[{"data":1,"prerenderedAt":823},["ShallowReactive",2],{"/en-us/blog/gitlab-com-stability-post-gcp-migration":3,"navigation-en-us":42,"banner-en-us":452,"footer-en-us":462,"blog-post-authors-en-us-Andrew Newdigate":704,"blog-related-posts-en-us-gitlab-com-stability-post-gcp-migration":718,"blog-promotions-en-us":760,"next-steps-en-us":813},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":29,"isFeatured":12,"meta":30,"navigation":31,"path":32,"publishedDate":20,"seo":33,"stem":37,"tagSlugs":38,"__hash__":41},"blogPosts/en-us/blog/gitlab-com-stability-post-gcp-migration.yml","Gitlab Com Stability Post Gcp Migration",[7],"andrew-newdigate",null,"engineering",{"slug":11,"featured":12,"template":13},"gitlab-com-stability-post-gcp-migration",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"What's up with GitLab.com? Check out the latest data on its stability","Let's take a look at the data on the stability of GitLab.com from before and after our recent migration from Azure to GCP, and dive into why things are looking up.",[18],"Andrew Newdigate","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749671280/Blog/Hero%20Images/gitlab-gke-integration-cover.png","2018-10-11","This post is inspired by [this comment on\nReddit](https://www.reddit.com/r/gitlab/comments/9f71nq/thanks_gitlab_team_for_improving_the_stability_of/), thanking us for improving the stability of GitLab.com. Thanks, hardwaresofton! Making GitLab.com ready for your mission-critical workloads has been top of mind for us for some time, and it's great to hear that users are noticing a difference.\n\n_Please note that the numbers in this post differ slightly from the Reddit post as the data has changed since that post._\n\nWe will continue to work hard on improving the availability and stability of the platform. Our current goal is to achieve 99.95 percent availability on GitLab.com – look out for an upcoming post about how we're planning to get there.\n\n## GitLab.com stability before and after the migration\n\nAccording to [Pingdom](http://stats.pingdom.com/81vpf8jyr1h9), GitLab.com's availability for the year to date, up until the migration was **[99.68 percent](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=527563485&range=F2)**, which equates to about 32 minutes of downtime per week on average.\n\nSince the migration, our availability has improved greatly, although we have much less data to compare with than in Azure.\n\n![Availability Chart](https://docs.google.com/spreadsheets/d/e/2PACX-1vQg_tdtdZYoC870W3u2R2icSK0Rd9qoOtDJqYHALaQlzhxXOmfY63X1NMMyFVEypQs7NngR4UUIZx5R/pubchart?oid=458170195&format=image)\n\nUsing data publicly available from Pingdom, here are some stats about our availability for the year to date:\n\n| Period | Mean-time between outage events |\n| --- | --- |\n| Pre-migration (Azure) | **1.3 days** |\n| Post-migration (GCP) | **7.3 days** |\n| Post-migration (GCP) excluding 1st day | **12 days** |\n\nThis is great news: we're experiencing outages less frequently. What does this mean for our availability, and are we on track to achieve our goal of 99.95 percent?\n\n| Period | Availability | Downtime per week |\n| --- | --- | --- |\n| Pre-migration (Azure) | **[99.68%](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=527563485&range=F2)** | **32 minutes** |\n| Post-migration (GCP) | **[99.88%](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=527563485&range=B3)** | **13 minutes** |\n| Target - not yet achieved | **99.95%** | **5 minutes** |\n\nDropping from 32 minutes per week average downtime to 13 minutes per week means we've experienced a **61 percent improvement** in our availability following our migration to Google Cloud Platform.\n\n## Performance\n\nWhat about the performance of GitLab.com since the migration?\n\nPerformance can be tricky to measure. In particular, averages are a terrible way of measuring performance, since they neglect outlying values. One of the better ways to measure performance is with a latency histogram chart. To do this, we imported the GitLab.com access logs for July (for Azure) and\nSeptember (for Google Cloud Platform) into [Google\nBigQuery](https://cloud.google.com/bigquery/), then selected the 100 most popular endpoints for each month and categorised these as either API, web, git, long-polling, or static endpoints. Comparing these histograms side-by-side allows us to study how the performance of GitLab.com has changed since the migration.\n\n![GitLab.com Latency\nHistogram](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/azure_v_gcp_latencies.gif)\n\nIn this histogram, higher values on the left indicate better performance.\nThe right of the graph is the \"_tail_\", and the \"_fatter the tail_\", the worse the user experience.\n\nThis graph shows us that with the move to GCP, more requests are completing within a satisfactory amount of time.\n\nHere's two more graphs showing the difference for API and Git requests respectively.\n\n![API Latency\nHistogram](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/api-performance-histogram.png)\n\n![Git Latency\nHistogram](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/git-performance-histogram.png)\n\n## Why these improvements?\n\nWe chose Google Cloud Platform because we believe that Google offer the most reliable cloud platform for our workload, particularly as we move towards running GitLab.com in [Kubernetes](/solutions/kubernetes/).\n\nHowever, there are many other reasons unrelated to our change in cloud provider for these improvements to stability and performance.\n\n> #### _“We chose Google Cloud Platform because we believe that Google offer\nthe most reliable cloud platform for our workload”_\n\nLike any large SaaS site, GitLab.com is a large, complicated system, and attributing availability changes to individual changes is extremely difficult, but here are a few factors which may be effecting our availability and performance:\n\n### Reason #1: Our Gitaly Fleet on GCP is much more powerful than before\n\nGitaly is responsible for all Git access in the GitLab application. Before\nGitaly, Git access occurred directly from within Rails workers. Because of the scale we run at, we require many servers serving the web application, and therefore, in order to share git data between all workers, we relied on\nNFS volumes. Unfortunately this approach doesn't scale well, which led to us building Gitaly, a dedicated Git service.\n\n> #### _“We've opted to give our fleet of 24 Gitaly servers a serious\nupgrade”_\n\n#### Our upgraded Gitaly fleet\n\nAs part of the migration, we've opted to give our fleet of 24 [Gitaly](/blog/the-road-to-gitaly-1-0/) servers a serious upgrade. If the old fleet was the equivalent of a nice family sedan, the new fleet are like a pack of snarling musclecars, ready to serve your Git objects.\n\n| Environment | Processor | Number of cores per instance | RAM per instance |\n| --- | --- | --- | --- |\n| Azure | Intel Xeon Ivy Bridge @ 2.40GHz | 8 | 55GB |\n| GCP | Intel Xeon Haswell @ 2.30GHz | **32** | **118GB** |\n\nOur new Gitaly fleet is much more powerful. This means that Gitaly can respond to requests more quickly, and deal better with unexpected traffic surges.\n\n#### IO performance\n\nAs you can probably imagine, serving [225TB of Git data](https://dashboards.gitlab.com/d/ZwfWfY2iz/vanity-metrics-dashboard?orgId=1)\nto roughly half-a-million active users a week is a fairly IO-heavy operation. Any performance improvements we can make to this will have a big impact on the overall performance of GitLab.com.\n\nFor this reason, we've focused on improving performance here too.\n\n| Environment | RAID | Volumes | Media | filesystem | Performance |\n| --- | --- | --- | --- | --- | --- |\n| Azure | RAID 5 (lvm) | 16 | magnetic | xfs | 5k IOPS, 200MB/s (_per disk_) / 32k IOPS **1280MB/s** (_volume group_) |\n| GCP | No raid | 1 | **SSD** | ext4 | **60k read IOPs**, 30k write IOPs, 800MB/s read 200MB/s write |\n\nHow does this translate into real-world performance? Here are average read and write times across our Gitaly fleet:\n\n##### IO performance is much higher\n\nHere are some comparative figures for our Gitaly fleet from Azure and GCP.\nIn each case, the performance in GCP is much better than in Azure, although this is what we would expect given the more powerful fleet.\n\n[![Disk read time graph](https://docs.google.com/spreadsheets/d/e/2PACX-1vQg_tdtdZYoC870W3u2R2icSK0Rd9qoOtDJqYHALaQlzhxXOmfY63X1NMMyFVEypQs7NngR4UUIZx5R/pubchart?oid=458168633&format=image)](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=1002437172)\n[![Disk write time graph](https://docs.google.com/spreadsheets/d/e/2PACX-1vQg_tdtdZYoC870W3u2R2icSK0Rd9qoOtDJqYHALaQlzhxXOmfY63X1NMMyFVEypQs7NngR4UUIZx5R/pubchart?oid=884528549&format=image)](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=1002437172)\n[![Disk Queue length graph](https://docs.google.com/spreadsheets/d/e/2PACX-1vQg_tdtdZYoC870W3u2R2icSK0Rd9qoOtDJqYHALaQlzhxXOmfY63X1NMMyFVEypQs7NngR4UUIZx5R/pubchart?oid=2135164979&format=image)](https://docs.google.com/spreadsheets/d/1uJ_zacNvJTsvJUfNpi1D_aPBg-vNJC1xJzsSwGKKt8g/edit#gid=1002437172)\n\nNote: For reference: for Azure, this uses the average times for the week leading up to the failover. For GCP, it's an average for the week up to\nOctober 2, 2018.\n\nThese stats clearly illustrate that our new fleet has far better IO performance than our old cluster. Gitaly performance is highly dependent on\nIO performance, so this is great news and goes a long way to explaining the performance improvements we're seeing.\n\n### Reason #2: Fewer \"unicorn worker saturation\" errors\n\n![HTTP 503 Status\nGitLab](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/facepalm-503.png)\n\nUnicorn worker saturation sounds like it'd be a good thing, but it's really not!\n\nWe ([currently](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/1899))\nrely on [unicorn](https://bogomips.org/unicorn/), a Ruby/Rack http server, for serving much of the application. Unicorn uses a single-threaded model, which uses a fixed pool of workers processes. Each worker can handle only one request at a time. If the worker gives no response within 60 seconds, it is terminated and another process is spawned to replace it.\n\n> #### _“Unicorn worker saturation sounds like it'd be a good thing, but\nit's really not!”_\n\nAdd to this the lack of autoscaling technologies to ramp the fleet up when we experience high load volumes, and this means that GitLab.com has a relatively static-sized pool of workers to handle incoming requests.\n\nIf a Gitaly server experiences load problems, even fast [RPCs](https://en.wikipedia.org/wiki/Remote_procedure_call) that would normally only take milliseconds, could take up to several seconds to respond\n– thousands of times slower than usual. Requests to the unicorn fleet that communicate with the slow server will take hundreds of times longer than expected. Eventually, most of the fleet is handling requests to that affected backend server. This leads to a queue which affects all incoming traffic, a bit like a tailback on a busy highway caused by a traffic jam on a single offramp.\n\nIf the request gets queued for too long – after about 60 seconds – the request will be cancelled, leading to a 503 error. This is indiscriminate – all requests, whether they interact with the affected server or not, will get cancelled. This is what I call unicorn worker saturation, and it's a very bad thing.\n\nBetween February and August this year we frequently experienced this phenomenon.\n\nThere are several approaches we've taken to dealing with this:\n\n- **Fail fast with aggressive timeouts and circuitbreakers**: Timeouts mean\nthat when a Gitaly request is expected to take a few milliseconds, they time out after a second, rather than waiting for the request to time out after 60 seconds. While some requests will still be affected, the cluster will remain generally healthy. Gitaly currently doesn't use circuitbreakers, but we plan to add this, possibly using [Istio](https://istio.io/docs/tasks/traffic-management/circuit-breaking/)\nonce we've moved to Kubernetes.\n\n- **Better abuse detection and limits**: More often than not, server load\nspikes are driven by users going against our fair usage policies. We built tools to better detect this and over the past few months, an abuse team has been established to deal with this. Sometimes, load is driven through huge repositories, and we're working on reinstating fair-usage limits which prevent 100GB Git repositories from affecting our entire fleet.\n\n- **Concurrency controls and rate limits**: For limiting the blast radius,\nrate limiters (mostly in HAProxy) and concurrency limiters (in Gitaly) slow overzealous users down to protect the fleet as a whole.\n\n### Reason #3: GitLab.com no longer uses NFS for any Git access\n\nIn early September we disabled Git NFS mounts across our worker fleet. This was possible because Gitaly had reached v1.0: the point at which it's sufficiently complete. You can read more about how we got to this stage in our [Road to Gitaly blog post](/blog/the-road-to-gitaly-1-0/).\n\n### Reason #4: Migration as a chance to reduce debt\n\nThe migration was a fantastic opportunity for us to improve our infrastructure, simplify some components, and otherwise make GitLab.com more stable and more observable, for example, we've rolled out new **structured logging infrastructure**.\n\nAs part of the migration, we took the opportunity to move much of our logging across to structured logs. We use [fluentd](https://www.fluentd.org/), [Google\nPub/Sub](https://cloud.google.com/pubsub/docs/overview), [Pubsubbeat](https://github.com/GoogleCloudPlatform/pubsubbeat), storing our logs in [Elastic Cloud](https://www.elastic.co/cloud) and [Google\nStackdriver Logging](https://cloud.google.com/logging/). Having reliable, indexed logs has allowed us to reduce our mean-time to detection of incidents, and in particular detect abuse. This new logging infrastructure has also been invaluable in detecting and resolving several security incidents.\n\n> #### _“This new logging infrastructure has also been invaluable in\ndetecting and resolving several security incidents”_\n\nWe've also focused on making our staging environment much more similar to our production environment. This allows us to test more changes, more accurately, in staging before rolling them out to production. Previously the team was maintaining a limited scaled-down staging environment and many changes were not adequately tested before being rolled out. Our environments now share a common configuration and we're working to automate all [terraform](https://gitlab.com/gitlab-com/gl-infra/infrastructure/issues/5079)\nand [chef](https://gitlab.com/gitlab-com/gl-infra/infrastructure/issues/5078)\nrollouts.\n\n### Reason #5: Process changes\n\nUnfortunately many of the worst outages we've experienced over the past few years have been self-inflicted. We've always been transparent about these — and will continue to be so — but as we rapidly grow, it's important that our processes scale alongside our systems and team.\n\n> #### _“It's important that our processes scale alongside our systems and\nteam”_\n\nIn order to address this, over the past few months, we've formalized our change and incident management processes. These processes respectively help us to avoid outages and resolve them quicker when they do occur.\n\nIf you're interested in finding out more about the approach we've taken to these two vital disciplines, they're published in our handbook:\n\n- [GitLab.com's Change Management\nProcess](https://handbook.gitlab.com/handbook/engineering/infrastructure/change-management/)\n\n- [GitLab.com's Incident Management\nProcess](https://handbook.gitlab.com/handbook/engineering/infrastructure/incident-management/)\n\n### Reason #6: Application improvement\n\nEvery GitLab release includes [performance and stability improvements](https://gitlab.com/gitlab-org/gitlab-ce/issues?scope=all&state=opened&label_name%5B%5D=performance); some of these have had a big impact on GitLab's stability and performance, particularly n+1 issues.\n\nTake Gitaly for example: like other distributed systems, Gitaly can suffer from a class of performance degradations known as \"n+1\" problems. This happens when an endpoint needs to make many queries (_\"n\"_) to fulfill a single request.\n\n> Consider an imaginary endpoint which queried Gitaly for all tags on a\nrepository, and then issued an additional query for each tag to obtain more information. This would result in n + 1 Gitaly queries: one for the initial tag, and then n for the tags. This approach would work fine for a project with 10 tags – issuing 11 requests, but a project with 1000 tags, this would result in 1001 Gitaly calls, each with a round-trip time, and issued in sequence.\n\n![Latency drop in Gitaly endpoints](https://about.gitlab.com/images/blogimages/whats-up-with-gitlab-com/drop-off.png)\n\nUsing data from Pingdom, this chart shows long-term performance trends since the start of the year. It's clear that latency improved a great deal on May 7, 2018. This date happens to coincide with the RC1 release of GitLab 10.8, and its deployment on GitLab.com.\n\nIt turns out that this was due to a [single fix on n+1 on the merge request page being resolved](https://gitlab.com/gitlab-org/gitlab-ce/issues/44052).\n\nWhen running in development or test mode, GitLab now detects n+1 situations and we have compiled [a list of known n+1s](https://gitlab.com/gitlab-org/gitlab-ce/issues?scope=all&utf8=%E2%9C%93&state=opened&label_name[]=performance&label_name[]=Gitaly&label_name[]=technical%20debt).\nAs these are resolved we expect even more performance improvements.\n\n![GitLab Summit - South Africa - 2018](https://about.gitlab.com/images/summits/2018_south-africa_team.jpg)\n\n### Reason #7: Infrastructure team growth and reorganization\n\nAt the start of May 2018, the Infrastructure team responsible for GitLab.com consisted of five engineers.\n\nSince then, we've had a new director join the Infrastructure team, two new managers, a specialist [Postgres\nDBRE](https://gitlab.com/gitlab-com/www-gitlab-com/merge_requests/13778), and four new [SREs](https://handbook.gitlab.com/job-families/engineering/infrastructure/site-reliability-engineer/).\nThe database team has been reorganized to be an embedded part of infrastructure group. We've also brought in [Ongres](https://www.ongres.com/), a specialist Postgres consultancy, to work alongside the team.\n\nHaving enough people in the team has allowed us to be able to split time between on-call, tactical improvements, and longer-term strategic work.\n\nOh, and we're still hiring! If you're interested, check out [our open positions](/jobs/) and choose the Infrastructure Team 😀\n\n## TL;DR: Conclusion\n\n1. GitLab.com is more stable: availability has improved 61 percent since we\nmigrated to GCP\n\n1. GitLab.com is faster: latency has improved since the migration\n\n1. We are totally focused on continuing these improvements, and we're\nbuilding a great team to do it\n\nOne last thing: our Grafana dashboards are open, so if you're interested in digging into our metrics in more detail, visit [dashboards.gitlab.com](https://dashboards.gitlab.com) and explore!\n",[23,24,25,26,27,28],"GKE","google","inside 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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.",[725],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[111,729,730,731],"DevOps platform","tutorial","features",{"featured":31,"template":13,"slug":733},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":735,"config":745},{"title":736,"description":737,"authors":738,"heroImage":740,"date":741,"body":742,"category":9,"tags":743},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[739],"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/)",[730,744,731],"product",{"featured":12,"template":13,"slug":746},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":748,"config":758},{"title":749,"description":750,"authors":751,"heroImage":753,"date":754,"category":9,"tags":755,"body":757},"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.",[752],"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",[264,626,756],"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":759,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":761},[762,776,787,799],{"id":763,"categories":764,"header":766,"text":767,"button":768,"image":773},"ai-modernization",[765],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":769,"config":770},"Get your AI maturity score",{"href":771,"dataGaName":772,"dataGaLocation":246},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":774},{"src":775},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":777,"categories":778,"header":779,"text":767,"button":780,"image":784},"devops-modernization",[744,572],"Are you just managing tools or shipping innovation?",{"text":781,"config":782},"Get your DevOps maturity score",{"href":783,"dataGaName":772,"dataGaLocation":246},"/assessments/devops-modernization-assessment/",{"config":785},{"src":786},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":788,"categories":789,"header":791,"text":767,"button":792,"image":796},"security-modernization",[790],"security","Are you trading speed for security?",{"text":793,"config":794},"Get your security maturity score",{"href":795,"dataGaName":772,"dataGaLocation":246},"/assessments/security-modernization-assessment/",{"config":797},{"src":798},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":800,"paths":801,"header":804,"text":805,"button":806,"image":811},"github-azure-migration",[802,803],"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":807,"config":808},"See how GitLab compares to GitHub",{"href":809,"dataGaName":810,"dataGaLocation":246},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":812},{"src":786},{"header":814,"blurb":815,"button":816,"secondaryButton":821},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":817,"config":818},"Get your free trial",{"href":819,"dataGaName":53,"dataGaLocation":820},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":508,"config":822},{"href":57,"dataGaName":58,"dataGaLocation":820},1776444480412]