[{"data":1,"prerenderedAt":818},["ShallowReactive",2],{"/en-us/blog/how-to-continously-test-web-apps-apis-with-hurl-and-gitlab-ci-cd":3,"navigation-en-us":39,"banner-en-us":449,"footer-en-us":459,"blog-post-authors-en-us-Michael Friedrich":699,"blog-related-posts-en-us-how-to-continously-test-web-apps-apis-with-hurl-and-gitlab-ci-cd":713,"blog-promotions-en-us":755,"next-steps-en-us":808},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":34,"tagSlugs":35,"__hash__":38},"blogPosts/en-us/blog/how-to-continously-test-web-apps-apis-with-hurl-and-gitlab-ci-cd.yml","How To Continously Test Web Apps Apis With Hurl And Gitlab Ci Cd",[7],"michael-friedrich",null,"engineering",{"slug":11,"featured":12,"template":13},"how-to-continously-test-web-apps-apis-with-hurl-and-gitlab-ci-cd",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to continuously test web apps and APIs with Hurl and GitLab CI/CD","Hurl as a CLI tool can be integrated into the DevSecOps platform to continuously verify, test, and monitor targets. It also offers integrated unit test reports in GitLab CI/CD.",[18],"Michael Friedrich","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659883/Blog/Hero%20Images/post-cover-image.jpg","2022-12-14","Testing websites, web applications, or generally everything reachable with the HTTP protocol, can be a challenging exercise. Thanks to tools like `curl` and `jq`, [DevOps workflows have become more productive](/blog/devops-workflows-json-format-jq-ci-cd-lint/) and even simple monitoring tasks can be automated with CI/CD pipeline schedules. Sometimes, use cases require specialized tooling with custom HTTP headers, parsing expected responses, and building end-to-end test pipelines. Stressful incidents also need good and fast tools that help analyze the root cause and quickly mitigate and fix problems.\n\n[Hurl](https://hurl.dev) is an open-source project developed and maintained by Orange, and uses libcurl from curl to provide HTTP test capabilities. It aims to tackle complex HTTP test challenges by providing a simple plain text configuration to describe HTTP requests. It can chain requests, capture values, and evaluate queries on headers and body responses. So far, so good: Hurl does not only support fetching data, it can be used to test HTTP sessions and XML (SOAP) and JSON (REST) APIs.\n\n## Getting Started\n\nHurl comes in various package formats to [install](https://hurl.dev/docs/installation.html). On macOS, a Homebrew package is available.\n\n```sh\n$ brew install hurl\n```\n\n## First steps with Hurl\n\nHurl proposes to start with the configuration file format first, which is a great way to learn the syntax step by step. The following example creates a new `gitlab-contribute.hurl` configuration file that will do two things: execute a GET HTTP request on `https://about.gitlab.com/community/contribute/` and check whether its HTTP response contains the HTTP protocol `2` and status code `200` (OK).\n\n```sh\n$ vim gitlab-contribute.hurl\n\nGET https://about.gitlab.com/community/contribute/\n\nHTTP/2 200\n$ hurl --test gitlab-contribute.hurl\ngitlab-contribute.hurl: Running [1/1]\ngitlab-contribute.hurl: Success (1 request(s) in 413 ms)\n--------------------------------------------------------------------------------\nExecuted files:  1\nSucceeded files: 1 (100.0%)\nFailed files:    0 (0.0%)\nDuration:        415 ms\n```\n\nInstead of creating configuration files, you can also use the `echo “...” | hurl` command pattern. The following command tests against about.gitlab.com and checks whether the HTTP response protocol is 1.1 and the status is OK (200). The two newline characters `\\n` are required for separation.\n\n```sh\n$ echo \"GET https://about.gitlab.com\\n\\nHTTP/1.1 200\" | hurl --test\n```\n\n![hurl CLI run against about.gitlab.com, failed request](https://about.gitlab.com/images/blogimages/hurl-continuous-website-testing/hurl_assert_failure.png)\n\nThe command failed, and it says that the response protocol version is actually `2`. Let's adjust the test run to expect `HTTP/2`:\n\n```sh\necho \"GET https://about.gitlab.com\\n\\nHTTP/2 200\" | hurl --test\n```\n## Asserting HTTP responses\n\nHurl allows defining [assertions](https://hurl.dev/docs/asserting-response.html) to control when the tests fail. These can be defined for different HTTP response types:\n\n- Expected HTTP protocol version and status\n- Headers\n- Body\n\nThe configuration language allows users to define queries with predicates that allow to compare, chain, and execute different assertions.\n\nThis is the easiest way to verify that the HTTP response contains what is expected to be a string or sentence on the website, for example. If the string does not exist, this can indicate that it was changed unexpectedly, or that the website is down. Let's revisit the example with testing GET https://about.gitlab.com/community/contribute/ and add an expected string `Everyone can contribute` as a new assertion, `body contains \u003Cstring>` is the expected configuration syntax for [body asserts](https://hurl.dev/docs/asserting-response.html#body-assert).\n\n```sh\n$ vim gitlab-contribute.hurl\n\nGET https://about.gitlab.com/community/contribute/\n\nHTTP/2 200\n\n[Asserts]\nbody contains \"Everyone should contribute\"\n\n$ hurl --test gitlab-contribute.hurl\n```\n\n**Exercise:** Fix the test by updating the asserts line to `Everyone can contribute` and run Hurl again.\n\n### Asserting responses: JSON and XML\n\n[JSONPath](https://hurl.dev/docs/asserting-response.html#jsonpath-assert) automatically parses the JSON response (a built-in `jq with curl` parser so to speak), and allows users to compare the value to verify the asserts (more below). The XML format can be found in an [RSS feed on about.gitlab.com](https://about.gitlab.com/atom.xml) and parsed using [XPath](https://hurl.dev/docs/asserting-response.html#xpath-assert). The following example from `atom.xml` should be verified with Hurl:\n\n```xml\n\u003Cfeed xmlns=\"http://www.w3.org/2005/Atom\">\n\u003Ctitle>GitLab\u003C/title>\n\u003Cid>https://about.gitlab.com/blog\u003C/id>\n\u003Clink href=\"https://about.gitlab.com/blog/\"/>\n\u003Cupdated>2022-11-21T00:00:00+00:00\u003C/updated>\n\u003Cauthor>\n\u003Cname>The GitLab Team\u003C/name>\n\u003C/author>\n\u003Centry>\n...\n\u003C/entry>\n\u003Centry>\n...\n\u003C/entry>\n\u003Centry>\n…\n```\n\nIt is important to note that XML namespaces need to be specified for parsing. Hurl allows users to replace the first default namespace with the `_` character to avoid adding `http://www.w3.org/2005/Atom` everywhere, the XPath is now shorter with `string(//_:feed/_:entry)` to get a list of all entries. This value is captured in the `entries` variable, which can be compared to match a specific string, `GitLab` in this example. Additionally, the feed id and author name is checked.\n\n```text\n$ vim gitlab-rss.hurl\n\nGET https://about.gitlab.com/atom.xml\n\nHTTP/2 200\n\n[Captures]\nentries: xpath \"string(//_:feed/_:entry)\"\n\n[Asserts]\nvariable \"entries\" matches \"GitLab\"\n\nxpath \"string(//_:feed/_:id)\" == \"https://about.gitlab.com/blog\"\nxpath \"string(//_:feed/_:author/_:name)\" == \"The GitLab Team\"\n\n$ hurl –test gitlab-rss.hurl\n```\n\nHurl allows users to capture the value from responses into [variables](https://hurl.dev/docs/templates.html#variables) that can be used later. This method can also be helpful to model end-to-end testing workflows: First, check the website health status and retrieve a CSRF token, and then try to log into the website by sending the token again.\n\nREST APIs that are expected to always return a specified field, or monitoring a website health state [becomes a breeze using Hurl](https://hurl.dev/docs/tutorial/chaining-requests.html#test-rest-api).\n\n## Use Hurl in GitLab CI/CD jobs\n\nThe easiest way to integrate Hurl into GitLab CI/CD is to use the official container image. The Hurl project provides a [container image on Docker Hub](https://hub.docker.com/r/orangeopensource/hurl), which did not work in CI/CD at first glance. After talking with the maintainers, the [entrypoint override](https://docs.gitlab.com/ee/ci/docker/using_docker_images.html#override-the-entrypoint-of-an-image) was identified as a solution for using the image in GitLab CI/CD. Note that the Alpine based image uses the libcurl library that does not support HTTP/2 yet - the test results are different to a Debian base image (follow [this issue report](https://github.com/Orange-OpenSource/hurl/issues/1082) for the problem analysis).\n\nThe following example is kept short to run the container image, override the entrypoint, and run Hurl with passing in the test using the `echo` CLI command.\n\n```yaml\nhurl-standalone:\n  image:\n    name: ghcr.io/orange-opensource/hurl:latest\n    entrypoint: [\"\"]\n  script:\n    - echo -e \"GET https://about.gitlab.com/community/contribute/\\n\\nHTTP/1.1 200\" | hurl --test --color\n\n```\n\nThe Hurl test report is printed into the CI/CD job trace log, and returns succesfully.\n\n```sh\n$ echo -e \"GET https://about.gitlab.com/community/contribute/\\n\\nHTTP/1.1 200\" | hurl --test --color\n-: Running [1/1]\n-: Success (1 request(s) in 280 ms)\n--------------------------------------------------------------------------------\nExecuted files:  1\nSucceeded files: 1 (100.0%)\nFailed files:    0 (0.0%)\nDuration:        283 ms\nCleaning up project directory and file based variables\n00:00\nJob succeeded\n```\n\nThe next iteration is to create a CI/CD job template that provides generic attributes, and allows users to dynamically run the job with an environment variable called `HURL_URL`.\n\n```yaml\n# Hurl job template\n.hurl-tmpl:\n  # Use the upstream container image and override the ENTRYPOINT to run CI/CD script\n  # https://docs.gitlab.com/ee/ci/docker/using_docker_images.html#override-the-entrypoint-of-an-image\n  image:\n    name: ghcr.io/orange-opensource/hurl:1.8.0\n    entrypoint: [\"\"]\n  variables:\n    HURL_URL: \"about.gitlab.com/community/contribute/\"\n  script:\n    - echo -e \"GET https://${HURL_URL}\\n\\nHTTP/1.1 200\" | hurl --test --color\n\nhurl-about-gitlab-com:\n  extends: .hurl-tmpl\n  variables:\n    HURL_URL: \"about.gitlab.com/jobs/\"\n\n```\n\nRunning GET commands with expected HTTP results is not the only use case, and the Hurl maintainers thought about this already. The next section explains how to create a custom container image; you can skip to the [DevSecOps workflows](#devSecOps-workflows-with-hurl) section to learn more about efficient Hurl configuration use cases.\n\n### Custom container image with Hurl\n\nMaintaining and building a custom container image adds more work, but also helps with avoiding running unknown container images in CI/CD pipelines. The latter is often a requirement for compliance and security. _Since the Hurl Debian package supports detecting HTTP/2 as a protocol, this blog post will focus on building a custom image, and run all tests using this image. If you plan on using the upstream container image, make sure to review the test configuration for the HTTP protocol version detection._\n\nThe Hurl documentation provides multiple ways to install Hurl. For this example, Debian 11 Bullseye (slim) is used. Hurl comes with a package dependency on `libxml2` which can either be installed manually with then running the `dpkg` command, or by using `apt install` to install a local package and automatically resolve the dependencies.\n\nThe following CI/CD example uses a job template which defines the Hurl version as environment variable to avoid repetitive use, and downloads and installs the Hurl Debian package. The `hurl-gitlab-com` job extends the CI/CD job template and runs a one-line test against `https://gitlab.com` and expects to return `HTTP/2` as HTTP protocol version, and `200` as status.\n\n```yaml\n# CI/CD job template\n.hurl-tmpl:\n  variables:\n    HURL_VERSION: 1.8.0\n  before_script:\n    - DEBIAN_FRONTEND=noninteractive apt update && apt -y install jq curl ca-certificates\n    - curl -LO \"https://github.com/Orange-OpenSource/hurl/releases/download/${HURL_VERSION}/hurl_${HURL_VERSION}_amd64.deb\"\n    - DEBIAN_FRONTEND=noninteractive apt -y install \"./hurl_${HURL_VERSION}_amd64.deb\"\n\nhurl-gitlab-com:\n  extends: .hurl-tmpl\n  script:\n    - echo -e \"GET https://gitlab.com\\n\\nHTTP/2 200\" | hurl --test --color\n\n```\n\nThe next section describes how to optimize the CI/CD pipelines for more efficient schedules and runs to monitor websites and not waste too many resources and CI/CD minutes. You can also skip it and [scroll down to more advanced Hurl examples in GitLab CI/CD](#devsecops-workflows-with-hurl).\n\n### CI/CD efficiency: Hurl container image\n\nThe installation steps for Hurl, and its dependencies, can waste resources and increase the pipeline job runtime every time. To make the CI/CD pipelines more efficient, we want to use a container image that already provides Hurl pre-installed. The following steps are required for creating a container image:\n\n- Use Debian 11 Slim (FROM).\n- Install dependencies to download Hurl (`curl`, `ca-certificates`). `jq` is installed for convenience to access it from CI/CD commands when needed later.\n- Download the Hurl Debian package, and use `apt install` to install its dependencies automatically.\n- Clear the apt lists cache to enforce apt update again, and avoid security issues.\n- Hurl is installed into the PATH, specify the default command being run. This allows running the container without having to specify a command.\n\nThe steps to install the packages are separated for better readability; an optimization for the `docker-build` job can happen by chaining the `RUN` commands into one long command.\n\n`Dockerfile`\n```text\nFROM debian:11-slim\n\nENV DEBIAN_FRONTEND noninteractive\n\nARG HURL_VERSION=1.8.0\n\nRUN apt update && apt install -y curl jq ca-certificates\nRUN curl -LO \"https://github.com/Orange-OpenSource/hurl/releases/download/${HURL_VERSION}/hurl_${HURL_VERSION}_amd64.deb\"\n# Use apt install to determine package dependencies instead of dpkg\nRUN apt -y install \"./hurl_${HURL_VERSION}_amd64.deb\"\nRUN rm -rf /var/lib/apt/lists/*\n\nCMD [\"hurl\"]\n```\n\nNote that the `HURL_VERSION` variable can be overridden by passing the variable and value into the container build job later. It is intentionally not using an automated script that always uses the [latest release](https://github.com/Orange-OpenSource/hurl/releases) to avoid breaking the behavior, and enforces a controlled upgrade cycle for container images in production.\n\nOn GitLab.com SaaS, you can include the `Docker.gitlab-ci.yml` CI/CD template which will automatically detect the `Dockerfile` file and start building the image using the shared runners, and push it to the [GitLab container registry](https://docs.gitlab.com/ee/user/packages/container_registry/). For self-managed instances or own runners on GitLab.com SaaS, it is recommended to decide whether to use and setup [Docker-in-Docker](https://docs.gitlab.com/ee/ci/docker/using_docker_build.html) or [Kaniko](https://docs.gitlab.com/ee/ci/docker/using_kaniko.html), Podman, or other container image build tools.\n\n```yaml\ninclude:\n  - template: Docker.gitlab-ci.yml\n\n```\n\nTo avoid running the Docker image build job every time, the job override definition specifies to [run it manually](https://docs.gitlab.com/ee/ci/yaml/#when). You can also use rules to [choose when to run the job](https://docs.gitlab.com/ee/ci/jobs/job_control.html), only when a Git tag is pushed for example.\n\n```yaml\ninclude:\n  - template: Docker.gitlab-ci.yml\n\n# Change Docker build to manual non-blocking\ndocker-build:\n  rules:\n    - if: '$CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH'\n      when: manual\n      allow_failure: true\n\n```\n\nOnce the container image is pushed to the registry, navigate into `Packages and Registries > Container Registries` and inspect the tagged image. Copy the image path for the latest tagged version and use it for the `image` attribute in the CI/CD job configuration.\n\n### Hurl container image in GitLab CI/CD example\n\nThe full example uses the previously built container image, and specifies the default `HURL_URL` variable. This can later be overridden by job definitions.\n\n_Please note that the image URL `registry.gitlab.com/everyonecancontribute/dev/hurl-playground:latest` is only used for demo purposes and not actively maintained or updated._\n\n```yaml\ninclude:\n  - template: Docker.gitlab-ci.yml\n\n# Change Docker build to manual non-blocking\ndocker-build:\n  rules:\n    - if: '$CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH'\n      when: manual\n      allow_failure: true\n\n# Hurl job template\n.hurl-tmpl:\n  image: registry.gitlab.com/everyonecancontribute/dev/hurl-playground:latest\n  variables:\n    HURL_URL: gitlab.com\n\n# Hurl jobs that check websites\nhurl-dnsmichi-at:\n  extends: .hurl-tmpl\n  variables:\n    HURL_URL: dnsmichi.at\n  script:\n    - echo -e \"GET https://${HURL_URL}\\n\\nHTTP/1.1 200\" | hurl --test --color\n\nhurl-opsindev-news:\n  extends: .hurl-tmpl\n  variables:\n    HURL_URL: opsindev.news\n  script:\n    - echo -e \"GET https://${HURL_URL}\\n\\nHTTP/2 200\" | hurl --test --color\n\n```\n\nThe CI/CD configuration can further be optimized:\n\n- Create job templates that execute the same scripts and only differ in the `HURL_URL` variable.\n- Use Hurl configuration files that allow specifying variables on the CLI or as environment variables. More on this in the next section.\n\n## DevSecOps workflows with Hurl\n\nHurl allows users to describe HTTP instructions in a configuration file with the `.hurl` suffix. You can add the configuration files to Git, and review and approve changes in merge requests - with the changes run in CI/CD and reporting back any failures before merging.\n\nInspect the `use-cases/` directory in the [example project](https://gitlab.com/everyonecancontribute/dev/hurl-playground), and fork it to make changes and commit and run the CI/CD pipelines and reports. You can also clone the project and run the `tree` command in the terminal.\n\n```sh\n$ tree use-cases\nuse-cases\n├── dnsmichi.at.hurl\n├── gitlab-com-api.hurl\n├── gitlab-contribute.hurl\n└── hackernews.hurl\n```\n\nHurl supports the glob option which collects all configuration files matching a specific pattern.\n\n![Hurl configuration file run](https://about.gitlab.com/images/blogimages/hurl-continuous-website-testing/hurl_multiple_config_files_run.png)\n\n### Chaining requests\n\nSimilar to CI/CD pipelines, jobs, and stages, testing HTTP endpoints with Hurl can require multiple steps. First, ping the website for being reachable, and then try parsing expected results. Separating the requirements into two steps helps to analyze errors.\n\n- HTTP endpoint reachable, but expected string not in response - static website was changed, REST API misses a field, etc.\n- HTTP endpoint is unreachable, don’t try to understand why the follow-up tests fail.\n\nThe following example first sends a ping probe to the dev instance, and a check towards the production environment in the second request.\n\n```sh\n$ vim use-cases/everyonecancontribute-com.hurl\n\nGET https://everyonecancontribute.dev\n\nHTTP/2 200\n\nGET https://everyonecancontribute.com\n\nHTTP/2 200\n$ hurl --test use-cases/everyonecancontribute-com.hurl\n```\n\nIn this scenario, the TLS certificate of the dev instance expired, and Hurl halts the test immediately.\n\n![Hurl chained requests, failing the first test with TLS certificate problems](https://about.gitlab.com/images/blogimages/hurl-continuous-website-testing/hurl_chained_request_fail.png)\n\n### Hurl reports as JUnit test reports\n\nTreat website monitoring and web app tests as unit and end-to-end tests. The Hurl developers thought of that too - the CLI command provides different output options for the report: `--report-junit \u003Coutputpath>` integrates with [GitLab JUnit report](https://docs.gitlab.com/ee/ci/testing/unit_test_reports.html) support into merge requests and pipeline views.\n\nThe following configuration generates a JUnit report file into the value of the `HURL_JUNIT_REPORT` variable. It exists to avoid typing the path three times. The Hurl tests are run from the `use-cases/` directory using a glob pattern.\n\n```yaml\n# Hurl job template\n.hurl-tmpl:\n    image: registry.gitlab.com/everyonecancontribute/dev/hurl-playground:latest\n    variables:\n        HURL_URL: gitlab.com\n        HURL_JUNIT_REPORT: hurl_junit_report.xml\n\n# Hurl tests from configuration file, generating JUnit report integration in GitLab CI/CD\nhurl-report:\n    extends: .hurl-tmpl\n    script:\n      - hurl --test use-cases/*.hurl --report-junit $HURL_JUNIT_REPORT\n    after_script:\n      # Hack: Workaround for 'id' instead of 'name' in JUnit report from Hurl. https://gitlab.com/gitlab-org/gitlab/-/issues/299086\n      - sed -i 's/id/name/g' $HURL_JUNIT_REPORT\n    artifacts:\n      when: always\n      paths:\n        - $HURL_JUNIT_REPORT\n      reports:\n        junit: $HURL_JUNIT_REPORT\n\n```\n\nThe JUnit format returned by Hurl 1.8.0 defines the `id` attribute, but the GitLab JUnit integration expects the `name` attribute to be present. While writing this blog post, [the problem was discussed](https://github.com/Orange-OpenSource/hurl/issues/1067#issuecomment-1343264751) with the maintainers, and [the `name` attribute was implemented](https://github.com/Orange-OpenSource/hurl/issues/1078) and will be available in future releases. As a workaround with Hurl 1.8.0, the CI/CD [after_script](https://docs.gitlab.com/ee/ci/yaml/#after_script) section uses `sed` to replace the attributes after generating the report.\n\nThe [following example](https://gitlab.com/everyonecancontribute/dev/hurl-playground/-/merge_requests/10) fails on purpose with checking a different HTTP protocol version.\n\n```text\nGET https://opsindev.news\n\n# This will fail on purpose\nHTTP/1.1 200\n\n[Asserts]\nbody contains \"Michael Friedrich\"\n```\n\n![Hurl test report in JUnit format integrated into GitLab](https://about.gitlab.com/images/blogimages/hurl-continuous-website-testing/hurl_gitlab_junit_integration_merge_request_widget_overlay.png)\n\nOnce the JUnit integration with Hurl tests from a glob pattern work, you can continue adding new `.hurl` configuration files to the GitLab repository and start testing in MRs, which will require review and approval workflows for production then.\n\n### Web review apps\n\nWebsite monitoring is only one aspect of using Hurl: Testing web applications deployed in review environments in the cloud, and in cloud-native clusters provides a native integration into [DevSecOps](https://about.gitlab.com/topics/devsecops/) workflows. The CI/CD pipelines will fail when Hurl tests are failing, and more insights are provided using merge request widgets reports.\n\n[Cloud Seed](https://docs.gitlab.com/ee/cloud_seed/index.html) provides the ability to deploy a web application to a major cloud provider, for example Google Cloud. After the deployment is successful, additional CI/CD jobs can be configured that verify that the deployed web app version does not introduce a regression, and provides all required data elements, API endpoints, etc. A similar workflow can be achieved by using review app environments with [webservers (Nginx, etc.), Docker, AWS, and Kubernetes](https://docs.gitlab.com/ee/ci/review_apps/#review-apps-examples). The review app [environment URL](https://docs.gitlab.com/ee/ci/environments/#create-a-dynamic-environment) is important for instrumenting the Hurl tests dynamically. The CI/CD variable [`CI_ENVIRONMENT_URL`](https://docs.gitlab.com/ee/ci/variables/predefined_variables.html) is available when `environment:url` is specified in the review app configuration.\n\nThe following example tests the review app for [this blog post when written in a merge request](https://gitlab.com/gitlab-com/www-gitlab-com/-/merge_requests/115548):\n\n```yaml\n# Test review apps with hurl for this blog post.\nhurl-review-test:\n  extends: .review-environment # inherits the environment settings\n  needs: [uncategorized-build-and-review-deploy] # waits until the website (sites/uncategorized) is deployed\n  stage: test\n  rules: # YAML anchor that runs the job only on merge requests\n    - \u003C\u003C: *if-merge-request-original-repo\n  image:\n    name: ghcr.io/orange-opensource/hurl:1.8.0\n    entrypoint: [\"\"]\n  script:\n    - echo -e \"GET ${CI_ENVIRONMENT_URL}\\n\\nHTTP/1.1 200\" | hurl --test --color\n\n```\n\nThe environment is specified in the [.review-environment job template](https://gitlab.com/gitlab-com/www-gitlab-com/-/blob/91d6fd72a424a3d913e79ebc2aefb23bbab85863/.gitlab-ci.yml#L332) and used to [deploy the website review job](https://gitlab.com/gitlab-com/www-gitlab-com/-/blob/91d6fd72a424a3d913e79ebc2aefb23bbab85863/.gitlab-ci.yml#L532). The relevant configuration snippet is shown here:\n\n```yaml\n.review-environment:\n  variables:\n    DEPLOY_TYPE: review\n  environment:\n    name: review/$CI_COMMIT_REF_SLUG\n    url: https://$CI_COMMIT_REF_SLUG.about.gitlab-review.app\n    on_stop: review-stop\n    auto_stop_in: 30 days\n\n```\n\nThe deployment of the www-gitlab-com project [uses buckets in Google Cloud](https://gitlab.com/gitlab-com/www-gitlab-com/-/blob/91d6fd72a424a3d913e79ebc2aefb23bbab85863/scripts/deploy) that serve the website content in the review app. There are different types of web applications that require different deployment methods - as long as the environment URL variable is available in CI/CD and the deployment URL is accessible from the GitLab Runner executing the CI/CD job, you can continously test web apps with Hurl!\n\n![Hurl test in GitLab CI/CD for review app environments](https://about.gitlab.com/images/blogimages/hurl-continuous-website-testing/hurl_gitlab_cicd_review_app_environment_tests_www-gitlab-com.png)\n\n## Development tips\n\nUse the [`--verbose` parameter](https://hurl.dev/docs/tutorial/debug-tips.html) to see the full request and response flow. Hurl also provides tips which `curl` command could be run to fetch more data. This can be helpful when starting to use or develop a new REST API, or learning to understand the JSON structure of HTTP responses. Chaining the `curl` command with `jq` (the `curl ... | jq` pattern) can still be helpful to fetch data, and build the HTTP tests in a second terminal or editor window.\n\n```sh\n$ curl -s 'https://gitlab.com/api/v4/projects' | jq\n$ curl -s 'https://gitlab.com/api/v4/projects' | jq -c '.[]' | jq\n\n{\"id\":41375401,\"description\":\"An example project for a GitLab pipeline.\",\"name\":\"Calculator\",\"name_with_namespace\":\"Iva Tee / Calculator\",\"path\":\"calculator\",\"path_with_namespace\":\"snufkins_hat/calculator\",\"created_at\":\"2022-11-26T00:32:33.825Z\",\"default_branch\":\"master\",\"tag_list\":[],\"topics\":[],\"ssh_url_to_repo\":\"git@gitlab.com:snufkins_hat/calculator.git\",\"http_url_to_repo\":\"https://gitlab.com/snufkins_hat/calculator.git\",\"web_url\":\"https://gitlab.com/snufkins_hat/calculator\",\"readme_url\":\"https://gitlab.com/snufkins_hat/calculator/-/blob/master/README.md\",\"avatar_url\":null,\"forks_count\":0,\"star_count\":0,\"last_activity_at\":\"2022-11-26T00:32:33.825Z\",\"namespace\":{\"id\":58849237,\"name\":\"Iva Tee\",\"path\":\"snufkins_hat\",\"kind\":\"user\",\"full_path\":\"snufkins_hat\",\"parent_id\":null,\"avatar_url\":\"https://secure.gravatar.com/avatar/a3efe834950275380d5f19c9b17c922c?s=80&d=identicon\",\"web_url\":\"https://gitlab.com/snufkins_hat\"}}\n```\n\nThe GitLab projects API returns an array of elements, where we can inspect the `id` and `name` attributes for a simple test - the first element’s name must not be empty, the second element’s id needs to be greater than 0.\n\n```sh\n$ vim gitlab-com-api.hurl\n\nGET https://gitlab.com/api/v4/projects\n\nHTTP/2 200\n\n[Asserts]\njsonpath \"$[0].name\" != \"\"\njsonpath \"$[1].id\" > 0\n\n$ hurl --test gitlab-com-api.hurl\n\ngitlab-com-api.hurl: Running [1/1]\ngitlab-com-api.hurl: Success (1 request(s) in 728 ms)\n--------------------------------------------------------------------------------\nExecuted files:  1\nSucceeded files: 1 (100.0%)\nFailed files:    0 (0.0%)\nDuration:        730 ms\n```\n\n## More use cases\n\n- Work with HTTP sessions and [cookies](https://hurl.dev/docs/request.html#cookies), test [forms with parameters](https://hurl.dev/docs/request.html#form-parameters).\n- Review existing API tests of your applications.\n- Build advanced chained workflows with GET, POST, PUT, DELETE, and more HTTP methods.\n- Integrate simple ping/HTTP monitoring health checks into the DevSecOps Platform using alerts and incident management.\n\nIf the Hurl checks cannot be integrated directly inside the project where the application is developed and deployed, another idea could be to create a standalone GitLab project that has CI/CD pipeline schedules enabled. It can continuously run the Hurl tests, and parse the reports or trigger an event when the pipeline is failing, and [create an alert](https://docs.gitlab.com/ee/operations/incident_management/alerts.html) by sending a JSON payload from the Hurl results to the [HTTP endpoint](https://docs.gitlab.com/ee/operations/incident_management/integrations.html#single-http-endpoint). Developers can send MRs to update the Hurl tests, and maintainers review and approve the new test suites being rolled out into production. Alternatively, move the complete CI/CD configuration into a group/project with different permissions, and specify the CI/CD configuration as remote URL in the web application project. This compliance level helps to control who can make changes to important tests and CI/CD configuration.\n\nHurl supports `--json` as parameter to only return the JSON formatted test result and build own custom reports and integrations.\n\n```sh\n$ echo -e \"GET https://about.gitlab.com/teamops/\\n\\nHTTP/2 200\" | hurl --json | jq\n```\n\nFor folks in DevRel, monitoring certain websites for keywords or checking APIs whether values increase a certain threshold can be interesting. Here is an example for monitoring Hacker News using the Algolia search API, inspired by the [Zapier integration used for GitLab Slack](https://handbook.gitlab.com/handbook/marketing/developer-relations/workflows-tools/zapier/#zaps-for-hacker-news). The `QueryStringParams` section allows users to define the query parameters as a readable list, which is easier to modify. The `jsonpath` checks searches for the `hits` key and its count being zero (not on the Hacker News front page means OK in this example).\n\n```text\n$ vim hackernews.hurl\n\nGET https://hn.algolia.com/api/v1/search\n[QueryStringParams]\nquery: gitlab\n#query: hurl\ntags: front_page\n\nHTTP/2 200\n\n[Asserts]\njsonpath \"$.hits\" count == 0\n\n$ hurl --test hackernews.hurl\n```\n\n## Limitations\n\nHurl works great for testing websites and web applications that serve static content, and by sending different HTTP request types, data, etc., and ensuring that responses match expectations. Compared to other end-to-end testing solutions (Selenium, etc.), Hurl does not provide a JavaScript engine and only can parse the raw DOM or JSON response. It does not support a DOM managed and rendered by JavaScript front-end frameworks. UI integration tests also need to be performed with different tools, similar to full end-to-end test workflows. Other examples are [accessibility testing](https://docs.gitlab.com/ee/ci/testing/accessibility_testing.html) and [browser performance testing](https://docs.gitlab.com/ee/ci/testing/browser_performance_testing.html). If you are curious how end-to-end testing is done for GitLab, the product, peek into the [development documentation](https://docs.gitlab.com/ee/development/testing_guide/end_to_end/).\n\n## Conclusion\n\nHurl provides an easy way to test HTTP endpoints (such as websites and APIs) in a fast and reliable way. The CLI commands can be integrated into CI/CD workflows, and the configuration syntax and files provide a single source of truth for everything. Additional support for JUnit report formats ensure that website testing is fully integrated into the [DevSecOps](https://about.gitlab.com/topics/devsecops/) platform, and increases visibility and extensibility with automating tests, and monitoring. There are known limitations with dynamic JavaScript websites and advanced UI/end-to-end testing workflows.\n\nHurl is open source, [created and maintained by Orange](https://opensource.orange.com/en/open-source-orange/), and written in Rust. This blog post inspired contributions to the [Debian/Ubuntu installation documentation](https://github.com/Orange-OpenSource/hurl/pull/1084) and [default issue templates](https://github.com/Orange-OpenSource/hurl/pull/1083).\n\n**Tip:** Practice using Hurl on the command line, and remember it when the next production incident shows a strange API behavior with POST requests.\n\nThanks to [Lee Tickett](/company/team/#leetickett-gitlab) who inspired me to test Hurl in GitLab CI/CD and write this blog post after seeing huge interest in a [Twitter share](https://twitter.com/dnsmichi/status/1595820546062778369).\n\nCover image by [Aaron Burden](https://unsplash.com/@aaronburden) on 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CI/CD tools can run a build and ship a deployment. 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.",[720],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[108,724,725,726],"DevOps platform","tutorial","features",{"featured":28,"template":13,"slug":728},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":730,"config":740},{"title":731,"description":732,"authors":733,"heroImage":735,"date":736,"body":737,"category":9,"tags":738},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[734],"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/)",[725,739,726],"product",{"featured":12,"template":13,"slug":741},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":743,"config":753},{"title":744,"description":745,"authors":746,"heroImage":748,"date":749,"category":9,"tags":750,"body":752},"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.",[747],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[261,621,751],"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":754,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":756},[757,771,782,794],{"id":758,"categories":759,"header":761,"text":762,"button":763,"image":768},"ai-modernization",[760],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":764,"config":765},"Get your AI maturity score",{"href":766,"dataGaName":767,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":772,"categories":773,"header":774,"text":762,"button":775,"image":779},"devops-modernization",[739,567],"Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":767,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":786,"text":762,"button":787,"image":791},"security-modernization",[785],"security","Are you trading speed for security?",{"text":788,"config":789},"Get your security maturity score",{"href":790,"dataGaName":767,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":792},{"src":793},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":795,"paths":796,"header":799,"text":800,"button":801,"image":806},"github-azure-migration",[797,798],"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":802,"config":803},"See how GitLab compares to GitHub",{"href":804,"dataGaName":805,"dataGaLocation":243},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":807},{"src":781},{"header":809,"blurb":810,"button":811,"secondaryButton":816},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":812,"config":813},"Get your free trial",{"href":814,"dataGaName":50,"dataGaLocation":815},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":505,"config":817},{"href":54,"dataGaName":55,"dataGaLocation":815},1776444490505]