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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 JavaScript SDK
A JavaScript library for LanceDB.
Installation
npm install @lancedb/lancedb
This will download the appropriate native library for your platform. We currently support:
- Linux (x86_64 and aarch64 on glibc and musl)
- MacOS (Intel and ARM/M1/M2)
- Windows (x86_64 and aarch64)
Usage
Basic Example
import * as lancedb from "@lancedb/lancedb";
const db = await lancedb.connect("data/sample-lancedb");
const table = await db.createTable("my_table", [
{ id: 1, vector: [0.1, 1.0], item: "foo", price: 10.0 },
{ id: 2, vector: [3.9, 0.5], item: "bar", price: 20.0 },
]);
const results = await table.vectorSearch([0.1, 0.3]).limit(20).toArray();
console.log(results);
The quickstart contains more complete examples.
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.