[{"data":1,"prerenderedAt":794},["ShallowReactive",2],{"/en-us/blog/how-we-spent-two-weeks-hunting-an-nfs-bug":3,"navigation-en-us":40,"banner-en-us":439,"footer-en-us":449,"blog-post-authors-en-us-Stan Hu":691,"blog-related-posts-en-us-how-we-spent-two-weeks-hunting-an-nfs-bug":705,"assessment-promotions-en-us":745,"next-steps-en-us":784},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/how-we-spent-two-weeks-hunting-an-nfs-bug.yml","How We Spent Two Weeks Hunting An Nfs Bug",[7],"stan-hu",null,"engineering",{"slug":11,"featured":12,"template":13},"how-we-spent-two-weeks-hunting-an-nfs-bug",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How we spent two weeks hunting an NFS bug in the Linux kernel","Here's an in-depth recap of debugging a GitLab issue that culminated in a patch for the Linux kernel.",[18],"Stan Hu","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749672173/Blog/Hero%20Images/nfs-bug-hunt-detective.jpg","2018-11-14","UPDATE 2019-08-06: This bug has now been resolved in the following\ndistributions:\n\n* [Red Hat Enterprise Linux 7](https://access.redhat.com/errata/RHSA-2019:2029)\n* [Ubuntu](https://bugs.launchpad.net/ubuntu/+source/linux/+bug/1802585)\n* Linux mainline: Backported to [4.14-stable](https://lkml.org/lkml/2019/8/2/562) and [4.19-stable](https://lkml.org/lkml/2019/8/2/639)\n\nOn Sep. 14, the GitLab support team escalated a critical\nproblem encountered by one of our customers: GitLab would run fine for a\nwhile, but after some time users encountered errors. When attempting to\nclone certain repositories via Git, users would see an opaque `Stale\nfile error` message. The error message persisted for a long time,\nblocking employees from being able to work, unless a system\nadministrator intervened manually by running `ls` in the directory\nitself.\n\nThus launched an investigation into the inner workings of Git and the\nNetwork File System (NFS). The investigation uncovered a bug with the\nLinux v4.0 NFS client and culiminated with a [kernel patch that was written by\nTrond Myklebust](https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?h=be189f7e7f03de35887e5a85ddcf39b91b5d7fc1)\nand [merged in the latest mainline Linux kernel](https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?h=c7a2c49ea6c9eebbe44ff2c08b663b2905ee2c13)\non Oct. 26.\n\nThis post describes the journey of investigating the issue and\ndetails the thought process and tools by which we tracked down the\nbug. It was inspired by the fine detective work in [How I spent two\nweeks hunting a memory leak in Ruby](http://www.be9.io/2015/09/21/memory-leak/)\nby Oleg Dashevskii.\n\nMore importantly, this experience exemplifies how open source software\ndebugging has become a team sport that involves expertise across\nmultiple people, companies, and locations. The GitLab motto \"[everyone can\ncontribute](https://handbook.gitlab.com/handbook/company/mission/#mission)\" applies not only to GitLab itself, but also to other open\nsource projects, such as the Linux kernel.\n\n## Reproducing the bug\n\nWhile we have run NFS on GitLab.com for many years, we have stopped\nusing it to access repository data across our application\nmachines. Instead, we have [abstracted all Git calls to\nGitaly](/blog/the-road-to-gitaly-1-0/).\nStill, NFS remains a supported configuration for our customers who\nmanage their own installation of GitLab, but we had never seen the exact\nproblem described by the customer before.\n\n[Our customer gave us a few important clues](https://gitlab.com/gitlab-org/gitlab-ce/issues/51437):\n\n1. The full error message read, `fatal: Couldn't read ./packed-refs: Stale file handle`.\n2. The error seemed to start when they started a manual Git garbage\ncollection run via `git gc`.\n3. The error would go away if a system administrator ran `ls` in the\ndirectory.\n4. The error also would go away after `git gc` process ended.\n\nThe first two items seemed obviously related. When you push to a branch\nin Git, Git creates a loose reference, a fancy name for a file that\npoints your branch name to the commit. For example, a push to `master`\nwill create a file called `refs/heads/master` in the repository:\n\n```bash\n$ cat refs/heads/master\n2e33a554576d06d9e71bfd6814ee9ba3a7838963\n```\n\n`git gc` has several jobs, but one of them is to collect these loose\nreferences (refs) and bundle them up into a single file called\n`packed-refs`. This makes things a bit faster by eliminating the need to\nread lots of little files in favor of reading one large one. For\nexample, after running `git gc`, an example `packed-refs` might look\nlike:\n\n```text\n# pack-refs with: peeled fully-peeled sorted\n564c3424d6f9175cf5f2d522e10d20d781511bf1 refs/heads/10-8-stable\nedb037cbc85225261e8ede5455be4aad771ba3bb refs/heads/11-0-stable\n94b9323033693af247128c8648023fe5b53e80f9 refs/heads/11-1-stable\n2e33a554576d06d9e71bfd6814ee9ba3a7838963 refs/heads/master\n```\n\nHow exactly is this `packed-refs` file created? To answer that, we ran\n`strace git gc` with a loose ref present. Here are the pertinent lines\nfrom that:\n\n```text\n28705 open(\"/tmp/libgit2/.git/packed-refs.lock\", O_RDWR|O_CREAT|O_EXCL|O_CLOEXEC, 0666) = 3\n28705 open(\".git/packed-refs\", O_RDONLY) = 3\n28705 open(\"/tmp/libgit2/.git/packed-refs.new\", O_RDWR|O_CREAT|O_EXCL|O_CLOEXEC, 0666) = 4\n28705 rename(\"/tmp/libgit2/.git/packed-refs.new\", \"/tmp/libgit2/.git/packed-refs\") = 0\n28705 unlink(\"/tmp/libgit2/.git/packed-refs.lock\") = 0\n```\n\nThe system calls showed that `git gc` did the following:\n\n1. Open `packed-refs.lock`. This tells other processes that `packed-refs` is locked and cannot be changed.\n1. Open `packed-refs.new`.\n1. Write loose refs to `packed-refs.new`.\n1. Rename `packed-refs.new` to `packed-refs`.\n1. Remove `packed-refs.lock`.\n1. Remove loose refs.\n\nThe fourth step is the key here: the rename where Git puts `packed-refs`\ninto action. In addition to collecting loose refs, `git gc` also\nperforms a more expensive task of scanning for unused objects and\nremoving them. This task can take over an hour for large\nrepositories.\n\nThat made us wonder: for a large repository, does `git gc` keep the file\nopen while it's running this sweep? Looking at the `strace` logs and\nprobing the process with `lsof`, we found that it did the following:\n\n![Git Garbage Collection](https://about.gitlab.com/images/blogimages/nfs-debug/git-gc-diagram.svg)\n\nNotice that `packed-refs` is closed only at the end, after the potentially\nlong `Garbage collect objects` step takes place.\n\nThat made us wonder: how does NFS behave when one node has `packed-refs`\nopen while another renames over that file?\n\nTo experiment, we asked the customer to run the following experiment on\ntwo different machines (Alice and Bob):\n\n1. On the shared NFS volume, create two files: `test1.txt` and\n`test2.txt` with different contents to make it easy to distinguish them:\n\n    ```bash\n    alice $ echo \"1 - Old file\" > /path/to/nfs/test1.txt\n    alice $ echo \"2 - New file\" > /path/to/nfs/test2.txt\n    ```\n\n2. On machine Alice, keep a file open to `test1.txt`:\n\n    ```bash\n     alice $ irb\n     irb(main):001:0> File.open('/path/to/nfs/test1.txt')\n    ```\n\n3. On machine Alice, show the contents of `test1.txt` continuously:\n\n    ```bash\n    alice $ while true; do cat test1.txt; done\n    ```\n\n4. Then on machine Bob, run:\n\n    ```bash\n    bob $ mv -f test2.txt test1.txt\n    ```\n\nThis last step emulates what `git gc` does with `packed-refs` by\noverwriting the existing file.\n\nOn the customer's machine, the result looked something like:\n\n```text\n1 - Old file\n1 - Old file\n1 - Old file\ncat: test1.txt: Stale file handle\n```\n\nBingo! We seemed to reproduce the problem in a controlled way. However,\nthe same experiment using a Linux NFS server did not have this\nproblem. The result was what you would expect: the new contents were\npicked up after the rename:\n\n```text\n1 - Old file\n1 - Old file\n1 - Old file\n2 - New file  \u003C--- RENAME HAPPENED\n2 - New file\n2 - New file\n```\n\nWhy the difference in behavior? It turns out that the customer was using\nan [Isilon NFS\nappliance](https://www.dellemc.com/en-us/storage/isilon/index.htm) that\nonly supported NFS v4.0. By switching the mount parameters to v4.0 via\nthe `vers=4.0` parameter in `/etc/fstab`, the test revealed a different\nresult with the Linux NFS server:\n\n```text\n1 - Old file\n1 - Old file\n1 - Old file\n1 - Old file \u003C--- RENAME HAPPENED\n1 - Old file\n1 - Old file\n```\n\nInstead of a `Stale file handle`, the Linux NFS v4.0 server showed stale\n*contents*. It turns out this difference in behavior can be explained by\nthe NFS spec. From [RFC\n3010](https://tools.ietf.org/html/rfc3010#page-153):\n\n> A filehandle may or may not become stale or expire on a rename.\n> However, server implementors are strongly encouraged to attempt to keep\n> file handles from becoming stale or expiring in this fashion.\n\nIn other words, NFS servers can choose how to behave if a file is\nrenamed; it's perfectly valid for any NFS server to return a `Stale file\nerror` when that happens. We surmised that even though the results were\ndifferent, the problem was likely related to the same issue. We\nsuspected some cache validation issue because running `ls` in the\ndirectory would \"clear\" the error. Now that we had a reproducible test\ncase, we asked the experts: the Linux NFS maintainers.\n\n## False path: NFS server delegations\n\nWith a clear set of reproduction steps, I [sent an email to the Linux\nNFS mailing list](https://marc.info/?l=linux-nfs&m=153721785231614&w=2)\ndescribing what we had found. Over the week, I went back and forth with\nBruce Fields, the Linux NFS server maintainer, who suggested this was a\nNFS bug and that it would be useful to look at the network traffic. He\nthought there might be an issue with NFS server delegations.\n\n### What is an NFS server delegation?\n\nIn a nutshell, NFS v4 introduced server delegations as a way to speed up file access. A server can\ndelegate read or write access to a client so that the client doesn't\nhave to keep asking the server whether that file has changed by another\nclient. In simpler terms, a write delegation is akin to someone lending\nyou a notebook and saying, \"Go ahead and write in here, and I'll take it\nback when I'm ready.\" Instead of having to ask to borrow the notebook\nevery time you want to write a new paragraph, you have free rein until\nthe owner reclaims the notebook. In NFS terms, this reclamation process\nis called a delegation recall.\n\nIndeed, a bug in the NFS delegation recall might explain the `Stale file\nhandle` problem. Remember that in the earlier experiment, Alice had\nan open file to `test1.txt` when it was replaced by `test2.txt` later.\nIt's possible that the server failed to recall the delegation on\n`test1.txt`, resulting in an incorrect state. To check whether this was\nan issue, we turned to `tcpdump` to capture NFS traffic and used\nWireshark to visualize it.\n\n[Wireshark](https://www.wireshark.org/) is a wonderful open source tool\nfor analyzing network traffic, and it's especially good for viewing NFS\nin action. We captured a trace using the following command on the NFS server:\n\n```text\ntcpdump -s 0 -w /tmp/nfs.pcap port 2049\n```\n\nThis command captures all NFS traffic, which typically is on TCP port 2049.\nBecause our experiment worked properly with NFS v4.1 but did not\n with NFS v4.0, we could compare and contrast how NFS behaved\nin a non-working and a working case. With Wireshark, we saw the\nfollowing behavior:\n\n### NFS v4.0 (stale file case)\n\n![NFS v4.0 flow](https://about.gitlab.com/images/blogimages/nfs-debug/nfs-4.0-flow.svg)\n\nIn this diagram, we can see in step 1 Alice opens `test1.txt` and gets\nback an NFS file handle along with a `stateid` of 0x3000. When Bob\nattempts to rename the file, the NFS server tells to Bob to retry via\nthe `NFS4ERR_DELAY` message while it recalls the delegation from Alice\nvia the `CB_RECALL` message (step 3). Alice then returns her delegation\nvia `DELEGRETURN` (step 4), and then Bob attempts to send another\n`RENAME` message (step 5). The `RENAME` completes in both cases, but\nAlice continues to read using the same file handle.\n\n### NFS v4.1 (working case)\n\n![NFS v4.1 flow](https://about.gitlab.com/images/blogimages/nfs-debug/nfs-4.1-flow.svg)\n\nThe main difference happens at the bottom at step 6. Notice in NFS v4.0\n(the stale file case), Alice attempts to reuse the same `stateid`. In\nNFS v4.1 (working case), Alice performs an additional `LOOKUP` and\n`OPEN`, which causes the server to return a different `stateid`. In v4.0,\nthese extra messages are never sent. This explains why Alice continues\nto see stale content because she uses the old file handle.\n\nWhat makes Alice decide to do the extra `LOOKUP`? The delegation recall\nseemed to work fine, but perhaps there was still an issue, such as a\nmissing invalidation step. To rule that out, we disabled NFS delegations\nby issuing this command on the NFS server itself:\n\n```sh\necho 0 > /proc/sys/fs/leases-enable\n```\n\nWe repeated the experiment, but the problem persisted. All this\nconvinced us this wasn't a NFS server issue or a problem with NFS\ndelegations; it was a problem that led us to look into the NFS client\nwithin the kernel.\n\n## Digging deeper: the Linux NFS client\n\nThe first question we had to answer for the NFS maintainers:\n\n### Was this problem still in the latest upstream kernel?\n\nThe issue occurred with both CentOS 7.2 and Ubuntu 16.04 kernels, which\nused versions 3.10.0-862.11.6 and 4.4.0-130, respectively. However, both\nthose kernels lagged the most recent kernel, which was 4.19-rc2 at the\ntime.\n\nWe deployed a new Ubuntu 16.04 virtual machine on Google Cloud Platform\n(GCP), cloned the latest Linux kernel, and set up a kernel development\nenvironment. After generating a `.config` file via `make menuconfig`, we\nchecked two items:\n\n1. The NFS driver was compiled as a module (`CONFIG_NFSD=m`).\n2. The [required GCP kernel settings](https://cloud.google.com/compute/docs/images/building-custom-os)\nwere set properly.\n\nJust as a geneticist would use fruit flies to study evolution in\nreal time, the first item allowed us to make quick changes in the NFS\nclient without having to reboot the kernel. The second item was required\nto ensure that the kernel would actually boot after it was\ninstalled. Fortunately, the default kernel settings had all the settings\nright out of the box.\n\nWith our custom kernel, we verified that the stale file problem still\nexisted in the latest version. That begged a number of questions:\n\n1. Where exactly was this problem happening?\n2. Why was this problem happening with NFS v4.0 but not in v4.1?\n\nTo answer these questions, we began to investigate the NFS [source\ncode](/solutions/source-code-management/). Since we didn't have a kernel debugger available, we sprinkled the\nsource code with two main types of calls:\n\n1. `pr_info()` ([what used to be `printk`](https://lwn.net/Articles/487437/)).\n2. `dump_stack()`: This would show the stack trace of the current function call.\n\nFor example, one of the first things we did was hook into the\n`nfs4_file_open()` function in `fs/nfs/nfs4file.c`:\n\n```c\nstatic int\nnfs4_file_open(struct inode *inode, struct file *filp)\n{\n...\n        pr_info(\"nfs4_file_open start\\n\");\n        dump_stack();\n\n```\n\nAdmittedly, we could have [activated the `dprintk` messages with the\nLinux dynamic\ndebug](https://www.kernel.org/doc/html/v4.15/admin-guide/dynamic-debug-howto.html)\nor used\n[`rpcdebug`](https://www.thegeekdiary.com/how-to-enable-nfs-debug-logging-using-rpcdebug/),\nbut it was nice to be able to add our own messages to verify changes\nwere being made.\n\nEvery time we made changes, we recompiled the module and reinstalled it\ninto the kernel via the commands:\n\n```sh\nmake modules\nsudo umount /mnt/nfs-test\nsudo rmmod nfsv4\nsudo rmmod nfs\nsudo insmod fs/nfs/nfs.ko\nsudo mount -a\n```\n\nWith our NFS module installed, repeating the experiments would print\nmessages that would help us understand the NFS code a bit more. For\nexample, you can see exactly what happens when an application calls `open()`:\n\n```text\nSep 24 20:20:38 test-kernel kernel: [ 1145.233460] Call Trace:\nSep 24 20:20:38 test-kernel kernel: [ 1145.233462]  dump_stack+0x8e/0xd5\nSep 24 20:20:38 test-kernel kernel: [ 1145.233480] nfs4_file_open+0x56/0x2a0 [nfsv4]\nSep 24 20:20:38 test-kernel kernel: [ 1145.233488]  ? nfs42_clone_file_range+0x1c0/0x1c0 [nfsv4]\nSep 24 20:20:38 test-kernel kernel: [ 1145.233490] do_dentry_open+0x1f6/0x360\nSep 24 20:20:38 test-kernel kernel: [ 1145.233492]  vfs_open+0x2f/0x40\nSep 24 20:20:38 test-kernel kernel: [ 1145.233493]  path_openat+0x2e8/0x1690\nSep 24 20:20:38 test-kernel kernel: [ 1145.233496]  ? mem_cgroup_try_charge+0x8b/0x190\nSep 24 20:20:38 test-kernel kernel: [ 1145.233497]  do_filp_open+0x9b/0x110\nSep 24 20:20:38 test-kernel kernel: [ 1145.233499]  ? __check_object_size+0xb8/0x1b0\nSep 24 20:20:38 test-kernel kernel: [ 1145.233501]  ? __alloc_fd+0x46/0x170\nSep 24 20:20:38 test-kernel kernel: [ 1145.233503]  do_sys_open+0x1ba/0x250\nSep 24 20:20:38 test-kernel kernel: [ 1145.233505]  ? do_sys_open+0x1ba/0x250\nSep 24 20:20:38 test-kernel kernel: [ 1145.233507] __x64_sys_openat+0x20/0x30\nSep 24 20:20:38 test-kernel kernel: [ 1145.233508]  do_syscall_64+0x65/0x130\n```\n\nWhat are the `do_dentry_open` and `vfs_open` calls above? Linux has a\n[virtual filesystem\n(VFS)](https://www.kernel.org/doc/Documentation/filesystems/vfs.txt), an\nabstraction layer which provides a common interface for all\nfilesystems. The VFS documentation explains:\n\n> The VFS implements the open(2), stat(2), chmod(2), and similar system\n> calls. The pathname argument that is passed to them is used by the VFS\n> to search through the directory entry cache (also known as the dentry\n> cache or dcache). This provides a very fast look-up mechanism to\n> translate a pathname (filename) into a specific dentry. Dentries live\n> in RAM and are never saved to disc: they exist only for performance.\n\n### This gave us a clue: what if this was a problem with the dentry cache?\n\nWe noticed a lot of dentry cache validation was done in\n`fs/nfs/dir.c`. In particular, `nfs4_lookup_revalidate()` sounded\npromising. As an experiment, we hacked that function to bail\nout early:\n\n\n```diff\ndiff --git a/fs/nfs/dir.c b/fs/nfs/dir.c\nindex 8bfaa658b2c1..ad479bfeb669 100644\n--- a/fs/nfs/dir.c\n+++ b/fs/nfs/dir.c\n@@ -1159,6 +1159,7 @@ static int nfs_lookup_revalidate(struct dentry *dentry, unsigned int flags)\n        trace_nfs_lookup_revalidate_enter(dir, dentry, flags);\n        error = NFS_PROTO(dir)->lookup(dir, &dentry->d_name, fhandle, fattr, label);\n        trace_nfs_lookup_revalidate_exit(dir, dentry, flags, error);\n+       goto out_bad;\n        if (error == -ESTALE || error == -ENOENT)\n                goto out_bad;\n        if (error)\n\n```\n\nThat made the stale file problem in our experiment go away! Now we were onto something.\n\nTo answer, \"Why does this problem not happen in NFS v4.1?\", we added\n`pr_info()` calls to every `if` block in that function. After running our\nexperiments with NFS v4.0 and v4.1, we found this special condition being run\nin the v4.1 case:\n\n```c\n\n        if (NFS_SB(dentry->d_sb)->caps & NFS_CAP_ATOMIC_OPEN_V1) {\n          goto no_open;\n        }\n\n```\n\nWhat is `NFS_CAP_ATOMIC_OPEN_V1`? We saw [this kernel\npatch](https://patchwork.kernel.org/patch/2300511/) mentioned this was\nan NFS v4.1-specific feature, and the code in `fs/nfs/nfs4proc.c`\nconfirmed that this flag was a capability present in v4.1 but not in v4.0:\n\n```c\nstatic const struct nfs4_minor_version_ops nfs_v4_1_minor_ops = {\n        .minor_version = 1,\n        .init_caps = NFS_CAP_READDIRPLUS\n                | NFS_CAP_ATOMIC_OPEN\n                | NFS_CAP_POSIX_LOCK\n                | NFS_CAP_STATEID_NFSV41\n                | NFS_CAP_ATOMIC_OPEN_V1\n\n```\n\nThat explained the difference in behavior: in the v4.1 case, the `goto\nno_open` would cause more validation to happen in\n`nfs_lookup_revalidate()`, but in v4.0, the `nfs4_lookup_revalidate()`\nwould return earlier. Now, how do we actually solve the problem?\n\n## The solution\n\nI reported the [findings to the NFS mailing\nlist](https://marc.info/?l=linux-nfs&m=153782129412452&w=2) and proposed\n[a naive patch](https://marc.info/?l=linux-nfs&m=153807208928650&w=2). A\nweek after the report, Trond Myklebust sent a [patch series to the list\nfixing this bug and found another related issue for NFS\nv4.1](https://marc.info/?l=linux-nfs&m=153816500525563&w=2).\n\nIt turns out the fix for the NFS v4.0 bug was deeper in the code base\nthan we had looked. Trond summarized it well in the\n[patch](https://marc.info/?l=linux-nfs&m=153816500525564&w=2):\n\n> We need to ensure that inode and dentry revalidation occurs correctly\n> on reopen of a file that is already open. Currently, we can end up not\n> revalidating either in the case of NFSv4.0, due to the 'cached open'\n> path.  Let's fix that by ensuring that we only do cached open for the\n> special cases of open recovery and delegation return.\n\nWe confirmed that this fix made the stale file problem go away and filed\nbug reports with\n[Ubuntu](https://bugs.launchpad.net/ubuntu/+source/linux/+bug/1802585)\nand [RedHat](https://bugzilla.redhat.com/show_bug.cgi?id=1648482).\n\nKnowing full well that kernel changes may take a while to make it to\nstable releases, we also added a [workaround in\nGitaly](https://gitlab.com/gitlab-org/gitaly/merge_requests/924) to deal\nwith this issue. We did experiments to test that calling `stat()` on the\n`packed-refs` file appears to cause the kernel to revalidate the dentry\ncache for the renamed file. For simplicity, this is implemented in\nGitaly regardless of whether the filesystem is NFS; we only do this once\nbefore Gitaly \"opens\" a repository, and there are already other `stat()`\ncalls that check for other files.\n\n## What we learned\n\nA bug can be anywhere in your software stack, and sometimes you have to\nlook beyond your application to find it. Having helpful partners in the\nopen source world makes that job much easier.\n\nWe are extremely grateful to Trond Myklebust for fixing the problem, and\nBruce Fields for responding to questions and helping us understand\nNFS. Their responsiveness and professionalism truly reflects the best of\nthe open source community.\n\nPhoto by [dynamosquito](https://www.flickr.com/photos/dynamosquito) on [Flickr](https://www.flickr.com/photos/dynamosquito/4265771518)\n",[23,24,25,26],"community","git","inside GitLab","open source","yml",{},true,"/en-us/blog/how-we-spent-two-weeks-hunting-an-nfs-bug",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":32,"ogSiteName":33,"ogType":34,"canonicalUrls":32},"https://about.gitlab.com/blog/how-we-spent-two-weeks-hunting-an-nfs-bug","https://about.gitlab.com","article","en-us/blog/how-we-spent-two-weeks-hunting-an-nfs-bug",[23,24,37,38],"inside-gitlab","open-source","dBxkLN0Vzr8zSsU_M5C2T1U0k_eqSIUgyIfaMj-fQuI",{"data":41},{"logo":42,"freeTrial":47,"sales":52,"login":57,"items":62,"search":369,"minimal":400,"duo":419,"pricingDeployment":429},{"config":43},{"href":44,"dataGaName":45,"dataGaLocation":46},"/","gitlab 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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":717,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":719,"config":728},{"title":720,"description":721,"authors":722,"heroImage":723,"date":724,"category":9,"tags":725,"body":727},"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.",[711],"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",[613,23,726],"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":729,"featured":29,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":731,"config":743},{"category":9,"tags":732,"body":734,"date":735,"updatedDate":736,"heroImage":737,"authors":738,"title":741,"description":742},[733,24,109],"tutorial","\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",[739,740],"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":29,"template":13,"slug":744},"migration-from-azure-devops-to-gitlab",{"promotions":746},[747,761,772],{"id":748,"categories":749,"header":751,"text":752,"button":753,"image":758},"ai-modernization",[750],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":754,"config":755},"Get your AI maturity score",{"href":756,"dataGaName":757,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":759},{"src":760},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":762,"categories":763,"header":764,"text":752,"button":765,"image":769},"devops-modernization",[726,559],"Are you just managing tools or shipping innovation?",{"text":766,"config":767},"Get your DevOps maturity score",{"href":768,"dataGaName":757,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":773,"categories":774,"header":776,"text":752,"button":777,"image":781},"security-modernization",[775],"security","Are you trading speed for security?",{"text":778,"config":779},"Get your security maturity score",{"href":780,"dataGaName":757,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":785,"blurb":786,"button":787,"secondaryButton":792},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":788,"config":789},"Get your free trial",{"href":790,"dataGaName":51,"dataGaLocation":791},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":495,"config":793},{"href":55,"dataGaName":56,"dataGaLocation":791},1772652081009]