[{"data":1,"prerenderedAt":806},["ShallowReactive",2],{"/en-us/blog/why-we-spent-the-last-month-eliminating-postgresql-subtransactions":3,"navigation-en-us":39,"banner-en-us":439,"footer-en-us":449,"blog-post-authors-en-us-Grzegorz Bizon|Stan Hu":691,"blog-related-posts-en-us-why-we-spent-the-last-month-eliminating-postgresql-subtransactions":717,"assessment-promotions-en-us":757,"next-steps-en-us":796},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":28,"isFeatured":13,"meta":29,"navigation":30,"path":31,"publishedDate":22,"seo":32,"stem":36,"tagSlugs":37,"__hash__":38},"blogPosts/en-us/blog/why-we-spent-the-last-month-eliminating-postgresql-subtransactions.yml","Why We Spent The Last Month Eliminating Postgresql Subtransactions",[7,8],"grzegorz-bizon","stan-hu",null,"engineering",{"slug":12,"featured":13,"template":14},"why-we-spent-the-last-month-eliminating-postgresql-subtransactions",false,"BlogPost",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"Why we spent the last month eliminating PostgreSQL subtransactions","How a mysterious stall in database queries uncovered a performance limitation with PostgreSQL.",[19,20],"Grzegorz Bizon","Stan Hu","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749669470/Blog/Hero%20Images/nessie.jpg","2021-09-29","Since last June, we noticed the database on GitLab.com would\nmysteriously stall for minutes, which would lead to users seeing 500\nerrors during this time. Through a painstaking investigation over\nseveral weeks, we finally uncovered the cause of this: initiating a\nsubtransaction via the [`SAVEPOINT` SQL query](https://www.postgresql.org/docs/current/sql-savepoint.html) while\na long transaction is in progress can wreak havoc on database\nreplicas. Thus launched a race, which we recently completed, to\neliminate all `SAVEPOINT` queries from our code. Here's what happened,\nhow we discovered the problem, and what we did to fix it.\n\n### The symptoms begin\n\nOn June 24th, we noticed that our CI/CD runners service reported a high\nerror rate:\n\n![runners errors](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/ci-runners-errors.png)\n\nA quick investigation revealed that database queries used to retrieve\nCI/CD builds data were timing out and that the unprocessed builds\nbacklog grew at a high rate:\n\n![builds queue](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/builds-queue.png)\n\nOur monitoring also showed that some of the SQL queries were waiting for\nPostgreSQL lightweight locks (`LWLocks`):\n\n![aggregated lwlocks](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/aggregated-lwlocks.png)\n\nIn the following weeks we had experienced a few incidents like this. We were\nsurprised to see how sudden these performance degradations were, and how\nquickly things could go back to normal:\n\n![ci queries latency](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/ci-queries-latency.png)\n\n### Introducing Nessie: Stalled database queries\n\nIn order to learn more, we extended our observability tooling [to sample\nmore data from `pg_stat_activity`](https://gitlab.com/gitlab-cookbooks/gitlab-exporters/-/merge_requests/231). In PostgreSQL, the `pg_stat_activity`\nvirtual table contains the list of all database connections in the system as\nwell as what they are waiting for, such as a SQL query from the\nclient. We observed a consistent pattern: the queries were waiting on\n`SubtransControlLock`. Below shows a graph of the URLs or jobs that were\nstalled:\n\n![endpoints locked](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/endpoints-locked.png)\n\nThe purple line shows the sampled number of transactions locked by\n`SubtransControlLock` for the `POST /api/v4/jobs/request` endpoint that\nwe use for internal communication between GitLab and GitLab Runners\nprocessing CI/CD jobs.\n\nAlthough this endpoint was impacted the most, the whole database cluster\nappeared to be affected as many other, unrelated queries timed out.\n\nThis same pattern would rear its head on random days. A week would pass\nby without incident, and then it would show up for 15 minutes and\ndisappear for days. Were we chasing the Loch Ness Monster?\n\nLet's call these stalled queries Nessie for fun and profit.\n\n### What is a `SAVEPOINT`?\n\nTo understand `SubtransControlLock` ([PostgreSQL\n13](https://www.postgresql.org/docs/13/monitoring-stats.html#MONITORING-PG-STAT-ACTIVITY-VIEW)\nrenamed this to `SubtransSLRU`), we first must understand how\nsubtransactions work in PostgreSQL. In PostgreSQL, a transaction can\nstart via a `BEGIN` statement, and a subtransaction can be started with\na subsequent `SAVEPOINT` query. PostgreSQL assigns each of these a\ntransaction ID (XID for short) [when a transaction or a subtransaction\nneeds one, usually before a client modifies data](https://gitlab.com/postgres/postgres/blob/a00c138b78521b9bc68b480490a8d601ecdeb816/src/backend/access/transam/README#L193-L198).\n\n#### Why would you use a `SAVEPOINT`?\n\nFor example, let's say you were running an online store and a customer\nplaced an order. Before the order is fullfilled, the system needs to\nensure a credit card account exists for that user. In Rails, a common\npattern is to start a transaction for the order and call\n[`find_or_create_by`](https://apidock.com/rails/v5.2.3/ActiveRecord/Relation/find_or_create_by). For\nexample:\n\n```ruby\nOrder.transaction do\n  begin\n    CreditAccount.transaction(requires_new: true) do\n      CreditAccount.find_or_create_by(customer_id: customer.id)\n  rescue ActiveRecord::RecordNotUnique\n    retry\n  end\n  # Fulfill the order\n  # ...\nend\n```\n\nIf two orders were placed around the same time, you wouldn't want the\ncreation of a duplicate account to fail one of the orders. Instead, you\nwould want the system to say, \"Oh, an account was just created; let me\nuse that.\"\n\nThat's where subtransactions come in handy: the `requires_new: true`\ntells Rails to start a new subtransaction if the application already is\nin a transaction. The code above translates into several SQL calls that\nlook something like:\n```sql\n--- Start a transaction\nBEGIN\nSAVEPOINT active_record_1\n--- Look up the account\nSELECT * FROM credit_accounts WHERE customer_id = 1\n--- Insert the account; this may fail due to a duplicate constraint\nINSERT INTO credit_accounts (customer_id) VALUES (1)\n--- Abort this by rolling back\nROLLBACK TO active_record_1\n--- Retry here: Start a new subtransaction\nSAVEPOINT active_record_2\n--- Find the newly-created account\nSELECT * FROM credit_accounts WHERE customer_id = 1\n--- Save the data\nRELEASE SAVEPOINT active_record_2\nCOMMIT\n```\n\nOn line 7 above, the `INSERT` might fail if the customer account was\nalready created, and the database unique constraint would prevent a\nduplicate entry. Without the first `SAVEPOINT` and `ROLLBACK` block, the\nwhole transaction would have failed. With that subtransaction, the\ntransaction can retry gracefully and look up the existing account.\n\n### What is `SubtransControlLock`?\n\nAs we mentioned earlier, Nessie returned at random times with queries\nwaiting for `SubtransControlLock`. `SubtransControlLock` indicates that\nthe query is waiting for PostgreSQL to load subtransaction data from\ndisk into shared memory.\n\nWhy is this data needed? When a client runs a `SELECT`, for example,\nPostgreSQL needs to decide whether each version of a row, known as a\ntuple, is actually visible within the current transaction. It's possible\nthat a tuple has been deleted or has yet to be committed by another\ntransaction. Since only a top-level transaction can actually commit\ndata, PostgreSQL needs to map a subtransaction ID (subXID) to its parent\nXID.\n\nThis mapping of subXID to parent XID is stored on disk in the\n`pg_subtrans` directory. Since reading from disk is slow, PostgreSQL\nadds a simple least-recently used (SLRU) cache in front for each\nbackend process. The lookup is fast if the desired page is already\ncached. However, as [Laurenz Albe discussed in his blog\npost](https://www.cybertec-postgresql.com/en/subtransactions-and-performance-in-postgresql/),\nPostgreSQL may need to read from disk if the number of active\nsubtransactions exceeds 64 in a given transaction, a condition\nPostgreSQL terms `suboverflow`. Think of it as the feeling you might get\nif you ate too many Subway sandwiches.\n\nSuboverflowing (is that a word?) can bog down performance because as\nLaurenz said, \"Other transactions have to update `pg_subtrans` to\nregister subtransactions, and you can see in the perf output how they\nvie for lightweight locks with the readers.\"\n\n### Hunting for nested subtransactions\n\nLaurenz's blog post suggested that we might be using too many\nsubtransactions in one transaction. At first, we suspected we might be\ndoing this in some of our expensive background jobs, such as project\nexport or import. However, while we did see numerous `SAVEPOINT` calls\nin these jobs, we didn't see an unusual degree of nesting in local\ntesting.\n\nTo isolate the cause, we started by [adding Prometheus metrics to track\nsubtransactions as a Prometheus metric by model](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/66477).\nThis led to nice graphs as the following:\n\n![subtransactions plot](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/subtransactions-plot.png)\n\nWhile this was helpful in seeing the rate of subtransactions over time,\nwe didn't see any obvious spikes that occurred around the time of the\ndatabase stalls. Still, it was possible that suboverflow was happening.\n\nTo see if that was happening, we [instrumented our application to track\nsubtransactions and log a message whenever we detected more than 32\n`SAVEPOINT` calls in a given transaction](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/67918). Rails\nmakes it possible for the application to subscribe to all of its SQL\nqueries via `ActiveSupport` notifications. Our instrumentation looked\nsomething like this, simplified for the purposes of discussion:\n\n```ruby\nActiveSupport::Notifications.subscribe('sql.active_record') do |event|\n  sql = event.payload.dig(:sql).to_s\n  connection = event.payload[:connection]\n  manager = connection&.transaction_manager\n\n  context = manager.transaction_context\n  return if context.nil?\n\n  if sql.start_with?('BEGIN')\n    context.set_depth(0)\n  elsif cmd.start_with?('SAVEPOINT', 'EXCEPTION')\n    context.increment_savepoints\n  elsif cmd.start_with?('ROLLBACK TO SAVEPOINT')\n    context.increment_rollbacks\n  elsif cmd.start_with?('RELEASE SAVEPOINT')\n    context.increment_releases\n  elsif sql.start_with?('COMMIT', 'ROLLBACK')\n    context.finish_transaction\n  end\nend\n```\n\nThis code looks for the key SQL commands that initiate transactions and\nsubtransactions and increments counters when they occurred. After a\n`COMMIT,` we log a JSON message that contained the backtrace and the\nnumber of `SAVEPOINT` and `RELEASES` calls. For example:\n\n```json\n{\n  \"sql\": \"/*application:web,correlation_id:01FEBFH1YTMSFEEHS57FA8C6JX,endpoint_id:POST /api/:version/projects/:id/merge_requests/:merge_request_iid/approve*/ BEGIN\",\n  \"savepoints_count\": 1,\n  \"savepoint_backtraces\": [\n    [\n      \"app/models/application_record.rb:75:in `block in safe_find_or_create_by'\",\n      \"app/models/application_record.rb:75:in `safe_find_or_create_by'\",\n      \"app/models/merge_request.rb:1859:in `ensure_metrics'\",\n      \"ee/lib/analytics/merge_request_metrics_refresh.rb:11:in `block in execute'\",\n      \"ee/lib/analytics/merge_request_metrics_refresh.rb:10:in `each'\",\n      \"ee/lib/analytics/merge_request_metrics_refresh.rb:10:in `execute'\",\n      \"ee/app/services/ee/merge_requests/approval_service.rb:57:in `calculate_approvals_metrics'\",\n      \"ee/app/services/ee/merge_requests/approval_service.rb:45:in `block in create_event'\",\n      \"ee/app/services/ee/merge_requests/approval_service.rb:43:in `create_event'\",\n      \"app/services/merge_requests/approval_service.rb:13:in `execute'\",\n      \"ee/app/services/ee/merge_requests/approval_service.rb:14:in `execute'\",\n      \"lib/api/merge_request_approvals.rb:58:in `block (3 levels) in \u003Cclass:MergeRequestApprovals>'\",\n    ]\n  \"rollbacks_count\": 0,\n  \"releases_count\": 1\n}\n```\n\nThis log message contains not only the number of subtransactions via\n`savepoints_count`, but it also contains a handy backtrace that\nidentifies the exact source of the problem. The `sql` field also\ncontains [Marginalia comments](https://github.com/basecamp/marginalia)\nthat we tack onto every SQL query. These comments make it possible to\nidentify what HTTP request initiated the SQL query.\n\n### Taking a hard look at PostgreSQL\n\nThe new instrumentation showed that while the application regularly used\nsubtransactions, it never exceeded 10 nested `SAVEPOINT` calls.\n\nMeanwhile, [Nikolay Samokhvalov](https://gitlab.com/NikolayS), founder\nof [Postgres.ai](https://postgres.ai/), performed a battery of tests [trying to replicate the problem](https://gitlab.com/postgres-ai/postgresql-consulting/tests-and-benchmarks/-/issues/20).\nHe replicated Laurenz's results when a single transaction exceeded 64\nsubtransactions, but that wasn't happening here.\n\nWhen the database stalls occurred, we observed a number of patterns:\n\n1. Only the replicas were affected; the primary remained unaffected.\n1. There was a long-running transaction, usually relating to\nPostgreSQL's autovacuuming, during the time. The stalls stopped quickly after the transaction ended.\n\nWhy would this matter? Analyzing the PostgreSQL source code, Senior\nSupport Engineer [Catalin Irimie](https://gitlab.com/cat) [posed an\nintriguing question that led to a breakthrough in our understanding](https://gitlab.com/gitlab-org/gitlab/-/issues/338410#note_652056284):\n\n> Does this mean that, having subtransactions spanning more than 32 cache pages, concurrently, would trigger the exclusive SubtransControlLock because we still end up reading them from the disk?\n\n### Reproducing the problem with replicas\n\nTo answer this, Nikolay immediately modified his test [to involve replicas and long-running transactions](https://gitlab.com/postgres-ai/postgresql-consulting/tests-and-benchmarks/-/issues/21#note_653453774). Within a day, he reproduced the problem:\n\n![Nikolay experiment](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/nikolay-experiment.png)\n\nThe image above shows that transaction rates remain steady around\n360,000 transactions per second (TPS). Everything was proceeding fine\nuntil the long-running transaction started on the primary. Then suddenly\nthe transaction rates plummeted to 50,000 TPS on the replicas. Canceling\nthe long transaction immediately caused the transaction rate to return.\n\n### What is going on here?\n\nIn his blog post, Nikolay called the problem [Subtrans SLRU overflow](https://v2.postgres.ai/blog/20210831-postgresql-subtransactions-considered-harmful#problem-4-subtrans-slru-overflow).\nIn a busy database, it's possible for the size of the subtransaction log\nto grow so large that the working set no longer fits into memory. This\nresults in a lot of cache misses, which in turn causes a high amount of\ndisk I/O and CPU as PostgreSQL furiously tries to load data from disk to\nkeep up with all the lookups.\n\nAs mentioned earlier, the subtransaction cache holds a mapping of the\nsubXID to the parent XID. When PostgreSQL needs to look up the subXID,\nit calculates in which memory page this ID would live, and then does a\nlinear search to find in the memory page. If the page is not in the\ncache, it evicts one page and loads the desired one into memory. The\ndiagram below shows the memory layout of the subtransaction SLRU.\n\n![Subtrans SLRU](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/subtrans-slru.png)\n\nBy default, each SLRU page is an 8K buffer holding 4-byte parent\nXIDs. This means 8192/4 = 2048 transaction IDs can be stored in each\npage.\n\nNote that there may be gaps in each page. PostgreSQL will cache XIDs as\nneeded, so a single XID can occupy an entire page.\n\nThere are 32 (`NUM_SUBTRANS_BUFFERS`) pages, which means up to 65K\ntransaction IDs can be stored in memory. Nikolay demonstrated that in a\nbusy system, it took about 18 seconds to fill up all 65K entries. Then\nperformance dropped off a cliff, making the database replicas unusable.\n\nTo our surprise, our experiments also demonstrated that a single\n`SAVEPOINT` during a long-transaction [could initiate this problem if\nmany writes also occurred simultaneously](https://gitlab.com/gitlab-org/gitlab/-/issues/338865#note_655312474). That\nis, it wasn't enough just to reduce the frequency of `SAVEPOINT`; we had\nto eliminate them completely.\n\n#### Why does a single `SAVEPOINT` cause problems?\n\nTo answer this question, we need to understand what happens when a\n`SAVEPOINT` occurs in one query while a long-running transaction is\nrunning.\n\nWe mentioned earlier that PostgreSQL needs to decide whether a given row\nis visible to support a feature called [multi-version concurrency control](https://www.postgresql.org/docs/current/mvcc.html), or MVCC for\nshort. It does this by storing hidden columns, `xmin` and `xmax`, in\neach tuple.\n\n`xmin` holds the XID of when the tuple was created, and `xmax` holds the\nXID when it was marked as dead (0 if the row is still present). In\naddition, at the beginning of a transaction, PostgreSQL records metadata\nin a database snapshot. Among other items, this snapshot records the\noldest XID and the newest XID in its own `xmin` and `xmax` values.\n\nThis metadata helps [PostgreSQL determine whether a tuple is visible](https://www.interdb.jp/pg/pgsql05.html).\nFor example, a committed XID that started before `xmin` is definitely\nvisible, while anything after `xmax` is invisible.\n\n### What does this have to do with long transactions?\n\nLong transactions are bad in general because they can tie up\nconnections, but they can cause a subtly different problem on a\nreplica. On the replica, a single `SAVEPOINT` during a long transaction\ncauses a snapshot to suboverflow. Remember that dragged down performance\nin the case where we had more than 64 subtransactions.\n\nFundamentally, the problem happens because a replica behaves differently\nfrom a primary when creating snapshots and checking for tuple\nvisibility. The diagram below illustrates an example with some of the\ndata structures used in PostgreSQL:\n\n![Diagram of subtransaction handling in replicas](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/pg-replica-subtransaction-diagram.png)\n\nOn the top of this diagram, we can see the XIDs increase at the\nbeginning of a subtransaction: the `INSERT` after the `BEGIN` gets 1,\nand the subsequent `INSERT` in `SAVEPOINT` gets 2. Another client comes\nalong and performs a `INSERT` and `SELECT` at XID 3.\n\nOn the primary, PostgreSQL stores the transactions in progress in a\nshared memory segment. The process array (`procarray`) stores XID 1 with\nthe first connection, and the database also writes that information to\nthe `pg_xact` directory. XID 2 gets stored in the `pg_subtrans`\ndirectory, mapped to its parent, XID 1.\n\nIf a read happens on the primary, the snapshot generated contains `xmin`\nas 1, and `xmax` as 3. `txip` holds a list of transactions in progress,\nand `subxip` holds a list of subtransactions in progress.\n\nHowever, neither the `procarray` nor the snapshot are shared directly\nwith the replica. The replica receives all the data it needs from the\nwrite-ahead log (WAL).\n\nPlaying the WAL back one entry at time, the replica populates a shared data\nstructure called `KnownAssignedIds`. It contains all the transactions in\nprogress on the primary. Since this structure can only hold a limited number of\nIDs, a busy database with a lot of active subtransactions could easily fill\nthis buffer. PostgreSQL made a design choice to kick out all subXIDs from this\nlist and store them in the `pg_subtrans` directory.\n\nWhen a snapshot is generated on the replica, notice how `txip` is\nblank. A PostgreSQL replica treats **all** XIDs as though they are\nsubtransactions and throws them into the `subxip` bucket. That works\nbecause if a XID has a parent XID, then it's a subtransaction. Otherwise, it's a normal transaction. [The code comments\nexplain the rationale](https://gitlab.com/postgres/postgres/blob/9f540f840665936132dd30bd8e58e9a67e648f22/src/backend/storage/ipc/procarray.c#L1665-L1681).\n\nHowever, this means the snapshot is missing subXIDs, and that could be\nbad for MVCC. To deal with that, the [replica also updates `lastOverflowedXID`](https://gitlab.com/postgres/postgres/blob/9f540f840665936132dd30bd8e58e9a67e648f22/src/backend/storage/ipc/procarray.c#L3176-L3182):\n\n```c\n\n * When we throw away subXIDs from KnownAssignedXids, we need to keep track of\n * that, similarly to tracking overflow of a PGPROC's subxids array.  We do\n * that by remembering the lastOverflowedXID, ie the last thrown-away subXID.\n * As long as that is within the range of interesting XIDs, we have to assume\n * that subXIDs are missing from snapshots.  (Note that subXID overflow occurs\n * on primary when 65th subXID arrives, whereas on standby it occurs when 64th\n * subXID arrives - that is not an error.)\n\n```\n\nWhat is this \"range of interesting XIDs\"? We can see this in [the code below](https://gitlab.com/postgres/postgres/blob/4bf0bce161097869be5a56706b31388ba15e0113/src/backend/storage/ipc/procarray.c#L1702-L1703):\n\n```c\nif (TransactionIdPrecedesOrEquals(xmin, procArray->lastOverflowedXid))\n    suboverflowed = true;\n\n```\n\nIf `lastOverflowedXid` is smaller than our snapshot's `xmin`, it means\nthat all subtransactions have completed, so we don't need to check for\nsubtransactions. However, in our example:\n\n1. `xmin` is 1 because of the transaction.\n2. `lastOverflowXid` is 2 because of the `SAVEPOINT`.\n\nThis means `suboverflowed` is set to `true` here, which tells PostgreSQL\nthat whenever a XID needs to be checked, check to see if it has a parent\nXID. Remember that this causes PostgreSQL to:\n\n1. Look up the subXID for the parent XID in the SLRU cache.\n1. If this doesn't exist in the cache, fetch the data from `pg_trans`.\n\nIn a busy system, the requested XIDs could span an ever-growing range of\nvalues, which could easily exhaust the 64K entries in the SLRU\ncache. This range will continue to grow as long as the transaction runs;\nthe rate of increase depends on how many updates are happening on the\nprmary. As soon as the transaction terminates, the `suboverflowed` state\ngets set to `false`.\n\nIn other words, we've replicated the same conditions as we saw with 64\nsubtransactions, only with a single `SAVEPOINT` and a long transaction.\n\n### What can we do about getting rid of Nessie?\n\nThere are three options:\n\n1. Eliminate `SAVEPOINT` calls completely.\n1. Eliminate all long-running transactions.\n1. Apply [Andrey Borodin's patches to PostgreSQL and increase the subtransaction cache](https://www.postgresql.org/message-id/flat/494C5E7F-E410-48FA-A93E-F7723D859561%40yandex-team.ru#18c79477bf7fc44a3ac3d1ce55e4c169).\n\nWe chose the first option because most uses of subtransaction could be\nremoved fairly easily. There were a [number of approaches](https://gitlab.com/groups/gitlab-org/-/epics/6540) we took:\n\n1. Perform updates outside of a subtransaction. Examples: [1](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/68471), [2](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/68690)\n1. Rewrite a query to use a `INSERT` or an `UPDATE` with an `ON CONFLICT` clause to deal with duplicate constraint violations. Examples: [1](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/68433), [2](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/69240), [3](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/68509)\n1. Live with a non-atomic `find_or_create_by`. We used this approach sparingly. Example: [1](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/68649)\n\nIn addition, we added [an alert whenever the application used a a single `SAVEPOINT`](https://gitlab.com/gitlab-com/runbooks/-/merge_requests/3881):\n\n![subtransaction alert](https://about.gitlab.com/images/blogimages/postgresql-subtransactions/subtransactions-alert-example.png)\n\nThis had the side benefit of flagging a [minor bug](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/70889).\n\n#### Why not eliminate all long-running transactions?\n\nIn our database, it wasn't practical to eliminate all long-running\ntransactions because we think many of them happened via [database\nautovacuuming](https://www.postgresql.org/docs/current/runtime-config-autovacuum.html),\nbut [we're not able to reproduce this yet](https://gitlab.com/postgres-ai/postgresql-consulting/tests-and-benchmarks/-/issues/21#note_669698320).\nWe are working on partitioning the tables and sharding the database, but this is a much more time-consuming problem\nthan removing all subtransactions.\n\n#### What about the PostgreSQL patches?\n\nAlthough we tested Andrey's PostgreSQL patches, we did not feel comfortable\ndeviating from the official PostgreSQL releases. Plus, maintaining a\ncustom patched release over upgrades would add a significant maintenance\nburden for our infrastructure team. Our self-managed customers would\nalso not benefit unless they used a patched database.\n\nAndrey's patches do two main things:\n\n1. Allow administrators to change the SLRU size to any value.\n1. Adds an [associative cache](https://www.youtube.com/watch?v=A0vR-ks3hsQ).\nto make it performant to use a large cache value.\n\nRemember that the SLRU cache does a linear search for the desired\npage. That works fine when there are only 32 pages to search, but if you\nincrease the cache size to 100 MB the search becomes much more\nexpensive. The associative cache makes the lookup fast by indexing pages\nwith a bitmask and looking up the entry with offsets from the remaining\nbits. This mitigates the problem because a transaction would need to be\nseveral magnitudes longer to cause a problem.\n\nNikolay demonstrated that the `SAVEPOINT` problem disappeared as soon as\nwe increased the SLRU size to 100 MB with those patches. With a 100 MB\ncache, PostgreSQL can cache 26.2 million IDs (104857600/4), far more\nthan the measely 65K.\n\nThese [patches are currently awaiting review](https://postgres.ai/blog/20210831-postgresql-subtransactions-considered-harmful#ideas-for-postgresql-development),\nbut in our opinion they should be given high priority for PostgreSQL 15.\n\n### Conclusion\n\nSince removing all `SAVEPOINT` queries, we have not seen Nessie rear her\nhead again. If you are running PostgreSQL with read replicas, we\nstrongly recommend that you also remove *all* subtransactions until\nfurther notice.\n\nPostgreSQL is a fantastic database, and its well-commented code makes it\npossible to understand its limitations under different configurations.\n\nWe would like to thank the GitLab community for bearing with us while we\niron out this production issue.\n\nWe are also grateful for the support from [Nikolay\nSamokhvalov](https://gitlab.com/NikolayS) and [Catalin\nIrimie](https://gitlab.com/cat), who contributed to understanding where our\nLoch Ness Monster was hiding.\n\nCover image by [Khadi Ganiev](https://www.istockphoto.com/portfolio/Ganiev?mediatype=photography) on [iStock](https://istock.com), licensed under [standard 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to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[723],"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/)",[728,729,730],"tutorial","product","features",{"featured":13,"template":14,"slug":732},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":734,"config":744},{"title":735,"description":736,"authors":737,"heroImage":739,"date":740,"category":10,"tags":741,"body":743},"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.",[738],"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",[261,613,742],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":745,"featured":13,"template":14},"how-iit-bombay-students-code-future-with-gitlab",{"content":747,"config":755},{"title":748,"description":749,"authors":750,"heroImage":751,"date":752,"category":10,"tags":753,"body":754},"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.",[738],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[613,261,729],"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":756,"featured":30,"template":14},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":758},[759,773,784],{"id":760,"categories":761,"header":763,"text":764,"button":765,"image":770},"ai-modernization",[762],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":766,"config":767},"Get your AI maturity score",{"href":768,"dataGaName":769,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":774,"categories":775,"header":776,"text":764,"button":777,"image":781},"devops-modernization",[729,559],"Are you just managing tools or shipping innovation?",{"text":778,"config":779},"Get your DevOps maturity score",{"href":780,"dataGaName":769,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":785,"categories":786,"header":788,"text":764,"button":789,"image":793},"security-modernization",[787],"security","Are you trading speed for security?",{"text":790,"config":791},"Get your security maturity score",{"href":792,"dataGaName":769,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":794},{"src":795},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":797,"blurb":798,"button":799,"secondaryButton":804},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":800,"config":801},"Get your free trial",{"href":802,"dataGaName":50,"dataGaLocation":803},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":495,"config":805},{"href":54,"dataGaName":55,"dataGaLocation":803},1773350825030]