[{"data":1,"prerenderedAt":794},["ShallowReactive",2],{"/en-us/blog/puma-nakayoshi-fork-and-compaction":3,"navigation-en-us":38,"banner-en-us":438,"footer-en-us":448,"blog-post-authors-en-us-Matthias Käppler":690,"blog-related-posts-en-us-puma-nakayoshi-fork-and-compaction":705,"assessment-promotions-en-us":745,"next-steps-en-us":784},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":34,"tagSlugs":35,"__hash__":37},"blogPosts/en-us/blog/puma-nakayoshi-fork-and-compaction.yml","Puma Nakayoshi Fork And Compaction",[7],"matthias-kppler",null,"engineering",{"slug":11,"featured":12,"template":13},"puma-nakayoshi-fork-and-compaction",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Ruby 2.7: Understand and debug problems with heap compaction","An overview of Ruby 2.7 heap compaction and the risks it adds to production Rails applications.",[18],"Matthias Käppler","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749669673/Blog/Hero%20Images/engineering.png","2021-04-28","The GitLab Rails application runs on [Puma](https://puma.io/), a multi-threaded Rack application server written in the new Ruby.\nWe recently updated Puma to major version 5, which introduced [a number of important\nchanges](https://github.com/puma/puma/blob/master/History.md#500--2020-09-17),\nincluding support for _compaction_, a technique to reduce memory fragmentation in the\nRuby heap.\n\nIn this post we will describe what Puma's \"nakayoshi fork\" does, what compaction is,\nand some of the challenges we faced when first deploying it.\n\n## Nakayoshi: A friendlier `fork`\n\nPuma 5 added a new configuration switch: `nakayoshi_fork`. This switch affects Puma's behavior when\nforking new workers from the primary process. It is largely based on a [Ruby gem of the same name](https://github.com/ko1/nakayoshi_fork)\nbut adds new functionality. More specifically, enabling `nakayoshi_fork` in Puma will result in two additional\nsteps prior to forking into new workers:\n\n1. **Tenuring objects.** By running several minor garbage collection cycles ahead of a `fork`, Ruby can promote survivors\n   from the young to the old generation (referred to as \"tenuring\"). These objects are often classes, modules, or long-lived\n   constants that are unlikely to change.\n   This process makes forking copy-on-write friendly because tagging an object as \"old\" implies a write\n   to the underlying heap page. Doing this prior to forking means the OS won't have\n   to copy this page from the parent to the worker process later. We won't be discussing copy-on-write in detail but\n   [this blog post offers a good introduction to the topic and how it relates to Ruby and pre-fork servers](https://brandur.org/ruby-memory).\n\n1. **Heap compaction.** Ruby 2.7 added a new method `GC.compact`, which\n   will reorganize the Ruby heap to pack objects closer together when invoked. `GC.compact` reduces Ruby heap fragmentation and\n   potentially frees up Ruby heap pages so that the physical memory consumed can be reclaimed by the OS.\n   This step only happens when `GC.compact` is available in the version of Ruby that is in use (for MRI, 2.7 or newer).\n\nIn the remainder of this post, we will look at:\n\n* How `GC.compact` works and its potential benefits.\n* Why using C-extensions can be problematic when using compaction.\n* How we resolved a production incident that crashed GitLab.\n* What to look out for before enabling compaction in your app, via `nakayoshi_fork` or otherwise.\n\n## How compacting garbage collection works\n\nThe primary goal of a compacting garbage collector (GC) is to use allocated memory more\neffectively, which increases the likelihood of the application using less memory over time.\nCompaction is especially important when processes can share memory, as is the case with Ruby pre-fork\nservers such as Puma or Unicorn. But how does Ruby accomplish this?\n\nRuby manages its own object heap by allocating chunks of memory from the operating system called pages\n(a confusing term since Ruby heap pages are distinct from the smaller memory pages managed by the OS itself).\nWhen an application asks to create a new object, Ruby will try to find a free object slot in one of these\npages and fill it. As objects are allocated and deallocated over the lifetime of the application,\nthis can lead to fragmentation, with pages being neither entirely full nor entirely empty. This is the\nprimary cause for Ruby's infamous runaway memory problem: Since the available space isn't optimally used,\npages will rarely be entirely empty and become \"tomb pages\" which means it is necessary for the pages to be empty for them to be deallocated.\n\nRuby 2.7 added a new method, `GC.compact`, which aims to address this problem by walking the entire\nRuby heap space and moving objects around to obtain tightly packed pages. This process will ideally make\nsome pages unused, and unused memory can be reclaimed by the OS. [Watch this video from RubyConf 2019](https://www.youtube.com/watch?v=H8iWLoarTZc) where Aaron Patterson, the author of this feature, gave a good introduction to compacting GC.\n\nCompaction is a fairly expensive task since Ruby needs to stop-the-world for a complete heap reorganization so\nits best to perform this task before forking a new worker process, which is why Puma 5 included this step when performing `nakayoshi_fork`. Moreover, running compaction before forking\ninto worker processes increases the chance of workers being able to share memory.\n\nWe were eager to enable this feature on GitLab to see if it would reduce memory consumption, but things didn't entirely go as planned.\n\n## Inside the incident\n\nAfter extensive testing via our automated performance test suite and in preproduction\nenvironments, we felt ready to explore compaction on production nodes. We kept a\n[detailed, public record of what happened\nduring this production incident](https://gitlab.com/gitlab-com/gl-infra/production/-/issues/3370), but the key details are summarized below:\n\n* The deployment passed the canary stage, meaning workers who had their heaps compacted were serving traffic\n  successfully at this point.\n* Sometime during the full fleet rollout, problems emerged: Error rates started spiking but not\n  across the entire fleet. This phenomenon is odd because errors tend to spread across all workers due to load balancing.\n* The error messages surfacing in Sentry were mysterious at best:\n  `ActionView::Template::Error\nuninitialized constant #\u003CClass:#GrapePathHelpers::DecoratedRoute:0x00007f95f10ea5b8>::UNDERSCORE`. Remember this error message for later.\n* We discovered the affected workers were segfaulting in [`hamlit`](https://github.com/k0kubun/hamlit),\n  a high-performance HAML compiler. Hamlit uses a C-extension to achieve better performance. The segfaulting and the fact\n  that we were rolling out an optimization that touches GC-internal structures was a tell-tale sign that\n  compaction was likely to be the cause.\n* We rolled back the change to quickly recover from the outage.\n\n## How we diagnosed the problem\n\nWe were disappointed by this setback and wanted to understand why the outage occurred. Fortunately,\nRuby provides detailed stack traces when crashing in C-extensions. The most effective way\nto quickly analyze these is to look for transitions where a C-extension calls into the Ruby VM\nor vice versa. These lines therefore caught our attention:\n\n```shell\n...\n/opt/gitlab/embedded/lib/libruby.so.2.7(sigsegv+0x52) [0x7f9601adb932] signal.c:946\n/lib/x86_64-linux-gnu/libc.so.6(0x7f960154c4c0) [0x7f960154c4c0]\n/opt/gitlab/embedded/lib/libruby.so.2.7(rb_id_table_lookup+0x1) [0x7f9601b15e11] id_table.c:227\n/opt/gitlab/embedded/lib/libruby.so.2.7(rb_const_lookup+0x1e) [0x7f9601b4861e] variable.c:3357\n/opt/gitlab/embedded/lib/libruby.so.2.7(rb_const_get+0x39) [0x7f9601b4a049] variable.c:2339\n# ^--- Ruby VM functions\n/opt/gitlab/embedded/lib/ruby/gems/2.7.0/gems/hamlit-2.11.0/lib/hamlit/hamlit.so(str_underscore+0x16) [0x7f95ee3518f8] hamlit.c:17\n/opt/gitlab/embedded/lib/ruby/gems/2.7.0/gems/hamlit-2.11.0/lib/hamlit/hamlit.so(rb_hamlit_build_id) hamlit.c:100\n# ^-- hamlit C-extension\n...\n```\n\nThe topmost stack frame reveals the preceeding calls led to a segmentation fault (`SIGSEGV`).\nWe highlighted the lines where Hamlit calls back into Ruby: In a function called `str_underscore` which\nwas called by `rb_hamlit_build_id`. The `rb_*` prefix tells us that this is a C-function we can call from Ruby,\nand indeed it is used by [`Hamlit::AttributeBuilder`](https://github.com/k0kubun/hamlit/blob/master/lib/hamlit/attribute_builder.rb) to construct DOM `id`s.\n\nBut we still don't know why it is crashing. Next, we need to inspect what happens in `str_underscore`.\nWe can see that this function performs a constant lookup on `mAttributeBuilder` – searching\nfor a constant called `UNDERSCORE`. When following the breadcrumbs it turns out to simply be the string `\"_\"`.\nIt is this lookup that failed.\n\nWait -- `UNDERSCORE`? That sounds familiar. Recall the top-level error messages:\n\n```text\nActionView::Template::Error\nuninitialized constant #\u003CClass:#GrapePathHelpers::DecoratedRoute:0x00007f95f10ea5b8>::UNDERSCORE\n```\n\nBut `GrapePathHelpers` is clearly not a Hamlit class. Hamlit is trying to look up its own `UNDERSCORE`\nconstant on a class in the [`grape`](https://github.com/ruby-grape/grape) gem, an entirely different library\nthat is not involved in HTML rendering at all and there is no such constant defined on Grape's\n`DecoratedRoute` class either.\n\nNow the penny dropped – remember how compaction moves around objects in Ruby's heap space? Classes in\nRuby are objects too, so `GC.compact` must have moved a Grape class into an object slot that was previously\noccupied by a Hamlit class object, but Hamlit's C-extension never saw it coming!\n\n## How we solved the problem\n\nTo be clear, what happened above should _not_ happen with a well-behaved C-extension. Compaction\nwas developed carefully with support for C-extensions that predate Ruby 2.7, so all\nexisting Ruby gems would continue to operate normally.\n\nSo what went wrong? When a C-extension allocates Ruby objects, it must _mark_ them for as long as\nthey are alive. A marked object will not be garbage collected and because the Ruby GC cannot reason about objects\noutside of its own purview (i.e., objects created from Ruby code), it needs to rely on C-extensions\nto correctly mark and unmark objects themselves.\n\nNow comes the twist: Marked objects can be moved during compaction and existing C-extensions\ncan't cope with an object they hold pointers to suddenly move into a different slot.\nTherefore, Ruby 2.7 does something clever: It \"pins\" objects allocated with the mark function that existed prior\nto Ruby 2.7, meaning the pinned objects are not allowed to move during compaction. For new code, it introduces\na special mark-but-don't-pin function that will also allow an object to move, giving gem authors the\nopportunity to make their libraries compaction-aware.\n\nHamlit does not implement compaction support, so this could only mean one thing:\nHamlit wasn't even properly marking those objects, otherwise Ruby 2.7\nwould have automatically pinned them so they wouldn't move during compaction.\nAfter [discussing an attempted fix we submitted](https://github.com/k0kubun/hamlit/pull/171) but without\na reliable way to reproduce the issue for everyone, the Hamlit author decided to sidestep the\nproblem by [resolving those constants statically instead](https://github.com/k0kubun/hamlit/pull/172)\nand marking each via `rb_gc_register_mark_object`.\nThis change landed in [Hamlit 2.14.2](https://github.com/k0kubun/hamlit/blob/master/CHANGELOG.md#2142---2021-01-21)\nwhich we confirmed resolves the issue.\n\n## The next steps\n\nIt is exciting to see that the Ruby community is making progress on making Ruby a more memory-efficient\nlanguage but we learned that we need to step carefully when introducing such wide-reaching changes to a large\napplication like GitLab. It is difficult to investigate and fix problems that crash the Ruby VM, which is more likely for\nany library that uses C-extensions.\n\nTwo particular action items we took away from this were:\n\n1. **More reliable detection of compaction-related issues in CI.** We're not going to sugar-coat this:\n   We detected the problem late. Our comprehensive test suite was passing, our QA and performance tests\n   on staging environments passed, and the problem didn't even show up in canary deployments. Ideally, we\n   would have caught this issue with our automated test suite. One way to test whether compaction causes problems\n   is by using `GC.verify_compaction_references` – this is a rather crude tool because it requires\n   keeping two copies of the Ruby heap, which can be prohibitively expensive in terms of memory use. We\n   have therefore not yet decided how to approach this.\n1. **Improve our ability to roll out system configuration gradually.** Puma is part of our core infrastructure,\n   since it sits in the path of every web request, which makes it especially risky to experiment with Puma\n   configuration. GitLab already supports [feature flags](https://docs.gitlab.com/ee/development/feature_flags/index.html)\n   to allow developers to roll out product changes gradually, but it presents us with a catch-22 when\n   making changes at the infrastructure level, because to query the state of a feature flag, the infrastructure\n   needs to already be up and running. It would be ideal to have a similar mechanism for system configuration, [which we are currently exploring](https://gitlab.com/gitlab-com/gl-infra/scalability/-/issues/154).\n\nWhile performance is a major focus for us at the moment it must not compromise availability.\nWe will continue to monitor developments in the Ruby community around compaction support, but decided to\nnot use it in production at this point in time since the gains don't appear to outweigh the risks.\n",[23,24,25],"production","performance","inside 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statement",{"items":680},[681,684,687],{"text":682,"config":683},"Terms",{"href":508,"dataGaName":509,"dataGaLocation":456},{"text":685,"config":686},"Cookies",{"dataGaName":518,"dataGaLocation":456,"id":519,"isOneTrustButton":28},{"text":688,"config":689},"Privacy",{"href":513,"dataGaName":514,"dataGaLocation":456},[691],{"id":692,"title":693,"body":8,"config":694,"content":696,"description":8,"extension":26,"meta":700,"navigation":28,"path":701,"seo":702,"stem":703,"__hash__":704},"blogAuthors/en-us/blog/authors/matthias-kppler.yml","Matthias Kppler",{"template":695},"BlogAuthor",{"name":18,"config":697},{"headshot":698,"ctfId":699},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749670351/Blog/Author%20Headshots/mkaeppler-headshot.jpg","mkaeppler",{},"/en-us/blog/authors/matthias-kppler",{},"en-us/blog/authors/matthias-kppler","ifYoCrN_DXBz-4NxWtEPrkhCLzL27iwXIaXEy_ogYFc",[706,721,734],{"content":707,"config":719},{"title":708,"description":709,"authors":710,"heroImage":712,"date":713,"body":714,"category":9,"tags":715},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[711],"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/)",[716,717,718],"tutorial","product","features",{"featured":12,"template":13,"slug":720},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":722,"config":732},{"title":723,"description":724,"authors":725,"heroImage":727,"date":728,"category":9,"tags":729,"body":731},"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.",[726],"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",[260,612,730],"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":733,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":735,"config":743},{"title":736,"description":737,"authors":738,"heroImage":739,"date":740,"category":9,"tags":741,"body":742},"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.",[726],"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",[612,260,717],"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":744,"featured":28,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":746},[747,761,772],{"id":748,"categories":749,"header":751,"text":752,"button":753,"image":758},"ai-modernization",[750],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":754,"config":755},"Get your AI maturity score",{"href":756,"dataGaName":757,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":759},{"src":760},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":762,"categories":763,"header":764,"text":752,"button":765,"image":769},"devops-modernization",[717,558],"Are you just managing tools or shipping innovation?",{"text":766,"config":767},"Get your DevOps maturity score",{"href":768,"dataGaName":757,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":773,"categories":774,"header":776,"text":752,"button":777,"image":781},"security-modernization",[775],"security","Are you trading speed for security?",{"text":778,"config":779},"Get your security maturity score",{"href":780,"dataGaName":757,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":785,"blurb":786,"button":787,"secondaryButton":792},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":788,"config":789},"Get your free trial",{"href":790,"dataGaName":49,"dataGaLocation":791},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":494,"config":793},{"href":53,"dataGaName":54,"dataGaLocation":791},1773350845090]