The pydbantic from dannyrohde

'db' within pydantic - A single model for shaping, creating, accessing, storing data within a Database

Key Features

Automatic Migration on Schema Changes
Flexible Data Types
One Model for type validation & database access
Dynamic Model Relationships
Integrated Redis Caching Support

Documentation

https://pydbantic.readthedocs.io/en/latest/

Setup

$ pip install pydbantic
$ pip install pydbantic[sqlite]
$ pip install pydbantic[mysql]
$ pip install pydbantic[postgres]

Basic Usage - Model

from typing import List, Optional, Union
from pydbantic import DataBaseModel, PrimaryKey

class Department(DataBaseModel):
    department_id: str = PrimaryKey()
    name: str
    company: str
    is_sensitive: bool = False
    positions: List[Optional['Positions']] = []  # One to Many

class Positions(DataBaseModel):
    position_id: str = PrimaryKey()
    name: str
    department: Department = None               # One to One mapping 
    employees: List[Optional['Employee']] = []  # One to Many

class EmployeeInfo(DataBaseModel):
    ssn: str = PrimaryKey()
    first_name: str
    last_name: str
    address: str
    address2: Optional[str]
    city: Optional[str]
    zip: Optional[int]
    new: Optional[str]
    employee: Optional[Union['Employee', dict]] = None # One to One 

class Employee(DataBaseModel):
    employee_id: str = PrimaryKey()
    employee_info: Optional[EmployeeInfo] = None  # One to One
    position: List[Optional[Positions]] = []      # One to Many 
    salary: float
    is_employed: bool
    date_employed: Optional[str]

Basic Usage - Connecting a Database to Models

import asyncio
from pydbantic import Database
from models import Employee, EmployeeInfo, Positions, Department

async def main():
    db = await Database.create(
        'sqlite:///test.db',
        tables=[
            Employee,
            EmployeeInfo,
            Positions,
            Department
        ]
    )

if __name__ == '__main__':
    asyncio.run(main())

Model Usage

Import and use the models where you need them. As long as DB as already been created, the Models can accesss the & Use the connected DB

from models import (
    Employee, 
    EmployeeInfo, 
    Position, 
    Department
)

Model - Creation

    # create department 
    hr_department = await Department.create(
        id='d1234',
        name='hr'
        company='abc-company',
        is_sensitive=True,
    )

Via instance using insert or save

    hr_department = Department.create(
        id='d1234',
        name='hr'
        company='abc-company',
        is_sensitive=True,
    )

    await hr_department.insert()
    await hr_department.save()

Insert with related models

    # create a Position in Hr Department
    hr_manager = Position.create(
        id='p1234',
        name='manager',
        department=hr_department
    )
    
    # create instance on an hr employee
    hr_emp_info = EmployeeInfo.create(
        ssn='123-456-789',
        first_name='john',
        last_name='doe',
        address='123 lane',
        city='snake city',
        zip=12345
    )

    # create an hr employee 
    hr_employee = await Employee.create(
        id='e1234',
        employee_info=hr_emp_info,
        position=hr_manager,
        is_employed=True,
        date_employed='1970-01-01'
    )

Filtering

    # get all hr managers currently employed
    managers = await Employee.filter(
        Employee.position==hr_manager, # conditional
        is_employed=True               # key-word argument
    )

    first_100_employees = await Employee.all(
        limit=100
    )

Deleting

    # remove all managers not employed anymore
    for manager in await Employee.filter(
        position=hr_manager,
        is_employed=False
    ):
        await manager.delete()

Updating

    # raise salary of all managers
    for manager in await Employee.filter(
        position=hr_manager,
        is_employed=False
    ):
        manager.salary = manager.salary + 1000.0
        await manager.update() # or manager.save()

.save() results in a new row created in Employee table as well as the related EmployeeInfo, Position, Department tables if not yet created. s

What is pydbantic

pydbantic was built to solve some of the most common pain developers may face working with databases.

migrations
model creation / managment
dynamic relationships
caching

pydbantic believes that related data should be stored together, in the shape the developer plans to use

pydbantic knows data is rarely flat or follows a set schema

pydbantic understand migrations are not fun, and does them for you

pydbantic speaks many types

Pillars

pydantic - Models, Type validation
databases - Database Connection Abstractions
sqlalchemy - Core Database Query and Database Model

Models

pydbantic most basic object is a DataBaseModel. This object may be comprised of almost any pickle-able python object, though you are encouraged to stay within the type-validation land by using pydantic's BaseModels and validators.

Primary Keys

DataBaseModel 's also have a priamry key, which is the first item defined in a model or marked with = PrimaryKey()

class NotesBm(DataBaseModel):
    id: str = PrimaryKey()
    text: Optional[str]  # optional
    data: DataModel      # required 
    coridinates: tuple   # required
    items: list          # required
    nested: dict = {'nested': True} # Optional - w/ Default

Model Types & Typing

DataBaseModel items are capable of being multiple layers deep following pydantic model validation

Primary Key - First Item, must be unique
Required - items without default values are assumed required
Optional - marked explicitly with typing.Optional or with a default value
Union - Accepts Either specified input type Union[str|int]
List[item] - Lists of specified items

Input datatypes without a natural / built in serialization path are serialized using pickle and stored as bytes. More on this later.

Migrations

pydbantic handles migrations automatically in response to detected model changes: New Field, Removed Field, Modified Field, Renamed Field, Primary Key Changes

Renaming an exiting column

Speical consideration is needed when renaming a field in a DataBaseModel, extra metadata __renamed__ is needed to ensure existing data is migrated:

# field `first_name` is renamed to `first_names`

class EmployeeInfo(DataBaseModel):
    __renamed__= [{'old_name': 'first_name', 'new_name': 'first_names'}]
    ssn: str = PrimaryKey()
    first_names: str
    last_name: str
    address: str
    address2: Optional[str]
    city: Optional[str]
    zip: Optional[int]

Cache

Adding cache with Redis is easy with pydbantic, and is complete with built in cache invalidation.

    db = await Database.create(
        'sqlite:///test.db',
        tables=[Employee],
        cache_enabled=True,
        redis_url="redis://localhost"
    )

Models with arrays of Foreign Objects

DataBaseModel models can support arrays of both BaseModels and other DataBaseModel. Just like single DataBaseModel references, data is stored in separate tables, and populated automatically when the child DataBaseModel is instantiated.

from uuid import uuid4
from datetime import datetime
from typing import List, Optional
from pydbantic import DataBaseModel, PrimaryKey


def time_now():
    return datetime.now().isoformat()
def get_uuid4():
    return str(uuid4())

class Coordinate(DataBaseModel):
    id: str = PrimaryKey(default=get_uuid4)
    lat_long: tuple
    journeys: List[Optional["Journey"]] = []

class Journey(DataBaseModel):
    trip_id: str = PrimaryKey(default=get_uuid4)
    waypoints: List[Optional[Coordinate]] = []

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pydbantic's Introduction