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A sensible configuration library for Python

Project description

Gestalt

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Gestalt (/ɡəˈSHtält/) - noun - an organized whole that is perceived as more than the sum of its parts.

Gestalt is an opinionated, strongly typed, configuration library for Python 3.6+. Gestalt aims to simplify configuration loading, getting/setting, and management by letting the user focus on writing programs, and letting Gestalt check configuration types, location, and environment variable overrides.

Install

pip install gestalt

Why use Gestalt?

In short, Gestalt does the following:

  1. Automatically loads configuration files from a directory
  2. Allows for runtime overrides
  3. Allows for runtime defaults
  4. Allows for default values when trying to get a config
  5. Allows for environment variable overrides
  6. Type checks everything

Specifically, Gestalt enforces configs in the following order:

  1. Calls to set_*
  2. Environment Variables
  3. Config File Directories and Single Config Files
  4. Defaults provided in get_*
  5. Set default values

Usage

TL;DR

from gestalt import gestalt

g = gestalt.Gestalt()
g.add_config_path('./testdata')
g.build_config()

my_val_1 = g.get_string('some.nested.string')
my_val_2 = g.get_int('someint')
my_val_3 = g.get_float('somekey', 57.29) # If 'somekey' doesn't exist, retrun default value

g.set_bool('my_custom_config', True)
g.get_list('my_custom_config') # Raises TypeError, as this key is a bool, and we asked for a list

Initialization

Beginning to use Gestalt is as simple as:

from gestalt import gestalt

g = gestalt.Gestalt()

Loading Configuration Files

Configuration files are supported in JSON and YAML format, with YAML files being given priority over JSON.

Loading a directory of configuration files is done by calling add_config_path

g.add_config_path('./testdata')

Multiple directory paths can be added:

g.add_config_path('./testdata2')
g.add_config_path('./testdata3')

Note that files loaded from a directory are loaded in alphabetical order, with JSON files being loaded first, then YAML files.

Individual files can also be loaded:

g.add_config_file('./testfile.json')
g.add_config_file('./testotherfile.yaml')

After all the directory paths and files are added, we can render them:

g.build_config()

Note that the the last added directory path takes the most precedence, and will override conflicting keys from previous paths. Individual files take precedence over directories. In addition to this, the rendering flattens the config, for example, the configuration:

{
  "yarn": "blue skies",
  "numbers": 12345678,
  "strangenumbers": 123.456,
  "truthy": true,
  "listing": ["dog", "cat"],
  "deep": {
    "nested1": "hello",
    "nested2": "world"
  }
}

Will be rendered to this in the internal data structure:

{
    "yarn": "blue skies",
    "numbers": 12345678,
    "strangenumbers": 123.456,
    "truthy": true,
    "listing": [
        "dog",
        "cat"
    ],
    "deep.nested1": "hello",
    "deep.nested2": "world"
}

The nested values are flattened and delimited by periods, making access simpler. Note, Gestalt will not normalize names in config files, so keys are case sensitive.

Environment Variables

Environment variable overrides are not enabled by default. To enable it:

g.auto_env()

With this, Gestalt will check to see if a corresponding environment variable exists, for example if checking for the key some.nested.key, Gestalt will search for SOME_NESTED_KEY, and attempt to convert it to the desired type.

Setting and Getting Values

Types

Currently, Gestalt supports 5 basic types:

  1. String
  2. Int
  3. Float
  4. Bool
  5. List

For each of these types, there is a corresponding get and set function, allowing for Gestalt to guarantee types.

Setting

To set a configuration value programmatically:

g.set_string('some.k', 'value')

Defined by the function signature:

set_string(self, key: str, value: str) -> None

The same applies to the remaining types that Gestalt supports:

set_int(key: str, value: int) -> None
set_float(key: str, value: float) -> None
set_bool(key: str, value: bool) -> None
set_list(key: str, value: List[Any]) -> None

Note that all of these functions, in addition from being type hinted, also strongly enforce types, and will raise TypeErrors in the following cases:

  1. Setting a key that is not a string
  2. Setting a value that does not match the function signature
  3. Overriding an existing set value of type a with type b

Setting Default Values

To set a default configuration value programmatically:

g.set_defualt_string('some.default.k', 'default value')

Defined by the function signature

set_default_string(key: str, value: str) -> None

The same applies to the remaining types that Gestalt supports:

set_default_int(key: str, value: int) -> None
set_default_float(key: str, value: float) -> None
set_default_bool(key: str, value: bool) -> None
set_default_list(key: str, value: List[Any]) -> None

Note that all of these functions, in addition from being type hinted, also strongly enforce types, and will raise TypeErrors in the following cases:

  1. Setting a key that is not a string
  2. Setting a value that does not match the function signature
  3. Overriding an existing default set value of type a with type b

Getting Values

To get a configuration value:

g.get_string('some.key')

This will attempt to retrieve the given key from the configuration, in order of precedence. If no such key exists in all configurations, a ValueError will be raised.

In addition to this, a default can be passed:

g.get_string('some.key', 'some default value')

The get function will raise TypeErrors in the following cases:

  1. The key is not a string
  2. The default value does not match the desired type
  3. The configuration has the key with a value of type a, when the user desires a value of type b

Working with Vault

To work with vault, connect gestalt with your vault cluster using:

g.add_vault_config_provider(vault_config, authentication_config)
  1. The vault_config is a dictionary requiring url, and token
  2. The authentication_config is a dictionary with Kubernetes role and jwt

Next, add a path to the secret for Vault to access the cluster which loads it into the running internal data structure configuration

g.add_vault_secret_path('path')

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