Skip to main content

CUBRID dialect for SQLAlchemy

Project description

sqlalchemy-cubrid

SQLAlchemy 2.0–2.1 dialect for the CUBRID database — Python ORM, schema reflection, Alembic migrations, and type mapping for SQLAlchemy and CUBRID-specific types.

🇰🇷 한국어 · 🇺🇸 English · 🇨🇳 中文 · 🇮🇳 हिन्दी · 🇩🇪 Deutsch · 🇷🇺 Русский

PyPI version python version ci workflow coverage license GitHub stars docs


Status: Beta. The core public API follows semantic versioning; minor releases may add features and bug fixes while the project remains under active development.

Why sqlalchemy-cubrid?

CUBRID is a high-performance open-source relational database, widely adopted in Korean public-sector and enterprise applications. Until now, there was no production-ready SQLAlchemy dialect that supports the modern 2.0–2.1 API.

sqlalchemy-cubrid bridges that gap:

  • Full SQLAlchemy 2.0–2.1 dialect with statement caching and PEP 561 typing
  • 577 offline tests with 99%+ code coverage — no database required to run them
  • Tested against 4 CUBRID versions (10.2, 11.0, 11.2, 11.4) across Python 3.10 -- 3.14
  • CUBRID-specific DML constructs: ON DUPLICATE KEY UPDATE, MERGE, REPLACE INTO
  • Alembic migration support out of the box
  • Three driver options — C-extension (cubrid://), pure Python (cubrid+pycubrid://), or async pure Python (cubrid+aiopycubrid://)

Architecture

flowchart TD
    app["Application"] --> sa["SQLAlchemy Core/ORM"]
    sa --> dialect["CubridDialect"]
    dialect --> pycubrid["pycubrid driver"]
    dialect --> cext["CUBRIDdb driver"]
    dialect --> aio["pycubrid.aio async driver"]
    pycubrid --> server["CUBRID Server"]
    cext --> server
    aio --> server
flowchart TD
    expr["SQL Expression"] --> compiler["CubridSQLCompiler"] --> sql["SQL String"]

Requirements

Installation

pip install sqlalchemy-cubrid

With the pure Python driver (no C build needed):

pip install "sqlalchemy-cubrid[pycubrid]"

With Alembic support:

pip install "sqlalchemy-cubrid[alembic]"

Quick Start

Core (Connection-Level)

from sqlalchemy import create_engine, text

engine = create_engine("cubrid://dba:password@localhost:33000/demodb")

with engine.connect() as conn:
    result = conn.execute(text("SELECT 1"))
    print(result.scalar())

ORM (Session-Level)

from sqlalchemy import create_engine, String
from sqlalchemy.orm import DeclarativeBase, Mapped, Session, mapped_column


class Base(DeclarativeBase):
    pass


class User(Base):
    __tablename__ = "users"

    id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True)
    name: Mapped[str] = mapped_column(String(100))
    email: Mapped[str] = mapped_column(String(200), unique=True)


engine = create_engine("cubrid://dba:password@localhost:33000/demodb")
Base.metadata.create_all(engine)

with Session(engine) as session:
    user = User(name="Alice", email="alice@example.com")
    session.add(user)
    session.commit()

Async

from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy import text

engine = create_async_engine("cubrid+aiopycubrid://dba:password@localhost:33000/demodb")

async with AsyncSession(engine) as session:
    result = await session.execute(text("SELECT 1"))
    print(result.scalar())

Features

  • Type mapping for SQLAlchemy standard and CUBRID-specific types — numeric, string, date/time, bit, LOB, collection, and JSON types
  • SQL compilation -- SELECT, JOIN, CAST, LIMIT/OFFSET, subqueries, CTEs, window functions
  • DML extensions -- ON DUPLICATE KEY UPDATE, MERGE, REPLACE INTO, FOR UPDATE, TRUNCATE
  • DDL support -- COMMENT, IF NOT EXISTS / IF EXISTS, AUTO_INCREMENT
  • Schema reflection -- tables, views, columns, PKs, FKs, indexes, unique constraints, comments
  • Alembic migrations via CubridImpl (auto-discovered entry point)
  • All 6 CUBRID isolation levels (dual-granularity: class-level + instance-level)
  • Async support (available since v1.1.0) — create_async_engine("cubrid+aiopycubrid://...") via pycubrid.aio

Known Limitations

  • No RETURNINGINSERT/UPDATE/DELETE ... RETURNING not supported; use cursor.lastrowid or LAST_INSERT_ID()
  • No sequences — CUBRID uses AUTO_INCREMENT only
  • No multi-schema — single schema per database
  • DDL auto-commits — migrations are not transactional (transactional_ddl = False)
  • SQLAlchemy 2.0–2.1 only — pinned to <2.2 due to internal API dependencies (details)
  • Async requires pycubrid ≥ 1.1.0 — the cubrid+aiopycubrid:// driver needs the async-capable pycubrid

Documentation

Guide Description
Connection Connection strings, URL format, driver setup, pool tuning
Type Mapping Full type mapping, CUBRID-specific types, collection types
DML Extensions ON DUPLICATE KEY UPDATE, MERGE, REPLACE INTO, query trace
Isolation Levels All 6 CUBRID isolation levels, configuration
Alembic Migrations Setup, configuration, limitations, batch workarounds
Feature Support Comparison with MySQL, PostgreSQL, SQLite
ORM Cookbook Practical ORM examples, relationships, queries
Development Dev setup, testing, Docker, coverage, CI/CD
Driver Compatibility CUBRID-Python driver versions and known issues
Troubleshooting Common issues, error solutions, debugging techniques
Async Connection Async engine setup with cubrid+aiopycubrid://

Compatibility Matrix

Component Supported versions
Python 3.10, 3.11, 3.12, 3.13, 3.14
CUBRID 10.2, 11.0, 11.2, 11.4
SQLAlchemy 2.0–2.1
Alembic >=1.7
pycubrid (sync) >=1.2.0
pycubrid (async) >=1.2.0

FAQ

How do I connect to CUBRID with SQLAlchemy?

from sqlalchemy import create_engine
engine = create_engine("cubrid://dba:password@localhost:33000/demodb")

For the pure Python driver (no C build needed): create_engine("cubrid+pycubrid://dba@localhost:33000/demodb")

Does sqlalchemy-cubrid support SQLAlchemy 2.0–2.1?

Yes. sqlalchemy-cubrid is built for SQLAlchemy 2.0–2.1 and supports the 2.0-style API including Session.execute(), typed Mapped[] columns, and statement caching.

Does sqlalchemy-cubrid support Alembic migrations?

Yes. Install with pip install "sqlalchemy-cubrid[alembic]". The dialect auto-registers via entry point. Note that CUBRID auto-commits DDL, so migrations are not transactional.

What Python versions are supported?

Python 3.10, 3.11, 3.12, 3.13, and 3.14.

Does CUBRID support RETURNING clauses?

No. CUBRID does not support INSERT ... RETURNING or UPDATE ... RETURNING. Use cursor.lastrowid or SELECT LAST_INSERT_ID() instead.

How do I use ON DUPLICATE KEY UPDATE with CUBRID?

from sqlalchemy_cubrid import insert
stmt = insert(users).values(name="Alice").on_duplicate_key_update(name="Alice Updated")

What's the difference between cubrid:// and cubrid+pycubrid://?

cubrid:// uses the C-extension driver (CUBRIDdb) which requires compilation. cubrid+pycubrid:// uses the pure Python driver which installs with pip alone — no build tools needed. cubrid+aiopycubrid:// uses the async variant of the pure Python driver for use with create_async_engine and AsyncSession.

Does sqlalchemy-cubrid support async?

Yes. Use create_async_engine("cubrid+aiopycubrid://...") with the pycubrid async driver. Requires pycubrid>=1.1.0. All Core and ORM features work with AsyncSession.

Related Projects

Roadmap

See ROADMAP.md for this project's direction and next milestones.

For the ecosystem-wide view, see the CUBRID Labs Ecosystem Roadmap and Project Board.

Contributing

See CONTRIBUTING.md for guidelines and docs/DEVELOPMENT.md for development setup.

Security

Report vulnerabilities via email -- see SECURITY.md. Do not open public issues for security concerns.

License

MIT -- see LICENSE.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sqlalchemy_cubrid-1.3.0.tar.gz (80.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

sqlalchemy_cubrid-1.3.0-py3-none-any.whl (40.0 kB view details)

Uploaded Python 3

File details

Details for the file sqlalchemy_cubrid-1.3.0.tar.gz.

File metadata

  • Download URL: sqlalchemy_cubrid-1.3.0.tar.gz
  • Upload date:
  • Size: 80.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for sqlalchemy_cubrid-1.3.0.tar.gz
Algorithm Hash digest
SHA256 f929e9804367dfb7ca2390dc82deae81bde96a60816d02bdbd06e68330f9120a
MD5 e986c820b563260d8892bb13533bd632
BLAKE2b-256 7483eacfcc6a17d8d41d3db302ce2daa397d7f47adbb98c8f70d1224cb9783ba

See more details on using hashes here.

Provenance

The following attestation bundles were made for sqlalchemy_cubrid-1.3.0.tar.gz:

Publisher: publish-pypi.yml on cubrid-labs/sqlalchemy-cubrid

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file sqlalchemy_cubrid-1.3.0-py3-none-any.whl.

File metadata

File hashes

Hashes for sqlalchemy_cubrid-1.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 4eceb91bc7bd32724a0d886411c98dbc038b787280b8e9d93b7fa7ce484bd91f
MD5 e8d16972804eb1ef9aeff1a54cf116c2
BLAKE2b-256 03824c24dc891f78db7d3d649f75370e22410b0d09fa67f5a2e84df6f8f84641

See more details on using hashes here.

Provenance

The following attestation bundles were made for sqlalchemy_cubrid-1.3.0-py3-none-any.whl:

Publisher: publish-pypi.yml on cubrid-labs/sqlalchemy-cubrid

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page