aws-resource-validator 2.0.3

Validate, get validation patterns and generate valid AWS resource names, plus Pydantic v2 models mirroring the boto3 typed-dicts for every AWS service.

AWS Resource Validator

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aws_resource_validator is a Python package that creates objects to validate, show constraints of common AWS resource names, and generate compatible patterns for tests. This helps ensure that AWS resource names comply with AWS naming rules and can be used for testing and validation purposes.

📜Documentation | Blogs website

Features

• Validation: Check if a given AWS resource name meets the AWS naming constraints.
• Constraint Display: Display constraints for different AWS resource names.
• Pattern Generation: Generate compatible patterns for AWS resource names for testing purposes.

Installation

The core package ships the resource validator and the BaseValidatorModel runtime; per-service Pydantic models are shipped as extras so you only download what you need.

# Core only (validators, generators, BaseValidatorModel).
pip install aws-resource-validator

# One or more individual services.
pip install 'aws-resource-validator[s3,ec2,lambda]'

# A whole domain shard (installs every service in that shard).
pip install 'aws-resource-validator[data]'       # storage, databases, analytics
pip install 'aws-resource-validator[security]'   # IAM, KMS, WAF, ...
pip install 'aws-resource-validator[compute]'    # EC2, Lambda, ECS, EKS, ...
pip install 'aws-resource-validator[ai]'         # Bedrock, SageMaker, ...
pip install 'aws-resource-validator[networking]' # VPC, DNS, CDN, ...
pip install 'aws-resource-validator[integration]' # SNS, SQS, EventBridge, ...
pip install 'aws-resource-validator[management]' # CloudWatch, CloudFormation, ...
pip install 'aws-resource-validator[rest]'       # Media, IoT, gaming, long tail

# Everything.
pip install 'aws-resource-validator[all]'

# Regenerate models yourself (maintainers only).
pip install 'aws-resource-validator[generator]'

See docs/packaging.md for the full list of standalone service packages, shard membership, and the detailed install matrix.

Usage Example

Here's a simple example demonstrating how to use aws_resource_validator:

from aws_resource_validator.class_definitions import Acm, class_registry

# Use type hint so that you can use `api_registry` with full class definitions
acm: Acm = class_registry.Acm

print(acm.Arn.pattern)
print(acm.Arn.type)
print(acm.Arn.validate("example-arn"))
print(acm.Arn.generate())

Using Pydantic models for boto3 models:

import boto3

from aws_resource_validator.pydantic_models.dynamodb.dynamodb_classes import ListTablesOutput

dynamodb = boto3.client('dynamodb')

def list_dynamo_tables() -> List[str]:
    return ListTablesOutput(**dynamodb.list_tables()).TableNames


if __name__ == "__main__":
    tables: List[str] = list_dynamo_tables()
    print("DynamoDB Tables:", tables)

Maintainer workflows

Day-to-day repo care is scripted under scripts/release/. All scripts are invoked as modules (python -m scripts.release.<name>) so the scripts/ package resolves correctly. See CLAUDE.md for the architectural invariants these scripts assume.

Regenerating Pydantic models after poetry update

poetry update can install a newer boto3-stubs whose TypedDict shapes differ from the committed pydantic_models/ tree. Run:

python -m scripts.release.regenerate           # Pipeline B + manifest sync + gauntlet
python -m scripts.release.regenerate --only s3 # scope to one service
python -m scripts.release.regenerate --pipeline-a  # also regen class_definitions.py (needs GITHUB_TOKEN)
python -m scripts.release.regenerate --dry-run     # preview without touching the tree
python -m scripts.release.regenerate --skip-checks # skip pytest + ruff + mypy

It wipes aws_resource_validator/pydantic_models/, reruns arv-generate pipeline-b, re-syncs pyproject.toml extras and docs/packaging.md, and runs the full pytest/ruff/mypy gauntlet. It leaves changes unstaged for review; the suggested commit is printed at the end. Pipeline A is off by default — it hits GitHub for live botocore shapes and doesn't need to run on every stubs bump.

Cutting a release

python -m scripts.release.prepare_release 2.0.3

Rewrites __version__ + [project].version, re-pins every extra to the new version via sync_extras --write, regenerates docs/packaging.md, and runs the gauntlet. Leaves changes unstaged. Then:

git add aws_resource_validator/__init__.py pyproject.toml docs/packaging.md
git commit -m "Bump version to 2.0.3"
git tag v2.0.3 && git push origin main v2.0.3
gh release create v2.0.3   # triggers release-package.yml

Publishing the GitHub release fires the release-package.yml workflow, which builds the main wheel + 423 per-service wheels + 8 shard metapackages and uploads them to PyPI via scripts/release/publish_wheels.py. The first release of a version pays the one-time new-project registration cap (~65 min/chunk); subsequent versions of existing projects upload in one shot.

Other release-tooling entry points

Script	Purpose
build_subpackages.py	Builds per-service + shard wheels into dist/ (invoked by CI).
publish_wheels.py	PyPI uploader with new-project rate-limit handling. Client-side --skip-existing via the JSON API.
verify_wheels.py	Asserts wheel contents (no stray __init__.py under pydantic_models/, etc.).
smoke_test.py	Installs each freshly-built wheel into a throwaway venv and imports it.
sync_extras.py --check / --write	Reconciles [project.optional-dependencies] with popular_services.txt + shards.toml. CI enforces --check.
sync_packaging_docs.py --check / --write	Same for docs/packaging.md.

Popular services and shards

• scripts/release/popular_services.txt — one service name per line (directory form, e.g. lambda_). Each gets a dedicated aws-resource-validator-<svc> wheel and a top-level extras key.
• scripts/release/shards.toml — groups services into domain shards (metapackage wheels). Exactly one shard must have catch_all = true and it absorbs every service not explicitly listed elsewhere.

After editing either file, run python -m scripts.release.sync_extras --write and python -m scripts.release.sync_packaging_docs --write so pyproject.toml and docs/packaging.md stay in sync.

Known gotchas

• The repo does not commit poetry.lock. The circular constraint between the main wheel and the per-service sub-packages (pinned via ==<version> in extras) can't be resolved by Poetry's lockfile resolver across a version bump. CI uses pip, not Poetry, for the same reason. See .gitignore for the why.
• pydantic_models/ is a PEP 420 namespace package. No __init__.py under it, ever — adding one breaks cross-wheel imports from the aws-resource-validator-<svc> family. verify_wheels.py enforces this.
• lambda is a Python keyword. The service directory is pydantic_models/lambda_/ but the extras key and PyPI project name are lambda and aws-resource-validator-lambda. Normalization happens in scripts/release/_naming.py::extra_key.

Contributing

We welcome contributions from everyone. Please see our CONTRIBUTING.md for more details.

Security

For information on reporting security vulnerabilities, please see our SECURITY.md.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

License

This project is licensed under the Apache-2.0 License. See the LICENSE file for details.

Contact

If you have any questions, feel free to reach out to us:

• Alexy Grabov: [e-mail]
• Yafit Tupman: [e-mail]