confarg 0.0.1.dev3

A tool to manage complex, dynamic configurations.

A tool to manage complex, dynamic configurations

What is confarg?

confarg is a Python library that helps you load your app configuration in a modular fashion from multiple sources: one or more configuration files, environment variables, and command line arguments.

It strives to have minimal footprint on your data and app, to make it easy to switch to it, or switch from it.

It can handle deeply nested configurations, type unions, derived classes, expressions and variable interpolation, configuration compositions, and can coexist with your favorite argument parser library such as argparse, click, typer or cyclopts.

If none of this makes sense to you, read along.

What is not confarg?

confarg is deliberately not a framework, but just a tool.

It does not own the interface with the command line, and it won't help you build a beautiful CLI. However, it can coexist with the one you might be already using.

It doesn't require you to use custom data classes, or to use custom annotations.

The scope of confarg is limited to the deserialization and serialization of complex configurations. By limiting itself to those transient moments in the lifetime of your application, the footprint of confarg in your app is limited to a few lines of code.

Install

pip install confarg

confarg is a stand-alone library that comes with no required dependencies. Installing libraries such as pyyaml unlocks the support of additioanl configuration file formats.

Getting started

All the examples presented in this section (and more) are available in the examples/ folder.

Imagine that you have an app that depends on some parameters that you have collected into a dataclass like so:

@dataclass
class DBConfig:
    host: str
    port: int
    name: str

In your app, you use confarg to instantiate this configuration:

db_config = confarg.load(DBConfig)

This allows you to construct a DBConfig object by collecting data from three possible sources.

1. From a configuration file. By passing --config <config_file> to your app, confarg will load the content of the file and fill the DBConfig object. For example, a config file could look like so:

   # config.yaml
   host: example.com
   port: 1234
   name: mydb
   

   You would then call your application as

   $ myapp.py --config config.yaml
   DBConfig(host='example.com', port=1234, name='mydb')
   

   Configuration files in TOML and JSON formats are also supported.

   You can change the default config flag to something else using the config_flag parameter.

2. From environment variables. You can declare

   MYAPP_HOST=example.com
   MYAPP_PORT=1234
   MYAPP_NAME=mydb
   

   for the same effect.

   Note that the environment variable prefix of your app should actually be passed to confarg.load like so:

   db_config = confarg.load(DBConfig, env_prefix="MYAPP_")
   

3. From command line arguments.

   $ my_app --host example.com --port 1234 --name mydb
   DBConfig(host='example.com', port=1234, name='mydb')
   

Progressive build-up

The examples above presented different sources to feed your configuration. They are not mutually exclusive — in fact, they are intended to be used simultaneously.

Note that no one source needs to provide a complete configuration, as long as the configuration resulting from this progressive build-up is complete.

For example, taking our previous example, you could have a partial configuration file containing only host information,

# partial_config.yaml
host: example.com
port: 1234

and provide the schema name from the command line:

$ myapp.py --config partial_config.yaml --name mydb
DBConfig(host='example.com', port=1234, name='mydb')

Source precedence

Configuration data is read in the following order, later read overwriting existing data:

1. configuration files are read first;
2. then environment variables;
3. finally, command line arguments.

This allows for surgical modifications of configuration files. For example, one could overwrite the schema configuration from our existing full_config from the command line like so:

$ # Overwrite the schema name defined in the config file from the command line
$ myapp.py --config config.yaml --name otherdb
DBConfig(host='example.com', port=1234, name='otherdb')

Unions

Let's say your app needs to support SQLite databases. You now have two different, incompatible DB configurations:

@dataclass
class DBServerConfig:
    host: str
    port: int
    name: str

@dataclass
class SQLiteConfig:
    dbpath: str

The DB configuration needs to be either one or the other, which we declare like so:

type DBConfig = SQLiteConfig | DBServerConfig

confarg can handle this new union type and figure out which configuration is desired based on the arguments it got:

$ # Pass DBServerConfig parameters, and you get a DBServerConfig
$ myapp.py --host example.com --port 1234 --name mydb
DBServerConfig(host='example.com', port=1234, name='mydb')
$ # Pass SQLiteConfig parameters, and you get a SQLiteConfig
$ myapp.py --dbpath db.sqlite
SQLiteConfig(dbpath='db.sqlite')

Disambiguation tags

For simple configurations, the above automatic disambiguation is enough and convenient.

In more complex configuration scenarios, this automatic disambiguation may not be not possible. For example, different configurations may share the exact same fields.

Even when disambiguation is possible, it may not be obvious to the human eye which object class should be return from the provided parameters.

Therefore, by necessity or for the sake of clarity, you can provide the class path of the required configuration by using the class tag, like so

$ # Explicitly ask for a SQLiteConfig
$ myapp.py --class myapp.SQLiteConfig --dbpath db.sqlite
SQLiteConfig(dbpath='db.sqlite')

One example where it is necessary to provide the class path is to overwrite the configuration with a new class. Without it, command line arguments are added to the configuration, resulting in an invalid input.

$ # Config file contains a DBServerConfig
$ myapp.py --config db_server.yaml
DBServerConfig(host='example.com', port=1234, name='mydb')
$ # Fails:  dbpath is not a DBServerConfig key
$ myapp.py --config db_server.yaml --dbpath db.sqlite
...
$ # OK: using class signals overwrite existing DB config
$ myapp.py --config db_server.yaml --class myapp.SQLiteConfig --dbpath db.sqlite
SQLiteConfig(dbpath='db.sqlite')

Inheritance

Another way to provide a flexible configuration is to derive akin configuration classes from a common base class.

@dataclass
class DBConfig:
    pass

@dataclass
class DBServerConfig(DBConfig):
    host: str
    port: int
    name: str

@dataclass
class SQLiteConfig(DBConfig):
    dbpath: str

This allows configurations to be easily extensible. Contrast with unions, where a class must be explicitly listed to be supported.

The downside is that the concrete class must be tagged, as confarg cannot discover classes derived from a given class.

$ # Fails:  derived class not specified
$ uv run myapp.py --dbpath db.sqlite
...
$ # OK: explicit class path provided
$ uv run myapp.py --dbpath db.sqlite --class myapp.SQLiteConfig
SQLiteConfig(dbpath='db.sqlite')

Configuration hierarchies

The configurations discussed so far has been rather simple, composed of values grouped together in a dataclass. However, it needs not be. Configurations are generally deeply nested hierarchies, which confarg supports.

Let's say you want to add a log level to your application. You place it at the root level of a new Config object, along with the DB configuration, that is now one level down under the db key.

@dataclass
class Config:
    db: DBConfig
    log_level: Literal["DEBUG", "INFO", "WARNING", "ERROR"] = "INFO"

You now parse your new top-level Config instead of DBConfig.

config = confarg.load(Config)

Our DB configuration, which used to be the root configuration, is now located under the db key. This has the following impact.

For command line arguments, we follow the common convention of using dot-separated paths to address nested fields. Previous command line arguments for DBConfig are now prefixed by db., like so:

$ myapp.py --db.class myapp.SQLiteConfig --db.dbpath db.sqlite
Config(db=SQLiteConfig(dbpath='db.sqlite'), log_level='INFO')

The configuration file is also modified accordingly,

# config.yaml
db:
  class: myapp.DBServerConfig
  host: example.com
  name: mydb
  port: 1234

and is used just like before:

$ myapp.py --config config.yaml
Config(db=DBServerConfig(host='example.com', port=1234, name='mydb'),
       log_level='DEBUG')

Leaf data type and type coercion

You may have noticed that the previous section introduced a log_level parameter that has two interesting features: first, it is not of a simple type (str, int, float, bool or None); second, it comes with a default value.

Default values are honored, and you may have noticed that we did not provide any value to log_level. You can of course override a default value.

As for leaf node data type, confarg coerces Enum and Path types as special exceptions to simple types. Other types are treated as classes and must follow the same rules.

Expressions and variable interpolation

Your application is becoming more complex by the day, and is now requiring a resources configuration.

@dataclass
class Resources:
    cpu_count: int
    memory_gb: int
    max_heap_size_mb: int

It is added to the global configuration under the resources key:

@dataclass
class Config:
    db: DBConfig
    resources: Resources
    log_level: Literal["DEBUG", "INFO", "WARNING", "ERROR"] = "INFO"

Your configuration file has become,

# config.yaml
db:
  class: myapp.DBServerConfig
  host: example.com
  name: mydb
  port: 1234

resources:
  cpu_count: 4
  memory_gb: 16
  max_heap_size_mb: 131072

This works fine. However, you want to better express the fact that max_heap_size_mb is chosen to be 80% of the host memory by default. To achieve this, you can write expressions relying on variable interpolation using the ${...} syntax, like so:

# expression_config.yaml
db:
  class: myapp.DBServerConfig
  host: example.com
  name: mydb
  port: 1234

resources:
  cpu_count: 4
  memory_gb: 16
  max_heap_size_mb: ${int(resources.memory_gb * 1024 * 0.8)}

$ myapp.py --config expression_config.yaml
Config(db=SQLiteConfig(dbpath='db.sqlite'),
       resources=Resources(cpu_count=4, memory_gb=16, max_heap_size_mb=13107),
       log_level='INFO')

Note that variable interpolation occurs after all configuration data is read. This means here that you can override memory_gb from the command line, and max_heap_size_mb will be adjusted accordingly, even though the expression is defined in the configuration file.

$ # Max heap is recomputed according to the expression in the config file
$ myapp.py --config expression_config.yaml --resources.memory_gb 8
Config(db=SQLiteConfig(dbpath='db.sqlite'),
       resources=Resources(cpu_count=4, memory_gb=8, max_heap_size_mb=6553),
       log_level='INFO')

Building large configurations from parts

Large configurations are often made up of independent components, and as such, you may want to split them accordingly. It is easier to navigate, but it also makes it possible to reuse configuration parts and to build multiple complex configurations from the same set of atomic configuration components.

Some configuration components may even be generated automatically, in which case being able to isolate those parts from the rest is a must.

confarg lets you do this in different ways.

From the command line, the --config flag can be suffixed with a key path to load configurations there. For example,

# Load a config file specific to the `db` key
$ myapp.py --config.db db_config.yaml
Config(db=DBServerConfig(host='example.com', port=1234, name='mydb'), log_level='INFO')

A similar pattern applies to environment variables:

$ MYAPP_CONFIG_DB=db_config.py myapp.py
Config(db=DBServerConfig(host='example.com', port=1234, name='mydb'), log_level='INFO')

Note that db_config.yaml does not contain the db key. It does not need to know the path it is loaded to.

In config files, you can load a configuration by specifying the special __include__ key, followed by the path to the sub-configuration to load, like so:

# set everything under the `db` key from another file
db:
  __include__: ./db_config.yaml

The __include__ keyword can also be used at the top-level, to create a new config that amends an existing config.

# start from this base configuration
__include__: base_config.yaml

# set or overwrite everything under the `db` key
db:
  __include__: ./db_config.yaml

confarg and command-line interfaces

Command line arguments are an essential part of confarg. We have seen how they are parsed and consumed implicitly by confarg.load.

Although it is not needed for confarg to work, application generally provide a command line interface to offer some help and parse parameters.

What to expect from a CLI regarding complex configurations

We are used to the great user experience provided by CLI libraries such as click, typer or cyclopts. However, porting this great UX to complex configurations is no small feat because of their size and dynamic nature. Inline help is bound to be both very long, reflecting the configuration's complexity, and incomplete, as options coming from derived classes are not available. This could be frustrating.

At the same time, the command line is not the main configuration interface: configuration files are. Building a great CLI UX for complex configuration has a somewhat poor benefit/effort ratio.

Using a CLI library

The python ecosystem offers many libraries to build powerful and beautiful CLI apps, such as click, typer or cyclopts. Those libraries parse and consume command line arguments, but they also offer a rich user experience by providing help on available commands, sometimes even auto-completion. Some like cyclopts also parse concrete nested dataclasses using the dot-separated field command line argument convention used by confarg and similar libraries.

Should you use such a library, confarg can coexist with them by parsing unused arguments. Currently however, confarg will essentially work in "suppress" (argparse terminology) or "hidden" (click terminology) mode: the arguments won't show in the help generated by those libraries.

Building your interface with argparse

If you manage your interface yourself with argparse, confarg can step in and provide (limited) help for command line arguments. This is currently an experimental feature.

Not registering confarg with your ArgumentParser and running in hidden mode is of course an option.

Optional command line argument prefix

When mixing confarg arguments with other application arguments, you may worry about name conflicts, or you may want to clearly identify which arguments belong to the configuration handled by confarg, especially if confarg is running in hidden arguments mode.

For this purpose, you can specify a prefix for confarg command line arguments, using the cli_prefix parameter:

config = confarg.load(Config, args=rgs, cli_prefix="settings")

The command line now cleanly conveys which arguments are routed to the configuration.

myapp.py --app_arg=hello --settings.config=config.yaml --settings.resources.cpu_count=2

Next steps

We have more than scratched the surface, and you should have enough knowledge to cover most of your needs.

Again, all of the examples above and more are in the examples/ folder, which is a great way to discover and experiment with the library features.

A documentation is also currently being written at https://confarg.github.io/confarg/.