pkonfig 2.0.0

Pythonic agile application configuration helpers

PKonfig

P stands for Python.

Prerequisites

• Pythonic configuration management helpers.
• Multiple sources of configs (environment variables, dotenv files, YAML, JSON, TOML, INI) with agile order configuration.
• Configs validation mechanics based on type hints or user defined classes.
• Minimal external dependencies.
• Follow Fail-fast principle.
• Autocomplete in modern IDEs.

Features

• User defined config source order: Define the order in which PKonfig looks for configuration values.
• Multilevel configs for environment variables and dotenv config sources: Allows for more granular control over configuration values.
• Custom aliases for fields or groups of configs: Create custom aliases for configuration values to make them easier to reference in code.
• Configs type casting: Automatically cast configuration values to the correct data type.
• Config values validation based on type and/or value: Validate configuration values to ensure they meet specific requirements.
• High performance: Designed to be fast and efficient.
• Extendable API: Easily extend PKonfig to meet your specific needs.

Installation

To install basic PKonfig without YAML and TOML support run:

pip install pkonfig

YAML files parsing is handled with PyYaml:

pip install pkonfig[yaml]

TOML files handled with help of Tomli:

pip install pkonfig[toml]

And if both TOML and YAML is needed:

pip install pkonfig[toml,yaml]

For production no .env files are needed but proper environment variables should be set. In case some of required variables missing ConfigValueNotFoundError exception raised while AppConfig instantiation.

Quickstart

The Config class is a Pythonic configuration management helper designed to provide a simple way of managing multiple sources of configuration values in your application. The most basic usage example when environment variables are used for production environment and DotEnv files are used for local development.

Create config module config.py:

from typing import Literal
from pkonfig import Config, LogLevel, Choice, Str, Int
from pkonfig.storage import Env
from pkonfig import DotEnv


class PG(Config):
    host: str = Str("localhost")
    port: int = Int(5432)
    user: str = Str("postgres")
    password: str = Str("postgres")


class AppConfig(Config):
    db1 = PG()
    db2 = PG()
    log_level: int = LogLevel("INFO")
    env: Literal["local", "prod", "test"] = Choice(["local", "prod", "test"], default="prod")


config = AppConfig(DotEnv(".env"), Env())

For local development create DotEnv file in root app folder .env:

APP_DB1_HOST=10.10.10.10
APP_DB1_USER=user
APP_DB1_PASSWORD=securedPass
APP_ENV=local
APP_LOG_LEVEL=debug

Then elsewhere in app you could run:

from config import config

print(config.env)           # 'local'
print(config.log_level)     # 20
print(config.db.host)       # 'localhost'
print(config.db.port)       # 5432
print(config.db.user)       # 'postgres'
print(config.db.password)   # 'postgres'

Usage

Config sources

PKonfig implements several config sources out of the box. Use DictStorage if some defaults should be stored from code rather than from field default values:

from pkonfig import Config, Str, DictStorage


class AppConfig(Config):
    foo: str = Str()    # foo has no default value and raise an exception if value not found in storage


CONFIG = AppConfig(DictStorage(foo="baz"))
print(CONFIG.foo)   # 'baz'

Environment variables

The most common way to configure application is environment variables. To parse environment variables and store values in multilevel structure class Env could be used. Common pattern is naming variables with multiple words describing the exact purpose more precise: PG_HOST, PG_PORT and REDIS_HOST, REDIS_PORT could be treated as two groups:

• PG
  • HOST
  • PORT
• REDIS
  • HOST
  • PORT

PKonfig respects this convention so that Env has two optional arguments:

• delimiter string that will be used to split configuration levels taken from keys;
• prefix string that is used to identify keys that are related to the given app and omit everything else.

from os import environ
from pkonfig.storage import Env

environ["APP_OUTER"] = "foo"
environ["APP_INNER_KEY"] = "baz"
environ["NOPE"] = "qwe"

source = Env(delimiter="_", prefix="APP")

print(source[("outer",)])  # foo
print(source[("inner", "key")])  # baz
print(source[("nope",)])  # raises KeyError

Env ignores key cases and ignores all keys starting not from prefix. To change this behaviour set prefix to None or an empty string. In this case you will get all key value pairs:

from os import environ
from pkonfig import Env

environ["NOPE"] = "qwe"

source = Env(prefix=None)

print(source[("nope",)])   # qwe

DotEnv

In the same manner as environment variables DotEnv files could be used. DotEnv requires file name as a string or a path and also accepts delimiter and prefix optional arguments. missing_ok argument defines whether DotEnv raises exception when given file not found. When file not found and missing_ok is set DotEnv contains empty dictionary.

from pkonfig import DotEnv

config_source = DotEnv("test.env", delimiter="_", prefix="APP", missing_ok=True)

Ini

INI files are quite common and class Ini is build on top of configparser.ConfigParser.

config.ini file example:

[DEFAULT]
ServerAliveInterval = 45

[bitbucket.org]
User = hg

Then in Python code:

from pkonfig.storage import Ini

storage = Ini("config.ini", missing_ok=False)
print(storage[("bitbucket.org", "User")])  # hg
print(storage[("bitbucket.org", "ServerAliveInterval")])  # 45

Ini also accepts missing_ok argument to ignore missing file. Most of ConfigParser arguments are also accepted to modify parser behaviour.

Json

Json class uses json.load to read given JSON file and respects missing_ok argument:

from pkonfig.storage import Json

storage = Json("config.json", missing_ok=False)

Yaml

To parse YAML files PyYaml could be used wrapped with Yaml class:

from pkonfig import Yaml

storage = Yaml("config.yaml", missing_ok=False)

Toml

TOML files are parsed with tomli wrapped with Toml helper class:

from pkonfig import Toml


storage = Toml("config.toml", missing_ok=False)

Source order

Any source for BaseConfig should implement Mapper protocol. So it is easy to implement custom or combine existing implementations. Recommended way to combine multiple sources of configs is ChainMap:

from pkonfig import Config, Env, Yaml, DotEnv, Str


class AppConfig(Config):
    foo: str = Str()


config = AppConfig(
    DotEnv("test.env", missing_ok=True),
    Env(),
    Yaml("base_config.yaml"),
)

In this example we created AppConfig that looks for key until finds one in the given mappers sequence. The first one source for configs is test.env file that might not exist and could be used for local development only. Then environment variables are used as the second one config source. The last one is base_config.yaml that should exist or FileNotFoundError exception raised. You can customize source order.

Config

To implement application config class user should inherit from pkonfig.config.Config class and define required fields:

from pkonfig import Config, Float, Int, DictStorage


class AppConfig(Config):
    foo: float = Float()
    baz: int = Int()


config = AppConfig(DictStorage(**{"foo": "0.33", "baz": 1}))

print(config.foo)   # 0.33
print(config.baz)   # 1

To build more granular config structure:

from pkonfig import Config, DictStorage, Float, Int, Str


class Inner(Config):
    key: str = Str()


class AppConfig(Config):
    inner = Inner()
    foo: float = Float()
    baz: int = Int()


storage = DictStorage(
    **{
        "foo": "0.33",
        "baz": 1,
        "inner": {"key": "value"}
    }
)
config = AppConfig(storage)

print(config.inner.key)   # value

Multilevel Config

Grouping might be useful when there are lots of config parameters. To achieve this Config class should be inherited like:

from pkonfig import Config, DotEnv, Str, Int


class PgConfig(Config):
    host: str = Str("localhost")
    port: int = Int(5432)


class RedisConfig(Config):
    host: str = Str("localhost")
    port: int = Int(6379)


class AppConfig(Config):
    pg = PgConfig()
    redis = RedisConfig()


config = AppConfig(
    DotEnv(".env", delimiter="__", prefix="APP")
)

print(config.pg.host)  # db_host
print(config.pg.port)  # 6432
print(config.redis.host)  # redis

.env content:

APP__PG__HOST=db_host
APP__PG__PORT=6432
APP__REDIS__HOST=redis

In this example we customized delimiter with two underscores, default is '_'.

Aliases

All Config fields accept alias argument. When storage class searches for config attribute in its source either attribute name is used or alias when it is set.

config.py:

from pkonfig import Config, Int, Str, DotEnv


class HostConfig(Config):
    host: str = Str("localhost")
    port: int = Int(5432)
    user: str = Str("user")
    password = Str(alias="pass")


class AppConfig(Config):
    pg = HostConfig(alias="db")
    foo_baz = Int(alias="my_alias")


config = AppConfig(DotEnv(".env", delimiter="__"))

.env content:

APP__DB__HOST=db_host
APP__DB__PORT=6432
APP__DB__PASS=password
APP__DB__USER=postgres
APP__MY_ALIAS=123

In this example storage will seek in dotenv file parameters named by given alias. Elsewhere in the app:

from config import config


print(config.foo_baz)       # 123
print(config.pg.password)   # password

PKonfig fields

All simple Python data types are implemented in field types: Bool, Int, Float, Str, Byte, ByteArray. All fields with known type converted to descriptors during class creation. Fields in Config classes may be defined in several ways:

Using types:

from pathlib import Path
from pkonfig import Config


class AppConfig(Config):
    foo: str
    baz: int
    flag: bool
    file: Path

Caching

All PKonfig field types are Python descriptors that are responsible for type casting and data validation. In most cases there is no need to do this job every time the value is accessed. To avoid undesirable calculations caching is used. So that type casting and validation is done only once during Config object initialization.

Default values

If value is not set in config source user can use default value. None could be used as default value:

from pkonfig import Config, Int, Str, DictStorage


class AppConfig(Config):
    int_attr = Int(None)
    str_attr = Str(None)

config = AppConfig(DictStorage())
print(config.str_attr)    # None
print(config.int_attr)    # None

When None is default value the field is treated as nullable.

Field nullability

To handle type casting and validation fields should not be nullable. In case None is a valid value and should be used without casting and validation option nullable could be set:

from pkonfig import Int, Config, DictStorage


class AppConfig(Config):
    int_attr = Int(nullable=True)

config = AppConfig(DictStorage(int_attr=None))
print(config.int_attr)    # None

In this example when None comes from storage type casting and validation is omitted.

By default, fields are treated as not nullable:

from pkonfig import Int, Config, DictStorage


class AppConfig(Config):
    int_attr = Int(default=1)

config = AppConfig(DictStorage(int_attr=None))  # ValueError("Not nullable") is raised here

Custom descriptor or property

from pkonfig import Config, Bool, DictStorage, Str


class AppConfig(Config):
    flag: bool = Bool(True)
    baz: str = Str("test")

    @property
    def value(self):
        return self.flag and self.baz == "test"


config = AppConfig(DictStorage())
print(config.value)  # True

Custom field types

User can customize how field validation and casting is done. The recommended way is to implement validate method:

from pkonfig import Config, Int


class OnlyPositive(Int):
    def validate(self, value) -> None:
        if value < 0:
            raise ValueError("Only positive values accepted")


class AppConfig(Config):
    positive = OnlyPositive()

Custom type casting is also available. To achieve this user should inherit abstract class Field and implement method cast:

from typing import List
from pkonfig import Field


class ListOfStrings(Field):
    def cast(self, value: str) -> List[str]:
        return value.split(",")

Available fields

Builtin Python types has appropriate Field types:

• bool -> Bool
• int -> Int
• float -> Float
• Decimal -> DecimalField
• str -> Str
• bytes -> Byte
• bytearray -> ByteArray

PathField

Basic path type that is parental for other two types and is used when you define field using pathlib.Path. This type raises FileNotFoundError exception during initialization if given path doesn't exist:

from pkonfig import Config, PathField


class AppConfig(Config):
    mandatory_existing_path = PathField()
    optional_path = PathField(missing_ok=True)

In given example field optional_path may not exist during initialization.

File

File inherits PathField but also checks whether given path is a file.

Folder

Folder inherits PathField and does checking whether given path is a folder.

EnumField

This field uses custom enum to validate input and cast it to given Enum:

from enum import Enum
from pkonfig import Config, EnumField, DictStorage, Int


class UserType(Enum):
    guest = Int(1)
    user = Int(2)
    admin = Int(3)


class AppConfig(Config):
    user_type = EnumField(UserType)


config = AppConfig(DictStorage(user_type="admin"))
print(config.user_type is UserType.admin)  # True

LogLevel

LogLevel field is useful to define logging level through configs. LogLevel accepts strings that define log level and casts that string to logging level integer value:

import logging
from pkonfig import Config, LogLevel, DictStorage


class AppConfig(Config):
    some_level = LogLevel()
    another_level = LogLevel()


config = AppConfig(
    DictStorage(
        some_level="info",
        another_level="Debug",
    )
)

print(config.some_level)        # 20
print(config.another_level)     # 10

print(config.another_level is logging.DEBUG)     # True

Choice

Choice field validates that config value is a member of the given sequence and also does optional type casting:

from pkonfig import Config, Choice, DictStorage


class AppConfig(Config):
    one_of_attr = Choice([10, 100], cast_function=int)


config = AppConfig(DictStorage(one_of_attr="10"))
print(config.one_of_attr == 10)  # True

config = AppConfig(DictStorage(one_of_attr="2"))    # raises TypeError exception

When cast_function is not given raw values from storage are used.

DebugFlag

DebugFlag helps to set widely used debug option. DebugFlag ignores value case and treats 'true' string as True and any other value as False:

from pkonfig import Config, Bool, DictStorage


class AppConfig(Config):
    lower_case = Bool()
    upper_case = Bool()
    random_string = Bool()


config = AppConfig(
    DictStorage(
        lower_case="true",
        upper_case="TRUE",
        random_string="foo",
    )
)
print(config.lower_case)        # True
print(config.upper_case)        # True
print(config.random_string)     # False

Per-environment config files

When your app is configured with different configuration files and each file is used only in an appropriate environment you can create a function to find which file should be used:

from pkonfig import Env, Config, Choice


CONFIG_FILES = {
    "prod": "configs/prod.yaml",
    "staging": "configs/staging.yaml",
    "local": "configs/local.yaml",
}


def get_config_file():
    class _Config(Config):
        env = Choice(
            ["prod", "local", "staging"],
            cast_function=str.lower,
            default="prod"
        )

    _config = _Config(Env())
    return CONFIG_FILES[_config.env]

get_config_file uses environment variables and predefined config files paths to check whether APP_ENV var is set, validate this variable and return appropriate config file name. Then actual application configuration:

from pkonfig import Env, Yaml, Config, Choice


CONFIG_FILES = {
    "prod": "configs/prod.yaml",
    "staging": "configs/staging.yaml",
    "local": "configs/local.yaml",
}


def get_config_file():
    class _Config(Config):
        env = Choice(
            ["prod", "local", "staging"],
            cast_function=str.lower,
            default="prod"
        )

    _config = _Config(Env())
    return CONFIG_FILES[_config.env]


class AppConfig(Config):
    env = Choice(
        ["prod", "local", "staging"],
        cast_function=str.lower,
        default="prod"
    )
    ...


config = AppConfig(Env(), Yaml(get_config_file()))

Fail fast

Very often it is helpful to check app configs existence and validate values before the app does something. To achieve this Config class runs check as the last step in it's __init__ method. check recursively gets from storage and verifies all defined config attributes. When this behaviour is not desirable for some reason user can set flag fail_fast to False:

from pkonfig import Config, DotEnv, ConfigValueNotFoundError


class AppConfig(Config):
    foo: str


try:
    config = AppConfig(DotEnv(".env"))
except ConfigValueNotFoundError as exc:
    print(exc)  # config.foo not found

config = AppConfig(DotEnv(".env"), fail_fast=False) # No error raised
config.foo  # This line actually causes `config.foo not found` exception