GitLab CI/CD Pipelines
Tips, Tricks, and Gotchas You Only Learn After the Pipeline Teaches You Humility
GitLab CI/CD pipelines are elegant, powerful, and deceptively simple. At first glance, it’s just a `.gitlab-ci.yml` file. How hard could that be? Then your pipeline fails, your job never starts, and GitLab calmly informs you that everything is working exactly as designed.
This is how the learning begins.
One of the first lessons GitLab teaches is that YAML is not forgiving. YAML is polite, quiet, and ruthless. One extra space can turn a valid pipeline into an abstract art project. GitLab will happily parse your file, accept it, and then skip every job because the indentation implied a different reality than the one you intended. The pipeline didn’t break. It followed instructions you didn’t realize you gave.
Stages are another early confidence trap. They look obvious. Build, test, deploy. Simple. Until you realize that jobs without a defined stage don’t run where you think they do, or that a missing stage definition means GitLab invents one for you. This is when you learn that explicit is always better than clever, especially when your deployment job quietly runs before your tests.
Rules and only/except conditions are where pipelines either become elegant or deeply confusing. Rules are powerful and expressive, but they require precision. GitLab will evaluate them exactly as written, not as intended. A rule that feels obvious in your head can result in jobs that run everywhere, nowhere, or only on Tuesdays when the moon is full. When in doubt, simplify. The pipeline appreciates honesty.
Variables feel like magic at first. Define once, reuse everywhere. Then you discover variable precedence. Project-level variables override group variables. Job variables override pipeline variables. Some variables exist only at runtime. Others are masked, protected, or silently unavailable depending on branch protection. GitLab is not hiding this from you. It just assumes you enjoy documentation.
Artifacts and caching are another area where expectations meet reality. Artifacts move files between jobs. Caches speed things up. Mixing them up leads to pipelines that are either very slow or very confused. Artifacts expire. Caches persist. Forgetting this distinction results in jobs that work once and then fail forever, which is an exciting way to spend an afternoon.
Runners deserve respect. Shared runners are convenient until they’re busy. Specific runners are fast until they’re offline. Tags are powerful until no runner matches them. When a job is stuck in pending, it’s not thinking. It’s waiting for a runner that does not exist in the way you think it does.
Security scanning jobs are helpful and judgmental. They will tell you things you did not ask to know, at times you did not want to know them. They also add time to pipelines, which encourages teams to disable them temporarily. Temporarily has a way of becoming permanent unless discipline intervenes.
One of the most important tricks is learning to read pipeline graphs. GitLab shows you exactly what happened. Which job ran. Which one didn’t. Why it skipped. The graph is honest even when it’s inconvenient. Senior users stop guessing and start trusting the visualization.
Another lesson arrives when pipelines grow. What started as ten lines becomes hundreds. Reuse becomes essential. Includes, templates, and anchors save sanity. Copy-paste works until it doesn’t. GitLab gives you tools to scale pipelines. Ignoring them leads to YAML archaeology later.
The biggest gotcha of all is treating the pipeline as a side effect instead of a product. GitLab CI/CD works best when pipelines are designed, reviewed, and maintained like code. When pipelines are owned by the team, they improve. When they’re ignored, they become fragile.
The reward for mastering GitLab CI/CD is subtle. Pipelines become boring. Builds run. Tests pass. Deployments happen quietly. Nobody asks who touched what. This is success.
GitLab CI/CD does not demand perfection. It demands clarity. When you give it clear instructions, it delivers reliably. When you are vague, it teaches you a lesson.
Usually more than once.