[{"data":1,"prerenderedAt":718},["ShallowReactive",2],{"/en-us/blog/making-ci-easier-with-gitlab/":3,"navigation-en-us":35,"banner-en-us":463,"footer-en-us":480,"Rob Ribeiro":690,"next-steps-en-us":703},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":16,"config":25,"_id":28,"_type":29,"title":30,"_source":31,"_file":32,"_stem":33,"_extension":34},"/en-us/blog/making-ci-easier-with-gitlab","blog",false,"",{"title":9,"description":10,"ogTitle":9,"ogDescription":10,"noIndex":6,"ogImage":11,"ogUrl":12,"ogSiteName":13,"ogType":14,"canonicalUrls":12,"schema":15},"Making CI/CD easier with GitLab","The team at Trek10 tries to consider the need for automation and repeatability with everything they do. One team member gives a crash course in GitLab CI/CD and explains how they use it.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749680423/Blog/Hero%20Images/making-ci-easier-with-gitlab.jpg","https://about.gitlab.com/blog/making-ci-easier-with-gitlab","https://about.gitlab.com","article","\n                        {\n        \"@context\": \"https://schema.org\",\n        \"@type\": \"Article\",\n        \"headline\": \"Making CI/CD easier with GitLab\",\n        \"author\": [{\"@type\":\"Person\",\"name\":\"Rob Ribeiro\"}],\n        \"datePublished\": \"2017-07-13\",\n      }",{"title":9,"description":10,"authors":17,"heroImage":11,"date":19,"body":20,"category":21,"tags":22},[18],"Rob Ribeiro","2017-07-13","At [Trek10](https://www.trek10.com/), we always try to consider the need for\nautomation and repeatability with everything that we do. That’s why we focus\non using tools like CloudFormation, [Serverless](/topics/serverless/), and\nCI/CD, as well as building other tools. Recently, I was tasked with doing\nvarious maintenance tasks on a number of internal tools/projects. Some\nneeded upgrades from Node.js 0.10, some needed code fixes, and most needed\nCI/CD. Today, we’re just going to focus on the CI/CD part.\n\n\n\u003C!-- more -->\n\n\nIn spite of my past experience with Jenkins and TeamCity and our team’s\nexperience with AWS (CodePipeline/CodeDeploy), I chose [GitLab\nCI/CD](/topics/ci-cd/) to standardize these projects. The biggest reason for\nthis choice is history. As a project evolves, its CI/CD configuration may\nchange. If you ever need to go back in time, you may have difficulty\ndeploying again. Since GitLab CI/CD is based on a `.gitlab-ci.yml` config\nfile that is committed with the code, as long as a commit built and deployed\nthen, it stands a pretty good chance of building and deploying now. Being\nable to tweak CI/CD without leaving my editor was an additional bonus.\n\n\n### Crash course in GitLab CI/CD\n\n\nGitLab CI/CD relies on having a `.gitlab-ci.yml` file in the root of your\nrepo. CI/CD for each commit is run against the `.gitlab-ci.yml` that is\ncurrent for that commit. The fundamental unit of CI/CD for GitLab is a\n“job”. A job is a construct that runs a bash script against a commit in a\nparticular context. You might have one job to run tests, other jobs to build\nfor staging or production, and other jobs to deploy to particular\nenvironments. In the config file, jobs are represented by top level maps\n(aka “objects”) that are not otherwise “reserved” GitLab CI/CD maps.\nExamples of reserved top level maps: `image` (Docker image in which your\njobs run), `services` (other Docker images that need to run while your jobs\nrun), `before_script` (runs before every `script`), `after_script` (runs\nafter every `script`), `stages` (redefines the stage names and order),\n`variables` (variables available to all jobs), and `cache` (controls what is\ncached between CI/CD runs; good for stuff from your package manager).\n\n\nEvery job must belong to a stage (if left out, `test` is the default).\nStages are run in a sequence, and all of the jobs in a stage run with max\nparallelism available. The default stage sequence is: `build`, `test`,\n`deploy`. Each job also has `before_script`, `after_script`, `variables`,\nand `cache`. Defining these at a job level will override the top-level\nconfiguration. The most important of these is `variables`, because your\nvariables are what make the production deploy job’s context different from\nthe staging deploy job’s context. `variables` is just a map with a bunch of\nkey value pairs. Variables are consumed with a syntax similar to bash:\n`${myVar}`. There are some limitations that you should know:\n\n\n* Variables do not support bash variable expansions, substitutions,\ndefaults, etc.\n\n* Variables do not recurse or have a sense of order of evaluation, but top\nlevel variables can be used in job level variables. See the following\nexamples:\n\n\n```\n\n# You CANNOT do this (referencing a sibling variable in the same map)\n\nvariables:\n    PROD_STAGE_NAME: prod\n    PROD_URL: https://thisismywebsite.com/${PROD_STAGE_NAME}\n```\n\n\n```\n\n# You CAN do this (referencing a top-level variable from a job's variables\nmap)\n\nvariables:\n    PROD_STAGE_NAME: prod\n\nmy_job:\n    variables:\n        STAGE_NAME: ${PROD_STAGE_NAME}\n```\n\n\n```\n\n# But you CANNOT do something like this (nested variables)\n\nvariables:\n    CURRENT_STAGE: PROD\n    PROD_STAGE_NAME: prod\n\nmy_job:\n    variables:\n        STAGE_NAME: ${${CURRENT_STAGE}_STAGE_NAME}\n```\n\n\nThat last example gives us a ton of power. We’ll be sure to abuse that as we\ngo.\n\n\nAs mentioned before, jobs run a bash script in a context. So every job must\nhave a `script`. The last big thing that you need is “flow control”. By\ndefault, a job will run on every commit. Using the `only`, `except`, and\n`when` keys allows you to control how jobs are triggered. `only` and\n`except` accept the following options:\n\n\n* Branch names, e.g. `master` or `develop`\n\n* Tag names\n\n* JS style RegExp literals to evaluate against branch/tag names\n\n* These special keywords: `api`, `branches`, `external`, `tags`, `pushes`,\n`schedules`, `triggers`, and `web`\n\n* Using `branches` and `tags` with `only` cause a job to be run for every\nbranch or tag, respectively\n\n* Repo path filters to deal with repo forks\n\n\nOne more important fact: jobs that start with a period character are\ndisabled, e.g.: `.my_disabled_job`\n\n\nThat should be enough to get us started. You can find more [GitLab CI/CD\ndocumentation here](https://docs.gitlab.com/ee/ci/). The most useful bit is\nthe `.gitlab-ci.yml` reference found\n[here](https://docs.gitlab.com/ee/ci/yaml/).\n\n\nAs with any new tool, I got to read and re-read the documentation and make\nsome mistakes getting things right. By the time I was knee-deep in this, I\nrealized there was a need to prevent anyone from having to do this again,\nmyself included. The solution requires two things: a well-designed CI/CD\ntemplate and a way to get that template into all of your new repositories.\nLet’s tackle template design next.\n\n\n### Designing a template\n\n\nThis part is hard to talk about in a completely generic manner. Instead,\nlet’s walk through our use case. Looking at our projects past and present, I\ncould usually bet on these characteristics:\n\n\n* Deploys to AWS (we are an AWS consultancy after all…)\n\n* Uses Serverless framework with Node.js or Python\n\n* May deploy production to multiple regions\n\n* May deploy different stages to different accounts\n\n\nIn addition, I realized that I needed these other options:\n\n\n* May need to “disable” dev/staging from doing real work\n\n* May want one dev environment per branch\n\n\nFinally, we decided on the following deployment strategy:\n\n\n* Production deploys via tags on `master`\n\n* Staging deploys on commits/merges to `master`\n\n* Dev deploys should work for all other branches (we’re not going to\nimplement this one in this post)\n\n\nMy roots are as a software developer, so making things reusable is a core\nskill at this point. A good template is going to make it super easy for the\nintended cases and be fairly adaptable for other uses. Here is the goal:\n\n\n* One script per stage. That means only one test script, one build script,\nand one deploy script. Oh, and keep it DRY.\n\n* Jobs should be as similar as possible, and differences should be tweaked\nby top level variables.\n\n\nLet’s focus on that single script per stage. We’re not going to cover how to\nwrite the deployment script, but we’ll focus on the deploy stage. But let’s\nsay we start with a deployment job like this:\n\n\n```\n\ndeploy:production:\n    stage: deploy\n    script: |\n        # assume ${DEPLOYMENT_ROLE} in AWS\n        # install dependencies\n        # run serverless deployment with ${STAGE_NAME} ${REGION}\n    variables:\n        DEPLOYMENT_ROLE: arn:aws:iam::1234567890:role/gitlab-ci-deployment\n        STAGE_NAME: prod\n        REGION: us-east-1\n        ACCOUNT: \"1234567890\"\n    only:\n        - tags\n```\n\n\n\nNow we could copy and tweak this for staging and dev, but that’s not what\nwe’re after. First, let’s break the script off to a reusable chunk and use\nit in our staging deploy:\n\n\n```\n\n.deployment_script: &deployment_script\n    stage: deploy\n    script: |\n        # assume ${DEPLOYMENT_ROLE} in AWS\n        # install dependencies\n        # run serverless deployment with ${STAGE_NAME} ${REGION}\n\ndeploy:production:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: arn:aws:iam::1234567890:role/gitlab-ci-deployment\n        STAGE_NAME: prod\n        REGION: us-east-1\n        ACCOUNT: \"1234567890\"\n        PRODUCTION: \"true\"\n    only:\n        - tags\n\ndeploy:staging:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: arn:aws:iam::0987654321:role/gitlab-ci-deployment\n        STAGE_NAME: staging\n        REGION: us-east-1\n        ACCOUNT: \"0987654321\"\n    only:\n        - master\n```\n\n\nUsing YAML anchors and references, we can inject the script into all of our\ndeployment jobs. Notice that the deployment script is disabled. This is\nbecause we don’t want it to run in parallel with all of our intended jobs.\nWe also added a `PRODUCTION` environment variable to just the production\ndeploy to allow our script to pick that up too. If your code knows about\nthis, you can use this to turn on/off production-only features. Now, we can\nmake this cleaner and easier for our developers by pulling all of the\n`variables` to a top-level variables map at the top of the file:\n\n\n```\n\nvariables:\n    PROD_ACCOUNT: \"1234567890\"\n    PROD_STAGE_NAME: prod\n    PROD_REGION: us-east-1\n    STAGING_ACCOUNT: \"0987654321\"\n    STAGING_STAGE_NAME: staging\n    STAGING_REGION: us-east-1\n\n.deployment_script: &deployment_script\n    stage: deploy\n    script: |\n        # assume ${DEPLOYMENT_ROLE} in AWS\n        # install dependencies\n        # run serverless deployment with ${STAGE_NAME}, ${REGION}, and ${ACCOUNT}\n\ndeploy:production:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: \"arn:aws:iam::${PROD_ACCOUNT}:role/gitlab-ci-deployment\"\n        STAGE_NAME: ${PROD_STAGE_NAME}\n        REGION: ${PROD_REGION}\n        ACCOUNT: ${PROD_ACCOUNT}\n        PRODUCTION: \"true\"        \n    only:\n        - tags\n\ndeploy:staging:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: \"arn:aws:iam::${STAGING_ACCOUNT}:role/gitlab-ci-deployment\"\n        STAGE_NAME: ${STAGING_STAGE_NAME}\n        REGION: ${STAGING_REGION}\n        ACCOUNT: ${STAGING_ACCOUNT}\n    only:\n        - master\n```\n\n\n\nNow, that’s looking more reusable, and we have accomplished our second goal\nof making the jobs very similar and controlled by top-level variables. This\nmakes it easy for anyone who fits the template’s use case perfectly to reuse\nit. We could easily add the dev environment, but we’ll skip that in favor of\nillustrating multi-region production deploys:\n\n\n```\n\nvariables:\n    PROD_ACCOUNT: \"1234567890\"\n    PROD_STAGE_NAME: prod\n    PROD1_REGION: us-east-1\n    PROD2_REGION: us-west-2\n    STAGING_ACCOUNT: \"0987654321\"\n    STAGING_STAGE_NAME: staging\n    STAGING_REGION: us-east-1\n\n.deployment_script: &deployment_script\n    stage: deploy\n    script: |\n        # assume ${DEPLOYMENT_ROLE} in AWS\n        # install dependencies\n        # run serverless deployment with ${STAGE_NAME}, ${REGION}, and ${ACCOUNT}\n\n.production_variables\n    DEPLOYMENT_ROLE: \"arn:aws:iam::${PROD_ACCOUNT}:role/gitlab-ci-deployment\"\n    STAGE_NAME: ${PROD_STAGE_NAME}\n    ACCOUNT: ${PROD_ACCOUNT}\n    PRODUCTION: \"true\"    \n\ndeploy:production_1: &deploy_production\n    \u003C\u003C: *deployment_script\n    variables:\n        \u003C\u003C: *production_variables\n        REGION: ${PROD1_REGION}\n    only:\n        - tags\n\ndeploy:production_2:\n    \u003C\u003C: *deploy_production\n    variables:\n        \u003C\u003C: *production_variables\n        REGION: ${PROD2_REGION}        \n\ndeploy:staging:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: \"arn:aws:iam::${STAGING_ACCOUNT}:role/gitlab-ci-deployment\"\n        STAGE_NAME: ${STAGING_STAGE_NAME}\n        REGION: ${STAGING_REGION}\n        ACCOUNT: ${STAGING_ACCOUNT}\n    only:\n        - master\n```\n\n\nNotice that we have changed the job names to reflect having multiple\nregions. In addition, we are making use of YAML anchors and references to\ncopy the entire `deploy:production_1` job into `deploy:production_2` and\nthen we just override the `REGION` variable. This makes adding additional\nregions super easy.\n\n\nWhat’s more useful at this point is that, as long as you have made your\nscript flexible enough, you can now distribute this to your development team\nas a template. If their project fits the script and configuration perfectly,\nthey should just have to fill in the correct values for the top-level\nvariables and go. For those needing something different, they should\nhopefully be able to just tweak the script. Now, we just need to solve the\nproblem of making sure that they actually use the template…\n\n\n### Automatic CI/CD injection with GitLab and AWS Lambda\n\n\nI was inspired by GitHub’s option to select a .gitignore and license during\nthe repo creation process. What if we could have that for CI? Forking GitLab\nand figuring out how to hack this in did not sound like a quick or easy\nthing to do. However, after a little research, I found that we could use a\nsystem hook to trigger a Lambda that could inject the desired template via\nthe commit API. This part is not as interesting to read about, so we did one\nbetter: we have open sourced this tool so you can deploy it in your\nenvironment. Check out the repo\n[here](https://github.com/trek10inc/gitlab-boilerplate-injector). And if\nyou’re looking for someone to help you implement these and other awesome\nautomations and AWS solutions, we would love to talk to you. Feel free to\nreach out to us at info@trek10.com for more. Thanks for reading!\n\n\n## About the Guest Author\n\n\nRob has spent his career honing his interpersonal, technical, and problem\nsolving skills. He spent five years in customer service and management,\nfollowed by over five years in software development and consulting. He has\nexperience working and consulting for everything from startups to Fortune\n500 enterprises in a variety of industries including manufacturing,\nhealthcare, and finance. 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