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nuget

README.md

Package assembly

This set of scripts collect CI artifacts from a local directory or S3, and assembles them into a package structure defined by a packaging class in a staging directory. For the NugetPackage class the NuGet tool is then run (from within docker) on this staging directory to create a proper NuGet package (with all the metadata). While the StaticPackage class creates a tarball.

The finalized nuget package maybe uploaded manually to NuGet.org

Requirements

  • Requires Python 3
  • Requires Docker
  • (if --s3) Requires private S3 access keys for the librdkafka-ci-packages bucket.

Usage

  1. Trigger CI builds by creating and pushing a new release (candidate) tag in the librdkafka repo. Make sure the tag is created on the correct branch.

    $ git tag v0.11.0-RC3 $ git push origin v0.11.0-RC3

  2. Wait for CI builds to finish, monitor the builds here:

New builds

Previous builds

Or if using SemaphoreCI, just have the packaging job depend on prior build jobs in the same pipeline.

  1. On a Linux host, run the release.py script to assemble the NuGet package

    $ cd packaging/nuget

    Specify the tag

    $ ./release.py v0.11.0-RC3

    Optionally, if the tag was moved and an exact sha is also required:

    $ ./release.py --sha v0.11.0-RC3

  2. If all artifacts were available the NuGet package will be built and reside in the current directory as librdkafka.redist..nupkg

  3. Test the package manually

  4. Upload the package to NuGet

  1. If you trust this process you can have release.py upload the package automatically to NuGet after building it:

    $ ./release.py --retries 100 --upload your-nuget-api.key v0.11.0-RC3

Other uses

Create static library bundles

To create a bundle (tarball) of librdkafka self-contained static library builds, use the following command:

$ ./release.py --class StaticPackage v1.1.0

Clean up S3 bucket

To clean up old non-release/non-RC builds from the S3 bucket, first check with:

$ AWS_PROFILE=.. ./cleanup-s3.py --age 360

Verify that the listed objects should really be deleted, then delete:

$ AWS_PROFILE=.. ./cleanup-s3.py --age 360 --delete