[{"data":1,"prerenderedAt":816},["ShallowReactive",2],{"/en-us/blog/gitlab-pg-upgrade":3,"navigation-en-us":35,"banner-en-us":445,"footer-en-us":455,"blog-post-authors-en-us-Jose Finotto":697,"blog-related-posts-en-us-gitlab-pg-upgrade":711,"blog-promotions-en-us":753,"next-steps-en-us":806},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":12,"meta":25,"navigation":26,"path":27,"publishedDate":20,"seo":28,"stem":32,"tagSlugs":33,"__hash__":34},"blogPosts/en-us/blog/gitlab-pg-upgrade.yml","Gitlab Pg Upgrade",[7],"jose-finotto",null,"engineering",{"slug":11,"featured":12,"template":13},"gitlab-pg-upgrade",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How we upgraded PostgreSQL at GitLab.com","We explain the precise maintenance process to execute a major version upgrade of PostgreSQL.",[18],"Jose Finotto","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668002/Blog/Hero%20Images/pg-gear.jpg","2020-09-11","\n\nWe teamed up with [OnGres](https://ongres.com/) to [perform a major version upgrade of GitLab.com's main Postgres cluster from version 9.6 to 11](https://status.gitlab.com/pages/maintenance/5b36dc6502d06804c08349f7/5ea322c1d1097004ba30d227) back in May 2020. We upgraded it during a maintenance window, and it all went according to plan. We unpack all that was involved – from planning, testing, and full process automation – to achieve a near-perfect execution of the PostgreSQL upgrade. The full operation was recorded and you can [watch it on GitLab Unfiltered](https://youtu.be/TKODwTtKWew).\n\nThe biggest challenge was to do a complete fleet major upgrade through an orchestrated [pg_upgrade](https://www.postgresql.org/docs/11/pgupgrade.html). We needed to have a rollback plan to optimize our capacity right after [Recovery Time Objective (RTO)](https://en.wikipedia.org/wiki/Disaster_recovery) while maintaining a 12-node cluster’s 6TB-data consistent serving 300.000 aggregated transactions per second from around six million users.\n\nThe best way to resolve an engineering challenge is to follow the blueprints and design docs. In the process of creating the blueprint, you define the problem that we are attempting to solve, evaluate the most suitable solutions, and consider the pros and cons of each solution. Here is a [link](https://gitlab.com/gitlab-com/gl-infra/readiness/-/tree/master/library/database/postgres/Postgresql-upgrade/blueprint/) to the blueprint from the project.\n\nAfter the blueprint comes the design process. The implementation is detailed in the design process, where we explain the steps and requirements involved in executing the design. The design doc from the project is [linked here](https://gitlab.com/gitlab-com/gl-infra/readiness/-/tree/master/library/database/postgres/Postgresql-upgrade/design).\n\n## Why we upgraded PostgreSQL\n\nWe made a business decision in GitLab 13.0 to discontinue support for Postgresql 10.0. PostgreSQL version 9.6 is becoming EOL in November 2021, so we needed to take action.\n\nHere are some of the main differences in features [between PostgreSQL versions 9.6 and 11](https://why-upgrade.depesz.com/show?from=9.6.18&to=11.7&keywords=):\n\n * Native table partitioning, supporting LIST, RANGE, and HASH.\n * Transaction supporting in stored procedures.\n * [Just-in-time (JIT) compilation](https://www.postgresql.org/about/news/1894/) for accelerating the execution of expressions in queries.\n * Query parallelism improvements and adds parallelized data definition capabilities.\n * The new PostgreSQL version comes with the \"[Logical Replication - A publish/subscribe framework for distributing data](https://www.postgresql.org/about/news/1786/)\" that was introduced in version 10. This feature enables smoother future upgrades and simplifies other relevant processes.\n * Quorum-based commit that would ensure our transactions are committed in the specified nodes from the cluster.\n * Improved performance for queries over partitioned tables\n\n## The environment and architecture\n\nThe infrastructure capacity of the PostgreSQL cluster consisted of 12 n1-highmem-96 GCP instances for OLTP and asynchronous pipelines purposes – plus two BI nodes within different specs, each one with 96 CPU cores and 614GB RAM. The cluster HA is managed and configured through [Patroni](https://github.com/zalando/patroni), which keeps a consistent leader election through a Consul cluster and all its replicas working with asynchronous streaming replication using replication slots and WAL shipping against a GCS storage bucket.\nOur setup currently uses Patroni HA solution, which constantly gathers critical information about the cluster, leader detection, and node availability. It is implemented using key features from Consul, such as DNS service, which in turn updates PgBouncer endpoints, keeping a different architecture for traffic read-write and read-only.\n\n![GitLab.com Architecture](https://about.gitlab.com/images/blogimages/pg-up-arch.png)\n[GitLab.com architecture](https://handbook.gitlab.com/handbook/engineering/infrastructure/production/architecture/#database-architecture)\n\n\nFor HA purposes, two of the replicas are out of the read-only server list pool, used by the API, and served by Consul DNS. After several enhancements to Gitlab's architecture, we were able to downscale the fleet to seven nodes.\n\nFurthermore, the entire cluster handles a weekly average of approximately 181,000 transactions per second. As the image below indicates, the traffic increases on Monday and maintains the throughput during the week right up to Friday/Saturday. The traffic data was critical to set up a proper maintenance window so we can impact the fewest users.\n\n![GitLab.com Connection Numbers](https://about.gitlab.com/images/blogimages/pg-up-prom1.png)\nNumber of connections at GitLab.com\n\n\nThe fleet is reaching 250,000 transactions per second in the busiest hours of the day.\n\n![GitLab.com Commits](https://about.gitlab.com/images/blogimages/pg-up-prom2.png)\nThe number of commits at GitLab.com.\n\n\nIt is also handling spikes of 300,000 transactions per second. GitLab.com is reaching 60,000 connections per second.\n\n## Our upgrade requirements\n\nWe established a number of requirements before proceeding with the upgrade at production.\n\n * No regressions should be on PostgreSQL 11. We developed a custom benchmark to perform extensive regression testing. The goal was to identify potential query performance degradation in PostgreSQL 11.\n * The upgrade should be done across the whole fleet within the maintenance window.\n * Use pg_upgrade which relies on physical, and not logical, replication.\n * Keep a 9.6 cluster sample: Not all the nodes should be upgraded, a few of them should be left in 9.6 as a rollback procedure.\n * The upgrade should be fully automated to reduce the chance of any human error.\n * Only 30 minutes of maintenance threshold time for all the database upgrades.\n * The upgrade will be recorded and published.\n\n## The project\n\nTo accomplish a smooth execution in production, the project had the following phases:\n\n### Phase one: Develop automation in a isolated environment\n\n* Develop the [ansible-playbook](https://gitlab.com/gitlab-com/gl-infra/db-migration/-/tree/master/pg-upgrade) and test on a PostgreSQL environment (created using a back-up from staging) for these tests.\n* We used a separate environment to have the freedom to stop, initiate or restore the backup at any time, to focus on the development, and be able to restore an environment shortly before the upgrade.\n* We used a backup from staging to get the upgrade project in contact with the environment, where we faced some challenges such as migrating the procedures that are different for monitoring in our database.\n\n### Phase two: Integrate development with our configuration management in staging\n\n* Integrate with our configuration management in Chef, and execute a snapshot from the database disk that could be used in a restore scenario.\n* We told our customers that we would schedule a maintenance window with the goals of having the least impact possible on their work and to execute a safe upgrade without any risk of data loss.\n* After iterating and testing the integration to our configuration management we started to execute end-to-end tests in staging. Those tests were announced internally, so the other teams that share this environment would know that staging would be unavailable for a period of time.\n\n### Phase three: Test the upgrade end-to-end in staging\n\n * Pre-flight checks on the environment. We sometimes found problems with credentials or made tiny adjustments to improve the efficiency of our tests.\n * Stop all the applications and traffic to GitLab.com, add a maintenance mode in CloudFlare and HA-proxy, and stop all the applications that accessed the database, sidekiq, workhorse, WEB-API, etc.\n * Upgrade three nodes from the six node cluster. We had a similar strategy in production with a rollback scenario in mind.\n * Execute the ansible-playbook for the PostgreSQL upgrade, first on the leader database node, and after on the secondaries nodes.\n * Regarding post upgrade: We executed some automated tests in our ansible-playbook, checking that the replication and data were consistent.\n * Next, we started the applications to enable our QA team to execute several tests suites. They executed local unit tests on the upgraded database. We investigated negative results.\n * Once we finished the test we stopped the applications again to restore the staging cluster to version 9.6 and shut down the upgraded nodes to version 11, and started the old cluster. Where Patroni will promote one of the nodes, start the applications and the cluster could receive the traffic back. We restored the Chef configuration to the cluster 9.6 and rebuilt those databases to have six nodes ready for the next test.\n\nWe executed seven tests in staging in total, iterating to perfect the team's execution.\n\n### Phase four: Upgrade in production\n\nIn production, the steps were very similar to staging, and our plan was to have eight nodes migrated and four left behind as a backup:\n\n * Execute the pre-checks for the project.\n * Announce the start of the maintenance.\n * Execute the ansible-playbook to stop the traffic and application.\n * Execute the ansible-playbook to carry out the PostgreSQL upgrade.\n * Start the validation tests and restore the traffic. We performed the minimum amount of tests required, so we could fit everything in the narrow maintenance window.\n\nThe rollback plan would only be called in case of any problems with the database consistency, or errors in the QA test. The steps included:\n\n * Stop the cluster with PostgreSQL 11.\n * Restore the configuration in Chef to PostgreSQL 9.6.\n * Initialize the cluster with the four nodes in version 9.6. With these four nodes, we could restore the activity for GitLab.com when traffic was quieter.\n * Start receiving traffic – with this approach we could minimize downtime.\n * Recreate the other nodes using disk snapshot image that were taken during the maintenance and before the upgrade.\n\nAll the steps of the upgrade are detailed in the template used to execute the project.\n\n## How pg_upgrade works\n\nThe [pg_upgrade](https://www.postgresql.org/docs/11/pgupgrade.html) process allows us to upgrade data files from PostgreSQL to a later PostgreSQL major version, without using a dump/reload strategy which would require more downtime.\n\nAs explained in the [official PostgreSQL documentation](https://www.postgresql.org/docs/11/pgupgrade.html), the pg_upgrade tool avoids performing the dump/restore method to upgrade the PostgreSQL version. There are some important details to review before proceeding with this tool. Major PostgreSQL releases add new features that often change the layout of the system tables, but the internal data storage format rarely changes. If a major release changes the data storage format, pg_upgrade could not be used, so we must verify what changes were included between the major versions.\n\nIt is important that any external modules are also binary-compatible, though this cannot be checked by pg_upgrade. For the GitLab upgrade, we uninstalled views/extensions such as [postgres_exporter](https://github.com/wrouesnel/postgres_exporter) before the upgrade, to recreate them after the upgrade (with slight modifications for compatibility reasons).\n\nBefore performing the upgrade, the new version binaries have to be installed. The new binaries from PostgreSQL and extensions were installed in the set of hosts, that were listed to be upgraded.\n\nThere are some options when using pg_upgrade. We chose to use pg_upgrade's link mode on the Leader node because of our narrow, two-hour maintenance window. This method avoids copying the 6TB data files by hard linking files through [inode](https://en.wikipedia.org/wiki/Inode). The drawback is the old data cluster could not be rolled back to 9.6. We provided a rollback path via the replicas kept in 9.6 and GCP snapshots as a secondary choice.\nRebuilding the replicas from scratch was not an option either so we used rsync to upgrade them using incremental features. pg_upgrade's documentation says: \"From a directory on the primary server that is above the old and new database cluster directories, run this on the primary for each standby server\".\n\nThe ansible-playbook implemented this step by having a task from the leader node to each replica, triggering the rsync command from the parent directory of both new and old datadirs.\n\n## Regression testing benchmarks\n\nAny migration or database upgrade requires a regression test before performing the final production upgrade. For the team, the database test was a key step in this process, executing performance tests based on the query load from production, captured in the table pg_stat_statements. These were executed in the same dataset - once for the 9.6 version and another iteration for version 11. The process was captured in the following public issues:\n\n * [Preparing the tool](https://gitlab.com/gitlab-com/gl-infra/infrastructure/-/issues/7817)\n * [Creating the test environment](https://gitlab.com/gitlab-com/gl-infra/infrastructure/-/issues/9177)\n * [Capacity planning](https://gitlab.com/gitlab-com/gl-infra/infrastructure/-/issues/9094)\n * [Run the benchmark with JMeter tool](https://gitlab.com/gitlab-com/gl-infra/infrastructure/-/issues/9545)\n\nFinally, based on OnGres work on this benchmark, GitLab will be following up with a new benchmark test for the future:\n\n * [Capacity assessment for our main production DB cluster](https://gitlab.com/gitlab-com/gl-infra/infrastructure/-/issues/10258)\n * [Database capacity and saturation analysis](https://gitlab.com/gitlab-com/gl-infra/infrastructure/-/issues/10340)\n\n### The upgrade process: automate everything\n\nDuring the upgrade project, the upgrade teams have a strong commitment to Infrastructure as Code (IaC) and automation: All the processes had to be fully automated in order to keep any human error to a minimum during the maintenance window. All the steps for pg_upgrade execution are detailed at this [Gitlab pg_upgrade template issue](https://gitlab.com/gitlab-com/gl-infra/db-migration/-/blob/master/.gitlab/issue_templates/pg_upgrade.md).\n\nThe GitLab.com environment is managed by Terraform and Chef. All the automation for the upgrade was scripted via Ansible 2.9 playbooks and roles, where we used two ansible-playbooks to automate the upgrade:\n\nOne [ansible-playbook](https://gitlab.com/gitlab-com/gl-infra/ansible-migrations/-/tree/master/maintenance-mode) controlled the traffic and the applications:\n\n * Put Cloudflare in maintenance and do not receive traffic.\n * Stop HA-proxy\n * Stop the middleware that accesses the database:\n   * Sidekiq\n   * Workhorse\n   * WEB-API\n\nThe second [ansible-playbook](https://gitlab.com/gitlab-com/gl-infra/db-migration/-/tree/master/pg-upgrade) executed the upgrade process:\n\n * Orchestrate all the database and pools traffic\n * Control Patroni cluster and Consul instances\n * Execute the upgrade on the primary and secondary nodes\n * Collect statistics after the upgrade\n * Synchronize the changes using Chef to keep the integrity with our configuration management\n * Verify the integrity and status of the cluster\n * Execute a GCP snapshot\n * Possible rollback process\n\nThe playbook was run interactively task by task, providing the operator with the ability to skip or pause in any given execution point. Every step was reviewed by all the teams that participated in the tests and iterations in staging for the upgrade.\nThe staging environment allowed us to rehearse and find issues using the same procedure that we planned to use in production. After executing and iterating the automated process in staging we reached a quasi-flawless upgrade of PostgreSQL 9.6 to version 11.\n\nTo complete the release, the QA GitLab team reported errors that happened on some of the tests. Find the reference for this work in [this issue note](https://gitlab.com/groups/gitlab-com/gl-infra/-/epics/106#note_332170837).\n\n### Pre-upgrade steps of the PostgreSQL\n\nThe first part of the process was the \"pre-upgrade\" section, which deals with the instances reserved for rollback purposes. We did the corresponding analysis to ensure that the new cluster could start with eight out of 12 instances of the fleet without losing throughput, reserving four instances for a potential rollback scenario - where they could be brought as a 9.6 cluster via standard Patroni cluster synchronization.\n\nIt was necessary also in this phase to stop Postgres-dependent services, such as PgBouncer, Chef Client, and Patroni services.\n\nBefore proceeding with the upgrade itself, Patroni had to be signaled to avoid any spurious leader election, take a consistent backup through GCP Snapshots (using corresponding [low-level backup API](https://www.cybertec-postgresql.com/en/exclusive-backup-deprecated-what-now/?gclid=CjwKCAjwltH3BRB6EiwAhj0IUBjiSxBdmS11SUpITLCmk-oPkBa7udOWyA6bK6hig8neaiJc8n1WexoCq8UQAvD_BwE)) and apply the new settings via Chef run.\n\n### The upgrade phase of the PostgreSQL\n\nFirst, we stopped all the nodes.\n\nWe executed these checks:\n\n* pg_upgrade's version check\n* Verify that all the nodes were synchronized and not receiving any traffic.\n\nOnce the primary node data was upgraded, an rsync process was triggered for syncing the data with the replicas. After the upgrade was done, the Patroni service was started up and all the replicas caught up easily with the new cluster configuration.\n\nThe binaries were installed by Chef and the setup of the new cluster on the version was defined in the same MR that would install the extensions used in the database, from GitLab.com.\n\nThe last stage involved resuming the traffic, running an earlier vacuum and finally starting the PgBouncer and Chef Client services.\n\n### The migration day\n\nFinally, fully prepared to perform the production upgrade, the team met on that Sunday (night time for some, and early morning for others) at 08:45 AM UTC. The service would be down for a max of two hours. When the last announcements were sent, the enginering team was given permission to start the procedure.\n\nThe upgrade process began by stopping the traffic and related services, to avoid users getting into the site.\n\nThe graph below shows the traffic and HTTP stats of the service before the upgrade, during the maintenance period (the \"gap\" in the graphs) and after, when the traffic was resumed.\n\n![GitLab.com Commits](https://about.gitlab.com/images/blogimages/pg-up-traf.png)\nGraphs of the traffic on GitLab.com before and after the upgrade maintenance.\n\n\nThe total elapsed time to do the entire job was four hours, it only required [two hours of downtime](https://status.gitlab.com/pages/maintenance/5b36dc6502d06804c08349f7/5ea322c1d1097004ba30d227).\n\n## It's on video\n\nWe recorded the full PostgreSQL upgrade and posted it to GitLab Unfiltered. Warm up the popcorn 🍿\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/TKODwTtKWew\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n\nThanks to [Alvaro Hernandez](https://twitter.com/ahachete) and [Sergio Ostapowicz](https://twitter.com/Cepxio_OS) for co-authoring this blog post, as well as the [OnGres team](https://ongres.com) for their contributions and performing the upgrade with the GitLab team.\n\n## References\n\nThe issues used to coordinate this project are public:\n\n* [Upgrade Postgresql to version 11.7 on GitLab.com](https://gitlab.com/groups/gitlab-com/gl-infra/-/epics/106)\n* [Execute PostgreSQL upgrade on staging](https://gitlab.com/gitlab-com/gl-infra/infrastructure/-/issues/9592)\n* [OnGres Inc on Twitter](https://twitter.com/ongresinc/status/1259441563614273537)\n* [Scheduled maintenance at GitLab.com](https://status.gitlab.com/pages/maintenance/5b36dc6502d06804c08349f7/5ea322c1d1097004ba30d227)\n\nCover image by [Tim Mossholder](https://unsplash.com/@timmossholder) on [Unsplash](https://unsplash.com/photos/GmvH5v9l3K4)\n\n",[23],"production","yml",{},true,"/en-us/blog/gitlab-pg-upgrade",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":29,"ogSiteName":30,"ogType":31,"canonicalUrls":29},"https://about.gitlab.com/blog/gitlab-pg-upgrade","https://about.gitlab.com","article","en-us/blog/gitlab-pg-upgrade",[23],"OqcdV1QnLMwQ9l60XkO4WDRdtKmOrxIqpxtvMC4J2oQ",{"data":36},{"logo":37,"freeTrial":42,"sales":47,"login":52,"items":57,"search":365,"minimal":396,"duo":415,"switchNav":424,"pricingDeployment":435},{"config":38},{"href":39,"dataGaName":40,"dataGaLocation":41},"/","gitlab logo","header",{"text":43,"config":44},"Get free 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Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n  \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n  \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - trigger\n\ntrigger-services:\n  stage: trigger\n  trigger:\n    include:\n      - local: '.gitlab/ci/api-service.yml'\n      - local: '.gitlab/ci/web-service.yml'\n      - local: '.gitlab/ci/worker-service.yml'\n    strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n  - build\n  - test\n\nbuild-api:\n  stage: build\n  script:\n    - echo \"Building API service\"\n\ntest-api:\n  stage: test\n  needs: [build-api]\n  script:\n    - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/ee/api/jobs.html#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n  - trigger-backend\n\nbuild-frontend:\n  stage: build\n  script:\n    - echo \"Building frontend and generating API contract...\"\n    - mkdir -p dist\n    - |\n      echo '{\n        \"api_version\": \"v2\",\n        \"breaking_changes\": false\n      }' > dist/api-contract.json\n    - cat dist/api-contract.json\n  artifacts:\n    paths:\n      - dist/api-contract.json\n    expire_in: 1 hour\n\ntest-frontend:\n  stage: test\n  script:\n    - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n  stage: trigger-backend\n  trigger:\n    project: my-org/backend-service\n    branch: main\n    strategy: depend\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n\nbuild-backend:\n  stage: build\n  script:\n    - echo \"All backend tests passed!\"\n\nintegration-test:\n  stage: test\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n  script:\n    - echo \"Fetching API contract from frontend...\"\n    - |\n      curl --silent --fail \\\n        --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n        --output api-contract.json \\\n        \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n    - cat api-contract.json\n    - |\n      if grep -q '\"breaking_changes\": true' api-contract.json; then\n        echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n        exit 1\n      fi\n      echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - generate\n  - trigger-environments\n\ngenerate-config:\n  stage: generate\n  script:\n    - |\n      # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n      # Default to dev, staging, prod if not set.\n      ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n      for ENV in $ENVIRONMENTS; do\n        cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n      stages:\n        - deploy\n        - verify\n      deploy-${ENV}:\n        stage: deploy\n        script:\n          - echo \"Deploying to ${ENV} environment\"\n      verify-${ENV}:\n        stage: verify\n        script:\n          - echo \"Running smoke tests on ${ENV}\"\n      EOF\n      done\n  artifacts:\n    paths:\n      - \"*.yml\"\n    exclude:\n      - \".gitlab-ci.yml\"\n\n.trigger-template:\n  stage: trigger-environments\n  trigger:\n    strategy: depend\n\ntrigger-dev:\n  extends: .trigger-template\n  trigger:\n    include:\n      - artifact: dev-pipeline.yml\n        job: generate-config\n\ntrigger-staging:\n  extends: .trigger-template\n  needs: [trigger-dev]\n  trigger:\n    include:\n      - artifact: staging-pipeline.yml\n        job: generate-config\n\ntrigger-prod:\n  extends: .trigger-template\n  needs: [trigger-staging]\n  trigger:\n    include:\n      - artifact: prod-pipeline.yml\n        job: generate-config\n  when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n*   [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n*   [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n*   [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n      when: never\n    - if: $CI_COMMIT_BRANCH\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n  - fast-checks\n  - expensive-tests\n  - deploy\n\nlint-code:\n  stage: fast-checks\n  script:\n    - echo \"Running linter\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n  stage: fast-checks\n  script:\n    - echo \"Running unit tests\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running integration tests (15 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running E2E tests (30 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n  stage: expensive-tests\n  script:\n    - echo \"Running full nightly suite (2 hours)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n  stage: deploy\n  script:\n    - echo \"Deploying to production\"\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n      when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n  inputs:\n    stage:\n      default: deploy\n    environment:\n      default: production\n---\ndeploy-job:\n  stage: $[[ inputs.stage ]]\n  script:\n    - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n    - echo \"Deploy URL: $DEPLOY_URL\"\n  environment:\n    name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n  APP_NAME: \"my-awesome-app\"\n  DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n  - component: gitlab.com/my-org/component-library/build@v1.0.6\n  - component: gitlab.com/my-org/component-library/test@v1.0.6\n  - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n    inputs:\n      environment: staging\n\nstages:\n  - build\n  - test\n  - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n*   A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n*   A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n*   A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n*   [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n*   [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n*   [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n*   [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n*   [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n*   [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n*   [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n*   [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n*   [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n*   [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n*   [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[718],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[104,722,723,724],"DevOps platform","tutorial","features",{"featured":26,"template":13,"slug":726},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":728,"config":738},{"title":729,"description":730,"authors":731,"heroImage":733,"date":734,"body":735,"category":9,"tags":736},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[732],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficult to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[723,737,724],"product",{"featured":12,"template":13,"slug":739},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":741,"config":751},{"title":742,"description":743,"authors":744,"heroImage":746,"date":747,"category":9,"tags":748,"body":750},"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.",[745],"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",[257,619,749],"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-agent-platform/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":752,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":754},[755,769,780,792],{"id":756,"categories":757,"header":759,"text":760,"button":761,"image":766},"ai-modernization",[758],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":762,"config":763},"Get your AI maturity score",{"href":764,"dataGaName":765,"dataGaLocation":239},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":770,"categories":771,"header":772,"text":760,"button":773,"image":777},"devops-modernization",[737,565],"Are you just managing tools or shipping innovation?",{"text":774,"config":775},"Get your DevOps maturity score",{"href":776,"dataGaName":765,"dataGaLocation":239},"/assessments/devops-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":781,"categories":782,"header":784,"text":760,"button":785,"image":789},"security-modernization",[783],"security","Are you trading speed for security?",{"text":786,"config":787},"Get your security maturity score",{"href":788,"dataGaName":765,"dataGaLocation":239},"/assessments/security-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":793,"paths":794,"header":797,"text":798,"button":799,"image":804},"github-azure-migration",[795,796],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":800,"config":801},"See how GitLab compares to GitHub",{"href":802,"dataGaName":803,"dataGaLocation":239},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":805},{"src":779},{"header":807,"blurb":808,"button":809,"secondaryButton":814},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":810,"config":811},"Get your free trial",{"href":812,"dataGaName":46,"dataGaLocation":813},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":501,"config":815},{"href":50,"dataGaName":51,"dataGaLocation":813},1776443045742]