feature tests. Where `find('.empty-div')` would have worked fine in\n    Poltergeist, we now need to use `visible: :any` to\n    select such elements.\n\n    ```ruby\n    # Before\n    find('.empty-div')\n\n    # After\n    find('.empty-div', visible: :any)\n    # or\n    find('.empty-div', visible: false)\n    ```\n\n    More on [Capybara and hidden elements](https://makandracards.com/makandra/7617-change-how-capybara-sees-or-ignores-hidden-elements).\n\n1.  {:#trigger-method} **Poltergeist's `Element.trigger('click')` method does not exist in Selenium**\n\n    In Capybara, when you use `find('.some-selector').click`, the element you\n    are clicking must be both visible and unobscured by any overlapping\n    element. Situations where links could not be clicked would sometimes occur\n    with Poltergeist/PhantomJS due to its poor CSS support sans-prefixes.\n    Here's one example:\n\n    ![Overlapping elements](https://about.gitlab.com/images/blogimages/moving-to-headless-chrome/overlapping-element.png)\n\n    The broken layout of the search form here was actually placing an invisible\n    element over the top of the \"Update all\" button, making it unclickable.\n    Poltergeist offers a `.trigger('click')` method to work around this.\n    Rather than actually clicking the element, this method would trigger a DOM\n    event to simulate a click. Utilizing this method was a bad practice, but\n    we ran into similar issues so often that many developers formed a habit\n    of using it everywhere. This began to lead to some lazy and sloppy test\n    writing. For instance, someone might use `.trigger` as a shortcut to click\n    on an link that was obscured behind an open dropdown menu, when a properly\n    written test should `.click` somewhere to close the dropdown, and _then_\n    `.click` on the item behind it.\n\n    Selenium does not support the `.trigger` method. Now that we were using a\n    more accurate rendering engine that won't break our layouts, many of these\n    instances could be resolved by simply replacing `.trigger('click')` with\n    `.click`, but due to some of the bad practice uses mentioned above, this\n    didn't always work.\n\n    There are of course some ways to hack a `.trigger` replacement. You could\n    simulate a click by focusing on an element and hitting the \"return\" key,\n    or use JavaScript to trigger a click event, but in most cases we decided to\n    take the time and actually correct these poorly implemented tests so that a\n    normal `.click` could again be used. After all, if our tests are meant to\n    simulate a real user interacting with the page, we should limit ourselves\n    to the actions a real user would be expected to use.\n\n    ```ruby\n    # Before\n    find('.obscured-link').trigger('click')\n\n    # After\n\n    # bad\n    find('.obscured-link').send_keys(:return)\n\n    # bad\n    execute_script(\"document.querySelector('.obscured-link').click();\")\n\n    # good\n    # do something to make link accessible, then\n    find('.link').click\n    ```\n\n1.  **`Element.send_keys` only works on focus-able elements**\n\n    We had a few places in our code where we would test out our keyboard\n    shortcuts using something like `find('.boards-list').native.send_keys('i')`.\n    It turns out Chrome will not allow you to `send_keys` to any element that\n    cannot be \"focused\", e.g. links, form elements, the document body, or\n    presumably anything with a tab index.\n\n    In all of the cases where we were doing this, triggering `send_keys` on the\n    body element would work since that's ultimately where our event handler was\n    listening anyway:\n\n    ```ruby\n    # Before\n    find('.some-div').native.send_keys('i')\n\n    # After\n    find('body').native.send_keys('i')\n    ```\n\n1.  **`Element.send_keys` does not support non-BMP characters (like emoji)**\n\n    In a few tests, we needed to fill out forms with emoji characters. With\n    Poltergeist we would do this like so:\n\n    ```ruby\n    # Before\n    find('#note-body').native.send_keys('@💃username💃')\n    ```\n\n    In Selenium we would get the following error message:\n\n    ```text\n    Selenium::WebDriver::Error::UnknownError:\n        unknown error: ChromeDriver only supports characters in the BMP\n    ```\n\n    To work around this, we added [a JavaScript method to our test bundle that\n    would simulate input and fire off the same DOM events](https://gitlab.com/gitlab-org/gitlab-ce/blob/a8b9852837/app/assets/javascripts/test_utils/simulate_input.js)\n    that an actual keyboard input would generate on every keystroke, then\n    wrapped this with a [ruby helper](https://gitlab.com/gitlab-org/gitlab-ce/blob/a8b9852837/spec/support/input_helper.rb)\n    method that could be called like so:\n\n    ```ruby\n    # After\n    include InputHelper\n\n    simulate_input('#note-body', \"@💃username💃\")\n    ```\n\n1.  **Setting cookies is much more complicated**\n\n    It's quite common to want to set some cookies before `visit`ing a page that\n    you intend to test, whether it's to mock a user session, or toggle a\n    setting. With Poltergeist, this process is really simple. You can use\n    `page.driver.set_cookie`, provide a simple key/value pair, and it will just\n    work as expected, setting a cookie with the correct domain and scope.\n\n    Selenium is quite a bit more strict. The method is now\n    `page.driver.browser.manage.add_cookie`, and it comes with two caveats:\n\n    - You cannot set cookies until you `visit` a page in the domain you intend\n      to scope your cookies to.\n    - Annoyingly, you cannot alter the `path` parameter (or at least we could\n      never get this to work), so it is best to set cookies at the root path.\n\n    Before you `visit` your page, Chrome's url is technically sitting at\n    something like `about:blank;`. When you attempt to set a cookie there, it\n    will refuse because there is no hostname, and you cannot coerce one by\n    providing a domain as an argument. The [Selenium\n    documentation](http://docs.seleniumhq.org/docs/03_webdriver.jsp#cookies)\n    suggests that you do the following:\n\n    > If you are trying to preset cookies before you start interacting with a\n    > site and your homepage is large / takes a while to load, an alternative is\n    > to find a smaller page on the site (typically the 404 page is small, e.g.\n    > `http://example.com/some404page`).\n\n    ```ruby\n    # Before\n    before do\n      page.driver.set_cookie('name', 'value')\n    end\n\n    # After\n    before do\n      visit '/some-root-path'\n      page.driver.browser.manage.add_cookie(name: 'name', value: 'value')\n    end\n    ```\n\n1.  **Page request/response inspection methods are missing**\n\n    Poltergeist very conveniently implemented methods like `page.status_code`\n    and `page.response_headers` which are also present in Capybara's default\n    `RackTest` driver, making it easy to inspect the raw response from the\n    server, in addition to the way that response is rendered by the browser. It\n    also allowed you to inject headers into the requests made to the server,\n    e.g.:\n\n    ```ruby\n    # Before\n    before do\n      page.driver.add_header('Accept', '*/*')\n    end\n\n    it 'returns a 404 page'\n      visit some_path\n\n      expect(page.status_code).to eq(404)\n      expect(page).to have_css('.some-selector')\n    end\n    ```\n\n    Selenium does not implement these methods, and [the authors do not intend\n    to add support for them](https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/141#issuecomment-191404986),\n    so we needed to develop a workaround. Several people have suggested running\n    a proxy alongside ChromeDriver that would intercept all traffic to and from\n    the server, but this seemed to us like overkill. Instead, we opted to\n    create a [lightweight Rack middleware](https://gitlab.com/gitlab-org/gitlab-ce/blob/a8b9852837/lib/gitlab/testing/request_inspector_middleware.rb)\n    and a corresponding [helper class](https://gitlab.com/gitlab-org/gitlab-ce/blob/a8b9852837/spec/support/inspect_requests.rb)\n    that would intercept the traffic for inspection. This is similar to our\n    [RequestBlockerMiddleware](https://gitlab.com/gitlab-org/gitlab-ce/blob/master/lib/gitlab/testing/request_blocker_middleware.rb)\n    that we were already using to intelligently `wait_for_requests` to complete\n    within our tests. It works like this:\n\n    ```ruby\n    # After\n    it 'returns a 404 page'\n      requests = inspect_requests do\n        visit some_path\n      end\n\n      expect(requests.first.status_code).to eq(404)\n      expect(page).to have_css('.some-selector')\n    end\n    ```\n\n    Within the `inspect_requests` block, the Rack middleware will log all\n    requests and responses, and return them as an array for inspection. This\n    will include the page being `visit`ed as well as the subsequent XHR and\n    asset requests, but the initial path request will be the first in the array.\n\n    You can also inject headers using the same helper like so:\n\n    ```ruby\n    # After\n    inspect_requests(inject_headers: { 'Accept' => '*/*' }) do\n      visit some_path\n    end\n    ```\n\n    This middleware should be injected early in the stack to ensure any other\n    middleware that might intercept or modify the request/response will be\n    seen by our tests. We include this line in our test environment config:\n\n    ```ruby\n    config.middleware.insert_before('ActionDispatch::Static', 'Gitlab::Testing::RequestInspectorMiddleware')\n    ```\n\n1.  **Browser console output is no longer output to the terminal**\n\n    Poltergeist would automatically output any `console` messages directly into\n    the terminal in real time as tests were run. If you had a bug in the frontend\n    code that caused a test to fail, this feature would make debugging much\n    easier as you could inspect the terminal output of the test for an error\n    message or a stack trace, or inject your own `console.log()` into the\n    JavaScript to see what is going on. With Selenium this is sadly no longer the\n    case.\n\n    You can, however, collect browser logs by configuring Capybara like so:\n\n    ```ruby\n    capabilities = Selenium::WebDriver::Remote::Capabilities.chrome(\n      loggingPrefs: {\n        browser: \"ALL\",\n        client: \"ALL\",\n        driver: \"ALL\",\n        server: \"ALL\"\n      }\n    )\n\n    # ...\n\n    Capybara::Selenium::Driver.new(\n      app,\n      browser: :chrome,\n      desired_capabilities: capabilities,\n      options: options\n    )\n    ```\n\n    This will allow you to access logs with the following, i.e. in the event of\n    a test failure:\n\n    ```ruby\n    page.driver.manage.get_log(:browser)\n    ```\n\n    This is far more cumbersome than it was in Poltergeist, but it's the best\n    method we've found so far. Thanks to [Larry Reid's blog post](http://technopragmatica.blogspot.com/2017/10/switching-to-headless-chrome-for-rails_31.html)\n    for the tip!\n\n## Results\n\nRegarding performance, we attempted to quantify the change with a\nnon-scientific analysis of 10 full-suite RSpec test runs _before_ this change,\nand 10 more runs from _after_ this change, factoring out any tests that were\nadded or removed between these pipelines. The end result was:\n\n**Before:** 5h 18m 52s\n**After:** 5h 12m 34s\n\nA savings of about six minutes, or roughly 2 percent of the total compute time, is\nstatistically insignificant, so I'm not going to claim we improved our test\nspeed with this change.\n\nWhat we did improve was test accuracy, and we vastly improved the tools at our\ndisposal to write and debug tests. Now, all of the Capybara screenshots\ngenerated when a CI/CD job fails look exactly as they do on your own browser\nrather than resembling the broken PhantomJS screenshot above. Inspecting a\nfailing test locally can now be done interactively by turning off headless\nmode, dropping a `byebug` line into the spec file, and watching the browser\nwindow as you type commands into the prompt. This technique proved extremely\nuseful while working on this project.\n\nYou can find all of the changes we made in [the original merge request page\non GitLab.com](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/12244).\n\n## What are some additional uses for headless Chrome?\n\nWe have also been utilizing headless Chrome to analyze frontend performance, and have found it to be useful in detecting issues.\n\nWe'd like to make it easier for other companies to embrace as well, so as part of the upcoming 10.3 release of GitLab we are releasing [Browser Performance Testing](https://docs.gitlab.com/ee/user/project/merge_requests/browser_performance_testing.html). Leveraging [GitLab CI/CD](/solutions/continuous-integration/), headless Chrome is launched against a set of pages and an overall performance score is calculated. Then for each merge request the scores are compared between the source and target branches, making it easier detect performance regressions prior to merge.\n\n## Acknowledgements\n\nI sincerely hope this information will prove useful to anybody else looking to\nmake the switch from PhantomJS to headless Chrome for their Rails application.\n\nThanks to the Google team for their very helpful documentation, thanks to the\nmany bloggers out there who shared their own experiences with hacking headless\nChrome in the early days of its availability, and special thanks to Vitaly\nSlobodin and the rest of the contributors to PhantomJS who provided us with an\nextremely useful tool that served us for many years. 🙇‍\n\n\u003Cstyle>\n\n.center {\n  text-align: center;\n  display: block;\n  margin-right: auto;\n  margin-left: auto;\n}\n\ncode, kbd {\n  font-size: 80%;\n}\n\n\u003C/style>\n",[23,24,25],"inside 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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",[107,725,726,727],"DevOps platform","tutorial","features",{"featured":28,"template":13,"slug":729},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":731,"config":741},{"title":732,"description":733,"authors":734,"heroImage":736,"date":737,"body":738,"category":9,"tags":739},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[735],"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/)",[726,740,727],"product",{"featured":12,"template":13,"slug":742},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":744,"config":754},{"title":745,"description":746,"authors":747,"heroImage":749,"date":750,"category":9,"tags":751,"body":753},"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.",[748],"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",[260,622,752],"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":755,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":757},[758,772,783,795],{"id":759,"categories":760,"header":762,"text":763,"button":764,"image":769},"ai-modernization",[761],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":765,"config":766},"Get your AI maturity score",{"href":767,"dataGaName":768,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":773,"categories":774,"header":775,"text":763,"button":776,"image":780},"devops-modernization",[740,568],"Are you just managing tools or shipping innovation?",{"text":777,"config":778},"Get your DevOps maturity score",{"href":779,"dataGaName":768,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":784,"categories":785,"header":787,"text":763,"button":788,"image":792},"security-modernization",[786],"security","Are you trading speed for security?",{"text":789,"config":790},"Get your security maturity score",{"href":791,"dataGaName":768,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":793},{"src":794},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":796,"paths":797,"header":800,"text":801,"button":802,"image":807},"github-azure-migration",[798,799],"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":803,"config":804},"See how GitLab compares to GitHub",{"href":805,"dataGaName":806,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":808},{"src":782},{"header":810,"blurb":811,"button":812,"secondaryButton":817},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":813,"config":814},"Get your free trial",{"href":815,"dataGaName":49,"dataGaLocation":816},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":818},{"href":53,"dataGaName":54,"dataGaLocation":816},1776449933783]