pydantic based ArangoDB ODM
Project description
Pydango - Asynchronous Pydantic ArangoDB ORM
pydangorm
is a Python ORM (Object-Relational Mapping) system tailored for ArangoDB, a multi-model NoSQL database. It provides a Pythonic interface for defining models, constructing queries, and interacting with ArangoDB, abstracting away the direct complexities of database interactions.
Features
-
Model Definitions with pydantic(v1): Easily define and validate your database models using
pydantic
.- VertexModel
- EdgeModel
-
Pythonic Query Building: Construct complex ArangoDB queries with a Pythonic API.
-
Session Management: Streamlined management of database sessions and connections.
-
Collection Management: Create indices, truncate collections, and perform other collection operations.
-
Asynchronous Support: Perform asynchronous database operations for optimized I/O-bound tasks.
Full Documentation
Installation
pip install pydangorm
Quick Start & Usage Examples
Defining Models
Using pydangorm
, you can define vertex and edge models with ease:
import datetime
from typing import Annotated
from pydango import (
EdgeModel,
VertexModel,
EdgeCollectionConfig,
VertexCollectionConfig,
Relation,
)
from pydango.indexes import PersistentIndex
class Visited(EdgeModel):
rating: int
on_date: datetime.date
class Collection(EdgeCollectionConfig):
name = "visited"
indexes = [
PersistentIndex(fields=["rating"]),
]
class LivesIn(EdgeModel):
since: datetime.datetime
class Collection(EdgeCollectionConfig):
name = "lives_in"
class Person(VertexModel):
name: str
age: int
lives_in: Annotated["City", Relation[LivesIn]]
visited: Annotated[list["City"], Relation[Visited]]
class Collection(VertexCollectionConfig):
name = "people"
indexes = [
PersistentIndex(fields=["name"]),
PersistentIndex(fields=["age"]),
]
class City(VertexModel):
name: str
population: int
class Collection(VertexCollectionConfig):
name = "cities"
indexes = [
PersistentIndex(fields=["name"]),
PersistentIndex(fields=["population"]),
]
Querying Data
Construct and execute queries in a Pythonic manner:
from aioarango import ArangoClient
from app.models import Person, City, Visited, LivesIn
from pydango import PydangoSession
from pydango.orm import for_
from pydango.connection.utils import get_or_create_db, deplete_cursor
person = Person(
name="John",
age=35,
lives_in=City(name="Buenos Aires", population=30000000),
visited=[
City(name="Amsterdam", population=123),
City(name="New Delhi", population=123),
],
edges={
Person.lives_in: LivesIn(since=datetime.datetime.now()),
Person.visited: [
Visited(rating=10, on_date=datetime.date.today()),
Visited(rating=10, on_date=datetime.date.today()),
],
},
)
async def main():
db = await get_or_create_db(ArangoClient(), "app")
session = PydangoSession(database=db)
# Retrieving users older than 10 years
await session.save(person)
assert person.id.startswith("people/")
db_person = await session.get(Person, person.key, fetch_edges=True, depth=(1, 1))
assert db_person == person
query = for_(Person).filter(Person.age > 10).sort(-Person.age).return_(Person)
query_result = await session.execute(query)
result = await deplete_cursor(query_result)
More detailed examples and scenarios can be found in the tests
directory, which showcases modeling and querying for different use-cases like cities, families, and e-commerce operations.
Detailed Documentation
For detailed documentation, please refer to the documentation.
Contributing
Contributions to pydangorm
are welcome! Please refer to the CONTRIBUTING.md
file for guidelines.
License
pydangorm
is licensed under MIT. See the LICENSE
file for details.
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