[{"data":1,"prerenderedAt":792},["ShallowReactive",2],{"/en-us/blog/delayed-replication-for-disaster-recovery-with-postgresql":3,"navigation-en-us":38,"banner-en-us":438,"footer-en-us":448,"blog-post-authors-en-us-Andreas Brandl":690,"blog-related-posts-en-us-delayed-replication-for-disaster-recovery-with-postgresql":704,"assessment-promotions-en-us":743,"next-steps-en-us":782},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/delayed-replication-for-disaster-recovery-with-postgresql.yml","Delayed Replication For Disaster Recovery With Postgresql",[7],"andreas-brandl",null,"engineering",{"slug":11,"featured":12,"template":13},"delayed-replication-for-disaster-recovery-with-postgresql",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How we used delayed replication for disaster recovery with PostgreSQL","Replication is no backup. Or is it? Let's take a look at delayed replication and how we used it to recover from accidental label deletion.",[18],"Andreas Brandl","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749683349/Blog/Hero%20Images/mathew-schwartz-397471-unsplash.jpg","2019-02-13","\nThe [infrastructure team](https://handbook.gitlab.com/handbook/engineering/infrastructure/) at GitLab is responsible for the operation of [GitLab.com](https://gitlab.com/), the largest GitLab instance in existence: With about 3 million users and nearly 7 million projects, it is one of the largest single-tenancy, open source SaaS sites on the internet. The PostgreSQL database system is a critical part of the infrastructure that powers GitLab.com and we employ various strategies to provide resiliency against all kinds of data-loss-inducing disasters. Those are highly unlikely of course, but we are well prepared with backup and replication mechanisms to recover from these scenarios.\n\nIt's a misconception to think of replication as a means to back up a database ([see below](#summing-up)). However, in this post, we're going to explore the power of delayed replication to recover data after an accidental deletion: On [GitLab.com](https://gitlab.com), a user [deleted a label](https://gitlab.com/gitlab-com/gl-infra/production/issues/509) for the [`gitlab-ce`](https://gitlab.com/gitlab-org/gitlab-ce/) project, thereby also losing the label's association with merge requests and issues.\n\nWith a delayed replica in place, we were able to recover and restore that data in under 90 minutes. We'll look into that process and how delayed replication helped to achieve this.\n\n### Point-in-time recovery with PostgreSQL\n\nPostgreSQL comes with a built-in feature to recover the state of a database to a certain point in time. This is called *[Point-in-Time Recovery](https://www.postgresql.org/docs/current/continuous-archiving.html)* (PITR), which leverages the same mechanics that are used to keep a replica up to date: Starting from a consistent snapshot of the whole database cluster (a *basebackup*), we apply the sequence of changes to the database state until a certain point in time has been reached.\n\nIn order to use this feature for a cold backup, we regularly take a basebackup of the database and store this in the *archive* (at GitLab, we keep the archive in [Google Cloud Storage](https://cloud.google.com/storage/)). Additionally, we keep track of changes to the database state by archiving the [*write-ahead log*](https://www.postgresql.org/docs/current/wal-intro.html) (WAL). With that in place, we can perform PITR to recover from a disaster: Start with a snapshot that was taken before the disaster happened and apply changes from the WAL archive until right before the disastrous event.\n\n### What is delayed replication?\n\n*Delayed replication* is the idea of applying time-delayed changes from the WAL. That is, a transaction that is committed at physical time `X` is only going to be visible on a replica with delay `d` at time `X + d`.\n\nFor PostgreSQL, there are two ways of setting up a physical replica of the database: *Archive recovery* and *streaming replication*. [Archive recovery](https://www.postgresql.org/docs/11/archive-recovery-settings.html) essentially works like PITR but in a continuous way: We keep retrieving changes from the WAL archive and apply them to the replica state in a continuous fashion. On the other hand, [streaming replication](https://wiki.postgresql.org/wiki/Streaming_Replication) directly retrieves the WAL stream from an upstream database host. We prefer archive recovery for delayed replication because it is simpler to manage and delivers an adequate level of performance to keep up with the production cluster.\n\n### How to set up delayed archive recovery\n\nConfiguration of [recovery options](https://www.postgresql.org/docs/11/recovery-config.html) mostly go into `recovery.conf`. Here's an example:\n\n```text\nstandby_mode = 'on'\nrestore_command = '/usr/bin/envdir /etc/wal-e.d/env /opt/wal-e/bin/wal-e wal-fetch -p 4 \"%f\" \"%p\"'\nrecovery_min_apply_delay = '8h'\nrecovery_target_timeline = 'latest'\n```\n\nWith these settings in place, we have configured a delayed replica with archive recovery. It uses [wal-e](https://github.com/wal-e/wal-e) to retrieve WAL segments (`restore_command`) from the archive and delays application of changes by eight hours (`recovery_min_apply_delay`). The replica is going to follow any timeline switches present in the archive, e.g. caused by a failover in the cluster (`recovery_target_timeline`).\n\nIt is possible to configure streaming replication with a delay using `recovery_min_apply_delay`. However, there are a few pitfalls regarding replication slots, hot standby feedback, and others that one needs to be aware of. In our case, we avoid them by replicating from the WAL archive instead of using streaming replication.\n\nIt is worth noting that `recovery_min_apply_delay` was only introduced in PostgreSQL 9.4. However, in previous versions, a delayed replica is typically implemented with a combination of [recovery management functions](https://www.postgresql.org/docs/9.3/functions-admin.html) (`pg_xlog_replay_pause(), pg_xlog_replay_resume()`) or by withholding WAL segments from the archive for the duration of the delay.\n\n### How does PostgreSQL implement it?\n\nIt is particularly interesting to see how PostgreSQL implements delayed recovery. So let's look at [`recoveryApplyDelay(XlogReaderState)`](https://gitlab.com/postgres/postgres/blob/c24dcd0cfd949bdf245814c4c2b3df828ee7db36/src/backend/access/transam/xlog.c#L6124) below. It is called from the [main redo apply loop](https://gitlab.com/postgres/postgres/blob/c24dcd0cfd949bdf245814c4c2b3df828ee7db36/src/backend/access/transam/xlog.c#L7196) for each record read from WAL.\n\n```c\nstatic bool\nrecoveryApplyDelay(XLogReaderState *record)\n{\n\tuint8\t\txact_info;\n\tTimestampTz xtime;\n\tlong\t\tsecs;\n\tint\t\t\tmicrosecs;\n\n\t/* nothing to do if no delay configured */\n\tif (recovery_min_apply_delay \u003C= 0)\n\t\treturn false;\n\n\t/* no delay is applied on a database not yet consistent */\n\tif (!reachedConsistency)\n\t\treturn false;\n\n\t/*\n\t * Is it a COMMIT record?\n\t *\n\t * We deliberately choose not to delay aborts since they have no effect on\n\t * MVCC. We already allow replay of records that don't have a timestamp,\n\t * so there is already opportunity for issues caused by early conflicts on\n\t * standbys.\n\t */\n\tif (XLogRecGetRmid(record) != RM_XACT_ID)\n\t\treturn false;\n\n\txact_info = XLogRecGetInfo(record) & XLOG_XACT_OPMASK;\n\n\tif (xact_info != XLOG_XACT_COMMIT &&\n\t\txact_info != XLOG_XACT_COMMIT_PREPARED)\n\t\treturn false;\n\n\tif (!getRecordTimestamp(record, &xtime))\n\t\treturn false;\n\n\trecoveryDelayUntilTime =\n\t\tTimestampTzPlusMilliseconds(xtime, recovery_min_apply_delay);\n\n\t/*\n\t * Exit without arming the latch if it's already past time to apply this\n\t * record\n\t */\n\tTimestampDifference(GetCurrentTimestamp(), recoveryDelayUntilTime,\n\t\t\t\t\t\t&secs, &microsecs);\n\tif (secs \u003C= 0 && microsecs \u003C= 0)\n\t\treturn false;\n\n\twhile (true)\n\t{\n        // Shortened:\n        // Use WaitLatch until we reached recoveryDelayUntilTime\n        // and then\n        break;\n\t}\n\treturn true;\n}\n```\n\nThe takeaway here is that the delay is based on the physical time that was recorded with the commit timestamp of the transaction (`xtime`). We can also see that the delay is only applied to commit records but not to other types of records: Any data changes are directly applied but the corresponding commit is delayed, so these changes only become visible after the configured delay.\n\n### How to use a delayed replica to recover data\n\nLet's say we have a production database cluster and a replica with eight hours of delay. How do we use this to recover data? Let's look at how this worked in the case of the [accidental label deletion](https://gitlab.com/gitlab-com/gl-infra/production/issues/509).\n\nAs soon as we were aware of the incident, we [paused archive recovery](https://www.postgresql.org/docs/9.3/functions-admin.html) on the delayed replica:\n\n```sql\nSELECT pg_xlog_replay_pause();\n```\n\nPausing the replica eliminated the risk of the replica replaying the `DELETE` query. This is useful if you need more time to investigate.\n\nThe recovery approach is to let the delayed replica catch up until right before the point the `DELETE` query occurred. In our case we knew roughly the physical time of the `DELETE` query. We removed `recovery_min_apply_delay` and added `recovery_target_time` to `recovery.conf`. This effectively lets the replica catch up as fast as possible (no delay) until a certain point in time:\n\n```text\nrecovery_target_time = '2018-10-12 09:25:00+00'\n```\n\nWhen operating with physical timestamps, it's worth adding a little margin for error. Obviously, the bigger the margin, the bigger the data loss. On the other hand, if the replica recovers beyond the actual incident timestamp it also replays the `DELETE` query and we would have to start over (or worse: use a cold backup to perform PITR).\n\nAfter restarting the delayed Postgres instance, we saw a lot of WAL segments being replayed until the target transaction time was reached. In order to get a sense of the progress during this phase, we can use this query:\n\n```sql\nSELECT\n  -- current location in WAL\n  pg_last_xlog_replay_location(),\n  -- current transaction timestamp (state of the replica)\n  pg_last_xact_replay_timestamp(),\n  -- current physical time\n  now(),\n  -- the amount of time still to be applied until recovery_target_time has been reached\n  '2018-10-12 09:25:00+00'::timestamptz - pg_last_xact_replay_timestamp() as delay;\n```\n\nWe know recovery is complete when the replay timestamp does not change any more. We can consider setting a [`recovery_target_action`](https://www.postgresql.org/docs/11/recovery-target-settings.html) in order to shut down, promote or pause the instance once replay has completed (the default is to pause).\n\nThe database is now in the state preceding the disastrous query. We can start to export data or otherwise make use of the database. In our case, we exported information about the label that was deleted and its association with issues and merge requests and imported that data into our production database. In other cases with more severe data loss, it can be favorable to promote the replica and continue to use it as a primary. However this means that we lose any data that was entered into the database after the point in time we recovered to.\n\nA more precise alternative to using physical timestamps for targeted recovery is using transaction ids. It is good practice to log transaction ids for e.g. DDL statements (like `DROP TABLE`) using `log_statements = 'ddl'`. If we had a transaction id at hand, we could have used `recovery_target_xid` instead in order to replay to the transaction that preceded the `DELETE` query.\n\nFor the delayed replica, the way back to normal is simple: Revert changes to `recovery.conf` and restart Postgres. After a while, the replica is going to show a delay of eight hours again – ready for any future disasters.\n\n### Benefits for recovery\n\nThe major benefit from a delayed replica over using a cold backup is that it eliminates the step of restoring a full snapshot from the archive. This can easily take hours, depending on network and storage speeds. In our case, it takes roughly five hours to retrieve the full ~2TB basebackup from the archive. In addition to that, we would have to apply 24 hours' worth of WAL in order to recover to the desired state (in the worst case).\n\nWith a delayed replica in place, we get two benefits over a cold backup:\n\n1. No need to retrieve a full basebackup from the archive and\n2. we have a *fixed* window of eight hours' worth of WAL that needs to be replayed to catch up.\n\nIn addition to that, we continuously test our ability to perform PITR from the WAL archive and would quickly realize WAL archive corruption or other WAL-related problems by monitoring the lag of the delayed replica.\n\nIn our example case, completing recovery took 50 minutes and translated to a recovery rate of 110 GB worth of WAL per hour (the archive was still on [AWS S3](https://aws.amazon.com/s3/) at that time). The incident was mitigated and data recovered and restored 90 minutes after work was started.\n\n### Summing up: Where delayed replication can be useful (and where it's not)\n\nDelayed replication can be used as a first resort to recover from accidental data loss and lends itself perfectly to situations where the loss-inducing event is noticed within the configured delay.\n\nLet's be clear though: *Replication is not a backup mechanism*.\n\nBackup and replication are two mechanisms with distinct purposes: A *cold backup* is useful to recover from a disaster, for example an accidental `DELETE` or `DROP TABLE` event. In this case, we utilize a backup from cold storage to restore an earlier state of a table or the whole database. On the other hand, a `DROP TABLE` query replicates nearly instantly to all replicas in a running cluster – hence normal replication on its own is not useful to recover from this scenario. 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With replication only, we'd be out of luck.\n\nNote: For [GitLab.com](https://gitlab.com/), we currently only provide system-level resiliency against data loss and do not provide user-level data recovery in general.\n\nPhoto by [Mathew Schwartz](https://unsplash.com/photos/sb7RUrRMaC4?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\n\n",[23,24],"inside GitLab","open 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statement",{"items":680},[681,684,687],{"text":682,"config":683},"Terms",{"href":508,"dataGaName":509,"dataGaLocation":456},{"text":685,"config":686},"Cookies",{"dataGaName":518,"dataGaLocation":456,"id":519,"isOneTrustButton":27},{"text":688,"config":689},"Privacy",{"href":513,"dataGaName":514,"dataGaLocation":456},[691],{"id":692,"title":18,"body":8,"config":693,"content":695,"description":8,"extension":25,"meta":699,"navigation":27,"path":700,"seo":701,"stem":702,"__hash__":703},"blogAuthors/en-us/blog/authors/andreas-brandl.yml",{"template":694},"BlogAuthor",{"name":18,"config":696},{"headshot":697,"ctfId":698},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749683343/Blog/Author%20Headshots/abrandl-headshot.jpg","abrandl",{},"/en-us/blog/authors/andreas-brandl",{},"en-us/blog/authors/andreas-brandl","QLSNQgA77cNHYZ_7hhNmSc9i5ft9KHX6rvscS_MsZ9U",[705,720,732],{"content":706,"config":718},{"title":707,"description":708,"authors":709,"heroImage":711,"date":712,"body":713,"category":9,"tags":714},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[710],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficulat to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[715,716,717],"tutorial","product","features",{"featured":12,"template":13,"slug":719},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":721,"config":730},{"title":722,"description":723,"authors":724,"heroImage":726,"date":727,"category":9,"tags":728,"body":729},"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.",[725],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[260,612,24],"The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":731,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":733,"config":741},{"title":734,"description":735,"authors":736,"heroImage":737,"date":738,"category":9,"tags":739,"body":740},"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.",[725],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[612,260,716],"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":742,"featured":27,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":744},[745,759,770],{"id":746,"categories":747,"header":749,"text":750,"button":751,"image":756},"ai-modernization",[748],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":752,"config":753},"Get your AI maturity score",{"href":754,"dataGaName":755,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":757},{"src":758},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":760,"categories":761,"header":762,"text":750,"button":763,"image":767},"devops-modernization",[716,558],"Are you just managing tools or shipping innovation?",{"text":764,"config":765},"Get your DevOps maturity score",{"href":766,"dataGaName":755,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":768},{"src":769},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":771,"categories":772,"header":774,"text":750,"button":775,"image":779},"security-modernization",[773],"security","Are you trading speed for security?",{"text":776,"config":777},"Get your security maturity score",{"href":778,"dataGaName":755,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":783,"blurb":784,"button":785,"secondaryButton":790},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":786,"config":787},"Get your free trial",{"href":788,"dataGaName":49,"dataGaLocation":789},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":494,"config":791},{"href":53,"dataGaName":54,"dataGaLocation":789},1773350803611]