Package: ormdantic
Asynchronous ORM that uses pydantic models to represent database tables ✨Ormdantic is a library for interacting with AsynchronousSQL databasesfrom Python code, with Python objects. It is designed to be intuitive, easy to use, compatible, and robust.Ormdanticis based onPypika, and powered byPydanticandSQLAlchemy, and Highly inspired bySqlmodel, Created by@tiangolo.What isPypika?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.RequirementsA recent and currently supported version of Python (right now,Python supports versions 3.10 and above).AsOrmdanticis based onPydanticandSQLAlchemyandPypika, it requires them. They will be automatically installed when you install Ormdantic.InstallationYou can add Ormdantic in a few easy steps. First of all, install the dependency:$pipinstallormdantic

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SuccessfullyinstalledOrmdanticInstall The specific Asynchronous ORM library for your database.# PostgreSQL$pipinstallormdantic[postgres]# SQLite$pipinstallormdantic[sqlite]ExampleTo 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 thedocumentation.But let's see how to use Ormdantic.Create SQLAlchemy engineOrmdantic uses SQLAlchemy under hood to run different queries, which is why we need to initialize by creating an asynchronous engine.Note: You will use theconnectionparameter to pass the connection to the engine directly.fromormdanticimportOrmdanticconnection="sqlite+aiosqlite:///db.sqlite3"database=Ormdantic(connection)Note: You can use any asynchronous engine, check out thedocumentationfor more information.Create a tableTo create tables decorate a pydantic model with thedatabase.tabledecorator, passing the database information ex.Primary key,foreign keys,Indexes,back_references,unique_constraintsetc. to the decorator call.Table RestrictionsTables must have a single column primary key.The primary key column must be the first column.Relationships mustunion-typethe foreign model and that models primary key.fromuuidimportuuid4frompydanticimportBaseModel,Field@database.table(pk="id",indexed=["name"])classFlavor(BaseModel):"""A coffee flavor."""id:UUID=Field(default_factory=uuid4)name:str=Field(max_length=63)QueriesNow 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 usedatabase.initwill Populate relations information and create the tables.asyncdefdemo()->None:asyncdef_init()->None:asyncwithdb._engine.begin()asconn:awaitdb.init()awaitconn.run_sync(db._metadata.drop_all)awaitconn.run_sync(db._metadata.create_all)await_init()InsertNow let's imagine we have another table calledCoffeethat has a foreign key toFlavor.@database.table(pk="id")classCoffee(BaseModel):"""Drink it in the morning."""id:UUID=Field(default_factory=uuid4)sweetener:str|None=Field(max_length=63)sweetener_count:int|None=Noneflavor:Flavor|UUIDAfter we create the table, we can insert data into the table, using thedatabase.insertmethod, is away we insert a Model Instance.# Create a Flavor called "Vanilla"vanilla=Flavor(name="Vanilla")# Insert the Flavor into the databaseawaitdatabase[Flavor].insert(vanilla)# Create a Coffee with the Vanilla Flavorcoffee=Coffee(sweetener="Sugar",sweetener_count=1,flavor=vanilla)# Insert the Coffee into the databaseawaitdatabase[Coffee].insert(coffee)Searching QueriesAs 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 thedatabase.find_oneordatabase.find_manymethods.Find_oneused to find a Model instance by Primary Key, its could also find withdepthparameter.# Find onevanilla=awaitdatabase[Flavor].find_one(flavor.id)print(vanilla.name)# Find one with depth.find_coffee=awaitdatabase[Coffee].find_one(coffee.id,depth=1)print(find_coffee.flavor.name)Find_manyused to find Model instances by some condition ex.where,order_by,order,limit,offset,depth.# Find manyawaitdatabase[Flavor].find_many()# Get paginated results.awaitdatabase[Flavor].find_many(where={"name":"vanilla"},order_by=["id","name"],limit=2,offset=2)Update / Upsert QueriesUpdateThe 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.# Update a Flavorflavor.name="caramel"awaitdatabase[Flavor].update(flavor)UpsertTheUpsertmethod is similar to the Synchronize method with one exception; theUpsertmethod does not delete any records. TheUpsertmethod 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.# Upsert a Flavorflavor.name="mocha"awaitdatabase[Flavor].upsert(flavor)DeleteTheDELETEstatement is used to delete existing records in a table.# Delete a Flavorawaitdatabase[Flavor].delete(flavor.id)CountTo count the number of rows of a table or in a result set you can use thecountfunction.# Countcount=awaitdatabase[Flavor].count()print(count)It's support alsoWhereandDepthcount_advanced=awaitdatabase[Coffee].count(where={"sweetener":2},depth=1)print(count_advanced)Generator FeatureWe introduce a new feature calledGenerator, 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.usingormdantic.generator.Generatorto generate a Model instance.fromenumimportauto,EnumfromuuidimportUUIDfromormdantic.generatorimportGeneratorfrompydanticimportBaseModelclassFlavor(Enum):MOCHA=auto()VANILLA=auto()classBrand(BaseModel):brand_name:strclassCoffee(BaseModel):id:UUIDdescription:strcream:boolsweetener:intflavor:Flavorbrand:Brandprint(Generator(Coffee))so the results will be:id=UUID('93b517c2-083b-457d-a0e5-6e1bd2a927e4')description='ctWOb'cream=Truesweetener=234flavor=<Flavor.VANILLA:2>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:python-mvenvvenv/sourcevenv/bin/activateAnd then install the development dependencies:# Install dependenciespipinstall-e.[sqlite,postgresql,test,lint,docs]Run tests 🌝You can run all the tests with:bashscripts/test.shNote: You can also generate a coverage report with:bashscripts/test_html.shFormat the code 🍂Execute the following command to applypre-commitformatting:bashscripts/format.shExecute the following command to applymypytype checking:bashscripts/lint.shLicenseThis project is licensed under the terms of the MIT license.