bas-metadata-library 0.1.1

Python library for generating metadata records

BAS Metadata Library

Python library for generating metadata records.

Purpose

This library is designed to assist in generating metadata records for the discovery of datasets. As a library, this package is intended to be embedded within other tools and services, to avoid the need to implement the complexity and verbosity of specific metadata standards.

This library is built around the needs of the British Antarctic Survey and NERC Polar Data Centre. This means only standards, and elements of these standards, used by BAS or the UK PDC are supported. Additions that would enable this library to be useful to others are welcome as contributions.

Supported standards

Standard	Implementation	Library Namespace
ISO 19115	ISO 19139	bas_metadata_library.standards.iso_19115_v1

Supported profiles

Standard	Profile	Implementation
ISO 19115	EU Inspire	UK Gemini

Installation

This package can be installed using Pip from PyPi:

$ pip install bas-metadata-library

Usage

...

Implementation

This library consists of a set of base classes for generating XML based metadata records from a configuration object.

Each supported standard implements these classes to generate each supported element within the standard, using values, or computed values, from the configuration object.

The configuration object is a python dict, the properties and values of which, including if they are required or controlled, are defined and validated by a JSON Schema.

The class for each metadata standard will validate the configuration object against the relevant JSON schema, create a XML tree of elements, and exporting the tree as an XML document. XML is generated using lxml.

See the development section for the base classes used across all standards and how to add a new standard.

Setup

$ git clone https://gitlab.data.bas.ac.uk/uk-pdc/metadata-infrastructure/metadata-generator.git
$ cd metadata-generator

Local development

Docker and Docker Compose are required to setup a local development environment of this application.

Local development - Docker Compose

If you have access to the BAS GitLab instance, you can pull the application Docker image from the BAS Docker Registry. Otherwise you will need to build the Docker image locally.

# If you have access to gitlab.data.bas.ac.uk
$ docker login docker-registry.data.bas.ac.uk
$ docker-compose pull
# If you don't have access
$ docker-compose build

Copy .env.example to .env and edit the file to set at least any required (uncommented) options.

To run the application using the Flask development server (which reloads automatically if source files are changed):

$ docker-compose up

To run other commands against the Flask application (such as Integration tests):

# in a separate terminal to `docker-compose up`
$ docker-compose run app flask [command]
# E.g.
$ docker-compose run app flask test
# List all available commands
$ docker-compose run app flask

Development

This API is developed as a Python library. A bundled Flask application is used to simulate its usage and to act as framework for running tests etc.

Library base classes

The bas_metadata_library module defines a series of modules for each standard (in bas_metadata_library.standards) as well *base' classes used across all standards and providing common functionality. See existing standards how they are used.

Adding a new standard

To add a new standard:

1. create a new module in bas_metadata_library.standards - e.g. bas_metadata_library.standards.foo
2. create a JSON schema, metadata-record-schema.json within this module and populate with required and permitted configuration options for the standard - e.g. bas_metadata_library/standards/foo/metadata-record-schema.json
3. define a series of test configurations (e.g. minimal, typical and complete) for generating test records in tests/config.py
4. update the inbuilt Flask application in app.py with a route for generating test records for the new standard
5. use the inbuilt Flask application to generate the test records and save to tests/resources/records/[standard]
6. add relevant Integration tests with methods to test each metadata element class and that the generated record matches the test records saved in tests/resources/records/[standard]

Code Style

PEP-8 style and formatting guidelines must be used for this project, with the exception of the 80 character line limit.

Flake8 is used to ensure compliance, and is ran on each commit through Continuous Integration.

To check compliance locally:

$ docker-compose run app flake8 . --ignore=E501

Dependencies

Python dependencies should be defined using Pip through the requirements.txt file. The Docker image is configured to install these dependencies into the application image for consistency across different environments. Dependencies should be periodically reviewed and updated as new versions are released.

To add a new dependency:

$ docker-compose run app ash
$ pip install [dependency]==
# this will display a list of available versions, add the latest to `requirements.txt`
$ exit
$ docker-compose down
$ docker-compose build

If you have access to the BAS GitLab instance, push the rebuilt Docker image to the BAS Docker Registry:

$ docker login docker-registry.data.bas.ac.uk
$ docker-compose push

Dependency vulnerability scanning

To ensure the security of this API, all dependencies are checked against Snyk for vulnerabilities.

Warning: Snyk relies on known vulnerabilities and can't check for issues that are not in it's database. As with all security tools, Snyk is an aid for spotting common mistakes, not a guarantee of secure code.

Some vulnerabilities have been ignored in this project, see .snyk for definitions and the Dependency exceptions section for more information.

Through Continuous Integration, on each commit current dependencies are tested and a snapshot uploaded to Snyk. This snapshot is then monitored for vulnerabilities.

Dependency vulnerability exceptions

This project contains known vulnerabilities that have been ignored for a specific reason.

• Py-Yaml yaml.load() function allows Arbitrary Code Execution
  • currently no known or planned resolution
  • indirect dependency, required through the bandit package
  • severity is rated high
  • risk judged to be low as we don't use the Yaml module in this application
  • ignored for 1 year for re-review

Static security scanning

To ensure the security of this API, source code is checked against Bandit for issues such as not sanitising user inputs or using weak cryptography.

Warning: Bandit is a static analysis tool and can't check for issues that are only be detectable when running the application. As with all security tools, Bandit is an aid for spotting common mistakes, not a guarantee of secure code.

Through Continuous Integration, each commit is tested.

To check locally:

$ docker-compose run app bandit -r .

Debugging

To debug using PyCharm:

• Run -> Edit Configurations
• Add New Configuration -> Python

In Configuration tab:

• Script path: [absolute path to project]/manage.py
• Python interpreter: Project interpreter (app service in project Docker Compose)
• Working directory: [absolute path to project]
• Path mappings: [absolute path to project]=/usr/src/app

Testing

Integration tests

This project uses integration tests to ensure features work as expected and to guard against regressions and vulnerabilities.

The Python UnitTest library is used for running tests using Flask's test framework. Test cases are defined in files within tests/ and are automatically loaded when using the test Flask CLI command.

Tests are automatically ran on each commit through Continuous Integration.

To run tests manually:

$ docker-compose run -e FLASK_ENV=testing app flask test --test-runner text

To run tests using PyCharm:

• Run -> Edit Configurations
• Add New Configuration -> Python Tests -> Unittests

In Configuration tab:

• Script path: [absolute path to project]/tests
• Python interpreter: Project interpreter (app service in project Docker Compose)
• Working directory: [absolute path to project]
• Path mappings: [absolute path to project]=/usr/src/app

Note: This configuration can be also be used to debug tests (by choosing debug instead of run).

JUnit support

To run integration tests to produce a JUnit compatible file, test-results.xml:

$ docker-compose run -e FLASK_ENV=testing app flask test --test-runner junit

Continuous Integration

All commits will trigger a Continuous Integration process using GitLab's CI/CD platform, configured in .gitlab-ci.yml.

This process will run the application Integration tests.

Pip dependencies are also checked and monitored for vulnerabilities.

Distribution

Both source and binary versions of the package are build using SetupTools, which can then be published to the Python package index for use in other applications. Package settings are defined in setup.py.

This project is built and published to PyPi automatically through Continuous Deployment.

To build the source and binary artifact's for this project manually:

$ docker-compose run app ash
# build package to /build, /dist and /bas-metadata-library.egg-info
$ python setup.py sdist bdist_wheel
$ exit
$ docker-compose down

To publish built artifact's for this project manually to PyPi testing:

$ docker-compose run app ash
$ python -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*
# project then available at: https://test.pypi.org/project/bas-metadata-library/
$ exit
$ docker-compose down

To publish manually to PyPi:

$ docker-compose run app ash
$ python -m twine upload --repository-url https://pypi.org/legacy/ dist/*
# project then available at: https://pypi.org/project/bas-metadata-library/
$ exit
$ docker-compose down

Continuous Deployment

A Continuous Deployment process using GitLab's CI/CD platform is configured in .gitlab-ci.yml. This will:

• build the source and binary artifact's for this project
• publish built artifact's for this project to the relevant PyPi repository

This process will deploy changes to PyPi testing on all commits to the master branch.

This process will deploy changes to PyPi on all tagged commits.

Release procedure

At release

1. create a release branch
2. bump version in setup.py as per SemVer
3. close release in CHANGELOG.md
4. push changes, merge the release branch into master and tag with version

The project will be built and published to PyPi automatically through Continuous Deployment.

Feedback

The maintainer of this project is the BAS Web & Applications Team, they can be contacted at: [e-mail].

Issue tracking

This project uses issue tracking, see the Issue tracker for more information.

Note: Read & write access to this issue tracker is restricted. Contact the project maintainer to request access.

License

© UK Research and Innovation (UKRI), 2019, British Antarctic Survey.

You may use and re-use this software and associated documentation files free of charge in any format or medium, under the terms of the Open Government Licence v3.0.

You may obtain a copy of the Open Government Licence at http://www.nationalarchives.gov.uk/doc/open-government-licence/