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GarlicConfig

GarlicConfig is a framework that makes it easy to deal with configurations in an easy yet flexible way. The core of this package is written in C++ and this Python package wraps the native code to provide an easy access to the configuration and some extra feature on top of it. The native library allows for quick retrieval of the configurations which can be used on any platform, this wrapper, however allows for defining advanced validation and config retrieval logic. For example, you could define your own conventions for getting localized version of configs.

The whole thing starts by defining the structure of your configs using models.

You can define models by inheriting from ConfigModel:

from garlicconfig import models

class DatabaseConfig(models.ConfigModel):
    pass

By adding attributes (or properties), you can define what this model recognizes and what format should each one of these attributes have.

For properties, you can use ConfigField. There are a set of built-in fields:

  • StringField
  • IntegerField
  • ArrayField
  • ModelField
  • BooleanField

You can also make your own custom ConfigModel and/or ConfigField.

for example:

from garlicconfig import fields
from garlicconfig.exceptions import ValidationError

class EvenIntegerField(IntegerField):

    def validate(self, value):
        if value % 2 != 0:
            raise ValidationError('bad integer')

    def to_model_value(self, value):
        return int(value)

    def to_garlic_value(self, value):
        return str(value)

The field class above stores str in the python dictionary representation. However, for the materialized config model, int is used. It also invalidates unaccepted values by raising ValidationError, in this case odd values.

Note that to_model_value is responsible for converting a basic type to a more complicated python type, perhaps constructing a Python-defined class.

to_garlic_value does the exact opposite, it should convert the value to a basic value. Basic value is defined to be one of the following types:

  • str
  • int
  • float
  • dict
  • set & list

This is primarily used for ease of encoding/decoding. By default, both of these methods return the given value without making any changes and I'd recommend not creating very complicated objects since it'll limit the accessibility of them in different platforms should you need to support them.

Next, you can define your own config model and use the custom ConfigField we just created.

for example:

class SomeRandomConfig(ConfigModel):

	value = EvenIntegerField(nullable=False, default=2)

You can use py_value method on config models to get a python dictionary with basic value types in it. This is handy to cache it in memory, or to use it for serialization.

from_dict will create a new config model from a python dictionary.

Furthermore, you can use garlic_value to construct a GarlicValue from the current config model and use from_garlic to construct a model from a GarlicValue.

GarlicValue is a type that keeps configuration objects in the native code and loads them in Python lazily. This allows you to lower memory usage while speeding up all operations. It also comes with a set of handy methods:

resolve

Allows for requested a specific value by providing a dot separated path.

for example:

class ParentConfig(models.ConfigModel):

    random_config_field = models.ModelField(SomeRandomConfig)


foo = ParentConfig()
foo.random_config_field.value = 8
garlic_value = foo.garlic_value()
print(garlic_value.resolve('random_config_field.value'))

In the above code, if value to the given path exists, the python representation of the value gets returned. Otherwise, None gets returned. This is helpful because you can simply give the path to the final value and get the requested value. Since all of this is happening in the native code, it's significantly faster to use GarlicValue over python dictionary or regular models.

Your goal should be to validate models using ConfigModel and store/read configurations using GarlicValue.

clone

Copy operations, specially deep copies in Python are very expensive. You can, however, clone GarlicValue instances much faster by using the native clone which copies the object without the need to use deep copy yet accomplish the same result.

for example:

garlic_value_1 = foo.garlic_value()
garlic_value_2 = foo.clone()

Serialization

You can use the following code to encode/decode configs. The default encoder is Json. However, you can write your own encoder and support other formats as needed.

from garlicconfig import encoding

config = DatabaseConfig()
serialized_string = encoding.encode(config, pretty=True)

Merging layers

You merge two configuration layers in order to support inheritance. Something that will come very handy if you plan to use localization or multi-layered configurations.

Any GarlicValue instance will have a apply method that will basically applies a second GarlicValue on top of itself.

for example:

from garlicconfig import models
from garlicconfig import fields


class ExtraConfig(models.ConfigModel):

    has_id = fields.BooleanField(default=False)
    has_degree = fields.BooleanField(default=False)


class DumbConfig(models.ConfigModel):

    name = fields.StringField(nullable=False)
    numbers = fields.ArrayField(IntegerField())
    extra = models.ModelField(ExtraConfig)

    def validate(self):
        super(DumbConfig, self).validate()
        if not self.name and not self.numbers:
            raise garlicconfig.exceptions.ValidationError('invalid config for some reason!')


config_1 = DumbConfig.from_dict({
    'name': 'Peyman',
    'numbers': [1, 2, 3]
    'extra': {
        'has_id': True
    }
}).garlic_value()

config_2 = DumbConfig.from_dict({
    'name': 'Patrick',
    'numbers': [4, 5, 6]
    'extra': {
        'has_degree': True
    }
}).garlic_value()

config_1.apply(config_2)
config_1.resolve('numbers')  # returns [4, 5, 6]
config_1.resolve('name')  # returns 'Patrick'
config_1.resolve('extra.has_id')  # returns True (from config_1)
config_1.resolve('extra.has_degree')  # returns True (from config_2)

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