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

Key Features

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

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
from pydantic import BaseModel, Field
from pydbantic import DataBaseModel, PrimaryKey

class Department(DataBaseModel):
    id: str = PrimaryKey()
    name: str
    company: str
    is_sensitive: bool = False

class Positions(DataBaseModel):
    id: str = PrimaryKey()
    name: str
    department: Department

class EmployeeInfo(DataBaseModel):
    ssn: str = PrimaryKey()
    first_name: str
    last_name: str
    address: str
    address2: Optional[str]
    city: Optional[str]
    zip: Optional[int]

class Employee(DataBaseModel):
    id: str = PrimaryKey()
    employee_info: EmployeeInfo
    position: Positions
    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

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

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

Model Usage

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

async def main():
    # db creation is above

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

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

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

Note: At this point only the models have been created, but nothing is saved in the database yet.

    # save to database
    await hr_employee.save()

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 non-existing.

What is pydbantic

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

• migrations
• model creation / managment
• 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):
    time: str = PrimaryKey(default=time_now)
    latitude: float
    longitude: float

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