confident 0.1.2

Loading configurations from multiple sources into a data model.

Confident

Confident helps you create configuration objects from multiple sources such as files and environment variables.
Confident configuration objects are data models that enforce validation and type hints by using pydantic library. For simple configuration loading from environment variables, you might want to check pydantic's BaseSettings model.

With Confident you can manage multiple configurations depend on the environment your code is deployed. While having lots of flexibility how to describe your config objects, Confident will provide visibility of the loading config process and help you expose mis-configuration as soon a possible.

Example

import os

from confident import Confident


# Creating your own config class by inheriting from `Confident`.
class MyAppConfig(Confident):
    port: int = 5000
    host: str = 'localhost'
    labels: list


# Illustrates some environment variables.
os.environ['host'] = '127.0.0.1'
os.environ['labels'] = '["FOO", "BAR"]'  # JSON strings can be used for more types.


# Creating the config object. `Confident` will load the values of the properties.
config = MyAppConfig()

print(config.host)
#> 127.0.0.1
print(config.json())
#> {"port": 5000, "host": "127.0.0.1", "labels": ["FOO", "BAR"]}
print(config)
#> port=5000 host='127.0.0.1' labels=['FOO', 'BAR']
print(config.full_details())
#> {
# 'port': ConfigProperty(name='port', value=5000, origin_value=5000, source_name='MyAppConfig', source_type='class_default', source_location=PosixPath('~/confident/readme_example.py')),
# 'host': ConfigProperty(name='host', value='127.0.0.1', origin_value='127.0.0.1', source_name='host', source_type='env_var', source_location='host'),
# 'labels': ConfigProperty(name='labels', value=['FOO', 'BAR'], origin_value='["FOO", "BAR"]', source_name='labels', source_type='env_var', source_location='labels')
# }

Installation

pip install confident

Capabilities

Confident object can load config fields from multiple sources:

• Environment variables.
• Config files such as 'json' and 'yaml'.
• '.env' files.
• Explicitly given fields.
• Default values.
• Deployment configs. (See below)

Confident object core functionality is based on pydantic library. That means the Confident config object has all the benefits of pydantic's BaseModel including Type validation, object transformation and many more features.

Usage

Load Config files

Confident supports json, yaml and .env files.

app_config/config1.json

{
  "title": "my_app_1",
  "retry": true,
  "timeout": 10
}

app_config/config2.yaml

title: my_yaml_ap
port: 3030

from confident import Confident


class MyConfig(Confident):
    title: str
    port: int = 5000
    retry: bool = False

config = MyConfig(files=['app_config/config1.json', 'app_config/config2.yaml'])

print(config)
#> title='my_app_1' port=3030 retry=True

Load deployment configs

Deployment config in Confident is basically a dictionary of configurations values that only one will be loaded in execution time depends on a given key. This key is called deployment_field and it is one of the config object properties.
For having the following deployment configurations (can also specified in a json or yaml file):

multi_configs = {
                    'prod': {
                        'host': 'https://prod_server',
                        'log_level': 'info'
                    },
                    'dev': {
                        'host': 'http://dev_server',
                        'log_level': 'debug'
                    },
                    'local': {
                        'host': 'localhost',
                        'log_level': 'debug'
                    },
}

app/configs.json

{
    "prod": {
        "host": "https://prod_server",
        "log_level": "info"
    },
    "dev": {
        "host": "http://dev_server",
        "log_level": "debug"
    },
    "local": {
        "host": "localhost",
        "log_level": "debug"
    }
}

The config class definition can be as follows:

from confident import Confident

class MainConfig(Confident):
    current_deployment: str = 'local'  # <-- This will be our `deployment_field`.
    host: str
    port: int = 5000
    log_level: str = 'error'

Now we can create the config object in several ways:

# Using python dict:
config_a = MainConfig(deployment_field='current_deployment', deployments=multi_configs)
print(config_a)
#> current_deployment='local' host='localhost' port=5000 log_level='debug'

# Same, but from a file path:
config_b = MainConfig(deployment_field='current_deployment', deployments='app/configs.json')
print(config_b)
#> current_deployment='local' host='localhost' port=5000 log_level='debug'

Notice that the deployment_field as every other field, can be loaded from a source.

os.environ['current_deployment'] = 'dev'  # Setting the field as an environment variable.
config_c = MainConfig(deployment_field='current_deployment', deployments='app/configs.json')
print(config_c)
#> current_deployment='dev' host='http://dev_server' port=5000 log_level='debug'

Selecting the deployment_field can be done in class definition using DeploymentField.
DeploymentField has the same functionality as pydantic Field.

import os

from confident import Confident, DeploymentField

class MainConfig(Confident):
    deployment: str = DeploymentField('local')  # <-- This will be our `deployment_field`.
    host: str
    port: int = 5000
    log_level: str = 'error'

os.environ['deployment'] = 'prod'
config_d = MainConfig(deployments='app/configs.json')  # <-- No need to mention the `deployment_field`.
print(config_d)
#> deployment='prod' host='https://prod_server' port=5000 log_level='info'

Contributing

To contribute to Confident, please make sure that any new features or changes to existing functionality include test coverage.

Creating Distribution

Build the distribution:
python setup.py sdist

Upload to pypi:
twine upload dist/confident-<version>.tar.gz