DefPixel

Define your data connections, models, and access in Python.

Define your pixels.
Shape your voxels.

DefPixel is a Python library for defining data source connections, models, and access. Use a provided data access implementation or build your own, and work with data directly or through standard dataclasses.

Connect · model · use

• Define connections: storage-specific configuration and data access capabilities.
• Define models: storage specifications and validation rules attached to standard dataclasses.
• Use your data: direct access without models, or storage and retrieval through models.

Configuration and Connection provide connection foundations. Database, KeyValue, Document, and Index Protocols describe capabilities. Modeling approaches and support vary by implementation. Validators also provide independent value validation and conversion.

Requirements and installation

Python 3.9+ is required.

pip install DefPixel

Usage at a glance

Database

SQLite

Execute SQL directly for dictionary results or map standard dataclasses to tables and retrieve objects. This complete example requires SQLite 3.35+ as used by Python.

from dataclasses import dataclass
from DefPixel.DataAccessObject.Implements.Sqlite import (
  Configuration, Connection, column, table,
)
from DefPixel.DataAccessObject.Implements.Sqlite.Constraints import PrimaryKey
from DefPixel.DataAccessObject.Implements.Sqlite.Executors import Create, Insert, Select
from DefPixel.DataAccessObject.Implements.Sqlite.Types import Integer, Text


@table(name='users', constraints=(PrimaryKey('id'),))
@dataclass
class User:
  id: int = column(Integer())
  name: str = column(Text())


connection = Connection(Configuration(':memory:'))
connection.connect()
try:
  with connection.session() as session:
    session.run(Create(User))
    session.run(Insert(User).values(id=1, name='Alice'))

    rows = session.execute(
      'SELECT id, name FROM users WHERE id = ?', 1,
    ).rows()
    print(rows)

    users = session.run(Select(User), fetch=User)
    print(users)
finally:
  connection.close()

Output:

[{'id': 1, 'name': 'Alice'}]
[User(id=1, name='Alice')]

Sessions commit on normal completion and roll back on exceptions. Direct SQL does not require models. Models remain ordinary Python objects; attribute assignment does not automatically persist changes. No custom Model base class or Service/Repository/DTO structure is required.

Supported implementations

• SQLite: direct SQL, dataclass mappings, CRUD, tables, views, constraints, indexes, joins, aggregates, and transactions. SQLite modeling requires SQLite 3.35+.

Build your own

Implement the capability Protocols for the store you need, without inheriting a provided implementation or adapting every store to SQL.

The source repository includes step-by-step implementation development guides and a SQLite user manual.