pycubrid 1.2.0

Pure Python DB-API 2.0 driver for CUBRID database

pycubrid

Pure Python DB-API 2.0 driver for the CUBRID database — no C extensions, no compilation, implements the PEP 249 (DB-API 2.0) interface.

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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 pycubrid?

CUBRID is a high-performance open-source relational database, widely adopted in Korean public-sector and enterprise applications. The existing C-extension driver (CUBRIDdb) had build dependencies and platform compatibility issues.

pycubrid solves these problems:

• Pure Python implementation — no C build dependencies, install with pip install only
• Implements PEP 249 (DB-API 2.0) — standard exception hierarchy, type objects, cursor interface
• 666 offline tests with 97%+ code coverage — no database required to run them
• Native asyncio support — async/await API via pycubrid.aio for high-concurrency applications
• PEP 561 typed package — py.typed marker for modern IDE and static analysis support
• Direct CUBRID CAS protocol implementation — no additional middleware required
• LOB (CLOB/BLOB) support — handle large text and binary data

Requirements

• Python 3.10+
• CUBRID database server 10.2+

Installation

pip install pycubrid

Quick Start

Basic Connection

import pycubrid

conn = pycubrid.connect(
    host="localhost",
    port=33000,
    database="testdb",
    user="dba",
    password="",
)

cur = conn.cursor()
cur.execute("SELECT 1 + 1")
print(cur.fetchone())  # (2,)

cur.close()
conn.close()

Context Manager

import pycubrid

with pycubrid.connect(host="localhost", port=33000, database="testdb", user="dba") as conn:
    with conn.cursor() as cur:
        cur.execute("CREATE TABLE IF NOT EXISTS cookbook_users (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(100))")
        cur.execute("INSERT INTO cookbook_users (name) VALUES (?)", ("Alice",))
        conn.commit()

        cur.execute("SELECT * FROM cookbook_users")
        for row in cur:
            print(row)

Async

import asyncio
import pycubrid.aio

async def main():
    conn = await pycubrid.aio.connect(
        host="localhost", port=33000, database="testdb", user="dba"
    )
    cur = await conn.cursor()
    await cur.execute("SELECT 1 + 1")
    print(await cur.fetchone())  # (2,)
    await cur.close()
    await conn.close()

asyncio.run(main())

Parameter Binding

# qmark style (question marks)
cur.execute("SELECT * FROM users WHERE name = ? AND age > ?", ("Alice", 25))

# Batch insert with executemany
data = [("Alice", 30), ("Bob", 25), ("Charlie", 35)]
cur.executemany("INSERT INTO users (name, age) VALUES (?, ?)", data)
conn.commit()

Parameterized Queries

sql = "SELECT * FROM users WHERE department = ?"

cur.execute(sql, ("Engineering",))
engineers = cur.fetchall()

cur.execute(sql, ("Marketing",))
marketers = cur.fetchall()

PEP 249 Compliance

Attribute	Value
apilevel	"2.0"
threadsafety	1 (connections cannot be shared between threads)
paramstyle	"qmark" (positional parameters ?)

• Full standard exception hierarchy: Warning, Error, InterfaceError, DatabaseError, OperationalError, IntegrityError, InternalError, ProgrammingError, NotSupportedError
• Standard type objects: STRING, BINARY, NUMBER, DATETIME, ROWID
• Standard constructors: Date(), Time(), Timestamp(), Binary(), DateFromTicks(), TimeFromTicks(), TimestampFromTicks()

Features

• Pure Python — no C extensions, no compilation, works everywhere Python runs
• Complete DB-API 2.0 — connect(), Cursor, fetchone/many/all, executemany, callproc
• Parameterized queries — cursor.execute(sql, params) with server-side PREPARE_AND_EXECUTE
• Batch operations — executemany() and executemany_batch() for bulk inserts
• LOB support — create_lob(), read/write CLOB and BLOB columns
• Schema introspection — get_schema_info() for tables, columns, indexes, constraints
• Auto-commit control — connection.autocommit property for transaction management
• Server version detection — connection.get_server_version() returns version string (e.g., "11.2.0.0378")
• Iterator protocol — iterate over cursor results with for row in cursor
• Context managers — with statements for both connections and cursors
• Async support — pycubrid.aio.connect() with AsyncConnection and AsyncCursor for asyncio event loops

Supported CUBRID Versions

The project targets CUBRID 11.x series and is validated in CI against:

• 11.2
• 11.4

SQLAlchemy Integration

pycubrid works as a driver for sqlalchemy-cubrid — the SQLAlchemy 2.0 dialect for CUBRID:

pip install "sqlalchemy-cubrid[pycubrid]"

from sqlalchemy import create_engine, text

engine = create_engine("cubrid+pycubrid://dba@localhost:33000/testdb")

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

SQLAlchemy features (ORM, Core, Alembic migrations, schema reflection) are accessible through the pycubrid driver when used with sqlalchemy-cubrid.

Documentation

Guide	Description
Connection	Connection strings, URL format, configuration
Type Mapping	Full type mapping, CUBRID-specific types, collection types
API Reference	Complete API documentation — modules, classes, functions
Protocol	CAS wire protocol reference
Development	Dev setup, testing, Docker, coverage, CI/CD
Examples	Practical usage examples with code
Troubleshooting	Connection errors, query problems, LOB handling, debugging

Compatibility

	Python 3.10	Python 3.11	Python 3.12	Python 3.13
Offline Tests	✅	✅	✅	✅
CUBRID 11.4	✅	--	✅	--
CUBRID 11.2	✅	--	✅	--

Architecture

graph TD
    app[Application]
    pycubrid[pycubrid Connection/Cursor]
    cas[CAS Protocol]
    server[CUBRID Server]

    app --> pycubrid
    pycubrid --> cas
    cas --> server

graph TD
    root[pycubrid/]
    init[__init__.py - Public API connect(), types, exceptions, __version__]
    connection[connection.py - Connection class connect/commit/rollback/cursor/LOB]
    cursor[cursor.py - Cursor class execute/fetch/executemany/callproc/iterator]
    types[types.py - DB-API 2.0 type objects and constructors]
    exceptions[exceptions.py - PEP 249 exception hierarchy]
    constants[constants.py - CAS function codes, data types, protocol constants]
    protocol[protocol.py - CAS wire protocol packet classes (18 packet types)]
    packet[packet.py - Low-level packet reader/writer]
    lob[lob.py - LOB support]
    typed[py.typed - PEP 561 marker]

    root --> init
    root --> connection
    root --> cursor
    root --> types
    root --> exceptions
    root --> constants
    root --> protocol
    root --> packet
    root --> lob
    root --> typed
    root --> aio
    aio[aio/ - AsyncConnection, AsyncCursor, async connect()]

FAQ

How do I connect to CUBRID with Python?

import pycubrid
conn = pycubrid.connect(host="localhost", port=33000, database="testdb", user="dba")

How do I install pycubrid?

pip install pycubrid — no C extensions or build tools required.

What parameter style does pycubrid use?

Question mark (qmark) style: cursor.execute("SELECT * FROM users WHERE id = ?", (1,))

Does pycubrid work with SQLAlchemy?

Yes. Install pip install "sqlalchemy-cubrid[pycubrid]" and use the connection URL cubrid+pycubrid://dba@localhost:33000/testdb.

What Python versions are supported?

Python 3.10, 3.11, 3.12, and 3.13.

Does pycubrid support LOBs (CLOB/BLOB)?

Yes. Insert strings/bytes directly into CLOB/BLOB columns. For reading, LOB columns return data that can be accessed through the cursor.

Is pycubrid thread-safe?

pycubrid has threadsafety = 1, meaning connections cannot be shared between threads. Create a separate connection per thread.

What CUBRID versions are supported?

CUBRID 10.2, 11.0, 11.2, and 11.4 are tested in CI.

Does pycubrid support async/await?

Yes. Use pycubrid.aio.connect() for native asyncio support. The async API mirrors the sync API — AsyncConnection and AsyncCursor provide the same methods with await.

Related Projects

• sqlalchemy-cubrid — SQLAlchemy 2.0 dialect for CUBRID
• cubrid-python-cookbook — Production-ready Python examples for CUBRID

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.