Martin Beránek
GitLab CI pipelines for a whole company
#1about 3 minutes
Overcoming the challenges of a legacy Jenkins pipeline
The initial Jenkins setup suffered from an undocumented and unversioned Groovy codebase, leading to high maintenance toil for the operations team.
#2about 4 minutes
Establishing new requirements for a modern CI system
The decision to migrate was driven by high support costs, while retaining successful concepts like monorepos and setting new goals for simplicity and clarity.
#3about 3 minutes
How GitLab CI pipelines use stages, jobs, and DAGs
GitLab CI pipelines are structured with sequential stages and parallel jobs, which can be optimized into a Directed Acyclic Graph (DAG) using the `needs` keyword.
#4about 4 minutes
Applying principles for scalable and maintainable pipelines
Key principles for success include recycling configurations with `include`, documenting complex jobs, assigning clear ownership, and tracking known issues as pitfalls.
#5about 4 minutes
Customizing pipeline runtimes and implementing robust testing
Pipeline execution is customized using variable Docker images, while testing is handled with Docker Compose and dynamic environments are managed for quality assurance.
#6about 3 minutes
Solving performance bottlenecks from data sharing and dependencies
Sharing large artifacts like `node_modules` is slow, so performance is improved by implementing local package caches and runner-level volume mounts.
#7about 3 minutes
Key lessons learned from the CI migration process
Important takeaways include educating developers on best practices, automating dependency updates with Renovate, and carefully managing changes in a shared monorepo.
#8about 4 minutes
A feature wishlist for improving GitLab CI workflows
Future improvements to GitLab CI could include native HashiCorp Vault integration, map support for variables, simpler rule evaluation, and faster artifact sharing.
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