[{"data":1,"prerenderedAt":816},["ShallowReactive",2],{"/en-us/blog/how-we-removed-all-502-errors-by-caring-about-pid-1-in-kubernetes":3,"navigation-en-us":35,"banner-en-us":445,"footer-en-us":455,"blog-post-authors-en-us-Steve Azzopardi":697,"blog-related-posts-en-us-how-we-removed-all-502-errors-by-caring-about-pid-1-in-kubernetes":711,"blog-promotions-en-us":753,"next-steps-en-us":806},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":12,"meta":25,"navigation":26,"path":27,"publishedDate":20,"seo":28,"stem":32,"tagSlugs":33,"__hash__":34},"blogPosts/en-us/blog/how-we-removed-all-502-errors-by-caring-about-pid-1-in-kubernetes.yml","How We Removed All 502 Errors By Caring About Pid 1 In Kubernetes",[7],"steve-azzopardi",null,"engineering",{"slug":11,"featured":12,"template":13},"how-we-removed-all-502-errors-by-caring-about-pid-1-in-kubernetes",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How we reduced 502 errors by caring about PID 1 in Kubernetes","For every deploy, scale down event, or pod termination, users of GitLab's Pages service were experiencing 502 errors. This explains how we found the root cause and rolled out a fix for it.",[18],"Steve Azzopardi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682305/Blog/Hero%20Images/KubeCon2022.jpg","2022-05-17","\n\n_This blog post and linked pages contain information related to upcoming products, features, and functionality. It is important to note that the information presented is for informational purposes only. Please do not rely on this information for purchasing or planning purposes. As with all projects, the items mentioned in this blog post and linked pages are subject to change or delay. The development, release, and timing of any products, features, or functionality remain at the sole discretion of GitLab Inc._\n\nOur [SRE on call](https://handbook.gitlab.com/handbook/engineering/infrastructure/incident-management/#engineer-on-call-eoc-responsibilities)\nwas getting paged daily that one of our\n[SLIs](https://www.youtube.com/watch?v=tEylFyxbDLE) was\nburning through our\n[SLOs](https://www.youtube.com/watch?v=tEylFyxbDLE) for the [GitLab\nPages](https://docs.gitlab.com/ee/user/project/pages/) service. It was\nintermittent and short-lived, but enough to cause user-facing impact which we\nweren't comfortable with. This turned into alert fatigue because there wasn't\nenough time for the SRE on call to investigate the issue and it wasn't\nactionable since it recovered on its own.\n\nWe decided to open up an [investigation issue](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497)\nfor these alerts. We had to find out what the issue was since we were\nshowing `502` errors to our users and we needed a\n[DRI](https://handbook.gitlab.com/handbook/people-group/directly-responsible-individuals/)\nthat wasn't on call to investigate.\n\n## What is even going on?\n\nAs an [SRE](https://handbook.gitlab.com/job-families/engineering/infrastructure/site-reliability-engineer/)\nat GitLab, you get to touch a lot of services that you didn't build yourself and\ninteract with system dependencies that you might have not touched before.\nThere's always detective work to do!\n\nWhen we looked at the GitLab Pages logs we found that it's always returning\n[`ErrDomainDoesNotExist`](https://gitlab.com/gitlab-org/gitlab-pages/-/blob/e1f1effa23c520d3b8b717d831ccab7ba3dd494f/internal/routing/middleware.go#L22-26)\nerrors which result in a `502` error to our users. GitLab Pages [sends a request](https://gitlab.com/gitlab-org/gitlab-pages/-/blob/e1f1effa23c520d3b8b717d831ccab7ba3dd494f/internal/source/gitlab/client/client.go#L101-127)\nto [GitLab Workhorse](https://docs.gitlab.com/ee/development/workhorse/),\nspecifically the `/api/v4/internal/pages` route.\n\nGitLab Workhorse is a Go service in front of our Ruby on Rails monolith and\nit's deployed as a [sidecar](https://www.magalix.com/blog/the-sidecar-pattern)\ninside of the `webservice pod`, which runs Ruby on Rails using the `Puma` web\nserver.\n\nWe used the internal IP to correlate the GitLab Pages requests with GitLab Workhorse\ncontainers. We looked at multiple requests and found that all the 502 requests\nhad the following error attached to them: [`502 Bad Gateway with dial tcp 127.0.0.1:8080: connect: connection refused`](https://gitlab.com/gitlab-org/gitlab/-/blob/f64be48cc737f5d12c1c30f724af540a836dcc94/workhorse/internal/badgateway/roundtripper.go#L43).\nThis means that GitLab Workhorse couldn't connect to the Puma web server. So we\nneeded to go another layer deeper.\n\nThe Puma web server is what runs the Ruby on Rails monolith which has an\ninternal API endpoint but Puma was never getting these requests since it wasn't\nrunning. What this tells us is that Kubernetes kept our pod in the\n[service](https://kubernetes.io/docs/concepts/services-networking/service/)\neven when Puma wasn't responding, despite having [readiness probes](https://gitlab.com/gitlab-org/charts/gitlab/-/blob/4bb638bccc6a676f9fdd5bbf800f7d2b977efd55/charts/gitlab/charts/webservice/templates/deployment.yaml#L279-287)\nconfigured.\n\nBelow is the request flow between GitLab Pages, GitLab Workhorse, and Puma/Webservice to try and make it more clear:\n\n![overview of the request flow](https://about.gitlab.com/images/blogimages/how-we-removed-all-502-errors-by-caring-about-pid-1-in-kubernetes/overview.png){: .shadow.center}\n\n## Attempt 1: Red herring\n\nWe shifted our focus on GitLab Workhorse and Puma to try and understand how\nGitLab Workhorse was returning 502 errors in the first place. We found some\n`502 Bad Gateway with dial tcp 127.0.0.1:8080: connect: connection refused`\nerrors during container startup time. How could this be? With the readiness\nprobe, the pod shouldn't be added to the\n[Endpoint](https://kubernetes.io/docs/concepts/services-networking/service/#over-capacity-endpoints)\nuntil [all readiness probes pass](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497#note_899321775).\nWe later found out that it's because of a [polling\nmechanisim](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497#note_899629314)\nthat we have for [Geo](https://docs.gitlab.com/ee/administration/geo/) which\nruns in the background, using a Goroutine in GitLab Workhorse, and pings Puma for Geo information.\nWe don't have Geo enabled on GitLab.com so we [simply disabled it](https://gitlab.com/gitlab-com/gl-infra/k8s-workloads/gitlab-com/-/merge_requests/1670)\nto reduce the noise.\n\nWe removed the 502 errors, but not the ones we want, just a red herring.\n\n## Attempt 2: Close but not quite\n\nAt this time, we were still burning through our SLO from time to time, so this\nwas still an urgent thing that we needed to fix. Now that we had cleaner logs for\n`502` errors it started to become a bit clearer that this is happening on pod\ntermination:\n\n```text\n2022-04-05 06:03:49.000 UTC: Readiness probe failed\n2022-04-05 06:03:51.000 UTC: Puma (127.0.0.1:8080) started shutdown.\n2022-04-05 06:04:04.526 UTC: Puma shutdown finished.\n2022-04-05 06:04:04.000 UTC - 2022-04-05 06:04:46.000 UTC: workhorse started serving 502 constantly. 42 seconds of serving 502 requests for any request that comes in apart from /api/v4/jobs/request\n```\n\nIn the timeline shown above, we see that we've kept serving requests well after\nour `Puma`/`webservice` container exited, and the first readiness probe failed.\nIf we look at the readiness probes we had on that pod we see the following:\n\n```shell\n$ kubectl -n gitlab get po gitlab-webservice-api-785cb54bbd-xpln2 -o jsonpath='{range .spec.containers[*]} {@.name \":\\n\\tliveness:\"} {@.livenessProbe} {\"\\n\\treadiness:\"} {@.readinessProbe} {\"\\n\"} {end}'\n webservice:\n liveness: {\"failureThreshold\":3,\"httpGet\":{\"path\":\"/-/liveness\",\"port\":8080,\"scheme\":\"HTTP\"},\"initialDelaySeconds\":20,\"periodSeconds\":60,\"successThreshold\":1,\"timeoutSeconds\":30}\n readiness: {\"failureThreshold\":3,\"httpGet\":{\"path\":\"/-/readiness\",\"port\":8080,\"scheme\":\"HTTP\"},\"initialDelaySeconds\":60,\"periodSeconds\":10,\"successThreshold\":1,\"timeoutSeconds\":2}\n gitlab-workhorse:\n liveness: {\"exec\":{\"command\":[\"/scripts/healthcheck\"]},\"failureThreshold\":3,\"initialDelaySeconds\":20,\"periodSeconds\":60,\"successThreshold\":1,\"timeoutSeconds\":30}\n readiness: {\"exec\":{\"command\":[\"/scripts/healthcheck\"]},\"failureThreshold\":3,\"periodSeconds\":10,\"successThreshold\":1,\"timeoutSeconds\":2}\n\n```\n\nThis meant that for the `webservice` pod to be marked unhealthy and removed\nfrom the endpoints, Kubernetes had to get 3 consecutive failures with an\ninterval of 10 seconds, so in total that's 30 seconds. That seems a bit slow.\n\nOur next logical step was to reduce the `periodSeconds` for the readiness probe\nfor the `webservice` pod so we don't wait 30 seconds before removing the pod\nfrom the service when it becomes unhealthy.\n\nBefore doing so we had to understand if sending more requests to `/-/readiness`\nendpoint would have any knock-on effect with using more memory or anything\nelse. We had to [understand what the `/-/readiness` endpoint was doing](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497#note_903812722)\nand if it was safe to increase the frequency at which we send requests. We\ndecided it was safe, and after enabling it on\n[staging](https://gitlab.com/gitlab-com/gl-infra/k8s-workloads/gitlab-com/-/merge_requests/1686#note_903877755),\nand\n[canary](https://gitlab.com/gitlab-com/gl-infra/k8s-workloads/gitlab-com/-/merge_requests/1688#note_904501848)\nwe didn't see any increase in CPU/Memory usage, as expected, and saw an\nimprovement in the removal of 502 errors, which made us more confident that\nthis was the issue. We rolled this out to\n[production](https://gitlab.com/gitlab-com/gl-infra/k8s-workloads/gitlab-com/-/merge_requests/1689)\nwith high hopes.\n\nAs usual, Production is a different story than Staging or Canary, and it showed\nthat it didn't remove all the 502 errors, just [enough to stop triggering the SLO every day](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497#note_905993144),\nbut at least we removed the alert fatigue on the SRE on call. We were close, but not quite.\n\n## Attempt 3: All gone!\n\nAt this point, we were a bit lost and weren't sure what to look at next. We had\na bit of tunnel vision and kept focusing/blaming that we aren't removing the\nPod from the `Endpoint` quickly enough. We even looked at [Google Cloud Platform\nNEGs](https://cloud.google.com/kubernetes-engine/docs/how-to/standalone-neg) to\nsee if we could have faster readiness probes and remove the pod quicker. However,\nthis wasn't ideal [because we wouldn't have solved this for our self-hosting customers](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497#note_908359286)\nwhich seem to be facing the same [problem](https://gitlab.com/gitlab-org/charts/gitlab/-/issues/2943).\n\nWhile researching we also came across a known problem with [running `Puma` in\nKubernetes](https://github.com/puma/puma/blob/bf2548ce300c2b4f671582bc756dcec5861e815f/docs/kubernetes.md),\nand thought that might be the solution. However, we already implemented a\n[blackout window](https://gitlab.com/gitlab-org/charts/gitlab/-/blob/c1b63f3a4867886bc1212d86985fc70e66b717c5/charts/gitlab/charts/webservice/templates/deployment.yaml#L223-224)\njust for this specific reason, so it couldn't be that either...in other words, it was another dead end.\n\nWe took a step back and looked at the [timelines one more time](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497#note_910106152)\nand then it hit us. The Puma/webservice container is terminating within a\nfew seconds, but the GitLab Workhorse one is always taking 30 seconds. Is it because\nof the [long polling from GitLab Runner](https://gitlab.com/gitlab-org/gitlab-foss/-/issues/21698)? 30 seconds\nis a \"special\" number for Kubernetes [pod termination](https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#pod-termination).\nWhen Kubernetes deletes a pod it firsts sends the `TERM` signal to the\ncontainer and waits 30 seconds, if the container hasn't exited yet, it will\nsend a `KILL` signal. This indicated that maybe GitLab Workhorse was never\nshutting down and Kubernetes had to kill it.\n\nOnce more we looked at GitLab Workhorse source code and [searched for the `SIGTERM` usage](https://gitlab.com/gitlab-org/gitlab/-/blob/d66f10e169a08cedcbfe70e3ea46cbfbb20d972d/workhorse/main.go#L238-258)\nand it did seem to support [graceful termination](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/62701) and\nit also had explicit logic about long polling requests, so is this just another\ndead end? Luckily when the `TERM` signal is sent, Workhorse [logs a message that\nit's shutting down](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/62701). We looked\nat our logs for this specific message and didn't see anything. Is this it? We\naren't gracefully shutting down? But how? Why does it result in 502 errors?\nWhy do the GitLab Pages keep using the same pod that is terminating?\n\nWe know that the `TERM` signal is being sent to PID 1 inside of the container,\nand that process should handle the `TERM` signal for graceful shutdown. We\nlooked at the GitLab Workhorse process tree and this is what we found:\n\n```sh\ngit@gitlab-webservice-default-5d85b6854c-sbx2z:/$ ps faux\nUSER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND\nroot 1015 0.0 0.0 805036 4588 ? Rsl 13:12 0:00 runc init\ngit 1005 0.3 0.0 5992 3784 pts/0 Ss 13:12 0:00 bash\ngit 1014 0.0 0.0 8592 3364 pts/0 R+ 13:12 0:00 \\_ ps faux\ngit 1 0.0 0.0 2420 532 ? Ss 12:52 0:00 /bin/sh -c /scripts/start-workhorse\ngit 16 0.0 0.0 5728 3408 ? S 12:52 0:00 /bin/bash /scripts/start-workhorse\ngit 19 0.0 0.3 1328480 33080 ? Sl 12:52 0:00 \\_ gitlab-workhorse -logFile stdout -logFormat json -listenAddr 0.0.0.0:8181 -documentRoot /srv/gitlab/public -secretPath /etc/gitlab/gitlab-workhorse/secret -config /srv/gitlab/config/workhorse-config.toml\n```\n\nBingo! `gitlab-workhorse` is PID 19 in this case, and a child process of a\n[script](https://gitlab.com/gitlab-org/build/CNG/-/blob/92d3e22e9ff6c5cbb685aeea99813751d5e19a9d/gitlab-workhorse/Dockerfile#L51)\nthat we invoke. Taking a close look at the\n[script](https://gitlab.com/gitlab-org/build/CNG/-/blob/92d3e22e9ff6c5cbb685aeea99813751d5e19a9d/gitlab-workhorse/scripts/start-workhors)\nwe check if it listens to `TERM` and it doesn't! So far everything indicated\nthat GitLab Workhorse was never getting the `TERM` signal which ended up in receiving\n`KILL` after 30 seconds. We updated our `scripts/start-workhorse` to use\n[`exec(1)`](https://linux.die.net/man/1/exec) so that `gitlab-workhorse`\nreplaced the PID of our bash script, that should have worked, right? When we tested\nthis locally we then saw the following process tree.\n\n```shell\ngit@gitlab-webservice-default-84c68fc9c9-xcsnm:/$ ps faux\nUSER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND\ngit 167 0.0 0.0 5992 3856 pts/0 Ss 14:27 0:00 bash\ngit 181 0.0 0.0 8592 3220 pts/0 R+ 14:27 0:00 \\_ ps faux\ngit 1 0.0 0.0 2420 520 ? Ss 14:24 0:00 /bin/sh -c /scripts/start-workhorse\ngit 17 0.0 0.3 1328228 32800 ? Sl 14:24 0:00 gitlab-workhorse -logFile stdout -logFormat json -listenAddr 0.0.0.0:8181 -documentRoot /srv/gitlab/public -secretPath /etc/gitlab/gitlab-workhorse/secret -config /srv/gitlab/config/workhorse-config.toml\n```\n\nThis changed a bit: this shows that `gitlab-workhorse` was no longer a child\nprocess of `/scripts/start-workhorse` however `/bin/sh` was still PID 1. What is even\ninvoking `/bin/sh` that we didn't see anywhere in our\n[Dockerfile](https://gitlab.com/gitlab-org/build/CNG/-/blob/92d3e22e9ff6c5cbb685aeea99813751d5e19a9d/gitlab-workhorse/Dockerfile)?\nAfter some thumb-twiddling, we had an idea that the container runtime is invoking\n`/bin/sh`. We went back to basics and looked at the\n[`CMD`](https://docs.docker.com/engine/reference/builder/#cmd) documentation to\nsee if we were missing something, and we were. We read the following:\n\n> If you use the shell form of the CMD, then the \u003Ccommand> will execute in `/bin/sh -c`:\n>\n> ```\n> FROM ubuntu\n> CMD echo \"This is a test.\" | wc -\n> ```\n>\n> If you want to run your \u003Ccommand> without a shell then you must express the command as a JSON array and give the full path to the executable. This array form is the preferred format of CMD. Any additional parameters must be individually expressed as strings in the array:\n>\n> ```\n> FROM ubuntu\n> CMD [\"/usr/bin/wc\",\"--help\"]\n> ```\n\nThis was exactly [what we were doing](https://gitlab.com/gitlab-org/build/CNG/-/blob/92d3e22e9ff6c5cbb685aeea99813751d5e19a9d/gitlab-workhorse/Dockerfile#L51)!\nwe weren't using `CMD` in `exec form`, but in `shell form`. Changing this confirmed\nthat `gitlab-workhorse` is now PID 1, and also receives the termination signal\nafter testing it locally:\n\n```shell\ngit@gitlab-webservice-default-84c68fc9c9-lzwmp:/$ ps faux\nUSER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND\ngit 65 1.0 0.0 5992 3704 pts/0 Ss 15:25 0:00 bash\ngit 73 0.0 0.0 8592 3256 pts/0 R+ 15:25 0:00 \\_ ps faux\ngit 1 0.2 0.3 1328228 32288 ? Ssl 15:24 0:00 gitlab-workhorse -logFile stdout -logFormat json -listenAddr 0.0.0.0:8181 -documentRoot /srv/gitlab/public -secretPath /etc/gitlab/gitlab-workhorse/secret -config /srv/gitlab/config/workhorse-config.toml\n```\n\n```text\n{\"level\":\"info\",\"msg\":\"shutdown initiated\",\"shutdown_timeout_s\":61,\"signal\":\"terminated\",\"time\":\"2022-04-13T15:27:57Z\"}\n{\"level\":\"info\",\"msg\":\"keywatcher: shutting down\",\"time\":\"2022-04-13T15:27:57Z\"}\n{\"error\":null,\"level\":\"fatal\",\"msg\":\"shutting down\",\"time\":\"2022-04-13T15:27:57Z\"}\n```\n\nOk, then we just needed to update `exec` and `CMD []` and we would have been\ndone, right? Almost. GitLab Workhorse proxies all of the requests for the API, Web, and Git requests so we couldn't just do a big change and expect that everything is going to be OK. We had to progressively roll this out to make\nsure we didn't break any existing working behavior since this affects all the\nrequests we get to GitLab.com. To do this, we hid it behind a [feature\nflag](https://gitlab.com/gitlab-org/build/CNG/-/merge_requests/972) so GitLab\nWorkhorse is only PID 1 when the `GITLAB_WORKHORSE_EXEC` environment variable\nis set. This allowed us to deploy the change and only enable it on a small part\nof our fleet to see if we see any problems. We were a bit more careful here and\nrolled it out zone by zone in Production since we run on 3 zones. When we\nrolled it out in the [first\nzone](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497#note_919259030)\nwe saw all 502 errors disappear! After fully rolling this out we see that [the\nproblem is fixed and it had no negative side\neffects](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497#note_920585707). Hurray!\n\nWe still had one question unanswered, why were GitLab Pages still trying to use\nthe same connection even after the Pod was removed from the Service because it was\nscheduled for deletion? When we looked at Go internals we see that [Go reuses\nTCP connections](https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/15497#note_920642770)\nif we close the body of the request. So even though it's not part of the Service\nwe can still keep the TCP connection open and send requests – this explains why\nwe kept seeing 502 on pod being terminated and always from the same GitLab\nPages pod.\n\nNow it's all gone!\n\n## More things that we can explore\n\n1. We've made graceful termination for GitLab Workhorse as [default](https://gitlab.com/gitlab-com/gl-infra/k8s-workloads/gitlab-com/-/merge_requests/1732).\n1. Audit all of our Dockerfiles that use `CMD command` and fix them. We've found 10, and [fixed all of them](https://gitlab.com/gitlab-org/charts/gitlab/-/issues/3249).\n1. [Better readiness Probe defaults for `webservice` pod](https://gitlab.com/gitlab-org/charts/gitlab/-/merge_requests/2518).\n1. Add [linting](https://gitlab.com/gitlab-org/charts/gitlab/-/issues/3253) for Dockerfiles.\n1. See if any of our child processes need [zombie process reaping](https://blog.phusion.nl/2015/01/20/docker-and-the-pid-1-zombie-reaping-problem/).\n\n## Takeaways\n\n1. We should always care about what is PID 1 in a container.\n1. Always try and use `CMD [\"executable\",\"param1\",\"param2\"]` in your Dockerfile.\n1. Pods are removed from the Service/Endpoint in async.\n1. If you are on GKE [NEGs](https://cloud.google.com/kubernetes-engine/docs/how-to/standalone-neg) might be better for readinessProbes.\n1. By default, there is a 30 second grace period between the `TERM` signal and the `KILL` signal when Pods terminate. You can update the time between the signals `terminationGracePeriodSeconds`.\n1. The Go `http.Client` will reuse the TCP connection if [the body is closed](https://cs.opensource.google/go/go/+/refs/tags/go1.18.2:src/net/http/response.go;l=59-64) which in this case made the issue worse.\n\nThank you to [@igorwwwwwwwwwwwwwwwwwwww](https://gitlab.com/igorwwwwwwwwwwwwwwwwwwww), [@gsgl](https://gitlab.com/gsgl), [@jarv](https://gitlab.com/jarv), and [@cmcfarland](https://gitlab.com/cmcfarland) for helping me debug this 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Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n - trigger\n\ntrigger-services:\n stage: trigger\n trigger:\n include:\n - local: '.gitlab/ci/api-service.yml'\n - local: '.gitlab/ci/web-service.yml'\n - local: '.gitlab/ci/worker-service.yml'\n strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n - build\n - test\n\nbuild-api:\n stage: build\n script:\n - echo \"Building API service\"\n\ntest-api:\n stage: test\n needs: [build-api]\n script:\n - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/ee/api/jobs.html#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n - build\n - test\n - trigger-backend\n\nbuild-frontend:\n stage: build\n script:\n - echo \"Building frontend and generating API contract...\"\n - mkdir -p dist\n - |\n echo '{\n \"api_version\": \"v2\",\n \"breaking_changes\": false\n }' > dist/api-contract.json\n - cat dist/api-contract.json\n artifacts:\n paths:\n - dist/api-contract.json\n expire_in: 1 hour\n\ntest-frontend:\n stage: test\n script:\n - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n stage: trigger-backend\n trigger:\n project: my-org/backend-service\n branch: main\n strategy: depend\n rules:\n - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n - build\n - test\n\nbuild-backend:\n stage: build\n script:\n - echo \"All backend tests passed!\"\n\nintegration-test:\n stage: test\n rules:\n - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n script:\n - echo \"Fetching API contract from frontend...\"\n - |\n curl --silent --fail \\\n --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n --output api-contract.json \\\n \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n - cat api-contract.json\n - |\n if grep -q '\"breaking_changes\": true' api-contract.json; then\n echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n exit 1\n fi\n echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n - generate\n - trigger-environments\n\ngenerate-config:\n stage: generate\n script:\n - |\n # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n # Default to dev, staging, prod if not set.\n ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n for ENV in $ENVIRONMENTS; do\n cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n stages:\n - deploy\n - verify\n deploy-${ENV}:\n stage: deploy\n script:\n - echo \"Deploying to ${ENV} environment\"\n verify-${ENV}:\n stage: verify\n script:\n - echo \"Running smoke tests on ${ENV}\"\n EOF\n done\n artifacts:\n paths:\n - \"*.yml\"\n exclude:\n - \".gitlab-ci.yml\"\n\n.trigger-template:\n stage: trigger-environments\n trigger:\n strategy: depend\n\ntrigger-dev:\n extends: .trigger-template\n trigger:\n include:\n - artifact: dev-pipeline.yml\n job: generate-config\n\ntrigger-staging:\n extends: .trigger-template\n needs: [trigger-dev]\n trigger:\n include:\n - artifact: staging-pipeline.yml\n job: generate-config\n\ntrigger-prod:\n extends: .trigger-template\n needs: [trigger-staging]\n trigger:\n include:\n - artifact: prod-pipeline.yml\n job: generate-config\n when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n* [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n* [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n* [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n rules:\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n when: never\n - if: $CI_COMMIT_BRANCH\n - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n - fast-checks\n - expensive-tests\n - deploy\n\nlint-code:\n stage: fast-checks\n script:\n - echo \"Running linter\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"push\"\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n stage: fast-checks\n script:\n - echo \"Running unit tests\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"push\"\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n stage: expensive-tests\n script:\n - echo \"Running integration tests (15 min)\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n stage: expensive-tests\n script:\n - echo \"Running E2E tests (30 min)\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n stage: expensive-tests\n script:\n - echo \"Running full nightly suite (2 hours)\"\n rules:\n - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n stage: deploy\n script:\n - echo \"Deploying to production\"\n rules:\n - if: $CI_COMMIT_BRANCH == \"main\"\n when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n inputs:\n stage:\n default: deploy\n environment:\n default: production\n---\ndeploy-job:\n stage: $[[ inputs.stage ]]\n script:\n - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n - echo \"Deploy URL: $DEPLOY_URL\"\n environment:\n name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n APP_NAME: \"my-awesome-app\"\n DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n - component: gitlab.com/my-org/component-library/build@v1.0.6\n - component: gitlab.com/my-org/component-library/test@v1.0.6\n - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n inputs:\n environment: staging\n\nstages:\n - build\n - test\n - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n* A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n* A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n* A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n* [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n* [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n* [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n* [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n* [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n* [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n* [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n* [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n* [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n* [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n* [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[718],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[104,722,723,724],"DevOps platform","tutorial","features",{"featured":26,"template":13,"slug":726},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":728,"config":738},{"title":729,"description":730,"authors":731,"heroImage":733,"date":734,"body":735,"category":9,"tags":736},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[732],"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/)",[723,737,724],"product",{"featured":12,"template":13,"slug":739},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":741,"config":751},{"title":742,"description":743,"authors":744,"heroImage":746,"date":747,"category":9,"tags":748,"body":750},"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.",[745],"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",[257,619,749],"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":752,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":754},[755,769,780,792],{"id":756,"categories":757,"header":759,"text":760,"button":761,"image":766},"ai-modernization",[758],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":762,"config":763},"Get your AI maturity score",{"href":764,"dataGaName":765,"dataGaLocation":239},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":770,"categories":771,"header":772,"text":760,"button":773,"image":777},"devops-modernization",[737,565],"Are you just managing tools or shipping innovation?",{"text":774,"config":775},"Get your DevOps maturity score",{"href":776,"dataGaName":765,"dataGaLocation":239},"/assessments/devops-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":781,"categories":782,"header":784,"text":760,"button":785,"image":789},"security-modernization",[783],"security","Are you trading speed for security?",{"text":786,"config":787},"Get your security maturity score",{"href":788,"dataGaName":765,"dataGaLocation":239},"/assessments/security-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":793,"paths":794,"header":797,"text":798,"button":799,"image":804},"github-azure-migration",[795,796],"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":800,"config":801},"See how GitLab compares to GitHub",{"href":802,"dataGaName":803,"dataGaLocation":239},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":805},{"src":779},{"header":807,"blurb":808,"button":809,"secondaryButton":814},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":810,"config":811},"Get your free trial",{"href":812,"dataGaName":46,"dataGaLocation":813},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":501,"config":815},{"href":50,"dataGaName":51,"dataGaLocation":813},1776449954016]