dbduck 0.3.0

Universal Data Object Model - one API for every database

DBDuck — One API for every database

DBDuck is a Universal Data Object Model (UDOM): one Python API for SQL, MongoDB, Neo4j, Qdrant, and async data workflows.

The problem

You use Postgres and MongoDB and Qdrant and Neo4j. That means four clients, four query styles, four error formats, and four security surfaces. Every feature team ends up rebuilding the same validation, retries, logging, and model plumbing. The more backends you add, the more your application code turns into adapter glue.

The solution

# BEFORE: four clients, four mental models
import asyncpg
from pymongo import MongoClient
from neo4j import GraphDatabase
from qdrant_client import QdrantClient

# 40+ lines of setup, auth, query translation, and result normalization

# AFTER: one API, one model, one error surface
from DBDuck import UDOM

sql = UDOM(url="postgresql+psycopg2://...")
mongo = UDOM(db_type="nosql", db_instance="mongodb", url="mongodb://localhost:27017/app")
graph = UDOM(db_type="graph", db_instance="neo4j", url="bolt://localhost:7687")
vector = UDOM(db_type="vector", db_instance="qdrant", url="http://localhost:6333")

orders = sql.find("orders", where={"paid": True})
profiles = mongo.find("profiles", where={"active": True})
related = graph.find_related("User", id="u1", rel_type="PURCHASED")
nearest = vector.search_similar("products", vector=[0.1, 0.2, 0.3], top_k=5)

Install

pip install dbduck
pip install dbduck[mongo]    # MongoDB support
pip install dbduck[async]    # AsyncUDOM
pip install dbduck[vector]   # Vector DB (Qdrant)
pip install dbduck[graph]    # Neo4j
pip install dbduck[all]      # Everything

Quick start

SQLite

from DBDuck import UDOM

db = UDOM(url="sqlite:///app.db")
db.create("users", {"id": 1, "name": "Asha", "active": True})
users = db.find("users", where={"active": True})
print(users)

MongoDB

from DBDuck import UDOM

db = UDOM(db_type="nosql", db_instance="mongodb", url="mongodb://localhost:27017/app")
db.create("profiles", {"id": "p1", "name": "Nila", "active": True})
profiles = db.find("profiles", where={"active": True})
print(profiles)

Async Postgres

import asyncio
from DBDuck.udom.async_udom import AsyncUDOM

async def main():
    db = AsyncUDOM(url="postgresql+psycopg2://postgres:pass@localhost:5432/app")
    await db.create("users", {"id": 1, "name": "Ishan", "active": True})
    print(await db.find("users", where={"active": True}))
    await db.close()

asyncio.run(main())

Qdrant

from DBDuck import UDOM

db = UDOM(db_type="vector", db_instance="qdrant", url="http://localhost:6333")
db.create_collection("products", vector_size=3, distance="cosine")
db.upsert_vector("products", id="p1", vector=[0.1, 0.2, 0.3], metadata={"name": "Widget"})
print(db.search_similar("products", vector=[0.1, 0.2, 0.3], top_k=3))

Neo4j

from DBDuck import UDOM

db = UDOM(db_type="graph", db_instance="neo4j", url="bolt://localhost:7687", auth=("neo4j", "password"))
db.create("User", {"id": "u1", "name": "Mira"})
db.create("Company", {"id": "c1", "name": "DBDuck"})
db.create_relationship("User", "u1", "WORKS_AT", "Company", "c1", {"role": "Engineer"})
print(db.find_related("User", id="u1", rel_type="WORKS_AT", target_label="Company"))

Developer-friendly setup

• For SQL backends, DBDuck can infer the backend directly from the URL:
  • UDOM(url="sqlite:///app.db")
  • UDOM(url="mysql+pymysql://root:pass@localhost:3306/app")
  • UDOM(url="postgresql+psycopg2://postgres:pass@localhost:5432/app")
  • UDOM(url="mssql+pyodbc:///?odbc_connect=...")
• Explicit configuration still works if you prefer it:
  • UDOM(db_type="sql", db_instance="postgres", url="...")
• Common SQL aliases are accepted:
  • postgres, postgresql, psql, pg
  • mssql, sqlserver

Supported backends

Backend	Type	Status	Install extra
SQLite	SQL	Production-capable	base
MySQL	SQL	Production-capable	base driver required
PostgreSQL	SQL	Production-capable	base driver required
SQL Server	SQL	Production-capable	mssql
MongoDB	NoSQL	Production-capable	mongo
Neo4j	Graph	Production-capable	graph
Qdrant	Vector	Production-capable	vector
Pinecone	Vector	Stub/TODO	planned
Weaviate	Vector	Stub/TODO	planned
Chroma	Vector	Stub/TODO	planned
AI backends	AI	Experimental pass-through	planned

Core API reference

• create(entity, data): insert one record, document, node, or vector payload.
• create_many(entity, rows): batch insert records or documents.
• find(entity, where=None, order_by=None, limit=None): fetch matching records.
• find_page(entity, page=1, page_size=20, where=None, order_by=None): offset pagination with safety caps.
• update(entity, data, where): update matching records safely.
• delete(entity, where): delete matching records safely.
• count(entity, where=None): count matching records.
• aggregate(...): backend-aware aggregation for SQL and MongoDB.
• begin() / commit() / rollback() / transaction(): transaction control.
• ping() / close(): lifecycle and health checks.
• uexecute(uql): execute UQL through backend-specific parameterized translation.
• create_relationship(...) / find_related(...) / shortest_path(...): graph-specific helpers.
• create_collection(...) / upsert_vector(...) / search_similar(...): vector-specific helpers.
• table(entity): returns a fluent QueryBuilder for chainable queries.

Full docs live in the codebase docstrings and examples.

Query Builder DSL

DBDuck 0.3.0 introduces a fluent Query Builder API for constructing queries with method chaining:

from DBDuck import UDOM

db = UDOM(url="sqlite:///app.db")

# Fluent query building
users = db.table("users").where(active=True).order("name").limit(10).find()
user = db.table("users").where(id=1).first()
count = db.table("users").where(role="admin").count()

# Field projection
db.table("users").select("id", "name", "email").find()

# Pagination
db.table("users").page(2, 25).find()
db.table("users").find_page(page=2, page_size=25)

# Comparison operators
db.table("users").where_gt(age=18).find()           # age > 18
db.table("users").where_gte(age=21).find()          # age >= 21
db.table("users").where_lt(age=65).find()           # age < 65
db.table("users").where_in("role", ["admin", "mod"]).find()

# Mutations
db.table("users").where(id=1).update({"name": "Updated"})
db.table("users").where(id=1).delete()
db.table("users").create({"name": "New User", "email": "[e-mail]"})

# Aggregation
db.table("orders").group_by("status").metrics(total="count").aggregate()

# Check existence
if db.table("users").where(email="[e-mail]").exists():
    print("User exists")

# Clone for reuse
base = db.table("users").where(active=True)
admins = base.clone().where(role="admin").find()
regular = base.clone().where(role="user").find()

Query Builder Across All Backends

The Query Builder works uniformly across all database types:

# SQL (SQLite, MySQL, PostgreSQL, SQL Server)
sql_db = UDOM(url="postgresql+psycopg2://postgres:pass@localhost/app")
sql_db.table("users").where(active=True).order("name").limit(10).find()

# NoSQL (MongoDB)
mongo_db = UDOM(db_type="nosql", db_instance="mongodb", url="mongodb://localhost:27017/app")
mongo_db.table("profiles").where(active=True).find()
mongo_db.table("orders").group_by("status").metrics(total="count").aggregate()

# Graph (Neo4j)
graph_db = UDOM(db_type="graph", db_instance="neo4j", url="bolt://localhost:7687")
graph_db.table("User").where(name="Alice").find()
graph_db.table("User").find_related(id="u1", rel_type="FOLLOWS", direction="out")
graph_db.table("User").create_relationship("u1", "FOLLOWS", "User", "u2")

# Vector (Qdrant)
vector_db = UDOM(db_type="vector", db_instance="qdrant", url="http://localhost:6333")
vector_db.table("embeddings").where(category="tech").search_similar([0.1, 0.2, 0.3], top_k=5)
vector_db.table("embeddings").upsert_vector("v1", [0.1, 0.2, 0.3], {"label": "test"})

CLI

dbduck ping --url "postgresql+psycopg2://postgres:pass@localhost:5432/app"
dbduck shell --url "sqlite:///app.db"
dbduck inspect --url "sqlite:///app.db" --entity users
dbduck version

For SQL backends, dbduck can infer the backend from the URL, so --type and --instance are optional. CLI output is quiet by default and colorized for easier scanning in the terminal.

UModel

from DBDuck import UDOM, UModel

class User(UModel):
    __entity__ = "users"
    __sensitive_fields__ = ["password"]
    id: int
    email: str
    password: str

User.bind(UDOM(url="sqlite:///app.db"))
user = User(id=1, email="[e-mail]", password="plain-text")
user.save()
print(User.find_one(where={"id": 1}).to_dict())
print(User.find_one(where={"id": 1}).verify_secret("password", "plain-text"))

UModel with Query Builder

UModel also supports the fluent Query Builder via Model.query():

from DBDuck import UDOM, UModel

class User(UModel):
    __entity__ = "users"
    id: int
    name: str
    role: str
    active: bool

User.bind(UDOM(url="sqlite:///app.db"))

# Fluent queries returning typed model instances
users = User.query().where(active=True).order("name").find()  # list[User]
user = User.query().where(id=1).first()                       # User | None
count = User.query().where(role="admin").count()              # int

# Chaining with comparison operators
adults = User.query().where_gte(age=18).where_lt(age=65).find()

# Clone for reusable base queries
active = User.query().where(active=True)
admins = active.clone().where(role="admin").find()
users = active.clone().where(role="user").find()

# Mutations
User.query().where(id=1).update({"name": "Updated"})
User.query().where(id=1).delete()

# Pagination with model instances
page = User.query().find_page(page=2, page_size=25)
for user in page["items"]:  # Each item is a User instance
    print(user.name)

Errors

• DBDuck maps backend failures to DBDuck exceptions:
  • ConnectionError
  • QueryError
  • TransactionError
• CLI commands return masked, developer-friendly messages instead of raw SQLAlchemy tracebacks.
• Use --debug-errors in dbduck shell if you want the full underlying traceback while debugging locally.

Security

• Parameterized SQL and parameterized Cypher generation.
• UQL string hardening for FIND, CREATE, and DELETE.
• Mongo operator-injection blocking.
• Identifier validation across entities, fields, labels, and relationship types.
• BCrypt hashing for sensitive fields.
• verify_secret() helper for BCrypt validation.
• Structured logging without raw SQL or user secrets in normal logs.
• Custom exception hierarchy with masked execution errors.
• Security audit logging for blocked operations.
• Per-caller rate limiting support.

Roadmap

DBDuck 0.2.0 delivers the hardened SQL core, Mongo support, Neo4j graph support, Qdrant vector support, AsyncUDOM, and the CLI. Next up: deeper vector backends, richer schema migration workflows, Redis and DynamoDB adapters, and first-class observability hooks.

Contributing

Issues, discussions, and pull requests are welcome. See CONTRIBUTING.md for development setup and contribution guidelines.