| Name | ddeutil-model JSON |
| Version |
0.0.4
JSON |
| download |
| home_page | None |
| Summary | Data Developer & Engineer Model Utility Objects |
| upload_time | 2024-05-20 04:45:13 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9.13 |
| license | MIT |
| keywords |
data
model
utility
pipeline
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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# Data Utility Package: *Model*
[](https://github.com/ddeutils/ddeutil-model/actions/workflows/tests.yml)
[](https://pypi.org/project/ddeutil-model/)
[](https://github.com/ddeutils/ddeutil-model)
**Table of Contents**:
- [Installation](#installation)
- [Models](#models)
- [Data Types](#data-types)
- [Constraints](#constraints)
- [Datasets](#datasets)
- [Usecase](#usecase)
This is **Utility Models**, it implements any models for **Data Pipeline**,
or **Data Platform** frameworks. The Model objects was implemented from the [Pydantic V2](https://docs.pydantic.dev/latest/)
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`).
## Installation
```shell
pip install -U ddeutil-model
```
## Models
### Data Types
```python
from ddeutil.model.dtype import StringType
dtype = StringType()
assert dtype.type == "string"
assert dtype.max_length == -1
```
### Constraints
```python
from ddeutil.model.const import Pk
const = Pk(of="foo", cols=["bar", "baz"])
assert const.name == "foo_bar_baz_pk"
assert const.cols == ["bar", "baz"]
```
### Datasets
```python
from ddeutil.model.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"
```
## Usecase
If I have some catalog config, it easy to pass this config to model object.
```python
import yaml
from ddeutil.model.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](LICENSE).
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"description": "# Data Utility Package: *Model*\n\n[](https://github.com/ddeutils/ddeutil-model/actions/workflows/tests.yml)\n[](https://pypi.org/project/ddeutil-model/)\n[](https://github.com/ddeutils/ddeutil-model)\n\n**Table of Contents**:\n\n- [Installation](#installation)\n- [Models](#models)\n - [Data Types](#data-types)\n - [Constraints](#constraints)\n - [Datasets](#datasets)\n- [Usecase](#usecase)\n\nThis is **Utility Models**, it implements any models for **Data Pipeline**,\nor **Data Platform** frameworks. The Model objects was implemented from the [Pydantic V2](https://docs.pydantic.dev/latest/)\nwhich is the powerful parsing and serializing data model to the Python object.\n\n> [!NOTE]\n> So, I use this project to learn and implement a limit and trick of the Pydantic\n> package.\n\nThe model able to handle common logic validations and able to adjust by custom code\nfor your specific requirements (Yeah, it just inherits Sub-Class from `BaseModel`).\n\n## Installation\n\n```shell\npip install -U ddeutil-model\n```\n\n## Models\n\n### Data Types\n\n```python\nfrom ddeutil.model.dtype import StringType\n\ndtype = StringType()\nassert dtype.type == \"string\"\nassert dtype.max_length == -1\n```\n\n### Constraints\n\n```python\nfrom ddeutil.model.const import Pk\n\nconst = Pk(of=\"foo\", cols=[\"bar\", \"baz\"])\nassert const.name == \"foo_bar_baz_pk\"\nassert const.cols == [\"bar\", \"baz\"]\n```\n\n### Datasets\n\n```python\nfrom ddeutil.model.datasets import Col, Tbl\n\ntbl = Tbl(\n name=\"table_foo\",\n features=[\n Col(name=\"id\", dtype=\"integer primary key\"),\n Col(name=\"foo\", dtype=\"varchar( 10 )\"),\n ],\n)\nassert tbl.name == \"table_foo\"\nassert tbl.features[0].name == \"id\"\nassert tbl.features[0].dtype.type == \"integer\"\n```\n\n## Usecase\n\nIf I have some catalog config, it easy to pass this config to model object.\n\n```python\nimport yaml\nfrom ddeutil.model.datasets import Scm\n\n\nconfig = yaml.safe_load(\"\"\"\nname: \"warehouse\"\ntables:\n - name: \"customer_master\"\n features:\n - name: \"id\"\n dtype: \"integer\"\n pk: true\n - name: \"name\"\n dtype: \"varchar( 256 )\"\n nullable: false\n\"\"\")\nschema = Scm.model_validate(config)\nassert len(schema.tables) == 1\nassert schema.tables[0].name == 'customer_master'\n```\n\n## License\n\nThis project was licensed under the terms of the [MIT license](LICENSE).\n",
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