Files
Kevin Dong 1f378c1b49 fix(python): preserve Decimal precision in table updates (#4431)
Python `Decimal` values and Arrow decimal scalars raise
`NotImplementedError` in `table.update(values=...)`. Register a Decimal
converter that emits quoted fixed-point text so the native cast to the
target column preserves every digit, including decimal128 and decimal256
values. Non-finite Decimal values raise `ValueError` with a static
message.

Closes #4360.

Validation with the native extension built from the current source at
7f593359, on macOS arm64 and Python 3.12.14:

- All 33 new regression cases failed before the fix and passed
afterward. They cover Python and Arrow scalar updates, signs, zero,
exponents, full 38/76-digit precision, and a Decimal context precision
of 6.
- 16 existing converter, update, and Decimal expression tests passed.
- Repository Ruff formatting and lint passed; formatting left all 156
files unchanged.
- A native round-trip check confirmed that an unquoted SQL number rounds
a high-precision value while the quoted Decimal update preserves it
exactly.

The full suite, other Python versions, and remote API were not
exercised.

AI assistance: Codex helped implement and validate the Decimal
conversion and its regression tests.
2026-10-09 16:51:56 -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.