Jack Ye
f39a7a4dd9
`StreamingDataset`, `PermutationBuilder`, and `Permutation` now work
against a `RemoteTable` (LanceDB Cloud and Enterprise), which unblocks
benchmarking the loader against the enterprise cluster cache.
```python
db = lancedb.connect("db://my-db", api_key=..., host_override=...)
ds = StreamingDataset(db.open_table("training"), world_size=8, rank=r)
```
Rows are addressed by `_rowid` exactly as before —
`PermutationReader::load_batch` already built the same `_rowid IN (...)`
filter that `Table::take_row_ids` sends, so the loader's fetch was
always the take path. It just was never allowed to run.
### The guard
`PermutationBuilder.__init__` rejected anything without `_inner`, so a
`RemoteTable` raised `TypeError` before reaching the PyO3 layer — which
already unwraps one via `_table._inner`.
### A bounded schema lookup
`PermutationReader::output_schema` reads the schema off a query plan,
and building a plan on a remote table *executes* the query
(`create_plan` → `execute_query`). With no limit that is `k =
isize::MAX`, so asking a remote table for its output schema pulled the
whole table over HTTP and threw it away — once per assigned split, on
every epoch, since `StreamingDataset.__iter__` constructs a
`Permutation` per split.
One row rather than zero, deliberately: lance gates its limit node on
`self.limit.unwrap_or(0) > 0`, so `Some(0)` means *no limit*.
### Tables with an LSM write spec are refused
A permutation references rows by row id, and rows that have not been
flushed to the base table do not have one yet. The loader could read
around them, but they would then be missing from training with nothing
said about it, so the build refuses such a table up front instead of
half supporting it.
### Fallible identity construction
`PermutationReader::identity` resolved `inner_new` with `unwrap`. That
was near total against a local dataset, but construction counts the base
table — an HTTP round trip for a remote one — so a transient network or
auth failure became a panic across the PyO3 boundary.
### Tests
End-to-end `permutation_builder` and `StreamingDataset` runs against a
mock server, the former torch-free so it runs wherever the suite does,
plus a test that a build succeeds without an LSM write spec and is
refused once one is installed.
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
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.