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GPU Functions that use a scalar callable currently invoke the callable once per row. The SDK documents Arrow-vectorized callables, but silently accepts a scalar GPU callable, which makes it easy to assume that the runtime will batch model work automatically. Warn during Function construction when a GPU callable does not use the documented vectorized form. A callable is recognized as vectorized when every input is annotated as `pyarrow.Array`, or when a single input is annotated as `pyarrow.RecordBatch`. The warning explains the resulting per-row invocation and shows how to opt into batch execution. CPU Functions and the vectorized GPU forms do not emit it. The warning is advisory and does not change registration serialization, the artifact, the runtime adapter, or execution behavior. It is one part of the ENT-3522 documentation-and-warning alternative; it does not claim that a single A10G meets the original batch throughput requirement. Validation on the designated EC2 host reused the compatible existing native extension from `ent3563-sdk-freshness-9336cfb`; this does not claim a new native package or GPU performance run. The final head has a clean source tree and ran: ```bash PYTHONPATH="$sdk/python/python" /home/ec2-user/.venvs/ent3563-0a839fa/bin/python -m pytest -q python/python/tests/test_first_class_function_slice2.py -k 'gpu_marker_uses_gpu_runtime or gpu_scalar_warning_names_the_vectorized_alternative' /home/ec2-user/.venvs/ent3563-0a839fa/bin/ruff format --check python/python/lancedb/functions.py python/python/tests/test_first_class_function_slice2.py /home/ec2-user/.venvs/ent3563-0a839fa/bin/ruff check python/python/lancedb/functions.py python/python/tests/test_first_class_function_slice2.py ``` The actual result was `2 passed, 123 deselected`; both Ruff commands passed. The original 2,000-row single-A10G throughput requirement remains a separate ENT-3522 acceptance gate.
LanceDB Python SDK
A Python library for LanceDB.
Installation
pip install lancedb
If no wheel is published for your platform, pip builds lancedb from the source distribution. That build needs a Rust toolchain and protoc (the Protocol Buffers compiler) on your PATH, and can take a while.
Pre-Haswell x86_64 hosts: lancedb-compat
The default lancedb wheel targets x86-64-haswell (AVX2 + FMA + F16C) for full performance on modern hardware. Pre-Haswell hosts — Intel Sandy Bridge / Ivy Bridge / Westmere; AMD Bulldozer / Piledriver / Steamroller — don't have AVX2 and crash with Illegal instruction at import lancedb.
For those hosts, install the lancedb-compat package instead:
pip install lancedb-compat
Same Python API (import lancedb works as usual). The compat wheel is compiled at the x86-64-v2 baseline (Nehalem-class) and uses runtime SIMD dispatch in the embedded lance crate to pick the right kernel tier (scalar / AVX / AVX+FMA / AVX2+FMA / AVX-512) at load time, so it still goes fast on modern hardware while running cleanly on the pre-Haswell silicon (if pylance is installed, lance.simd_info() reports which tier was selected).
lancedb and lancedb-compat install to the same lancedb/ namespace and conflict at install time. Pick one. To switch, pip uninstall lancedb first, then pip install lancedb-compat (or vice-versa).
If you need a custom baseline (or lancedb-compat isn't yet published for your platform), build from source with the override:
RUSTFLAGS="-C target-cpu=x86-64-v2" maturin build --release
pip install ./target/wheels/lancedb-*.whl
Preview Releases
Stable releases are created about every 2 weeks. For the latest features and bug fixes, you can install the preview release. These releases receive the same level of testing as stable releases, but are not guaranteed to be available for more than 6 months after they are released. Once your application is stable, we recommend switching to stable releases.
pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
Most applications only need lancedb. Install the optional pylance extra and
include the Lance preview index only when building query engines or connectors,
calling format-level lance APIs directly, or converting plain-scan lazy or
legacy blob columns with to_pandas():
pip install --pre \
--extra-index-url https://pypi.fury.io/lancedb/ \
--extra-index-url https://pypi.fury.io/lance-format/ \
"lancedb[pylance]"
With uv, also select the best matching version across the indexes:
uv pip install --prerelease allow \
--index https://pypi.fury.io/lancedb/ \
--index https://pypi.fury.io/lance-format/ \
--index-strategy unsafe-best-match \
"lancedb[pylance]"
The LanceDB preview index contains old pylance releases; current Lance previews
are published to the lance-format index. By default, uv only considers versions
from the first index containing a package, which can select an old pylance or
backtrack to an older LanceDB release. unsafe-best-match searches all configured
indexes, like pip; use it only with indexes you trust. Install the extra rather
than an unconstrained pylance so LanceDB's minimum supported version is enforced.
Threading in CPU-limited containers
LanceDB uses separate pools for compute work and storage I/O. On a container with two visible CPUs, current releases intentionally use one compute worker by default; no manual configuration is needed. If every query logs an I/O core reservation warning on a two-CPU container, upgrade from LanceDB 0.21.1 or earlier.
The two commonly tuned environment variables control different resources:
LANCE_CPU_THREADSoverrides the number of compute workers. One worker is the appropriate setting for a two-CPU container when an explicit override is needed.LANCE_IO_THREADScontrols concurrent storage operations, not reserved CPU cores. Its default can be greater than the number of CPUs because I/O workers spend much of their time waiting for storage.
Keep the defaults unless measurements show that the workload benefits from an override. See the Lance threading model for the current defaults and tuning guidance.
Usage
Basic Example
import lancedb
db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>')
table = db.open_table('my_table')
results = table.search([0.1, 0.3]).limit(20).to_list()
print(results)
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.
Opening SQL blob descriptors
A SQL blob_descriptor(video) result keeps the Blob V2 storage metadata and a
source struct containing its table URI, version, commit timestamp, branch,
column and row identity. This example uses a remote SQL connection whose SQL
service produces those source-bearing descriptors. Open the reference on the
source table to read incrementally:
rows = db.execute_query("""
SELECT f.timestamp, blob_descriptor(v.video) AS video
FROM videos v JOIN frames f ON v.id = f.video_id
""").read_all()
videos = db.open_table("videos")
for row in rows.to_pylist():
if row["video"] is not None:
with videos.open_blob_file(row["video"]) as video:
frame = extract_frame(video, row["timestamp"])
open_blob_file accepts the dictionary or an Arrow struct scalar directly. It
keeps the query's version and branch without changing the table handle's checkout.
Payload bytes load on reads. An external blob with an unspecified size may need a
metadata-only HEAD request to open.
videos.open_blob_files(rows["video"]) returns an iterator over the same handles,
preserving order, duplicates and nulls (None). It accepts Arrow arrays/chunked
arrays or another iterable and opens each handle as iteration advances. Close each
non-null handle after use. Async tables offer await videos.open_blob_file(...)
and async for video in videos.open_blob_files(...).
for video in videos.open_blob_files(rows["video"]):
if video is not None:
with video:
header = video.read(4096)
To extract a frame while retaining each row's timestamp, iterate over the handles and rows together:
for row, video in zip(rows.to_pylist(), videos.open_blob_files(rows["video"])):
if video is not None:
with video:
frame = extract_frame(video, row["timestamp"])