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- split the xtask crate into structured modules for - bindings - ci - dev - util - no-std - Adding commands for - ci-release/ci-debug - MUSL/no-std - per- binding language smoke tests - developer tasks (fmt, clippy, pre-commit, pre-push) - refresh Cargo manifests/locks, binding readmes, and shared FFI helpers so every binding reuses the same preparation steps - refactor GitHub Actions (release/debug, extensions, CodeQL, clippy, bindings) to call the new xtask commands - Use rust-cache in ci workflows (microsoft qdk also does this) - extend README with a contributor workflow section describing how xtask mirrors CI expectations - update pre-commit and pre-push hooks to use the xtask dev commands WORKAROUND: When dotnet is run from an xtask, codeql tracer intercepts it an routes to a nonexistent binary. Therefore in codeql workflow, xtask is not used for c# and instead dotnet is directly invoked. Tracked by #545 closes #475 Signed-off-by: Anand Krishnamoorthi <anakrish@microsoft.com>
regorus
Regorus is
- Rego-Rus(t) - A fast, light-weight Rego interpreter written in Rust.
- Rigorous - A rigorous enforcer of well-defined Rego semantics.
Regorus can be used in Python via regorus package. (It is not yet available in PyPI, but can be manually built.)
See Repository.
Automation
Run cargo xtask build-python to produce wheels via maturin, or cargo xtask test-python to reinstall the package locally and execute the sample script and pytest suite.
To build this binding, see building
Usage
import regorus
# Create engine
engine = regorus.Engine()
# Load policies
engine.add_policy_from_file('../../tests/aci/framework.rego')
engine.add_policy_from_file('../../tests/aci/api.rego')
engine.add_policy_from_file('../../tests/aci/policy.rego')
# Add policy data
data = {
"metadata": {
"devices": {
"/run/layers/p0-layer0": "1b80f120dbd88e4355d6241b519c3e25290215c469516b49dece9cf07175a766",
"/run/layers/p0-layer1": "e769d7487cc314d3ee748a4440805317c19262c7acd2fdbdb0d47d2e4613a15c",
"/run/layers/p0-layer2": "eb36921e1f82af46dfe248ef8f1b3afb6a5230a64181d960d10237a08cd73c79",
"/run/layers/p0-layer3": "41d64cdeb347bf236b4c13b7403b633ff11f1cf94dbc7cf881a44d6da88c5156",
"/run/layers/p0-layer4": "4dedae42847c704da891a28c25d32201a1ae440bce2aecccfa8e6f03b97a6a6c",
"/run/layers/p0-layer5": "fe84c9d5bfddd07a2624d00333cf13c1a9c941f3a261f13ead44fc6a93bc0e7a"
}
}
}
engine.add_data(data)
# Set input
input = {
"containerID": "container0",
"layerPaths": [
"/run/layers/p0-layer0",
"/run/layers/p0-layer1",
"/run/layers/p0-layer2",
"/run/layers/p0-layer3",
"/run/layers/p0-layer4",
"/run/layers/p0-layer5"
],
"target": "/run/gcs/c/container0/rootfs"
}
engine.set_input(input)
# Eval rule
value = engine.eval_rule('data.framework.mount_overlay')
# Print value
print(value)