Jack Ye
f12996557f
## Summary
Align the experimental materialized-view HTTP transport with the
equivalent Table API shape and add remote materialized-view support
across Rust, Python, and TypeScript. This is an intentional breaking
change to the experimental materialized-view surface.
Materialized-view creation performs an initial refresh by default. The
create endpoint returns `202 Accepted` with `{ "job_id": "..." }`;
blocking SDK creation waits for that job before returning a populated
view. `with_no_data` / `withNoData` explicitly creates only the
definition and empty backing table.
## Route comparison
| Operation | Materialized-view API | Equivalent Table API |
| --- | --- | --- |
| Create | `POST /v1/materialized_view/{id}/create` | `POST
/v1/table/{id}/create` |
| Describe/open | `POST /v1/materialized_view/{id}/describe` | `POST
/v1/table/{id}/describe` |
| List | `GET /v1/namespace/{id}/materialized_view/list` | `GET
/v1/namespace/{id}/table/list` |
| Refresh | `POST /v1/materialized_view/{id}/refresh` | asynchronous
Table mutation pattern |
| Drop | `POST /v1/materialized_view/{id}/drop` | `POST
/v1/table/{id}/drop` |
Create, describe, refresh, and drop identify the target in the singular
item path instead of duplicating it in the request body. Create and drop
require `202 Accepted` with a valid job ID. List is a namespace-scoped
GET with opaque pagination tokens. The Rust list API now returns view
names, matching Table listing and the existing Python and TypeScript
APIs.
## Python API changes
| Operation | Synchronous API | Asynchronous API | Table/job pattern |
| --- | --- | --- | --- |
| Create and wait | `DBConnection.create_materialized_view(...)` |
`await AsyncConnection.create_materialized_view(...)` | Returns a
materialized-view handle after its initial-population job finishes |
| Submit create | `DBConnection.create_materialized_view_async(...) ->
Job[None]` | `await AsyncConnection.create_materialized_view_async(...)
-> AsyncJob[None]` | Matches job-returning Table mutations such as
`create_index_async` |
| Open | `DBConnection.open_materialized_view(...)` | `await
AsyncConnection.open_materialized_view(...)` | Opens the backing Table
plus its definition |
| List | `DBConnection.list_materialized_views()` | `await
AsyncConnection.list_materialized_views()` | Returns names like Table
listing |
| Refresh and wait | `MaterializedView.refresh(...)` | `await
AsyncMaterializedView.refresh(...)` | Returns the typed refresh result
after the job finishes |
| Submit refresh | `MaterializedView.refresh_async(...) ->
Job[RefreshMaterializedViewResult]` | `await
AsyncMaterializedView.refresh_async(...) ->
AsyncJob[RefreshMaterializedViewResult]` | Matches
`Table.refresh_column_async`; remote job handles expose the server job
ID |
| Drop | `DBConnection.drop_materialized_view(...)` | `await
AsyncConnection.drop_materialized_view(...)` | Matches blocking
`drop_table` |
| Submit drop | `DBConnection.drop_materialized_view_async(...) ->
Job[None]` | `await AsyncConnection.drop_materialized_view_async(...) ->
AsyncJob[None]` | Matches `drop_table_async`; remote handles expose the
server cleanup job ID |
The materialized-view handle exposes its backing Table through `.table`,
so normal Table query, search, and index APIs apply. Definition lookup
and refresh are backend-aware rather than depending on local schema
metadata. TypeScript exposes the equivalent blocking/job drop pair as
`dropMaterializedView` and `dropMaterializedViewAsync`.
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.