lancedb/python/README.md

# LanceDB Python SDK

A Python library for [LanceDB](https://github.com/lancedb/lancedb).

## Installation

```bash
pip install lancedb
```

If no wheel is published for your platform, pip builds `lancedb` from the source distribution. That build needs a [Rust toolchain](https://rustup.rs/) and [protoc](https://grpc.io/docs/protoc-installation/) (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:

```bash
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:

```bash
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.


```bash
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()`:

```bash
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:

```bash
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_THREADS` overrides the number of compute workers. One worker is the
  appropriate setting for a two-CPU container when an explicit override is needed.
- `LANCE_IO_THREADS` controls 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](https://lance.org/guide/performance/#threading-model)
for the current defaults and tuning guidance.

## Usage

### Basic Example

```python
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](./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:

```python
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(...)`.

```python
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:

```python
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"])
```