Files
lancedb/python
lancedb-gatefixer[bot] c7cb0b9afa docs(python): clarify threading on two-CPU containers (#3807)
## Summary

- document that current LanceDB releases use one compute worker without
warning on two-vCPU containers
- distinguish compute-worker tuning from storage I/O concurrency
- direct users of affected LanceDB 0.21.1 installations to upgrade and
link the current threading guidance

## Root cause

The Lance version bundled with LanceDB 0.21.1 warned whenever the
detected CPU count was less than or equal to its default two-core I/O
reservation. A two-vCPU deployment therefore emitted the warning on
every query even though falling back to one compute worker was the
intended behavior. Lance fixed that warning condition upstream in
lance-format/lance#3710, and LanceDB current main already pins a version
containing the runtime fix; the Python package documentation did not
explain the corrected behavior or the distinct thread controls.

## Validation

- `git diff --check`
- verified the linked Lance threading-model documentation returns HTTP
200

Fixes #2326

<!-- lance-gatekeeper-fix:v1 agent=9f141242416a6dbeb43be0e80404dd4d
generation=1 -->

Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
2026-08-21 16:13:49 -07:00
..
2025-01-29 08:27:07 -08:00
2024-04-05 16:22:59 -07:00

LanceDB Python SDK

A Python library for LanceDB.

Installation

pip install lancedb

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

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 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.