Config Maps

Config maps in Confident is basically a dictionary of configurations values. Only one configuration will be loaded in execution time depends on a given map_name. Config maps can be either python dict or a file (json or yaml).

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 BaseConfig class definition can be as follows:

from confident import BaseConfig

class MainConfig(BaseConfig):
    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(_map_name='local', _config_map=multi_configs)
print(config_a)

#> host='localhost' port=5000 log_level='debug'

# Same, but from a file path:
config_b = MainConfig(_map_name='local', _config_map='app/configs.json')
print(config_b)

#> host='localhost' port=5000 log_level='debug'

Map Field

If we want more flexibility in selecting the map to load, we can use a property to do so. map_field is a field declared in the Config object that its value will define what will be the map_name.

from confident import BaseConfig

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

    class Config:
        map_field = 'current_map'

Now we can create the config object:

config_a = MainConfig(_config_map=multi_configs)
print(config_a)

#> current_map='local' host='localhost' port=5000 log_level='debug'

In the above example the map_field is current_map, the map_name in run time is local so the matching properties are loaded from the config_map. Notice that the map_field as every other field, can be loaded from a source:

os.environ['current_map'] = 'dev'  # Setting the field as an environment variable.
config_c = MainConfig(_config_map='app/configs.json')
print(config_c)

#> current_deployment='dev' host='http://dev_server' port=5000 log_level='debug'

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

Declaration with MapField:

from confident import BaseConfig, MapField

class MainConfig(BaseConfig):
    my_map: str = MapField('local')  # <-- This will be our `map_field`.
    host: str
    port: int = 5000
    log_level: str = 'error'

Declaration with Config class:

from confident import Confident

class MainConfig(Confident):
    my_map: str = 'local'
    host: str
    port: int = 5000
    log_level: str = 'error'

    class ConfidentConfig:
        map_field = 'my_map'  # <-- Marking `my_map` as our `map_field`.

Usage is the same in both ways:

import os

os.environ['my_map'] = 'prod'

config = MainConfig(_config_map='app/configs.json')
print(config)

#> my_map='prod' host='https://prod_server' port=5000 log_level='info'