[{"data":1,"prerenderedAt":822},["ShallowReactive",2],{"/en-us/blog/environment-friction-cycle":3,"navigation-en-us":41,"banner-en-us":451,"footer-en-us":461,"blog-post-authors-en-us-Darwin Sanoy":701,"blog-related-posts-en-us-environment-friction-cycle":717,"blog-promotions-en-us":759,"next-steps-en-us":812},{"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__":40},"blogPosts/en-us/blog/environment-friction-cycle.yml","Environment Friction Cycle",[7],"darwin-sanoy",null,"engineering",{"slug":11,"featured":12,"template":13},"environment-friction-cycle",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How GitLab can eliminate the massive value stream friction of developer environment provisioning and cleanup","It is important to have the complete picture of scaled effects in view when designing automation.",[18],"Darwin Sanoy","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682507/Blog/Hero%20Images/sandeep-singh-3KbACriapqQ-unsplash.jpg","2022-11-17","A strong DevOps value stream drives developer empowerment as far left as possible. In GitLab, this is embodied in per-feature branch merge requests that are rich with automated code quality and defect information - including not only findings - but automated remediation capabilities and collaboration. Some defects and code quality issues can only be found by analyzing a running copy of the application, including DAST, IAST, fuzzing and many others. GitLab has built a fully automated, seamless developer environment lifecycle management approach right into the developer experience. In fact, it’s so seamlessly built-in, it can be easy to overlook how critical developer environment lifecycle management is. This article will highlight why and how GitLab adds value using developer environment automation. In addition, while GitLab provides out of the box developer environment lifecycle management for Kubernetes, this article demonstrates an approach and a working example of how to extend that capability to other common cloud-based application framework PaaS offerings.\n\n## Provisioning of development environments is generally a negative feedback loop\n\nIn a prior job, I worked on a DevOps transformation team that supported multiple massive shared development environments in AWS. They were accessible to more than 4,000 developers working to build more than 100 SaaS applications and utility stacks. In the journey to the AWS Cloud, each development team took ownership of the automation required to deploy their applications. Since developers were able to self-service, over time this solved the problem of development friction generated by waiting for environments to be provisioned for testing, feature experiments, integration experiments, etc. \n\nHowever, the other half of the problem then ballooned - environment sprawl - with an untold number of environments idling without management and without knowledge of when they could be torn down. Over time the development environment cost became a significant multiple of production costs. The cloud has solved problems with environment provisioning bottlenecks due to hardware acquisition and provisioning, but this can also inadvertently fuel the high costs of unmanaged sprawl. This problem understandably causes organizations to raise administrative barriers to new development environments.\n\nIn many organizations this becomes a vicious cycle - most especially if developer environments are operated by a different team, or worse, on an independent budget. Environment justification friction usually comes quickly after discovering the true cost of the current running environments. Developers then have to justify the need for new environment requests and they have to make the gravest of promises to disband the environment as soon as they are done. Another friction arises when a separate group is tasked with cost controls and environment provisioning and cleanup. This introduces friction in the form of administrative and work queueing delays. Coordination friction also crops up because an accurate understanding of exactly what is needed for an environment can be challenging to convey. When mistakes are made or key information is missing, developers must go back and forth on support requests to get the configuration completely correct.\n\n## Partial automation can worsen the problem\n\nThat’s the first half of the environment lifecycle, but as I mentioned, even if that is fully automated and under the control of developers, the other half of the feedback loop comes into play. When a given development environment has fulfilled its initial justification reason, the team does not want to destroy it because environments are so hard to justify and create. Then the sprawl starts and, of course, the barriers to new environments are raised even higher. This is a classic negative feedback loop.\n\nSystems theory shows us that sometimes there are just a few key factors in stopping or even reversing a negative feedback loop. Lets take this specific problem apart and talk about how GitLab solves for it.\n\n## Treat developer environments as a complete lifecycle\n\nIn the prior example it is evident that by leaving out the last stage of the environment lifecycle - retirement or tear down - we still end up with a negative feedback loop. Removing provisioning friction actually makes the problem worse if retirement friction is not also addressed at the same time. Solutions to this problem need to address the entire lifecycle to avoid impacting value stream velocity. Neglecting or avoiding the retirement stage of a lifecycle is a common problem across all types of systems. In contrast, by addressing the entire lifecycle we can transform it from being a negative feedback loop to a managed lifecycle.\n\n## The problems of who and when\n\nBuried inside the insidious friction loop are a couple key coordination problems we’ll call “Who and When.” Basically, \"Who\" should create environments and \"When\" should they be created to ensure reasonable cost optimization? Then again, _Who_ should cleanup environments and _When_ do you know that the environment is no longer needed with certainty? Even with highly collaborative teams working hard together for maximum business value, these questions present a difficulty that frequently results in environments running for a long time before they are used and after they are no longer needed. The knowledge of appropriate timing plays a critical role in gaining control over this source of friction.\n\n## The problem of non-immutable development environments\n\nFriction in environment lifecycle management creates a substantial knock-on problem associated with long-lived environments. Long-lived environments that are updated multiple times for various independent projects start to accumulate configuration rot; they become snowflakes with small changes that are left over from non-implemented experiments, software or configuration removals, and other irrelevant bits and pieces. Immutability is the practice of not doing “in place” updates to a computing element, but rather destroying it and replacing it with a fresh, built-from-scratch, element. Docker has made this concept very accepted and effective in production workloads, but development environments frequently do not have this attribute due to automating without the design constraint of immutability, so they are updated in-place for reuse by various initiatives. If the environment lifecycle is not fully automated, it impossible to make them workable on a per-feature branch basis.\n\n## The problem of non-isolated development environments \n\nWhen environments are manually provisioned or when there is a lot of cost or administrative friction to setting them up, environment sharing becomes more common place. This creates sharing contention at many levels. Waiting to schedule into use an environment, pressure to complete work quickly so others can use the environment, and restrictions on the types of changes that can be made to shared environments are just some of the common sharing contention elements that arise. If environments can be isolated, then sharing contention friction evaporates. Pushing this to the extreme of a per-feature branch granularity brings many benefits, but is also difficult.\n\n## Effect on the development value stream\n\nThe effect that a friction-filled environment lifecycle has on the value stream can be immense - how many stories have you heard of projects waylaid for weeks or months while waiting on environment provisioning? What about defects shipped to production because a shared environment had left over configuration during testing? Frequently this friction is tolerated in the value stream because no one will argue that unlimited environment sprawl is an unwise use of company resources. We all turn off the lights in our home when we are no longer using a room and it is good business sense and good stewardship not to leave idle resources running at work.\n\nThe concept of good stewardship of planetary resources is actually becoming an architectural level priority in the technology sector. This is in evidenced in AWS’ [introduction of the “Sustainability” pillar to the AWS Well Architected principals in 2021](https://aws.amazon.com/blogs/aws/sustainability-pillar-well-architected-framework/) and many other green initiatives in the technology sector.\n\nIt’s imperative that efforts to improve the development value stream consider whether developer environment management friction is hampering the breadth, depth and velocity of product management and software development.\n\n## Seamless and fully automated review environment lifecycle management\n\nWhat if this negative feedback loop could be stopped? What if new environments were seamless and automatically created right at the moment they were needed? What if developers were completely happy to immediately tear down an environment when they were done because it takes no justification nor effort on their part to create new one at will?\n\nEnter GitLab Review Environments!\n\nGitLab review apps are created by the developer action of creating a new branch. No humans are involved as the environment is deployed while the developer is musing their first code changes on their branch.\n\nAs the developer pushes code updates the review apps are automatically updated with the changes and all quality checks and security scanning are run to ensure the developer understands that they introduced a vulnerability or quality defect. This is done within the shortest possible amount of time after the defect was introduced.\n\nWhen the developer merges their code, the review app is automatically torn down.\n\nThis seamless approach to developer environment provisioning and cleanup addresses enough of the critical factors in the negative feedback loop that it is effectively nullified.\n\nConsider:\n\n- Developer environment provisioning and cleanup are fully automated, transparent, developer-initiated activities. They do not consume people nor human process resources, which are always legions slower and more expensive than technology solutions.\n- Provisioning and cleanup timing are exactly synchronized with the developer’s need, preventing inefficiencies in idle time before or after environment usage.\n- They are immutable on a new branch basis - a new branch always creates a new environment from fresh copy of the latest code.\n- They are isolated - no sharing contention and no mixing of varying configuration.\n- They treat developer environments as a lifecycle.\n\nIt is so transparent that some developers may not even realize that their feature branch has an isolated environment associated with it.\n\n## Hard dollar costs are important and opportunity costs are paramount\n\nGitLab environments positively contribute to the value stream in two critical ways. First, the actual waste of idle machines is dramatically reduced. However, more importantly, all the human processes that end up being applied to managing that waste also disappear. Machines running in the cloud are only lost money. Inefficient use of people’s time carries a high dollar cost but it also carries a higher opportunity cost. There are so many value-generating activities people can do when their time is unencumbered by cost-control administration.\n\n## Multiplying the value stream contributions of developer review environments\n\nDeveloper environment friction is an industry-wide challenge and GitLab nearly eliminates the core problems of this feedback cycle. However, GitLab has also gone way beyond simply addressing this problem by creating a lot of additional value through seamless per-feature branch developer environments.\n\nHere is a visualization of where dynamic review environments plug into the overall GitLab developer workflow.\n\n![](https://about.gitlab.com/images/blogimages/environment-friction-lifecycle/gitlabenvironmentlifecycle.png)\n\n**Figure 1: Review environments with AWS Cloud Services**\n\nFigure 1 is showing GitLab’s full development cycle support with a little art of the possible thrown in around interfacing with AWS deployment services. The green dashed arrow indicates that GitLab deploys a review environment when the branch is first created. Since the green arrow is part of the developer's iteration loop, the green arrow is also depicting that review app updates are done on each code push. \n\nThe light purple box is showing that the iterative development and CI checks are all within the context of a merge request (MR), which provides a Single Pane of Glass (SPOG) for all quality checks, vulnerabilities and collaboration. Finally, when the merge is done, the review environment is cleaned up. The feature branch merge request is the furthest left that visibility and remediation can be shifted. GitLab’s shifting of this into the developer feature branch is what gives developers a semi-private opportunity to fix any quality or security findings with the specific code they have added or updated.\n\nOne other thing to note here is that when GitLab CD code is engineered to handle review environments, it is reused for all other preproduction and production environments. The set of AWS icons after the “Release” icon would be using the same deployment code. However, if the GitLab CD code is engineered only around deploying to a set of static environments, it is not automatically capable of review environments. Review environment support is a superset of static environment support.\n\n## Review environments enable a profound shift left of visibility and remediation\n\nAt GitLab “shift left” is not just about “problem visibility” but also about “full developer enablement to resolve problems” while in-context. GitLab merge requests provide critical elements that encourage developers to get into a habit of defect remediation:\n\n- **Context** - Defect and vulnerability reporting is only for code the developer changed in their branch and is tracked by the merge request (MR) for that branch.\n- **Responsibility** - Since MRs and branches are associated to an individual, it is evident to the developer (and the whole team) what defects were introduced or discovered by which developers.\n- **Timing** - Developers become aware of defects nearly as soon as they are introduced, not weeks or months after having integrated with other code. If they were working on a physical product, we can envision that all the parts are still on the assembly bench.\n- **Visibility - Appropriately Local, Then Appropriately Global** - Visibility of defects is context specific. While a developer has an open MR that is still a work in progress, they can be left alone to remedy accidentally-introduced defects with little concern from others because the visibility is local to the MR. However, once they seek approvals to merge their code, then the approval process for the MR will cause the visibility of any unresolved defects and vulnerabilities to come to the attention of everyone involved in the approval process. This ensures that oversight happens with just the right timing - not too early and not forgotten. This makes a large-scale contribution to human efficiency in the development value stream.\n- **Advisement** - As much as possible GitLab integrates tools and advice right into the feature branch MR context where the defects are visible. Developers are given full vulnerability details and can take just-in-time training on specific vulnerabilities. \n- **Automated Remediation** - Developers can choose to apply auto-remediations when they are available.\n- **Collaboration** - They can use MR comments and new issues to collaborate with team mates throughout the organization on resolving defects of all types.\n\nHaving seamless, effortless review environments at a per-feature branch granularity is a critical ingredient in GitLab’s ability to maximize the shift left of the above developer capabilities. This is most critical in the developer checks that require a running copy of application, which is provided by the review environments. These checks include things such as DAST, IAST, API fuzzing and accessibility testing. The industry is also continuing to multiply the types of defect scanners that require an actively running copy of the application.\n\n## Extending GitLab review environments to other cloud application framework PaaS\n\nSo you may be thinking, “I love GitLab review environments, but not all of our applications are targeting Kubernetes.” It is true that the out- of-the-box showcasing of GitLab review environments depends on Kubernetes. One of the key reasons for this is that Kubernetes provides an integrated declarative deployment capability known as deployment manifests. The environment isolation capability, known as namespaces, also provides a critical capability. GitLab wires these Kubernetes capabilities up to a few key pieces of GitLab CD to accomplish the magic of isolated, per-feature branch review environments.\n\nAs far as I know there is no formal or defacto industry term for what I’ll call “Cloud Application Framework PaaS.” Cloud-provided PaaS can be targeted at various “levels” of the problem of building applications. For instance, primitive components such as AWS ELB address the problem of application load balancing by providing a variety of virtual, cloud-scaling and secured appliances that you can use as a component of building an application. Another example is [AWS Cognito](https://aws.amazon.com/cognito/) to help with providing user login and profile services to an application build.\n\nHowever, there are also cloud PaaS offerings that seek to solve the entire problem of rapid application building and maintenance. These are services like AWS Amplify and AWS AppRunner. These services frequently knit together primitive PaaS components (such as described above) into a composite that attempts to accelerate the entire process of building applications. Frequently these PaaS also include special CLIs or other developer tools that attempt to abstract the creation, maintenance and deployment of an Infrastructure as Code layer. They also tend to be [GitOps](/topics/gitops/)-oriented by storing this IaC in the same repository as the application code, which enables full control over deployments via Git controls such as branches and merge requests.\n\nThis approach relieves developers of early stage applications from having to learn IaC or hire IaC operations professionals too early. Basically it allows avoidance of overly early optimization of onboarding IaC skills. If the application is indeed successful it is quite common to outgrow the integrated IaC support provided by these specialized PaaS, however, the evolution is very natural because the managed IaC can simply start to be developed by specialists.\n\nThe distinction of cloud application framework PaaS is important when understanding where GitLab can create compound value with Dynamic Review Environments. I will refer to this kind of PaaS as “Cloud Application Infrastructure PaaS” that tries to solve the entire “Building Applications Problem.”\n\nSo we have a bunch of GitLab interfaces and conventions for implementing seamless developer review environments and we have non-Kubernetes cloud application infrastructures that provide declarative deployment interfaces and we can indeed make them work together! Interesting it is all done in GitLab CI YAML, which means that once you see the art of the possible, you can start implementing dynamic review environment lifecycle management for many custom environment types with the existing GitLab features. \n\n## A working, non-Kubernetes example of dynamic review environments in action\n\n![](https://about.gitlab.com/images/blogimages/environment-friction-lifecycle/CloudFormationDeployAnimatedGif.gif)\n\n**Figure 2: Working CD example of review environments for AWS CloudFormation**\n\nFigure 2 shows the details of an actual non-Kubernetes working example called CloudFormation AutoDeploy With Dynamic Review Environments. This project enables any AWS CloudFormation template to be deployed. It specifically supports an isolated stack deployment whenever a review branch is created and then also destroys that environment when the branch is merged. \n\nHere are some of the key design constraints and best practices that allow it to support automated review environments.:\n\n- **The code is implemented as an include.** Notice that the main [.gitlab-ci.yml](https://gitlab.com/guided-explorations/aws/cloudformation-deploy/-/blob/main/.gitlab-ci.yml) files have only variables applicable to this project and then the inclusion of Deploy-AWSCloudFormation.gitlab-ci.yml. This allows you to treat the CloudFormation integration as a managed process, shared include to be improved and updated. If the stress of backward compatibility of managing a shared dependency is too much, you can encourage developers to make a copy of this file to essentially version peg it with their project.\n\n- **Avoids Conflict with Auto DevOps CI Stage Names** - The [standard stages of Auto Devops are here](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/ci/templates/Auto-DevOps.gitlab-ci.yml#L70). This constraint allows the auto deploy template to be leveraged. \n\n- **Creates and Sequences Custom Stages as Necessary** - For instance, you can see we’ve added `create-changeset` stage and jobs.\n\n- The `deploy-review` job and it’s `environment:` section must have a very specific construction, let’s look at the important details:\n\n  ```text\n    rules:\n      - if: '$CI_COMMIT_BRANCH == \"main\"'\n        when: never\n      - if: '$REVIEW_DISABLED'\n        when: never\n      - if: '($CI_COMMIT_TAG || $CI_COMMIT_BRANCH) && $REQUIRE_CHANGESET_APPROVALS == \"true\"'\n        when: manual\n      - if: '($CI_COMMIT_TAG || $CI_COMMIT_BRANCH) && $REQUIRE_CHANGESET_APPROVALS != \"true\"'\n    artifacts:\n      reports:\n        dotenv: envurl.env\n    environment:\n      name: review/$CI_COMMIT_REF_SLUG\n      url: $DYNAMIC_ENVIRONMENT_URL\n      on_stop: stop_review\n  ```\n\n  \n\n  - `rules:` are used to ensure this job only runs when we are not on the main branch. The main branch implements long lived stage and prod environments.\n  - `artifacts:reports:dotenv` allows variables populated during a CI job to become pipeline level variables. The most critical role this does in this job is to allow the URL retrieved from CloudFormation Outputs to be populated into the variable DYNAMIC_ENVIRONMENT_URL. The file `enviurl.env` would have at least the line `DYNAMIC_ENVIRONMENT_URL={url-from-cloudformation}` in it. You can see this in the job code as `echo \"DYNAMIC_ENVIRONMENT_URL=${STACK_ENV_URL}\" >> envurl.env`\n  - `environment:name:` is using the Auto Deploy convention of placing review apps under the review environments top level called `review` The reference $CI_COMMIT_REF_SLUG ensures that the branch (or tag name) is used, but with all illegal characters removed. By your development convention, the Environment Name should become a part of the IaC constructs that ensure both uniqueness as well as identifiability by this pipeline. In GitLab's standard auto deploy for Kubernetes this is done by constructing a namespace that contains the name in this provided parameter. In CloudFormation we make it part of the Stack Name. The value here is exposed in the job as the variable ${ENVRONMENT}.\n  - `environment:url:` it is not self-evident here that the variable DYNAMIC_ENVIRONMENT_URL was populated by the deployment job and added to the file `enviro.env` so that it would contain the right value at this time. This causes the GitLab “Environment” page to have a clickable link to visit the environment. It also is used by DAST and other live application scan engines to find and scan the isolated environment.\n  - `environment:on_stop:` in the deploy-review job is what maps to the `stop_review` named job. This is the magic sauce behind automatic environment deletion when a feature branch is merged. `stop_review` must be written with the correct commands to accomplish the teardown.\n\n## A reusable engineering pattern\n\nThis CloudFormation pattern serves as a higher-level pattern of how GitLab review environments can be adopted to any other cloud “Application Level PaaS.” This is a term I use to indicate a cloud PaaS that is abstracted highly enough that developers think of it as “a place to deploy applications.” Perhaps a good way to contrast it with PaaS that does not claim to serve as an entire application platform. Cloud-based load balancers are a good example of a PaaS that performs a utility function for applications but is not a place to build an entire cloud application. \n\n## Application PaaS for abstracting IaC concerns for developers\n\nGitLab auto deploy combines well with the cloud application framework PaaS that has a disposition toward developer productivity by reducing or eliminating IaC management required by developers. AWS Amplify has such productivity support in the form of a developer specific CLI which allows impacting to be authored and updated in the same Git repository where the application code is stored. 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Architect",{"headshot":709,"linkedin":710,"ctfId":711},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659751/Blog/Author%20Headshots/Darwin-Sanoy-headshot-395-square-gitlab-teampage-avatar.png","https://linkedin.com/in/darwinsanoy","DarwinJS",{},"/en-us/blog/authors/darwin-sanoy",{},"en-us/blog/authors/darwin-sanoy","UkMMwmU5o2e6Y-wBltA9E_z96LvHuB-bG6VW9DsLzIY",[718,733,746],{"content":719,"config":731},{"body":720,"title":721,"description":722,"authors":723,"heroImage":725,"date":726,"category":9,"tags":727},"Most CI/CD tools can run a build and ship a deployment. Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n  \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n  \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - trigger\n\ntrigger-services:\n  stage: trigger\n  trigger:\n    include:\n      - local: '.gitlab/ci/api-service.yml'\n      - local: '.gitlab/ci/web-service.yml'\n      - local: '.gitlab/ci/worker-service.yml'\n    strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n  - build\n  - test\n\nbuild-api:\n  stage: build\n  script:\n    - echo \"Building API service\"\n\ntest-api:\n  stage: test\n  needs: [build-api]\n  script:\n    - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/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.",[724],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[110,728,729,730],"DevOps platform","tutorial","features",{"featured":28,"template":13,"slug":732},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":734,"config":744},{"title":735,"description":736,"authors":737,"heroImage":739,"date":740,"body":741,"category":9,"tags":742},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[738],"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/)",[729,743,730],"product",{"featured":12,"template":13,"slug":745},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":747,"config":757},{"title":748,"description":749,"authors":750,"heroImage":752,"date":753,"category":9,"tags":754,"body":756},"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.",[751],"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",[263,623,755],"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":758,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":760},[761,775,786,798],{"id":762,"categories":763,"header":765,"text":766,"button":767,"image":772},"ai-modernization",[764],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":768,"config":769},"Get your AI maturity score",{"href":770,"dataGaName":771,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":773},{"src":774},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":776,"categories":777,"header":778,"text":766,"button":779,"image":783},"devops-modernization",[743,569],"Are you just managing tools or shipping innovation?",{"text":780,"config":781},"Get your DevOps maturity score",{"href":782,"dataGaName":771,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":784},{"src":785},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":787,"categories":788,"header":790,"text":766,"button":791,"image":795},"security-modernization",[789],"security","Are you trading speed for security?",{"text":792,"config":793},"Get your security maturity score",{"href":794,"dataGaName":771,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":796},{"src":797},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":799,"paths":800,"header":803,"text":804,"button":805,"image":810},"github-azure-migration",[801,802],"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":806,"config":807},"See how GitLab compares to GitHub",{"href":808,"dataGaName":809,"dataGaLocation":245},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":811},{"src":785},{"header":813,"blurb":814,"button":815,"secondaryButton":820},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":816,"config":817},"Get your free trial",{"href":818,"dataGaName":52,"dataGaLocation":819},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":507,"config":821},{"href":56,"dataGaName":57,"dataGaLocation":819},1776442966890]