Supports configuration from yaml files and env.

The order of configuration overload(from highest priority to lowest):

• Parameters set in environment variables;
• Files specified via the environment variable {PROJECT_NAME}_CONFIG;
• Files specified in the process startup parameter --config(-c);
• Files specified by the config_paths parameter in the initialization of the ConfigManager class.

Manager Parameters:

• config_cls(pydantic.BaseModel) - class, used for validating and typing config fields;
• project_metadata(dict) - Basic information about the project (name, version). name is used to generate environment variables;
• config_paths(List[str]) - Paths to the application configuration files;
• project_metadata_as: (Optional[str]) - This key will be assigned to project_metadata in the configuration object. By default project. If None is set, project_metadata will not be added to the config;
• root_config_dir(Optional[str]) - Base path to the directory with configuration files to set relative paths;
• pre_validation_hook(Callable[[dict], dict]) - The method called after loading the configuration before validation. If it is necessary to perform any transformations (downloading key files from the file system, etc.);
• parse_config_paths_from_args(bool) - Specifies whether it is necessary to automatically search for configuration files in the command line options or not;
• parse_config_paths_from_env(bool) - Specifies whether it is necessary to automatically search for configuration files in an environment variable({PROJECT_NAME}_CONFIG) or not;
• multiprocessing_mode(bool) - Specifies whether it is necessary to use multiprocessing.Manager().dict() to store the configuration.

Manager Methods:

• load_config - Loads the configuration from configuration files and environment variables. It can be called repeatedly to update the configuration object. Returns the proxy configuration object;
• get_config - Returns a new proxy object to the configuration without loading the configuration;
• get_multiprocessing_config_dict - Returns multiprocessing.Manager().dict() for passing to child processes. This option is available only with multiprocessing_mode=True;
• set_multiprocessing_config_dict - Accepts multiprocessing.Manager().dict(). Sets as the configuration source. It must be installed in child processes during initialization. Available only with multiprocessing_mode=True.

Creation of environment variable names:

A transformation is used for each element of the name:

import re
re.sub(pattern='[^a-zA-Z0-9]', repl='_', flags=re.DOTALL, string=name.upper())

For example, the project name Test project is converted to TEST_PROJECT.

The variable name is formed from {PROJECT_NAME}_ + '_'.join(path_to_variable).

from pydantic import BaseModel
from qstd_config import BaseConfig, ConfigManager

class Config(BaseConfig):
    class Example(BaseModel):
        class Child(BaseModel):
            value: str
        
        child: Child
        value: int
        
    example: Example

manager = ConfigManager(Config, {'name': 'Test project', 'version': 'version'})

Generates the following environment variables:

• TEST_PROJECT_EXAMPLE_VALUE;
• TEST_PROJECT_EXAMPLE_CHILD_VALUE.

Basic example

from pydantic import BaseModel

from qstd_config import ConfigManager, BaseConfig

class Config(BaseConfig):
    class Example(BaseModel):
        example: str
    
    example: Example
        
        
manager = ConfigManager(
    Config,
    {'name': 'Project name', 'version': 'project version'}
)

config = manager.load_config()

Basic multiprocessing example

from multiprocessing import get_context
from pydantic import BaseModel

from qstd_config import ConfigManager, BaseConfig

class Config(BaseConfig):
    class Example(BaseModel):
        example: str
    
    example: Example
        
        
manager = ConfigManager(
    Config,
    {'name': 'Project name', 'version': 'project version'},
    multiprocessing_mode=True
)

config = manager.load_config()

def run_child_process(config_dict):
    manager.set_multiprocessing_config_dict(config_dict)
    ...


get_context('spawn').Process(
    target=run_child_process,
    daemon=True,
    args=(manager.get_multiprocessing_config_dict(),)
).start()