Skip to main content

Simple dataclasses configuration management for Python with hocon/json/yaml/properties/env-vars/dict support.

Project description

Dataconf

Actions Status PyPI version

Simple dataclasses configuration management for Python with hocon/json/yaml/properties/env-vars/dict/cli support.

Getting started

Requires at least Python 3.9.

# pypi
pip install dataconf
poetry add dataconf

# remote master
pip install --upgrade git+https://github.com/zifeo/dataconf.git
poetry add git+https://github.com/zifeo/dataconf.git

# local repo/dev
poetry install
pre-commit install

Usage

import os
from dataclasses import dataclass, field
from typing import List, Dict, Text, Union, Tuple
from dateutil.relativedelta import relativedelta
from datetime import datetime, timedelta
import dataconf

conf = """
str_name = test
str_name = ${?HOME}
dash-to-underscore = true
float_num = 2.2
iso_datetime = "2000-01-01T20:00:00"
iso_duration = "P123DT4H5M6S"
variable_length_tuple_data = [
    1
    2
    3
]
tuple_data = [
    a
    P1D
]
# this is a comment
list_data = [
    a
    b
]
nested {
    a = test
    b : 1
}
nested_list = [
    {
        a = test1
        b : 2.5
    }
]
duration = 2s
union = 1
people {
    name = Thailand
}
zone {
    area_code = 42
}
"""

class AbstractBaseClass:
    pass

@dataclass
class Person(AbstractBaseClass):
    name: Text

@dataclass
class Zone(AbstractBaseClass):
    area_code: int

@dataclass
class Nested:
    a: Text
    b: float

@dataclass
class Config:
    str_name: Text
    dash_to_underscore: bool
    float_num: float
    iso_datetime: datetime
    iso_duration: timedelta
    variable_length_tuple_data: Tuple[int, ...]
    tuple_data: Tuple[Text, timedelta]
    list_data: List[Text]
    nested: Nested
    nested_list: List[Nested]
    duration: relativedelta
    union: Union[Text, int]
    people: AbstractBaseClass
    zone: AbstractBaseClass
    default: Text = 'hello'
    default_factory: Dict[Text, Text] = field(default_factory=dict)

print(dataconf.string(conf, Config))
# Config(
#   str_name='/users/root/',
#   dash_to_underscore=True,
#   float_num=2.2,
#   iso_datetime=datetime.datetime(2000, 1, 1, 20, 0),
#   iso_duration=datetime.timedelta(days=123, seconds=14706),
#   variable_length_tuple_data=(1,2,3),
#   tuple_data=('a', datetime.timedelta(days=1)),
#   list_data=['a', 'b'],
#   nested=Nested(a='test', b=1),
#   nested_list=[Nested(a='test1', b=2.5)],
#   duration=relativedelta(seconds=+2),
#   union=1,
#   people=Person(name='Thailand'),
#   zone=Zone(area_code=42),
#   default='hello',
#   default_factory={}
# )

@dataclass
class Example:
    hello: str
    world: str
    foo: List[str]

os.environ['DC_WORLD'] = 'monde'

print(
    dataconf
    .multi
    .url('https://raw.githubusercontent.com/zifeo/dataconf/main/confs/simple.hocon')
    .env('DC')
    .on(Example)
)
# Example(hello='bonjour',world='monde')

API

import dataconf

conf = dataconf.string('{ name: Test }', Config)
conf = dataconf.string('name:\n\tvalue: Test', Config, loader=dataconf.YAML)  # dataconf.HOCON by default
conf = dataconf.env('PREFIX_', Config)
conf = dataconf.dict({'name': 'Test'}, Config)
conf = dataconf.url('https://raw.githubusercontent.com/zifeo/dataconf/master/confs/test.hocon', Config)  # hocon, json, yaml, properties
conf = dataconf.file('confs/test.hocon', Config)  # hocon, json, yaml, properties
conf = dataconf.cli(sys.argv, Config)

# Aggregation
conf = dataconf.multi.string(...).env(...).url(...).file(...).dict(...).cli(...).on(Config)

# Same api as Python json/yaml packages (e.g. `load`, `loads`, `dump`, `dumps`)
conf = dataconf.load('confs/test.hocon', Config)  # hocon, json, yaml, properties
conf = dataconf.load('confs/test.yaml', Config, loader=dataconf.YAML)  # dataconf.HOCON by default
dataconf.dump('confs/test.hocon', conf, out='hocon')
dataconf.dump('confs/test.json', conf, out='json')
dataconf.dump('confs/test.yaml', conf, out='yaml')
dataconf.dump('confs/test.properties', conf, out='properties')

For full HOCON capabilities see here.

Parse env vars

PREFIX_VAR=a
PREFIX_VAR_NAME=b
PREFIX_TEST__NAME=c
PREFIX_LS_0=d
PREFIX_LS_1=e
PREFIX_LSLS_0_0=f
PREFIX_LSOB_0__NAME=g
PREFIX_NESTED_="{ name: Test }"
PREFIX_SUB_="{ value: ${PREFIX_VAR} }"

is equivalent to

{
    var = a
    var_name = b
    test {
        name = c
    }
    ls = [
        d
        e
    ]
    lsls = [
        [
            f
        ]
    ]
    lsob = [
        {
            name = g
        }
    ]
    nested {
        # parse nested config by suffixing env var with `_`
        name: Test
    }
    sub {
        # will have value "a" at parsing, useful for aliases
        value = ${PREFIX_VAR}
    }
}

Note that when using .env source, the strict mode is disabled and value might be casted.

Parse CLI arguments

Same as env vars parse (dashes are converted to underscore, e.g. TEST_A--test-a).

CLI usage

Can be used for validation or converting between supported file formats (-o).

dataconf -c confs/test.hocon -m tests.configs -d TestConf -o hocon
# dataconf.exceptions.TypeConfigException: expected type <class 'datetime.timedelta'> at .duration, got <class 'int'>

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dataconf-3.3.0.tar.gz (15.3 kB view details)

Uploaded Source

Built Distribution

dataconf-3.3.0-py3-none-any.whl (16.1 kB view details)

Uploaded Python 3

File details

Details for the file dataconf-3.3.0.tar.gz.

File metadata

  • Download URL: dataconf-3.3.0.tar.gz
  • Upload date:
  • Size: 15.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.5 Linux/6.5.0-1025-azure

File hashes

Hashes for dataconf-3.3.0.tar.gz
Algorithm Hash digest
SHA256 da656c5e45bae9071a380d991183db98024c13a861e3d8fd225629bd9a76dd6f
MD5 1fc0e391dfba792f5b0541fcf580e829
BLAKE2b-256 c1b6a41874c4ebf4e2bb0c49e6b03563cf2503258235fa4affec464db4e7d00e

See more details on using hashes here.

File details

Details for the file dataconf-3.3.0-py3-none-any.whl.

File metadata

  • Download URL: dataconf-3.3.0-py3-none-any.whl
  • Upload date:
  • Size: 16.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.5 Linux/6.5.0-1025-azure

File hashes

Hashes for dataconf-3.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 5f80bc6f5c130f2c3e463ea8cbc57221cf5e789c580e3880e19b284bee25db41
MD5 b4676dfc728421ed233fe18e461a41d2
BLAKE2b-256 cf4780a04c27f9bb66f7d46a8cd9a49cbb48a894328da47420cc592e2f252d9c

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page