[{"data":1,"prerenderedAt":788},["ShallowReactive",2],{"/en-us/blog/how-to-configure-sidekiq-for-gitlab-at-scale":3,"navigation-en-us":33,"banner-en-us":433,"footer-en-us":443,"blog-post-authors-en-us-Craig Miskell":685,"blog-related-posts-en-us-how-to-configure-sidekiq-for-gitlab-at-scale":699,"assessment-promotions-en-us":739,"next-steps-en-us":778},{"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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statement",{"items":675},[676,679,682],{"text":677,"config":678},"Terms",{"href":503,"dataGaName":504,"dataGaLocation":451},{"text":680,"config":681},"Cookies",{"dataGaName":513,"dataGaLocation":451,"id":514,"isOneTrustButton":24},{"text":683,"config":684},"Privacy",{"href":508,"dataGaName":509,"dataGaLocation":451},[686],{"id":687,"title":18,"body":8,"config":688,"content":690,"description":8,"extension":22,"meta":694,"navigation":24,"path":695,"seo":696,"stem":697,"__hash__":698},"blogAuthors/en-us/blog/authors/craig-miskell.yml",{"template":689},"BlogAuthor",{"name":18,"config":691},{"headshot":692,"ctfId":693},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667372/Blog/Author%20Headshots/cmiskell-headshot.jpg","cmiskell",{},"/en-us/blog/authors/craig-miskell",{},"en-us/blog/authors/craig-miskell","VPOHmA7ISNMfEE3Y0SkxSJD1DRgIqQVOLz9bf8UCnh8",[700,715,728],{"content":701,"config":713},{"title":702,"description":703,"authors":704,"heroImage":706,"date":707,"body":708,"category":9,"tags":709},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[705],"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 difficulat 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/)",[710,711,712],"tutorial","product","features",{"featured":12,"template":13,"slug":714},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":716,"config":726},{"title":717,"description":718,"authors":719,"heroImage":721,"date":722,"category":9,"tags":723,"body":725},"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.",[720],"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",[255,607,724],"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/) 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":727,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":729,"config":737},{"title":730,"description":731,"authors":732,"heroImage":733,"date":734,"category":9,"tags":735,"body":736},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[720],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[607,255,711],"Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1.  **Deliver a modern, integrated DevSecOps curriculum.**\n\n2.  **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3.  **Empower students to make AI-assisted open source contributions.**\n\n4.  **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":738,"featured":24,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":740},[741,755,766],{"id":742,"categories":743,"header":745,"text":746,"button":747,"image":752},"ai-modernization",[744],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":748,"config":749},"Get your AI maturity score",{"href":750,"dataGaName":751,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":753},{"src":754},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":756,"categories":757,"header":758,"text":746,"button":759,"image":763},"devops-modernization",[711,553],"Are you just managing tools or shipping innovation?",{"text":760,"config":761},"Get your DevOps maturity score",{"href":762,"dataGaName":751,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":764},{"src":765},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":767,"categories":768,"header":770,"text":746,"button":771,"image":775},"security-modernization",[769],"security","Are you trading speed for security?",{"text":772,"config":773},"Get your security maturity score",{"href":774,"dataGaName":751,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":776},{"src":777},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":779,"blurb":780,"button":781,"secondaryButton":786},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":782,"config":783},"Get your free trial",{"href":784,"dataGaName":44,"dataGaLocation":785},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":489,"config":787},{"href":48,"dataGaName":49,"dataGaLocation":785},1773350820799]