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Figcan - minimalistic configuration handling library for Python

Project description

Figcan - Minimalistic Configuration Handling Library

Figcan is a minimalistic configuration handling library for Python.

It is designed to help you manage runtime configuration coming from different sources, without making any assumptions about configuration file formats and locations, and while staying super simple to use for common use cases.

Figcan has no runtime dependencies other than Python versions 2.7 or 3.4 and up.

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Figcan's Philosophy

Figcan's design is based on a few basic assumptions:

  • Configuration is important in any but the most simple projects
  • Configuration can easily be described as a set of nested key-value pairs where values can have a few native scalar types (booleans, strings, numbers) or container types (lists, mappings)
  • Python dictionaries are almost perfect for configuration. Almost.
  • Configuration keys can be known in advance. The structure of your expected configuration is almost always known to your project's code and thus can be described in advance.
  • Configuration can come from multiple sources: in-code defaults, multiple configuration files, environment variables, command line arguments, database-persisted key-value pairs etc.
  • But realistically, objects read from these sources are not that different from each other: they can almost always be represented as Python object attributes or dictionaries
  • There is already a Python module in out there that handles reading values from these sources and converting them to some kind of native dictionary or object

With those in mind, here is what Figcan will do:

  • Provide a dictionary-like object containing configuration
  • This object is created from a dictionary specifying your default configuration
  • Additional configuration values (in the form of Python dictionaries or objects) can be "layered" on top of this default configuration to override values

And here is what Figcan will not do for you in one line - but supports doing very easily with just a few lines of custom code you will need to write:

  • Read and parse files in specific formats (INI, JSON, YAML etc.)
  • Look for configuration files in specific places, based on OS or environment
  • Read values from a specific command line argument parsers (argparse, optparse, click etc.)
  • Manage saving configuration to files or anywhere else
  • Provide any API to accessing configuration beyond what the Python dict interface provides (which, if you ask us, should be enough for everybody)

We plan to provide some documentation and examples on how to get these done with Figcan.

Getting Started


It is recommended to add Figcan to your project using pip:

pip install figcan

You should also be able to install directly from the source tree pulled from git:


Using in your project

Typically, Figcan is used by reading configuration from all sources at the beginning of your program (e.g. in your main), and making the configuration object available to all other parts of the program as needed.

Here is a very basic (but not unrealistic) usage example:

import os
from figcan import Configuration
from my_project.config import default_config  # A dictionary defining default configuration values

def main():
    config = Configuration(default_config)

    # Apply configuration overrides from environment variables
    config.apply_flat(os.environ, prefix='MYPROJECT')

    # Do something with the configuration:
    db_engine = sqlalchemy.create_engine(config['db']['url'])

Applying configuration from YAML or JSON files:

If your configuration is saved in a file format that can be parsed into a Python dict, you can easily get Figcan to work with it. For example:

import yaml
from figcan import Configuration
from my_project.config import default_config  # A dictionary defining default configuration values

def main(config_file_path):
    config = Configuration(default_config)

    with open(config_file_path) as f:

    # Do something with the configuration:
    db_engine = sqlalchemy.create_engine(config['db']['url'])

Note that Configuration.apply will raise an exception if it encounters a configuration key that is not present in your default_config. This can be changed like so:

config.apply(yaml.safe_load(f), raise_on_unknown_key=False)

If you want to allow merging new configuration keys into a configuration section, you will need to define that section as Extensible in the base configuration:

from figcan import Configuration, Extensible

default_config = dict({  # Base configuration keys are known ahead and static 
    'bind_port': 5656,
    'db': {  # Database settings keys are known ahead and static
        'hostname': 'db.local',
        'username': 'foobar',
        'password': 'blahblah'
    } ,
    'logging': Extensible({  # But logging settings are flexible, and new handlers / loggers can be defined
        'handlers': {
            'handler_1': '...'

config = Configuration(default_config)

# This will not raise an exception and 'handler_2' config will be available in `config`:
config.apply({"logging": {"handlers": {"handler_2": "... more config ..."}}})

Applying configuration from environment variables:

Applying configuration from command line arguments:

Some Alternatives to Consider

There are many configuration handling libraries for Python. Some may be more suitable for you than Figcan (some we have tried before deciding to write Figcan):

TODO / Planned Features

Schema based type coercion and validation of configuration values

the idea here is that the initial default_config dict will also contain some type annotations in some form. These will be used to coerce override values (e.g. when coming as strings from environment variables) and to do some validation when configuration is applied.

Allow defining "flexible" vs "non-flexible" configuration mapping

For example, a logging section used for logging.config.dictConfig typically needs to have a flexible structure. However, making everything flexible can lead to typos etc. not being detected.


Figcan was created by the Shoppimon team and is in use by Shoppimon in highly used, critical production code.


© 2018 Shoppimon LTD, all rights reserved

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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