# PKonfig
__P__ stands for __Python__.
[](https://pypi.python.org/pypi/pkonfig)
[](https://pepy.tech/project/pkonfig)
[](https://github.com/ngladkikh/pkonfig)
[](https://github.com/ngladkikh/pkonfig/blob/master/LICENSE)
[](https://mypy.readthedocs.io/en/stable/)
[](https://www.pylint.org/)
[](https://github.com/psf/black)
[](https://pycqa.github.io/isort/)
[](https://codecov.io/github/ngladkikh/pkonfig)
## 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](https://en.wikipedia.org/wiki/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:
```bash
pip install pkonfig
```
YAML files parsing is handled with [PyYaml](https://pypi.org/project/PyYAML/):
```bash
pip install pkonfig[yaml]
```
TOML files handled with help of [Tomli](https://pypi.org/project/tomli/):
```bash
pip install pkonfig[toml]
```
And if both TOML and YAML is needed:
```bash
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__:
```python
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__:
```dotenv
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:
```python
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:
```python
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.
```python
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:
```python
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.
```python
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`](https://docs.python.org/3/library/configparser.html).
**config.ini** file example:
```ini
[DEFAULT]
ServerAliveInterval = 45
[bitbucket.org]
User = hg
```
Then in Python code:
```python
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:
```python
from pkonfig.storage import Json
storage = Json("config.json", missing_ok=False)
```
#### Yaml
To parse YAML files [PyYaml](https://pyyaml.org/wiki/PyYAMLDocumentation) could be used wrapped with `Yaml` class:
```python
from pkonfig import Yaml
storage = Yaml("config.yaml", missing_ok=False)
```
#### Toml
TOML files are parsed with [tomli](https://pypi.org/project/tomli/) wrapped with `Toml` helper class:
```python
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`:
```python
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:
```python
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:
```python
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:
```python
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:
```dotenv
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__:
```python
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:
```dotenv
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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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
```python
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:
```python
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`:
```python
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:
```python
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`:
```python
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:
```python
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:
```python
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`:
```python
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:
```python
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:
```python
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`:
```python
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
```
Raw data
{
"_id": null,
"home_page": null,
"name": "pkonfig",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.9",
"maintainer_email": null,
"keywords": "config, configuration, configurations, settings, env, environment, environments, dotenv, application, python-config, yaml, toml, ini",
"author": "Nikita Gladkikh",
"author_email": "gladkikh.nikita@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/2c/d2/22a5be96dc87a74b7b29d9fd398a101c8f712452de31010e0c90d4ed8508/pkonfig-2.0.0.tar.gz",
"platform": null,
"description": "# PKonfig\n\n__P__ stands for __Python__.\n\n[](https://pypi.python.org/pypi/pkonfig)\n[](https://pepy.tech/project/pkonfig)\n[](https://github.com/ngladkikh/pkonfig)\n[](https://github.com/ngladkikh/pkonfig/blob/master/LICENSE)\n[](https://mypy.readthedocs.io/en/stable/)\n[](https://www.pylint.org/)\n[](https://github.com/psf/black)\n[](https://pycqa.github.io/isort/)\n[](https://codecov.io/github/ngladkikh/pkonfig)\n\n## Prerequisites\n\n- Pythonic configuration management helpers.\n- Multiple sources of configs (environment variables, dotenv files, YAML, JSON, TOML, INI)\n with agile order configuration.\n- Configs validation mechanics based on type hints or user defined classes.\n- Minimal external dependencies.\n- Follow [Fail-fast](https://en.wikipedia.org/wiki/Fail-fast) principle.\n- Autocomplete in modern IDEs.\n\n## Features\n\n- User defined config source order: Define the order in which PKonfig looks for configuration values.\n- Multilevel configs for environment variables and dotenv config sources: Allows for more granular control over configuration values.\n- Custom aliases for fields or groups of configs: Create custom aliases for configuration values to make them easier to reference in code.\n- Configs type casting: Automatically cast configuration values to the correct data type.\n- Config values validation based on type and/or value: Validate configuration values to ensure they meet specific requirements.\n- High performance: Designed to be fast and efficient.\n- Extendable API: Easily extend PKonfig to meet your specific needs.\n\n## Installation\n\nTo install basic PKonfig without YAML and TOML support run:\n\n```bash\npip install pkonfig\n```\n\nYAML files parsing is handled with [PyYaml](https://pypi.org/project/PyYAML/):\n\n```bash\npip install pkonfig[yaml]\n```\n\nTOML files handled with help of [Tomli](https://pypi.org/project/tomli/):\n\n```bash\npip install pkonfig[toml]\n```\n\nAnd if both TOML and YAML is needed:\n\n```bash\npip install pkonfig[toml,yaml]\n```\n\nFor production no __.env__ files are needed but proper environment variables should be set.\nIn case some of required variables missing __ConfigValueNotFoundError__ exception raised while __AppConfig__\ninstantiation.\n\n## Quickstart\n\nThe Config class is a Pythonic configuration management helper designed\nto provide a simple way of managing multiple sources of configuration values in your application.\nThe most basic usage example when environment variables are used for production\nenvironment and DotEnv files are used for local development.\n\nCreate __config__ module __config.py__:\n\n```python\nfrom typing import Literal\nfrom pkonfig import Config, LogLevel, Choice, Str, Int\nfrom pkonfig.storage import Env\nfrom pkonfig import DotEnv\n\n\nclass PG(Config):\n host: str = Str(\"localhost\")\n port: int = Int(5432)\n user: str = Str(\"postgres\")\n password: str = Str(\"postgres\")\n\n\nclass AppConfig(Config):\n db1 = PG()\n db2 = PG()\n log_level: int = LogLevel(\"INFO\")\n env: Literal[\"local\", \"prod\", \"test\"] = Choice([\"local\", \"prod\", \"test\"], default=\"prod\")\n\n\nconfig = AppConfig(DotEnv(\".env\"), Env())\n```\n\nFor local development create DotEnv file in root app folder __.env__:\n\n```dotenv\nAPP_DB1_HOST=10.10.10.10\nAPP_DB1_USER=user\nAPP_DB1_PASSWORD=securedPass\nAPP_ENV=local\nAPP_LOG_LEVEL=debug\n```\n\nThen elsewhere in app you could run:\n\n```python\nfrom config import config\n\nprint(config.env) # 'local'\nprint(config.log_level) # 20\nprint(config.db.host) # 'localhost'\nprint(config.db.port) # 5432\nprint(config.db.user) # 'postgres'\nprint(config.db.password) # 'postgres'\n```\n\n## Usage\n\n### Config sources\n\n__PKonfig__ implements several config sources out of the box.\nUse `DictStorage` if some defaults should be stored from code rather than from field default values:\n\n```python\nfrom pkonfig import Config, Str, DictStorage\n\n\nclass AppConfig(Config):\n foo: str = Str() # foo has no default value and raise an exception if value not found in storage\n\n\nCONFIG = AppConfig(DictStorage(foo=\"baz\"))\nprint(CONFIG.foo) # 'baz'\n```\n\n#### Environment variables\n\nThe most common way to configure application is environment variables.\nTo parse environment variables and store values in multilevel structure class `Env` could be used.\nCommon pattern is naming variables with multiple words describing the exact purpose\nmore precise: __PG_HOST__, __PG_PORT__ and __REDIS_HOST__, __REDIS_PORT__ could be treated as two groups:\n\n- PG\n - HOST\n - PORT\n- REDIS\n - HOST\n - PORT\n\nPKonfig respects this convention so that `Env` has two optional arguments:\n\n- `delimiter` string that will be used to split configuration levels taken from keys;\n- `prefix` string that is used to identify keys that are related to the given app and omit everything else.\n\n```python\nfrom os import environ\nfrom pkonfig.storage import Env\n\nenviron[\"APP_OUTER\"] = \"foo\"\nenviron[\"APP_INNER_KEY\"] = \"baz\"\nenviron[\"NOPE\"] = \"qwe\"\n\nsource = Env(delimiter=\"_\", prefix=\"APP\")\n\nprint(source[(\"outer\",)]) # foo\nprint(source[(\"inner\", \"key\")]) # baz\nprint(source[(\"nope\",)]) # raises KeyError\n```\n\n`Env` ignores key cases and ignores all keys starting not from __prefix__.\nTo change this behaviour set __prefix__ to `None` or an empty string.\nIn this case you will get all key value pairs:\n\n```python\nfrom os import environ\nfrom pkonfig import Env\n\nenviron[\"NOPE\"] = \"qwe\"\n\nsource = Env(prefix=None)\n\nprint(source[(\"nope\",)]) # qwe\n```\n\n#### DotEnv\n\nIn the same manner as environment variables DotEnv files could be used.\n`DotEnv` requires file name as a string or a path and also accepts `delimiter` and `prefix` optional arguments.\n`missing_ok` argument defines whether `DotEnv` raises exception when given file not found.\nWhen file not found and `missing_ok` is set `DotEnv` contains empty dictionary.\n\n```python\nfrom pkonfig import DotEnv\n\nconfig_source = DotEnv(\"test.env\", delimiter=\"_\", prefix=\"APP\", missing_ok=True)\n```\n\n#### Ini\n\n__INI__ files are quite common and class `Ini`\nis build on top of [`configparser.ConfigParser`](https://docs.python.org/3/library/configparser.html).\n\n**config.ini** file example:\n\n```ini\n[DEFAULT]\nServerAliveInterval = 45\n\n[bitbucket.org]\nUser = hg\n```\n\nThen in Python code:\n\n```python\nfrom pkonfig.storage import Ini\n\nstorage = Ini(\"config.ini\", missing_ok=False)\nprint(storage[(\"bitbucket.org\", \"User\")]) # hg\nprint(storage[(\"bitbucket.org\", \"ServerAliveInterval\")]) # 45\n```\n\n`Ini` also accepts `missing_ok` argument to ignore missing file.\nMost of `ConfigParser` arguments are also accepted to modify parser behaviour.\n\n#### Json\n\n`Json` class uses `json.load` to read given JSON file and respects `missing_ok` argument:\n\n```python\nfrom pkonfig.storage import Json\n\nstorage = Json(\"config.json\", missing_ok=False)\n```\n\n#### Yaml\n\nTo parse YAML files [PyYaml](https://pyyaml.org/wiki/PyYAMLDocumentation) could be used wrapped with `Yaml` class:\n\n```python\nfrom pkonfig import Yaml\n\nstorage = Yaml(\"config.yaml\", missing_ok=False)\n```\n\n#### Toml\n\nTOML files are parsed with [tomli](https://pypi.org/project/tomli/) wrapped with `Toml` helper class:\n\n```python\nfrom pkonfig import Toml\n\n\nstorage = Toml(\"config.toml\", missing_ok=False)\n```\n\n### Source order\n\nAny source for `BaseConfig` should implement `Mapper` protocol.\nSo it is easy to implement custom or combine existing implementations.\nRecommended way to combine multiple sources of configs is `ChainMap`:\n\n```python\nfrom pkonfig import Config, Env, Yaml, DotEnv, Str\n\n\nclass AppConfig(Config):\n foo: str = Str()\n\n\nconfig = AppConfig(\n DotEnv(\"test.env\", missing_ok=True),\n Env(),\n Yaml(\"base_config.yaml\"),\n)\n```\n\nIn this example we created `AppConfig` that looks for key until finds one in the given mappers sequence.\nThe first one source for configs is **test.env** file that might not exist and could be used for local development only.\nThen environment variables are used as the second one config source.\nThe last one is **base_config.yaml** that should exist or `FileNotFoundError` exception raised.\nYou can customize source order.\n\n### Config\n\nTo implement application config class user should inherit from `pkonfig.config.Config` class and define\nrequired fields:\n\n```python\nfrom pkonfig import Config, Float, Int, DictStorage\n\n\nclass AppConfig(Config):\n foo: float = Float()\n baz: int = Int()\n\n\nconfig = AppConfig(DictStorage(**{\"foo\": \"0.33\", \"baz\": 1}))\n\nprint(config.foo) # 0.33\nprint(config.baz) # 1\n```\n\nTo build more granular config structure:\n\n```python\nfrom pkonfig import Config, DictStorage, Float, Int, Str\n\n\nclass Inner(Config):\n key: str = Str()\n\n\nclass AppConfig(Config):\n inner = Inner()\n foo: float = Float()\n baz: int = Int()\n\n\nstorage = DictStorage(\n **{\n \"foo\": \"0.33\",\n \"baz\": 1,\n \"inner\": {\"key\": \"value\"}\n }\n)\nconfig = AppConfig(storage)\n\nprint(config.inner.key) # value\n```\n\n### Multilevel Config\n\nGrouping might be useful when there are lots of config parameters.\nTo achieve this `Config` class should be inherited like:\n\n```python\nfrom pkonfig import Config, DotEnv, Str, Int\n\n\nclass PgConfig(Config):\n host: str = Str(\"localhost\")\n port: int = Int(5432)\n\n\nclass RedisConfig(Config):\n host: str = Str(\"localhost\")\n port: int = Int(6379)\n\n\nclass AppConfig(Config):\n pg = PgConfig()\n redis = RedisConfig()\n\n\nconfig = AppConfig(\n DotEnv(\".env\", delimiter=\"__\", prefix=\"APP\")\n)\n\nprint(config.pg.host) # db_host\nprint(config.pg.port) # 6432\nprint(config.redis.host) # redis\n```\n\n__.env__ content:\n```dotenv\nAPP__PG__HOST=db_host\nAPP__PG__PORT=6432\nAPP__REDIS__HOST=redis\n```\nIn this example we customized delimiter with two underscores, default is '**_**'.\n\n### Aliases\n\nAll __Config__ fields accept __alias__ argument.\nWhen storage class searches for config attribute in its source either attribute\nname is used or alias when it is set.\n\n__config.py__:\n\n```python\nfrom pkonfig import Config, Int, Str, DotEnv\n\n\nclass HostConfig(Config):\n host: str = Str(\"localhost\")\n port: int = Int(5432)\n user: str = Str(\"user\")\n password = Str(alias=\"pass\")\n\n\nclass AppConfig(Config):\n pg = HostConfig(alias=\"db\")\n foo_baz = Int(alias=\"my_alias\")\n\n\nconfig = AppConfig(DotEnv(\".env\", delimiter=\"__\"))\n```\n\n__.env__ content:\n\n```dotenv\nAPP__DB__HOST=db_host\nAPP__DB__PORT=6432\nAPP__DB__PASS=password\nAPP__DB__USER=postgres\nAPP__MY_ALIAS=123\n```\n\nIn this example storage will seek in dotenv file parameters named by given alias.\nElsewhere in the app:\n\n```python\nfrom config import config\n\n\nprint(config.foo_baz) # 123\nprint(config.pg.password) # password\n```\n\n### PKonfig fields\n\nAll simple Python data types are implemented in field types: `Bool`, `Int`, `Float`, `Str`, `Byte`, `ByteArray`.\nAll fields with known type converted to descriptors during class creation.\nFields in `Config` classes may be defined in several ways:\n\n#### Using types:\n```python\nfrom pathlib import Path\nfrom pkonfig import Config\n\n\nclass AppConfig(Config):\n foo: str\n baz: int\n flag: bool\n file: Path\n```\n\n#### Caching\n\nAll __PKonfig__ field types are Python descriptors that are responsible for type casting and data validation.\nIn most cases there is no need to do this job every time the value is accessed.\nTo avoid undesirable calculations caching is used.\nSo that type casting and validation is done only once during `Config` object initialization.\n\n#### Default values\n\nIf value is not set in config source user can use default value.\n`None` could be used as default value:\n\n```python\nfrom pkonfig import Config, Int, Str, DictStorage\n\n\nclass AppConfig(Config):\n int_attr = Int(None)\n str_attr = Str(None)\n\nconfig = AppConfig(DictStorage())\nprint(config.str_attr) # None\nprint(config.int_attr) # None\n```\n\nWhen `None` is default value the field is treated as nullable.\n\n#### Field nullability\n\nTo handle type casting and validation fields should not be nullable.\nIn case `None` is a valid value and should be used without casting and validation\noption `nullable` could be set:\n\n```python\nfrom pkonfig import Int, Config, DictStorage\n\n\nclass AppConfig(Config):\n int_attr = Int(nullable=True)\n\nconfig = AppConfig(DictStorage(int_attr=None))\nprint(config.int_attr) # None\n```\n\nIn this example when `None` comes from storage type casting and validation is omitted.\n\nBy default, fields are treated as not nullable:\n\n```python\nfrom pkonfig import Int, Config, DictStorage\n\n\nclass AppConfig(Config):\n int_attr = Int(default=1)\n\nconfig = AppConfig(DictStorage(int_attr=None)) # ValueError(\"Not nullable\") is raised here\n```\n\n### Custom descriptor or property\n\n```python\nfrom pkonfig import Config, Bool, DictStorage, Str\n\n\nclass AppConfig(Config):\n flag: bool = Bool(True)\n baz: str = Str(\"test\")\n\n @property\n def value(self):\n return self.flag and self.baz == \"test\"\n\n\nconfig = AppConfig(DictStorage())\nprint(config.value) # True\n```\n\n### Custom field types\n\nUser can customize how field validation and casting is done.\nThe recommended way is to implement `validate` method:\n\n```python\nfrom pkonfig import Config, Int\n\n\nclass OnlyPositive(Int):\n def validate(self, value) -> None:\n if value < 0:\n raise ValueError(\"Only positive values accepted\")\n\n\nclass AppConfig(Config):\n positive = OnlyPositive()\n```\n\nCustom type casting is also available.\nTo achieve this user should inherit abstract class `Field` and implement method `cast`:\n\n```python\nfrom typing import List\nfrom pkonfig import Field\n\n\nclass ListOfStrings(Field):\n def cast(self, value: str) -> List[str]:\n return value.split(\",\")\n```\n\n### Available fields\n\nBuiltin Python types has appropriate `Field` types:\n\n- bool -> `Bool`\n- int -> `Int`\n- float -> `Float`\n- Decimal -> `DecimalField`\n- str -> `Str`\n- bytes -> `Byte`\n- bytearray -> `ByteArray`\n\n#### PathField\n\nBasic path type that is parental for other two types and is used when you define field using `pathlib.Path`.\nThis type raises `FileNotFoundError` exception during initialization if given path doesn't exist:\n\n```python\nfrom pkonfig import Config, PathField\n\n\nclass AppConfig(Config):\n mandatory_existing_path = PathField()\n optional_path = PathField(missing_ok=True)\n```\n\nIn given example field `optional_path` may not exist during initialization.\n\n##### File\n\n`File` inherits `PathField` but also checks whether given path is a file.\n\n#### Folder\n\n`Folder` inherits `PathField` and does checking whether given path is a folder.\n\n#### EnumField\n\nThis field uses custom enum to validate input and cast it to given `Enum`:\n\n```python\nfrom enum import Enum\nfrom pkonfig import Config, EnumField, DictStorage, Int\n\n\nclass UserType(Enum):\n guest = Int(1)\n user = Int(2)\n admin = Int(3)\n\n\nclass AppConfig(Config):\n user_type = EnumField(UserType)\n\n\nconfig = AppConfig(DictStorage(user_type=\"admin\"))\nprint(config.user_type is UserType.admin) # True\n```\n\n#### LogLevel\n\n`LogLevel` field is useful to define `logging` level through configs.\n`LogLevel` accepts strings that define log level and casts\nthat string to `logging` level integer value:\n\n```python\nimport logging\nfrom pkonfig import Config, LogLevel, DictStorage\n\n\nclass AppConfig(Config):\n some_level = LogLevel()\n another_level = LogLevel()\n\n\nconfig = AppConfig(\n DictStorage(\n some_level=\"info\",\n another_level=\"Debug\",\n )\n)\n\nprint(config.some_level) # 20\nprint(config.another_level) # 10\n\nprint(config.another_level is logging.DEBUG) # True\n```\n\n#### Choice\n\n`Choice` field validates that config value is a member of the given sequence and also does optional type casting:\n\n```python\nfrom pkonfig import Config, Choice, DictStorage\n\n\nclass AppConfig(Config):\n one_of_attr = Choice([10, 100], cast_function=int)\n\n\nconfig = AppConfig(DictStorage(one_of_attr=\"10\"))\nprint(config.one_of_attr == 10) # True\n\nconfig = AppConfig(DictStorage(one_of_attr=\"2\")) # raises TypeError exception\n```\n\nWhen `cast_function` is not given raw values from storage are used.\n\n#### DebugFlag\n\n`DebugFlag` helps to set widely used __debug__ option.\n`DebugFlag` ignores value case and treats __'true'__ string as `True` and any other value as `False`:\n\n```python\nfrom pkonfig import Config, Bool, DictStorage\n\n\nclass AppConfig(Config):\n lower_case = Bool()\n upper_case = Bool()\n random_string = Bool()\n\n\nconfig = AppConfig(\n DictStorage(\n lower_case=\"true\",\n upper_case=\"TRUE\",\n random_string=\"foo\",\n )\n)\nprint(config.lower_case) # True\nprint(config.upper_case) # True\nprint(config.random_string) # False\n```\n\n### Per-environment config files\n\nWhen your app is configured with different configuration files\nand each file is used only in an appropriate environment you can create a function\nto find which file should be used:\n\n```python\nfrom pkonfig import Env, Config, Choice\n\n\nCONFIG_FILES = {\n \"prod\": \"configs/prod.yaml\",\n \"staging\": \"configs/staging.yaml\",\n \"local\": \"configs/local.yaml\",\n}\n\n\ndef get_config_file():\n class _Config(Config):\n env = Choice(\n [\"prod\", \"local\", \"staging\"],\n cast_function=str.lower,\n default=\"prod\"\n )\n\n _config = _Config(Env())\n return CONFIG_FILES[_config.env]\n```\n\n__get_config_file__ uses environment variables and predefined config files paths\nto check whether __APP_ENV__ var is set, validate this variable and return appropriate\nconfig file name.\nThen actual application configuration:\n\n```python\nfrom pkonfig import Env, Yaml, Config, Choice\n\n\nCONFIG_FILES = {\n \"prod\": \"configs/prod.yaml\",\n \"staging\": \"configs/staging.yaml\",\n \"local\": \"configs/local.yaml\",\n}\n\n\ndef get_config_file():\n class _Config(Config):\n env = Choice(\n [\"prod\", \"local\", \"staging\"],\n cast_function=str.lower,\n default=\"prod\"\n )\n\n _config = _Config(Env())\n return CONFIG_FILES[_config.env]\n\n\nclass AppConfig(Config):\n env = Choice(\n [\"prod\", \"local\", \"staging\"],\n cast_function=str.lower,\n default=\"prod\"\n )\n ...\n\n\nconfig = AppConfig(Env(), Yaml(get_config_file()))\n```\n\n### Fail fast\n\nVery often it is helpful to check app configs existence and validate values before the app does something.\nTo achieve this `Config` class runs `check` as the last step in it's `__init__` method.\n`check` recursively gets from storage and verifies all defined config attributes.\nWhen this behaviour is not desirable for some reason user can set flag `fail_fast` to `False`:\n\n```python\nfrom pkonfig import Config, DotEnv, ConfigValueNotFoundError\n\n\nclass AppConfig(Config):\n foo: str\n\n\ntry:\n config = AppConfig(DotEnv(\".env\"))\nexcept ConfigValueNotFoundError as exc:\n print(exc) # config.foo not found\n\nconfig = AppConfig(DotEnv(\".env\"), fail_fast=False) # No error raised\nconfig.foo # This line actually causes `config.foo not found` exception\n```\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Pythonic agile application configuration helpers",
"version": "2.0.0",
"project_urls": {
"documentation": "https://github.com/ngladkikh/pkonfig",
"homepage": "https://github.com/ngladkikh/pkonfig",
"repository": "https://github.com/ngladkikh/pkonfig"
},
"split_keywords": [
"config",
" configuration",
" configurations",
" settings",
" env",
" environment",
" environments",
" dotenv",
" application",
" python-config",
" yaml",
" toml",
" ini"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "c062cb0c29ea6765e6afe854c447ad7809a944a1403319c6555638ca757f854d",
"md5": "9fcfd7bc1a3606eb81043aad7026e7b6",
"sha256": "71514660191857abb8c32d0b367871f56ab1a60a5e277081cf30cf9a7a0662eb"
},
"downloads": -1,
"filename": "pkonfig-2.0.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "9fcfd7bc1a3606eb81043aad7026e7b6",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<4.0,>=3.9",
"size": 15806,
"upload_time": "2024-04-30T13:28:50",
"upload_time_iso_8601": "2024-04-30T13:28:50.523116Z",
"url": "https://files.pythonhosted.org/packages/c0/62/cb0c29ea6765e6afe854c447ad7809a944a1403319c6555638ca757f854d/pkonfig-2.0.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2cd222a5be96dc87a74b7b29d9fd398a101c8f712452de31010e0c90d4ed8508",
"md5": "e74f50c0302a2ceff32cc95eb7d74c95",
"sha256": "b8c7548bdff591a2a96640b9e6c71342a6433a9850e345eccb7535247184f8d1"
},
"downloads": -1,
"filename": "pkonfig-2.0.0.tar.gz",
"has_sig": false,
"md5_digest": "e74f50c0302a2ceff32cc95eb7d74c95",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<4.0,>=3.9",
"size": 19138,
"upload_time": "2024-04-30T13:28:52",
"upload_time_iso_8601": "2024-04-30T13:28:52.415402Z",
"url": "https://files.pythonhosted.org/packages/2c/d2/22a5be96dc87a74b7b29d9fd398a101c8f712452de31010e0c90d4ed8508/pkonfig-2.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-30 13:28:52",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "ngladkikh",
"github_project": "pkonfig",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "pkonfig"
}
JSON
