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Bruno RamirezandClaude Sonnet 5
a9ad499781
`Table::optimize()` previously returned `NotSupported` for remote tables. This PR wires it to the LanceDB Enterprise server's `OPTIMIZE TABLE ... REWRITE DATA` SQL statement, submitted over Flight SQL. Only `OptimizeAction::Compact` without a custom `remap_options` or non-default `CompactionOptions` is supported remotely. `OptimizeAction::All`, `::Prune`, and `::Index` all return a clear `NotSupported` error instead of silently doing the wrong thing. A branch-scoped table handle submits a `WITH (branch = '<branch>')` clause; the statement doesn't validate the branch exists itself, so an unknown branch surfaces as an async job failure rather than an error from this call. The server's SQL job adapter doesn't expose compaction metrics, so `OptimizeStats::compaction` is `Some(CompactionMetrics::default())`, a placeholder rather than real numbers — it must still be `Some`, since callers (including the Python/Node bindings) unwrap it unconditionally on success. This mirrors how `refresh_materialized_view_async` already handles the same limitation for its own metrics. The Python and Node `optimize()` convenience methods call a new shared `Table::optimize_full`, which runs `Compact` and then, on a remote table, skips `Prune` and `Index` instead of attempting and failing on them — returning zeroed prune stats rather than submitting a compaction job and then raising. Python's `RemoteTable.optimize()` also now forwards `compaction_options` to the Rust layer, so a non-default value is rejected before submission instead of being silently dropped. --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
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.