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fix: support LargeList label list indexes (#3529)
## Summary This PR extends nested-field regression coverage across Rust local/remote, Python sync/async, and Node so canonical escaped paths stay consistent across scalar, vector, and FTS index lifecycle behavior. It also aligns LanceDB's LabelList type gate with Lance by accepting `LargeList<primitive>` columns while keeping `List<Struct<...>>` unsupported until Lance defines stable membership semantics for struct labels. Part of #3406.
This commit is contained in:
Xuanwo
+173
-12
@@ -911,10 +911,22 @@ describe("When creating an index", () => {
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expect(indices2.length).toBe(0);
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});
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it("should create and search a nested vector index", async () => {
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it("should preserve canonical nested field paths across index lifecycle", async () => {
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const db = await connect(tmpDir.name);
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const nestedSchema = new Schema([
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new Field("id", new Int32(), true),
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new Field("rowId", new Int32(), true),
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new Field("row-id", new Int32(), true),
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new Field("userId", new Int32(), true),
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new Field(
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"metadata",
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new Struct([new Field("user_id", new Int32(), true)]),
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true,
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),
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new Field(
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"MetaData",
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new Struct([new Field("userId", new Int32(), true)]),
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true,
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),
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new Field(
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"image",
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new Struct([
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@@ -926,28 +938,147 @@ describe("When creating an index", () => {
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]),
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true,
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),
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new Field(
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"payload",
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new Struct([new Field("text", new Utf8(), true)]),
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true,
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),
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new Field(
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"meta-data",
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new Struct([new Field("user-id", new Int32(), true)]),
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true,
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),
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new Field(
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"literal",
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new Struct([new Field("a.b", new Int32(), true)]),
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true,
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),
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]);
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const nestedTable = await db.createTable(
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"nested_vector",
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"nested_field_index_lifecycle",
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makeArrowTable(
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Array.from({ length: 300 }, (_, id) => ({
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id,
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image: { embedding: [id, id + 1] },
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Array.from({ length: 300 }, (_, rowId) => ({
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rowId,
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"row-id": rowId,
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userId: rowId,
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metadata: { ["user_id"]: rowId },
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["MetaData"]: { userId: rowId },
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image: { embedding: [rowId, rowId + 1] },
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payload: { text: `document ${rowId}` },
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"meta-data": { "user-id": rowId },
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literal: { "a.b": rowId },
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})),
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{ schema: nestedSchema },
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),
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);
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await nestedTable.createIndex("rowId", {
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config: Index.btree(),
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name: "row_id_idx",
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});
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await nestedTable.createIndex("`row-id`", {
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config: Index.btree(),
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name: "row_dash_id_idx",
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});
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await nestedTable.createIndex("userId", {
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config: Index.btree(),
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name: "top_user_id_idx",
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});
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await nestedTable.createIndex("metadata.user_id", {
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config: Index.btree(),
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name: "nested_user_id_idx",
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});
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await nestedTable.createIndex("MetaData.userId", {
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config: Index.btree(),
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name: "mixed_case_metadata_user_id_idx",
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});
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await nestedTable.createIndex("`meta-data`.`user-id`", {
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config: Index.btree(),
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name: "escaped_names_idx",
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});
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await nestedTable.createIndex("literal.`a.b`", {
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config: Index.btree(),
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name: "literal_dot_idx",
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});
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await nestedTable.createIndex("image.embedding", {
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name: "image_embedding_idx",
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});
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const indices = await nestedTable.listIndices();
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expect(indices).toContainEqual({
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name: "image_embedding_idx",
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indexType: "IvfPq",
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columns: ["image.embedding"],
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await nestedTable.createIndex("payload.text", {
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config: Index.fts({ withPosition: false }),
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name: "payload_text_idx",
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});
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const indices = await nestedTable.listIndices();
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expect(indices).toEqual(
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expect.arrayContaining([
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{
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name: "row_id_idx",
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indexType: "BTree",
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columns: ["rowId"],
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},
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{
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name: "row_dash_id_idx",
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indexType: "BTree",
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columns: ["`row-id`"],
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},
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{
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name: "top_user_id_idx",
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indexType: "BTree",
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columns: ["userId"],
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},
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{
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name: "nested_user_id_idx",
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indexType: "BTree",
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columns: ["metadata.user_id"],
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},
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{
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name: "mixed_case_metadata_user_id_idx",
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indexType: "BTree",
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columns: ["MetaData.userId"],
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},
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{
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name: "escaped_names_idx",
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indexType: "BTree",
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columns: ["`meta-data`.`user-id`"],
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},
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{
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name: "literal_dot_idx",
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indexType: "BTree",
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columns: ["literal.`a.b`"],
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},
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{
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name: "image_embedding_idx",
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indexType: "IvfPq",
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columns: ["image.embedding"],
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},
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{
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name: "payload_text_idx",
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indexType: "FTS",
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columns: ["payload.text"],
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},
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]),
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);
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const stats = await nestedTable.indexStats(
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"mixed_case_metadata_user_id_idx",
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);
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expect(stats?.numIndexedRows).toEqual(300);
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expect(stats?.indexType).toEqual("BTREE");
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const filtered = await nestedTable
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.query()
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.where("MetaData.userId = 42")
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.limit(1)
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.toArray();
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expect(filtered[0].MetaData.userId).toEqual(42);
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const escapedFiltered = await nestedTable
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.query()
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.where("`row-id` = 43")
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.limit(1)
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.toArray();
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expect(escapedFiltered[0]["row-id"]).toEqual(43);
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const explicit = await nestedTable
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.query()
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.nearestTo([0.0, 1.0])
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@@ -959,7 +1090,37 @@ describe("When creating an index", () => {
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.nearestTo([0.0, 1.0])
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.limit(1)
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.toArray();
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expect(inferred[0].id).toEqual(explicit[0].id);
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expect(inferred[0].rowId).toEqual(explicit[0].rowId);
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await nestedTable.add([
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{
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rowId: 300,
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"row-id": 300,
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userId: 300,
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metadata: { ["user_id"]: 300 },
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["MetaData"]: { userId: 300 },
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image: { embedding: [300.0, 301.0] },
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payload: { text: "document 300" },
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"meta-data": { "user-id": 300 },
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literal: { "a.b": 300 },
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},
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]);
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await nestedTable.optimize();
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const indicesAfterOptimize = await nestedTable.listIndices();
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expect(indicesAfterOptimize).toEqual(
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expect.arrayContaining([
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{
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name: "mixed_case_metadata_user_id_idx",
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indexType: "BTree",
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columns: ["MetaData.userId"],
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},
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{
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name: "image_embedding_idx",
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indexType: "IvfPq",
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columns: ["image.embedding"],
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},
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]),
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);
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});
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it("should report multiple nested vector candidates", async () => {
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