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https://github.com/yezz123/ormdantic

Asynchronous ORM that uses pydantic models to represent database tables ✨
https://github.com/yezz123/ormdantic

async asyncio database hacktoberfest mysql orm postgres postgresql pydantic pypika python sql sqlalchemy sqlite typing

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Asynchronous ORM that uses pydantic models to represent database tables ✨

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README 

        
![Logo](https://raw.githubusercontent.com/yezz123/ormdantic/main/.github/logo.png)





    Asynchronous ORM that uses pydantic models to represent database tables ✨








    Test





    





    Package version





    Supported Python versions






Ormdantic is a library for interacting with Asynchronous SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, compatible, and robust.



**Ormdantic** is based on [Pypika](https://github.com/kayak/pypika), and powered by Pydantic and SQLAlchemy, and Highly inspired by Sqlmodel, Created by [@tiangolo](https://github.com/tiangolo).



> What is [Pypika](https://github.com/kayak/pypika)?

>

> PyPika is a Python API for building SQL queries. The motivation behind PyPika is to provide a simple interface for building SQL queries without limiting the flexibility of handwritten SQL. Designed with data analysis in mind, PyPika leverages the builder design pattern to construct queries to avoid messy string formatting and concatenation. It is also easily extended to take full advantage of specific features of SQL database vendors.



The key features are:



* **Easy to use**: It has sensible defaults and does a lot of work underneath to simplify the code you write.

* **Compatible**: It combines SQLAlchemy, Pydantic and Pypika tries to simplify the code you write as much as possible, allowing you to reduce the code duplication to a minimum, but while getting the best developer experience possible.

* **Extensible**: You have all the power of SQLAlchemy and Pypika underneath.

* **Short Queries**: You can write queries in a single line of code, and it will be converted to the appropriate syntax for the database you are using.



## Requirements



A recent and currently supported version of Python (right now, Python supports versions 3.10 and above).



As **Ormdantic** is based on **Pydantic** and **SQLAlchemy** and **Pypika**, it requires them. They will be automatically installed when you install Ormdantic.



## Installation



You can add Ormdantic in a few easy steps. First of all, install the dependency:



```shell

$ pip install ormdantic



---> 100%



Successfully installed Ormdantic

```



* Install The specific Asynchronous ORM library for your database.



```shell

# PostgreSQL

$ pip install ormdantic[postgres]



# SQLite

$ pip install ormdantic[sqlite]

```



## Example



To understand SQL, Sebastian the Creator of FastAPI and SQLModel created an amazing documentation that could help you understand the basics of SQL, ex. `CREATE TABLE`, `INSERT`, `SELECT`, `UPDATE`, `DELETE`, etc.



Check out the [documentation](https://sqlmodel.tiangolo.com/).



But let's see how to use Ormdantic.



### Create SQLAlchemy engine



Ormdantic uses SQLAlchemy under hood to run different queries, which is why we need to initialize by creating an asynchronous engine.



> **Note**: You will use the `connection` parameter to pass the connection to the engine directly.



```python

from ormdantic import Ormdantic



connection = "sqlite+aiosqlite:///db.sqlite3"



database = Ormdantic(connection)

```



**Note**: You can use any asynchronous engine, check out the [documentation](https://docs.sqlalchemy.org/en/14/core/engines.html) for more information.



### Create a table



To create tables decorate a pydantic model with the `database.table` decorator, passing the database information ex. `Primary key`, `foreign keys`, `Indexes`, `back_references`, `unique_constraints` etc. to the decorator call.



#### Table Restrictions



* Tables must have a single column primary key.

* The primary key column must be the first column.

* Relationships must `union-type` the foreign model and that models primary key.



```python

from uuid import uuid4

from pydantic import BaseModel, Field



@database.table(pk="id", indexed=["name"])

class Flavor(BaseModel):

     """A coffee flavor."""



     id: UUID = Field(default_factory=uuid4)

     name: str = Field(max_length=63)

```



### Queries



Now after we create the table, we can initialize the database with the table and then run different queries.



#### Init()



* Register models as ORM models and initialize the database.



We use `database.init` will Populate relations information and create the tables.



```python

async def demo() -> None:

    async def _init() -> None:

        async with db._engine.begin() as conn:

            await db.init()

            await conn.run_sync(db._metadata.drop_all)

            await conn.run_sync(db._metadata.create_all)

    await _init()

```



#### Insert



Now let's imagine we have another table called `Coffee` that has a foreign key to `Flavor`.



```python

@database.table(pk="id")

class Coffee(BaseModel):

     """Drink it in the morning."""



     id: UUID = Field(default_factory=uuid4)

     sweetener: str | None = Field(max_length=63)

     sweetener_count: int | None = None

     flavor: Flavor | UUID

```



After we create the table, we can insert data into the table, using the `database.insert` method, is away we insert a Model Instance.



```python

# Create a Flavor called "Vanilla"

vanilla = Flavor(name="Vanilla")



# Insert the Flavor into the database

await database[Flavor].insert(vanilla)



# Create a Coffee with the Vanilla Flavor

coffee = Coffee(sweetener="Sugar", sweetener_count=1, flavor=vanilla)



# Insert the Coffee into the database

await database[Coffee].insert(coffee)

```



#### Searching Queries



As we know, in SQL, we can search for data using different methods, ex. `WHERE`, `LIKE`, `IN`, `BETWEEN`, etc.



In Ormdantic, we can search for data using the `database.find_one` or `database.find_many` methods.



* `Find_one`  used to find a Model instance by Primary Key, its could also find with `depth` parameter.



```python

     # Find one

     vanilla = await database[Flavor].find_one(flavor.id)

     print(vanilla.name)



     # Find one with depth.

     find_coffee = await database[Coffee].find_one(coffee.id, depth=1)

     print(find_coffee.flavor.name)

```



* `Find_many` used to find Model instances by some condition ex. `where`, `order_by`, `order`, `limit`, `offset`, `depth`.



```python

     # Find many

     await database[Flavor].find_many()



     # Get paginated results.

     await database[Flavor].find_many(

          where={"name": "vanilla"}, order_by=["id", "name"], limit=2, offset=2

     )

```



#### Update / Upsert Queries



##### Update



The modification of data that is already in the database is referred to as updating. You can update individual rows, all the rows in a table, or a subset of all rows. Each column can be updated separately; the other columns are not affected.



```python

     # Update a Flavor

     flavor.name = "caramel"

     await database[Flavor].update(flavor)

```



##### Upsert



The `Upsert` method is similar to the Synchronize method with one exception; the `Upsert` method does not delete any records. The `Upsert` method will result in insert or update operations. If the record exists, it will be updated. If the record does not exist, it will be inserted.



```python

     # Upsert a Flavor

     flavor.name = "mocha"

     await database[Flavor].upsert(flavor)

```



### Delete



The `DELETE` statement is used to delete existing records in a table.



```python

     # Delete a Flavor

     await database[Flavor].delete(flavor.id)

```



### Count



To count the number of rows of a table or in a result set you can use the `count` function.



```python

     # Count

     count = await database[Flavor].count()

     print(count)

```



* It's support also `Where` and `Depth`



```python

     count_advanced = await database[Coffee].count(

          where={"sweetener": 2}, depth=1

     )

     print(count_advanced)

```



## Generator Feature



We introduce a new feature called `Generator`, which is a way to generate a Model instance with random data.



So, Given a Pydantic model type can generate instances of that model with randomly generated values.



using `ormdantic.generator.Generator` to generate a Model instance.



```python

from enum import auto, Enum

from uuid import UUID



from ormdantic.generator import Generator

from pydantic import BaseModel



class Flavor(Enum):

    MOCHA = auto()

    VANILLA = auto()



class Brand(BaseModel):

    brand_name: str



class Coffee(BaseModel):

    id: UUID

    description: str

    cream: bool

    sweetener: int

    flavor: Flavor

    brand: Brand



print(Generator(Coffee))

```



so the results will be:



```shell

id=UUID('93b517c2-083b-457d-a0e5-6e1bd2a927e4')

description='ctWOb' cream=True sweetener=234

flavor= brand=Brand(brand_name='LMrIf')

```



We can integrate this with our database while testing our application (Live Tests).



## Development 🚧



### Setup environment 📦



You should create a virtual environment and activate it:



```bash

python -m venv venv/

```



```bash

source venv/bin/activate

```



And then install the development dependencies:



```bash

# Install dependencies

pip install -r requirements/all.txt

```



### Run tests 🌝



You can run all the tests with:



```bash

bash scripts/test.sh

```



### Format the code 🍂



Execute the following command to apply `pre-commit` formatting:



```bash

bash scripts/format.sh

```



Execute the following command to apply `mypy` type checking:



```bash

bash scripts/lint.sh

```



## License



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