Files
lancedb/nodejs/examples/basic.test.ts
Will Jones 7747c9bcbf feat(node): parse arrow types in alterColumns() (#2208)
Previously, users could only specify new data types in `alterColumns` as
strings:

```ts
await tbl.alterColumns([
  path: "price",
  dataType: "float"
]);
```

But this has some problems:

1. It wasn't clear what were valid types
2. It was impossible to specify nested types, like lists and vector
columns.

This PR changes it to take an Arrow data type, similar to how the Python
API works. This allows casting vector types:

```ts
await tbl.alterColumns([
  {
    path: "vector",
    dataType: new arrow.FixedSizeList(
      2,
      new arrow.Field("item", new arrow.Float16(), false),
    ),
  },
]);
```

Closes #2185
2025-03-12 09:57:36 -07:00

207 lines
5.6 KiB
TypeScript

// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
import { expect, test } from "@jest/globals";
// --8<-- [start:imports]
import * as lancedb from "@lancedb/lancedb";
import * as arrow from "apache-arrow";
import {
Field,
FixedSizeList,
Float16,
Int32,
Schema,
Utf8,
} from "apache-arrow";
// --8<-- [end:imports]
import { withTempDirectory } from "./util.ts";
test("basic table examples", async () => {
await withTempDirectory(async (databaseDir) => {
// --8<-- [start:connect]
const db = await lancedb.connect(databaseDir);
// --8<-- [end:connect]
{
// --8<-- [start:create_table]
const _tbl = await db.createTable(
"myTable",
[
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
],
{ mode: "overwrite" },
);
// --8<-- [end:create_table]
const data = [
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
];
{
// --8<-- [start:create_table_exists_ok]
const tbl = await db.createTable("myTable", data, {
existOk: true,
});
// --8<-- [end:create_table_exists_ok]
expect(await tbl.countRows()).toBe(2);
}
{
// --8<-- [start:create_table_overwrite]
const tbl = await db.createTable("myTable", data, {
mode: "overwrite",
});
// --8<-- [end:create_table_overwrite]
expect(await tbl.countRows()).toBe(2);
}
}
await db.dropTable("myTable");
{
// --8<-- [start:create_table_with_schema]
const schema = new arrow.Schema([
new arrow.Field(
"vector",
new arrow.FixedSizeList(
2,
new arrow.Field("item", new arrow.Float32(), true),
),
),
new arrow.Field("item", new arrow.Utf8(), true),
new arrow.Field("price", new arrow.Float32(), true),
]);
const data = [
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
];
const tbl = await db.createTable("myTable", data, {
schema,
});
// --8<-- [end:create_table_with_schema]
expect(await tbl.countRows()).toBe(2);
}
{
// --8<-- [start:create_empty_table]
const schema = new arrow.Schema([
new arrow.Field("id", new arrow.Int32()),
new arrow.Field("name", new arrow.Utf8()),
]);
const emptyTbl = await db.createEmptyTable("empty_table", schema);
// --8<-- [end:create_empty_table]
expect(await emptyTbl.countRows()).toBe(0);
}
{
// --8<-- [start:open_table]
const _tbl = await db.openTable("myTable");
// --8<-- [end:open_table]
}
{
// --8<-- [start:table_names]
const tableNames = await db.tableNames();
// --8<-- [end:table_names]
expect(tableNames).toEqual(["empty_table", "myTable"]);
}
const tbl = await db.openTable("myTable");
{
// --8<-- [start:add_data]
const data = [
{ vector: [1.3, 1.4], item: "fizz", price: 100.0 },
{ vector: [9.5, 56.2], item: "buzz", price: 200.0 },
];
await tbl.add(data);
// --8<-- [end:add_data]
}
// --8<-- [start:add_columns]
await tbl.addColumns([
{ name: "double_price", valueSql: "cast((price * 2) as Float)" },
]);
// --8<-- [end:add_columns]
// --8<-- [start:alter_columns]
await tbl.alterColumns([
{
path: "double_price",
rename: "dbl_price",
dataType: "float",
nullable: true,
},
]);
// --8<-- [end:alter_columns]
// --8<-- [start:alter_columns_vector]
await tbl.alterColumns([
{
path: "vector",
dataType: new arrow.FixedSizeList(
2,
new arrow.Field("item", new arrow.Float16(), false),
),
},
]);
// --8<-- [end:alter_columns_vector]
// --8<-- [start:drop_columns]
await tbl.dropColumns(["dbl_price"]);
// --8<-- [end:drop_columns]
{
// --8<-- [start:vector_search]
const res = await tbl.search([100, 100]).limit(2).toArray();
// --8<-- [end:vector_search]
expect(res.length).toBe(2);
}
{
const data = Array.from({ length: 1000 })
.fill(null)
.map(() => ({
vector: [Math.random(), Math.random()],
item: "autogen",
price: Math.round(Math.random() * 100),
}));
await tbl.add(data);
}
// --8<-- [start:create_index]
await tbl.createIndex("vector");
// --8<-- [end:create_index]
// --8<-- [start:delete_rows]
await tbl.delete('item = "fizz"');
// --8<-- [end:delete_rows]
// --8<-- [start:drop_table]
await db.dropTable("myTable");
// --8<-- [end:drop_table]
await db.dropTable("empty_table");
{
// --8<-- [start:create_f16_table]
const db = await lancedb.connect(databaseDir);
const dim = 16;
const total = 10;
const f16Schema = new Schema([
new Field("id", new Int32()),
new Field(
"vector",
new FixedSizeList(dim, new Field("item", new Float16(), true)),
false,
),
]);
const data = lancedb.makeArrowTable(
Array.from(Array(total), (_, i) => ({
id: i,
vector: Array.from(Array(dim), Math.random),
})),
{ schema: f16Schema },
);
const _table = await db.createTable("f16_tbl", data);
// --8<-- [end:create_f16_table]
await db.dropTable("f16_tbl");
}
});
});