[{"data":1,"prerenderedAt":819},["ShallowReactive",2],{"/en-us/blog/the-trouble-with-technical-interviews":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Sara Kassabian":700,"blog-related-posts-en-us-the-trouble-with-technical-interviews":714,"blog-promotions-en-us":756,"next-steps-en-us":809},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":34,"tagSlugs":35,"__hash__":37},"blogPosts/en-us/blog/the-trouble-with-technical-interviews.yml","The Trouble With Technical Interviews",[7],"sara-kassabian",null,"engineering",{"slug":11,"featured":12,"template":13},"the-trouble-with-technical-interviews",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"The trouble with technical interviews? They aren't like the job you're interviewing for","Forget the coding exercise. Here's how to create realistic scenarios for engineering candidates in technical interviews.",[18],"Sara Kassabian","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749681148/Blog/Hero%20Images/nycbrooklyn.jpg","2020-03-19","\n\nInterviewing for an engineering job in the tech world can mean [you’ll be asked all sorts of questions](https://stackify.com/devops-interview-questions/). Sometimes, the job interview questions can be pretty straightforward: “Tell me about a time that you have implemented an effective monitoring solution for a production system.” Other times, the questions are impossible to answer and designed to spark your creativity: “How many windows are in New York City?” After passing the initial interview, the applicant or candidate graduates to the next tier of interviewing: The often-dreaded technical interview.\n\n## What is a technical interview?\n\nA technical interview is one that is conducted to gauge a candidate’s skill level for positions in the information technology, engineering, and science fields. It may also determine how much a candidate knows in more niche areas of a company, such as marketing, sales, and HR.\n\n## How to prepare for a technical Interviews\n\nProspective engineers often face a challenge when it comes to preparing for the technical interview, largely because there is no playbook for how companies set them up technical. It’s unclear whether to prepare by memorizing many different topics, or focusing on specific projects. Is it better to practice with a computer or a peer engineer? There are an overwhelming number of resources available online, but with little clarity as to what the standard is for a technical interview and little guidance from the company on what to expect, most of the time engineers start technical interviews in the dark.\n\nInconsistencies in the technical interview process isn’t just a job candidate problem. In fact, many companies struggle to set up a technical interview process that is effective, equitable, and allows the hiring manager to compare candidates. The problem with technical interviewing compounds when a company is experiencing rapid growth.\n\n## What are the challenges of conducting technical interviews at a growing company\n\n\"Imagine you had a hiring target of doubling your team size and all your interviews are conducted remotely. Welcome to GitLab,\" says Clement Ho, [frontend engineering manager on the Monitor: Health team](/company/team/#ClemMakesApps) at GitLab.\n\n![Hiring chart shows GitLab more than doubled the number of hires from around 400 in 2019 to roughly 1300 by end of 2020](https://about.gitlab.com/images/blogimages/fei_hiringchart.jpg){: .shadow.medium.center}\n\nGitLab more than doubled the number of hires from around 400 in 2019 to roughly 1300 by end of 2020.\n\n\nWe identifed three core challenges with orchestrating technical interviews as GitLab grows.\n\n1. We didn't have enough interviewers for the pipeline of candidates.\n2. Our technical interviewing process was inconsistent and even a little biased.\n3. It was difficult to measure whether or not we were raising the bar.\n\n\"And by raising the bar, I mean making sure each candidate that joins the team makes the team better,\" says Clement.\n\nThese problems are by no means unique to GitLab. Any engineering company that is scaling rapidly will encounter some growing pains when it comes to hiring, and many will end up falling back on some of the typical models for conducting technical interviews.\n\n## The typical technical interview methods\n\nDuring his talk, [\"Using GitLab to Power our Frontend Technical Interviews\" at GitLab Commit San Francisco](https://www.youtube.com/watch?v=jSbCt8b_4ug), Clement explained the four different techniques that are often employed in technical interviews. Each method comes with advantages and disadvantages from the perspective of the hiring manager.\n\n## What are good technical interview questions\n\nA good technical interview needs to be about more than practical skills – it’s about the whole package.A candidate should possess the ideal coding skills but also be a team and culture fit and be able to discuss developer topics efficiently. A technical interview should include both situational interview questions and a skills assessment to discern a candidate’s potential.\n\nThe types of questions to ask can concern a candidate’s technical abilities and background, their career journey so far, and queries specific to the team or company.\n\n## Types of questions asked during a technical interview and their purpose\nEven though employers have already reviewed your resume and cover letter, they will want you to flesh that out during the interview to learn more about how you attained those skills. In order to assess your level of experience, they will likely also ask you to provide concrete examples from prior jobs.\nMake sure you are prepared—do your research on the company and the type of questions you may be potentially asked. This will help build your confidence level and reduce any nervousness you might feel. It’s also an opportunity for you to set yourself apart from other candidates by showcasing your knowledge and additional skills you can bring to the job.\nIt is important to be honest about your skill set because that is something employers value. You may find the company will be willing to hire someone who is transparent about the areas where they need to improve and where they’d like to gain more skills.\n\nExamples of common questions to expect in a technical interview:\n\n- What coding languages are you most familiar with?\n- What is your experience with Kubernetes with a specific example?\n- What’s the purpose of continuous integration in an automated build?\n- How have your previous technical roles prepared you for this job?\n- Tell me about a time when you received an unexpected assignment: how did you react, and what did the experience teach you?\n- Please provide more details about your educational background and how it prepared you for this position.\n- How did you go about teaching yourself a necessary technical skill while you were working on a project?\n- What are your strengths, and where do you think you need to improve your skills?\n- Do you have any technical certifications?\n- Please detail the work you did on the project you are most proud of.\n- What are your favorite and least favorite tech tools, and why?\n- What are the pros/cons of working in an agile environment?\n\n### Sample technical interview questions and answers\n- **How do you stay current with your technical knowledge and skills?** It’s a good idea to list online content you use to educate yourself, as well as tutorials and conferences you have attended to gain more knowledge. Perhaps you have also worked closely with vendors or attended sessions to learn about new product features.\n- **How do you troubleshoot technical problems?** Discuss the steps you take when you are answering a question. This will give employers a sense of how you problem-solve, and it provides a good overview of how well you understand the relevant concepts. Even if you don’t answer a question correctly, it will show the interviewer your process and reasoning, which are also important. You can mention resources you use, such as GitLab and Stack Exchange, as well as the developer community and any publications you read for advice.\n- **What is your level of experience with the software programs mentioned on your resume?** Describe how many years you have used the tools, your impressions of them, and bring up the companies you used them at, with specific examples.\n- **What programming language are you most proficient in?** You should discuss how you have become proficient in this language and why it is the one you are most comfortable using. You can also cite other languages you are familiar with.\n- **Describe a time you made an error and how you resolved it.** Don’t use an example of an egregious error since that may put you in a negative light. Be sure to emphasize that you took responsibility and acted with integrity, and did whatever it took to resolve the issue.\n\n## What are some soft skills and coding skills to highlight in a technical interview\n\nA technical interview assesses your technical expertise, coding skills, and ability to fit into a team. However, soft skills are just as important and often aid in the development of more technical skills – particularly in a team setting.\n\nAs the technical interview progresses, be prepared to tackle some questions about soft skills like:\n\n- **Communication skills:** How does the candidate contribute to group discussions, confront problems, or give and receive feedback?\n- **Organizational skills:** What are the ways in which the candidate provides visibility into their work processes and their methods of staying on task?\n- **Collaboration skills:** Are they interested in helping their teammates? What do they think are the keys to successfully navigating a team project? How have they collaborated on past projects?\n- **Creative problem solving:** How do they work through a problem in a project? Do they use both analytical and creative thinking to come up with solutions?\n\n### How to prepare for verbal technical questions\n\nThere are countless articles online that try to prepare job candidates for a verbal technical interview, but whether this method truly effective for evaluating the technical competency of a software engineer is debatable.\n\nIn the typical scenario, the interviewer asks the candidate to describe a technical concept and tries to measure their fluency in said concept based on the quality of the conversation.\n\nThe advantage of this method is that the interviewer can understand how the candidate communicates, which is of particular importance when the engineering team is all-remote, as is the case at GitLab. The drawback? Being a good communicator does not necessarily mean the candidate knows how to code effectively.\n\n\"So I've interviewed candidates that could talk the talk, but they couldn't really write the code,” says Clement. \"And that's not a great situation for an engineer to join GitLab.\" Clement’s team has moved away from using verbal technical questions as a method for evaluating candidates.\n\n### Live coding exercises\n\nOne of the more popular methods for evaluating engineers is through live coding. While it allows the evaluator to see how engineering candidates answer data structure questions, it also has its disadvantages.\n\nA key advantage of live coding data structures is that it offers a fairly consistent measurement and evaluation.\n\n\"I can talk to another manager or another interviewer and be able to communicate, 'Hey, this person wasn't able to do a linked list, they got stuck here. They weren't able to understand a runtime efficiency here.' So it's pretty consistent,\" says Clement.\n\nBut the ability to create data structures is not always the best indicator of ability. Oftentimes engineers with a very traditional background or recent graduates will shine here, but someone who is more senior and able to do a lot of great things, but is perhaps not as brushed up on data structures, may struggle.\n\nLive coding interviews probably aren’t going anywhere fast, but the pitfalls of this method are well documented by engineers and hiring managers. Brennan Moore, a product engineer in New York City, explains why he does not conduct live coding interviews when evaluating a prospective candidate:\n\n> \"Much like the SAT when applying for college, live coding is a structured test. I didn’t go to a school that trained me to do live coding, and so will probably fail the test. As I’ve experienced it, live coding isn’t the meritocratic space that it pretends to be. Live coding interviews weed out the people who are good at live coding interviews,\" says Brennan in his [blog post](https://www.zamiang.com/post/why-i-don-t-do-live-coding-interviews).\n\nAt GitLab, we found that live coding exercises don't accurately represent engineering capability. Oftentimes, a recent computer science graduate will outperform a more senior candidate with a lot of valuable experience. In summary, live coding exercises will often disadvantage more senior candidates, people who are nervous in high-pressure situations (read: everyone), and advantages more junior engineers or people who have practiced live coding.\n\n### Digital prompt\n\nA third common method for evaluating candidates is to ask the engineer to code a UI using an online editor while on screen share with the evaluator.\n\nThe advantage of this method is that it allows the evaluator to observe how a candidate builds. The drawbacks here are similar to those with live coding. First, the engineer is under pressure to build while the evaluator watches on, making it a nerve-wracking situation. The other drawbacks come from an evaluation perspective: It is challenging to measure the effectiveness of this method and it is hard to compare between candidates.\n\n### Take-home project\n\nAny engineer (or writer, for that matter) can tell you, the supplemental take-home project is a very common ask when going through an interview process. The advantage here for us is that this assignment closely mimics the reality of building environments while working remotely at GitLab.\n\nBut this task comes with major drawbacks, mainly that it disadvantages candidates who may not have the time or capacity to complete the project.\n\n\"... imagine a scenario where you're a single parent and you have kids; you may not have as much opportunity to take dedicated time, a couple of hours after work to really focus on a take-home project compared to someone from a more privileged background,\" says Clement. \"They might be able to dedicate and output something better.\"\n\n[Diversity and inclusion is a core value](https://handbook.gitlab.com/handbook/company/culture/inclusion/) for GitLab, and anything that disadvantages candidates from underrepresented groups is not inclusive, and therefore suboptimal for evaluating candidates based on their engineering abilities.\n\n## What are they looking for during a technical interview?\n\nCompanies want candidates who can discuss the industry in the context of the job they are applying for. Be prepared to discuss examples of your work. Many will want to hear about soft skills, too—your ability to communicate and collaborate and work with others to problem-solve issues.\n\nThey will also want to see how passionate and enthusiastic you are and whether you have the self-motivation to not only do the job but take the initiative to do more than what you’re tasked with.\n\nAlso, interviewers will want to see whether candidates have the desire to increase their technical knowledge.\n\n## What are some online preparation tools and resources for technical interviews\n\n- Indeed offers a career guide to [help prepare for](https://www.indeed.com/career-advice/interviewing/what-is-a-technical-interview) a technical interview.\n- Interview Kickstart has several [webinars](https://learn.interviewkickstart.com/) to help prepare engineers for interviews.\n- Udemy offers a course in [Technical Interview Skills](https://www.udemy.com/course/technical-interview-skills/?utm_source=bing&utm_medium=udemyads&utm_campaign=BG-DSA_Webindex_la.EN_cc.BE&utm_content=deal4584&utm_term=_._ag_1222657343651662_._ad__._kw_udemy_._de_c_._dm__._pl__._ti_dat-2328215871879260%3Aloc-190_._li_103429_._pd__._&matchtype=b&msclkid=9f5132d9c84c17b02f7951a4f46279d6).\n- [Codecademy](https://www.codecademy.com/learn/technical-interview-practice-python?utm_id=t_kwd-79027793284383:loc-190:ag_1264438993811076:cp_370314525:n_o:d_c&msclkid=550de1275d811b2cfc0f82592b6d9626&utm_source=bing&utm_medium=cpc&utm_campaign=US%20Language%3A%20Pro%20-%20Broad&utm_term=%2Btechnical%20%2Binterview%20%2Bprep&utm_content=technical%20interview%20practice) also offers a course called - Technical Interview Practice with Python.\n- Here are some more general [interview tips](https://www.roberthalf.com/blog/job-interview-tips/interview-tips-to-help-you-land-the-job-you-want) that are applicable to all candidates.\n\n## Meaningful questions to ask the interviewer\n\nCandidates will also be given a chance to ask questions they might have to learn more about the company. This is a great opportunity to gain more insight into how the company operates, what its philosophy is, and its vision for the long term.\n\nIt’s also a good way to glean how the company views its IT team. If you don’t ask questions, that could give the impression you are unprepared or not terribly interested in the job.\n\nQuestions to ask can include:\n\n- What does a typical day looks like in this role?\n- Are there opportunities for training and further advancement?\n- What software development methodology do you use?\n- What are your code review practices?\n- Do you have on-call rotations? If so, how long is one rotation?\n- What are the responsibilities of the person on call?\n- Please provide more details about the team I will be working with, such as how many people are there, what their roles are, what the hierarchy is, and what areas of improvement you would like to see on the team.\n\n## The new way\n\nWhile each method for conducting a technical interview comes with advantages, there are also numerous disadvantages when it comes to conducting an effective and measurable evaluation and creating an equitable interview process. Under the guidance of Clement, the [Monitor:Health team](https://handbook.gitlab.com/handbook/engineering/monitoring/) decided to interview frontend engineers in an entirely new way using GitLab.\n\nNow let's take a deep dive into the nuts and bolts of reinventing the technical interview for frontend engineers at GitLab. Just wondering about the key takeaways? [Skip ahead](#why-this-new-model-for-technical-interviews-is-better). As we continue to iterate on a more effective and measurable technical interview process, we hope this inspires other engineering organizations to rethink theirs and share learnings with us.\n\nOur first step: Standardize the interview process.\n\n### Fixing an MR on a test project\n\nThe team standardized the interview process by creating an open source test project, called `project-seeder`, which seeds projects to different candidates using a GitLab Bot. Candidates are assigned a merge request to troubleshoot in the project created for the technical interview. The `project-seeder` is powered by the GitLab Bot so the interviewer doesn't have to worry about API keys, and works in four steps:\n\n1. Exports the template project\n2. Imports template project\n3. Adds users with expiration\n4. Triggers pipeline for candidate to review MR\n\nThe candidate is sent an email with a link to the MR the candidate is assigned to fix as part of the technical interview.\n\n### Standardize the evaluation rubric\n\nThe team also created a standardized rubric for how the candidate's performance on a technical interview is evaluated.\n\n\"We don't want to be in a situation where unconscious bias or bias of one candidate over another plays a part because of our preconceived notions,\" says Clement.\n\nCreating a rubric that looks at multiple categories allows the evaluator to look at the performance of the candidate from a more holistic perspective, as opposed to looking at a candidate's performance on one technology.\n\nThe team created a [Periscope dashboard](https://handbook.gitlab.com/handbook/engineering/frontend/interview-metrics/) to create a feedback loop between the candidates and evaluators to identify opportunities for improvement in the technical interviewing process.\n\n![Frontend team used Periscrope to collect feedback from candidates who participate in technical interviews](https://about.gitlab.com/images/blogimages/fei_periscopedashboard.jpg){: .shadow.medium.center}\n\nThe frontend engineering team used Periscope to collect feedback from candidates who participate in technical interviews.\n\n\n## Demoing the technical interview\n\n### Inside the technical interview project\n\nClement created a sample project to demonstrate how we use GitLab to power our technical interviews.\n\nIn the [gl-commit-example](https://gitlab.com/gl-commit-example) group, there is a subgroup with all the interview projects we are seeding to the imaginary candidates, a template, and a project seeder.\n\n![A screenshot of the sample project shows the interview project's subgroup, template, and project seeder application](https://about.gitlab.com/images/blogimages/fei_interviewproject.jpg){: .shadow.medium.center}\n\nThe interview project's subgroup, template, and project seeder application lives inside the sample project for the technical interview.\n\n\n[Inside the template](https://gitlab.com/gl-commit-example/template), there are GitLab pages and the [interview test merge request](https://gitlab.com/gl-commit-example/template/-/merge_requests/1).\n\nThe assignment here is pretty simple. The candidate needs to update the website to say \"Hello GitLab Commit SF,\" but in order to accomplish this, the candidate will need to fix the failing pipeline.\n\n### Powering project-seeder\n\nWe use variables from GitLab CI to configure the [project-seeder application](https://gitlab.com/gl-commit-example/project-seeder).\n\n![Screenshot of the project for the project-seeder application](https://about.gitlab.com/images/blogimages/fei_projseederapp.jpg){: .shadow.medium.center}\n\nInside the project-seeder application which seeds the interview projects to job candidates.\n\n\n\"I'm creating `new-project-example-two`, and I'm adding this bot user that I created and the expiration, so I can just easily run this pipeline and it'll seed this project,\" says Clement.\n\n![We use variables from the GitLab CI to configure the project-seeder applications](https://about.gitlab.com/images/blogimages/fei_variables.jpg){: .shadow.medium.center}\n\nThe next step is to run the setup pipeline, which will create the project, import the project, export the project, and share it with the job candidate.\n\n![A look inside the pipeline that will create the test project](https://about.gitlab.com/images/blogimages/fei_insidethepipeline.jpg){: .shadow.medium.center}\nA look inside the pipeline that will create the test project.\n\n\nLooking inside example-one, we can see there is a project and [broken MR](https://gitlab.com/gl-commit-example/interview-projects/example-1/-/merge_requests/1).\n\n\"And an example for a candidate – they would probably look at the CI and see, 'Oh there's a failing test. Let's see what that's about. Oh, it looks like it's checking for \"hello world\". So since we changed the message earlier, we can just change this and get this test passing and then pass this interview,'\" says Clement.\n\n## Why this new model for technical interviews is better\n\nThe new model surpasses the old model because we created realistic scenarios that reflect what it's like to actually work for GitLab, and we established a more consistent method of measurement.\n\n\"So we're able to get better candidates overall. Candidates that pass through this technical interview, we're sure that they're going to be successful at GitLab,\" says Clement.\n\nBy designing our technical interviews this way, we can ensure that the interview project matches our actual product architecture at GitLab, which in this case is Ruby on Rails for Vue JS.\n\nWe also struggled in the past with finding a good way to check that the candidate knows how to use Git, and can navigate pipelines and testing. By using GitLab for interviews, we're able to confirm a candidate's competency with Git implicitly by evaluating their performance on the technical interviews.\n\nWe wanted to mirror the actual experience of troubleshooting a broken MR while working at GitLab, so we allow our candidates to use the internet during their technical interview. This allows the evaluator to see how the candidate solves problems and see their resourcefulness.\n\n\"If you're already using GitLab for your tooling, you're just exposing them to what it's like to work at GitLab; it's a more accurate representation,\" says Clement. \"And you can also make sure you're measuring testing proficiency and you make sure they understand how that works before they join your company.\"\n\n## Four key takeaways from our technical interview update\n\nWhether or not a company uses GitLab, there are a few key lessons that we learned by iterating on how we conduct technical interviews for engineers.\n\n1. **Make technical interviews as much like real work as possible**: Nine times out of ten, an engineering manager isn't going to sit back and watch an engineer break a sweat in a live coding exercise, any more than they will watch on as an engineer builds in UI. Create realistic scenarios based on the actual work and evaluate based on the candidate's performance.\n\n2. **Make any technical interview process \"open-book\"**: Engineering doesn't involve much rote memorization. Instead, allow the engineering candidate to use the internet (and in our case, the [GitLab Handbook](https://handbook.gitlab.com/handbook/)) to look up their questions. It's better to see how a candidate applies their knowledge and troubleshoots the inevitable problems that may arise. This change will likely improve your candidate experience too.\n\n3. **Standardize your rubric**: However the technical interview is done, make sure that the rubric is as objective as possible and that the candidate is evaluated based on various criteria, not on their familiarity with a particular technology. 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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.",[721],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[107,725,726,727],"DevOps platform","tutorial","features",{"featured":27,"template":13,"slug":729},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":731,"config":741},{"title":732,"description":733,"authors":734,"heroImage":736,"date":737,"body":738,"category":9,"tags":739},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[735],"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/)",[726,740,727],"product",{"featured":12,"template":13,"slug":742},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":744,"config":754},{"title":745,"description":746,"authors":747,"heroImage":749,"date":750,"category":9,"tags":751,"body":753},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[748],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[260,622,752],"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":755,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":757},[758,772,783,795],{"id":759,"categories":760,"header":762,"text":763,"button":764,"image":769},"ai-modernization",[761],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":765,"config":766},"Get your AI maturity score",{"href":767,"dataGaName":768,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":773,"categories":774,"header":775,"text":763,"button":776,"image":780},"devops-modernization",[740,568],"Are you just managing tools or shipping innovation?",{"text":777,"config":778},"Get your DevOps maturity score",{"href":779,"dataGaName":768,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":784,"categories":785,"header":787,"text":763,"button":788,"image":792},"security-modernization",[786],"security","Are you trading speed for security?",{"text":789,"config":790},"Get your security maturity score",{"href":791,"dataGaName":768,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":793},{"src":794},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":796,"paths":797,"header":800,"text":801,"button":802,"image":807},"github-azure-migration",[798,799],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":803,"config":804},"See how GitLab compares to GitHub",{"href":805,"dataGaName":806,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":808},{"src":782},{"header":810,"blurb":811,"button":812,"secondaryButton":817},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":813,"config":814},"Get your free trial",{"href":815,"dataGaName":49,"dataGaLocation":816},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":818},{"href":53,"dataGaName":54,"dataGaLocation":816},1776444476819]