Caching and artifacts can significantly enhance the speed of AWS VPC deployments on GitLab CI. By storing dependencies and build artifacts, you can reduce the time taken for the CI/CD pipeline to execute, which in turn leads to faster and more efficient deployments.
Caching allows you to save specific files or folders between different jobs in your pipeline. When properly configured, GitLab CI can reuse these cached items during future runs, avoiding the need to re-download or rebuild them each time.
Artifacts are files generated by your CI job that you want to keep after the job finishes. These can include compiled binaries, test results, or packages. By storing these artifacts, subsequent jobs can access them without needing to recreate them.
stages:
- build
- deploy
cache:
paths:
- vendor/
- node_modules/
build_job:
stage: build
script:
- composer install
- npm install
artifacts:
paths:
- vendor/
- node_modules/
deploy_job:
stage: deploy
dependencies:
- build_job
script:
- echo "Deploying to AWS VPC..."
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?