[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"/en-us/blog/making-gitlab-faster":3,"navigation-en-us":33,"banner-en-us":433,"footer-en-us":443,"blog-post-authors-en-us-Yorick Peterse":685,"blog-related-posts-en-us-making-gitlab-faster":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/making-gitlab-faster.yml","Making Gitlab Faster",[7],"yorick-peterse",null,"engineering",{"slug":11,"featured":12,"template":13},"making-gitlab-faster",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9},"Making GitLab Faster","In GitLab 8.5 we shipped numerous performance improvements. In this article we'll take a look at some of these changes and the process involved in finding and resolving these issues.",[18],"Yorick Peterse","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663397/Blog/Hero%20Images/logoforblogpost.jpg","2016-02-25","\nIn GitLab 8.5 we shipped numerous performance improvements. In this article\nwe'll take a look at some of these changes and the process involved in finding\nand resolving these issues. In particular we'll look at the following merge\nrequests:\n\n* [Optimize fetching issues closed by a merge request][mr2625]\n* [Improve performance of retrieving last update times for events][mr2613]\n* [Only set autocrlf when creating/updating files][mr2859]\n\n\u003C!--more-->\n\n## Performance Monitoring & Tooling\n\nWithout a proper production performance monitoring system and a good set of\ntools it's nearly impossible to find and resolve performance problems. GitLab\ncomes with two systems to make it possible to measure application performance:\n\n* [GitLab Performance Monitoring][monitoring]: a monitoring system using\n  [InfluxDB][influxdb] to track application performance of production\n  environments (though you can also use it during development). Data is then\n  visualized using [Grafana][grafana], though users can use any software capable\n  of extracting data from InfluxDB.\n* Sherlock: a development only monitoring system. Due to the overhead of\n  Sherlock it's not suitable for production environments. For example, Sherlock\n  uses [rblineprof][rblineprof] to track execution timings on a per line basis\n  but this adds quite a bit of overhead.\n\nAnother very useful library is [benchmark-ips][benchmark-ips]. This library can\nbe used to measure the performance of snippets of code while taking care of\nwarming up any caches, Just In Time compilers, etc. For more information see the\n[benchmark-ips README][benchmark-ips-readme].\n\n### Limitations of Benchmarks\n\nWhile we're on the topic of benchmarks it's worth mentioning that benchmarks are\nonly really useful to see the impact of a certain change. For example, if\nbenchmark X can run Y iterations in a certain time period this gives you no\ninsight in how this will perform in a production environment; all it indicates\nis that it can run a certain number of iterations. However, when a certain\nchange results in the benchmark now completing twice as fast things start\ngetting interesting. While we still don't really know how the change will affect\nour production environment we at least know that in the most ideal case\nperformance will be twice as fast.\n\nIn short, just benchmarks aren't enough; you always have to measure (and _keep_\nmeasuring) the performance of code in a production environment. This may seem\nlike common knowledge but a few too many projects out there make bold claims\nabout their performance based solely on a set of benchmarks.\n\nWith that out of the way, let's get started.\n\n## Optimize fetching issues closed by a merge request\n\nCommit messages can be used to automatically close issues by adding the text\n\"Fixes #X\" or \"Closes #X\" to a commit message (where X refers to an issue ID).\nIn turn each merge request shows the list of issues that will be closed whenever\nthe merge request is merged. The description of a merge request can also include\ninclude text such as \"Fixes #X\" to close issues. In other words, the list of\nissues to close is a set composed out of the issues to close as extracted from\nthe commit messages and the issues to close as extracted from the merge\nrequest's description.\n\nWhich brings us to the method `MergeRequest#closes_issues`. This method is used\nto return the list of issues to close (as an Array of `Issue` instances). If we\nlook at the performance of this method over time we see the following:\n\n![MergeRequest#closes_issues Timings][mr2625-timings]\n\nThe small gap at the start of the graph is due to monitoring data only being\nretained for 30 days.\n\nTo summarize the timings:\n\n* A mean of around 500 milliseconds\n* A 95th percentile between 1 and 1.5 seconds\n* A 99th percentile between 1.5 and 2 seconds\n\n2 seconds (in the worst case) to retrieve a list of issues to close is not\nacceptable so it was clear there was some work to be done.\n\nPrior to 8.5 this method was implemented as the following:\n\n    def closes_issues(current_user = self.author)\n       if target_branch == project.default_branch\n         issues = commits.flat_map { |c| c.closes_issues(current_user) }\n         issues.push(*Gitlab::ClosingIssueExtractor.new(project, current_user).\n                    closed_by_message(description))\n         issues.uniq(&:id)\n       else\n         []\n       end\n    end\n\nWhen the target branch of a merge request equals the project's default branch\nthis method takes the following steps:\n\n1. For every commit in the merge request, grab the issues that should be closed\n   when the merge request is merged.\n2. Append the list of issues to close based on the merge request's description\n   to the list of issues created in step 1.\n3. Remove any duplicate issues (based on the issue IDs) from the resulting list.\n\nWhat stood out here is the following line:\n\n    issues = commits.flat_map { |c| c.closes_issues(current_user) }\n\nFor every commit the method `Commit#closes_issues` would be called, which in\nturn was implemented as the following:\n\n    def closes_issues(current_user = self.committer)\n      Gitlab::ClosingIssueExtractor.new(project, current_user).closed_by_message(safe_message)\n    end\n\nFurther digging revealed that `Gitlab::ClosingIssueExtractor#closed_by_message`\nwould perform two steps:\n\n1. Extract the referenced issue IDs from a String\n2. Run a database query to return a list of corresponding `Issue` objects\n\nNote that the above steps would be performed for _every_ commit in a merge\nrequest, regardless of whether a commit would actually reference an issue or\nnot. As such the more commits a merge request would contain the slower things\nwould get.\n\nIf we look at how `Gitlab::ClosingIssueExtractor#closed_by_message` is\nimplemented and used we see that it operates on a single String and doesn't\nreally care what it contains or where it comes from as long as it contains\nreferences to issue IDs:\n\n    def closed_by_message(message)\n      return [] if message.nil?\n\n      closing_statements = []\n      message.scan(ISSUE_CLOSING_REGEX) do\n        closing_statements \u003C\u003C Regexp.last_match[0]\n      end\n\n      @extractor.analyze(closing_statements.join(\" \"))\n\n      @extractor.issues\n    end\n\nThis got me thinking: what if we concatenate all commit messages together and\npass the resulting String to `Gitlab::ClosingIssueExtractor#closed_by_message`?\nDoing so would mean performance is no longer affected by the amount of commits\nin a merge request.\n\nTo test this I wrote a benchmark to compare the old setup versus the idea I was\ngoing for:\n\n    require 'benchmark/ips'\n\n    project = Project.find_with_namespace('gitlab-org/gitlab-ce')\n    user    = User.find_by_username('yorickpeterse')\n    commits = ['Fixes #1', 'Fixes #2', 'Fixes #3']\n    desc    = 'This MR fixes #1 #2 #3'\n\n    Benchmark.ips do |bench|\n      # A somewhat simplified version of the old code (excluding any actual\n      # commit/merge request objects).\n      bench.report 'old' do\n        issues = commits.flat_map do |message|\n          Gitlab::ClosingIssueExtractor.new(project, user).\n            closed_by_message(message)\n        end\n\n        issues.push(*Gitlab::ClosingIssueExtractor.new(project, user).\n                   closed_by_message(desc))\n\n        issues.uniq(&:id)\n      end\n\n      # The new code\n      bench.report 'new' do\n        messages = commits + [desc]\n\n        Gitlab::ClosingIssueExtractor.new(project, user).\n          closed_by_message(messages.join(\"\\n\"))\n      end\n\n      bench.compare!\n    end\n\nWhen running this benchmark we get the following output:\n\n    Calculating -------------------------------------\n                     old     1.000  i/100ms\n                     new     1.000  i/100ms\n    -------------------------------------------------\n                     old      1.377  (± 0.0%) i/s -      7.000\n                     new      2.807  (± 0.0%) i/s -     15.000  in   5.345900s\n\n    Comparison:\n                     new:        2.8 i/s\n                     old:        1.4 i/s - 2.04x slower\n\nSo in this benchmark alone the new code is around 2 times faster than the old\ncode. The actual number of iterations isn't very relevant, we just want to know\nif we're on the right track or not.\n\nRunning the test suite showed no tests were broken by these changes so it was\ntime to set up a merge request and deploy this to GitLab.com (and of course\ninclude it in the next release, 8.5 in this case) to see the impact in a\nproduction environment. The merge request for this was [\"Optimize fetching\nissues closed by a merge request\"][mr2625]. These changes were deployed around\nthe 12th of February and we can see the impact on GitLab.com in the following\ngraph:\n\n![MergeRequest#closes_issues Timings][mr2625-timings]\n\nThat's right, we went from timings between 0.5 and 2.5 seconds to timings of\nless than 15 milliseconds (method call timings below 15 milliseconds are not\ntracked). Ship it!\n\n## Improve performance of retrieving last update times for events\n\nFor certain activity feeds we provide Atom feeds that users can subscribe to.\nFor example \u003Chttps://gitlab.com/yorickpeterse.atom> provides an Atom feed of\nmy public GitLab.com activity. The feed is built by querying a list of records\nfrom the database called \"events\". The SQL query is rather large as the list of\nevents to return is based on the projects a user has access to (in case of user\nactivity feeds). For example, for my own user profile the query would be as\nfollowing:\n\n    SELECT events.*\n    FROM events\n    LEFT OUTER JOIN projects ON projects.id = events.project_id\n    LEFT OUTER JOIN namespaces ON namespaces.id = projects.namespace_id\n    WHERE events.author_id IS NOT NULL\n    AND events.author_id = 209240\n    AND (\n        projects.id IN (\n            SELECT projects.id\n            FROM projects\n            WHERE projects.id IN (\n                -- All projects directly owned by a user.\n                SELECT projects.id\n                FROM projects\n                INNER JOIN namespaces ON projects.namespace_id = namespaces.id\n                WHERE namespaces.owner_id = 209240\n                AND namespaces.type IS NULL\n\n                UNION\n\n                -- All projects of the groups a user is a member of\n                SELECT projects.id\n                FROM projects\n                INNER JOIN namespaces ON projects.namespace_id = namespaces.id\n                INNER JOIN members ON namespaces.id = members.source_id\n                WHERE namespaces.type IN ('Group')\n                AND members.type IN ('GroupMember')\n                AND members.source_type = 'Namespace'\n                AND members.user_id = 209240\n\n                UNION\n\n                -- All projects (that don't belong to one of the groups of a\n                -- user) a user is a member of\n                SELECT projects.id\n                FROM projects\n                INNER JOIN members ON projects.id = members.source_id\n                WHERE members.type IN ('ProjectMember')\n                AND members.source_type = 'Project'\n                AND members.user_id = 209240\n            )\n\n            UNION\n\n            -- All publicly available projects, regardless of whether we still\n            -- have access or not.\n            SELECT projects.id\n            FROM projects\n            WHERE projects.visibility_level IN (20, 10)\n        )\n    )\n    ORDER BY events.id DESC;\n\nThis particular query is quite the behemoth but currently this is the easiest\nway of getting a list of events for projects a user has access to.\n\nOne of the bits of information provided by an Atom feed is a timestamp\nindicating the time the feed was updated. This timestamp was generated using the\nmethod `Event.latest_update_time` which would take a collection of events and\nreturn the most recent update time. This method was implemented as the following:\n\n    def latest_update_time\n      row = select(:updated_at, :project_id).reorder(id: :desc).take\n\n      row ? row.updated_at : nil\n    end\n\nThis method is broken up in two steps:\n\n1. Order the collection in descending order, take the first record\n2. If there was a record return the `updated_at` value, otherwise return `nil`\n\nThis method was then used as the following in the Atom feed (here `xml.updated`\nwould generate an `\u003Cupdated>` XML element):\n\n    xml.updated @events.latest_update_time.xmlschema if @events.any?\n\nPerformance of this method was less than stellar (the blue bars are the timings\nof `Event.latest_update_time`):\n\n![Event.latest_update_time Timings][mr2613-timings]\n\nIn this graph we can see the timings quite often hover around 10 seconds. That's\n10 seconds _just_ to get the latest update time from the database. Ouch!\n\nAt first I started messing around with using the SQL `max()` function instead of\na combination of `ORDER BY` and `LIMIT 1`. We were using this in the past and I\nexplicitly removed it because it was performing worse at the time. Since quite a\nbit changed since then I figured it was worth re-investigating the use of this\nfunction. The process of looking into this as well as my findings can be found\nin issue [12415](https://gitlab.com/gitlab-org/gitlab-ce/issues/12415).\n\nA couple of days after I first started looking into this issue I realized there\nwas a far easier solution to this problem. Since retrieving the list of events\nitself (without using the above code) is already quite fast and is already\nsorted in the right order we can simply re-use this list. That is, we'd take the\nfollowing steps:\n\n1. Query the list of events.\n2. Cast the list of events from an ActiveRecord query result to an Array (this\n   is done anyway later on as we have to generate XML for every event).\n3. Take the `updated_at` value of the first event in this list, if present.\n\nThis led to merge request\n[\"Improve performance of retrieving last update times for events\"][mr2613]. This\nmerge request also contains a few other changes so certain records aren't loaded\ninto memory when not needed, but the gist of it is that instead of this:\n\n    xml.updated @events.latest_update_time.xmlschema if @events.any?\n\nWe now use this:\n\n    xml.updated @events[0].updated_at.xmlschema if @events[0]\n\nAs a result of this the method `Event.latest_update_time` was no longer needed\nand thus was removed. This in turn drastically reduced the loading times of all\nAtom feeds (not just user feeds).\n\n## Only set autocrlf when creating/updating files\n\nGit has an option called `core.autocrlf` which can be used to automatically\nconvert line endings in text files. This option can be set to 3 values:\n\n1. `true`: CRLF line endings are always converted to LF line endings\n2. `false`: no conversion takes place\n3. `input`: converts CRLF line endings to LF upon committing changes\n\nGitLab supports 3 ways of committing changes to a Git repository:\n\n1. Via a Git client\n2. Via the web editor\n3. Via the API\n\nIn the last 2 cases we want to make sure CRLF line endings are replaced with LF\nline endings. For example, browsers use CRLF even on non Windows platforms. To\ntake care of this our documentation recommends users to configure Git to set\n`core.autocrlf` to `input`, however we still need to take care of this ourselves\nin case a user didn't configure Git to convert line endings by default. This\nprocess took place in a method called `Repository#raw_repository` which was\nimplemented as the following:\n\n    def raw_repository\n      return nil unless path_with_namespace\n\n      @raw_repository ||= begin\n        repo = Gitlab::Git::Repository.new(path_to_repo)\n        repo.autocrlf = :input\n        repo\n      rescue Gitlab::Git::Repository::NoRepository\n        nil\n      end\n    end\n\nThis particular method is used in quite a number of places and is used on almost\nevery (if not every) project-specific page (issues, milestones, the project\nhomepage, etc). Performance of this method was, well, bad:\n\n![Gitlab::Git::Repository#autocrlf= Timings][mr2859-bars]\n\nThis particular graph plots the 95th percentile of the method\n`Gitlab::Git::Repository#autocrlf=` which is used to set the `core.autocrlf`\noption. We can see that on average the 95th percentile hovers around 500\nmilliseconds. That's 500 milliseconds on almost every page to set a Git option\nthat's already set 99% of the time. More importantly, that's 500 milliseconds of\ntime wasted on many pages where no changes are ever written to a Git repository,\nthus never using this option.\n\nIt's clear that we _don't_ want to run this on every page, especially when the\noption is not going to be used. However, we still have to make sure this option\nis set when we _do_ need it. At this point my first thought was to see the\noverhead of always writing this option versus only writing this when actually\nneeded. In Ruby code this would roughly translate to:\n\n    repo = Gitlab::Git::Repository.new(path_to_repo)\n\n    # Only set autocrlf to :input if it's not already set to :input\n    repo.autocrlf = :input unless repo.autocrlf == :input\n\nThe idea was that when sharing a disk over the network (e.g. via an NFS server)\na read is probably much faster than a write. A write may also end up locking\nfiles for the duration, possibly blocking other read operations. To test this I\nwrote a script that would perform said operation a number of times and write the\ntimings to InfluxDB. This script is as the following:\n\n    require 'rugged'\n    require 'thread'\n    require 'benchmark'\n    require 'influxdb'\n\n    Thread.abort_on_exception = true\n\n    path = '/var/opt/gitlab/git-data/repositories/yorickpeterse/cat-pictures.git'\n    key  = 'core.autocrlf'\n    read = true\n\n    influx_options = { udp: { host: 'HOST', port: PORT } }\n\n    threads = 10.times.map do\n      Thread.new do\n        client = InfluxDB::Client.new(influx_options)\n\n        while read\n          time = Benchmark.measure do\n            repo = Rugged::Repository.new(path)\n\n            repo.config[key] = 'input' unless repo.config[key] == 'input'\n          end\n\n          ms = time.real * 1000\n\n          client.write_point('rugged_config_cas', values: { duration: ms })\n\n          sleep 0.05\n        end\n      end\n    end\n\n    sleep(120)\n\n    read = false\n\n    threads.each(&:join)\n\n    Rugged::Repository.new(path).config[key] = 'input'\n\nHere HOST and PORT were replaced with the hostname and port number of our\nInfluxDB server.\n\nRunning this script produced the following graph:\n\n![Timings for writing autocrlf when needed](https://about.gitlab.com/images/making_gitlab_faster/autocrlf_write_when_needed.png)\n\nNext I modified this script to simply always write the autocrlf option, this\nproduced the following graph:\n\n![Timings for always writing autocrlf](https://about.gitlab.com/images/making_gitlab_faster/autocrlf_always_write.png)\n\nFinally I modified the script to simply load the repository as-is, this produced\nthe following graph:\n\n![Timings for only reading](https://about.gitlab.com/images/making_gitlab_faster/autocrlf_read_only.png)\n\nIn all 3 cases we can see there's not really a clear difference in timings,\nleading me to believe there's no particular benefit to only writing the option\nwhen not already set to \"input\".\n\nI spent some more time trying out different things to see how they would impact\nperformance but sadly didn't get much out of it. The details can be found in the\nvarious comments for [issue 13457](https://gitlab.com/gitlab-org/gitlab-ce/issues/13457).\n\nA day later I and [Jacob Vosmaer][jacob] decided to double check the idea of\nwriting only when needed by applying a small patch to GitLab.com. This patch\nmodified `Repository#raw_repository` to the autocrlf option would only be\nwritten when needed just like the script above. We also made sure to measure the\ntimings of both reading and writing this option. After deploying this patch and\nwaiting for about half an hour to get enough data the timings were as the\nfollowing:\n\n![autocrlf reads vs writes](https://about.gitlab.com/images/making_gitlab_faster/autocrlf_reads_vs_writes.png)\n\nThis graph shows a nice drop in timings for writing the autocrlf option, sadly\nat the cost of an increase in timings for reading the autocrlf option. In other\nwords, this change didn't actually solve anything but instead just moved the\nproblem from writing an option to just reading the option.\n\nAfter discussing this with Jacob he suggested it may be an even better idea to\nonly set this option where we actually need it to, instead of checking (and\npotentially writing) it on every page that happens to use\n`Repository#raw_repository`. After all, the best way to speed code up is to\nremove it entirely (or at least as much as possible).\n\nThis lead to merge request\n[\"Only set autocrlf when creating/updating files\"][mr2859] which does exactly\nthat. The impact of this change can be seen in the following graph:\n\n![Merge Request Timings Impact](https://about.gitlab.com/images/making_gitlab_faster/autocrlf_timings_impact.png)\n\nThis graph shows the 95th percentile, 99th percentile, and the mean per 30\nminutes. The drop around the 20th is after the above merge request was deployed\nto GitLab.com. The changes in this merge request resulted in the timings going\nfrom between 70 milliseconds and 2.1 seconds to less than 15 milliseconds.\n\n## Conclusion\n\nIn this article I only highlighted 3 merge requests that made it into 8.5.0. The\nfollowing performance related merge requests are also included in 8.5.0:\n\n* [First pass at deleting projects in the background](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2569)\n* [Background process note logic](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2631)\n* [Page project list on dashboard](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2689)\n* [Cache BroadcastMessage.current](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2633)\n* [Smarter flushing of branch statistics caches](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2769)\n* [Cache various Repository Git operations](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2752)\n* [Dedicated method for counting commits between refs](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2707)\n\nThese are just a few of the performance changes we've made over the past few\nmonths, and they certainly won't be the last as there's still a lot of work to\nbe done.\n\n[mr2625]: https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2625\n[mr2625-timings]: https://about.gitlab.com/images/making_gitlab_faster/merge_request_closes_issues.png\n[mr2613]: https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2613\n[mr2613-timings]: https://about.gitlab.com/images/making_gitlab_faster/event_latest_update_time.png\n[mr2859]: https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/2859\n[mr2859-bars]: https://about.gitlab.com/images/making_gitlab_faster/gitlab_git_repository_autocrlf_bars.png\n[monitoring]: http://doc.gitlab.com/ce/monitoring/performance/introduction.html\n[influxdb]: https://influxdata.com/time-series-platform/influxdb/\n[grafana]: http://grafana.org/\n[rblineprof]: https://github.com/peek/peek-rblineprof\n[benchmark-ips]: https://github.com/evanphx/benchmark-ips\n[benchmark-ips-readme]: https://github.com/evanphx/benchmark-ips/blob/master/README.md\n[jacob]: 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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},1772652076679]