Data Utility Package: IO

Table of Contents:

• Installation
• Features
  • Files
  • Config
  • Register
  • Models

This Utility IO Object was created for load the config data from any file format types like .yaml, .json, or .toml, and manage retention and version of this config file lifecycle.

Installation

pip install ddeutil-io

This package need to install ddeutil for core package namespace.

Features

Files

File Objects that use to read or write data to that format.

For example, I will represent YamlEnvFl object that passing environment variable to reading content before passing to the Yaml loader.

data:
  get: HELLO ${HELLO}

import os
from ddeutil.io import YamlEnvFl

os.environ["HELLO"] = "WORLD"

content = YamlEnvFl('./.pre-commit-config.yaml').read(safe=True)
assert content['data']['get'] == "HELLO WORLD"

Config

Config Object is the dir system handler object that manage any files in that input dir path like load, save, load_stage, save_stage, or files.

from pathlib import Path
from ddeutil.io.config import ConfFl

config: ConfFl = ConfFl(path=Path('./conf'), compress="gzip")

data = config.load('config_file.yaml')
config.save_stage('./stage/file.json', data)

Register

The Register Object is the metadata generator object for the config data. If you passing name and configs to this object, it will find the config name in any stage storage and generate its metadata to you.

from ddeutil.io.register import Register
from ddeutil.io.param import Params

registry: Register = Register(
    name='examples:conn_data_local_file',
    params=Params.model_validate({
        "stages": {
            "raw": {"format": "{naming:%s}.{timestamp:%Y%m%d_%H%M%S}"},
        },
    }),
)
registry.move(stage="raw")

The raw data of this config was written in conn_file.yaml file.

conf/
  examples/
    conn_file.yaml

When call move method, it will transfer data from .yaml file to json file with the data hashing algorithm.

data/
  raw/
    conn_file_20240101_000000.json

Models

The Model objects was implemented from the Pydantic V2 which is the powerful parsing and serializing data model to the Python object.

Note

So, I use this project to learn and implement a limit and trick of the Pydantic package.

The model able to handle common logic validations and able to adjust by custom code for your specific requirements (Yeah, it just inherits Sub-Class from BaseModel).

Data Types

from ddeutil.io.models.dtype import StringType

dtype = StringType()
assert dtype.type == "string"
assert dtype.max_length == -1

Constraints

from ddeutil.io.models.const import Pk

const = Pk(of="foo", cols=["bar", "baz"])
assert const.name == "foo_bar_baz_pk"
assert const.cols == ["bar", "baz"]

Datasets

from ddeutil.io.models.datasets import Col, Tbl

tbl = Tbl(
  name="table_foo",
  features=[
    Col(name="id", dtype="integer primary key"),
    Col(name="foo", dtype="varchar( 10 )"),
  ],
)
assert tbl.name == "table_foo"
assert tbl.features[0].name == "id"
assert tbl.features[0].dtype.type == "integer"

Examples

If I have some catalog config, it easy to pass this config to model object.

import yaml
from ddeutil.io.models.datasets import Scm


config = yaml.safe_load("""
name: "warehouse"
tables:
  - name: "customer_master"
    features:
      - name: "id"
        dtype: "integer"
        pk: true
      - name: "name"
        dtype: "varchar( 256 )"
        nullable: false
""")
schema = Scm.model_validate(config)
assert len(schema.tables) == 1
assert schema.tables[0].name == 'customer_master'

License

This project was licensed under the terms of the MIT license.