nx-config 0.2.0b5

A convenient way to configure python applications that makes it easy and natural to follow best practices and solves a variety of common issues encountered when using e.g. the 'configparser' library.

PyConfig

TL;DR

PyConfig helps you write configurable applications with ease and takes care of config validation at loading time. It allows the end-user to choose their configuration language and whether to use files or environment variables or both. The library is designed to make best practices the natural way to do things and to remove the need to write and maintain documentation of the configuration options.

STL;INRAOT (Still Too Long; I’m Not Reading All Of That)

Like configparser but, like, waaay cooler. And safer. And with dot-autocompletion.

Introduction by example

You can find a complete guide of the library further down, but for simple use cases it might suffice to just look at an example, so that’s how we’ll start.

In this example we pretend to build an app that greets the user and exits. The user can provide a name through the --name argument. The greeting also includes a suggestion to go out or stay home depending on whether it’s going to rain.

$ python -m demo
Hello, world! It's a beautiful day outside. Have fun!
$ python -m demo --name Dave
Hello, Dave! You should probably stay home today...

Install PyConfig (package name nx_config)

With pip:

$ pip install nx_config

Or with poetry:

$ poetry add nx_config

Create a config class and its sections classes

Start by adding a new file, say config.py, to your app. In it, you’ll define a few “section classes” (which are subclasses of ConfigSection) and a “config class” (which is a subclass of Config), and then initialize a global instance of it (see further down why this is okay):

# demo/config.py
from datetime import timedelta
from typing import Optional

from nx_config import Config, ConfigSection, URL, SecretString, validate


class GreetingSection(ConfigSection):
    num_exclamation_marks: int = 1
    all_caps: bool = False

    @validate
    def positive_exclamation_marks(self):
        if self.num_exclamation_marks <= 0:
            raise ValueError("Number of exclamation marks must be positive!")


class WeatherSection(ConfigSection):
    service_url: URL
    username: Optional[str] = None
    password: Optional[SecretString] = None
    timeout_s: float = 70.0

    @validate
    def username_and_password_go_together(self):
        if (self.username is None) ^ (self.password is None):
            raise ValueError("Must either provide both username and password or neither of them!")

    def timeout(self) -> timedelta:
        return timedelta(seconds=self.timeout_s)


class DemoConfig(Config):
    greet: GreetingSection
    weather: WeatherSection


config = DemoConfig()

Here we make the following configurable:

• How many exclamation marks are added after “world” or the user’s name.

• Whether the whole greeting is printed in upper case letters or not.

• Which web service will be used to get the weather data (rain probability).

• User credentials for the weather service.

• The client-side timeout for requests to the weather service.

Note that the username and password are of optional types, i.e., can be None (some weather services might be free). Also, some entries in each section have a default value, while others don’t (which means the user must provide a value through a config file or an environment variable).

We see here the URL and SecretString types. The values of such entries are just ordinary python strings. These type-hints are used to convey intent to the user and to allow PyConfig to perform validations and other special behaviour. For example, an entry of type SecretString is not allowed to have a default value (unless it is optional and the default value is None). Furthermore, when you print a config or just a section, entries of type SecretString will be replaced with asterisks "*****".

The methods annotated with @validate() will be called automatically right after the config is loaded (ideally at the startup of your app). Each is used to validate an individual section and sections can have multiple validators.

The combination of the entry timeout_s and the method timeout above helps us avoid ambiguity for the users while being able to work with a unit-agnostic type: The name of the actual config field timeout_s clearly tells users they must provide the value in seconds, but in our code we instead use the timeout method and therefore work only with timedelta objects, never having to worry about measurement units.

Finally, the use of a global config object may seem dangerous (especially in python), but Config and ConfigSection objects are always* immutable, so there’s no global state to worry about.

*: There are two ways in which the contents of the config can be mutated. One is when loading it with fill_config() or fill_config_from_path(). The other is with test_utils.update_section(). You can quickly find all usages of these functions in your repository. Loading functions are ideally used only once and only at startup. And using the test_utils module in production code should be entirely forbidden!

Use the configuration in your code

The core of our app will be implemented in the greet.py module, where we use the global config several times:

# demo/greet.py
from datetime import timedelta
from random import random
from typing import Mapping

from demo.config import config


def _get_rain_probability(
    url: str, params: Mapping[str, str], timeout: timedelta
) -> float:
    return random()  # Just as reliable as a weather service...


def greet(name: str):
    msg = f"Hello, {name}" + ("!" * config.greet.num_exclamation_marks)  # <= config used here

    if config.greet.all_caps:  # <= and here
        msg = msg.upper()

    if config.weather.username is None:  # <= here too
        params = {}
    else:
        params = {
            "username": config.weather.username,  # <= and here
            "password": config.weather.password,  # <= and again
        }

    rain_prob = _get_rain_probability(
        url=config.weather.service_url,  # <= once more
        params=params,
        timeout=config.weather.timeout(),  # <= last time
    )

    if rain_prob > 0.5:
        msg += " You should probably stay home today..."
    else:
        msg += " It's a beautiful day outside. Have fun!"

    print(msg)

Your IDE will probably offer auto-completion for section names and entries within sections. In contrast to the usual approach with dictionaries (e.g. with configparser), it’s very unlikely that you’ll make a typing error this way. And even if you do, you’ll be trying to get an attribute that doesn’t exist and in PyConfig the attributes of configs and sections are determined by the class declaration (they do not depend on the configuration file provided by the user at runtime). This means that if you test your code and don’t get an AttributeError, you can be certain you won’t get an AttributeError in production either, regardless of what your users write in their configuration files.

Load the configuration on startup

# demo/__main__.py
from argparse import ArgumentParser

from demo.config import config
from demo.greet import greet
from nx_config import add_cli_options, resolve_config_path, fill_config_from_path

parser = ArgumentParser()
parser.add_argument("--name")
add_cli_options(parser, config_t=type(config))
args = parser.parse_args()

fill_config_from_path(config, path=resolve_config_path(cli_args=args))

greet(name=args.name or "world")

The magic here happens in fill_config_from_path(). This function will read a configuration file and fill the config object’s entries with the corresponding values. The path can be hard-coded (not recommended) or you can use resolve_config_path() without arguments, in which case the path is provided through the CONFIG_PATH environment variable (better), or you can use an argparse.ArgumentParser as above to allow the user to provide the config-path as a CLI argument (best). The helper add_cli_options() will add the option --config-path (among other things), which resolve_config_path() will try to read. If the user does not provide a path on the command line, resolve_config_path() will still use the CONFIG_PATH environment variable as a fallback.

The format of the config file will be determined by the path’s extension (e.g. .yaml for YAML). Note that it’s fine (and a common practice) to not provide a config file at all (neither through --config-path nor through CONFIG_PATH). In this case, the configuration values will be read from environment variables named SECTIONNAME__ENTRYNAME (double underscore!). Even if a config file is provided, values can still be overriden through these environment variables, as we’ll see below.

Write a configuration file

The add_cli_options() function above also adds a --generate-config option that prints out a template config file and exits. It is intended to be used as follows:

$ python -m demo --generate-config=yaml > demo/config.yaml

which in this example results in the following file:

# demo/config.yaml
greet:
  #num_exclamation_marks:
  #all_caps:
weather:
  service_url:
  #username:
  #password:
  #timeout_s:

All entries and all sections are present, but entries that have a default value are commented-out, so you know exactly what you need to fill out for the program to run. We can fill out the service_url in this file, say

service_url: www.weatherservice24.com/rain

and use it to run our app. We can still change other entries (or even override values from this file) using canonically named environment variables such as GREET__NUM_EXCLAMATION_MARKS:

$ export GREET__NUM_EXCLAMATION_MARKS=5
$ python -m demo --name Dave --config-path demo/config.yaml
Hello, Dave!!!!! It's a beautiful day outside. Have fun!

Why?

What’s so great about PyConfig? Why should you bother learning to use yet another library when configparser already does a pretty good job? Also: There are dozens of configuration libraries for python already! What makes PyConfig different?

Avoiding hard-coded paths

The configparser.ConfigParser.read() method takes a string or PathLike (or several) as argument. I have seen and worked on many, many projects where this argument was written as a hard-coded, version-controlled string. This is, of course, in most cases a bad idea. It makes it difficult to try out the code locally, or deploy it on multiple servers automatically, can result in clashes with different applications using the same path (and therefore making it impossible to configure them independently), cause headaches due to missing permissions and so on. It also makes it annoying and slow to use different configurations for different runs of the same application.

Most developers working on those projects knew it was a bad idea and knew how to avoid it (e.g. get the path from a CLI argument or from an environment variable) but (a) these solutions would require a bit of extra work and (b) they would require teaching the user how to provide the config path… for each application!

PyConfig offers two really simple solutions to this, making the best practice nearly the easiest thing to do. First, you can use the function resolve_config_path() with no arguments. This will return a pathlib.Path from the value of the CONFIG_PATH environment variable if defined, and None otherwise. With a little extra effort, by using an argparse.ArgumentParser and add_cli_options() you can allow your end-users to provide a config path either through the --config-path CLI option or the CONFIG_PATH environment variable:

parser = ArgumentParser()
add_cli_options(parser, config_t=DemoConfig)
args = parser.parse_args()
path = resolve_config_path(cli_args=args)

If you have multiple apps sharing environment variables or you use multiple config classes for a single app (should rarely be necessary), you can add a prefix to both the CLI option and the path environment variable:

parser = ArgumentParser()
add_cli_options(parser, prefix="demo", config_t=DemoConfig)
args = parser.parse_args()
path = resolve_config_path("demo", cli_args=args)

Now the CLI option --demo-config-path and the environment variable DEMO_CONFIG_PATH will be used instead.

Most importantly, this solution offers a standardized way for users to provide config files, through arguments that follow a simple naming convention, for all apps using PyConfig.

Immutability

Some might argue that in the example above we shouldn’t have created a global config object that’s just loaded at startup, but instead we should have created and loaded a config object in __main__.py and then injected it into the greet call. In most cases, I’d agree with this advice. But it is aimed at avoiding global state, i.e., global variables that can be read and modified from anywhere in the code, usually causing trouble.

In the case of Config instances we don’t have to worry*. The config object, each of its sections and each of their entries are all immutable** so an instance is just a namespace for some constants. The supported types for section entries are also all immutable, including the supported collection types tuple and frozenset.

Many configuration libraries allow the config object to be modified freely at any time, which is particularly problematic with long-running services. If a critical error or even a crash occurs, you don’t have any guarantees that the configuration you provided at startup is still the one being used. The current configuration might be completely different from the values you see in your config files. This makes it difficult to understand and replicate bugs. With PyConfig it’s very easy to check whether the config can ever change by searching for uses of fill_config() and fill_config_from_path() in the project. Ideally it will be loaded once and only once at startup but even if your app allows for config updates while running, the logic coordinating this will at least be easy to find. Also, check out the section on ‘logging’ below, which can be very helpful to make your app easy to debug.

To facilitate testing with different configurations, we’ve added the function test_utils.update_section() (which can only be imported through the module test_utils, not directly from nx_config):

# tests/test_greeting.py
from unittest import TestCase
from nx_config.test_utils import update_section
from demo.config import config

class DemoTests(TestCase):
    def setUp(self):
        ...  # load your base config values for testing

    def test_something(self):
        update_section(config.greet, num_exclamation_marks=7)
        ...  # call code that uses config

Again, you can easily scan your project for uses of test_utils. It should obviously be used only in tests and never in production code. And that’s it! fill_config(), fill_config_from_path() and test_utils.update_section() are the only ways to modify a config instance***.

*, ** and ***: Of course… this is python… There are always dark ways to cheat by messing with the internal attributes of configs and sections. Let’s just assume all contributors to your project are well-meaning grown ups.

Config file formats

Unlike many configuration libraries, PyConfig completely separates your code (and the modeling of your configuration options) from the input formats the end-user is allowed to choose for configuration. You only write python and don’t need to think for a second about YAML, INI, JSON, .ENV or whatever. Your code is config-format-agnostic.

PyConfig currently supports YAML, INI and environment variables. However, it is designed to be easily extensible and we’ll be listening to the community to see what other formats would be good candidates. When new formats are added, all you need to do as a developer is install the latest version and your end-users can start enjoying the extra flexibility, even though your code stays the same.

This freedom of choice can also be interesting for companies with teams using different programming languages. They have the option of defining a single, company-wide “configuration language” to be used in all projects. This is convenient for everyone and allows, for example, the use of centralized configuration files in production (e.g. with credentials to different services, common URLs and so on). At the same time, individual programmers can still pick a different “configuration language” for local testing if they want.

Documenting configuration options

One of the biggest advantages of using PyConfig is that the contents of the config model (i.e. which sections it should have, which entries each section should have, what their types should be etc) are defined only in code.

With configparser, for example, it is common practice to have 3 independent “definitions” of the configuration options. One is the usage of the config mapping in the source code, which is spread throughout the repository and not always easy to find. The second is the documentation written for end-users, usually in PDF of markdown format, listing all the sections, entries, types and how to use each entry. The third is sometimes a template INI file that the end-users can copy and then fill out with their chosen values. These 3 “definitions” have to be maintained and kept in sync with each other, which is rarely the case. Very often developers might, for instance, delete some code that used a configuration value, or add code using a brand new config entry, or change the default value of an entry… and forget to update the documentation or the INI template. And even if you’re extra careful and put a lot of work into keeping your docs up-to-date, experienced end-users will still not trust your docs because they’ve fallen into that trap enough times in the past already.

Enter: PyConfig! The code, i.e. your class definitions, is the only definition of the configuration options. It is the definitive truth, is always up-to-date and documents every detail of the config, including types, default values and validity criteria. And if you add docstrings to the config class and the section classes, they are much more likely to be kept up-to-date because they’re right next to the code they reference. Some tools even support docstrings directly below class attributes, so feel free to try it out.

If you use the add_cli_options() function applied to an argparse.ArgumentParser, your end-users get the --generate-config CLI option for free, with which they can generate config templates for any supported file format, e.g.:

$ python -m demo --generate-config=yaml
greet:
  #num_exclamation_marks:
  #all_caps:
weather:
  service_url:
  #username:
  #password:
  #timeout_s:

Using add_cli_options() also adds the --config-help CLI option. It shows a message specifically documenting the app’s config model, followed by cheat-sheet-style, general instructions for configuring with PyConfig (aimed at end-users).

This means all the documentation your app needs (in terms of configuration options) is easily, automagically generated from your class definitions and is always up-to-date! Even if you want to have the documentation directly available on your website or on github, you can setup the pipeline to re-generate it after every release. No maintenance needed.

Contributors to your project are even happier: they only have to look at the python code, just the one module (often called config.py), without any additional PDFs or markdown files or webpages, and they’re guaranteed to find all relevant, current information there.

Automatic validation and failing at startup

PyConfig always validates the configuration input against the type-hints used in the ConfigSection subclass declaration. In the case of environment variables or INI files, the values are initially interpreted as strings, so “checking the type” means checking that the provided strings can be transformed into the intended types (i.e. the string "3.14" is fine for a float, but no good for a UUID). In the case of YAML or JSON files, for example, there are already standard libraries that parse them into python objects of different types, so only smaller conversions will be made (e.g. str to Path or list to frozenset) depending on the provided type-hints.

Two more out-of-the-box automatic checks are:

• Users must provide a value for every field that doesn’t have a default.

• Secrets cannot have default values. They must always be provided by the end-user. (But Optional[SecretString] can have default None, tuple[SecretString, ...] can have default () etc.)

On top of these, you can add validating methods (single parameter self, no return value) to your section classes through the @validate() annotation. These methods will be called right after filling in the values for the section in fill_config() or fill_config_from_path() (see examples above).

If you use PyConfig and follow the best practice of loading all configuration at the app’s startup (and only then), you’ll never have to worry about an invalid configuration value causing trouble days after your long-running service went up, in the middle of the night or during your soon-to-be-cut-short vacation. Can you do the same with other configuration libraries? Certainly. PyConfig is just friendly and convenient.

Logging (and secrets)

Both Config and ConfigSection subclasses can be very nicely printed with ease. The __str__ method produces an inline description, while the __repr__ method gives a multi-line and indented version. Moreover, secrets (i.e. section entries type-annotated as SecretString) are automatically masked with asterisks, including optional secrets and collections of secrets*.

Here are example outputs using the DemoConfig class from above:

>>> print(str(config))
DemoConfig(greet=GreetingSection(num_exclamation_marks=1, all_caps=False), weather=WeatherSection(service_url='www.weatherservice24.dummy', username='Dave', password='*****', timeout_s=70.0))
>>> print(str(config.greet))
GreetingSection(num_exclamation_marks=1, all_caps=False)
>>> print(repr(config))
DemoConfig(
    greet=GreetingSection(
        num_exclamation_marks=1,
        all_caps=False,
    ),
    weather=WeatherSection(
        service_url='www.weatherservice24.dummy',
        username='Dave',
        password='*****',
        timeout_s=70.0,
    ),
)
>>> print(repr(config.greet))
GreetingSection(
    num_exclamation_marks=1,
    all_caps=False,
)

Having both formats available is very convenient when writing log messages, and indeed you should take advantage of this and log your app’s configuration in certain situations. A good idea would be to log the configuration right after it’s loaded at startup. Another approach would be to log the configuration whenever a serious error happens (this is more convenient for debugging, since all important information is bundled together with the error message). It’s also handy to just always log the entire configuration, instead of trying to guess a subset of its values that you think will be sufficient when debugging. And if you always log entire configs (or at least entire sections), you don’t have to worry about accidentally exposing your end-user’s secrets.

The choice of which method gets which format was made with debugging in mind. In the REPL, if you just type the object you want to inspect, the result will be printed using __repr__:

>>> config.weather
WeatherSection(
    service_url='www.weatherservice24.dummy',
    username='Dave',
    password='*****',
    timeout_s=70.0,
)

And if you use PyCharm, the “Variables” view on the console and the debugger displays values next to variable names using __str__, and the one-line description is much more suitable in that case.

*: Secrets are masked only when you use the methods __str__ and __repr__ of Config and ConfigSection. Remember that the actual value of my_config.my_section.my_secret is just an ordinary built-in str, so if you print it in your logs it will not be masked!

Attributes instead of strings

Using attributes for sections and section-entries (cfg.a_section.an_entry) instead of the mapping style with strings used in many configuration libraries (cfg["a_section"]["an_entry"]) is more than just shorter, prettier and easier to type.

Your IDE can help you with dot-autocompletion to (a) present the available sections and section-entries and (b) avoid typing errors. This is especially important because even if your configuration is thoroughly validated at startup, a typing error when using the configuration might only cause trouble much, much later, when no one is watching and ready to take action. (Of course, this could never happen in your company, since every one of your projects has 100% code coverage…)

In theory, there’s even more the IDE could do. If you make typing errors in such attributes (because you didn’t use autocompletion), the static analyzer could highlight them and warn you. And if you decide to change the name of a section or section-entry, the IDE could help with automatic refactoring. Unfortunately, we haven’t managed to get them to work with PyConfig sections and entries yet. We know this is due to limitations of the IDE and the fact that PyConfig uses a lot of magic behind the scenes, but we’re still trying to understand exactly why it doesn’t work.

Still, autocompletion + shorter + prettier is plenty of reason to prefer attributes over mappings.

Handy configuration through environment variables

There are situations in which configuring apps with files can be annoying, such as when doing quick tests and experiments locally on a terminal and changing just one or two configuration options all the time.

With PyConfig you can always override any configurations from files with environment variables. The standard naming convention is SECTIONNAME__ENTRYNAME (yes, double underscore, which makes the separation clearer when the section name or the entry name also contain underscores). In the example above, we’ve seen how to override the config.greet.num_exclamation_marks entry by setting the GREET__NUM_EXCLAMATION_MARKS environment variable.

If you have several configs in a single app or several apps sharing some environment variables, it’s also possible to use a prefix to make variable names more specific. For example, you could instead use the environment variable FOO__GREET__NUM_EXCLAMATION_MARKS and pass an env_prefix argument to fill_config_from_path() when loading the configuration, as in fill_config_from_path(config, path=..., env_prefix="FOO").

Finally, even the path to the configuration file can be provided through an environment variable, namely CONFIG_PATH. Again, it’s possible to use a prefix to make this name more specific. For example, you could use the variable BAR_CONFIG_PATH instead, and get the path with resolve_config_path("bar", cli_args=...). Note: If you use the cli_args argument in this case, resolve_config_path() will look for the option --bar-config-path instead of --config-path, so make sure you use the same prefix when adding options to the argparse.ArgumentParser by calling add_cli_options() with a prefix argument, as in add_cli_options(parser, prefix="bar", config_t=type(config)).

Support for the most useful types

After loading the config values, you should ideally be able to use them out of the box, without having to first convert them into something else. Most use cases should be covered by the types already supported by PyConfig (and there might be more on the way):

• Base supported types are int, float, bool, str, datetime.datetime, uuid.UUID, pathlib.Path, nx_config.SecretString, and nx_config.URL.

• Collection supported types are typing.Tuple[base, ...] and typing.FrozenSet[base] in all python versions, and tuple[base, ...] and frozenset[base] for python 3.9 and later (where base is one of the base supported types above). Note that the Ellipsis (...) in the tuple types is meant literally here, i.e., they represent tuples of arbitrary length where all elements are of the same type.

• Optional supported types are typing.Optional[base_or_coll] (where base_or_coll is either one of the base or one of the collection supported types listed above). Note that “Optional” must be the outer-most layer, i.e. you cannot have collections of optional elements, such as tuple[Optional[int], ...].

However, if you want to use your own, custom types, you’ll have to work a little harder. For example, if you want to use a unit-agnostic Temperature type, your end-users will have to provide a unit-bound value (e.g. surface_temp_celsius: float) and then you’ll have to convert it yourself (e.g. through a method def surface_temp(self) -> Temperature in the same section).

A note on imports

Everything you need from PyConfig for production code can (and should) be imported directly from the nx_config module:

from nx_config import Config, ConfigSection, SecretString, fill_config, ...

Everything you need from PyConfig for tests can (and should) be imported directly from the nx_config.test_utils module:

from nx_config.test_utils import update_section

And that’s everything. If you find yourself importing stuff from other submodules: it’s probably not meant for you. I’ve made an effort to keep everything else protected behind underscores, but something may have slipped through, or might slip through in the future.

A note on configuring libraries vs apps

It usually doesn’t make much sense to use configuration from files and environment variables directly into libraries. Configuration should be required from and received by applications, which can then inject any necessary values into library classes and functions. Libraries should at least offer the application the possibility of injecting all relevant values as input parameters. This makes it easier and more convenient to write tests, and can even be important for performance.

I’ve seen libraries offering classes that parsed configuration files when initialized (using default, hard-coded paths). Very well-informed users would initialize such objects rarely in their applications and keep them around for as long as possible. But most users just assumed initialization would have near-zero cost and created a new object whenever one was needed, unknowingly parsing files and throwing the information away over and over again.

App writers should have the ultimate control over how and when files are read and parsed.

Adding a Config subclass to a library is a very bad idea. It would force the app writers to use that class for that specific library and then use a different class for their own configuration options. Adding a ConfigSection subclass to a library can be a friendly feature for application writers, who can use such sections in their own Config classes. But even that might carry some rigidity with it: App writers might only want to give their users some control over the configuration of a library, but the ConfigSection provided by the library would likely give them full control.

Keep it simple: Use PyConfig in applications. Use injection in libraries.

A note on pydantic

If you’re unfamiliar with pydantic: It is a general “modeling” python library that offers pretty much everything that PyConfig does and much more (seriously). It is far more powerful and flexible and full of features and can be used brilliantly for configuration. It is also much older and more mature than PyConfig.

When I first ran into pydantic, I was actually very surprised with some of the similarities to parts of PyConfig, like the @validator() annotation they offer, the NamedTuple-style class declaration and even the SecretStr type! In this last case, the nx_config.SecretString type turns into an ordinary str at runtime, while the pydantic.SecretStr type is a wrapper around str and you need to call the get_secret_value() method to use the wrapped string. But that was even more interesting to see, because that’s exactly the approach I used in the first version of PyConfig, except my method was called get_value_at_own_peril() and it returned the protected member _dont_you_dare_use_me. Then some of my colleagues said they found secret strings annoying to use and made me change my mind.

I have no criticism about pydantic and I honestly don’t see other libraries as “competition”. We’re all in this together. But I do think there are times to use pydantic and times to use PyConfig. If you’re already using pydantic in your project, or you’re already very familiar with it, or you actually need it for modeling things other than configuration, please, by all means, go for it.

If, however, you’re just looking specifically for a better and safer way to add configuration to your app, then maybe you should check out PyConfig. It is minimal, single-purpose and simple. There’s effectively no learning curve and the package is fairly small, with no unnecessary features. It also enforces immutability, which is optional in pydantic. In my opinion, you need to know what you’re doing and be disciplined when using pydantic (specifically in terms of app configuration), while PyConfig just naturally guides you towards the best practices. But hey, I’m definitely biased…

Detailed documentation

The full docs for PyConfig are still very much under construction and can be found here.

FAQ

1. Why can’t I nest sections into other sections?

   This was not the easiest design choice. One of the most important requirements when writing PyConfig was that it should support INI files, and those only (really) support 1 level of nesting. In the end, even though this question is asked fairly often, there are barely any use cases for deeper nesting in configs. And in the few such use cases I’ve seen, the problem could be elegantly solved by using more than one Config subclass in the application.

2. Why can’t I have entries directly in the Config subclass? Why must all entries be in a section?

   Firstly, it would add more complexity to the implementation. Secondly, INI doesn’t allow entries without sections. Thirdly, this isn’t much of an issue, really. You can always just add a general section to your config.

3. Why aren’t dictionaries supported as types for section-entries?

   INI. The answer is almost always INI. I’ve chosen to support the iterable types tuple and frozenset because it’s so common and natural to interpret comma-separated values as sequences, and these types are incredibly helpful in configurations. Moreover, I’d already seen several projects where configuration values were being transformed into sequences via comma-separation, except that developers had to parse the strings themselves.

   For dictionaries, there’s no such simple, elegant and commonplace representation. Gladly, there’s also very little demand for dictionaries as section-entries.

4. Regarding the standard naming for environment variables: What happens if I have a section called foo__bar with an entry called baz, and also a section called foo with an entry called bar__baz?

   Honestly, I haven’t thought about it. Bad things, probably.

5. Are all these questions really frequently asked, or are you making them up as you go?

   Yes.