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0d30b31998
## Summary Split out from #3354 Adds `LsmWriteSpec` and `Table::set_lsm_write_spec` / `unset_lsm_write_spec` to install and clear the spec that selects Lance's MemWAL LSM-style write path for `merge_insert`. `LsmWriteSpec` offers three sharding strategies, all built on Lance's `InitializeMemWalBuilder`: - `LsmWriteSpec::bucket(column, num_buckets)` — hash-bucket sharding by the single-column unenforced primary key. - `LsmWriteSpec::identity(column)` — identity sharding by the raw value of a scalar column. - `LsmWriteSpec::unsharded()` — a single MemWAL shard. Each can be refined with `with_maintained_indexes(...)` (indexes the MemWAL keeps up to date as rows are appended) and `with_writer_config_defaults(...)` (default `ShardWriter` configuration recorded in the MemWAL index, so every writer starts from the same defaults). All variants require the table to have an unenforced primary key. - `set_lsm_write_spec` installs the spec by initializing the MemWAL index; `unset_lsm_write_spec` removes it (dropping the MemWAL index), reverting to the standard `merge_insert` path. `unset` is idempotent. - Bindings: Python (`LsmWriteSpec.bucket` / `.identity` / `.unsharded`, `set_lsm_write_spec` / `unset_lsm_write_spec`) and TypeScript (`setLsmWriteSpec` with `specType` `"bucket"` / `"identity"` / `"unsharded"`). `RemoteTable` returns `NotSupported`. The actual `merge_insert` LSM dispatch and `ShardWriter` write path are a follow-up — this PR only installs and clears the spec.
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.