[{"data":1,"prerenderedAt":814},["ShallowReactive",2],{"/en-us/blog/how-to-configure-sidekiq-for-gitlab-at-scale":3,"navigation-en-us":33,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Craig Miskell":695,"blog-related-posts-en-us-how-to-configure-sidekiq-for-gitlab-at-scale":709,"blog-promotions-en-us":751,"next-steps-en-us":804},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":22,"isFeatured":12,"meta":23,"navigation":24,"path":25,"publishedDate":20,"seo":26,"stem":30,"tagSlugs":31,"__hash__":32},"blogPosts/en-us/blog/how-to-configure-sidekiq-for-gitlab-at-scale.yml","How To Configure Sidekiq For Gitlab At Scale",[7],"craig-miskell",null,"engineering",{"slug":11,"featured":12,"template":13},"how-to-configure-sidekiq-for-gitlab-at-scale",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9},"How to configure Sidekiq for specialized or large-scale GitLab instances","This tutorial unpacks how to configure Sidekiq that suits your GitLab deployment.",[18],"Craig Miskell","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667068/Blog/Hero%20Images/sidekiqmountain.jpg","2021-09-27","Configuring Sidekiq in your own deployment of GitLab is a little complicated, but entirely possible. In this blog post, we share how to set up Sidekiq for GitLab in special cases and at a large scale by sharing some exmaples that may be useful to you.\n\n## Why consider special configuration?\n\nWhile Sidekiq (both in general, and in a GitLab deployment) will usually _just work_ most of the time, there can be some sharp edges and limits. Raw scale is a clear and obvious driver for needing to take action, and although it may be fine to simply scale out multiple Sidekiq nodes each listening to all the queues, at some point:\n\n1. The uniqueness of workload distribution and job characteristics may require dedicated workers, either sharded on job attributes (as for GitLab.com), or specific workers (based on your workloads), or\n1. Simple saturation on Redis means you need to listen to fewer queues\n\n**[We share [all we learned about configuring Sidekiq on GitLab.com](/blog/specialized-sidekiq-configuration-lessons-from-gitlab-dot-com/)]**\n\n### Example: Demo systems\n\nIn early 2021, our Demo Systems team were running a GitLab deployment for training purposes. Many users would join a training session where the first task was to import a sample project into the provided GitLab instance to work on further during the class. Imports are implemented with a Sidekiq job because they can take anything from a few seconds to hours. What the Demo Systems team found was that the default Sidekiq configuration simply couldn't keep up. The deployment wasn't huge, and neither was the user count, it was the very specific usage of the system that ran into difficulties. So, the team split off a dedicated Sidekiq VM for running imports, with suitably tuned concurrency (based on CPU contention), CPU + memory, and number of workers.\n\n**[[Discover how we scaled our use of Sidekiq on a GitLab instance](/blog/scaling-our-use-of-sidekiq/)]**\n\nThe key lesson here is that large scale isn't always the driver for customizing Sidekiq configuration, and the reason may be specific to your workloads, which means first you have to be able to identify the pain points.\n\n### Using metrics to identify problems\n\n{: #using-metrics-to-identify-problems}\nUser experience may tell you something isn't going well, but how do you tell where the actual problem lies? The GitLab UI exposes the Sidekiq UI to administrators, at `/admin/background_jobs` – in the 'Queues' tab, you can see how many jobs are currently pending, and a breakdown by queue. However, that is a snapshot of a point-in-time, and stored metrics/graphs are better for long term visibility, particularly for figuring out what happened an hour ago when someone reported slow pipelines, or to debug that thing that happens twice a day but never when anyone is watching.\n\nTo get some visibility, consider installing [gitlab-exporter](https://gitlab.com/gitlab-org/gitlab-exporter/) on (or pointed to) your Redis nodes, with:\n\n* `probe_queues` enabled to get the `sidekiq_queue_size` metric, and/or\n* `probe_jobs_limit` to get `sidekiq_enqueued_jobs`.\n\n`sidekiq_queue_size` reports the length of the all the Sidekiq queues in Redis (equivalent to the data exposed by the Sidekiq UI), but now it's exposed as a Prometheus metric for scraping and graphing. `sidekiq_enqueued_jobs` deserializes the job descriptions as well, meaning it can look inside a routing rule-based named queue with more than one class of jobs in it, and report the distribution of work by class. It has to limit (hence the name) the inspection to the first 1000 jobs in any given queue to contain the potential impact of blocking Redis with many calls to [LRANGE](https://redis.io/commands/lrange) with large responses. Usually this situation is fine. If you have > 1000 jobs in any given queue for a non-trivial amount of time, just knowing what's at the head of the queue is likely sufficient and `sidekiq_queue_size` will still show you the full magnitude of the backlog.\n\nIf we were to really simplify it - because there are always exceptions - both those metrics should be at or close to 0 most of the time. In practice, there's often small, brief spikes when batches of work land and cannot be processed immediately, and it may be quite acceptable for some large/slow jobs to be queued for some significant time (e.g., project exports). But a prolonged backlog (or perpetual growth) indicates some class of work is not being processed, either at all, or \"fast enough\" to keep up. If your team is encountering these problems, it might be time to customize your Sidekiq configuration.\n\nHowever the backlog in queues may not be the whole story – queuing might be occurring because all your Sidekiq workers are busy with long-running jobs, causing all the other jobs to stall. To observe that you need the `sidekiq_running_jobs` metric, which can be scraped from the [sidekiq exporter](https://docs.gitlab.com/ee/administration/monitoring/prometheus/gitlab_metrics.html#sidekiq-metrics). This is enabled by default on port 8082 for Omnibus, and 3807 in Kubernetes when using our helmcharts. Graphing `sum by (worker) (sidekiq_running_jobs)` will show you what your Sidekiq workers are actively up to right now, and may highlight which worker/queue is causing the problem.\n\nConsider also keeping an eye on your Redis CPU usage – on a modern CPU at smaller scales there's a lot of headroom, but if you're at the point of considering a specialized Sidekiq configuration, now is the time to add a little monitoring and alerting so it doesn't sneak up on you in the future. We use [Process Exporter](https://github.com/ncabatoff/process-exporter) inspecting the `redis-server` process, with `threads=true` (on the command line) to get per-thread details. In Prometheus we use `sum by (threadname) (rate(namedprocess_namegroup_thread_cpu_seconds_total[1m]))`. On Redis 6, the core thread is named 'redis-server'. As always, set your alert level so that you won't get false positives, but will have plenty of headway before saturation becomes a real problem.\n\n### How to customize your Sidekiq configuration\n\nAfter identifying one or more queues/workers that are backed up, the main task is to get more Sidekiq processing power deployed. As mentioned above, it may be sufficient to simply add one or more [Sidekiq nodes](https://docs.gitlab.com/ee/administration/sidekiq/index.html) or Sidekiq workload in Kubernetes, allowing you to listen to all the queues in a default configuration. If you choose this approach, make sure you're keeping an eye on Redis CPU per the [metrics](#using-metrics-to-identify-problems) above.\n\nAn alternative is to choose to provision some dedicated Sidekiq processing for just the problem work. It could even be said that any complex configuration of Sidekiq for GitLab is just the result of a series of these decisions, progressively adding dedicated processing for specific workloads with a \"catchall\" or \"default\" workload picking up the rest, so I'll describe just one such step and you can take it as far as you need.\n\nThere is a critical decision to make first, and that's whether to:\n\n1. use queue-selectors on the workers and continue with a queue per worker for all jobs, or\n1. use routing rules.\n\nAnd if using routing rules, decide whether to:\n\n1. Go entirely to one-queue-per-shard, or\n1. Use a mix of custom-named queues and the default worker-named queues.\n\nHaving worked in this area for a little over a year now, **I strongly recommend using routing rules and one-queue-per-shard** for the following reasons:\n\n1. Routing rules are more obvious in their effect/ordering than trying to configure disjointed sets of queues across Sidekiq workloads,\n1. Correlating the target queue names in routing rules with the names of queues listened to by workers is simpler,\n1. There is *far* less complexity in configuring the default/catchall workers,\n1. Load on Redis is significantly reduced with fewer named queues.\n\nIt may be easier to see why with an example. In the next section, we run through an example where we assume that you want to provide dedicated resources for `project_exports` because it sees heavy use, and Sidekiq is regularly spending all it's time on that. We'll skip the early phase and assume that you have identified from metrics that the queue name is project_export.\n\n#### Using queue-selectors only\n\nLet's say you want to continue to use one queue per worker and configure each Sidekiq workload to listen to a subset of jobs using queue selectors. The syntax and location for configuring queue selectors is available in our documentation under [Queue selector](https://docs.gitlab.com/ee/administration/sidekiq/extra_sidekiq_processes.html) and [Worker matcher query](https://docs.gitlab.com/ee/administration/sidekiq/processing_specific_job_classes.html) sections.\n\nAfter creating your new, dedicated Sidekiq workload, configure this in `gitlab.rb` on that workload:\n\n```ruby\nsidekiq['enable'] = true\nsidekiq['queue_selector'] = true\nsidekiq['queue_groups'] = [ 'name=project_export' ]\n```\n\nKeep in mind that this will only run one Sidekiq process which, while multithreaded with one job potentially executing on each thread, can only use one CPU – read up on [multiple processes](https://docs.gitlab.com/ee/administration/sidekiq/extra_sidekiq_processes.html) and [concurrency threading](https://about.gitlab.com/blog/specialized-sidekiq-configuration-lessons-from-gitlab-dot-com/) for a little more detail, but in short, if you had a 4 CPU VM and you wanted to run 4 project_export processes, you'd configure gitlab.rb like this:\n\n```ruby\nsidekiq['enable'] = true\nsidekiq['queue_selector'] = true\nsidekiq['queue_groups'] = [ 'name=project_export', 'name=project_export', 'name=project_export', 'name=project_export' ]\n```\n\nThis also reveals another approach. If your existing workload is running somewhere with spare CPU you could add processes with different sets of queues, gaining some control of workload prioritization without having to deploy new compute resources. For example:\n\n```ruby\nsidekiq['enable'] = true\nsidekiq['queue_selector'] = true\nsidekiq['queue_groups'] = [ 'name=project_export', 'name!=project_export' ]\n```\n\nThis may look a little odd at first glance, but it means that one process will be listening to `project_export`, and the other will be listening to every queue that _isn't_ project_export.\n\nA couple of caveats:\n\n1. Concurrency (threading) is set once in `gitlab.rb`, so all jobs running on that node will need to be compatible with that concurrency. Read up on [Concurrency (threading) in the previous blog post](/blog/specialized-sidekiq-configuration-lessons-from-gitlab-dot-com/) to learn more.\n1. Using the GitLab helmcharts, each pod only runs one process, so there you'd adjust maxReplicas instead.\n\nSpeaking of helmcharts, these have the queue-selector configured with the [`queues`](https://docs.gitlab.com/charts/charts/gitlab/sidekiq/#queues) attribute of the pod:\n\n```yaml\nqueues: name=project_export\n```\n\nWhere, despite being named `queues`, it can take the full queue-selector expression.\n\nAfter these configurations, your new workload will be listening exclusively to the `project_export` queue/worker. But what is to stop your original workload from also running `project_export`? Absolutely nothing! A default/baseline workload of Sidekiq for GitLab will listen on all the queues. This **may** be acceptable in a simple case – you've provided additional capacity dedicated to the named queue, and occasionally those jobs will still run on the original Sidekiq. In practice, because of the way Sidekiq uses BRPOP with a randomized order of queues, and how Redis distributes work when clients are already waiting on a named queue, the new dedicated workload will most likely pick up the **vast** majority of the work on that queue. But this may not isolate problem work as much as you desire. This could also lead to difficulty in reasoning clearly about what the system is going to do as your customization grows and becomes more specific. Therefore, I strongly recommend that you ensure the sets of queues are disjoint (that is, no overlap). The final step is to configure your original/default Sidekiq with either:\n\n```ruby\nsidekiq['enable'] = true\nsidekiq['negate'] = true\nsidekiq['queue_selector'] = true\nsidekiq['queue_groups'] = [ 'name=project_export' ]\n```\n\nor\n\n```ruby\nsidekiq['enable'] = true\nsidekiq['queue_selector'] = true\nsidekiq['queue_groups'] = [ \"name!=project_export\" ]\n```\n\nThen, as you add more customized workloads in future steps, you would extend the expression to exclude the work that is being picked up elsewhere, e.g., in the negate case if you had added a further workload executing only `feature_category=importers`:\n\n```ruby\nsidekiq['negate'] = true\nsidekiq['queue_groups'] = [ 'name=project_export&feature_category=importers' ]\n```\n\nThis is where setting `negate` to \"true\" can be better – this catchall/default expression can be a simple concatenation of the expressions used on every other workload, separated with `&`. The expression may end up complex, but it can be generated trivially with code. Not using negate and inverting the operators works for simple cases, but may run into difficulty expressing edge cases when the individual expressions become more nuanced or complicated.\n\n#### Use routing rules\n\nAnother option is to use [routing rules](https://docs.gitlab.com/ee/administration/sidekiq/processing_specific_job_classes.html) to achieve the same thing. First, add a new Sidekiq workload configured with:\n\n```ruby\nsidekiq['enable'] = true\nsidekiq['queue_selector'] = false # This is the default and is included only to be explicit\nsidekiq['queue_groups'] = [ 'export' ]\n```\n\nAs in the queue-selector approach, you can run more than one by repeating the expression in queue_groups:\n\n```ruby\nsidekiq['queue_groups'] = [ 'export', 'export', 'export', 'export' ]\n```\n\nWhen using [helm charts](https://docs.gitlab.com/charts/charts/gitlab/sidekiq/index.html#queues) it would be simply the following in the Sidekiq pod definition:\n\n```yaml\nqueues: name=export\n```\n\nThis is simply explicitly naming queues, but having made up an arbitrary named \"export\" rather than using a queue name derived from the job class. Next, and most importantly, add the following to `gitlab.rb` on **all** your workloads. In the queue-selector approach, we only had to configure the Sidekiq workload, but here we need to ensure that **everywhere that enqueues Sidekiq jobs has the routing rules** – meaning anywhere running the Rails portion of GitLab, i.e., puma (web) as well as Sidekiq:\n\n```ruby\nsidekiq['routing_rules'] = [\n  ['name=project_export', 'export'],\n  ['*', nil]\n]\n```\n\nAnd when using [helmcharts](https://docs.gitlab.com/charts/charts/globals.html#sidekiq-routing-rules-settings) deployment:\n\n```yaml\nglobal:\n  appConfig:\n    sidekiq:\n      routingRules:\n      - [\"name=project_export\", \"export\"]\n      - [\"*\", null]\n\n```\n\nSome caveats:\n\n1. You most likely want a workload listening to the new queue **before** reconfiguring the routing rules, otherwise jobs will be put into the queue with nothing ready to execute them.\n1. The destination name (`export`) is arbitrary, but must match exactly in Sidekiq queue configuration and the routing rules.\n1. In `gitlab.rb` we use \"nil\", but in YAML we must use \"null\".\n\nBy using null/nil as the target for `*` this example continues to use the default worker-per-queue strategy for all the other jobs. So you will have gained routing/prioritization control, but Redis will still be doing a lot of work to listen to the other 440+ queues. To avoid that, you can change the target of the final `*` routing rule to \"default\", e.g.\n\n```ruby\nsidekiq['routing_rules'] = [\n  ['name=project_export', 'export'],\n  ['*', 'default']\n]\n```\n\nIn this context \"default\" is literal. Conveniently there is a built-in 'default' queue that GitLab Sidekiq listens to, although nothing uses it out of the box. These rules will route all remaining jobs to that queue and the original/default Sidekiq workload will pick them up immediately. Then, at your convenience, you can reconfigure the original Sidekiq workload to listen **only** to \"default\" in the same way you configured the new workload to listen to \"export\", and gain the performance benefit in Redis.\n\n#### Edge cases\n\nThe routing rules example above is simplified slightly for clarity. In practice there are still a small set of queues that need to remain in their **original** dedicated named queue for a variety of reasons. We're working on resolving the blockers, but that may take a while to work through. You can follow along in [this issue](https://gitlab.com/gitlab-com/gl-infra/scalability/-/issues/1087), or you can keep an eye on the routingRules [configuration for GitLab.com](https://gitlab.com/gitlab-com/gl-infra/k8s-workloads/gitlab-com/-/blob/master/releases/gitlab/values/gprd.yaml.gotmpl) – special cases will be at the very top of the rules, routed by worker_name or name, and there will be a comment about why and a link to any related issues, which will help you determine if each is relevant to your needs. Some special cases may be there for GitLab.com-specific reasons and may not be generally applicable. In the long term we expect the list of special cases to reduce, not increase.\n\nAlso take into consideration that the special cases may be used for features that you do not use. Specifically:\n\n1. EmailReceiverWorker & ServiceDeskEmailReceiverWorker are for [Incoming email](https://docs.gitlab.com/ee/administration/incoming_email.html)\n1. ProjectImportScheduleWorker is for project mirroring\n\nSo you might be able to just ignore them, or route them to a queue that no worker is listening to and alert if `sidekiq_queue_size` is above zero on those queues.\n\n### Migrating when using routing rules\n\nThere is one more thing to note. When migrating an active GitLab deployment (rather than configuring this from scratch on a fresh GitLab deployment) the order of steps taken is important, and there's one additional step I haven't mentioned yet:\n\n1. Ensure a Sidekiq workload is listening to the new queues\n1. Change the routing rules\n1. Run the Sidekiq job migration [Rake task](https://docs.gitlab.com/ee/administration/sidekiq/sidekiq_job_migration.html)\n   * Any jobs that are scheduled for the future will be migrated to the new queue for correct execution\n1. Stop listening to queues that are no longer in use\n\nThese steps will ensure a clean migration. If you do not do step 3, then at future times deferred jobs will be picked up out of their holding place in Redis and might be scheduled to a queue that no Sidekiq is listening to anymore. This is exactly the process we took on GitLab.com when migrating our configuration to one queue per shard.\n\n## Simplifying complex Sidekiq configurations\n\nAny complicated Sidekiq configuration can be broken down into a series of these individual migrations, identifying (using metrics) queues or workers that need specialized handling, spinning up a workload to run them, and then sending/routing the jobs to this new workload.\n\nCover image by [Jerry Zhang](https://unsplash.com/@z734923105) on 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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.",[716],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[102,720,721,722],"DevOps platform","tutorial","features",{"featured":24,"template":13,"slug":724},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":726,"config":736},{"title":727,"description":728,"authors":729,"heroImage":731,"date":732,"body":733,"category":9,"tags":734},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[730],"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/)",[721,735,722],"product",{"featured":12,"template":13,"slug":737},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":739,"config":749},{"title":740,"description":741,"authors":742,"heroImage":744,"date":745,"category":9,"tags":746,"body":748},"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.",[743],"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",[255,617,747],"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":750,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":752},[753,767,778,790],{"id":754,"categories":755,"header":757,"text":758,"button":759,"image":764},"ai-modernization",[756],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":760,"config":761},"Get your AI maturity score",{"href":762,"dataGaName":763,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":765},{"src":766},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":768,"categories":769,"header":770,"text":758,"button":771,"image":775},"devops-modernization",[735,563],"Are you just managing tools or shipping innovation?",{"text":772,"config":773},"Get your DevOps maturity score",{"href":774,"dataGaName":763,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":776},{"src":777},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":779,"categories":780,"header":782,"text":758,"button":783,"image":787},"security-modernization",[781],"security","Are you trading speed for security?",{"text":784,"config":785},"Get your security maturity score",{"href":786,"dataGaName":763,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":788},{"src":789},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":791,"paths":792,"header":795,"text":796,"button":797,"image":802},"github-azure-migration",[793,794],"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":798,"config":799},"See how GitLab compares to GitHub",{"href":800,"dataGaName":801,"dataGaLocation":237},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":803},{"src":777},{"header":805,"blurb":806,"button":807,"secondaryButton":812},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":808,"config":809},"Get your free trial",{"href":810,"dataGaName":44,"dataGaLocation":811},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":813},{"href":48,"dataGaName":49,"dataGaLocation":811},1776454420423]