Dan RammerandClaude Opus 5
2216586023
Implementing a PK-based dedup to support WAL in hybrid search. Basically, the existing code just calls a vector query + FTS query and dedups locally using the row ID. Since row ID is unsupported in WAL (currently), we update the algorithm to use the tables PK (which is required in WAL, and uniquely linked to a PK on updates). To enable PK-dedup branch, we've chosen to fallback on error rather than fire an exploratory query. Specifically, we issue the regular hybrid query, using row IDs, and when that fails with "WAL does not support row IDs" we fallback to the PK-based variant, and then cache the fallback for future calls from the same client. The alternative is that we chec if WAL is enabled before issuing the query. This would require a `get_lsm_write_spec` call, slowing down all the existing non-WAL paths unnecessarily. The ultimate goal is to get row ID supported in WAL, but that is pending stable row ID GA and subsequent integration into the enterprise stack. Once that lands, we will rip out the PK-based dedup that is only for WAL-enabled tables and use the existing row ID code. This is the main reason for building this adaptively. I had spec-ed out a refactor so that rather than duplicating code between python and rust we build this in rust and have python call that through PyO3. However, that made the reverse path (ie. ) more difficult. So I think the tradeoff of maintaining duplicate code in the short term for the ease of reverting this in the medium term (hopefully 2-3 months). --------- Co-authored-by: Claude Opus 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.