📄️ GitLab CI
GitLab CI/CD: continuous integration and delivery, pipelines, runners, automation, workflow, stages, jobs, artifacts, variables, and continuous deployment with YAML.
📄️ Initial Concepts
GitLab CI fundamental concepts: groups, projects, subgroups, runners, executors, environment variables, triggers, pipeline triggers, and structural organization.
📄️ Runners
GitLab runner configuration: shared runners, group runners, project runners, installation, docker executor, kubernetes, helm chart, and minute limitations.
📄️ First Pipeline
First GitLab CI pipeline: creating .gitlab-ci.yml, configuring tags, runners, environment variables, jobs, shared runners, and basic execution.
📄️ Stages
GitLab CI stage configuration: defining sequential stages, parallel jobs, artifacts, dependencies between stages, and pipeline organization.
📄️ Base Project
Getting to know the Node.js project that will be used to create GitLab CI pipelines: structure, dependencies, DevBox, fork, configuration, and environment preparation.
📄️ Performance and Limits
GitLab CI performance optimization: Docker Hub limits, image caching, private registry, pipeline speed, and strategies to reduce execution time.
📄️ Build
Build execution in GitLab CI pipelines: dependency installation, npm ci vs npm install, artifacts, stage configuration and Node.js build optimization.
📄️ Tests
Running tests in GitLab CI pipelines: unit tests, linter, security, code quality, parallel jobs and automated testing strategies.
📄️ Reports
GitLab CI reports configuration: unit test reports, coverage, junit, GitLab interface integration and results visualization.
📄️ Extends
GitLab CI templates and inheritance: extends, code reuse, before_script, after_script, job templates, and efficient pipeline organization.
📄️ Variables
GitLab CI environment variables: execution context, predefined variables, custom variable configuration, secrets, masked variables, and best practices.
📄️ Environment and Anchors
GitLab CI environments and YAML anchors: environment management, environment variables, protected branches, staging/production deployment, and code reuse.
📄️ Includes
GitLab CI pipeline organization with includes: modularization, YAML file separation, reusable templates, and clean project structure.
📄️ Rules
GitLab CI flow control with rules: execution conditions, when, if, changes, exists, only/except migration, and advanced pipeline management.
📄️ Services
GitLab CI services: auxiliary containers, docker-in-docker, databases, container networking, PostgreSQL, privileged mode, and advanced configurations.
📄️ Dependencies
GitLab CI dependencies: needs, dependencies, flow control between jobs, pipeline optimization, artifacts, and sequential vs parallel execution.
📄️ Cache
Performance optimization with GitLab CI cache: temporary storage, node_modules, dependencies, reuse between pipelines, and efficient cache strategies.
📄️ Deploy
Automated GitLab CI deployment: Kubernetes, Netlify, environments, GitLab Agent, operators, deployment strategies and continuous integration.
📄️ Workflow
GitLab CI pipeline execution control with workflow rules: global configuration, conditions, branches, resources, optimization, and trigger management.