Jack Ye
6b6778e684
Infer Blob V2 input access from the Python Function signature. `video:
BlobReader` requests a lazy, seekable reader; `video: bytes` requests
the complete payload. Users do not need a separate `blob_inputs`
registration option.
## Python experience
For a video table joined to a table of frame timestamps, the Function
can pass its reader directly to a media decoder:
```python
import io
import av
from lancedb import BlobReader, udf
@udf(pip=["av", "Pillow"])
def extract_frame(video: BlobReader, timestamp_s: float) -> bytes:
with av.open(video) as container:
stream = container.streams.video[0]
container.seek(int(timestamp_s / stream.time_base), stream=stream)
for frame in container.decode(stream):
if frame.time is not None and frame.time >= timestamp_s:
output = io.BytesIO()
frame.to_image().save(output, format="JPEG")
return output.getvalue()
raise ValueError("No frame at or after the requested timestamp")
```
After registering `extract_frame`, the query keeps the usual Function
call:
```sql
SELECT f.video_id, f.timestamp_s,
extract_frame(v.video, f.timestamp_s) AS image
FROM videos AS v
JOIN frames AS f ON v.id = f.video_id
```
The returned JPEG can feed a subsequent image-model step. The reader
provides `read`, `readinto`, `seek`, `tell`, `size()`, and
`read_range(offset, length)`; range reads leave the sequential cursor
unchanged. Each row invocation owns its reader, which closes after the
call. Decoder seeks can request the parts of the MP4 needed to locate
and decode the requested frame; the amount read depends on the file
layout and codec.
Functions that need the whole value can keep a bytes signature:
```python
@udf
def video_length(video: bytes) -> int:
return len(video)
```
## Execution contract
- Signature inference maps `BlobReader` to a Blob V2 field and records
`blob_access="reader"`. `bytes` keeps its binary Arrow type and records
`blob_access="bytes"`; it can also bind to a Blob V2 source. Explicit
schemas support the same choice, including a Blob V2 field annotated as
`bytes`.
- Reader execution carries a compact blob reference into the Function
worker. Payload reads happen when Python calls the reader, with requests
split into at most 1 MiB ranges. The full video does not need to enter
the Arrow input batch just to invoke the Function.
- Bytes execution materializes the full source before the Function runs
and remains subject to eager-input memory admission. Calling
`reader.read()` without a size also materializes the remaining contents
in Python; lazy access depends on how the Function consumes it.
- Input access metadata survives Python/Rust Function serialization and
registration. Packaged Functions import the worker-provided reader
implementation. This PR defines the SDK contract; execution requires
corresponding server support.
- This changes blob inputs. The example still returns ordinary image
bytes; it does not add streamed outputs or run an image model.
Adds coverage for signature inference, explicit schemas, nullable
readers, signature round trips, seeking, bounded range reads, buffering,
and invalid responses.
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. Use lance.simd_info() from Python to verify 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
If you also need the optional lance Python API, install the pylance extra
and include the Lance preview index:
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.