ci: move to build branch model

Currently, runtrybot triggers a repository_dispatch build via the
test_dispatch workflow.

This dispatch workflow then starts the process of updating the CL with a
"starting" notification, running the build matrix, and then finally
updating the CL with the result state.

However, because this all happens as part of the dispatch workflow, it
all happens using the _tip_ definitions of the dispatch workflow, not
the definition in the commit under test.

This means that when we need to make changes to the workflow definition,
the try bot result from testing that CL is not the result of using the
changes to the workflow in that commit. Hence we must blindly submit
that CL and hope that it doesn't break the workflow (the next tip build
will tell us but still). This is clearly very brittle.

This change switches us to a model of using build branches that are
created by the initial repository_dispatch. These branches then trigger
a regular branch build, albeit using a special git ref
(ci/$CHANGE_ID/$COMMIT). Status updates to the corresponding CL happen
as before, but this time from the build branch workflow. When a CL build
branch workflow has completed, the corresponding build branch is deleted
(regardless of the test result).  Note that for now we do not delete
this branch: we first want to ensure that a master build succeeds.

Note that from a security perspective this is fine (tm). In order to
trigger a repository_dispatch event in the first place a user must have
write permission to the CUE repo. So there is no privilege escalation
here.

The initial repository dispatch is triggered by a user with write
privileges, but then the subsequent push of the build branch happens as
cueckoo.

Closes #513

Change-Id: I2738fc488d6a8ef08e7e83b151b12934b9f1ee15
Reviewed-on: https://cue-review.googlesource.com/c/cue/+/8212
Reviewed-by: Paul Jolly <paul@myitcv.org.uk>
5 files changed
tree: cda4a2e57f180ee511efa8be71b1f48a58fcf98e
  1. .github/
  2. cmd/
  3. cue/
  4. cue.mod/
  5. cuego/
  6. doc/
  7. encoding/
  8. internal/
  9. pkg/
  10. tools/
  11. .dockerignore
  12. .gitattributes
  13. .gitignore
  14. .goreleaser.yml
  15. AUTHORS
  16. codereview.cfg
  17. CONTRIBUTING.md
  18. Dockerfile
  19. gen.go
  20. genpkgtests.go
  21. go.mod
  22. go.sum
  23. LICENSE
  24. README.md
  25. tmp.cue
  26. tools.go
README.md

GoDoc Github GolangCI Go 1.13+ platforms

The CUE Data Constraint Language

Configure, Unify, Execute

CUE is an open source data constraint language which aims to simplify tasks involving defining and using data.

It is a superset of JSON, allowing users familiar with JSON to get started quickly.

What is it for?

You can use CUE to

  • define a detailed validation schema for your data (manually or automatically from data)
  • reduce boilerplate in your data (manually or automatically from schema)
  • extract a schema from code
  • generate type definitions and validation code
  • merge JSON in a principled way
  • define and run declarative scripts

How?

CUE merges the notion of schema and data. The same CUE definition can simultaneously be used for validating data and act as a template to reduce boilerplate. Schema definition is enriched with fine-grained value definitions and default values. At the same time, data can be simplified by removing values implied by such detailed definitions. The merging of these two concepts enables many tasks to be handled in a principled way.

Constraints provide a simple and well-defined, yet powerful, alternative to inheritance, a common source of complexity with configuration languages.

CUE Scripting

The CUE scripting layer defines declarative scripting, expressed in CUE, on top of data. This solves three problems: working around the closedness of CUE definitions (we say CUE is hermetic), providing an easy way to share common scripts and workflows for using data, and giving CUE the knowledge of how data is used to optimize validation.

There are many tools that interpret data or use a specialized language for a specific domain (Kustomize, Ksonnet). This solves dealing with data on one level, but the problem it solves may repeat itself at a higher level when integrating other systems in a workflow. CUE scripting is generic and allows users to define any workflow.

Tooling

CUE is designed for automation. Some aspects of this are:

  • convert existing YAML and JSON
  • automatically simplify configurations
  • rich APIs designed for automated tooling
  • formatter
  • arbitrary-precision arithmetic
  • generate CUE templates from source code
  • generate source code from CUE definitions (TODO)

Download and Install

Install using Homebrew

Using Homebrew, you can install using the CUE Homebrew tap:

brew install cuelang/tap/cue

Install From Source

If you already have Go installed, the short version is:

go get -u cuelang.org/go/cmd/cue

This will install the cue command line tool.

For more details see Installing CUE.

Learning CUE

The fastest way to learn the basics is to follow the tutorial on basic language constructs.

A more elaborate tutorial demonstrating of how to convert and restructure an existing set of Kubernetes configurations is available in written form.

References

Contributing

Our canonical Git repository is located at https://cue.googlesource.com.

To contribute, please read the Contribution Guide.

To report issues or make a feature request, use the issue tracker.

Changes can be contributed using Gerrit or Github pull requests.

Contact

You can get in touch with the cuelang community in the following ways:


Unless otherwise noted, the CUE source files are distributed under the Apache 2.0 license found in the LICENSE file.

This is not an officially supported Google product.