pynocular 0.4.0

Lightweight ORM that lets you query your database using Pydantic models and asyncio

pynocular

Pynocular is a lightweight ORM that lets you query your database using Pydantic models and asyncio.

With Pynocular you can decorate your existing Pydantic models to sync them with the corresponding table in your database, allowing you to persist changes without ever having to think about the database. Transaction management is automatically handled for you so you can focus on the important parts of your code. This integrates seamlessly with frameworks that use Pydantic models such as FastAPI.

Features:

• Fully supports asyncio to write to SQL databases
• Provides simple methods for basic SQLAlchemy support (create, delete, update, read)
• Contains access to more advanced functionality such as custom SQLAlchemy selects
• Contains helper functions for creating new database tables
• Advanced transaction management system allows you to conditionally put requests in transactions

Table of Contents:

• Installation
• Guide
  • Basic Usage
  • Advanced Usage
  • Creating database tables
• Development

Installation

pynocular requires Python 3.6 or above.

pip install pynocular
# or
poetry add pynocular

Guide

Basic Usage

Pynocular works by decorating your base Pydantic model with the function database_model. Once decorated with the proper information, you can proceed to use that model to interface with your specified database table.

The first step is to define a DBInfo object. This will contain the connection information to your database.

from pynocular.engines import DatabaseType, DBInfo


# Example below shows how to connect to a locally-running Postgres database
connection_string = f"postgresql://{db_user_name}:{db_user_password}@localhost:5432/{db_name}?sslmode=disable"
)
db_info = DBInfo(DatabaseType.aiopg_engine, connection_string)

Pynocular supports connecting to your database through two different asyncio engines; aiopg and asyncpgsa. You can pick which one you want to use by passing the correct DatabaseType enum value into DBInfo.

Once you define a db_info object, you are ready to decorate your Pydantic models and interact with your database!

from pydantic import BaseModel, Field
from pynocular.database_model import database_model, UUID_STR


@database_model("organizations", db_info)
class Org(BaseModel):

    id: Optional[UUID_STR] = Field(primary_key=True, fetch_on_create=True)
    serial_id: Optional[int]
    name: str = Field(max_length=45)
    slug: str = Field(max_length=45)
    tag: Optional[str] = Field(max_length=100)

    created_at: Optional[datetime] = Field(fetch_on_create=True)
    updated_at: Optional[datetime] = Field(fetch_on_update=True)


# Create a new Org via `create`
org = await Org.create("new org", "new-org")


# Create a new Org via `save`
org2 = Org("new org2", "new-org2")
await org2.save()


# Update an org
org.name = "renamed org"
await org.save()


# Delete org
await org.delete()


# Fetch org
org3 = await Org.get(org2.id)
assert org3 == org2

# Fetch a list of orgs
orgs = await Org.get_list()

# Fetch a filtered list of orgs
orgs = await Org.get_list(tag="green")

#  Fetch orgs that have several different tags
orgs = await Org.get_list(tag=["green", "blue", "red"])

With Pynocular you can set fields to be optional and set by the database. This is useful if you want to let the database autogenerate your primary key or created_at and updated_at fields on your table. To do this you must:

• Wrap the typehint in Optional
• Provide keyword arguments of fetch_on_create=True or fetch_on_update=True to the Field class

Advanced Usage

For most use cases, the basic usage defined above should suffice. However, there are certain situations where you don't necessarily want to fetch each object or you need to do more complex queries that are not exposed by the DatabaseModel interface. Below are some examples of how those situations can be addressed using Pynocular.

Batch operations on tables

Sometimes you want to insert a bunch of records into a database and you don't want to do an insert for each one. This can be handled by the create_list function.

org_list = [
    Org(name="org1", slug="org-slug1"),
    Org(name="org2", slug="org-slug2"),
    Org(name="org3", slug="org-slug3"),
]
await Org.create_list(org_list)

This function will insert all records into your database table in one batch.

If you have a use case that requires deleting a bunch of records based on some field value, you can use delete_records:

# Delete all records with the tag "green"
await Org.delete_records(tag="green")

# Delete all records with if their tag has any of the following: "green", "blue", "red"
await Org.delete_records(tag=["green", "blue", "red"])

Sometimes you may want to update the value of a record in a database without having to fetch it first. This can be accomplished by using the update_record function:

await Org.update_record(
    id="05c0060c-ceb8-40f0-8faa-dfb91266a6cf",
    tag="blue"
)
org = await Org.get("05c0060c-ceb8-40f0-8faa-dfb91266a6cf")
assert org.tag == "blue"

Complex queries

Sometimes your application will require performing complex queries, such as getting the count of each unique field value for all records in the table. Because Pynocular is backed by SQLAlchemy, we can access table columns directly to write pure SQLAlchemy queries as well!

from sqlalchemy import func, select
from pynocular.engines import DBEngine
async def generate_org_stats():
    query = (
        select([func.count(Org.column.id), Org.column.tag])
        .group_by(Org.column.tag)
        .order_by(func.count().desc())
    )
    async with await DBEngine.transaction(Org._database_info, is_conditional=False) as conn:
        result = await conn.execute(query)
        return [dict(row) async for row in result]

NOTE: DBengine.transaction is used to create a connection to the database using the credentials passed in. If is_conditional is False, then it will add the query to any transaction that is opened in the call chain. This allows us to make database calls in different functions but still have them all be under the same database transaction. If there is no transaction opened in the call chain it will open a new one and any subsequent calls underneath that context manager will be added to the new transaction.

If is_conditional is True and there is no transaction in the call chain, then the connection will not create a new transaction. Instead, the query will be performed without a transaction.

Creating database tables

When you decorate a Pydantic model with Pynocular, it creates a SQLAlchemy table as a private variable. This can be accessed via the _table property (although accessing private variables is not recommended). Using this, along with Pynocular's create_tracked_table function, allows you to create tables in your database based off of Pydantic models!

from pynocular.db_utils import create_tracked_table

from my_package import Org

# Creates the table "organizations" in the database defined by db_info
await create_tracked_table(Org._database_info, Org._table)

Development

To develop pynocular, install dependencies and enable the pre-commit hook:

pip install pre-commit poetry
poetry install
pre-commit install

To run tests:

poetry run pytest