ChilePiquinandClaude Sonnet 5
a53b6df689
The previous commit's shutdown() freed the shared Tokio runtime as soon as it looked unused (an AtomicPtr swap + Box::from_raw), but callers only ever held a bare reference into it, with no way to tell shutdown they were still using it. A concurrent caller could dereference memory shutdown had already freed. Switching to an ArcSwapOption<Runtime> so every caller holds an owned Arc clone for as long as it's using the runtime fixes the use-after-free, but isn't sufficient on its own, for two reasons: - future_into_py spawns a task that, once running, spawns a second one to do the real work and awaits its JoinHandle. If both nested calls independently re-resolved "the current runtime" via get_runtime(), a reclaim landing between them could bind the outer and inner task to two different runtime instances, permanently stalling the outer task if its own runtime was the one torn down while it awaited the inner one. - Runtime::shutdown_timeout gives spawned (non-blocking) tasks no grace period at all: a task keeps running until it next yields, then is dropped. An outermost caller that only held its Arc for the instant it submitted a task let the runtime's reference count fall back to baseline immediately, long before the task itself finished, so shutdown could reclaim the runtime while a top-level task was still genuinely in flight, abandoning it silently. spawn/spawn_blocking now use Handle::try_current() to pin any nested call to whatever runtime is already executing it, and the outermost call moves its Arc clone into the spawned task itself so the reference count stays elevated for the task's whole lifetime, not just its submission. Verified with two new targeted unit tests plus the existing concurrent stress test, and empirically against a concurrent reproducer built from this PR's own review feedback: 100+ clean runs with no hangs or crashes, versus a consistent ~35-55% failure rate beforehand. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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_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.