This release is a pre-release and may not be stable for production use.
bareduckdb
Minimal Python bindings to DuckDB 2.0, dynamically linked to the new stable duckdb_v2_* ABI. Free-threaded w/ minimal dependencies.
Highlights
- Supports latest versions CPython 3.15, GraalPy 25.3
- Free-threaded support for 3.14t and 3.15t, plus a cp312 abi3 wheel covering 3.12+.
- DuckDB C API v2, using only entry points upstream marks
stable. - Free threaded Every engine call runs under
nogil, every module that touches the engine setsfreethreading_compatible=True, and no Python object guards an engine call. - Minimal. ~3.5k lines of Cython, ~1.2k of declarations, ~2.3k of Python. The Cython exists to cross the C boundary and hold the free-threading guarantees; everything else is Python.
- No runtime dependencies. pyarrow, polars and pandas are all optional and imported on demand.
- Dynamically linked against DuckDB's official shared library, resolved or downloaded at first import rather than vendored.
- Native polars, end to end
Key Differences
- No relation API: Use ibis or narwhals instead
- Replacement scans: No implicit registration, use explicit registration of arrow/polars/pandas frames.
- Arrow-backed Pandas dataframes, not numpy
Roadmap
These features were implemented in bareduckdb v0.12.155 but need to be ported to the 2.0 API:
- Pushdown Filters Native arrow & polars pushdowns
- Appender
- Table Functions
These features require interaction with the GIL from duckdb threads, so need some planning/thought:
- Scalar UDFs
- Filesystems / fsspec
Getting Started
pip install bareduckdb
import bareduckdb
with bareduckdb.connect() as conn:
conn.execute("SELECT 42 AS answer").fetchall() # [(42,)]
conn.execute("SELECT * FROM range(10)").pl() # Polars
conn.execute("SELECT * FROM range(10)").arrow_table() # Arrow table
Query a frame by name, in one line. data= names the sources for that call only, so there is
nothing to register or clean up:
import polars as pl
df = pl.DataFrame({"a": [1, 2, 3]})
bareduckdb.connect().execute("SELECT sum(a) AS s FROM t1", data={"t1": df}).pl()
register() is the longer-lived form, for a name you want to query more than once:
with bareduckdb.connect() as conn:
conn.register("t1", df)
conn.execute("SELECT sum(a) AS s FROM t1").pl()
conn.unregister("t1")
Both take polars DataFrames and LazyFrames, pyarrow Tables, Datasets, Scanners and
RecordBatchReaders, pandas DataFrames, and anything exposing __arrow_c_stream__. A LazyFrame
is streamed in batches, never collected.
Usage
import bareduckdb
with bareduckdb.connect() as conn:
conn.execute("SELECT * FROM range(1000)").arrow_table()
conn.execute("SELECT * FROM range(1000)").pl()
conn.execute("SELECT * FROM range(1000)").df()
conn.execute("SELECT * FROM range(1000)").fetchall()
conn.execute("CREATE TABLE t AS SELECT * FROM range(100) AS r(a)")
conn.execute("SELECT * FROM t WHERE a > ?", [10]) # positional
conn.execute("SELECT * FROM t WHERE a > $x", {"x": 10}) # named
Registering in-memory data, by name or inline:
import polars as pl
with bareduckdb.connect() as conn:
conn.register("t", pl.LazyFrame({"a": [1, 2, 3]})) # streamed, never collected
conn.execute("SELECT sum(a) FROM t").pl()
frame = pl.DataFrame({"a": [1, 2, 3]})
conn.execute("SELECT * FROM frame", data={"frame": frame})
Streaming rather than materializing:
with bareduckdb.connect() as conn:
conn.execute("SELECT * FROM range(1000000)", output_type="arrow_reader")
for batch in conn.arrow_reader():
...
conn.execute("SELECT * FROM range(1000000)", batch_size=100_000) # cap the Arrow batch
Async, over a connection pool:
from bareduckdb.aio.async_connection import AsyncConnectionPool
async with AsyncConnectionPool(":memory:", pool_size=4) as pool:
rows = await pool.execute("SELECT * FROM range(?)", parameters=(10,))
Progress and cancellation. See README_PROGRESS.md for tqdm examples, the settings, and what the numbers mean:
import threading
from bareduckdb import QueryCancelled, enable_progress, poll_progress
with bareduckdb.connect() as conn:
enable_progress(conn)
with poll_progress(conn, lambda p: print(f"{p.percentage:.0f}%")):
conn.execute("SELECT ... FROM big").pl()
interrupt() stops the query running on that connection, from any thread. The query raises
QueryCancelled, and the connection is reusable straight after:
with bareduckdb.connect() as conn:
threading.Timer(5.0, conn.interrupt).start() # a crude query timeout
try:
conn.execute("SELECT ... FROM big").fetchall()
except QueryCancelled:
...
conn.execute("SELECT 42").fetchall() # works
Cancelling an awaited pool query interrupts it, so the cursor comes straight back:
try:
await asyncio.wait_for(pool.execute(slow_sql), timeout=0.5)
except asyncio.TimeoutError:
...
Development
uv pip install -e . # scikit-build-core + CMake; downloads libduckdb on first build
# Windows needs MSVC; no submodules, the v2 header is vendored
uv run pytest
uv run pre-commit run --all-files
uv pip install --group dev-gil --only-binary :all:
Editing a .pyx or .pxd requires a rebuild.
Disclaimer
Not affiliated with DuckDB Labs or the DuckDB Foundation.
License
MIT. See LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for bareduckdb 0.20.200a4
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Transparency logRelease files / bareduckdb-0.20.200a4-cp312-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
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Transparency logRelease files / bareduckdb-0.20.200a4-cp312-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
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Transparency logRelease files / bareduckdb-0.20.200a4-cp312-abi3-macosx_11_0_arm64.whl
| Download URL | bareduckdb-0.20.200a4-cp312-abi3-macosx_11_0_arm64.whl |
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| Size | 314.1 kB |
| Tags | CPython 3.12 abi3 macOS 11.0+ ARM64 |
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