[{"data":1,"prerenderedAt":790},["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":741,"next-steps-en-us":780},{"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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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.",[705],"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",[255,607,709],"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":712,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":714,"config":723},{"title":715,"description":716,"authors":717,"heroImage":718,"date":719,"category":9,"tags":720,"body":722},"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.",[705],"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",[607,255,721],"product","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":724,"featured":24,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":726,"config":739},{"category":9,"tags":727,"body":730,"date":731,"updatedDate":732,"heroImage":733,"authors":734,"title":737,"description":738},[728,729,102],"tutorial","git","\nEnterprise teams are increasingly migrating from Azure DevOps to GitLab to gain strategic advantages and accelerate secure software delivery. \n\n\n- GitLab comes with integrated controls, policies, and [compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that allow organizations to implement software delivery standards at scale. This is especially important for regulated industries.\n\n- [Security testing](https://docs.gitlab.com/user/application_security/) is embedded in the pipeline and results show in the developer workflow, including static application security testing (SAST), source code analysis (SCA), dynamic application security testing (DAST), infrastructure-as-code scanning (IaC), container scanning, and API scanning.\n\n- [AI capabilities](https://about.gitlab.com/gitlab-duo-agent-platform/) across the full software delivery lifecycle include advanced agent orchestration and customizable flows to support how your organizational teams work.\n\n\nGitLab's open-source, open-core approach, flexible deployment options such as single-tenant dedicated and self-managed, and truly unified platform eliminate integration complexity and security gaps. \n\n\nFor teams facing mounting pressure to accelerate delivery while strengthening security posture and maintaining regulatory compliance, GitLab represents not just a migration but a platform evolution.\n\n\nMigrating from Azure DevOps to GitLab can seem like a daunting task, but with the right approach and tools, it can be a smooth and efficient process. This guide will walk you through the steps needed to successfully migrate your projects, repositories, and pipelines from Azure DevOps to GitLab.\n\n\n## Overview\n\nGitLab provides both [Congregate](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/) (maintained by [GitLab Professional Services](https://about.gitlab.com/professional-services/) organization) and [a built-in Git repository import](https://docs.gitlab.com/user/project/import/repo_by_url/) for migrating projects from Azure DevOps (ADO). These options support repository-by-repository or bulk migration and preserve git commit history, branches, and tags. With Congregate and professional services tools, we support additional assets such as wikis, work items, CI/CD variables, container images, packages, pipelines, and more (see this [feature matrix](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/blob/master/customer/ado-migration-features-matrix.md)). Use this guide to plan and execute your migration and complete post-migration follow-up tasks.\n\n\nEnterprises migrating from ADO to GitLab commonly follow a multi-phase approach:\n\n\n- Migrate repositories from ADO to GitLab using Congregate or GitLab's built-in repository migration.\n\n- Migrate pipelines from Azure Pipelines to GitLab CI/CD.\n\n- Migrate remaining assets such as boards, work items, and artifacts to GitLab Issues, Epics, and the Package and Container Registries.\n\n\nHigh-level migration phases:\n\n\n```mermaid\ngraph LR\n    subgraph Prerequisites\n        direction TB\n        A[\"Set up identity provider (IdP) and\u003Cbr/>provision users\"]\n        A --> B[\"Set up runners and\u003Cbr/>third-party integrations\"]\n        B --> I[\"Users enablement and\u003Cbr/>change management\"]\n    end\n    \n    subgraph MigrationPhase[\"Migration phase\"]\n        direction TB\n        C[\"Migrate source code\"]\n        C --> D[\"Preserve contributions and\u003Cbr/> format history\"]\n        D --> E[\"Migrate work items and\u003Cbr/>map to \u003Ca href=\"https://docs.gitlab.com/topics/plan_and_track/\">GitLab Plan \u003Cbr/>and track work\"]\n    end\n    \n    subgraph PostMigration[\"Post-migration steps\"]\n        direction TB\n        F[\"Create or translate \u003Cbr/>ADO pipelines to GitLab CI\"]\n        F --> G[\"Migrate other assets\u003Cbr/>packages and container images\"]\n        G --> H[\"Introduce \u003Ca href=\"https://docs.gitlab.com/user/application_security/secure_your_application/\">security\u003C/a> and\u003Cbr/>SDLC improvements\"]\n    end\n    \n    Prerequisites --> MigrationPhase\n    MigrationPhase --> PostMigration\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style I fill:#FC6D26\n    style C fill:#8C929D\n    style D fill:#8C929D\n    style E fill:#8C929D\n    style F fill:#FFA500\n    style G fill:#FFA500\n    style H fill:#FFA500\n```\n\n\n## Planning your migration\n\n\n**To plan your migration, ask these questions:**\n\n\n- How soon do we need to complete the migration?\n\n- Do we understand what will be migrated?\n\n- Who will run the migration?\n\n- What organizational structure do we want in GitLab?\n\n- Are there any constraints, limitations, or pitfalls that need to be taken into account?\n\n\nDetermine your timeline, as it will largely dictate your migration approach. Identify champions or groups familiar with both ADO and GitLab platforms (such as early adopters) to help drive adoption and provide guidance.\n\n\n**Inventory what you need to migrate:**\n\n\n- The number of repositories, pull requests, and contributors\n\n- The number and complexity of work items and pipelines\n\n- Repository sizes and dependency relationships\n\n- Critical integrations and runner requirements (agent pools with specific capabilities)\n\n\nUse GitLab Professional Services's [Evaluate](https://gitlab.com/gitlab-org/professional-services-automation/tools/utilities/evaluate#beta-azure-devops) tool to produce a complete inventory of your entire Azure DevOps organization, including repositories, PR counts, contributor lists, number of pipelines, work items, CI/CD variables and more. If you're working with the GitLab Professional Services team, share this report with your engagement manager or technical architect to help plan the migration.\n\n\nMigration timing is primarily driven by pull request count, repository size, and amount of contributions (e.g. comments in PR, work items, etc). For example, 1,000 small repositories with few PRs and limited contributors can migrate much faster than a smaller set of repositories containing tens of thousands of PRs and thousands of contributors. Use your inventory data to estimate effort and plan test runs before proceeding with production migrations.\n\n\nCompare inventory against your desired timeline and decide whether to migrate all repositories at once or in batches. If teams cannot migrate simultaneously, batch and stagger migrations to align with team schedules. For example, in Professional Services engagements, we organize migrations into waves of 200-300 projects to manage complexity and respect API rate limits, both in [GitLab](https://docs.gitlab.com/security/rate_limits/) and [ADO](https://learn.microsoft.com/en-us/azure/devops/integrate/concepts/rate-limits?view=azure-devops).\n\n\nGitLab's built-in [repository importer](https://docs.gitlab.com/user/project/import/repo_by_url/) migrates Git repositories (commits, branches, and tags) one-by-one. Congregate is designed to preserve pull requests (known in GitLab as merge requests), comments, and related metadata where possible; the simple built-in repository import focuses only on the Git data (history, branches, and tags).\n\n\n**Items that typically require separate migration or manual recreation:**\n\n\n- Azure Pipelines - create equivalent GitLab CI/CD pipelines (consult with [CI/CD YAML](https://docs.gitlab.com/ci/yaml/) and/or with [CI/CD components](https://docs.gitlab.com/ci/components/)). Alternatively, consider using AI-based pipeline conversion available in Congregate.\n\n- Work items and boards - map to GitLab Issues, Epics, and Issue Boards.\n\n- Artifacts, container images (ACR) - migrate to GitLab Package Registry or Container Registry.\n\n- Service hooks and external integrations - recreate in GitLab.\n\n- [Permissions models](https://docs.gitlab.com/user/permissions/) differ between ADO and GitLab; review and plan permissions mapping rather than assuming exact preservation.\n\n\nReview what each tool (Congregate vs. built-in import) will migrate and choose the one that fits your needs. Make a list of any data or integrations that must be migrated or recreated manually.\n\n\n**Who will run the migration?**\n\n\nMigrations are typically run by a GitLab group owner or instance administrator, or by a designated migrator who has been granted the necessary permissions on the destination group/project. Congregate and the GitLab import APIs require valid authentication tokens for both Azure DevOps and GitLab.\n\n\n- Decide whether a group owner/admin will perform the migrations or whether you will grant a specific team/person delegated access.\n\n- Ensure the migrator has correctly configured personal access tokens (Azure DevOps and GitLab) with the scopes required by your chosen migration tool (for example, api/read_repository scopes and any tool-specific requirements). \n\n- Test tokens and permissions with a small pilot migration.\n\n**Note:** Congregate leverages file-based import functionality for ADO migrations and requires instance administrator permissions to run ([see our documentation](https://docs.gitlab.com/user/project/settings/import_export/#migrate-projects-by-uploading-an-export-file)). If you are migrating to GitLab.com, consider engaging Professional Services. For more information, see the [Professional Services Full Catalog](https://about.gitlab.com/professional-services/catalog/). Non-admin account cannot preserve contribution attribution!\n\n\n**What organizational structure do we want in GitLab?**\n\nWhile it's possible to map ADO structure directly to GitLab structure, it's recommended to rationalize and simplify the structure during migration. Consider how teams will work in GitLab and design the structure to facilitate collaboration and access management. Here is a way to think about mapping ADO structure to GitLab structure:\n\n\n```mermaid\ngraph TD\n    subgraph GitLab\n        direction TB\n        A[\"Top-level Group\"]\n        B[\"Subgroup (optional)\"]\n        C[\"Projects\"]\n        A --> B\n        A --> C\n        B --> C\n    end\n\n    subgraph AzureDevOps[\"Azure DevOps\"]\n        direction TB\n        F[\"Organizations\"]\n        G[\"Projects\"]\n        H[\"Repositories\"]\n        F --> G\n        G --> H\n    end\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style C fill:#FC6D26\n    style F fill:#8C929D\n    style G fill:#8C929D\n    style H fill:#8C929D\n```\n\nRecommended approach:\n\n\n- Map each ADO organization to a GitLab group (or a small set of groups), not to many small groups. Avoid creating a GitLab group for every ADO team project. Use migration as an opportunity to rationalize your GitLab structure.\n\n- Use subgroups and project-level permissions to group related repositories.\n\n- Manage access to sets of projects by using GitLab groups and group membership (groups and subgroups) rather than one group per team project.\n\n- Review GitLab [permissions](https://docs.gitlab.com/ee/user/permissions.html) and consider [SAML Group Links](https://docs.gitlab.com/user/group/saml_sso/group_sync/) to implement an enterprise RBAC model for your GitLab instance (or a GitLab.com namespace).\n\n\n**ADO Boards and work items: State of migration**\n\n\nIt's important to understand how work items migrate from ADO into GitLab Plan (issues, epics, and boards).\n\n\n- ADO Boards and work items map to GitLab Issues, Epics, and Issue Boards. Plan how your workflows and board configurations will translate.\n\n- ADO Epics and Features become GitLab Epics.\n\n- Other work item types (e.g., user stories, tasks, bugs) become project-scoped issues.\n\n- Most standard fields are preserved; selected custom fields can be migrated when supported.\n\n- Parent-child relationships are retained so Epics reference all related issues.\n\n- Links to pull requests are converted to merge request links to maintain development traceability.\n\n\nExample: Migration of an individual work item to a GitLab Issue, including field accuracy and relationships:\n\n\n![Example: Migration of an individual work item to a GitLab Issue](https://res.cloudinary.com/about-gitlab-com/image/upload/v1764769188/ztesjnxxfbwmfmtckyga.png)\n\n\nBatching guidance:\n\n\n- If you need to run migrations in batches, use your new group/subgroup structure to define batches (for example, by ADO organization or by product area).\n\n- Use inventory reports to drive batch selection and test each batch with a pilot migration before scaling.\n\n\n**Pipelines migration**\n\n\nCongregate [recently introduced](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/merge_requests/1298) AI-powered conversion for multi-stage YAML pipelines from Azure DevOps to GitLab CI/CD. This automated conversion works best for simple, single-file pipelines and is designed to provide a working starting point rather than a production-ready `.gitlab-ci.yml` file. The tool generates a functionally equivalent GitLab pipeline that you can then refine and optimize for your specific needs.\n\n\n- Converts Azure Pipelines YAML to `.gitlab-ci.yml` format automatically.\n\n- Best suited for straightforward, single-file pipeline configurations.\n\n- Provides a boilerplate to accelerate migration, not a final production artifact.\n\n- Requires review and adjustment for complex scenarios, custom tasks, or enterprise requirements.\n\n- Does not support Azure DevOps classic release pipelines — [convert these to multi-stage YAML](https://learn.microsoft.com/en-us/azure/devops/pipelines/release/from-classic-pipelines?view=azure-devops) first.\n\n\nRepository owners should review the [GitLab CI/CD documentation](https://docs.gitlab.com/ci/) to further optimize and enhance their pipelines after the initial conversion.\n\n\nExample of converted pipelines:\n\n\n```yml \n\n# azure-pipelines.yml\n\ntrigger:\n  - main\n\nvariables:\n  imageName: myapp\n\nstages:\n  - stage: Build\n    jobs:\n      - job: Build\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Build Docker image\n            inputs:\n              command: build\n              repository: $(imageName)\n              Dockerfile: '**/Dockerfile'\n              tags: |\n                $(Build.BuildId)\n\n  - stage: Test\n    jobs:\n      - job: Test\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          # Example: run tests inside the container\n          - script: |\n              docker run --rm $(imageName):$(Build.BuildId) npm test\n            displayName: Run tests\n\n  - stage: Push\n    jobs:\n      - job: Push\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Login to ACR\n            inputs:\n              command: login\n              containerRegistry: '\u003Cyour-acr-service-connection>'\n\n          - task: Docker@2\n            displayName: Push image to ACR\n            inputs:\n              command: push\n              repository: $(imageName)\n              tags: |\n                $(Build.BuildId)\n\n```\n\n```yaml\n\n# .gitlab-ci.yml\n\nvariables:\n  imageName: myapp\n\nstages:\n  - build\n  - test\n  - push\n\nbuild:\n  stage: build\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker build -t $imageName:$CI_PIPELINE_ID -f $(find . -name Dockerfile) .\n  only:\n    - main\n\ntest:\n  stage: test\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker run --rm $imageName:$CI_PIPELINE_ID npm test\n  only:\n    - main\n\npush:\n  stage: push\n  image: docker:latest\n  services:\n    - docker:dind\n  before_script:\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n  script:\n    - docker tag $imageName:$CI_PIPELINE_ID $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n    - docker push $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n  only:\n    - main\n\n```\n\n**Final checklist:**\n\n\n- Decide timeline and batch strategy.\n\n- Produce a full inventory of repositories, PRs, and contributors.\n\n- Choose Congregate or the built-in import based on scope (PRs and metadata vs. Git data only).\n\n- Decide who will run migrations and ensure tokens/permissions are configured.\n\n- Identify assets that must be migrated separately (pipelines, work items, artifacts, and hooks) and plan those efforts.\n\n- Run pilot migrations, validate results, then scale according to your plan.\n\n\n## Running your migrations\n\n\nAfter planning, execute migrations in stages, starting with trial runs. Trial migrations help surface org-specific issues early and let you measure duration, validate outcomes, and fine-tune your approach before production.\n\n\nWhat trial migrations validate:\n\n\n- Whether a given repository and related assets migrate successfully (history, branches, tags; plus MRs/comments if using Congregate)\n\n- Whether the destination is usable immediately (permissions, runners, CI/CD variables, integrations)\n\n- How long each batch takes, to set schedules and stakeholder expectations\n\n\nDowntime guidance:\n\n\n- GitLab's built-in Git import and Congregate do not inherently require downtime.\n\n- For production waves, freeze changes in ADO (branch protections or read-only) to avoid missed commits, PR updates, or work items created mid-migration.\n\n- Trial runs do not require freezes and can be run anytime.\n\n\nBatching guidance:\n\n\n- Run trial batches back-to-back to shorten elapsed time; let teams validate results asynchronously.\n\n- Use your planned group/subgroup structure to define batches and respect API rate limits.\n\n\nRecommended steps:\n\n\n1. Create a test destination in GitLab for trials:\n\n\n  - GitLab.com: create a dedicated group/namespace (for example, my-org-sandbox)\n\n  - Self-managed: create a top-level group or a separate test instance if needed\n\n\n2. Prepare authentication:\n\n\n  - Azure DevOps PAT with required scopes.\n\n  - GitLab Personal Access Token with api and read_repository (plus admin access for file-based imports used by Congregate).\n\n\n3. Run trial migrations:\n\n\n  - Repos only: use GitLab's built-in import (Repo by URL)\n\n  - Repos + PRs/MRs and additional assets: use Congregate\n\n\n4. Post-trial follow-up:\n\n\n  - Verify repo history, branches, tags; merge requests (if migrated), issues/epics (if migrated), labels, and relationships.\n\n  - Check permissions/roles, protected branches, required approvals, runners/tags, variables/secrets, integrations/webhooks.\n\n  - Validate pipelines (`.gitlab-ci.yml`) or converted pipelines where applicable.\n\n\n5. Ask users to validate functionality and data fidelity.\n\n6. Resolve issues uncovered during trials and update your runbooks.\n\n7. Network and security:\n\n\n  - If your destination uses IP allow lists, add the IPs of your migration host and any required runners/integrations so imports can succeed.\n\n\n8. Run production migrations in waves:\n\n\n  - Enforce change freezes in ADO during each wave.\n\n  - Monitor progress and logs; retry or adjust batch sizes if you hit rate limits.\n\n\n9. Optional: remove the sandbox group or archive it after you finish.\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/ibIXGfrVbi4?si=ZxOVnXjCF-h4Ne0N\" frameborder=\"0\" allowfullscreen=\"true\">\u003C/iframe>\n\u003C/figure>\n\n\n## Terminology reference for GitLab and Azure DevOps\n\n| GitLab                                                           | Azure DevOps                                 | Similarities & Key Differences                                                                                                                                          |\n| ---------------------------------------------------------------- | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Group                                                            | Organization                                 | Top-level namespace, membership, policies. ADO org contains Projects; GitLab Group contains Subgroups and Projects.                                                   |\n| Group or Subgroup                                                | Project                                      | Logical container, permissions boundary. ADO Project holds many repos; GitLab Groups/Subgroups organize many Projects.                                                |\n| Project (includes a Git repo)                                    | Repository (inside a Project)                | Git history, branches, tags. In GitLab, a \"Project\" is the repo plus issues, CI/CD, wiki, etc. One repo per Project.                                                  |\n| Merge Request (MR)                                               | Pull Request (PR)                            | Code review, discussions, approvals. MR rules include approvals, required pipelines, code owners.                                                                     |\n| Protected Branches, MR Approval Rules, Status Checks             | Branch Policies                              | Enforce reviews and checks. GitLab combines protections + approval rules + required status checks.                                                                    |\n| GitLab CI/CD                                                     | Azure Pipelines                              | YAML pipelines, stages/jobs, logs. ADO also has classic UI pipelines; GitLab centers on .gitlab-ci.yml.                                                               |\n| .gitlab-ci.yml                                                   | azure-pipelines.yml                          | Defines stages/jobs/triggers. Syntax/features differ; map jobs, variables, artifacts, and triggers.                                                                   |\n| Runners (shared/specific)                                        | Agents / Agent Pools                         | Execute jobs on machines/containers. Target via demands (ADO) vs tags (GitLab). Registration/scoping differs.                                                         |\n| CI/CD Variables (project/group/instance), Protected/Masked       | Pipeline Variables, Variable Groups, Library | Pass config/secrets to jobs. GitLab supports group inheritance and masking/protection flags.                                                                          |\n| Integrations, CI/CD Variables, Deploy Keys                       | Service Connections                          | External auth to services/clouds. Map to integrations or variables; cloud-specific helpers available.                                                                 |\n| Environments & Deployments (protected envs)                      | Environments (with approvals)                | Track deploy targets/history. Approvals via protected envs and manual jobs in GitLab.                                                                                 |\n| Releases (tag + notes)                                           | Releases (classic or pipelines)              | Versioned notes/artifacts. GitLab Release ties to tags; deployments tracked separately.                                                                               |\n| Job Artifacts                                                    | Pipeline Artifacts                           | Persist job outputs. Retention/expiry configured per job or project.                                                                                                  |\n| Package Registry (NuGet/npm/Maven/PyPI/Composer, etc.)           | Azure Artifacts (NuGet/npm/Maven, etc.)      | Package hosting. Auth/namespace differ; migrate per package type.                                                                                                     |\n| GitLab Container Registry                                        | Azure Container Registry (ACR) or others     | OCI images. GitLab provides per-project/group registries.                                                                                                             |\n| Issue Boards                                                     | Boards                                       | Visualize work by columns. GitLab boards are label-driven; multiple boards per project/group.                                                                         |\n| Issues (types/labels), Epics                                     | Work Items (User Story/Bug/Task)             | Track units of work. Map ADO types/fields to labels/custom fields; epics at group level.                                                                              |\n| Epics, Parent/Child Issues                                       | Epics/Features                               | Hierarchy of work. Schema differs; use epics + issue relationships.                                                                                                   |\n| Milestones and Iterations                                        | Iteration Paths                              | Time-boxing. GitLab Iterations (group feature) or Milestones per project/group.                                                                                       |\n| Labels (scoped labels)                                           | Area Paths                                   | Categorization/ownership. Replace hierarchical areas with scoped labels.                                                                                              |\n| Project/Group Wiki                                               | Project Wiki                                 | Markdown wiki. Backed by repos in both; layout/auth differ slightly.                                                                                                  |\n| Test reports via CI, Requirements/Test Management, integrations  | Test Plans/Cases/Runs                        | QA evidence/traceability. No 1:1 with ADO Test Plans; often use CI reports + issues/requirements.                                                                     |\n| Roles (Owner/Maintainer/Developer/Reporter/Guest) + custom roles | Access levels + granular permissions         | Control read/write/admin. Models differ; leverage group inheritance and protected resources.                                                                          |\n| Webhooks                                                         | Service Hooks                                | Event-driven integrations. Event names/payloads differ; reconfigure endpoints.                                                                                        |\n| Advanced Search                                                  | Code Search                                  | Full-text repo search. Self-managed GitLab may need Elasticsearch/OpenSearch for advanced features.                                                                   |\n","2025-12-03","2026-01-16","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749658924/Blog/Hero%20Images/securitylifecycle-light.png",[735,736],"Evgeny Rudinsky","Michael Leopard","Guide: Migrate from Azure DevOps to GitLab","Learn how to carry out the full migration from Azure DevOps to GitLab using GitLab Professional Services migration tools — from planning and execution to post-migration follow-up tasks.",{"featured":24,"template":13,"slug":740},"migration-from-azure-devops-to-gitlab",{"promotions":742},[743,757,768],{"id":744,"categories":745,"header":747,"text":748,"button":749,"image":754},"ai-modernization",[746],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":750,"config":751},"Get your AI maturity score",{"href":752,"dataGaName":753,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":755},{"src":756},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":758,"categories":759,"header":760,"text":748,"button":761,"image":765},"devops-modernization",[721,553],"Are you just managing tools or shipping innovation?",{"text":762,"config":763},"Get your DevOps maturity score",{"href":764,"dataGaName":753,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":769,"categories":770,"header":772,"text":748,"button":773,"image":777},"security-modernization",[771],"security","Are you trading speed for security?",{"text":774,"config":775},"Get your security maturity score",{"href":776,"dataGaName":753,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":781,"blurb":782,"button":783,"secondaryButton":788},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":784,"config":785},"Get your free trial",{"href":786,"dataGaName":44,"dataGaLocation":787},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":489,"config":789},{"href":48,"dataGaName":49,"dataGaLocation":787},1772652069258]