[{"data":1,"prerenderedAt":819},["ShallowReactive",2],{"/en-us/blog/how-we-spent-two-weeks-hunting-an-nfs-bug":3,"navigation-en-us":40,"banner-en-us":449,"footer-en-us":459,"blog-post-authors-en-us-Stan Hu":701,"blog-related-posts-en-us-how-we-spent-two-weeks-hunting-an-nfs-bug":715,"blog-promotions-en-us":756,"next-steps-en-us":809},{"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,"switchNav":428,"pricingDeployment":439},{"config":43},{"href":44,"dataGaName":45,"dataGaLocation":46},"/","gitlab 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Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n  \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n  \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - trigger\n\ntrigger-services:\n  stage: trigger\n  trigger:\n    include:\n      - local: '.gitlab/ci/api-service.yml'\n      - local: '.gitlab/ci/web-service.yml'\n      - local: '.gitlab/ci/worker-service.yml'\n    strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n  - build\n  - test\n\nbuild-api:\n  stage: build\n  script:\n    - echo \"Building API service\"\n\ntest-api:\n  stage: test\n  needs: [build-api]\n  script:\n    - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/ee/api/jobs.html#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n  - trigger-backend\n\nbuild-frontend:\n  stage: build\n  script:\n    - echo \"Building frontend and generating API contract...\"\n    - mkdir -p dist\n    - |\n      echo '{\n        \"api_version\": \"v2\",\n        \"breaking_changes\": false\n      }' > dist/api-contract.json\n    - cat dist/api-contract.json\n  artifacts:\n    paths:\n      - dist/api-contract.json\n    expire_in: 1 hour\n\ntest-frontend:\n  stage: test\n  script:\n    - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n  stage: trigger-backend\n  trigger:\n    project: my-org/backend-service\n    branch: main\n    strategy: depend\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n\nbuild-backend:\n  stage: build\n  script:\n    - echo \"All backend tests passed!\"\n\nintegration-test:\n  stage: test\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n  script:\n    - echo \"Fetching API contract from frontend...\"\n    - |\n      curl --silent --fail \\\n        --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n        --output api-contract.json \\\n        \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n    - cat api-contract.json\n    - |\n      if grep -q '\"breaking_changes\": true' api-contract.json; then\n        echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n        exit 1\n      fi\n      echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - generate\n  - trigger-environments\n\ngenerate-config:\n  stage: generate\n  script:\n    - |\n      # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n      # Default to dev, staging, prod if not set.\n      ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n      for ENV in $ENVIRONMENTS; do\n        cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n      stages:\n        - deploy\n        - verify\n      deploy-${ENV}:\n        stage: deploy\n        script:\n          - echo \"Deploying to ${ENV} environment\"\n      verify-${ENV}:\n        stage: verify\n        script:\n          - echo \"Running smoke tests on ${ENV}\"\n      EOF\n      done\n  artifacts:\n    paths:\n      - \"*.yml\"\n    exclude:\n      - \".gitlab-ci.yml\"\n\n.trigger-template:\n  stage: trigger-environments\n  trigger:\n    strategy: depend\n\ntrigger-dev:\n  extends: .trigger-template\n  trigger:\n    include:\n      - artifact: dev-pipeline.yml\n        job: generate-config\n\ntrigger-staging:\n  extends: .trigger-template\n  needs: [trigger-dev]\n  trigger:\n    include:\n      - artifact: staging-pipeline.yml\n        job: generate-config\n\ntrigger-prod:\n  extends: .trigger-template\n  needs: [trigger-staging]\n  trigger:\n    include:\n      - artifact: prod-pipeline.yml\n        job: generate-config\n  when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n*   [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n*   [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n*   [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n      when: never\n    - if: $CI_COMMIT_BRANCH\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n  - fast-checks\n  - expensive-tests\n  - deploy\n\nlint-code:\n  stage: fast-checks\n  script:\n    - echo \"Running linter\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n  stage: fast-checks\n  script:\n    - echo \"Running unit tests\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running integration tests (15 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running E2E tests (30 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n  stage: expensive-tests\n  script:\n    - echo \"Running full nightly suite (2 hours)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n  stage: deploy\n  script:\n    - echo \"Deploying to production\"\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n      when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n  inputs:\n    stage:\n      default: deploy\n    environment:\n      default: production\n---\ndeploy-job:\n  stage: $[[ inputs.stage ]]\n  script:\n    - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n    - echo \"Deploy URL: $DEPLOY_URL\"\n  environment:\n    name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n  APP_NAME: \"my-awesome-app\"\n  DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n  - component: gitlab.com/my-org/component-library/build@v1.0.6\n  - component: gitlab.com/my-org/component-library/test@v1.0.6\n  - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n    inputs:\n      environment: staging\n\nstages:\n  - build\n  - test\n  - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n*   A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n*   A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n*   A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n*   [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n*   [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n*   [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n*   [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n*   [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n*   [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n*   [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n*   [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n*   [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n*   [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n*   [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[722],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[109,726,727,728],"DevOps platform","tutorial","features",{"featured":29,"template":13,"slug":730},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":732,"config":742},{"title":733,"description":734,"authors":735,"heroImage":737,"date":738,"body":739,"category":9,"tags":740},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[736],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficult to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[727,741,728],"product",{"featured":12,"template":13,"slug":743},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":745,"config":754},{"title":746,"description":747,"authors":748,"heroImage":750,"date":751,"category":9,"tags":752,"body":753},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[749],"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",[23,623,26],"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-agent-platform/) 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":755,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":757},[758,772,783,795],{"id":759,"categories":760,"header":762,"text":763,"button":764,"image":769},"ai-modernization",[761],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":765,"config":766},"Get your AI maturity score",{"href":767,"dataGaName":768,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":773,"categories":774,"header":775,"text":763,"button":776,"image":780},"devops-modernization",[741,569],"Are you just managing tools or shipping innovation?",{"text":777,"config":778},"Get your DevOps maturity score",{"href":779,"dataGaName":768,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":784,"categories":785,"header":787,"text":763,"button":788,"image":792},"security-modernization",[786],"security","Are you trading speed for security?",{"text":789,"config":790},"Get your security maturity score",{"href":791,"dataGaName":768,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":793},{"src":794},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":796,"paths":797,"header":800,"text":801,"button":802,"image":807},"github-azure-migration",[798,799],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":803,"config":804},"See how GitLab compares to GitHub",{"href":805,"dataGaName":806,"dataGaLocation":244},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":808},{"src":782},{"header":810,"blurb":811,"button":812,"secondaryButton":817},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":813,"config":814},"Get your free trial",{"href":815,"dataGaName":51,"dataGaLocation":816},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":505,"config":818},{"href":55,"dataGaName":56,"dataGaLocation":816},1776449945378]