bareduckdb 0.8.144

Minimal Cython-based DuckDB Bindings

bareduckdb

Simplified, Dynamically Linked DuckDB Python Bindings — Fast, simple, and free-threaded.

Overview

bareduckdb provides extensible and easy to build Python bindings to DuckDB using Cython.

• Simple ~2k lines of C++ and ~2k lines of Python - easy to extend or customize
• Arrow-first data conversion supporting Polars, PyArrow, and Pandas
• Support for latest Python features Free threading, subinterpreters, ABI3 and asyncio
• Dynamically linked to DuckDB's official library
• Experimental Enhancements

Experimental Enhancements

• Explicit Stream vs Materialization Modes - At connection & execution time, select whether you want materialized arrow_tables or streaming arrow_readers.
• Arrow Deadlock Detection - certain use cases involving reuse of Arrow Readers can cause deadlocks
• Table Statistics - Extracts and passes table statistics at registration time
• Polars - No PyArrow Required - Polars can be read and produced without importing / installing PyArrow
• Polars - Native LazyFrame Pushdown - whereas DuckDB collects() LazyFrames before pushdown, bareduckdb pushes down native Polars predicates
• Inline Registration - bareduckdb.execute("query", data={...}) allows registration at call time
• User Defined Table Functions - extracts UDTFs at parse time and executes registered functions
• **Appender - Row by Row ** Exposes DuckDB's appender API for fast sequential writes to duckdb databases

Installation

From PyPI

pip install bareduckdb

From Source

git clone --recurse-submodules https://github.com/paultiq/bareduckdb.git
cd bareduckdb
uv sync -v # or: pip install -e .

Basic Usage

import bareduckdb

# Connect to in-memory database
conn = bareduckdb.connect()

# Execute query and get Arrow Table
result = conn.execute("SELECT 42 as answer").arrow_table()
print(result)

# Convert to Polars/Pandas/PyArrow
df_polars = conn.execute("SELECT * FROM range(100)").pl()
df_pandas = conn.execute("SELECT * FROM range(100)").df()

Async API

import asyncio
from bareduckdb.aio import connect_async

async def run_query():
    async with await connect_async() as conn:
        result = await conn.execute("SELECT * FROM generate_series(1, 1000)")
        return result

result = asyncio.run(run_query())

Polars Integration

import bareduckdb
import polars as pl

conn = bareduckdb.connect()

# Polars -> DuckDB (Arrow Capsule protocol)
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
conn.register("my_table", df)

# DuckDB -> Polars (direct conversion)
result = conn.execute("SELECT * FROM my_table", output_type="polars")

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Architecture

Design Principles

1. Keep it in Python — Business logic lives in Python, not Cython/C++
2. No GIL interaction from DuckDB threads — All Python operations happen before/after query execution
3. Semantic Versioning — Strict stability guarantees
4. Arrow-first — All data types map through Arrow's type system

Why Arrow-First?

By forcing all conversions through Arrow, bareduckdb achieves:

• Consistent type mappings across Polars/Pandas/PyArrow
• Reduced code complexity (no per-library conversion paths)
• Better memory efficiency (zero-copy where possible)
• Future-proof (Arrow is the lingua franca for columnar data)

Thread Safety & Free-Threading

Free-threading support (Python 3.13+):

• No global locks in critical paths
• DuckDB threads never acquire the GIL
• Safe concurrent query execution in --disable-gil mode
• Atomic operations for Arrow stream coordination

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APIs

bareduckdb provides multiple API layers for different use cases:

1. Core API (bareduckdb.core)

Minimal, no-frills interface for maximum performance.

from bareduckdb.core import Connection
conn = Connection()
result = conn.execute("SELECT 1")

2. Async API (bareduckdb.aio)

Non-blocking operations with async/await.

from bareduckdb.aio import connect_async
conn = await connect_async()
result = await conn.execute("SELECT 1")

3. Compatibility API (bareduckdb.compat)

Familiar interface similar to duckdb-python (with intentional differences).

import bareduckdb
conn = bareduckdb.connect()
result = conn.sql("SELECT 1")  # Eager execution

4. DBAPI 2.0 (bareduckdb.dbapi)

Standard Python database interface for compatibility with tools like SQLAlchemy.

from bareduckdb.dbapi import connect
conn = connect()
cursor = conn.cursor()
cursor.execute("SELECT 1")

---

Key Differences

Experimental Features

When pyarrow is installed, two experimental features are available -

Arrow Statistics and Cardinality

In duckdb-python, Arrow Tables, Readers and Capsules are all converted to Streams via DataSet->Scanner->Reader. These Streams have no cardinality (number of rows) nor statistics (such as: min max, number of distinct values, contains nulls).

Cardinality is used at determining whether to use TopN, which significantly speeds up (w/ less memory) "order by X limit N" queries when N is small relative to size of table. Statistics are used for query planning by the optimizer.

In bareduckdb, Arrow Tables are registered directly (as Tables, not Streams) and used by arrow_scan_dataset which can then retrieve cardinality and column level statistics.

Statistics Options:

The register() method accepts a statistics parameter to control which columns have statistics computed:

import bareduckdb

conn = bareduckdb.connect()

# No statistics (fastest registration, default)
conn.register("table", df, statistics=None)

# Numeric columns only (recommended for most use cases)
conn.register("table", df, statistics="numeric")

# All columns (slowest - includes string min/max)
conn.register("table", df, statistics=True)

# Specific columns by name
conn.register("table", df, statistics=["id", "price", "date"])

# Regex pattern to match column names
conn.register("table", df, statistics=".*_id")  # all columns ending with _id

Setting a Default:

Configure the default statistics mode at connection level:

# All register() calls will use numeric statistics by default
conn = bareduckdb.connect(default_statistics="numeric")
conn.register("table1", df1)  # uses numeric stats
conn.register("table2", df2)  # uses numeric stats
conn.register("table3", df3, statistics=False)  # override: no stats

Performance Impact (500K rows, 2 numeric + 2 string columns):

Mode	Registration Time	Use Case
None	~0.4ms	No filter pushdown needed
"numeric"	~10ms	JOIN/filter on numeric columns
True	~22ms	Filter pushdown on all columns

The "numeric" option provides the best balance: fast registration with statistics for the columns most commonly used in filters and JOINs (IDs, dates, prices).

Arrow Pushdown

Arrow projection and filter pushdowns are implemented using the Arrow C++ library. Pushdowns are only implemented for Tables currently.

Relational API

• Use Ibis

Replacement Scans

Automatically discover Arrow tables in the caller's scope without explicit registration:

import bareduckdb
import pyarrow as pa

conn = bareduckdb.connect(enable_replacement_scan=True)
my_data = pa.table({"a": [1, 2, 3], "b": [4, 5, 6]})

result = conn.execute("SELECT * FROM my_data").arrow_table()

Customization: Override _get_replacement(name) method for custom discovery logic (e.g., loading from disk, fetching from API).

Manual Registration: Use .register() for explicit control or .execute(..., data={"name": df}) for inline registration.

Not (Yet?) Supported

• No Python UDFs (scalar functions)
• No fsspec integration

User Defined Table Functions

Table functions execute in Python before query execution, enabling data generation and connection injection without GIL interaction:

import bareduckdb
import pyarrow as pa

def generate_data(rows: int, multiplier: int = 1) -> pa.Table:
    return pa.table({
        "id": range(rows),
        "value": [i * multiplier for i in range(rows)]
    })

conn = bareduckdb.connect()
conn.register_udtf("generate_data", generate_data)

result = conn.execute("""
    SELECT * FROM generate_data(100, 10)
    WHERE value > 500
""").arrow_table()

Features:

• AST-based query preprocessing - pure Python
• Connection injection: Add conn parameter to access connection during execution
• Supports any Arrow-compatible object: PyArrow Table, Polars DataFrame, Pandas DataFrame

Arrow Enhancements

• Deadlock detection

Type Mappings

All types convert through Arrow:

• UUIDs: Returned as strings (Arrow doesn't have native UUID type)
• Decimals: Arrow Decimal128/Decimal256
• Timestamps: Arrow Timestamp with timezone preservation
• Nested Types: Struct/List/Map fully supported

Development

Building from Source

# Clone with submodules (sparse checkout is automatic)
git clone --recurse-submodules https://github.com/iqmo-org/bareduckdb.git
cd bareduckdb

# Install development dependencies
uv sync

# Build in development mode
pip install -e .

* Note 1: DuckDB submodule version must match the library version. * Note 2: PyArrow version must match the runtime version for Table registration / Pushdown

Disclaimer

For official Python bindings, see: https://github.com/duckdb/duckdb-python

License

bareduckdb is licensed under the MIT License. See LICENSE for details.

All original copyrights are retained by their respective owners, including DuckDB and DuckDB-Python