typed-config 2.0.3

Typed, extensible, dependency free configuration reader for Python projects for multiple config sources and working well in IDEs for great autocomplete performance.

typed-config

Typed, extensible, dependency free configuration reader for Python projects for multiple config sources and working well in IDEs for great autocomplete performance.

pip install typed-config

Requires python 3.6 or above. (Also, needs typing_extensions for Python <= 3.7, so not completely dependency free if using old Python).

Basic usage

# my_app/config.py
from typedconfig import Config, key, section
from typedconfig.source import EnvironmentConfigSource

@section('database')
class AppConfig(Config):
    host = key(cast=str)
    port = key(cast=int)
    timeout = key(cast=float)

config = AppConfig()
config.add_source(EnvironmentConfigSource())
config.read()

# my_app/main.py
from my_app.config import config
print(config.host)

In PyCharm, and hopefully other IDEs, it will recognise the datatypes of your configuration and allow you to autocomplete. No more remembering strings to get the right thing out!

Upgrading

From 0.x.x

There is one breaking change when moving from 0.x.x to 1.x.x. The key function now expects all arguments to be keyword arguments. So simply replace any calls like so:

key('section', 'key', True, str, 'default')  # 0.x.x
key(section_name='section', key_name='key', required=True, cast=str, default='default')  # 1.x.x

The reason for this change is to tie down the return type of key properly. Previously, when required=False or when cast=None the return type would not include the possibility of None or string. The type checker should now be able to infer the return type based on the values of required, cast and default.

From 1.x.x

When upgrading from 1.x.x to 2.x.x, Python 3.7 no longer supported as it is end of life. As long as you are using Python >= 3.8, everything should just work.

How it works

Configuration is always supplied in a two level structure, so your source configuration can have multiple sections, and each section contains multiple key/value configuration pairs. For example:

[database]
host = 127.0.0.1
port = 2000

[algorithm]
max_value = 10
min_value = 20

You then create your configuration hierarchy in code (this can be flat or many levels deep) and supply the matching between strings in your config sources and properties of your configuration classes.

You provide one or more ConfigSources, from which the config for your application can be read. For example, you might supply an EnvironmentConfigSource, and two IniFileConfigSources. This would make your application first look for a configuration value in environment variables, if not found there it would then look at the first INI file (perhaps a user-specific file), before falling back to the second INI file (perhaps a default configuration shared between all users). If a parameter is still not found and is a required parameter, an error would be thrown.

There is emphasis on type information being available for everything so that an IDE will autocomplete when trying to use your config across your application.

Multiple data sources

from typedconfig import Config, key, section, group_key
from typedconfig.source import EnvironmentConfigSource, IniFileConfigSource

@section('database')
class DatabaseConfig(Config):
    host = key(cast=str)
    port = key(cast=int)
    username = key(cast=str)
    password = key(cast=str)

config = DatabaseConfig()
config.add_source(EnvironmentConfigSource(prefix="EXAMPLE"))
config.add_source(IniFileConfigSource("config.cfg"))

# OR provide sources directly to the constructor
config = DatabaseConfig(sources=[
    EnvironmentConfigSource(prefix="EXAMPLE"),
    IniFileConfigSource("config.cfg")
])

Since you don't want to hard code your secret credentials, you might supply them through the environment. So for the above configuration, the environment might look like this:

export EXAMPLE_DATABASE_USERNAME=my_username
export EXAMPLE_DATABASE_PASSWORD=my_very_secret_password
export EXAMPLE_DATABASE_PORT=2001

Those values which couldn't be found in the environment would then be read from the INI file, which might look like this:

[database]
HOST = db1.mydomain.com
PORT = 2000

Note after this, config.port will be equal to 2001 as the value in the environment took priority over the value in the INI file.

Caching

When config values are first used, they are read. This is lazy evaluation by default so that not everything is read if not necessary.

After first use, they are cached in memory so that there should be no further I/O if the config value is used again.

For fail fast behaviour, and also to stop unexpected latency when a config value is read partway through your application (e.g. your config could be coming across a network), the option is available to read all config values at the start. Just call

config.read()

This will throw an exception if any required config value cannot be found, and will also keep all read config values in memory for next time they are used. If you do not use read you will only get the exception when you first try to use the offending config key.

Hierarchical configuration

Use group_key to represent a "sub-config" of a configuration. Set up "sub-configs" exactly as demonstrated above, and then create a parent config to compose them in one place.

from typedconfig import Config, key, section, group_key
from typedconfig.source import EnvironmentConfigSource, IniFileConfigSource

@section('database')
class DatabaseConfig(Config):
    host = key(cast=str)
    port = key(cast=int)

@section('algorithm')
class AlgorithmConfig(Config):
    max_value = key(cast=float)
    min_value = key(cast=float)

class ParentConfig(Config):
    database = group_key(DatabaseConfig)
    algorithm = group_key(AlgorithmConfig)
    description = key(cast=str, section_name="general")

config = ParentConfig()
config.add_source(EnvironmentConfigSource(prefix="EXAMPLE"))
config.add_source(IniFileConfigSource("config.cfg"))
config.read()

The first time the config.database or config.algorithm is accessed (which in the case above is when read() is called), then an instance will be instantiated. Notice that it is the class definition, not an instance of the class, which is passed to the group_key function.

Custom section/key names, optional parameters, default values

Let's take a look at this:

from typedconfig import Config, key, section

@section('database')
class AppConfig(Config):
    host1 = key()
    host2 = key(section_name='database', key_name='HOST2',
                required=True, cast=str, default=None)

Both host1 and host2 are legitimate configuration key definitions.

• section_name - this name of the section in the configuration source from which this parameter should be read. This can be provided on a key-by-key basis, but if it is left out then the section name supplied by the @section decorator is used. If all keys supply a section_name, the class decorator is not needed. If both section_name and a decorator are provided, the section_name argument takes priority.
• key_name - the name of this key in the configuration source from which this parameter is read. If not supplied, some magic uses the object property name as the key name.
• required - default True. If False, and the configuration value can't be found, no error will be thrown and the default value will be used, if provided. If a default not provided, None will be used.
• cast - probably the most important option for typing. If you want autocomplete typing support you must specify this. It's just a function which takes a string as an input and returns a parsed value. See the casting section for more. If not supplied, the value remains as a string.
• default - only applicable if required is false. When required is false this value is used if a value cannot be found.

Types

from typedconfig import Config, key, section
from typing import List

def split_str(s: str) -> List[str]:
    return [x.strip() for x in s.split(",")]

@section('database')
class AppConfig(Config):
    host = key()
    port = key(cast=int)
    users = key(cast=split_str)
    zero_based_index = key(cast=lambda x: int(x)-1)
config = AppConfig(sources=[...])

In this example we have three ways of casting:

1. Not casting at all. This defaults to returning a str, but your IDE won't know that so if you want type hints use cast=str
2. Casting to an built in type which can take a string input and parse it, for example int
3. Defining a custom function. Your function should take one string input and return one output of any type. To get type hint, just make sure your function has type annotations.
4. Using a lambda expression. The type inference may or may not work depending on your expression, so if it doesn't just write it as a function with type annotations.

Validation

You can validate what has been supplied by providing a custom cast function to a key, which validates the configuration value in addition to parsing it.

Extending configuration using shared ConfigProvider

Multiple application modules may use different configuration schemes while sharing the same configuration source. Analogously, various Config classes may provide different view of the same configuration data, sharing the same ConfigProvider.

# app/config.py
from typedconfig.provider import ConfigProvider
from typedconfig.source import EnvironmentConfigSource, IniFileConfigSource

provider = ConfigProvider()
provider.add_source(EnvironmentConfigSource(prefix="EXAMPLE"))
provider.add_source(IniFileConfigSource("config.cfg"))

__all__ = ["provider"]

# app/database/config.py
from typedconfig import Config, key, section
from app.config import provider

@section('database')
class DatabaseConfig(Config):
    host = key(cast=str)
    port = key(cast=int)

database_config = DatabaseConfig(provider=provider)

# app/algorithm/config.py
from typedconfig import Config, key, section
from app.config import provider

@section('algorithm')
class AlgorithmConfig(Config):
    max_value = key(cast=float)
    min_value = key(cast=float)

algorithm_config = AlgorithmConfig(provider=provider)

Shared configuration provider can be used by plugins, which may need to declare additional configuration sections within the same configuration files as the main application. Let's assume we have [database] section used by main application and [app_extension] that provides 3rd party plugin configuration:

[database]
host = 127.0.0.1
port = 2000

[app_extension]
api_key = secret

e.g. app/config.py may look like that:

from typedconfig import Config, key, section, group_key
from typedconfig.source import EnvironmentConfigSource, IniFileConfigSource

@section('database')
class DatabaseConfig(Config):
    """Database configuration"""
    host = key(cast=str)
    port = key(cast=int)

class ApplicationConfig(Config):
    """Main configuration object"""
    database = group_key(DatabaseConfig)

app_config = ApplicationConfig(sources=[
    EnvironmentConfigSource(),
    IniFileConfigSource("config.cfg")
])

and plugin can read additional sections by using the same configuration provider as main application config.

e.g. plugin/config.py:

from typedconfig import Config, key, section, group_key

from app.config import ApplicationConfig, app_config

@section('app_extension')
class ExtensionConfig(Config):
    """Extension configuration"""
    api_key = key(cast=str)

# ExtendedAppConfig extends ApplicationConfig 
# so original sections are also included
class ExtendedAppConfig(ApplicationConfig):
    """Extended main configuration object"""
    app_extension = group_key(ExtensionConfig)
    
# ExtendedAppConfig uses the same provider as the main app_config
extended_config = ExtendedAppConfig(provider=app_config.provider)

from plugin.config import extended_config

# Plugin can access both main and extra sections
print(extended_config.app_extension.api_key)
print(extended_config.database.host)

Configuration variables which depend on other configuration variables

Sometimes you may wish to set the value of some configuration variables based on others. You may also wish to validate some variables, for example allowed values may be different depending on the value of another config variable. For this you can add a post_read_hook.

The default implementation of post_read_hook returns an empty dict. You can override this by implementing your own post_read_hook method. It should receive only self as an input, and return a dict. This dict should be a simple mapping from config keys to values. For hierarchical configurations, you can nest the dictionaries. If you provide a post_read_hook in both a parent and a child class which both make changes to the same keys (don't do this) then the values returned by the child method will overwrite those by the parent.

This hook is called whenever you call the read method. If you use lazy loading and skip calling the read method, you cannot use this hook.

# my_app/config.py
from typedconfig import Config, key, group_key, section
from typedconfig.source import EnvironmentConfigSource

@section('child')
class ChildConfig(Config):
    http_port_plus_one = key(cast=int, required=False)

@section('app')
class AppConfig(Config):
    use_https = key(cast=bool)
    http_port = key(key_name='port', cast=int, required=False)
    child = group_key(ChildConfig)
    
    def post_read_hook(self) -> dict:
        config_updates = dict()
        # If the port has not been provided, set it based on the value of use_https
        if self.http_port is None:
            config_updates.update(http_port=443 if self.use_https else 80)
        else:
            # Modify child config
            config_updates.update(child=dict(http_port_plus_one=self.http_port + 1))
            
        # Validate that the port number has a sensible value
        # It is recommended to do validation inside the cast method for individual keys, however for dependent keys it can be useful here
        if self.http_port is not None:
            if self.use_https:
                assert self.http_port in [443, 444, 445]
            else:
                assert self.http_port in [80, 81, 82]

        return config_updates
            
config = AppConfig()
config.add_source(EnvironmentConfigSource())
config.read()

Configuration Sources

Configuration sources are how your main Config class knows where to get its data from. These are totally extensible so that you can read in your configuration from wherever you like - from a database, from S3, anywhere that you can write code for.

You supply your configuration source to your config after you've instantiated it, but before you try to read any data from it:

config = AppConfig()
config.add_source(my_first_source)
config.add_source(my_second_source)
config.read()

Or you can supply the sources directly in the constructor like this:

config = AppConfig(sources=[my_first_source, my_second_source])
config.read()

Modifying or refreshing configuration after it has been loaded

In general it is bad practice to modify configuration at runtime because the configuration for your program should be fixed for the duration of it. However, there are cases where it may be necessary.

To completely replace the set of config sources, you can use

config = AppConfig(sources=[my_first_source, my_second_source])
config.set_sources([my_first_new_source, my_second_new_source])

To replace a specific config source, for example because a config file has changed and you need to re-read it from disk, you can use replace_source:

from typedconfig.source import IniFileConfigSource
original_source = IniFileConfigSource("config.cfg")
config = AppConfig(sources=[source])
# Now say you change the contents to config.cfg and need to read it again
new_source = IniFileConfigSource("config.cfg")  # re-reads file during construction
config.replace_source(original_source, new_source)

Important: if you add or modify the config sources the config has been read, or need to refresh the config for some reason, you'll need to clear any cached values in order to force the config to be fetched from the ConfigSources again. You can do this by

config.clear_cache()
config.read()          # Read all configuration values again

Supplied Config Sources

EnvironmentConfigSource

This just reads configuration from environment variables.

from typedconfig.source import EnvironmentConfigSource
source = EnvironmentConfigSource(prefix="XYZ")
# OR just
source = EnvironmentConfigSource()

It just takes one optional input argument, a prefix. This can be useful to avoid name clashes in environment variables.

• If prefix is provided, environment variables are expected to look like {PREFIX}_{SECTION}_{KEY}, for example export XYZ_DATABASE_PORT=2000.
• If no prefix is provided, environment variables should look like {SECTION}_{KEY}, for example export DATABASE_PORT=2000.

IniFileConfigSource

This reads from an INI file using Python's built in configparser. Read the docs for configparser for more about the structure of the file.

from typedconfig.source import IniFileConfigSource
source = IniFileConfigSource("config.cfg", encoding='utf-8', must_exist=True)

• The first argument is the filename (absolute or relative to the current working directory).
• encoding is the text encoding of the file. configparser's default is used if not supplied.
• must_exist - default True. If the file can't be found, an error will be thrown by default. Setting must_exist to be False allows the file not to be present, in which case this source will just report that it can't find any configuration values and your Config class will move onto looking in the next ConfigSource.

IniStringConfigSource

This reads from a string instead of a file

from typedconfig.source import IniStringConfigSource
source = IniStringConfigSource("""
[section_name]
key_name=key_value
""")

DictConfigSource

The most basic source, entirely in memory, and also useful when writing tests. It is case insensitive.

from typedconfig.source import DictConfigSource
source = DictConfigSource({
    'database': dict(HOST='db1', PORT='2000'),
    'algorithm': dict(MAX_VALUE='20', MIN_VALUE='10')
})

It expects data type Dict[str, Dict[str, str]], i.e. such that string_value = d['section_name']['key_name']. Everything should be provided as string data so that it can be parsed in the same way as if data was coming from a file or elsewhere.

This is an alternative way of supplying default values instead of using the default option when defining your keys. Just provide a DictConfigSource as the lowest priority source, containing your defaults.

CmdConfigSource

This reads configuration from command line arguments

from typedconfig.source import CmdConfigSource
source = CmdConfigSource(prefix="XYZ")
# OR just
source = CmdConfigSource()

It will use Python's argparse library to extract the relevant command line arguments. It takes an optional input argument, the prefix. This can be useful if you also accept other command line arguments and wish to avoid clashes with your own arguments.

• Arguments will be expected in the format --{PREFIX}_{SECTION}_{KEY} {VALUE}, for example --xyz_database_port 2000
• If no prefix is provided, arguments will be expected in the format --{SECTION}_{KEY} {VALUE}, for example --database_port 2000

The names are case insensitive so, for example, --DATABASE_PORT and --database_port would be treated the same.

Writing your own ConfigSources

An abstract base class ConfigSource is supplied. You should extend it and implement the method get_config_value as demonstrated below, which takes a section name and key name, and returns either a str config value, or None if the value could not be found. It should not error if the value cannot be found, Config will throw an error later if it still can't find the value in any of its other available sources. To make it easier for the user try to make your source case insensitive.

Here's an outline of how you might implement a source to read your config from a JSON file, for example. Use the __init__ method to provide any information your source needs to fetch the data, such as filename, api details, etc. You can do sanity checks in the __init__ method and throw an error if something is wrong.

import json
from typing import Optional
from typedconfig.source import ConfigSource

class JsonConfigSource(ConfigSource):
    def __init__(self, filename: str):
        # Read data - will raise an exception if problem with file
        with open(filename, 'r') as f:
            self.data = json.load(f)
        # Quick checks on data format
        assert type(self.data) is dict
        for k, v in self.data.items():
            assert type(k) is str
            assert type(v) is dict
            for v_k, v_v in v.items():
                assert type(v_k) is str
                assert type(v_v) is str
        # Convert all keys to lowercase
        self.data = {
            k.lower(): {
                v_k.lower(): v_v
                for v_k, v_v in v.items()
            }
            for k, v in self.data.items()
        }    

    def get_config_value(self, section_name: str, key_name: str) -> Optional[str]:
        # Extract info from data which we read in during __init__
        section = self.data.get(section_name.lower(), None)
        if section is None:
            return None
        return section.get(key_name.lower(), None)

Additional config sources

In order to keep typed-config dependency free, ConfigSources requiring additional dependencies are in separate packages, which also have typed-config as a dependency.

These are listed here:

pip install name	import name	Description
typed-config-aws-sources	typedconfig_awssource	Config sources using boto3 to get config e.g. from S3 or DynamoDB

Cast function library

The typedconfig.casts module contains helper functions that implement common casting operations. These would generally be passed to the cast parameter of the key() function

Casting to an Enum type with enum_cast

the enum_cast function converts a string input from a source to a member of an Enum type.

For example:

from enum import Enum
from typedconfig.casts import enum_cast
from typedconfig import Config, key
...
class ColorEnum(Enum):
    RED = 1
    GREEN = 2
    BLUE = 3
...

class MyConfig(Config):
    color = key(cast=enum_cast(ColorEnum))

In this example, if the ConfigSource reads the string "RED", the value of color will be set to ColorEnum.RED

Note that the enum_cast function is designed to read the name of an enum member, not its value (1, 2 or 3 in the example above)

Casting to a tuple with tuple_cast

If the source contains a list of items, tuple_cast will parse them as a python tuple, optionally applying a base_cast function to each element. The default behavior is to call strip() on each element and ignore trailing delimiters.

For example, given the input

nums = 1, 2, 3, 4,

Then you could use

nums = key(cast=tuple_cast())

to read the string input and cast it to ("1", "2", "3", "4")

key(cast=tuple_cast(base_cast=int))

Would cast the input to (1, 2, 3, 4)

key(cast=tuple_cast(ignore_trailing_delimiter=False))

Would cast the input to ("1", "2", "3", "4", "")

key(cast=tuple_cast(strip=False))

Would cast the input to ("1", " 2", " 3", " 4")

Finally, if a delimiter other that "," is used - say the input string is "1:2:3:4" - that can be handled like

key(cast=tuple_cast(delimiter=":"))

Casting to a bool with boolean_cast

If the source is a boolean value, it can be converted to Python's True or False using this cast.

key(cast=boolean_cast)

boolean_cast supports the following values and is case-insensitive. Any other values will result in a KeyError while parsing.

Value	Strings
True	"true", "yes", "on", "1"
False	"false", "no", "off", "0"

Casting to an Optional[bool] with optional_boolean_cast

If the source is a boolean value which can also be None, it can be converted to Python's True or False or None using this cast.

key(cast=optional_boolean_cast)

optional_boolean_cast supports the following values and is case-insensitive. Any other values will result in a KeyError while parsing.

Value	Strings
True	"true", "yes", "on", "1"
False	"false", "no", "off", "0"
None	"none", "unknown"

Contributing

Ideas for new features and pull requests are welcome. PRs must come with tests included. This is developed using Python 3.9 but Github actions will run tests with all versions 3.8-3.12 too.

Development setup

1. Clone the git repository
2. Create a virtual environment virtualenv venv
3. Activate the environment venv/scripts/activate
4. Install development dependencies pip install -r requirements.txt

Code style

Code style is black and this is checked by the CI. To autoformat your code as black before committing, just run black typedconfig test

Running tests

pytest

To run with coverage:

pytest --cov

Making a release

1. Bump version number in typedconfig/__version__.py
2. Add changes to CHANGELOG.md
3. Commit your changes and tag with git tag -a v0.1.0 -m "Summary of changes"
4. Github actions will deploy the release to PyPi for you.

Staging release

If you want to check how a release will look on PyPi before tagging and making it live, you can do the following:

1. pip install twine if you don't already have it
2. Bump version number in typedconfig/__version__.py
3. Clear the dist directory rm -r dist
4. python setup.py sdist bdist_wheel
5. twine check dist/*
6. Upload to the test PyPI twine upload --repository-url https://test.pypi.org/legacy/ dist/*
7. Check all looks ok at https://test.pypi.org/project/typed-config
8. If all looks good you can git tag and push for deploy to live PyPi

Here is a good tutorial on publishing packages to PyPI.