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* feat: add Azure Policy alias normalization/denormalization Add normalizer and denormalizer for ARM JSON resources, enabling Azure Policy alias short names to become direct paths into a flat structure. - Normalizer: flattens properties wrappers, lowercases keys, resolves per-alias versioned ARM paths, handles sub-resource array flattening, element-level field remaps, and array base renames - Denormalizer: reverses all transformations with casing restoration - AliasRegistry: loads production alias catalogs and data policy manifests - Types: serde deserialization for ARM provider alias formats - YAML test suite: 13 test files covering normalize, denormalize, round-trip, data-plane, edge cases, malformed input, sub-resources, and registry API - Benchmark suite for normalization performance * feat: add FFI and C# bindings for alias normalization - FFI: alias_registry.rs with C-compatible API for loading catalogs, normalizing resources, and denormalizing back to ARM JSON - C#: AliasRegistry wrapper class with NativeMethods P/Invoke bindings and integration tests - Updated Cargo.lock files for new serde_json dependency
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)