yaml-config-wrapper 1.0.4

A YAML configuration wrapper.

YAML Config Wrapper

About

A YAML configuration wrapper. PYPI Package

Table of Contents

• Using the library
  • Installing and using the library
  • Creating a config file
  • Set the required environment variables
• Manually install the library
  • Prerequisites
  • Install the requirements
  • Run the Unit Tests
• Continuous Integration
• Update PyPI package
• License

Using the library

Installing and using the library

First, you need to install the library either using pip:

$ pip install yaml_config_wrapper

Then, import it and use it like so:

from yaml_config_wrapper import Configuration

# The `config_schema_path` argument is optional
conf = Configuration(config_src='confs/template_conf.yml',
                     config_schema_path='yml_schemas/default_schema.json')

Creating a config file

There are two already example yml configs under confs. An example structure is the following:

tag: production
cloudstore:
  config:
    api_key: !ENV ${DROPBOX_API_KEY}
  type: dropbox
datastore:
  config:
    hostname: !ENV ${MYSQL_HOST}
    username: !ENV ${MYSQL_USERNAME}
    password: !ENV ${MYSQL_PASSWORD}
    db_name: !ENV ${MYSQL_DB_NAME}
    port: 3306
  type: mysql
email_app:
  config:
    email_address: !ENV ${EMAIL_ADDRESS}
    api_key: !ENV ${GMAIL_API_KEY}
  type: gmail

The !ENV flag indicates that you are passing an environmental value to this attribute. You can change the values/environmental var names as you wish.

There is also the option to create a validation schema the enforces a specific yaml structure. The default dummy version is the default_schema.json file.

Set the required environment variables

In order to use the !ENV flag in you config, you need to set the corresponding environment variables like so:

$ export DROPBOX_API_KEY=123
$ export MYSQL_HOST=foo.rds.amazonaws.com
$ export MYSQL_USERNAME=user
$ export MYSQL_PASSWORD=pass
$ export MYSQL_DB_NAME=Test_schema
$ export EMAIL_ADDRESS=Gmail Bot <[e-mail]>
$ export GMAIL_API_KEY=123

The best way to do that, is to create a .env file (example), and source it before running the code.

Manually install the library

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

You need to have a machine with anaconda installed and any Bash based shell (e.g. zsh) installed.

$ conda -V
conda 4.10.1

$ echo $SHELL
/usr/bin/zsh

Install the requirements

All the installation steps are being handled by the Makefile.

First, modify the python version (min_python) and everything else you need in the settings.ini.

Then, execute the following commands:

$ make create_env
$ conda activate yaml_config_wrapper
$ make dist

Now you are ready to use and modify the library.

Run the Unit Tests

If you want to run the unit tests, execute the following command:

$ make tests

Continuous Integration

For the continuous integration, the CircleCI service is being used. For more information you can check the setup guide.

Again, you should set the above-mentioned environmental variables (reference) and for any modifications, edit the circleci config .

Update PyPI package

This is mainly for future reference for the developers of this project. First, create a file called ~/.pypirc with your pypi login details, as follows:

[pypi]
username = your_pypi_username
password = your_pypi_password

Then, modify the python version (min_python), project status (status), release version (version) and everything else you need in the settings.ini.

Finally, execute the following commands:

$ make create_env
$ conda activate yaml_config_wrapper
$ make release

For a dev release, change the testing_version and instead of make release, run make release_test.

License

This project is licensed under the MIT License - see the LICENSE file for details.