Will JonesandClaude Opus 5
1d2a5d084b
Two ways of writing to a `json` column failed or silently corrupted data. **All-null batches were rejected.** `add()` refused a batch whose values for a `json` column were all null, while every plain Arrow type accepted the same batch. This bites row-at-a-time inserts hardest: a one-row batch with no value for an optional column is trivially all-null, so most such writes failed. pyarrow infers `null` as the column's type, and the write path had no handling for it — casting to the table's type dropped the field metadata that identifies the column as `lance.json`, so lance rejected the batch (`` `val` should have type json but type was large_binary ``). A null-typed input column now becomes typed nulls matching the table's field exactly, metadata included. **Unlabelled JSON text was stored raw.** JSON supplied as plain strings (what pyarrow infers for a column of `str`) was cast to the column's `LargeBinary` storage type and relabelled `lance.json`, putting unparsed text where JSONB was expected. Reads returned the text unnormalized and `json_extract` failed with `InvalidJsonb`. Lance-core does the JSONB encoding, but only for input labelled `arrow.json`, so string input is now labelled rather than cast — at the top level and inside structs. Both fixes are in the shared Rust write path, so they apply to any binding, including hand-built Arrow tables that never pass through Python's list-of-dicts type inference. `_align_field` gets the same JSON-string fix for the legacy Python `_sanitize_data` path, which `on_bad_vectors` and embedding functions still route through. The blob v2 half of the issue landed separately in #4065, which added a `DataType::Null` arm to blob coercion. This PR keeps that implementation and adds end-to-end add-path coverage for it. The tests from #4066 are included here and pass, so that PR's Python-layer inference changes are no longer needed to close the issue. Fixes #3759 --------- Co-authored-by: Claude Opus 5 (1M context) <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.