pds.registry-client

PDS Registry Client

This is a prototype implementation of a request-signing utility for use with serverless OpenSearch (AOSS). It is (currently) intended to provide a curl-like interface for querying PDS Registry's AOSS instance using a Cognito user identity.

Additional functionality may be built out in the future.

Prerequisites

• Personal user/pass credentials for a Cognito user authorized for Registry AOSS
• Python >=3.9
• Environment variables (contact developer for values)

    export REQUEST_SIGNER_AWS_ACCOUNT=''
    export REQUEST_SIGNER_AWS_REGION=''
    export REQUEST_SIGNER_CLIENT_ID=''
    export REQUEST_SIGNER_USER_POOL_ID=''
    export REQUEST_SIGNER_IDENTITY_POOL_ID=''
    export REQUEST_SIGNER_AOSS_ENDPOINT=''
    export REQUEST_SIGNER_COGNITO_USER=''
    export REQUEST_SIGNER_COGNITO_PASSWORD=''
  

Developer Quickstart

1. Clone the repository

   git clone https://github.com/NASA-PDS/registry-client.git
   cd registry-client
   

2. Create a virtual environment

   python -m venv venv
   source ./venv/bin/activate
   

3. Install the tool to the virtual environment

   pip install --editable .[dev]
   

4. Run the tool directly

   registry-client --help
   

Code of Conduct

All users and developers of the NASA-PDS software are expected to abide by our Code of Conduct. Please read this to ensure you understand the expectations of our community.

Development

To develop this project, use your favorite text editor, or an integrated development environment with Python support, such as PyCharm.

Contributing

For information on how to contribute to NASA-PDS codebases please take a look at our Contributing guidelines.

Installation

Install in editable mode and with extra developer dependencies into your virtual environment of choice:

pip install --editable '.[dev]'

Make a baseline for any secrets (email addresses, passwords, API keys, etc.) in the repository:

detect-secrets scan . \
    --all-files \
    --disable-plugin AbsolutePathDetectorExperimental \
    --exclude-files '\.secrets..*' \
    --exclude-files '\.git.*' \
    --exclude-files '\.mypy_cache' \
    --exclude-files '\.pytest_cache' \
    --exclude-files '\.tox' \
    --exclude-files '\.venv' \
    --exclude-files 'venv' \
    --exclude-files 'dist' \
    --exclude-files 'build' \
    --exclude-files '.*\.egg-info' > .secrets.baseline

Review the secrets to determine which should be allowed and which are false positives:

detect-secrets audit .secrets.baseline

Please remove any secrets that should not be seen by the public. You can then add the baseline file to the commit:

git add .secrets.baseline

Then, configure the pre-commit hooks:

pre-commit install
pre-commit install -t pre-push
pre-commit install -t prepare-commit-msg
pre-commit install -t commit-msg

These hooks then will check for any future commits that might contain secrets. They also check code formatting, PEP8 compliance, type hints, etc.

👉 Note: A one time setup is required both to support detect-secrets and in your global Git configuration. See the wiki entry on Secrets to learn how.

Packaging

To isolate and be able to re-produce the environment for this package, you should use a Python Virtual Environment. To do so, run:

python -m venv venv

Then exclusively use venv/bin/python, venv/bin/pip, etc.

If you have tox installed and would like it to create your environment and install dependencies for you run:

tox --devenv <name you'd like for env> -e dev

Dependencies for development are specified as the dev extras_require in setup.cfg; they are installed into the virtual environment as follows:

pip install --editable '.[dev]'

All the source code is in a sub-directory under src.

You should update the setup.cfg file with:

• name of your module
• license, default apache, update if needed
• description
• download url, when you release your package on github add the url here
• keywords
• classifiers
• install_requires, add the dependencies of you package
• extras_require, add the development Dependencies of your package
• entry_points, when your package can be called in command line, this helps to deploy command lines entry points pointing to scripts in your package

For the packaging details, see https://packaging.python.org/tutorials/packaging-projects/ as a reference.

Configuration

It is convenient to use ConfigParser package to manage configuration. It allows a default configuration which can be overwritten by the user in a specific file in their environment. See https://pymotw.com/2/ConfigParser/

For example:

candidates = ['my_pds_module.ini', 'my_pds_module.ini.default']
found = parser.read(candidates)

Logs

You should not use print()vin the purpose of logging information on the execution of your code. Depending on where the code runs these information could be redirected to specific log files.

To make that work, start each Python file with:

"""My module."""
import logging

logger = logging.getLogger(__name__)

To log a message:

logger.info("my message")

In your main routine, include:

logging.basicConfig(level=logging.INFO)

to get a basic logging system configured.

Tooling

The dev extras_require included in the template repo installs black, flake8 (plus some plugins), and mypy along with default configuration for all of them. You can run all of these (and more!) with:

tox -e lint

Code Style

So that your code is readable, you should comply with the PEP8 style guide. Our code style is automatically enforced in via black and flake8. See the Tooling section for information on invoking the linting pipeline.

❗Important note for template users❗ The included pre-commit configuration file executes flake8 (along with mypy) across the entire src folder and not only on changed files. If you're converting a pre-existing code base over to this template that may result in a lot of errors that you aren't ready to deal with.

You can instead execute flake8 only over a diff of the current changes being made by modifying the pre-commit entry line:

entry: git diff -u | flake8 --diff

Or you can change the pre-commit config so flake8 is only called on changed files which match a certain filtering criteria:

-   repo: local
    hooks:
    -   id: flake8
        name: flake8
        entry: flake8
        files: ^src/|tests/
        language: system

Recommended Libraries

Python offers a large variety of libraries. In PDS scope, for the most current usage we should use:

Library	Usage
configparser	manage and parse configuration files
argparse	command line argument documentation and parsing
requests	interact with web APIs
lxml	read/write XML files
json	read/write JSON files
pyyaml	read/write YAML files
pystache	generate files from templates

Some of these are built into Python 3; others are open source add-ons you can include in your requirements.txt.

Tests

This section describes testing for your package.

A complete "build" including test execution, linting (mypy, black, flake8, etc.), and documentation build is executed via:

tox

Unit tests

Your project should have built-in unit tests, functional, validation, acceptance, etc., tests.

For unit testing, check out the unittest module, built into Python 3.

Tests objects should be in packages test modules or preferably in project 'tests' directory which mirrors the project package structure.

Our unit tests are launched with command:

pytest

If you want your tests to run automatically as you make changes start up pytest in watch mode with:

ptw

Integration/Behavioral Tests

One should use the behave package and push the test results to "testrail".

See an example in https://github.com/NASA-PDS/pds-doi-service#behavioral-testing-for-integration--testing

Documentation

Your project should use Sphinx to build its documentation. PDS' documentation template is already configured as part of the default build. You can build your projects docs with:

python setup.py build_sphinx

You can access the build files in the following directory relative to the project root:

build/sphinx/html/

Build

pip install wheel
python setup.py sdist bdist_wheel

Publication

NASA PDS packages can publish automatically using the Roundup Action, which leverages GitHub Actions to perform automated continuous integration and continuous delivery. A default workflow that includes the Roundup is provided in the .github/workflows/unstable-cicd.yaml file. (Unstable here means an interim release.)

Manual Publication

Create the package:

python setup.py bdist_wheel

Publish it as a Github release.

Publish on PyPI (you need a PyPI account and configure $HOME/.pypirc):

pip install twine
twine upload dist/*

Or publish on the Test PyPI (you need a Test PyPI account and configure $HOME/.pypirc):

pip install twine
twine upload --repository testpypi dist/*

CI/CD

The template repository comes with our two "standard" CI/CD workflows, stable-cicd and unstable-cicd. The unstable build runs on any push to main (± ignoring changes to specific files) and the stable build runs on push of a release branch of the form release/<release version>. Both of these make use of our GitHub actions build step, Roundup. The unstable-cicd will generate (and constantly update) a SNAPSHOT release. If you haven't done a formal software release you will end up with a v0.0.0-SNAPSHOT release (see NASA-PDS/roundup-action#56 for specifics).