Bump version: 0.19.1-beta.2 → 0.19.1-beta.3

Signed-off-by: BubbleCal <bubble-cal@outlook.com>

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.15.1-beta.2"
+current_version = "0.16.1-beta.2"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ repos:
     -   id: trailing-whitespace
 -   repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.2.2
+    rev: v0.8.4
     hooks:
     - id: ruff
 - repo: local

Cargo.toml

@@ -21,16 +21,16 @@ categories = ["database-implementations"]
 rust-version = "1.78.0"
 
 [workspace.dependencies]
-lance = { "version" = "=0.23.0", "features" = [
+lance = { "version" = "=0.23.1", "features" = [
     "dynamodb",
-], git = "https://github.com/lancedb/lance.git", tag = "v0.23.0-beta.4" }
-lance-io = { version = "=0.23.0", git = "https://github.com/lancedb/lance.git", tag = "v0.23.0-beta.4" }
-lance-index = { version = "=0.23.0", git = "https://github.com/lancedb/lance.git", tag = "v0.23.0-beta.4" }
-lance-linalg = { version = "=0.23.0", git = "https://github.com/lancedb/lance.git", tag = "v0.23.0-beta.4" }
-lance-table = { version = "=0.23.0", git = "https://github.com/lancedb/lance.git", tag = "v0.23.0-beta.4" }
-lance-testing = { version = "=0.23.0", git = "https://github.com/lancedb/lance.git", tag = "v0.23.0-beta.4" }
-lance-datafusion = { version = "=0.23.0", git = "https://github.com/lancedb/lance.git", tag = "v0.23.0-beta.4" }
-lance-encoding = { version = "=0.23.0", git = "https://github.com/lancedb/lance.git", tag = "v0.23.0-beta.4" }
+], git = "https://github.com/lancedb/lance.git", tag = "v0.23.1-beta.4"}
+lance-io = {version = "=0.23.1", tag="v0.23.1-beta.4", git = "https://github.com/lancedb/lance.git"}
+lance-index = {version = "=0.23.1", tag="v0.23.1-beta.4", git = "https://github.com/lancedb/lance.git"}
+lance-linalg = {version = "=0.23.1", tag="v0.23.1-beta.4", git = "https://github.com/lancedb/lance.git"}
+lance-table = {version = "=0.23.1", tag="v0.23.1-beta.4", git = "https://github.com/lancedb/lance.git"}
+lance-testing = {version = "=0.23.1", tag="v0.23.1-beta.4", git = "https://github.com/lancedb/lance.git"}
+lance-datafusion = {version = "=0.23.1", tag="v0.23.1-beta.4", git = "https://github.com/lancedb/lance.git"}
+lance-encoding = {version = "=0.23.1", tag="v0.23.1-beta.4", git = "https://github.com/lancedb/lance.git"}
 # Note that this one does not include pyarrow
 arrow = { version = "53.2", optional = false }
 arrow-array = "53.2"
@@ -42,18 +42,22 @@ arrow-arith = "53.2"
 arrow-cast = "53.2"
 async-trait = "0"
 chrono = "0.4.35"
-datafusion-common = "44.0"
+datafusion = { version = "44.0", default-features = false }
+datafusion-catalog = "44.0"
+datafusion-common = { version = "44.0", default-features = false }
+datafusion-execution = "44.0"
+datafusion-expr = "44.0"
 datafusion-physical-plan = "44.0"
-env_logger = "0.10"
+env_logger = "0.11"
 half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"
 log = "0.4"
-moka = { version = "0.11", features = ["future"] }
-object_store = "0.10.2"
+moka = { version = "0.12", features = ["future"] }
+object_store = "0.11.0"
 pin-project = "1.0.7"
-snafu = "0.7.4"
+snafu = "0.8"
 url = "2"
 num-traits = "0.2"
 rand = "0.8"

docs/openapi.yml

@@ -38,6 +38,13 @@ components:
       required: true
       schema:
         type: string
+    index_name:
+      name: index_name
+      in: path
+      description: name of the index
+      required: true
+      schema:
+        type: string
   responses:
     invalid_request:
       description: Invalid request
@@ -485,3 +492,22 @@ paths:
           $ref: "#/components/responses/unauthorized"
         "404":
           $ref: "#/components/responses/not_found"
+  /v1/table/{name}/index/{index_name}/drop/:
+    post:
+      description: Drop an index from the table
+      tags:
+        - Tables
+      summary: Drop an index from the table
+      operationId: dropIndex
+      parameters:
+        - $ref: "#/components/parameters/table_name"
+        - $ref: "#/components/parameters/index_name"
+      responses:
+        "200":
+          description: Index successfully dropped
+        "400":
+          $ref: "#/components/responses/invalid_request"
+        "401":
+          $ref: "#/components/responses/unauthorized"
+        "404":
+          $ref: "#/components/responses/not_found"

docs/src/ann_indexes.ts

@@ -3,6 +3,7 @@ import * as vectordb from "vectordb";
 // --8<-- [end:import]
 
 (async () => {
+  console.log("ann_indexes.ts: start");
   // --8<-- [start:ingest]
   const db = await vectordb.connect("data/sample-lancedb");
 
@@ -49,5 +50,5 @@ import * as vectordb from "vectordb";
     .execute();
   // --8<-- [end:search3]
 
-  console.log("Ann indexes: done");
+  console.log("ann_indexes.ts: done");
 })();

docs/src/basic_legacy.ts

@@ -107,7 +107,6 @@ const example = async () => {
   // --8<-- [start:search]
   const query = await tbl.search([100, 100]).limit(2).execute();
   // --8<-- [end:search]
-  console.log(query);
 
   // --8<-- [start:delete]
   await tbl.delete('item = "fizz"');
@@ -119,8 +118,9 @@ const example = async () => {
 };
 
 async function main() {
+  console.log("basic_legacy.ts: start");
   await example();
-  console.log("Basic example: done");
+  console.log("basic_legacy.ts: done");
 }
 
 main();

docs/src/js/classes/Connection.md

@@ -131,6 +131,20 @@ Return a brief description of the connection
 
 ***
 
+### dropAllTables()
+
+```ts
+abstract dropAllTables(): Promise<void>
+```
+
+Drop all tables in the database.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
 ### dropTable()
 
 ```ts

docs/src/js/functions/makeArrowTable.md

@@ -22,8 +22,6 @@ when creating a table or adding data to it)
 This function converts an array of Record<String, any> (row-major JS objects)
 to an Arrow Table (a columnar structure)
 
-Note that it currently does not support nulls.
-
 If a schema is provided then it will be used to determine the resulting array
 types.  Fields will also be reordered to fit the order defined by the schema.
 
@@ -31,6 +29,9 @@ If a schema is not provided then the types will be inferred and the field order
 will be controlled by the order of properties in the first record.  If a type
 is inferred it will always be nullable.
 
+If not all fields are found in the data, then a subset of the schema will be
+returned.
+
 If the input is empty then a schema must be provided to create an empty table.
 
 When a schema is not specified then data types will be inferred.  The inference
@@ -38,6 +39,7 @@ rules are as follows:
 
  - boolean => Bool
  - number => Float64
+ - bigint => Int64
  - String => Utf8
  - Buffer => Binary
  - Record<String, any> => Struct

docs/src/js/interfaces/ClientConfig.md

@@ -8,6 +8,14 @@
 
 ## Properties
 
+### extraHeaders?
+
+```ts
+optional extraHeaders: Record<string, string>;
+```
+
+***
+
 ### retryConfig?
 
 ```ts

docs/src/js/interfaces/CreateTableOptions.md

@@ -8,7 +8,7 @@
 
 ## Properties
 
-### dataStorageVersion?
+### ~~dataStorageVersion?~~
 
 ```ts
 optional dataStorageVersion: string;
@@ -19,6 +19,10 @@ The version of the data storage format to use.
 The default is `stable`.
 Set to "legacy" to use the old format.
 
+#### Deprecated
+
+Pass `new_table_data_storage_version` to storageOptions instead.
+
 ***
 
 ### embeddingFunction?
@@ -29,7 +33,7 @@ optional embeddingFunction: EmbeddingFunctionConfig;
 
 ***
 
-### enableV2ManifestPaths?
+### ~~enableV2ManifestPaths?~~
 
 ```ts
 optional enableV2ManifestPaths: boolean;
@@ -41,6 +45,10 @@ turning this on will make the dataset unreadable for older versions
 of LanceDB (prior to 0.10.0). To migrate an existing dataset, instead
 use the LocalTable#migrateManifestPathsV2 method.
 
+#### Deprecated
+
+Pass `new_table_enable_v2_manifest_paths` to storageOptions instead.
+
 ***
 
 ### existOk
@@ -90,17 +98,3 @@ Options already set on the connection will be inherited by the table,
 but can be overridden here.
 
 The available options are described at https://lancedb.github.io/lancedb/guides/storage/
-
-***
-
-### useLegacyFormat?
-
-```ts
-optional useLegacyFormat: boolean;
-```
-
-If true then data files will be written with the legacy format
-
-The default is false.
-
-Deprecated. Use data storage version instead.

docs/src/search_legacy.ts

@@ -20,6 +20,7 @@ async function setup() {
 }
 
 async () => {
+  console.log("search_legacy.ts: start");
   await setup();
 
   // --8<-- [start:search1]
@@ -37,5 +38,5 @@ async () => {
     .execute();
   // --8<-- [end:search2]
 
-  console.log("search: done");
+  console.log("search_legacy.ts: done");
 };

docs/src/sql_legacy.ts

@@ -1,6 +1,7 @@
 import * as vectordb from "vectordb";
 
 (async () => {
+  console.log("sql_legacy.ts: start");
   const db = await vectordb.connect("data/sample-lancedb");
 
   let data = [];
@@ -34,5 +35,5 @@ import * as vectordb from "vectordb";
   await tbl.filter("id = 10").limit(10).execute();
   // --8<-- [end:sql_search]
 
-  console.log("SQL search: done");
+  console.log("sql_legacy.ts: done");
 })();

java/core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
         <groupId>com.lancedb</groupId>
         <artifactId>lancedb-parent</artifactId>
-        <version>0.15.1-beta.2</version>
+        <version>0.16.1-beta.2</version>
         <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.15.1-beta.2</version>
+    <version>0.16.1-beta.2</version>
     <packaging>pom</packaging>
 
     <name>LanceDB Parent</name>

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.15.1-beta.2",
+  "version": "0.16.1-beta.2",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.15.1-beta.2",
+      "version": "0.16.1-beta.2",
       "cpu": [
         "x64",
         "arm64"
@@ -52,14 +52,14 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.15.1-beta.2",
-        "@lancedb/vectordb-darwin-x64": "0.15.1-beta.2",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.15.1-beta.2",
-        "@lancedb/vectordb-linux-arm64-musl": "0.15.1-beta.2",
-        "@lancedb/vectordb-linux-x64-gnu": "0.15.1-beta.2",
-        "@lancedb/vectordb-linux-x64-musl": "0.15.1-beta.2",
-        "@lancedb/vectordb-win32-arm64-msvc": "0.15.1-beta.2",
-        "@lancedb/vectordb-win32-x64-msvc": "0.15.1-beta.2"
+        "@lancedb/vectordb-darwin-arm64": "0.16.1-beta.2",
+        "@lancedb/vectordb-darwin-x64": "0.16.1-beta.2",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.16.1-beta.2",
+        "@lancedb/vectordb-linux-arm64-musl": "0.16.1-beta.2",
+        "@lancedb/vectordb-linux-x64-gnu": "0.16.1-beta.2",
+        "@lancedb/vectordb-linux-x64-musl": "0.16.1-beta.2",
+        "@lancedb/vectordb-win32-arm64-msvc": "0.16.1-beta.2",
+        "@lancedb/vectordb-win32-x64-msvc": "0.16.1-beta.2"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",
@@ -329,110 +329,6 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
-    "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.15.1-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.15.1-beta.2.tgz",
-      "integrity": "sha512-hq5VkIW7oP+S030+o14TfSnsanjtKNuYMtv4ANBAsV7Lwr/q7EKwMZLwrbLW4Y/1hrjTjdwZF2ePgd5UwXdq1w==",
-      "cpu": [
-        "arm64"
-      ],
-      "license": "Apache-2.0",
-      "optional": true,
-      "os": [
-        "darwin"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.15.1-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.15.1-beta.2.tgz",
-      "integrity": "sha512-Xvms7y1PG52gDlbaWSotYjYhiT7oF9eF8T29H6wk5FA43Eu6fg1+wrrvMR8+7gDfwjsCN3kNabk9Cu4DDYtldg==",
-      "cpu": [
-        "x64"
-      ],
-      "license": "Apache-2.0",
-      "optional": true,
-      "os": [
-        "darwin"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.15.1-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.15.1-beta.2.tgz",
-      "integrity": "sha512-jTJEan8TLTtly1cucWvQZrh3N7fuJWDBz+ADmHUMQbRH6SDUoGqIekgs0u1WrgYuLFIPAi6V1VI66qbCWncCLQ==",
-      "cpu": [
-        "arm64"
-      ],
-      "license": "Apache-2.0",
-      "optional": true,
-      "os": [
-        "linux"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-linux-arm64-musl": {
-      "version": "0.15.1-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-musl/-/vectordb-linux-arm64-musl-0.15.1-beta.2.tgz",
-      "integrity": "sha512-KRTcMTsMIdkYz2u1BZ5xPB8q1unyyKEXXQZSj4Sye0vP5lBm1vvH4IAwZ3BkxffULFDLHpPegGSnnq0xNBWp2g==",
-      "cpu": [
-        "arm64"
-      ],
-      "license": "Apache-2.0",
-      "optional": true,
-      "os": [
-        "linux"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.15.1-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.15.1-beta.2.tgz",
-      "integrity": "sha512-oILD1M8BYAgNmdIuK1jqueqVtdkAvXfpuMNMOiUq2NhUaa/vFxO6Tb2XR8tOWoE14/VJkWTLpk+nLhjgfiNNTA==",
-      "cpu": [
-        "x64"
-      ],
-      "license": "Apache-2.0",
-      "optional": true,
-      "os": [
-        "linux"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-linux-x64-musl": {
-      "version": "0.15.1-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-musl/-/vectordb-linux-x64-musl-0.15.1-beta.2.tgz",
-      "integrity": "sha512-LO3WE7UUDOJIAN4hifTpaX8tzjfXRtTghqCilUGei8uaZbOD2T1pJKT89Ub7AurNVr3NEWT4NYu/Fa7ilfkb6w==",
-      "cpu": [
-        "x64"
-      ],
-      "license": "Apache-2.0",
-      "optional": true,
-      "os": [
-        "linux"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-win32-arm64-msvc": {
-      "version": "0.15.1-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-arm64-msvc/-/vectordb-win32-arm64-msvc-0.15.1-beta.2.tgz",
-      "integrity": "sha512-5H52qrtS1GIif6HC3lvuilKpzKhPJNkkAB5R9bx7ly2EN3d+ZFF4fwdlXhyoUaHI0SUf7z00hzgIVHqWnDadCg==",
-      "cpu": [
-        "arm64"
-      ],
-      "license": "Apache-2.0",
-      "optional": true,
-      "os": [
-        "win32"
-      ]
-    },
-    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.15.1-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.15.1-beta.2.tgz",
-      "integrity": "sha512-9sqH6uw8WWgQSZPzHCV9Ncurlx2YMwr6dnQtQGQ/KTDm7n/V2bz9m0t9+07jhyVzfJIxBkR/UlDMC3U3M/uAuQ==",
-      "cpu": [
-        "x64"
-      ],
-      "license": "Apache-2.0",
-      "optional": true,
-      "os": [
-        "win32"
-      ]
-    },
     "node_modules/@neon-rs/cli": {
       "version": "0.0.160",
       "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.160.tgz",

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.15.1-beta.2",
+  "version": "0.16.1-beta.2",
   "description": " Serverless, low-latency vector database for AI applications",
   "private": false,
   "main": "dist/index.js",
@@ -92,13 +92,13 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-x64": "0.15.1-beta.2",
-    "@lancedb/vectordb-darwin-arm64": "0.15.1-beta.2",
-    "@lancedb/vectordb-linux-x64-gnu": "0.15.1-beta.2",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.15.1-beta.2",
-    "@lancedb/vectordb-linux-x64-musl": "0.15.1-beta.2",
-    "@lancedb/vectordb-linux-arm64-musl": "0.15.1-beta.2",
-    "@lancedb/vectordb-win32-x64-msvc": "0.15.1-beta.2",
-    "@lancedb/vectordb-win32-arm64-msvc": "0.15.1-beta.2"
+    "@lancedb/vectordb-darwin-x64": "0.16.1-beta.2",
+    "@lancedb/vectordb-darwin-arm64": "0.16.1-beta.2",
+    "@lancedb/vectordb-linux-x64-gnu": "0.16.1-beta.2",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.16.1-beta.2",
+    "@lancedb/vectordb-linux-x64-musl": "0.16.1-beta.2",
+    "@lancedb/vectordb-linux-arm64-musl": "0.16.1-beta.2",
+    "@lancedb/vectordb-win32-x64-msvc": "0.16.1-beta.2",
+    "@lancedb/vectordb-win32-arm64-msvc": "0.16.1-beta.2"
   }
 }

node/src/index.ts

@@ -47,7 +47,8 @@ const {
   tableSchema,
   tableAddColumns,
   tableAlterColumns,
-  tableDropColumns
+  tableDropColumns,
+  tableDropIndex
   // eslint-disable-next-line @typescript-eslint/no-var-requires
 } = require("../native.js");
 
@@ -604,6 +605,13 @@ export interface Table<T = number[]> {
    */
   dropColumns(columnNames: string[]): Promise<void>
 
+  /**
+   * Drop an index from the table
+   *
+   * @param indexName The name of the index to drop
+   */
+  dropIndex(indexName: string): Promise<void>
+
   /**
    * Instrument the behavior of this Table with middleware.
    *
@@ -1206,6 +1214,10 @@ export class LocalTable<T = number[]> implements Table<T> {
     return tableDropColumns.call(this._tbl, columnNames);
   }
 
+  async dropIndex(indexName: string): Promise<void> {
+    return tableDropIndex.call(this._tbl, indexName);
+  }
+
   withMiddleware(middleware: HttpMiddleware): Table<T> {
     return this;
   }

node/src/remote/index.ts

@@ -471,6 +471,18 @@ export class RemoteTable<T = number[]> implements Table<T> {
       )
     }
   }
+  async dropIndex (index_name: string): Promise<void> {
+    const res = await this._client.post(
+        `/v1/table/${encodeURIComponent(this._name)}/index/${encodeURIComponent(index_name)}/drop/`
+    )
+    if (res.status !== 200) {
+      throw new Error(
+          `Server Error, status: ${res.status}, ` +
+          // eslint-disable-next-line @typescript-eslint/restrict-template-expressions
+          `message: ${res.statusText}: ${await res.body()}`
+      )
+    }
+  }
 
   async countRows (filter?: string): Promise<number> {
     const result = await this._client.post(`/v1/table/${encodeURIComponent(this._name)}/count_rows/`, {

node/src/test/test.ts

@@ -894,6 +894,27 @@ describe("LanceDB client", function () {
       expect(stats.distanceType).to.equal("l2");
       expect(stats.numIndices).to.equal(1);
     }).timeout(50_000);
+
+    // not yet implemented
+    // it("can drop index", async function () {
+    //   const uri = await createTestDB(32, 300);
+    //   const con = await lancedb.connect(uri);
+    //   const table = await con.openTable("vectors");
+    //   await table.createIndex({
+    //     type: "ivf_pq",
+    //     column: "vector",
+    //     num_partitions: 2,
+    //     max_iters: 2,
+    //     num_sub_vectors: 2
+    //   });
+    //
+    //   const indices = await table.listIndices();
+    //   expect(indices).to.have.lengthOf(1);
+    //   expect(indices[0].name).to.equal("vector_idx");
+    //
+    //   await table.dropIndex("vector_idx");
+    //   expect(await table.listIndices()).to.have.lengthOf(0);
+    // }).timeout(50_000);
   });
 
   describe("when using a custom embedding function", function () {

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.15.1-beta.2"
+version = "0.16.1-beta.2"
 license.workspace = true
 description.workspace = true
 repository.workspace = true

nodejs/__test__/arrow.test.ts

@@ -55,6 +55,7 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       Float64,
       Struct,
       List,
+      Int16,
       Int32,
       Int64,
       Float,
@@ -108,13 +109,16 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
           false,
         ),
       ]);
-
       const table = (await tableCreationMethod(
         records,
         recordsReversed,
         schema,
         // biome-ignore lint/suspicious/noExplicitAny: <explanation>
       )) as any;
+
+      // We expect deterministic ordering of the fields
+      expect(table.schema.names).toEqual(schema.names);
+
       schema.fields.forEach(
         (
           // biome-ignore lint/suspicious/noExplicitAny: <explanation>
@@ -141,13 +145,13 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
     describe("The function makeArrowTable", function () {
       it("will use data types from a provided schema instead of inference", async function () {
         const schema = new Schema([
-          new Field("a", new Int32()),
-          new Field("b", new Float32()),
+          new Field("a", new Int32(), false),
+          new Field("b", new Float32(), true),
           new Field(
             "c",
             new FixedSizeList(3, new Field("item", new Float16())),
           ),
-          new Field("d", new Int64()),
+          new Field("d", new Int64(), true),
         ]);
         const table = makeArrowTable(
           [
@@ -165,12 +169,15 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
         expect(actual.numRows).toBe(3);
         const actualSchema = actual.schema;
         expect(actualSchema).toEqual(schema);
+        expect(table.getChild("a")?.toJSON()).toEqual([1, 4, 7]);
+        expect(table.getChild("b")?.toJSON()).toEqual([2, 5, 8]);
+        expect(table.getChild("d")?.toJSON()).toEqual([9n, 10n, null]);
       });
 
       it("will assume the column `vector` is FixedSizeList<Float32> by default", async function () {
         const schema = new Schema([
           new Field("a", new Float(Precision.DOUBLE), true),
-          new Field("b", new Float(Precision.DOUBLE), true),
+          new Field("b", new Int64(), true),
           new Field(
             "vector",
             new FixedSizeList(
@@ -181,9 +188,9 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
           ),
         ]);
         const table = makeArrowTable([
-          { a: 1, b: 2, vector: [1, 2, 3] },
-          { a: 4, b: 5, vector: [4, 5, 6] },
-          { a: 7, b: 8, vector: [7, 8, 9] },
+          { a: 1, b: 2n, vector: [1, 2, 3] },
+          { a: 4, b: 5n, vector: [4, 5, 6] },
+          { a: 7, b: 8n, vector: [7, 8, 9] },
         ]);
 
         const buf = await fromTableToBuffer(table);
@@ -193,6 +200,19 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
         expect(actual.numRows).toBe(3);
         const actualSchema = actual.schema;
         expect(actualSchema).toEqual(schema);
+
+        expect(table.getChild("a")?.toJSON()).toEqual([1, 4, 7]);
+        expect(table.getChild("b")?.toJSON()).toEqual([2n, 5n, 8n]);
+        expect(
+          table
+            .getChild("vector")
+            ?.toJSON()
+            .map((v) => v.toJSON()),
+        ).toEqual([
+          [1, 2, 3],
+          [4, 5, 6],
+          [7, 8, 9],
+        ]);
       });
 
       it("can support multiple vector columns", async function () {
@@ -206,7 +226,7 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
           ),
           new Field(
             "vec2",
-            new FixedSizeList(3, new Field("item", new Float16(), true)),
+            new FixedSizeList(3, new Field("item", new Float64(), true)),
             true,
           ),
         ]);
@@ -219,7 +239,7 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
           {
             vectorColumns: {
               vec1: { type: new Float16() },
-              vec2: { type: new Float16() },
+              vec2: { type: new Float64() },
             },
           },
         );
@@ -307,6 +327,53 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
           false,
         );
       });
+
+      it("will allow subsets of columns if nullable", async function () {
+        const schema = new Schema([
+          new Field("a", new Int64(), true),
+          new Field(
+            "s",
+            new Struct([
+              new Field("x", new Int32(), true),
+              new Field("y", new Int32(), true),
+            ]),
+            true,
+          ),
+          new Field("d", new Int16(), true),
+        ]);
+
+        const table = makeArrowTable([{ a: 1n }], { schema });
+        expect(table.numCols).toBe(1);
+        expect(table.numRows).toBe(1);
+
+        const table2 = makeArrowTable([{ a: 1n, d: 2 }], { schema });
+        expect(table2.numCols).toBe(2);
+
+        const table3 = makeArrowTable([{ s: { y: 3 } }], { schema });
+        expect(table3.numCols).toBe(1);
+        const expectedSchema = new Schema([
+          new Field("s", new Struct([new Field("y", new Int32(), true)]), true),
+        ]);
+        expect(table3.schema).toEqual(expectedSchema);
+      });
+
+      it("will work even if columns are sparsely provided", async function () {
+        const sparseRecords = [{ a: 1n }, { b: 2n }, { c: 3n }, { d: 4n }];
+        const table = makeArrowTable(sparseRecords);
+        expect(table.numCols).toBe(4);
+        expect(table.numRows).toBe(4);
+
+        const schema = new Schema([
+          new Field("a", new Int64(), true),
+          new Field("b", new Int32(), true),
+          new Field("c", new Int64(), true),
+          new Field("d", new Int16(), true),
+        ]);
+        const table2 = makeArrowTable(sparseRecords, { schema });
+        expect(table2.numCols).toBe(4);
+        expect(table2.numRows).toBe(4);
+        expect(table2.schema).toEqual(schema);
+      });
     });
 
     class DummyEmbedding extends EmbeddingFunction<string> {

nodejs/__test__/connection.test.ts

@@ -17,14 +17,14 @@ describe("when connecting", () => {
   it("should connect", async () => {
     const db = await connect(tmpDir.name);
     expect(db.display()).toBe(
-      `NativeDatabase(uri=${tmpDir.name}, read_consistency_interval=None)`,
+      `ListingDatabase(uri=${tmpDir.name}, read_consistency_interval=None)`,
     );
   });
 
   it("should allow read consistency interval to be specified", async () => {
     const db = await connect(tmpDir.name, { readConsistencyInterval: 5 });
     expect(db.display()).toBe(
-      `NativeDatabase(uri=${tmpDir.name}, read_consistency_interval=5s)`,
+      `ListingDatabase(uri=${tmpDir.name}, read_consistency_interval=5s)`,
     );
   });
 });
@@ -61,6 +61,26 @@ describe("given a connection", () => {
     await expect(tbl.countRows()).resolves.toBe(1);
   });
 
+  it("should be able to drop tables`", async () => {
+    await db.createTable("test", [{ id: 1 }, { id: 2 }]);
+    await db.createTable("test2", [{ id: 1 }, { id: 2 }]);
+    await db.createTable("test3", [{ id: 1 }, { id: 2 }]);
+
+    await expect(db.tableNames()).resolves.toEqual(["test", "test2", "test3"]);
+
+    await db.dropTable("test2");
+
+    await expect(db.tableNames()).resolves.toEqual(["test", "test3"]);
+
+    await db.dropAllTables();
+
+    await expect(db.tableNames()).resolves.toEqual([]);
+
+    // Make sure we can still create more tables after dropping all
+
+    await db.createTable("test4", [{ id: 1 }, { id: 2 }]);
+  });
+
   it("should fail if creating table twice, unless overwrite is true", async () => {
     let tbl = await db.createTable("test", [{ id: 1 }, { id: 2 }]);
     await expect(tbl.countRows()).resolves.toBe(2);
@@ -96,14 +116,15 @@ describe("given a connection", () => {
     const data = [...Array(10000).keys()].map((i) => ({ id: i }));
 
     // Create in v1 mode
-    let table = await db.createTable("test", data, { useLegacyFormat: true });
+    let table = await db.createTable("test", data, {
+      storageOptions: { newTableDataStorageVersion: "legacy" },
+    });
 
     const isV2 = async (table: Table) => {
       const data = await table
         .query()
         .limit(10000)
         .toArrow({ maxBatchLength: 100000 });
-      console.log(data.batches.length);
       return data.batches.length < 5;
     };
 
@@ -122,7 +143,7 @@ describe("given a connection", () => {
     const schema = new Schema([new Field("id", new Float64(), true)]);
 
     table = await db.createEmptyTable("test_v2_empty", schema, {
-      useLegacyFormat: false,
+      storageOptions: { newTableDataStorageVersion: "stable" },
     });
 
     await table.add(data);

nodejs/__test__/remote.test.ts

@@ -104,4 +104,26 @@ describe("remote connection", () => {
       },
     );
   });
+
+  it("should pass on requested extra headers", async () => {
+    await withMockDatabase(
+      (req, res) => {
+        expect(req.headers["x-my-header"]).toEqual("my-value");
+
+        const body = JSON.stringify({ tables: [] });
+        res.writeHead(200, { "Content-Type": "application/json" }).end(body);
+      },
+      async (db) => {
+        const tableNames = await db.tableNames();
+        expect(tableNames).toEqual([]);
+      },
+      {
+        clientConfig: {
+          extraHeaders: {
+            "x-my-header": "my-value",
+          },
+        },
+      },
+    );
+  });
 });

nodejs/__test__/table.test.ts

@@ -253,6 +253,31 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       const arrowTbl = await table.toArrow();
       expect(arrowTbl).toBeInstanceOf(ArrowTable);
     });
+
+    it("should be able to handle missing fields", async () => {
+      const schema = new arrow.Schema([
+        new arrow.Field("id", new arrow.Int32(), true),
+        new arrow.Field("y", new arrow.Int32(), true),
+        new arrow.Field("z", new arrow.Int64(), true),
+      ]);
+      const db = await connect(tmpDir.name);
+      const table = await db.createEmptyTable("testNull", schema);
+      await table.add([{ id: 1, y: 2 }]);
+      await table.add([{ id: 2 }]);
+
+      await table
+        .mergeInsert("id")
+        .whenNotMatchedInsertAll()
+        .execute([
+          { id: 3, z: 3 },
+          { id: 4, z: 5 },
+        ]);
+
+      const res = await table.query().toArrow();
+      expect(res.getChild("id")?.toJSON()).toEqual([1, 2, 3, 4]);
+      expect(res.getChild("y")?.toJSON()).toEqual([2, null, null, null]);
+      expect(res.getChild("z")?.toJSON()).toEqual([null, null, 3n, 5n]);
+    });
   },
 );
 

nodejs/examples/full_text_search.test.ts

@@ -42,4 +42,4 @@ test("full text search", async () => {
     expect(result.length).toBe(10);
     // --8<-- [end:full_text_search]
   });
-});
+}, 10_000);

nodejs/lancedb/arrow.ts

@@ -2,31 +2,37 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 import {
+  Data as ArrowData,
   Table as ArrowTable,
   Binary,
+  Bool,
   BufferType,
   DataType,
+  Dictionary,
   Field,
   FixedSizeBinary,
   FixedSizeList,
   Float,
   Float32,
+  Float64,
   Int,
+  Int32,
+  Int64,
   LargeBinary,
   List,
   Null,
   RecordBatch,
   RecordBatchFileReader,
   RecordBatchFileWriter,
-  RecordBatchReader,
   RecordBatchStreamWriter,
   Schema,
   Struct,
   Utf8,
   Vector,
+  makeVector as arrowMakeVector,
   makeBuilder,
   makeData,
-  type makeTable,
+  makeTable,
   vectorFromArray,
 } from "apache-arrow";
 import { Buffers } from "apache-arrow/data";
@@ -236,8 +242,6 @@ export class MakeArrowTableOptions {
  * This function converts an array of Record<String, any> (row-major JS objects)
  * to an Arrow Table (a columnar structure)
  *
- * Note that it currently does not support nulls.
- *
  * If a schema is provided then it will be used to determine the resulting array
  * types.  Fields will also be reordered to fit the order defined by the schema.
  *
@@ -245,6 +249,9 @@ export class MakeArrowTableOptions {
  * will be controlled by the order of properties in the first record.  If a type
  * is inferred it will always be nullable.
  *
+ * If not all fields are found in the data, then a subset of the schema will be
+ * returned.
+ *
  * If the input is empty then a schema must be provided to create an empty table.
  *
  * When a schema is not specified then data types will be inferred.  The inference
@@ -252,6 +259,7 @@ export class MakeArrowTableOptions {
  *
  *  - boolean => Bool
  *  - number => Float64
+ *  - bigint => Int64
  *  - String => Utf8
  *  - Buffer => Binary
  *  - Record<String, any> => Struct
@@ -322,126 +330,316 @@ export function makeArrowTable(
   options?: Partial<MakeArrowTableOptions>,
   metadata?: Map<string, string>,
 ): ArrowTable {
+  const opt = new MakeArrowTableOptions(options !== undefined ? options : {});
+  let schema: Schema | undefined = undefined;
+  if (opt.schema !== undefined && opt.schema !== null) {
+    schema = sanitizeSchema(opt.schema);
+    schema = validateSchemaEmbeddings(
+      schema as Schema,
+      data,
+      options?.embeddingFunction,
+    );
+  }
+
+  let schemaMetadata = schema?.metadata || new Map<string, string>();
+  if (metadata !== undefined) {
+    schemaMetadata = new Map([...schemaMetadata, ...metadata]);
+  }
+
   if (
     data.length === 0 &&
     (options?.schema === undefined || options?.schema === null)
   ) {
     throw new Error("At least one record or a schema needs to be provided");
-  }
-
-  const opt = new MakeArrowTableOptions(options !== undefined ? options : {});
-  if (opt.schema !== undefined && opt.schema !== null) {
-    opt.schema = sanitizeSchema(opt.schema);
-    opt.schema = validateSchemaEmbeddings(
-      opt.schema as Schema,
-      data,
-      options?.embeddingFunction,
-    );
-  }
-  const columns: Record<string, Vector> = {};
-  // TODO: sample dataset to find missing columns
-  // Prefer the field ordering of the schema, if present
-  const columnNames =
-    opt.schema != null ? (opt.schema.names as string[]) : Object.keys(data[0]);
-  for (const colName of columnNames) {
-    if (
-      data.length !== 0 &&
-      !Object.prototype.hasOwnProperty.call(data[0], colName)
-    ) {
-      // The field is present in the schema, but not in the data, skip it
-      continue;
-    }
-    // Extract a single column from the records (transpose from row-major to col-major)
-    let values = data.map((datum) => datum[colName]);
-
-    // By default (type === undefined) arrow will infer the type from the JS type
-    let type;
-    if (opt.schema !== undefined) {
-      // If there is a schema provided, then use that for the type instead
-      type = opt.schema?.fields.filter((f) => f.name === colName)[0]?.type;
-      if (DataType.isInt(type) && type.bitWidth === 64) {
-        // wrap in BigInt to avoid bug: https://github.com/apache/arrow/issues/40051
-        values = values.map((v) => {
-          if (v === null) {
-            return v;
-          }
-          if (typeof v === "bigint") {
-            return v;
-          }
-          if (typeof v === "number") {
-            return BigInt(v);
-          }
-          throw new Error(
-            `Expected BigInt or number for column ${colName}, got ${typeof v}`,
-          );
-        });
-      }
+  } else if (data.length === 0) {
+    if (schema === undefined) {
+      throw new Error("A schema must be provided if data is empty");
     } else {
-      // Otherwise, check to see if this column is one of the vector columns
-      // defined by opt.vectorColumns and, if so, use the fixed size list type
-      const vectorColumnOptions = opt.vectorColumns[colName];
-      if (vectorColumnOptions !== undefined) {
-        const firstNonNullValue = values.find((v) => v !== null);
-        if (Array.isArray(firstNonNullValue)) {
-          type = newVectorType(
-            firstNonNullValue.length,
-            vectorColumnOptions.type,
-          );
+      schema = new Schema(schema.fields, schemaMetadata);
+      return new ArrowTable(schema);
+    }
+  }
+
+  let inferredSchema = inferSchema(data, schema, opt);
+  inferredSchema = new Schema(inferredSchema.fields, schemaMetadata);
+
+  const finalColumns: Record<string, Vector> = {};
+  for (const field of inferredSchema.fields) {
+    finalColumns[field.name] = transposeData(data, field);
+  }
+
+  return new ArrowTable(inferredSchema, finalColumns);
+}
+
+function inferSchema(
+  data: Array<Record<string, unknown>>,
+  schema: Schema | undefined,
+  opts: MakeArrowTableOptions,
+): Schema {
+  // We will collect all fields we see in the data.
+  const pathTree = new PathTree<DataType>();
+
+  for (const [rowI, row] of data.entries()) {
+    for (const [path, value] of rowPathsAndValues(row)) {
+      if (!pathTree.has(path)) {
+        // First time seeing this field.
+        if (schema !== undefined) {
+          const field = getFieldForPath(schema, path);
+          if (field === undefined) {
+            throw new Error(
+              `Found field not in schema: ${path.join(".")} at row ${rowI}`,
+            );
+          } else {
+            pathTree.set(path, field.type);
+          }
         } else {
-          throw new Error(
-            `Column ${colName} is expected to be a vector column but first non-null value is not an array.  Could not determine size of vector column`,
-          );
+          const inferredType = inferType(value, path, opts);
+          if (inferredType === undefined) {
+            throw new Error(`Failed to infer data type for field ${path.join(".")} at row ${rowI}. \
+                             Consider providing an explicit schema.`);
+          }
+          pathTree.set(path, inferredType);
+        }
+      } else if (schema === undefined) {
+        const currentType = pathTree.get(path);
+        const newType = inferType(value, path, opts);
+        if (currentType !== newType) {
+          new Error(`Failed to infer schema for data. Previously inferred type \
+                     ${currentType} but found ${newType} at row ${rowI}. Consider \
+                     providing an explicit schema.`);
         }
       }
     }
-
-    try {
-      // Convert an Array of JS values to an arrow vector
-      columns[colName] = makeVector(values, type, opt.dictionaryEncodeStrings);
-    } catch (error: unknown) {
-      // eslint-disable-next-line @typescript-eslint/restrict-template-expressions
-      throw Error(`Could not convert column "${colName}" to Arrow: ${error}`);
-    }
   }
 
-  if (opt.schema != null) {
-    // `new ArrowTable(columns)` infers a schema which may sometimes have
-    // incorrect nullability (it assumes nullable=true always)
-    //
-    // `new ArrowTable(schema, columns)` will also fail because it will create a
-    // batch with an inferred schema and then complain that the batch schema
-    // does not match the provided schema.
-    //
-    // To work around this we first create a table with the wrong schema and
-    // then patch the schema of the batches so we can use
-    // `new ArrowTable(schema, batches)` which does not do any schema inference
-    const firstTable = new ArrowTable(columns);
-    const batchesFixed = firstTable.batches.map(
-      (batch) => new RecordBatch(opt.schema as Schema, batch.data),
-    );
-    let schema: Schema;
-    if (metadata !== undefined) {
-      let schemaMetadata = opt.schema.metadata;
-      if (schemaMetadata.size === 0) {
-        schemaMetadata = metadata;
-      } else {
-        for (const [key, entry] of schemaMetadata.entries()) {
-          schemaMetadata.set(key, entry);
+  if (schema === undefined) {
+    function fieldsFromPathTree(pathTree: PathTree<DataType>): Field[] {
+      const fields = [];
+      for (const [name, value] of pathTree.map.entries()) {
+        if (value instanceof PathTree) {
+          const children = fieldsFromPathTree(value);
+          fields.push(new Field(name, new Struct(children), true));
+        } else {
+          fields.push(new Field(name, value, true));
         }
       }
+      return fields;
+    }
+    const fields = fieldsFromPathTree(pathTree);
+    return new Schema(fields);
+  } else {
+    function takeMatchingFields(
+      fields: Field[],
+      pathTree: PathTree<DataType>,
+    ): Field[] {
+      const outFields = [];
+      for (const field of fields) {
+        if (pathTree.map.has(field.name)) {
+          const value = pathTree.get([field.name]);
+          if (value instanceof PathTree) {
+            const struct = field.type as Struct;
+            const children = takeMatchingFields(struct.children, value);
+            outFields.push(
+              new Field(field.name, new Struct(children), field.nullable),
+            );
+          } else {
+            outFields.push(
+              new Field(field.name, value as DataType, field.nullable),
+            );
+          }
+        }
+      }
+      return outFields;
+    }
+    const fields = takeMatchingFields(schema.fields, pathTree);
+    return new Schema(fields);
+  }
+}
 
-      schema = new Schema(opt.schema.fields as Field[], schemaMetadata);
+function* rowPathsAndValues(
+  row: Record<string, unknown>,
+  basePath: string[] = [],
+): Generator<[string[], unknown]> {
+  for (const [key, value] of Object.entries(row)) {
+    if (isObject(value)) {
+      yield* rowPathsAndValues(value, [...basePath, key]);
     } else {
-      schema = opt.schema as Schema;
+      yield [[...basePath, key], value];
     }
-    return new ArrowTable(schema, batchesFixed);
   }
-  const tbl = new ArrowTable(columns);
-  if (metadata !== undefined) {
-    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-    (<any>tbl.schema).metadata = metadata;
+}
+
+function isObject(value: unknown): value is Record<string, unknown> {
+  return (
+    typeof value === "object" &&
+    value !== null &&
+    !Array.isArray(value) &&
+    !(value instanceof RegExp) &&
+    !(value instanceof Date) &&
+    !(value instanceof Set) &&
+    !(value instanceof Map) &&
+    !(value instanceof Buffer)
+  );
+}
+
+function getFieldForPath(schema: Schema, path: string[]): Field | undefined {
+  let current: Field | Schema = schema;
+  for (const key of path) {
+    if (current instanceof Schema) {
+      const field: Field | undefined = current.fields.find(
+        (f) => f.name === key,
+      );
+      if (field === undefined) {
+        return undefined;
+      }
+      current = field;
+    } else if (current instanceof Field && DataType.isStruct(current.type)) {
+      const struct: Struct = current.type;
+      const field = struct.children.find((f) => f.name === key);
+      if (field === undefined) {
+        return undefined;
+      }
+      current = field;
+    } else {
+      return undefined;
+    }
+  }
+  if (current instanceof Field) {
+    return current;
+  } else {
+    return undefined;
+  }
+}
+
+/**
+ * Try to infer which Arrow type to use for a given value.
+ *
+ * May return undefined if the type cannot be inferred.
+ */
+function inferType(
+  value: unknown,
+  path: string[],
+  opts: MakeArrowTableOptions,
+): DataType | undefined {
+  if (typeof value === "bigint") {
+    return new Int64();
+  } else if (typeof value === "number") {
+    // Even if it's an integer, it's safer to assume Float64. Users can
+    // always provide an explicit schema or use BigInt if they mean integer.
+    return new Float64();
+  } else if (typeof value === "string") {
+    if (opts.dictionaryEncodeStrings) {
+      return new Dictionary(new Utf8(), new Int32());
+    } else {
+      return new Utf8();
+    }
+  } else if (typeof value === "boolean") {
+    return new Bool();
+  } else if (value instanceof Buffer) {
+    return new Binary();
+  } else if (Array.isArray(value)) {
+    if (value.length === 0) {
+      return undefined; // Without any values we can't infer the type
+    }
+    if (path.length === 1 && Object.hasOwn(opts.vectorColumns, path[0])) {
+      const floatType = sanitizeType(opts.vectorColumns[path[0]].type);
+      return new FixedSizeList(
+        value.length,
+        new Field("item", floatType, true),
+      );
+    }
+    const valueType = inferType(value[0], path, opts);
+    if (valueType === undefined) {
+      return undefined;
+    }
+    // Try to automatically detect embedding columns.
+    if (valueType instanceof Float && path[path.length - 1] === "vector") {
+      // We default to Float32 for vectors.
+      const child = new Field("item", new Float32(), true);
+      return new FixedSizeList(value.length, child);
+    } else {
+      const child = new Field("item", valueType, true);
+      return new List(child);
+    }
+  } else {
+    // TODO: timestamp
+    return undefined;
+  }
+}
+
+class PathTree<V> {
+  map: Map<string, V | PathTree<V>>;
+
+  constructor(entries?: [string[], V][]) {
+    this.map = new Map();
+    if (entries !== undefined) {
+      for (const [path, value] of entries) {
+        this.set(path, value);
+      }
+    }
+  }
+  has(path: string[]): boolean {
+    let ref: PathTree<V> = this;
+    for (const part of path) {
+      if (!(ref instanceof PathTree) || !ref.map.has(part)) {
+        return false;
+      }
+      ref = ref.map.get(part) as PathTree<V>;
+    }
+    return true;
+  }
+  get(path: string[]): V | undefined {
+    let ref: PathTree<V> = this;
+    for (const part of path) {
+      if (!(ref instanceof PathTree) || !ref.map.has(part)) {
+        return undefined;
+      }
+      ref = ref.map.get(part) as PathTree<V>;
+    }
+    return ref as V;
+  }
+  set(path: string[], value: V): void {
+    let ref: PathTree<V> = this;
+    for (const part of path.slice(0, path.length - 1)) {
+      if (!ref.map.has(part)) {
+        ref.map.set(part, new PathTree<V>());
+      }
+      ref = ref.map.get(part) as PathTree<V>;
+    }
+    ref.map.set(path[path.length - 1], value);
+  }
+}
+
+function transposeData(
+  data: Record<string, unknown>[],
+  field: Field,
+  path: string[] = [],
+): Vector {
+  if (field.type instanceof Struct) {
+    const childFields = field.type.children;
+    const childVectors = childFields.map((child) => {
+      return transposeData(data, child, [...path, child.name]);
+    });
+    const structData = makeData({
+      type: field.type,
+      children: childVectors as unknown as ArrowData<DataType>[],
+    });
+    return arrowMakeVector(structData);
+  } else {
+    const valuesPath = [...path, field.name];
+    const values = data.map((datum) => {
+      let current: unknown = datum;
+      for (const key of valuesPath) {
+        if (isObject(current) && Object.hasOwn(current, key)) {
+          current = current[key];
+        } else {
+          return null;
+        }
+      }
+      return current;
+    });
+    return makeVector(values, field.type);
   }
-  return tbl;
 }
 
 /**
@@ -491,6 +689,31 @@ function makeVector(
 ): Vector<any> {
   if (type !== undefined) {
     // No need for inference, let Arrow create it
+    if (type instanceof Int) {
+      if (DataType.isInt(type) && type.bitWidth === 64) {
+        // wrap in BigInt to avoid bug: https://github.com/apache/arrow/issues/40051
+        values = values.map((v) => {
+          if (v === null) {
+            return v;
+          } else if (typeof v === "bigint") {
+            return v;
+          } else if (typeof v === "number") {
+            return BigInt(v);
+          } else {
+            return v;
+          }
+        });
+      } else {
+        // Similarly, bigint isn't supported for 16 or 32-bit ints.
+        values = values.map((v) => {
+          if (typeof v == "bigint") {
+            return Number(v);
+          } else {
+            return v;
+          }
+        });
+      }
+    }
     return vectorFromArray(values, type);
   }
   if (values.length === 0) {
@@ -902,7 +1125,7 @@ function validateSchemaEmbeddings(
   schema: Schema,
   data: Array<Record<string, unknown>>,
   embeddings: EmbeddingFunctionConfig | undefined,
-) {
+): Schema {
   const fields = [];
   const missingEmbeddingFields = [];
 

nodejs/lancedb/connection.ts

@@ -52,6 +52,8 @@ export interface CreateTableOptions {
    *
    * The default is `stable`.
    * Set to "legacy" to use the old format.
+   *
+   * @deprecated Pass `new_table_data_storage_version` to storageOptions instead.
    */
   dataStorageVersion?: string;
 
@@ -61,17 +63,11 @@ export interface CreateTableOptions {
    * turning this on will make the dataset unreadable for older versions
    * of LanceDB (prior to 0.10.0). To migrate an existing dataset, instead
    * use the {@link LocalTable#migrateManifestPathsV2} method.
+   *
+   * @deprecated Pass `new_table_enable_v2_manifest_paths` to storageOptions instead.
    */
   enableV2ManifestPaths?: boolean;
 
-  /**
-   * If true then data files will be written with the legacy format
-   *
-   * The default is false.
-   *
-   * Deprecated. Use data storage version instead.
-   */
-  useLegacyFormat?: boolean;
   schema?: SchemaLike;
   embeddingFunction?: EmbeddingFunctionConfig;
 }
@@ -215,6 +211,11 @@ export abstract class Connection {
    * @param {string} name The name of the table to drop.
    */
   abstract dropTable(name: string): Promise<void>;
+
+  /**
+   * Drop all tables in the database.
+   */
+  abstract dropAllTables(): Promise<void>;
 }
 
 /** @hideconstructor */
@@ -256,6 +257,28 @@ export class LocalConnection extends Connection {
     return new LocalTable(innerTable);
   }
 
+  private getStorageOptions(
+    options?: Partial<CreateTableOptions>,
+  ): Record<string, string> | undefined {
+    if (options?.dataStorageVersion !== undefined) {
+      if (options.storageOptions === undefined) {
+        options.storageOptions = {};
+      }
+      options.storageOptions["newTableDataStorageVersion"] =
+        options.dataStorageVersion;
+    }
+
+    if (options?.enableV2ManifestPaths !== undefined) {
+      if (options.storageOptions === undefined) {
+        options.storageOptions = {};
+      }
+      options.storageOptions["newTableEnableV2ManifestPaths"] =
+        options.enableV2ManifestPaths ? "true" : "false";
+    }
+
+    return cleanseStorageOptions(options?.storageOptions);
+  }
+
   async createTable(
     nameOrOptions:
       | string
@@ -272,20 +295,14 @@ export class LocalConnection extends Connection {
       throw new Error("data is required");
     }
     const { buf, mode } = await parseTableData(data, options);
-    let dataStorageVersion = "stable";
-    if (options?.dataStorageVersion !== undefined) {
-      dataStorageVersion = options.dataStorageVersion;
-    } else if (options?.useLegacyFormat !== undefined) {
-      dataStorageVersion = options.useLegacyFormat ? "legacy" : "stable";
-    }
+
+    const storageOptions = this.getStorageOptions(options);
 
     const innerTable = await this.inner.createTable(
       nameOrOptions,
       buf,
       mode,
-      cleanseStorageOptions(options?.storageOptions),
-      dataStorageVersion,
-      options?.enableV2ManifestPaths,
+      storageOptions,
     );
 
     return new LocalTable(innerTable);
@@ -309,22 +326,14 @@ export class LocalConnection extends Connection {
       metadata = registry.getTableMetadata([embeddingFunction]);
     }
 
-    let dataStorageVersion = "stable";
-    if (options?.dataStorageVersion !== undefined) {
-      dataStorageVersion = options.dataStorageVersion;
-    } else if (options?.useLegacyFormat !== undefined) {
-      dataStorageVersion = options.useLegacyFormat ? "legacy" : "stable";
-    }
-
+    const storageOptions = this.getStorageOptions(options);
     const table = makeEmptyTable(schema, metadata);
     const buf = await fromTableToBuffer(table);
     const innerTable = await this.inner.createEmptyTable(
       name,
       buf,
       mode,
-      cleanseStorageOptions(options?.storageOptions),
-      dataStorageVersion,
-      options?.enableV2ManifestPaths,
+      storageOptions,
     );
     return new LocalTable(innerTable);
   }
@@ -332,6 +341,10 @@ export class LocalConnection extends Connection {
   async dropTable(name: string): Promise<void> {
     return this.inner.dropTable(name);
   }
+
+  async dropAllTables(): Promise<void> {
+    return this.inner.dropAllTables();
+  }
 }
 
 /**

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.15.1-beta.2",
+	"version": "0.16.1-beta.2",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.15.1-beta.2",
+	"version": "0.16.1-beta.2",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

nodejs/npm/linux-arm64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.15.1-beta.2",
+	"version": "0.16.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-arm64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.15.1-beta.2",
+	"version": "0.16.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",

nodejs/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.15.1-beta.2",
+	"version": "0.16.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/linux-x64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.15.1-beta.2",
+	"version": "0.16.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",

nodejs/npm/win32-arm64-msvc/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.15.1-beta.2",
+  "version": "0.16.1-beta.2",
   "os": [
     "win32"
   ],

nodejs/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.15.1-beta.2",
+	"version": "0.16.1-beta.2",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.15.1-beta.2",
+  "version": "0.16.1-beta.2",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.15.1-beta.2",
+      "version": "0.16.1-beta.2",
       "cpu": [
         "x64",
         "arm64"

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.15.1-beta.2",
+  "version": "0.16.1-beta.2",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

nodejs/src/connection.rs

@@ -2,17 +2,15 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 use std::collections::HashMap;
-use std::str::FromStr;
 
+use lancedb::database::CreateTableMode;
 use napi::bindgen_prelude::*;
 use napi_derive::*;
 
-use crate::error::{convert_error, NapiErrorExt};
+use crate::error::NapiErrorExt;
 use crate::table::Table;
 use crate::ConnectionOptions;
-use lancedb::connection::{
-    ConnectBuilder, Connection as LanceDBConnection, CreateTableMode, LanceFileVersion,
-};
+use lancedb::connection::{ConnectBuilder, Connection as LanceDBConnection};
 use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};
 
 #[napi]
@@ -124,8 +122,6 @@ impl Connection {
         buf: Buffer,
         mode: String,
         storage_options: Option<HashMap<String, String>>,
-        data_storage_options: Option<String>,
-        enable_v2_manifest_paths: Option<bool>,
     ) -> napi::Result<Table> {
         let batches = ipc_file_to_batches(buf.to_vec())
             .map_err(|e| napi::Error::from_reason(format!("Failed to read IPC file: {}", e)))?;
@@ -137,14 +133,6 @@ impl Connection {
                 builder = builder.storage_option(key, value);
             }
         }
-        if let Some(data_storage_option) = data_storage_options.as_ref() {
-            builder = builder.data_storage_version(
-                LanceFileVersion::from_str(data_storage_option).map_err(|e| convert_error(&e))?,
-            );
-        }
-        if let Some(enable_v2_manifest_paths) = enable_v2_manifest_paths {
-            builder = builder.enable_v2_manifest_paths(enable_v2_manifest_paths);
-        }
         let tbl = builder.execute().await.default_error()?;
         Ok(Table::new(tbl))
     }
@@ -156,8 +144,6 @@ impl Connection {
         schema_buf: Buffer,
         mode: String,
         storage_options: Option<HashMap<String, String>>,
-        data_storage_options: Option<String>,
-        enable_v2_manifest_paths: Option<bool>,
     ) -> napi::Result<Table> {
         let schema = ipc_file_to_schema(schema_buf.to_vec()).map_err(|e| {
             napi::Error::from_reason(format!("Failed to marshal schema from JS to Rust: {}", e))
@@ -172,14 +158,6 @@ impl Connection {
                 builder = builder.storage_option(key, value);
             }
         }
-        if let Some(data_storage_option) = data_storage_options.as_ref() {
-            builder = builder.data_storage_version(
-                LanceFileVersion::from_str(data_storage_option).map_err(|e| convert_error(&e))?,
-            );
-        }
-        if let Some(enable_v2_manifest_paths) = enable_v2_manifest_paths {
-            builder = builder.enable_v2_manifest_paths(enable_v2_manifest_paths);
-        }
         let tbl = builder.execute().await.default_error()?;
         Ok(Table::new(tbl))
     }
@@ -209,4 +187,9 @@ impl Connection {
     pub async fn drop_table(&self, name: String) -> napi::Result<()> {
         self.get_inner()?.drop_table(&name).await.default_error()
     }
+
+    #[napi(catch_unwind)]
+    pub async fn drop_all_tables(&self) -> napi::Result<()> {
+        self.get_inner()?.drop_all_tables().await.default_error()
+    }
 }

nodejs/src/remote.rs

@@ -1,6 +1,8 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+use std::collections::HashMap;
+
 use napi_derive::*;
 
 /// Timeout configuration for remote HTTP client.
@@ -67,6 +69,7 @@ pub struct ClientConfig {
     pub user_agent: Option<String>,
     pub retry_config: Option<RetryConfig>,
     pub timeout_config: Option<TimeoutConfig>,
+    pub extra_headers: Option<HashMap<String, String>>,
 }
 
 impl From<TimeoutConfig> for lancedb::remote::TimeoutConfig {
@@ -104,6 +107,7 @@ impl From<ClientConfig> for lancedb::remote::ClientConfig {
                 .unwrap_or(concat!("LanceDB-Node-Client/", env!("CARGO_PKG_VERSION")).to_string()),
             retry_config: config.retry_config.map(Into::into).unwrap_or_default(),
             timeout_config: config.timeout_config.map(Into::into).unwrap_or_default(),
+            extra_headers: config.extra_headers.unwrap_or_default(),
         }
     }
 }

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.18.1-beta.3"
+current_version = "0.19.1-beta.3"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/.gitignore

@@ -0,0 +1,2 @@
+# Test data created by some example tests
+data/

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.18.1-beta.3"
+version = "0.19.1-beta.3"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/Makefile

@@ -29,4 +29,4 @@ doctest:	## Run documentation tests.
 
 .PHONY: test
 test:		## Run tests.
-	pytest python/tests -vv --durations=10 -m "not slow"
+	pytest python/tests -vv --durations=10 -m "not slow and not s3_test"

python/pyproject.toml

@@ -4,7 +4,7 @@ name = "lancedb"
 dynamic = ["version"]
 dependencies = [
     "deprecation",
-    "pylance==0.23.0b4",
+    "pylance==0.23.0",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
     "packaging",

python/python/lancedb/_lancedb.pyi

@@ -15,8 +15,6 @@ class Connection(object):
         mode: str,
         data: pa.RecordBatchReader,
         storage_options: Optional[Dict[str, str]] = None,
-        data_storage_version: Optional[str] = None,
-        enable_v2_manifest_paths: Optional[bool] = None,
     ) -> Table: ...
     async def create_empty_table(
         self,
@@ -24,8 +22,6 @@ class Connection(object):
         mode: str,
         schema: pa.Schema,
         storage_options: Optional[Dict[str, str]] = None,
-        data_storage_version: Optional[str] = None,
-        enable_v2_manifest_paths: Optional[bool] = None,
     ) -> Table: ...
     async def rename_table(self, old_name: str, new_name: str) -> None: ...
     async def drop_table(self, name: str) -> None: ...

python/python/lancedb/arrow.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-from typing import List, Optional, Union
+from typing import List, Optional, Tuple, Union
 
 import pyarrow as pa
 
@@ -66,3 +66,17 @@ class AsyncRecordBatchReader:
         batches = table.to_batches(max_chunksize=max_batch_length)
         for batch in batches:
             yield batch
+
+
+def peek_reader(
+    reader: pa.RecordBatchReader,
+) -> Tuple[pa.RecordBatch, pa.RecordBatchReader]:
+    if not isinstance(reader, pa.RecordBatchReader):
+        raise TypeError("reader must be a RecordBatchReader")
+    batch = reader.read_next_batch()
+
+    def all_batches():
+        yield batch
+        yield from reader
+
+    return batch, pa.RecordBatchReader.from_batches(batch.schema, all_batches())

python/python/lancedb/db.py

@@ -14,6 +14,7 @@ from overrides import EnforceOverrides, override  # type: ignore
 from lancedb.common import data_to_reader, sanitize_uri, validate_schema
 from lancedb.background_loop import LOOP
 
+from . import __version__
 from ._lancedb import connect as lancedb_connect  # type: ignore
 from .table import (
     AsyncTable,
@@ -26,6 +27,8 @@ from .util import (
     validate_table_name,
 )
 
+import deprecation
+
 if TYPE_CHECKING:
     import pyarrow as pa
     from .pydantic import LanceModel
@@ -119,19 +122,11 @@ class DBConnection(EnforceOverrides):
             See available options at
             <https://lancedb.github.io/lancedb/guides/storage/>
         data_storage_version: optional, str, default "stable"
-            The version of the data storage format to use. Newer versions are more
-            efficient but require newer versions of lance to read.  The default is
-            "stable" which will use the legacy v2 version.  See the user guide
-            for more details.
-        enable_v2_manifest_paths: bool, optional, default False
-            Use the new V2 manifest paths. These paths provide more efficient
-            opening of datasets with many versions on object stores.  WARNING:
-            turning this on will make the dataset unreadable for older versions
-            of LanceDB (prior to 0.13.0). To migrate an existing dataset, instead
-            use the
-            [Table.migrate_manifest_paths_v2][lancedb.table.Table.migrate_v2_manifest_paths]
-            method.
-
+            Deprecated.  Set `storage_options` when connecting to the database and set
+            `new_table_data_storage_version` in the options.
+        enable_v2_manifest_paths: optional, bool, default False
+            Deprecated.  Set `storage_options` when connecting to the database and set
+            `new_table_enable_v2_manifest_paths` in the options.
         Returns
         -------
         LanceTable
@@ -302,6 +297,12 @@ class DBConnection(EnforceOverrides):
         """
         raise NotImplementedError
 
+    def drop_all_tables(self):
+        """
+        Drop all tables from the database
+        """
+        raise NotImplementedError
+
     @property
     def uri(self) -> str:
         return self._uri
@@ -452,8 +453,6 @@ class LanceDBConnection(DBConnection):
             fill_value=fill_value,
             embedding_functions=embedding_functions,
             storage_options=storage_options,
-            data_storage_version=data_storage_version,
-            enable_v2_manifest_paths=enable_v2_manifest_paths,
         )
         return tbl
 
@@ -496,9 +495,19 @@ class LanceDBConnection(DBConnection):
         """
         LOOP.run(self._conn.drop_table(name, ignore_missing=ignore_missing))
 
+    @override
+    def drop_all_tables(self):
+        LOOP.run(self._conn.drop_all_tables())
+
+    @deprecation.deprecated(
+        deprecated_in="0.15.1",
+        removed_in="0.17",
+        current_version=__version__,
+        details="Use drop_all_tables() instead",
+    )
     @override
     def drop_database(self):
-        LOOP.run(self._conn.drop_database())
+        LOOP.run(self._conn.drop_all_tables())
 
 
 class AsyncConnection(object):
@@ -595,9 +604,6 @@ class AsyncConnection(object):
         storage_options: Optional[Dict[str, str]] = None,
         *,
         embedding_functions: Optional[List[EmbeddingFunctionConfig]] = None,
-        data_storage_version: Optional[str] = None,
-        use_legacy_format: Optional[bool] = None,
-        enable_v2_manifest_paths: Optional[bool] = None,
     ) -> AsyncTable:
         """Create an [AsyncTable][lancedb.table.AsyncTable] in the database.
 
@@ -640,23 +646,6 @@ class AsyncConnection(object):
             connection will be inherited by the table, but can be overridden here.
             See available options at
             <https://lancedb.github.io/lancedb/guides/storage/>
-        data_storage_version: optional, str, default "stable"
-            The version of the data storage format to use. Newer versions are more
-            efficient but require newer versions of lance to read.  The default is
-            "stable" which will use the legacy v2 version.  See the user guide
-            for more details.
-        use_legacy_format: bool, optional, default False. (Deprecated)
-            If True, use the legacy format for the table. If False, use the new format.
-            This method is deprecated, use `data_storage_version` instead.
-        enable_v2_manifest_paths: bool, optional, default False
-            Use the new V2 manifest paths. These paths provide more efficient
-            opening of datasets with many versions on object stores.  WARNING:
-            turning this on will make the dataset unreadable for older versions
-            of LanceDB (prior to 0.13.0). To migrate an existing dataset, instead
-            use the
-            [AsyncTable.migrate_manifest_paths_v2][lancedb.table.AsyncTable.migrate_manifest_paths_v2]
-            method.
-
 
         Returns
         -------
@@ -795,17 +784,12 @@ class AsyncConnection(object):
         if mode == "create" and exist_ok:
             mode = "exist_ok"
 
-        if not data_storage_version:
-            data_storage_version = "legacy" if use_legacy_format else "stable"
-
         if data is None:
             new_table = await self._inner.create_empty_table(
                 name,
                 mode,
                 schema,
                 storage_options=storage_options,
-                data_storage_version=data_storage_version,
-                enable_v2_manifest_paths=enable_v2_manifest_paths,
             )
         else:
             data = data_to_reader(data, schema)
@@ -814,8 +798,6 @@ class AsyncConnection(object):
                 mode,
                 data,
                 storage_options=storage_options,
-                data_storage_version=data_storage_version,
-                enable_v2_manifest_paths=enable_v2_manifest_paths,
             )
 
         return AsyncTable(new_table)
@@ -885,9 +867,19 @@ class AsyncConnection(object):
             if f"Table '{name}' was not found" not in str(e):
                 raise e
 
+    async def drop_all_tables(self):
+        """Drop all tables from the database."""
+        await self._inner.drop_all_tables()
+
+    @deprecation.deprecated(
+        deprecated_in="0.15.1",
+        removed_in="0.17",
+        current_version=__version__,
+        details="Use drop_all_tables() instead",
+    )
     async def drop_database(self):
         """
         Drop database
         This is the same thing as dropping all the tables
         """
-        await self._inner.drop_db()
+        await self._inner.drop_all_tables()

python/python/lancedb/embeddings/base.py

@@ -116,7 +116,7 @@ class EmbeddingFunction(BaseModel, ABC):
         )
 
     @abstractmethod
-    def ndims(self):
+    def ndims(self) -> int:
         """
         Return the dimensions of the vector column
         """

python/python/lancedb/pydantic.py

@@ -199,18 +199,29 @@ else:
         ]
 
 
+def _pydantic_type_to_arrow_type(tp: Any, field: FieldInfo) -> pa.DataType:
+    if inspect.isclass(tp):
+        if issubclass(tp, pydantic.BaseModel):
+            # Struct
+            fields = _pydantic_model_to_fields(tp)
+            return pa.struct(fields)
+        if issubclass(tp, FixedSizeListMixin):
+            return pa.list_(tp.value_arrow_type(), tp.dim())
+    return _py_type_to_arrow_type(tp, field)
+
+
 def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
     """Convert a Pydantic FieldInfo to Arrow DataType"""
-
     if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
         origin = field.annotation.__origin__
         args = field.annotation.__args__
+
         if origin is list:
             child = args[0]
             return pa.list_(_py_type_to_arrow_type(child, field))
         elif origin == Union:
             if len(args) == 2 and args[1] is type(None):
-                return _py_type_to_arrow_type(args[0], field)
+                return _pydantic_type_to_arrow_type(args[0], field)
     elif sys.version_info >= (3, 10) and isinstance(field.annotation, types.UnionType):
         args = field.annotation.__args__
         if len(args) == 2:
@@ -218,14 +229,7 @@ def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
                 if typ is type(None):
                     continue
                 return _py_type_to_arrow_type(typ, field)
-    elif inspect.isclass(field.annotation):
-        if issubclass(field.annotation, pydantic.BaseModel):
-            # Struct
-            fields = _pydantic_model_to_fields(field.annotation)
-            return pa.struct(fields)
-        elif issubclass(field.annotation, FixedSizeListMixin):
-            return pa.list_(field.annotation.value_arrow_type(), field.annotation.dim())
-    return _py_type_to_arrow_type(field.annotation, field)
+    return _pydantic_type_to_arrow_type(field.annotation, field)
 
 
 def is_nullable(field: FieldInfo) -> bool:

python/python/lancedb/remote/__init__.py

@@ -109,6 +109,7 @@ class ClientConfig:
     user_agent: str = f"LanceDB-Python-Client/{__version__}"
     retry_config: RetryConfig = field(default_factory=RetryConfig)
     timeout_config: Optional[TimeoutConfig] = field(default_factory=TimeoutConfig)
+    extra_headers: Optional[dict] = None
 
     def __post_init__(self):
         if isinstance(self.retry_config, dict):

python/python/lancedb/remote/table.py

@@ -526,6 +526,9 @@ class RemoteTable(Table):
     def drop_columns(self, columns: Iterable[str]):
         return LOOP.run(self._table.drop_columns(columns))
 
+    def drop_index(self, index_name: str):
+        return LOOP.run(self._table.drop_index(index_name))
+
     def uses_v2_manifest_paths(self) -> bool:
         raise NotImplementedError(
             "uses_v2_manifest_paths() is not supported on the LanceDB Cloud"

python/python/lancedb/table.py

@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import inspect
+import warnings
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from datetime import datetime, timedelta
@@ -23,6 +24,7 @@ from typing import (
 from urllib.parse import urlparse
 
 import lance
+from lancedb.arrow import peek_reader
 from lancedb.background_loop import LOOP
 from .dependencies import _check_for_pandas
 import pyarrow as pa
@@ -73,17 +75,19 @@ pl = safe_import_polars()
 QueryType = Literal["vector", "fts", "hybrid", "auto"]
 
 
-def _into_pyarrow_table(data) -> pa.Table:
+def _into_pyarrow_reader(data) -> pa.RecordBatchReader:
     if _check_for_hugging_face(data):
         # Huggingface datasets
         from lance.dependencies import datasets
 
         if isinstance(data, datasets.Dataset):
             schema = data.features.arrow_schema
-            return pa.Table.from_batches(data.data.to_batches(), schema=schema)
+            return pa.RecordBatchReader.from_batches(schema, data.data.to_batches())
         elif isinstance(data, datasets.dataset_dict.DatasetDict):
             schema = _schema_from_hf(data, schema)
-            return pa.Table.from_batches(_to_batches_with_split(data), schema=schema)
+            return pa.RecordBatchReader.from_batches(
+                schema, _to_batches_with_split(data)
+            )
     if isinstance(data, LanceModel):
         raise ValueError("Cannot add a single LanceModel to a table. Use a list.")
 
@@ -95,41 +99,41 @@ def _into_pyarrow_table(data) -> pa.Table:
         if isinstance(data[0], LanceModel):
             schema = data[0].__class__.to_arrow_schema()
             data = [model_to_dict(d) for d in data]
-            return pa.Table.from_pylist(data, schema=schema)
+            return pa.Table.from_pylist(data, schema=schema).to_reader()
         elif isinstance(data[0], pa.RecordBatch):
-            return pa.Table.from_batches(data)
+            return pa.Table.from_batches(data).to_reader()
         else:
-            return pa.Table.from_pylist(data)
+            return pa.Table.from_pylist(data).to_reader()
     elif _check_for_pandas(data) and isinstance(data, pd.DataFrame):
         table = pa.Table.from_pandas(data, preserve_index=False)
         # Do not serialize Pandas metadata
         meta = table.schema.metadata if table.schema.metadata is not None else {}
         meta = {k: v for k, v in meta.items() if k != b"pandas"}
-        return table.replace_schema_metadata(meta)
+        return table.replace_schema_metadata(meta).to_reader()
     elif isinstance(data, pa.Table):
-        return data
+        return data.to_reader()
     elif isinstance(data, pa.RecordBatch):
-        return pa.Table.from_batches([data])
+        return pa.RecordBatchReader.from_batches(data.schema, [data])
     elif isinstance(data, LanceDataset):
-        return data.scanner().to_table()
+        return data.scanner().to_reader()
     elif isinstance(data, pa.dataset.Dataset):
-        return data.to_table()
+        return data.scanner().to_reader()
     elif isinstance(data, pa.dataset.Scanner):
-        return data.to_table()
+        return data.to_reader()
     elif isinstance(data, pa.RecordBatchReader):
-        return data.read_all()
+        return data
     elif (
         type(data).__module__.startswith("polars")
         and data.__class__.__name__ == "DataFrame"
     ):
-        return data.to_arrow()
+        return data.to_arrow().to_reader()
     elif (
         type(data).__module__.startswith("polars")
         and data.__class__.__name__ == "LazyFrame"
     ):
-        return data.collect().to_arrow()
+        return data.collect().to_arrow().to_reader()
     elif isinstance(data, Iterable):
-        return _iterator_to_table(data)
+        return _iterator_to_reader(data)
     else:
         raise TypeError(
             f"Unknown data type {type(data)}. "
@@ -139,30 +143,28 @@ def _into_pyarrow_table(data) -> pa.Table:
         )
 
 
-def _iterator_to_table(data: Iterable) -> pa.Table:
-    batches = []
-    schema = None  # Will get schema from first batch
-    for batch in data:
-        batch_table = _into_pyarrow_table(batch)
-        if schema is not None:
-            if batch_table.schema != schema:
+def _iterator_to_reader(data: Iterable) -> pa.RecordBatchReader:
+    # Each batch is treated as it's own reader, mainly so we can
+    # re-use the _into_pyarrow_reader logic.
+    first = _into_pyarrow_reader(next(data))
+    schema = first.schema
+
+    def gen():
+        yield from first
+        for batch in data:
+            table: pa.Table = _into_pyarrow_reader(batch).read_all()
+            if table.schema != schema:
                 try:
-                    batch_table = batch_table.cast(schema)
+                    table = table.cast(schema)
                 except pa.lib.ArrowInvalid:
                     raise ValueError(
                         f"Input iterator yielded a batch with schema that "
                         f"does not match the schema of other batches.\n"
-                        f"Expected:\n{schema}\nGot:\n{batch_table.schema}"
+                        f"Expected:\n{schema}\nGot:\n{batch.schema}"
                     )
-        else:
-            # Use the first schema for the remainder of the batches
-            schema = batch_table.schema
-        batches.append(batch_table)
+            yield from table.to_batches()
 
-    if batches:
-        return pa.concat_tables(batches)
-    else:
-        raise ValueError("Input iterable is empty")
+    return pa.RecordBatchReader.from_batches(schema, gen())
 
 
 def _sanitize_data(
@@ -173,7 +175,7 @@ def _sanitize_data(
     fill_value: float = 0.0,
     *,
     allow_subschema: bool = False,
-) -> pa.Table:
+) -> pa.RecordBatchReader:
     """
     Handle input data, applying all standard transformations.
 
@@ -206,20 +208,20 @@ def _sanitize_data(
     # 1. There might be embedding columns missing that will be added
     #    in the add_embeddings step.
     # 2. If `allow_subschemas` is True, there might be columns missing.
-    table = _into_pyarrow_table(data)
+    reader = _into_pyarrow_reader(data)
 
-    table = _append_vector_columns(table, target_schema, metadata=metadata)
+    reader = _append_vector_columns(reader, target_schema, metadata=metadata)
 
     # This happens before the cast so we can fix vector columns with
     # incorrect lengths before they are cast to FSL.
-    table = _handle_bad_vectors(
-        table,
+    reader = _handle_bad_vectors(
+        reader,
         on_bad_vectors=on_bad_vectors,
         fill_value=fill_value,
     )
 
     if target_schema is None:
-        target_schema = _infer_target_schema(table)
+        target_schema, reader = _infer_target_schema(reader)
 
     if metadata:
         new_metadata = target_schema.metadata or {}
@@ -228,25 +230,25 @@ def _sanitize_data(
 
     _validate_schema(target_schema)
 
-    table = _cast_to_target_schema(table, target_schema, allow_subschema)
+    reader = _cast_to_target_schema(reader, target_schema, allow_subschema)
 
-    return table
+    return reader
 
 
 def _cast_to_target_schema(
-    table: pa.Table,
+    reader: pa.RecordBatchReader,
     target_schema: pa.Schema,
     allow_subschema: bool = False,
-) -> pa.Table:
+) -> pa.RecordBatchReader:
     # pa.Table.cast expects field order not to be changed.
     # Lance doesn't care about field order, so we don't need to rearrange fields
     # to match the target schema. We just need to correctly cast the fields.
-    if table.schema == target_schema:
+    if reader.schema == target_schema:
         # Fast path when the schemas are already the same
-        return table
+        return reader
 
     fields = []
-    for field in table.schema:
+    for field in reader.schema:
         target_field = target_schema.field(field.name)
         if target_field is None:
             raise ValueError(f"Field {field.name} not found in target schema")
@@ -259,12 +261,16 @@ def _cast_to_target_schema(
 
     if allow_subschema and len(reordered_schema) != len(target_schema):
         fields = _infer_subschema(
-            list(iter(table.schema)), list(iter(reordered_schema))
+            list(iter(reader.schema)), list(iter(reordered_schema))
         )
-        subschema = pa.schema(fields, metadata=target_schema.metadata)
-        return table.cast(subschema)
-    else:
-        return table.cast(reordered_schema)
+        reordered_schema = pa.schema(fields, metadata=target_schema.metadata)
+
+    def gen():
+        for batch in reader:
+            # Table but not RecordBatch has cast.
+            yield pa.Table.from_batches([batch]).cast(reordered_schema).to_batches()[0]
+
+    return pa.RecordBatchReader.from_batches(reordered_schema, gen())
 
 
 def _infer_subschema(
@@ -343,7 +349,10 @@ def sanitize_create_table(
     if metadata:
         schema = schema.with_metadata(metadata)
         # Need to apply metadata to the data as well
-        data = data.replace_schema_metadata(metadata)
+        if isinstance(data, pa.Table):
+            data = data.replace_schema_metadata(metadata)
+        elif isinstance(data, pa.RecordBatchReader):
+            data = pa.RecordBatchReader.from_batches(schema, data)
 
     return data, schema
 
@@ -380,11 +389,11 @@ def _to_batches_with_split(data):
 
 
 def _append_vector_columns(
-    data: pa.Table,
+    reader: pa.RecordBatchReader,
     schema: Optional[pa.Schema] = None,
     *,
     metadata: Optional[dict] = None,
-) -> pa.Table:
+) -> pa.RecordBatchReader:
     """
     Use the embedding function to automatically embed the source columns and add the
     vector columns to the table.
@@ -395,28 +404,43 @@ def _append_vector_columns(
         metadata = schema.metadata or metadata or {}
     functions = EmbeddingFunctionRegistry.get_instance().parse_functions(metadata)
 
+    if not functions:
+        return reader
+
+    fields = list(reader.schema)
     for vector_column, conf in functions.items():
-        func = conf.function
-        no_vector_column = vector_column not in data.column_names
-        if no_vector_column or pc.all(pc.is_null(data[vector_column])).as_py():
-            col_data = func.compute_source_embeddings_with_retry(
-                data[conf.source_column]
-            )
+        if vector_column not in reader.schema.names:
             if schema is not None:
-                dtype = schema.field(vector_column).type
+                field = schema.field(vector_column)
             else:
-                dtype = pa.list_(pa.float32(), len(col_data[0]))
-            if no_vector_column:
-                data = data.append_column(
-                    pa.field(vector_column, type=dtype), pa.array(col_data, type=dtype)
-                )
-            else:
-                data = data.set_column(
-                    data.column_names.index(vector_column),
-                    pa.field(vector_column, type=dtype),
-                    pa.array(col_data, type=dtype),
-                )
-    return data
+                dtype = pa.list_(pa.float32(), conf.function.ndims())
+                field = pa.field(vector_column, type=dtype, nullable=True)
+            fields.append(field)
+    schema = pa.schema(fields, metadata=reader.schema.metadata)
+
+    def gen():
+        for batch in reader:
+            for vector_column, conf in functions.items():
+                func = conf.function
+                no_vector_column = vector_column not in batch.column_names
+                if no_vector_column or pc.all(pc.is_null(batch[vector_column])).as_py():
+                    col_data = func.compute_source_embeddings_with_retry(
+                        batch[conf.source_column]
+                    )
+                    if no_vector_column:
+                        batch = batch.append_column(
+                            schema.field(vector_column),
+                            pa.array(col_data, type=schema.field(vector_column).type),
+                        )
+                    else:
+                        batch = batch.set_column(
+                            batch.column_names.index(vector_column),
+                            schema.field(vector_column),
+                            pa.array(col_data, type=schema.field(vector_column).type),
+                        )
+            yield batch
+
+    return pa.RecordBatchReader.from_batches(schema, gen())
 
 
 def _table_path(base: str, table_name: str) -> str:
@@ -2085,10 +2109,37 @@ class LanceTable(Table):
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
         embedding_functions: list of EmbeddingFunctionModel, default None
             The embedding functions to use when creating the table.
+        data_storage_version: optional, str, default "stable"
+            Deprecated.  Set `storage_options` when connecting to the database and set
+            `new_table_data_storage_version` in the options.
+        enable_v2_manifest_paths: optional, bool, default False
+            Deprecated.  Set `storage_options` when connecting to the database and set
+            `new_table_enable_v2_manifest_paths` in the options.
         """
         self = cls.__new__(cls)
         self._conn = db
 
+        if data_storage_version is not None:
+            warnings.warn(
+                "setting data_storage_version directly on create_table is deprecated. ",
+                "Use database_options instead.",
+                DeprecationWarning,
+            )
+            if storage_options is None:
+                storage_options = {}
+            storage_options["new_table_data_storage_version"] = data_storage_version
+        if enable_v2_manifest_paths is not None:
+            warnings.warn(
+                "setting enable_v2_manifest_paths directly on create_table is ",
+                "deprecated. Use database_options instead.",
+                DeprecationWarning,
+            )
+            if storage_options is None:
+                storage_options = {}
+            storage_options["new_table_enable_v2_manifest_paths"] = (
+                enable_v2_manifest_paths
+            )
+
         self._table = LOOP.run(
             self._conn._conn.create_table(
                 name,
@@ -2100,8 +2151,6 @@ class LanceTable(Table):
                 fill_value=fill_value,
                 embedding_functions=embedding_functions,
                 storage_options=storage_options,
-                data_storage_version=data_storage_version,
-                enable_v2_manifest_paths=enable_v2_manifest_paths,
             )
         )
         return self
@@ -2332,11 +2381,13 @@ class LanceTable(Table):
 
 
 def _handle_bad_vectors(
-    table: pa.Table,
+    reader: pa.RecordBatchReader,
     on_bad_vectors: Literal["error", "drop", "fill", "null"] = "error",
     fill_value: float = 0.0,
-) -> pa.Table:
-    for field in table.schema:
+) -> pa.RecordBatchReader:
+    vector_columns = []
+
+    for field in reader.schema:
         # They can provide a 'vector' column that isn't yet a FSL
         named_vector_col = (
             (
@@ -2356,22 +2407,28 @@ def _handle_bad_vectors(
         )
 
         if named_vector_col or likely_vector_col:
-            table = _handle_bad_vector_column(
-                table,
-                vector_column_name=field.name,
-                on_bad_vectors=on_bad_vectors,
-                fill_value=fill_value,
-            )
+            vector_columns.append(field.name)
 
-    return table
+    def gen():
+        for batch in reader:
+            for name in vector_columns:
+                batch = _handle_bad_vector_column(
+                    batch,
+                    vector_column_name=name,
+                    on_bad_vectors=on_bad_vectors,
+                    fill_value=fill_value,
+                )
+            yield batch
+
+    return pa.RecordBatchReader.from_batches(reader.schema, gen())
 
 
 def _handle_bad_vector_column(
-    data: pa.Table,
+    data: pa.RecordBatch,
     vector_column_name: str,
     on_bad_vectors: str = "error",
     fill_value: float = 0.0,
-) -> pa.Table:
+) -> pa.RecordBatch:
     """
     Ensure that the vector column exists and has type fixed_size_list(float)
 
@@ -2459,8 +2516,11 @@ def has_nan_values(arr: Union[pa.ListArray, pa.ChunkedArray]) -> pa.BooleanArray
     return pc.is_in(indices, has_nan_indices)
 
 
-def _infer_target_schema(table: pa.Table) -> pa.Schema:
-    schema = table.schema
+def _infer_target_schema(
+    reader: pa.RecordBatchReader,
+) -> Tuple[pa.Schema, pa.RecordBatchReader]:
+    schema = reader.schema
+    peeked = None
 
     for i, field in enumerate(schema):
         if (
@@ -2468,8 +2528,10 @@ def _infer_target_schema(table: pa.Table) -> pa.Schema:
             and (pa.types.is_list(field.type) or pa.types.is_large_list(field.type))
             and pa.types.is_floating(field.type.value_type)
         ):
+            if peeked is None:
+                peeked, reader = peek_reader(reader)
             # Use the most common length of the list as the dimensions
-            dim = _modal_list_size(table.column(i))
+            dim = _modal_list_size(peeked.column(i))
 
             new_field = pa.field(
                 VECTOR_COLUMN_NAME,
@@ -2483,8 +2545,10 @@ def _infer_target_schema(table: pa.Table) -> pa.Schema:
             and (pa.types.is_list(field.type) or pa.types.is_large_list(field.type))
             and pa.types.is_integer(field.type.value_type)
         ):
+            if peeked is None:
+                peeked, reader = peek_reader(reader)
             # Use the most common length of the list as the dimensions
-            dim = _modal_list_size(table.column(i))
+            dim = _modal_list_size(peeked.column(i))
             new_field = pa.field(
                 VECTOR_COLUMN_NAME,
                 pa.list_(pa.uint8(), dim),
@@ -2493,7 +2557,7 @@ def _infer_target_schema(table: pa.Table) -> pa.Schema:
 
             schema = schema.set(i, new_field)
 
-    return schema
+    return schema, reader
 
 
 def _modal_list_size(arr: Union[pa.ListArray, pa.ChunkedArray]) -> int:

python/python/tests/test_db.py

@@ -299,12 +299,12 @@ def test_create_exist_ok(tmp_db: lancedb.DBConnection):
 @pytest.mark.asyncio
 async def test_connect(tmp_path):
     db = await lancedb.connect_async(tmp_path)
-    assert str(db) == f"NativeDatabase(uri={tmp_path}, read_consistency_interval=None)"
+    assert str(db) == f"ListingDatabase(uri={tmp_path}, read_consistency_interval=None)"
 
     db = await lancedb.connect_async(
         tmp_path, read_consistency_interval=timedelta(seconds=5)
     )
-    assert str(db) == f"NativeDatabase(uri={tmp_path}, read_consistency_interval=5s)"
+    assert str(db) == f"ListingDatabase(uri={tmp_path}, read_consistency_interval=5s)"
 
 
 @pytest.mark.asyncio
@@ -396,13 +396,16 @@ async def test_create_exist_ok_async(tmp_db_async: lancedb.AsyncConnection):
 
 @pytest.mark.asyncio
 async def test_create_table_v2_manifest_paths_async(tmp_path):
-    db = await lancedb.connect_async(tmp_path)
+    db_with_v2_paths = await lancedb.connect_async(
+        tmp_path, storage_options={"new_table_enable_v2_manifest_paths": "true"}
+    )
+    db_no_v2_paths = await lancedb.connect_async(
+        tmp_path, storage_options={"new_table_enable_v2_manifest_paths": "false"}
+    )
     # Create table in v2 mode with v2 manifest paths enabled
-    tbl = await db.create_table(
+    tbl = await db_with_v2_paths.create_table(
         "test_v2_manifest_paths",
         data=[{"id": 0}],
-        use_legacy_format=False,
-        enable_v2_manifest_paths=True,
     )
     assert await tbl.uses_v2_manifest_paths()
     manifests_dir = tmp_path / "test_v2_manifest_paths.lance" / "_versions"
@@ -410,11 +413,9 @@ async def test_create_table_v2_manifest_paths_async(tmp_path):
         assert re.match(r"\d{20}\.manifest", manifest)
 
     # Start a table in V1 mode then migrate
-    tbl = await db.create_table(
+    tbl = await db_no_v2_paths.create_table(
         "test_v2_migration",
         data=[{"id": 0}],
-        use_legacy_format=False,
-        enable_v2_manifest_paths=False,
     )
     assert not await tbl.uses_v2_manifest_paths()
     manifests_dir = tmp_path / "test_v2_migration.lance" / "_versions"
@@ -498,6 +499,10 @@ def test_delete_table(tmp_db: lancedb.DBConnection):
     # if ignore_missing=True
     tmp_db.drop_table("does_not_exist", ignore_missing=True)
 
+    tmp_db.drop_all_tables()
+
+    assert tmp_db.table_names() == []
+
 
 @pytest.mark.asyncio
 async def test_delete_table_async(tmp_db: lancedb.DBConnection):
@@ -583,7 +588,7 @@ def test_empty_or_nonexistent_table(mem_db: lancedb.DBConnection):
 
 
 @pytest.mark.asyncio
-async def test_create_in_v2_mode(mem_db_async: lancedb.AsyncConnection):
+async def test_create_in_v2_mode():
     def make_data():
         for i in range(10):
             yield pa.record_batch([pa.array([x for x in range(1024)])], names=["x"])
@@ -594,10 +599,13 @@ async def test_create_in_v2_mode(mem_db_async: lancedb.AsyncConnection):
     schema = pa.schema([pa.field("x", pa.int64())])
 
     # Create table in v1 mode
-    tbl = await mem_db_async.create_table(
-        "test", data=make_data(), schema=schema, data_storage_version="legacy"
+
+    v1_db = await lancedb.connect_async(
+        "memory://", storage_options={"new_table_data_storage_version": "legacy"}
     )
 
+    tbl = await v1_db.create_table("test", data=make_data(), schema=schema)
+
     async def is_in_v2_mode(tbl):
         batches = (
             await tbl.query().limit(10 * 1024).to_batches(max_batch_length=1024 * 10)
@@ -610,10 +618,12 @@ async def test_create_in_v2_mode(mem_db_async: lancedb.AsyncConnection):
     assert not await is_in_v2_mode(tbl)
 
     # Create table in v2 mode
-    tbl = await mem_db_async.create_table(
-        "test_v2", data=make_data(), schema=schema, use_legacy_format=False
+    v2_db = await lancedb.connect_async(
+        "memory://", storage_options={"new_table_data_storage_version": "stable"}
     )
 
+    tbl = await v2_db.create_table("test_v2", data=make_data(), schema=schema)
+
     assert await is_in_v2_mode(tbl)
 
     # Add data (should remain in v2 mode)
@@ -622,20 +632,18 @@ async def test_create_in_v2_mode(mem_db_async: lancedb.AsyncConnection):
     assert await is_in_v2_mode(tbl)
 
     # Create empty table in v2 mode and add data
-    tbl = await mem_db_async.create_table(
-        "test_empty_v2", data=None, schema=schema, use_legacy_format=False
-    )
+    tbl = await v2_db.create_table("test_empty_v2", data=None, schema=schema)
     await tbl.add(make_table())
 
     assert await is_in_v2_mode(tbl)
 
-    # Create empty table uses v1 mode by default
-    tbl = await mem_db_async.create_table(
-        "test_empty_v2_default", data=None, schema=schema, data_storage_version="legacy"
-    )
+    # Db uses v2 mode by default
+    db = await lancedb.connect_async("memory://")
+
+    tbl = await db.create_table("test_empty_v2_default", data=None, schema=schema)
     await tbl.add(make_table())
 
-    assert not await is_in_v2_mode(tbl)
+    assert await is_in_v2_mode(tbl)
 
 
 def test_replace_index(mem_db: lancedb.DBConnection):

python/python/tests/test_embeddings.py

@@ -107,7 +107,7 @@ def test_embedding_with_bad_results(tmp_path):
         vector: Vector(model.ndims()) = model.VectorField()
 
     table = db.create_table("test", schema=Schema, mode="overwrite")
-    with pytest.raises(ValueError):
+    with pytest.raises(RuntimeError):
         # Default on_bad_vectors is "error"
         table.add([{"text": "hello world"}])
 
@@ -341,6 +341,7 @@ def test_add_optional_vector(tmp_path):
     assert not (np.abs(tbl.to_pandas()["vector"][0]) < 1e-6).all()
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize(
     "embedding_type",
     [

python/python/tests/test_pydantic.py

@@ -10,6 +10,7 @@ import pyarrow as pa
 import pydantic
 import pytest
 from lancedb.pydantic import PYDANTIC_VERSION, LanceModel, Vector, pydantic_to_schema
+from pydantic import BaseModel
 from pydantic import Field
 
 
@@ -252,3 +253,104 @@ def test_lance_model():
 
     t = TestModel()
     assert t == TestModel(vec=[0.0] * 16, li=[1, 2, 3])
+
+
+def test_optional_nested_model():
+    class WAMedia(BaseModel):
+        url: str
+        mimetype: str
+        filename: Optional[str]
+        error: Optional[str]
+        data: bytes
+
+    class WALocation(BaseModel):
+        description: Optional[str]
+        latitude: str
+        longitude: str
+
+    class ReplyToMessage(BaseModel):
+        id: str
+        participant: str
+        body: str
+
+    class Message(BaseModel):
+        id: str
+        timestamp: int
+        from_: str
+        fromMe: bool
+        to: str
+        body: str
+        hasMedia: Optional[bool]
+        media: WAMedia
+        mediaUrl: Optional[str]
+        ack: Optional[int]
+        ackName: Optional[str]
+        author: Optional[str]
+        location: Optional[WALocation]
+        vCards: Optional[List[str]]
+        replyTo: Optional[ReplyToMessage]
+
+    class AnyEvent(LanceModel):
+        id: str
+        session: str
+        metadata: Optional[str] = None
+        engine: str
+        event: str
+
+    class MessageEvent(AnyEvent):
+        payload: Message
+
+    schema = pydantic_to_schema(MessageEvent)
+
+    payload = schema.field("payload")
+    assert payload.type == pa.struct(
+        [
+            pa.field("id", pa.utf8(), False),
+            pa.field("timestamp", pa.int64(), False),
+            pa.field("from_", pa.utf8(), False),
+            pa.field("fromMe", pa.bool_(), False),
+            pa.field("to", pa.utf8(), False),
+            pa.field("body", pa.utf8(), False),
+            pa.field("hasMedia", pa.bool_(), True),
+            pa.field(
+                "media",
+                pa.struct(
+                    [
+                        pa.field("url", pa.utf8(), False),
+                        pa.field("mimetype", pa.utf8(), False),
+                        pa.field("filename", pa.utf8(), True),
+                        pa.field("error", pa.utf8(), True),
+                        pa.field("data", pa.binary(), False),
+                    ]
+                ),
+                False,
+            ),
+            pa.field("mediaUrl", pa.utf8(), True),
+            pa.field("ack", pa.int64(), True),
+            pa.field("ackName", pa.utf8(), True),
+            pa.field("author", pa.utf8(), True),
+            pa.field(
+                "location",
+                pa.struct(
+                    [
+                        pa.field("description", pa.utf8(), True),
+                        pa.field("latitude", pa.utf8(), False),
+                        pa.field("longitude", pa.utf8(), False),
+                    ]
+                ),
+                True,  # Optional
+            ),
+            pa.field("vCards", pa.list_(pa.utf8()), True),
+            pa.field(
+                "replyTo",
+                pa.struct(
+                    [
+                        pa.field("id", pa.utf8(), False),
+                        pa.field("participant", pa.utf8(), False),
+                        pa.field("body", pa.utf8(), False),
+                    ]
+                ),
+                True,
+            ),
+        ]
+    )

python/python/tests/test_query.py

@@ -232,6 +232,71 @@ async def test_distance_range_async(table_async: AsyncTable):
     assert res["_distance"].to_pylist() == [min_dist, max_dist]
 
 
+@pytest.mark.asyncio
+async def test_distance_range_with_new_rows_async():
+    conn = await lancedb.connect_async(
+        "memory://", read_consistency_interval=timedelta(seconds=0)
+    )
+    data = pa.table(
+        {
+            "vector": pa.FixedShapeTensorArray.from_numpy_ndarray(
+                np.random.rand(256, 2)
+            ),
+        }
+    )
+    table = await conn.create_table("test", data)
+    table.create_index("vector", config=IvfPq(num_partitions=1, num_sub_vectors=2))
+
+    q = [0, 0]
+    rs = await table.query().nearest_to(q).to_arrow()
+    dists = rs["_distance"].to_pylist()
+    min_dist = dists[0]
+    max_dist = dists[-1]
+
+    # append more rows so that execution plan would be mixed with ANN & Flat KNN
+    new_data = pa.table(
+        {
+            "vector": pa.FixedShapeTensorArray.from_numpy_ndarray(np.random.rand(4, 2)),
+        }
+    )
+    await table.add(new_data)
+
+    res = (
+        await table.query()
+        .nearest_to(q)
+        .distance_range(upper_bound=min_dist)
+        .to_arrow()
+    )
+    assert len(res) == 0
+
+    res = (
+        await table.query()
+        .nearest_to(q)
+        .distance_range(lower_bound=max_dist)
+        .to_arrow()
+    )
+    for dist in res["_distance"].to_pylist():
+        assert dist >= max_dist
+
+    res = (
+        await table.query()
+        .nearest_to(q)
+        .distance_range(upper_bound=max_dist)
+        .to_arrow()
+    )
+    for dist in res["_distance"].to_pylist():
+        assert dist < max_dist
+
+    res = (
+        await table.query()
+        .nearest_to(q)
+        .distance_range(lower_bound=min_dist)
+        .to_arrow()
+    )
+    for dist in res["_distance"].to_pylist():
+        assert dist >= min_dist
+
+
 @pytest.mark.parametrize(
     "multivec_table", [pa.float16(), pa.float32(), pa.float64()], indirect=True
 )

python/python/tests/test_remote_db.py

@@ -32,15 +32,16 @@ def make_mock_http_handler(handler):
 @contextlib.contextmanager
 def mock_lancedb_connection(handler):
     with http.server.HTTPServer(
-        ("localhost", 8080), make_mock_http_handler(handler)
+        ("localhost", 0), make_mock_http_handler(handler)
     ) as server:
+        port = server.server_address[1]
         handle = threading.Thread(target=server.serve_forever)
         handle.start()
 
         db = lancedb.connect(
             "db://dev",
             api_key="fake",
-            host_override="http://localhost:8080",
+            host_override=f"http://localhost:{port}",
             client_config={
                 "retry_config": {"retries": 2},
                 "timeout_config": {
@@ -57,22 +58,24 @@ def mock_lancedb_connection(handler):
 
 
 @contextlib.asynccontextmanager
-async def mock_lancedb_connection_async(handler):
+async def mock_lancedb_connection_async(handler, **client_config):
     with http.server.HTTPServer(
-        ("localhost", 8080), make_mock_http_handler(handler)
+        ("localhost", 0), make_mock_http_handler(handler)
     ) as server:
+        port = server.server_address[1]
         handle = threading.Thread(target=server.serve_forever)
         handle.start()
 
         db = await lancedb.connect_async(
             "db://dev",
             api_key="fake",
-            host_override="http://localhost:8080",
+            host_override=f"http://localhost:{port}",
             client_config={
                 "retry_config": {"retries": 2},
                 "timeout_config": {
                     "connect_timeout": 1,
                 },
+                **client_config,
             },
         )
 
@@ -254,6 +257,9 @@ def test_table_create_indices():
                 )
             )
             request.wfile.write(payload.encode())
+        elif "/drop/" in request.path:
+            request.send_response(200)
+            request.end_headers()
         else:
             request.send_response(404)
             request.end_headers()
@@ -265,6 +271,9 @@ def test_table_create_indices():
         table.create_scalar_index("id")
         table.create_fts_index("text")
         table.create_scalar_index("vector")
+        table.drop_index("vector_idx")
+        table.drop_index("id_idx")
+        table.drop_index("text_idx")
 
 
 @contextlib.contextmanager
@@ -522,3 +531,19 @@ def test_create_client():
 
     with pytest.warns(DeprecationWarning):
         lancedb.connect(**mandatory_args, request_thread_pool=10)
+
+
+@pytest.mark.asyncio
+async def test_pass_through_headers():
+    def handler(request):
+        assert request.headers["foo"] == "bar"
+        request.send_response(200)
+        request.send_header("Content-Type", "application/json")
+        request.end_headers()
+        request.wfile.write(b'{"tables": []}')
+
+    async with mock_lancedb_connection_async(
+        handler, extra_headers={"foo": "bar"}
+    ) as db:
+        table_names = await db.table_names()
+        assert table_names == []

python/python/tests/test_util.py

@@ -14,7 +14,7 @@ from lancedb.table import (
     _append_vector_columns,
     _cast_to_target_schema,
     _handle_bad_vectors,
-    _into_pyarrow_table,
+    _into_pyarrow_reader,
     _sanitize_data,
     _infer_target_schema,
 )
@@ -145,19 +145,19 @@ def test_append_vector_columns():
         schema=schema,
     )
     output = _append_vector_columns(
-        data,
+        data.to_reader(),
         schema,  # metadata passed separate from schema
         metadata=metadata,
-    )
+    ).read_all()
     assert output.schema == schema
     assert output["vector"].null_count == 0
 
     # Adds if missing
     data = pa.table({"text": ["hello"]})
     output = _append_vector_columns(
-        data,
+        data.to_reader(),
         schema.with_metadata(metadata),
-    )
+    ).read_all()
     assert output.schema == schema
     assert output["vector"].null_count == 0
 
@@ -170,9 +170,9 @@ def test_append_vector_columns():
         schema=schema,
     )
     output = _append_vector_columns(
-        data,
+        data.to_reader(),
         schema.with_metadata(metadata),
-    )
+    ).read_all()
     assert output == data  # No change
 
     # No provided schema
@@ -182,9 +182,9 @@ def test_append_vector_columns():
         }
     )
     output = _append_vector_columns(
-        data,
+        data.to_reader(),
         metadata=metadata,
-    )
+    ).read_all()
     expected_schema = pa.schema(
         {
             "text": pa.string(),
@@ -204,9 +204,9 @@ def test_handle_bad_vectors_jagged(on_bad_vectors):
     if on_bad_vectors == "error":
         with pytest.raises(ValueError) as e:
             output = _handle_bad_vectors(
-                data,
+                data.to_reader(),
                 on_bad_vectors=on_bad_vectors,
-            )
+            ).read_all()
         output = exception_output(e)
         assert output == (
             "ValueError: Vector column 'vector' has variable length vectors. Set "
@@ -217,10 +217,10 @@ def test_handle_bad_vectors_jagged(on_bad_vectors):
         return
     else:
         output = _handle_bad_vectors(
-            data,
+            data.to_reader(),
             on_bad_vectors=on_bad_vectors,
             fill_value=42.0,
-        )
+        ).read_all()
 
     if on_bad_vectors == "drop":
         expected = pa.array([[1.0, 2.0], [4.0, 5.0]])
@@ -240,9 +240,9 @@ def test_handle_bad_vectors_nan(on_bad_vectors):
     if on_bad_vectors == "error":
         with pytest.raises(ValueError) as e:
             output = _handle_bad_vectors(
-                data,
+                data.to_reader(),
                 on_bad_vectors=on_bad_vectors,
-            )
+            ).read_all()
         output = exception_output(e)
         assert output == (
             "ValueError: Vector column 'vector' has NaNs. Set "
@@ -253,10 +253,10 @@ def test_handle_bad_vectors_nan(on_bad_vectors):
         return
     else:
         output = _handle_bad_vectors(
-            data,
+            data.to_reader(),
             on_bad_vectors=on_bad_vectors,
             fill_value=42.0,
-        )
+        ).read_all()
 
     if on_bad_vectors == "drop":
         expected = pa.array([[3.0, 4.0]])
@@ -274,7 +274,7 @@ def test_handle_bad_vectors_noop():
         [[[1.0, 2.0], [3.0, 4.0]]], type=pa.list_(pa.float64(), 2)
     )
     data = pa.table({"vector": vector})
-    output = _handle_bad_vectors(data)
+    output = _handle_bad_vectors(data.to_reader()).read_all()
     assert output["vector"] == vector
 
 
@@ -325,7 +325,7 @@ class TestModel(lancedb.pydantic.LanceModel):
 )
 def test_into_pyarrow_table(data):
     expected = pa.table({"a": [1], "b": [2]})
-    output = _into_pyarrow_table(data())
+    output = _into_pyarrow_reader(data()).read_all()
     assert output == expected
 
 
@@ -349,7 +349,7 @@ def test_infer_target_schema():
             "vector": pa.list_(pa.float32(), 2),
         }
     )
-    output = _infer_target_schema(data)
+    output, _ = _infer_target_schema(data.to_reader())
     assert output == expected
 
     # Handle large list and use modal size
@@ -370,7 +370,7 @@ def test_infer_target_schema():
             "vector": pa.list_(pa.float32(), 2),
         }
     )
-    output = _infer_target_schema(data)
+    output, _ = _infer_target_schema(data.to_reader())
     assert output == expected
 
     # ignore if not list
@@ -386,7 +386,7 @@ def test_infer_target_schema():
         schema=example,
     )
     expected = example
-    output = _infer_target_schema(data)
+    output, _ = _infer_target_schema(data.to_reader())
     assert output == expected
 
 
@@ -476,7 +476,7 @@ def test_sanitize_data(
         target_schema=schema,
         metadata=metadata,
         allow_subschema=True,
-    )
+    ).read_all()
 
     assert output_data == expected
 
@@ -519,7 +519,7 @@ def test_cast_to_target_schema():
             "vec2": pa.list_(pa.float32(), 2),
         }
     )
-    output = _cast_to_target_schema(data, target)
+    output = _cast_to_target_schema(data.to_reader(), target)
     expected = pa.table(
         {
             "id": [1],
@@ -550,8 +550,10 @@ def test_cast_to_target_schema():
         }
     )
     with pytest.raises(Exception):
-        _cast_to_target_schema(data, target)
-    output = _cast_to_target_schema(data, target, allow_subschema=True)
+        _cast_to_target_schema(data.to_reader(), target)
+    output = _cast_to_target_schema(
+        data.to_reader(), target, allow_subschema=True
+    ).read_all()
     expected_schema = pa.schema(
         {
             "id": pa.int64(),
@@ -576,3 +578,22 @@ def test_cast_to_target_schema():
         schema=expected_schema,
     )
     assert output == expected
+
+
+def test_sanitize_data_stream():
+    # Make sure we don't collect the whole stream when running sanitize_data
+    schema = pa.schema({"a": pa.int32()})
+
+    def stream():
+        yield pa.record_batch([pa.array([1, 2, 3])], schema=schema)
+        raise ValueError("error")
+
+    reader = pa.RecordBatchReader.from_batches(schema, stream())
+
+    output = _sanitize_data(reader)
+
+    first = next(output)
+    assert first == pa.record_batch([pa.array([1, 2, 3])], schema=schema)
+
+    with pytest.raises(ValueError):
+        next(output)

python/src/connection.rs

@@ -1,10 +1,10 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-use std::{collections::HashMap, str::FromStr, sync::Arc, time::Duration};
+use std::{collections::HashMap, sync::Arc, time::Duration};
 
 use arrow::{datatypes::Schema, ffi_stream::ArrowArrayStreamReader, pyarrow::FromPyArrow};
-use lancedb::connection::{Connection as LanceConnection, CreateTableMode, LanceFileVersion};
+use lancedb::{connection::Connection as LanceConnection, database::CreateTableMode};
 use pyo3::{
     exceptions::{PyRuntimeError, PyValueError},
     pyclass, pyfunction, pymethods, Bound, FromPyObject, PyAny, PyRef, PyResult, Python,
@@ -80,15 +80,13 @@ impl Connection {
         future_into_py(self_.py(), async move { op.execute().await.infer_error() })
     }
 
-    #[pyo3(signature = (name, mode, data, storage_options=None, data_storage_version=None, enable_v2_manifest_paths=None))]
+    #[pyo3(signature = (name, mode, data, storage_options=None))]
     pub fn create_table<'a>(
         self_: PyRef<'a, Self>,
         name: String,
         mode: &str,
         data: Bound<'_, PyAny>,
         storage_options: Option<HashMap<String, String>>,
-        data_storage_version: Option<String>,
-        enable_v2_manifest_paths: Option<bool>,
     ) -> PyResult<Bound<'a, PyAny>> {
         let inner = self_.get_inner()?.clone();
 
@@ -101,32 +99,19 @@ impl Connection {
             builder = builder.storage_options(storage_options);
         }
 
-        if let Some(enable_v2_manifest_paths) = enable_v2_manifest_paths {
-            builder = builder.enable_v2_manifest_paths(enable_v2_manifest_paths);
-        }
-
-        if let Some(data_storage_version) = data_storage_version.as_ref() {
-            builder = builder.data_storage_version(
-                LanceFileVersion::from_str(data_storage_version)
-                    .map_err(|e| PyValueError::new_err(e.to_string()))?,
-            );
-        }
-
         future_into_py(self_.py(), async move {
             let table = builder.execute().await.infer_error()?;
             Ok(Table::new(table))
         })
     }
 
-    #[pyo3(signature = (name, mode, schema, storage_options=None, data_storage_version=None, enable_v2_manifest_paths=None))]
+    #[pyo3(signature = (name, mode, schema, storage_options=None))]
     pub fn create_empty_table<'a>(
         self_: PyRef<'a, Self>,
         name: String,
         mode: &str,
         schema: Bound<'_, PyAny>,
         storage_options: Option<HashMap<String, String>>,
-        data_storage_version: Option<String>,
-        enable_v2_manifest_paths: Option<bool>,
     ) -> PyResult<Bound<'a, PyAny>> {
         let inner = self_.get_inner()?.clone();
 
@@ -140,17 +125,6 @@ impl Connection {
             builder = builder.storage_options(storage_options);
         }
 
-        if let Some(enable_v2_manifest_paths) = enable_v2_manifest_paths {
-            builder = builder.enable_v2_manifest_paths(enable_v2_manifest_paths);
-        }
-
-        if let Some(data_storage_version) = data_storage_version.as_ref() {
-            builder = builder.data_storage_version(
-                LanceFileVersion::from_str(data_storage_version)
-                    .map_err(|e| PyValueError::new_err(e.to_string()))?,
-            );
-        }
-
         future_into_py(self_.py(), async move {
             let table = builder.execute().await.infer_error()?;
             Ok(Table::new(table))
@@ -196,12 +170,11 @@ impl Connection {
         })
     }
 
-    pub fn drop_db(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+    pub fn drop_all_tables(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.get_inner()?.clone();
-        future_into_py(
-            self_.py(),
-            async move { inner.drop_db().await.infer_error() },
-        )
+        future_into_py(self_.py(), async move {
+            inner.drop_all_tables().await.infer_error()
+        })
     }
 }
 
@@ -249,6 +222,7 @@ pub struct PyClientConfig {
     user_agent: String,
     retry_config: Option<PyClientRetryConfig>,
     timeout_config: Option<PyClientTimeoutConfig>,
+    extra_headers: Option<HashMap<String, String>>,
 }
 
 #[derive(FromPyObject)]
@@ -300,6 +274,7 @@ impl From<PyClientConfig> for lancedb::remote::ClientConfig {
             user_agent: value.user_agent,
             retry_config: value.retry_config.map(Into::into).unwrap_or_default(),
             timeout_config: value.timeout_config.map(Into::into).unwrap_or_default(),
+            extra_headers: value.extra_headers.unwrap_or_default(),
         }
     }
 }

python/src/query.rs

@@ -7,8 +7,7 @@ use arrow::pyarrow::FromPyArrow;
 use lancedb::index::scalar::FullTextSearchQuery;
 use lancedb::query::QueryExecutionOptions;
 use lancedb::query::{
-    ExecutableQuery, HasQuery, Query as LanceDbQuery, QueryBase, Select,
-    VectorQuery as LanceDbVectorQuery,
+    ExecutableQuery, Query as LanceDbQuery, QueryBase, Select, VectorQuery as LanceDbVectorQuery,
 };
 use pyo3::exceptions::PyRuntimeError;
 use pyo3::prelude::{PyAnyMethods, PyDictMethods};
@@ -313,7 +312,8 @@ impl VectorQuery {
     }
 
     pub fn nearest_to_text(&mut self, query: Bound<'_, PyDict>) -> PyResult<HybridQuery> {
-        let fts_query = Query::new(self.inner.mut_query().clone()).nearest_to_text(query)?;
+        let base_query = self.inner.clone().into_plain();
+        let fts_query = Query::new(base_query).nearest_to_text(query)?;
         Ok(HybridQuery {
             inner_vec: self.clone(),
             inner_fts: fts_query,
@@ -411,10 +411,14 @@ impl HybridQuery {
     }
 
     pub fn get_limit(&mut self) -> Option<u32> {
-        self.inner_fts.inner.limit.map(|i| i as u32)
+        self.inner_fts
+            .inner
+            .current_request()
+            .limit
+            .map(|i| i as u32)
     }
 
     pub fn get_with_row_id(&mut self) -> bool {
-        self.inner_fts.inner.with_row_id
+        self.inner_fts.inner.current_request().with_row_id
     }
 }

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-node"
-version = "0.15.1-beta.2"
+version = "0.16.1-beta.2"
 description = "Serverless, low-latency vector database for AI applications"
 license.workspace = true
 edition.workspace = true

rust/ffi/node/src/lib.rs

@@ -169,5 +169,6 @@ fn main(mut cx: ModuleContext) -> NeonResult<()> {
     cx.export_function("tableAddColumns", JsTable::js_add_columns)?;
     cx.export_function("tableAlterColumns", JsTable::js_alter_columns)?;
     cx.export_function("tableDropColumns", JsTable::js_drop_columns)?;
+    cx.export_function("tableDropIndex", JsTable::js_drop_index)?;
     Ok(())
 }

rust/ffi/node/src/table.rs

@@ -638,4 +638,8 @@ impl JsTable {
 
         Ok(promise)
     }
+
+    pub(crate) fn js_drop_index(_cx: FunctionContext) -> JsResult<JsPromise> {
+        todo!("not implemented")
+    }
 }

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.15.1-beta.2"
+version = "0.16.1-beta.2"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
@@ -19,7 +19,10 @@ arrow-ord = { workspace = true }
 arrow-cast = { workspace = true }
 arrow-ipc.workspace = true
 chrono = { workspace = true }
+datafusion-catalog.workspace = true
 datafusion-common.workspace = true
+datafusion-execution.workspace = true
+datafusion-expr.workspace = true
 datafusion-physical-plan.workspace = true
 object_store = { workspace = true }
 snafu = { workspace = true }
@@ -33,7 +36,7 @@ lance-table = { workspace = true }
 lance-linalg = { workspace = true }
 lance-testing = { workspace = true }
 lance-encoding = { workspace = true }
-moka = { workspace = true}
+moka = { workspace = true }
 pin-project = { workspace = true }
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
 log.workspace = true
@@ -82,7 +85,8 @@ aws-sdk-s3 = { version = "1.38.0" }
 aws-sdk-kms = { version = "1.37" }
 aws-config = { version = "1.0" }
 aws-smithy-runtime = { version = "1.3" }
-http-body = "1" # Matching reqwest
+datafusion.workspace = true
+http-body = "1"                                        # Matching reqwest
 
 
 [features]
@@ -98,7 +102,7 @@ sentence-transformers = [
     "dep:candle-core",
     "dep:candle-transformers",
     "dep:candle-nn",
-    "dep:tokenizers"
+    "dep:tokenizers",
 ]
 
 # TLS

rust/lancedb/src/database.rs

@@ -0,0 +1,133 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! The database module defines the `Database` trait and related types.
+//!
+//! A "database" is a generic concept for something that manages tables and their metadata.
+//!
+//! We provide a basic implementation of a database that requires no additional infrastructure
+//! and is based off listing directories in a filesystem.
+//!
+//! Users may want to provider their own implementations for a variety of reasons:
+//!  * Tables may be arranged in a different order on the S3 filesystem
+//!  * Tables may be managed by some kind of independent application (e.g. some database)
+//!  * Tables may be managed by a database system (e.g. Postgres)
+//!  * A custom table implementation (e.g. remote table, etc.) may be used
+
+use std::collections::HashMap;
+use std::sync::Arc;
+
+use arrow_array::RecordBatchReader;
+use lance::dataset::ReadParams;
+
+use crate::error::Result;
+use crate::table::{BaseTable, TableDefinition, WriteOptions};
+
+pub mod listing;
+
+pub trait DatabaseOptions {
+    fn serialize_into_map(&self, map: &mut HashMap<String, String>);
+}
+
+/// A request to list names of tables in the database
+#[derive(Clone, Debug, Default)]
+pub struct TableNamesRequest {
+    /// If present, only return names that come lexicographically after the supplied
+    /// value.
+    ///
+    /// This can be combined with limit to implement pagination by setting this to
+    /// the last table name from the previous page.
+    pub start_after: Option<String>,
+    /// The maximum number of table names to return
+    pub limit: Option<u32>,
+}
+
+/// A request to open a table
+#[derive(Clone, Debug)]
+pub struct OpenTableRequest {
+    pub name: String,
+    pub index_cache_size: Option<u32>,
+    pub lance_read_params: Option<ReadParams>,
+}
+
+pub type TableBuilderCallback = Box<dyn FnOnce(OpenTableRequest) -> OpenTableRequest + Send>;
+
+/// Describes what happens when creating a table and a table with
+/// the same name already exists
+pub enum CreateTableMode {
+    /// If the table already exists, an error is returned
+    Create,
+    /// If the table already exists, it is opened.  Any provided data is
+    /// ignored.  The function will be passed an OpenTableBuilder to customize
+    /// how the table is opened
+    ExistOk(TableBuilderCallback),
+    /// If the table already exists, it is overwritten
+    Overwrite,
+}
+
+impl CreateTableMode {
+    pub fn exist_ok(
+        callback: impl FnOnce(OpenTableRequest) -> OpenTableRequest + Send + 'static,
+    ) -> Self {
+        Self::ExistOk(Box::new(callback))
+    }
+}
+
+impl Default for CreateTableMode {
+    fn default() -> Self {
+        Self::Create
+    }
+}
+
+/// The data to start a table or a schema to create an empty table
+pub enum CreateTableData {
+    /// Creates a table using data, no schema required as it will be obtained from the data
+    Data(Box<dyn RecordBatchReader + Send>),
+    /// Creates an empty table, the definition / schema must be provided separately
+    Empty(TableDefinition),
+}
+
+/// A request to create a table
+pub struct CreateTableRequest {
+    /// The name of the new table
+    pub name: String,
+    /// Initial data to write to the table, can be None to create an empty table
+    pub data: CreateTableData,
+    /// The mode to use when creating the table
+    pub mode: CreateTableMode,
+    /// Options to use when writing data (only used if `data` is not None)
+    pub write_options: WriteOptions,
+}
+
+impl CreateTableRequest {
+    pub fn new(name: String, data: CreateTableData) -> Self {
+        Self {
+            name,
+            data,
+            mode: CreateTableMode::default(),
+            write_options: WriteOptions::default(),
+        }
+    }
+}
+
+/// The `Database` trait defines the interface for database implementations.
+///
+/// A database is responsible for managing tables and their metadata.
+#[async_trait::async_trait]
+pub trait Database:
+    Send + Sync + std::any::Any + std::fmt::Debug + std::fmt::Display + 'static
+{
+    /// List the names of tables in the database
+    async fn table_names(&self, request: TableNamesRequest) -> Result<Vec<String>>;
+    /// Create a table in the database
+    async fn create_table(&self, request: CreateTableRequest) -> Result<Arc<dyn BaseTable>>;
+    /// Open a table in the database
+    async fn open_table(&self, request: OpenTableRequest) -> Result<Arc<dyn BaseTable>>;
+    /// Rename a table in the database
+    async fn rename_table(&self, old_name: &str, new_name: &str) -> Result<()>;
+    /// Drop a table in the database
+    async fn drop_table(&self, name: &str) -> Result<()>;
+    /// Drop all tables in the database
+    async fn drop_all_tables(&self) -> Result<()>;
+    fn as_any(&self) -> &dyn std::any::Any;
+}

rust/lancedb/src/database/listing.rs

@@ -0,0 +1,542 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! Provides the `ListingDatabase`, a simple database where tables are folders in a directory
+
+use std::fs::create_dir_all;
+use std::path::Path;
+use std::{collections::HashMap, sync::Arc};
+
+use arrow_array::RecordBatchIterator;
+use lance::dataset::{ReadParams, WriteMode};
+use lance::io::{ObjectStore, ObjectStoreParams, ObjectStoreRegistry, WrappingObjectStore};
+use lance_encoding::version::LanceFileVersion;
+use lance_table::io::commit::commit_handler_from_url;
+use object_store::local::LocalFileSystem;
+use snafu::{OptionExt, ResultExt};
+
+use crate::connection::ConnectRequest;
+use crate::error::{CreateDirSnafu, Error, InvalidTableNameSnafu, Result};
+use crate::io::object_store::MirroringObjectStoreWrapper;
+use crate::table::NativeTable;
+use crate::utils::validate_table_name;
+
+use super::{
+    BaseTable, CreateTableData, CreateTableMode, CreateTableRequest, Database, DatabaseOptions,
+    OpenTableRequest, TableNamesRequest,
+};
+
+/// File extension to indicate a lance table
+pub const LANCE_FILE_EXTENSION: &str = "lance";
+
+pub const OPT_NEW_TABLE_STORAGE_VERSION: &str = "new_table_data_storage_version";
+pub const OPT_NEW_TABLE_V2_MANIFEST_PATHS: &str = "new_table_enable_v2_manifest_paths";
+
+/// Controls how new tables should be created
+#[derive(Clone, Debug, Default)]
+pub struct NewTableConfig {
+    /// The storage version to use for new tables
+    ///
+    /// If unset, then the latest stable version will be used
+    pub data_storage_version: Option<LanceFileVersion>,
+    /// Whether to enable V2 manifest paths for new tables
+    ///
+    /// V2 manifest paths are more efficient than V2 manifest paths but are not
+    /// supported by old clients.
+    pub enable_v2_manifest_paths: Option<bool>,
+}
+
+/// Options specific to the listing database
+#[derive(Debug, Default, Clone)]
+pub struct ListingDatabaseOptions {
+    /// Controls what kind of Lance tables will be created by this database
+    pub new_table_config: NewTableConfig,
+}
+
+impl ListingDatabaseOptions {
+    fn parse_from_map(map: &HashMap<String, String>) -> Result<Self> {
+        let new_table_config = NewTableConfig {
+            data_storage_version: map
+                .get(OPT_NEW_TABLE_STORAGE_VERSION)
+                .map(|s| s.parse())
+                .transpose()?,
+            enable_v2_manifest_paths: map
+                .get(OPT_NEW_TABLE_V2_MANIFEST_PATHS)
+                .map(|s| {
+                    s.parse::<bool>().map_err(|_| Error::InvalidInput {
+                        message: format!(
+                            "enable_v2_manifest_paths must be a boolean, received {}",
+                            s
+                        ),
+                    })
+                })
+                .transpose()?,
+        };
+        Ok(Self { new_table_config })
+    }
+}
+
+impl DatabaseOptions for ListingDatabaseOptions {
+    fn serialize_into_map(&self, map: &mut HashMap<String, String>) {
+        if let Some(storage_version) = &self.new_table_config.data_storage_version {
+            map.insert(
+                OPT_NEW_TABLE_STORAGE_VERSION.to_string(),
+                storage_version.to_string(),
+            );
+        }
+        if let Some(enable_v2_manifest_paths) = self.new_table_config.enable_v2_manifest_paths {
+            map.insert(
+                OPT_NEW_TABLE_V2_MANIFEST_PATHS.to_string(),
+                enable_v2_manifest_paths.to_string(),
+            );
+        }
+    }
+}
+
+/// A database that stores tables in a flat directory structure
+///
+/// Tables are stored as directories in the base path of the object store.
+///
+/// It is called a "listing database" because we use a "list directory" operation
+/// to discover what tables are available.  Table names are determined from the directory
+/// names.
+///
+/// For example, given the following directory structure:
+///
+/// ```text
+/// /data
+///  /table1.lance
+///  /table2.lance
+/// ```
+///
+/// We will have two tables named `table1` and `table2`.
+#[derive(Debug)]
+pub struct ListingDatabase {
+    object_store: ObjectStore,
+    query_string: Option<String>,
+
+    pub(crate) uri: String,
+    pub(crate) base_path: object_store::path::Path,
+
+    // the object store wrapper to use on write path
+    pub(crate) store_wrapper: Option<Arc<dyn WrappingObjectStore>>,
+
+    read_consistency_interval: Option<std::time::Duration>,
+
+    // Storage options to be inherited by tables created from this connection
+    storage_options: HashMap<String, String>,
+
+    // Options for tables created by this connection
+    new_table_config: NewTableConfig,
+}
+
+impl std::fmt::Display for ListingDatabase {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(
+            f,
+            "ListingDatabase(uri={}, read_consistency_interval={})",
+            self.uri,
+            match self.read_consistency_interval {
+                None => {
+                    "None".to_string()
+                }
+                Some(duration) => {
+                    format!("{}s", duration.as_secs_f64())
+                }
+            }
+        )
+    }
+}
+
+const LANCE_EXTENSION: &str = "lance";
+const ENGINE: &str = "engine";
+const MIRRORED_STORE: &str = "mirroredStore";
+
+/// A connection to LanceDB
+impl ListingDatabase {
+    /// Connect to a listing database
+    ///
+    /// The URI should be a path to a directory where the tables are stored.
+    ///
+    /// See [`ListingDatabaseOptions`] for options that can be set on the connection (via
+    /// `storage_options`).
+    pub async fn connect_with_options(request: &ConnectRequest) -> Result<Self> {
+        let uri = &request.uri;
+        let parse_res = url::Url::parse(uri);
+
+        let options = ListingDatabaseOptions::parse_from_map(&request.storage_options)?;
+
+        // TODO: pass params regardless of OS
+        match parse_res {
+            Ok(url) if url.scheme().len() == 1 && cfg!(windows) => {
+                Self::open_path(
+                    uri,
+                    request.read_consistency_interval,
+                    options.new_table_config,
+                )
+                .await
+            }
+            Ok(mut url) => {
+                // iter thru the query params and extract the commit store param
+                let mut engine = None;
+                let mut mirrored_store = None;
+                let mut filtered_querys = vec![];
+
+                // WARNING: specifying engine is NOT a publicly supported feature in lancedb yet
+                // THE API WILL CHANGE
+                for (key, value) in url.query_pairs() {
+                    if key == ENGINE {
+                        engine = Some(value.to_string());
+                    } else if key == MIRRORED_STORE {
+                        if cfg!(windows) {
+                            return Err(Error::NotSupported {
+                                message: "mirrored store is not supported on windows".into(),
+                            });
+                        }
+                        mirrored_store = Some(value.to_string());
+                    } else {
+                        // to owned so we can modify the url
+                        filtered_querys.push((key.to_string(), value.to_string()));
+                    }
+                }
+
+                // Filter out the commit store query param -- it's a lancedb param
+                url.query_pairs_mut().clear();
+                url.query_pairs_mut().extend_pairs(filtered_querys);
+                // Take a copy of the query string so we can propagate it to lance
+                let query_string = url.query().map(|s| s.to_string());
+                // clear the query string so we can use the url as the base uri
+                // use .set_query(None) instead of .set_query("") because the latter
+                // will add a trailing '?' to the url
+                url.set_query(None);
+
+                let table_base_uri = if let Some(store) = engine {
+                    static WARN_ONCE: std::sync::Once = std::sync::Once::new();
+                    WARN_ONCE.call_once(|| {
+                        log::warn!("Specifying engine is not a publicly supported feature in lancedb yet. THE API WILL CHANGE");
+                    });
+                    let old_scheme = url.scheme().to_string();
+                    let new_scheme = format!("{}+{}", old_scheme, store);
+                    url.to_string().replacen(&old_scheme, &new_scheme, 1)
+                } else {
+                    url.to_string()
+                };
+
+                let plain_uri = url.to_string();
+
+                let registry = Arc::new(ObjectStoreRegistry::default());
+                let storage_options = request.storage_options.clone();
+                let os_params = ObjectStoreParams {
+                    storage_options: Some(storage_options.clone()),
+                    ..Default::default()
+                };
+                let (object_store, base_path) =
+                    ObjectStore::from_uri_and_params(registry, &plain_uri, &os_params).await?;
+                if object_store.is_local() {
+                    Self::try_create_dir(&plain_uri).context(CreateDirSnafu { path: plain_uri })?;
+                }
+
+                let write_store_wrapper = match mirrored_store {
+                    Some(path) => {
+                        let mirrored_store = Arc::new(LocalFileSystem::new_with_prefix(path)?);
+                        let wrapper = MirroringObjectStoreWrapper::new(mirrored_store);
+                        Some(Arc::new(wrapper) as Arc<dyn WrappingObjectStore>)
+                    }
+                    None => None,
+                };
+
+                Ok(Self {
+                    uri: table_base_uri,
+                    query_string,
+                    base_path,
+                    object_store,
+                    store_wrapper: write_store_wrapper,
+                    read_consistency_interval: request.read_consistency_interval,
+                    storage_options,
+                    new_table_config: options.new_table_config,
+                })
+            }
+            Err(_) => {
+                Self::open_path(
+                    uri,
+                    request.read_consistency_interval,
+                    options.new_table_config,
+                )
+                .await
+            }
+        }
+    }
+
+    async fn open_path(
+        path: &str,
+        read_consistency_interval: Option<std::time::Duration>,
+        new_table_config: NewTableConfig,
+    ) -> Result<Self> {
+        let (object_store, base_path) = ObjectStore::from_uri(path).await?;
+        if object_store.is_local() {
+            Self::try_create_dir(path).context(CreateDirSnafu { path })?;
+        }
+
+        Ok(Self {
+            uri: path.to_string(),
+            query_string: None,
+            base_path,
+            object_store,
+            store_wrapper: None,
+            read_consistency_interval,
+            storage_options: HashMap::new(),
+            new_table_config,
+        })
+    }
+
+    /// Try to create a local directory to store the lancedb dataset
+    fn try_create_dir(path: &str) -> core::result::Result<(), std::io::Error> {
+        let path = Path::new(path);
+        if !path.try_exists()? {
+            create_dir_all(path)?;
+        }
+        Ok(())
+    }
+
+    /// Get the URI of a table in the database.
+    fn table_uri(&self, name: &str) -> Result<String> {
+        validate_table_name(name)?;
+
+        let path = Path::new(&self.uri);
+        let table_uri = path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
+
+        let mut uri = table_uri
+            .as_path()
+            .to_str()
+            .context(InvalidTableNameSnafu {
+                name,
+                reason: "Name is not valid URL",
+            })?
+            .to_string();
+
+        // If there are query string set on the connection, propagate to lance
+        if let Some(query) = self.query_string.as_ref() {
+            uri.push('?');
+            uri.push_str(query.as_str());
+        }
+
+        Ok(uri)
+    }
+}
+
+#[async_trait::async_trait]
+impl Database for ListingDatabase {
+    async fn table_names(&self, request: TableNamesRequest) -> Result<Vec<String>> {
+        let mut f = self
+            .object_store
+            .read_dir(self.base_path.clone())
+            .await?
+            .iter()
+            .map(Path::new)
+            .filter(|path| {
+                let is_lance = path
+                    .extension()
+                    .and_then(|e| e.to_str())
+                    .map(|e| e == LANCE_EXTENSION);
+                is_lance.unwrap_or(false)
+            })
+            .filter_map(|p| p.file_stem().and_then(|s| s.to_str().map(String::from)))
+            .collect::<Vec<String>>();
+        f.sort();
+        if let Some(start_after) = request.start_after {
+            let index = f
+                .iter()
+                .position(|name| name.as_str() > start_after.as_str())
+                .unwrap_or(f.len());
+            f.drain(0..index);
+        }
+        if let Some(limit) = request.limit {
+            f.truncate(limit as usize);
+        }
+        Ok(f)
+    }
+
+    async fn create_table(&self, mut request: CreateTableRequest) -> Result<Arc<dyn BaseTable>> {
+        let table_uri = self.table_uri(&request.name)?;
+        // Inherit storage options from the connection
+        let storage_options = request
+            .write_options
+            .lance_write_params
+            .get_or_insert_with(Default::default)
+            .store_params
+            .get_or_insert_with(Default::default)
+            .storage_options
+            .get_or_insert_with(Default::default);
+        for (key, value) in self.storage_options.iter() {
+            if !storage_options.contains_key(key) {
+                storage_options.insert(key.clone(), value.clone());
+            }
+        }
+
+        let storage_options = storage_options.clone();
+
+        let mut write_params = request.write_options.lance_write_params.unwrap_or_default();
+
+        if let Some(storage_version) = &self.new_table_config.data_storage_version {
+            write_params.data_storage_version = Some(*storage_version);
+        } else {
+            // Allow the user to override the storage version via storage options (backwards compatibility)
+            if let Some(data_storage_version) = storage_options.get(OPT_NEW_TABLE_STORAGE_VERSION) {
+                write_params.data_storage_version = Some(data_storage_version.parse()?);
+            }
+        }
+        if let Some(enable_v2_manifest_paths) = self.new_table_config.enable_v2_manifest_paths {
+            write_params.enable_v2_manifest_paths = enable_v2_manifest_paths;
+        } else {
+            // Allow the user to override the storage version via storage options (backwards compatibility)
+            if let Some(enable_v2_manifest_paths) = storage_options
+                .get(OPT_NEW_TABLE_V2_MANIFEST_PATHS)
+                .map(|s| s.parse::<bool>().unwrap())
+            {
+                write_params.enable_v2_manifest_paths = enable_v2_manifest_paths;
+            }
+        }
+
+        if matches!(&request.mode, CreateTableMode::Overwrite) {
+            write_params.mode = WriteMode::Overwrite;
+        }
+
+        let data = match request.data {
+            CreateTableData::Data(data) => data,
+            CreateTableData::Empty(table_definition) => {
+                let schema = table_definition.schema.clone();
+                Box::new(RecordBatchIterator::new(vec![], schema))
+            }
+        };
+        let data_schema = data.schema();
+
+        match NativeTable::create(
+            &table_uri,
+            &request.name,
+            data,
+            self.store_wrapper.clone(),
+            Some(write_params),
+            self.read_consistency_interval,
+        )
+        .await
+        {
+            Ok(table) => Ok(Arc::new(table)),
+            Err(Error::TableAlreadyExists { name }) => match request.mode {
+                CreateTableMode::Create => Err(Error::TableAlreadyExists { name }),
+                CreateTableMode::ExistOk(callback) => {
+                    let req = OpenTableRequest {
+                        name: request.name.clone(),
+                        index_cache_size: None,
+                        lance_read_params: None,
+                    };
+                    let req = (callback)(req);
+                    let table = self.open_table(req).await?;
+
+                    let table_schema = table.schema().await?;
+
+                    if table_schema != data_schema {
+                        return Err(Error::Schema {
+                            message: "Provided schema does not match existing table schema"
+                                .to_string(),
+                        });
+                    }
+
+                    Ok(table)
+                }
+                CreateTableMode::Overwrite => unreachable!(),
+            },
+            Err(err) => Err(err),
+        }
+    }
+
+    async fn open_table(&self, mut request: OpenTableRequest) -> Result<Arc<dyn BaseTable>> {
+        let table_uri = self.table_uri(&request.name)?;
+
+        // Inherit storage options from the connection
+        let storage_options = request
+            .lance_read_params
+            .get_or_insert_with(Default::default)
+            .store_options
+            .get_or_insert_with(Default::default)
+            .storage_options
+            .get_or_insert_with(Default::default);
+        for (key, value) in self.storage_options.iter() {
+            if !storage_options.contains_key(key) {
+                storage_options.insert(key.clone(), value.clone());
+            }
+        }
+
+        // Some ReadParams are exposed in the OpenTableBuilder, but we also
+        // let the user provide their own ReadParams.
+        //
+        // If we have a user provided ReadParams use that
+        // If we don't then start with the default ReadParams and customize it with
+        // the options from the OpenTableBuilder
+        let read_params = request.lance_read_params.unwrap_or_else(|| {
+            let mut default_params = ReadParams::default();
+            if let Some(index_cache_size) = request.index_cache_size {
+                default_params.index_cache_size = index_cache_size as usize;
+            }
+            default_params
+        });
+
+        let native_table = Arc::new(
+            NativeTable::open_with_params(
+                &table_uri,
+                &request.name,
+                self.store_wrapper.clone(),
+                Some(read_params),
+                self.read_consistency_interval,
+            )
+            .await?,
+        );
+        Ok(native_table)
+    }
+
+    async fn rename_table(&self, _old_name: &str, _new_name: &str) -> Result<()> {
+        Err(Error::NotSupported {
+            message: "rename_table is not supported in LanceDB OSS".to_string(),
+        })
+    }
+
+    async fn drop_table(&self, name: &str) -> Result<()> {
+        let dir_name = format!("{}.{}", name, LANCE_EXTENSION);
+        let full_path = self.base_path.child(dir_name.clone());
+        self.object_store
+            .remove_dir_all(full_path.clone())
+            .await
+            .map_err(|err| match err {
+                // this error is not lance::Error::DatasetNotFound,
+                // as the method `remove_dir_all` may be used to remove something not be a dataset
+                lance::Error::NotFound { .. } => Error::TableNotFound {
+                    name: name.to_owned(),
+                },
+                _ => Error::from(err),
+            })?;
+
+        let object_store_params = ObjectStoreParams {
+            storage_options: Some(self.storage_options.clone()),
+            ..Default::default()
+        };
+        let mut uri = self.uri.clone();
+        if let Some(query_string) = &self.query_string {
+            uri.push_str(&format!("?{}", query_string));
+        }
+        let commit_handler = commit_handler_from_url(&uri, &Some(object_store_params))
+            .await
+            .unwrap();
+        commit_handler.delete(&full_path).await.unwrap();
+        Ok(())
+    }
+
+    async fn drop_all_tables(&self) -> Result<()> {
+        self.object_store
+            .remove_dir_all(self.base_path.clone())
+            .await?;
+        Ok(())
+    }
+
+    fn as_any(&self) -> &dyn std::any::Any {
+        self
+    }
+}

rust/lancedb/src/error.rs

@@ -15,6 +15,8 @@ pub enum Error {
     InvalidInput { message: String },
     #[snafu(display("Table '{name}' was not found"))]
     TableNotFound { name: String },
+    #[snafu(display("Index '{name}' was not found"))]
+    IndexNotFound { name: String },
     #[snafu(display("Embedding function '{name}' was not found. : {reason}"))]
     EmbeddingFunctionNotFound { name: String, reason: String },
 

rust/lancedb/src/index.rs

@@ -8,7 +8,7 @@ use serde::Deserialize;
 use serde_with::skip_serializing_none;
 use vector::IvfFlatIndexBuilder;
 
-use crate::{table::TableInternal, DistanceType, Error, Result};
+use crate::{table::BaseTable, DistanceType, Error, Result};
 
 use self::{
     scalar::{BTreeIndexBuilder, BitmapIndexBuilder, LabelListIndexBuilder},
@@ -65,14 +65,14 @@ pub enum Index {
 ///
 /// The methods on this builder are used to specify options common to all indices.
 pub struct IndexBuilder {
-    parent: Arc<dyn TableInternal>,
+    parent: Arc<dyn BaseTable>,
     pub(crate) index: Index,
     pub(crate) columns: Vec<String>,
     pub(crate) replace: bool,
 }
 
 impl IndexBuilder {
-    pub(crate) fn new(parent: Arc<dyn TableInternal>, columns: Vec<String>, index: Index) -> Self {
+    pub(crate) fn new(parent: Arc<dyn BaseTable>, columns: Vec<String>, index: Index) -> Self {
         Self {
             parent,
             index,

rust/lancedb/src/index/vector.rs

@@ -23,7 +23,19 @@ impl VectorIndex {
         let fields = index
             .fields
             .iter()
-            .map(|i| manifest.schema.fields[*i as usize].name.clone())
+            .map(|field_id| {
+                manifest
+                    .schema
+                    .field_by_id(*field_id)
+                    .unwrap_or_else(|| {
+                        panic!(
+                            "field {field_id} of index {} must exist in schema",
+                            index.name
+                        )
+                    })
+                    .name
+                    .clone()
+            })
             .collect();
         Self {
             columns: fields,

rust/lancedb/src/lib.rs

@@ -193,6 +193,7 @@
 pub mod arrow;
 pub mod connection;
 pub mod data;
+pub mod database;
 pub mod embeddings;
 pub mod error;
 pub mod index;

rust/lancedb/src/query.rs

@@ -20,12 +20,12 @@ use lance_index::scalar::FullTextSearchQuery;
 use lance_index::vector::DIST_COL;
 use lance_io::stream::RecordBatchStreamAdapter;
 
-use crate::arrow::SendableRecordBatchStream;
 use crate::error::{Error, Result};
 use crate::rerankers::rrf::RRFReranker;
 use crate::rerankers::{check_reranker_result, NormalizeMethod, Reranker};
-use crate::table::TableInternal;
+use crate::table::BaseTable;
 use crate::DistanceType;
+use crate::{arrow::SendableRecordBatchStream, table::AnyQuery};
 
 mod hybrid;
 
@@ -449,7 +449,7 @@ pub trait QueryBase {
 }
 
 pub trait HasQuery {
-    fn mut_query(&mut self) -> &mut Query;
+    fn mut_query(&mut self) -> &mut QueryRequest;
 }
 
 impl<T: HasQuery> QueryBase for T {
@@ -577,6 +577,65 @@ pub trait ExecutableQuery {
     fn explain_plan(&self, verbose: bool) -> impl Future<Output = Result<String>> + Send;
 }
 
+/// A basic query into a table without any kind of search
+///
+/// This will result in a (potentially filtered) scan if executed
+#[derive(Debug, Clone)]
+pub struct QueryRequest {
+    /// limit the number of rows to return.
+    pub limit: Option<usize>,
+
+    /// Offset of the query.
+    pub offset: Option<usize>,
+
+    /// Apply filter to the returned rows.
+    pub filter: Option<String>,
+
+    /// Perform a full text search on the table.
+    pub full_text_search: Option<FullTextSearchQuery>,
+
+    /// Select column projection.
+    pub select: Select,
+
+    /// If set to true, the query is executed only on the indexed data,
+    /// and yields faster results.
+    ///
+    /// By default, this is false.
+    pub fast_search: bool,
+
+    /// If set to true, the query will return the `_rowid` meta column.
+    ///
+    /// By default, this is false.
+    pub with_row_id: bool,
+
+    /// If set to false, the filter will be applied after the vector search.
+    pub prefilter: bool,
+
+    /// Implementation of reranker that can be used to reorder or combine query
+    /// results, especially if using hybrid search
+    pub reranker: Option<Arc<dyn Reranker>>,
+
+    /// Configure how query results are normalized when doing hybrid search
+    pub norm: Option<NormalizeMethod>,
+}
+
+impl Default for QueryRequest {
+    fn default() -> Self {
+        Self {
+            limit: Some(DEFAULT_TOP_K),
+            offset: None,
+            filter: None,
+            full_text_search: None,
+            select: Select::All,
+            fast_search: false,
+            with_row_id: false,
+            prefilter: true,
+            reranker: None,
+            norm: None,
+        }
+    }
+}
+
 /// A builder for LanceDB queries.
 ///
 /// See [`crate::Table::query`] for more details on queries
@@ -591,59 +650,15 @@ pub trait ExecutableQuery {
 /// times.
 #[derive(Debug, Clone)]
 pub struct Query {
-    parent: Arc<dyn TableInternal>,
-
-    /// limit the number of rows to return.
-    pub limit: Option<usize>,
-
-    /// Offset of the query.
-    pub(crate) offset: Option<usize>,
-
-    /// Apply filter to the returned rows.
-    pub(crate) filter: Option<String>,
-
-    /// Perform a full text search on the table.
-    pub(crate) full_text_search: Option<FullTextSearchQuery>,
-
-    /// Select column projection.
-    pub(crate) select: Select,
-
-    /// If set to true, the query is executed only on the indexed data,
-    /// and yields faster results.
-    ///
-    /// By default, this is false.
-    pub(crate) fast_search: bool,
-
-    /// If set to true, the query will return the `_rowid` meta column.
-    ///
-    /// By default, this is false.
-    pub with_row_id: bool,
-
-    /// If set to false, the filter will be applied after the vector search.
-    pub(crate) prefilter: bool,
-
-    /// Implementation of reranker that can be used to reorder or combine query
-    /// results, especially if using hybrid search
-    pub(crate) reranker: Option<Arc<dyn Reranker>>,
-
-    /// Configure how query results are normalized when doing hybrid search
-    pub(crate) norm: Option<NormalizeMethod>,
+    parent: Arc<dyn BaseTable>,
+    request: QueryRequest,
 }
 
 impl Query {
-    pub(crate) fn new(parent: Arc<dyn TableInternal>) -> Self {
+    pub(crate) fn new(parent: Arc<dyn BaseTable>) -> Self {
         Self {
             parent,
-            limit: Some(DEFAULT_TOP_K),
-            offset: None,
-            filter: None,
-            full_text_search: None,
-            select: Select::All,
-            fast_search: false,
-            with_row_id: false,
-            prefilter: true,
-            reranker: None,
-            norm: None,
+            request: QueryRequest::default(),
         }
     }
 
@@ -691,38 +706,98 @@ impl Query {
     pub fn nearest_to(self, vector: impl IntoQueryVector) -> Result<VectorQuery> {
         let mut vector_query = self.into_vector();
         let query_vector = vector.to_query_vector(&DataType::Float32, "default")?;
-        vector_query.query_vector.push(query_vector);
+        vector_query.request.query_vector.push(query_vector);
         Ok(vector_query)
     }
+
+    pub fn into_request(self) -> QueryRequest {
+        self.request
+    }
+
+    pub fn current_request(&self) -> &QueryRequest {
+        &self.request
+    }
 }
 
 impl HasQuery for Query {
-    fn mut_query(&mut self) -> &mut Query {
-        self
+    fn mut_query(&mut self) -> &mut QueryRequest {
+        &mut self.request
     }
 }
 
 impl ExecutableQuery for Query {
     async fn create_plan(&self, options: QueryExecutionOptions) -> Result<Arc<dyn ExecutionPlan>> {
-        self.parent
-            .clone()
-            .create_plan(&self.clone().into_vector(), options)
-            .await
+        let req = AnyQuery::Query(self.request.clone());
+        self.parent.clone().create_plan(&req, options).await
     }
 
     async fn execute_with_options(
         &self,
         options: QueryExecutionOptions,
     ) -> Result<SendableRecordBatchStream> {
+        let query = AnyQuery::Query(self.request.clone());
         Ok(SendableRecordBatchStream::from(
-            self.parent.clone().plain_query(self, options).await?,
+            self.parent.clone().query(&query, options).await?,
         ))
     }
 
     async fn explain_plan(&self, verbose: bool) -> Result<String> {
-        self.parent
-            .explain_plan(&self.clone().into_vector(), verbose)
-            .await
+        let query = AnyQuery::Query(self.request.clone());
+        self.parent.explain_plan(&query, verbose).await
+    }
+}
+
+/// A request for a nearest-neighbors search into a table
+#[derive(Debug, Clone)]
+pub struct VectorQueryRequest {
+    /// The base query
+    pub base: QueryRequest,
+    /// The column to run the search on
+    ///
+    /// If None, then the table will need to auto-detect which column to use
+    pub column: Option<String>,
+    /// The vector(s) to search for
+    pub query_vector: Vec<Arc<dyn Array>>,
+    /// The number of partitions to search
+    pub nprobes: usize,
+    /// The lower bound (inclusive) of the distance to search for.
+    pub lower_bound: Option<f32>,
+    /// The upper bound (exclusive) of the distance to search for.
+    pub upper_bound: Option<f32>,
+    /// The number of candidates to return during the refine step for HNSW,
+    /// defaults to 1.5 * limit.
+    pub ef: Option<usize>,
+    /// A multiplier to control how many additional rows are taken during the refine step
+    pub refine_factor: Option<u32>,
+    /// The distance type to use for the search
+    pub distance_type: Option<DistanceType>,
+    /// Default is true. Set to false to enforce a brute force search.
+    pub use_index: bool,
+}
+
+impl Default for VectorQueryRequest {
+    fn default() -> Self {
+        Self {
+            base: QueryRequest::default(),
+            column: None,
+            query_vector: Vec::new(),
+            nprobes: 20,
+            lower_bound: None,
+            upper_bound: None,
+            ef: None,
+            refine_factor: None,
+            distance_type: None,
+            use_index: true,
+        }
+    }
+}
+
+impl VectorQueryRequest {
+    pub fn from_plain_query(query: QueryRequest) -> Self {
+        Self {
+            base: query,
+            ..Default::default()
+        }
     }
 }
 
@@ -737,39 +812,30 @@ impl ExecutableQuery for Query {
 /// the query and retrieve results.
 #[derive(Debug, Clone)]
 pub struct VectorQuery {
-    pub(crate) base: Query,
-    // The column to run the query on. If not specified, we will attempt to guess
-    // the column based on the dataset's schema.
-    pub(crate) column: Option<String>,
-    // IVF PQ - ANN search.
-    pub(crate) query_vector: Vec<Arc<dyn Array>>,
-    pub(crate) nprobes: usize,
-    // The lower bound (inclusive) of the distance to search for.
-    pub(crate) lower_bound: Option<f32>,
-    // The upper bound (exclusive) of the distance to search for.
-    pub(crate) upper_bound: Option<f32>,
-    // The number of candidates to return during the refine step for HNSW,
-    // defaults to 1.5 * limit.
-    pub(crate) ef: Option<usize>,
-    pub(crate) refine_factor: Option<u32>,
-    pub(crate) distance_type: Option<DistanceType>,
-    /// Default is true. Set to false to enforce a brute force search.
-    pub(crate) use_index: bool,
+    parent: Arc<dyn BaseTable>,
+    request: VectorQueryRequest,
 }
 
 impl VectorQuery {
     fn new(base: Query) -> Self {
         Self {
-            base,
-            column: None,
-            query_vector: Vec::new(),
-            nprobes: 20,
-            lower_bound: None,
-            upper_bound: None,
-            ef: None,
-            refine_factor: None,
-            distance_type: None,
-            use_index: true,
+            parent: base.parent,
+            request: VectorQueryRequest::from_plain_query(base.request),
+        }
+    }
+
+    pub fn into_request(self) -> VectorQueryRequest {
+        self.request
+    }
+
+    pub fn current_request(&self) -> &VectorQueryRequest {
+        &self.request
+    }
+
+    pub fn into_plain(self) -> Query {
+        Query {
+            parent: self.parent,
+            request: self.request.base,
         }
     }
 
@@ -781,7 +847,7 @@ impl VectorQuery {
     /// This parameter must be specified if the table has more than one column
     /// whose data type is a fixed-size-list of floats.
     pub fn column(mut self, column: &str) -> Self {
-        self.column = Some(column.to_string());
+        self.request.column = Some(column.to_string());
         self
     }
 
@@ -797,7 +863,7 @@ impl VectorQuery {
     /// result.
     pub fn add_query_vector(mut self, vector: impl IntoQueryVector) -> Result<Self> {
         let query_vector = vector.to_query_vector(&DataType::Float32, "default")?;
-        self.query_vector.push(query_vector);
+        self.request.query_vector.push(query_vector);
         Ok(self)
     }
 
@@ -822,15 +888,15 @@ impl VectorQuery {
     /// your actual data to find the smallest possible value that will still give
     /// you the desired recall.
     pub fn nprobes(mut self, nprobes: usize) -> Self {
-        self.nprobes = nprobes;
+        self.request.nprobes = nprobes;
         self
     }
 
     /// Set the distance range for vector search,
     /// only rows with distances in the range [lower_bound, upper_bound) will be returned
     pub fn distance_range(mut self, lower_bound: Option<f32>, upper_bound: Option<f32>) -> Self {
-        self.lower_bound = lower_bound;
-        self.upper_bound = upper_bound;
+        self.request.lower_bound = lower_bound;
+        self.request.upper_bound = upper_bound;
         self
     }
 
@@ -842,7 +908,7 @@ impl VectorQuery {
     /// Increasing this value will increase the recall of your query but will
     /// also increase the latency of your query.  The default value is 1.5*limit.
     pub fn ef(mut self, ef: usize) -> Self {
-        self.ef = Some(ef);
+        self.request.ef = Some(ef);
         self
     }
 
@@ -874,7 +940,7 @@ impl VectorQuery {
     /// and the quantized result vectors.  This can be considerably different than the true
     /// distance between the query vector and the actual uncompressed vector.
     pub fn refine_factor(mut self, refine_factor: u32) -> Self {
-        self.refine_factor = Some(refine_factor);
+        self.request.refine_factor = Some(refine_factor);
         self
     }
 
@@ -891,7 +957,7 @@ impl VectorQuery {
     ///
     /// By default [`DistanceType::L2`] is used.
     pub fn distance_type(mut self, distance_type: DistanceType) -> Self {
-        self.distance_type = Some(distance_type);
+        self.request.distance_type = Some(distance_type);
         self
     }
 
@@ -903,16 +969,19 @@ impl VectorQuery {
     /// the vector index can give you ground truth results which you can use to
     /// calculate your recall to select an appropriate value for nprobes.
     pub fn bypass_vector_index(mut self) -> Self {
-        self.use_index = false;
+        self.request.use_index = false;
         self
     }
 
     pub async fn execute_hybrid(&self) -> Result<SendableRecordBatchStream> {
         // clone query and specify we want to include row IDs, which can be needed for reranking
-        let fts_query = self.base.clone().with_row_id();
+        let mut fts_query = Query::new(self.parent.clone());
+        fts_query.request = self.request.base.clone();
+        fts_query = fts_query.with_row_id();
+
         let mut vector_query = self.clone().with_row_id();
 
-        vector_query.base.full_text_search = None;
+        vector_query.request.base.full_text_search = None;
         let (fts_results, vec_results) = try_join!(fts_query.execute(), vector_query.execute())?;
 
         let (fts_results, vec_results) = try_join!(
@@ -928,7 +997,7 @@ impl VectorQuery {
         let mut fts_results = concat_batches(&fts_schema, fts_results.iter())?;
         let mut vec_results = concat_batches(&vec_schema, vec_results.iter())?;
 
-        if matches!(self.base.norm, Some(NormalizeMethod::Rank)) {
+        if matches!(self.request.base.norm, Some(NormalizeMethod::Rank)) {
             vec_results = hybrid::rank(vec_results, DIST_COL, None)?;
             fts_results = hybrid::rank(fts_results, SCORE_COL, None)?;
         }
@@ -937,14 +1006,20 @@ impl VectorQuery {
         fts_results = hybrid::normalize_scores(fts_results, SCORE_COL, None)?;
 
         let reranker = self
+            .request
             .base
             .reranker
             .clone()
             .unwrap_or(Arc::new(RRFReranker::default()));
 
-        let fts_query = self.base.full_text_search.as_ref().ok_or(Error::Runtime {
-            message: "there should be an FTS search".to_string(),
-        })?;
+        let fts_query = self
+            .request
+            .base
+            .full_text_search
+            .as_ref()
+            .ok_or(Error::Runtime {
+                message: "there should be an FTS search".to_string(),
+            })?;
 
         let mut results = reranker
             .rerank_hybrid(&fts_query.query, vec_results, fts_results)
@@ -952,12 +1027,12 @@ impl VectorQuery {
 
         check_reranker_result(&results)?;
 
-        let limit = self.base.limit.unwrap_or(DEFAULT_TOP_K);
+        let limit = self.request.base.limit.unwrap_or(DEFAULT_TOP_K);
         if results.num_rows() > limit {
             results = results.slice(0, limit);
         }
 
-        if !self.base.with_row_id {
+        if !self.request.base.with_row_id {
             results = results.drop_column(ROW_ID)?;
         }
 
@@ -969,14 +1044,15 @@ impl VectorQuery {
 
 impl ExecutableQuery for VectorQuery {
     async fn create_plan(&self, options: QueryExecutionOptions) -> Result<Arc<dyn ExecutionPlan>> {
-        self.base.parent.clone().create_plan(self, options).await
+        let query = AnyQuery::VectorQuery(self.request.clone());
+        self.parent.clone().create_plan(&query, options).await
     }
 
     async fn execute_with_options(
         &self,
         options: QueryExecutionOptions,
     ) -> Result<SendableRecordBatchStream> {
-        if self.base.full_text_search.is_some() {
+        if self.request.base.full_text_search.is_some() {
             let hybrid_result = async move { self.execute_hybrid().await }.boxed().await?;
             return Ok(hybrid_result);
         }
@@ -990,13 +1066,14 @@ impl ExecutableQuery for VectorQuery {
     }
 
     async fn explain_plan(&self, verbose: bool) -> Result<String> {
-        self.base.parent.explain_plan(self, verbose).await
+        let query = AnyQuery::VectorQuery(self.request.clone());
+        self.parent.explain_plan(&query, verbose).await
     }
 }
 
 impl HasQuery for VectorQuery {
-    fn mut_query(&mut self) -> &mut Query {
-        &mut self.base
+    fn mut_query(&mut self) -> &mut QueryRequest {
+        &mut self.request.base
     }
 }
 
@@ -1015,7 +1092,7 @@ mod tests {
     use lance_testing::datagen::{BatchGenerator, IncrementingInt32, RandomVector};
     use tempfile::tempdir;
 
-    use crate::{connect, connection::CreateTableMode, Table};
+    use crate::{connect, database::CreateTableMode, Table};
 
     #[tokio::test]
     async fn test_setters_getters() {
@@ -1036,7 +1113,13 @@ mod tests {
         let vector = Float32Array::from_iter_values([0.1, 0.2]);
         let query = table.query().nearest_to(&[0.1, 0.2]).unwrap();
         assert_eq!(
-            *query.query_vector.first().unwrap().as_ref().as_primitive(),
+            *query
+                .request
+                .query_vector
+                .first()
+                .unwrap()
+                .as_ref()
+                .as_primitive(),
             vector
         );
 
@@ -1054,15 +1137,21 @@ mod tests {
             .refine_factor(999);
 
         assert_eq!(
-            *query.query_vector.first().unwrap().as_ref().as_primitive(),
+            *query
+                .request
+                .query_vector
+                .first()
+                .unwrap()
+                .as_ref()
+                .as_primitive(),
             new_vector
         );
-        assert_eq!(query.base.limit.unwrap(), 100);
-        assert_eq!(query.base.offset.unwrap(), 1);
-        assert_eq!(query.nprobes, 1000);
-        assert!(query.use_index);
-        assert_eq!(query.distance_type, Some(DistanceType::Cosine));
-        assert_eq!(query.refine_factor, Some(999));
+        assert_eq!(query.request.base.limit.unwrap(), 100);
+        assert_eq!(query.request.base.offset.unwrap(), 1);
+        assert_eq!(query.request.nprobes, 1000);
+        assert!(query.request.use_index);
+        assert_eq!(query.request.distance_type, Some(DistanceType::Cosine));
+        assert_eq!(query.request.refine_factor, Some(999));
     }
 
     #[tokio::test]

rust/lancedb/src/remote.rs

@@ -14,6 +14,7 @@ pub(crate) mod util;
 const ARROW_STREAM_CONTENT_TYPE: &str = "application/vnd.apache.arrow.stream";
 #[cfg(test)]
 const ARROW_FILE_CONTENT_TYPE: &str = "application/vnd.apache.arrow.file";
+#[cfg(test)]
 const JSON_CONTENT_TYPE: &str = "application/json";
 
 pub use client::{ClientConfig, RetryConfig, TimeoutConfig};

rust/lancedb/src/remote/client.rs

@@ -1,8 +1,9 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-use std::{future::Future, time::Duration};
+use std::{collections::HashMap, future::Future, str::FromStr, time::Duration};
 
+use http::HeaderName;
 use log::debug;
 use reqwest::{
     header::{HeaderMap, HeaderValue},
@@ -15,7 +16,7 @@ use crate::remote::db::RemoteOptions;
 const REQUEST_ID_HEADER: &str = "x-request-id";
 
 /// Configuration for the LanceDB Cloud HTTP client.
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct ClientConfig {
     pub timeout_config: TimeoutConfig,
     pub retry_config: RetryConfig,
@@ -23,6 +24,7 @@ pub struct ClientConfig {
     /// name and version.
     pub user_agent: String,
     // TODO: how to configure request ids?
+    pub extra_headers: HashMap<String, String>,
 }
 
 impl Default for ClientConfig {
@@ -31,12 +33,13 @@ impl Default for ClientConfig {
             timeout_config: TimeoutConfig::default(),
             retry_config: RetryConfig::default(),
             user_agent: concat!("LanceDB-Rust-Client/", env!("CARGO_PKG_VERSION")).into(),
+            extra_headers: HashMap::new(),
         }
     }
 }
 
 /// How to handle timeouts for HTTP requests.
-#[derive(Default, Debug)]
+#[derive(Clone, Default, Debug)]
 pub struct TimeoutConfig {
     /// The timeout for creating a connection to the server.
     ///
@@ -62,7 +65,7 @@ pub struct TimeoutConfig {
 }
 
 /// How to handle retries for HTTP requests.
-#[derive(Default, Debug)]
+#[derive(Clone, Default, Debug)]
 pub struct RetryConfig {
     /// The number of times to retry a request if it fails.
     ///
@@ -256,6 +259,7 @@ impl RestfulLanceDbClient<Sender> {
                 host_override.is_some(),
                 options,
                 db_prefix,
+                &client_config,
             )?)
             .user_agent(client_config.user_agent)
             .build()
@@ -291,6 +295,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
         has_host_override: bool,
         options: &RemoteOptions,
         db_prefix: Option<&str>,
+        config: &ClientConfig,
     ) -> Result<HeaderMap> {
         let mut headers = HeaderMap::new();
         headers.insert(
@@ -345,6 +350,18 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
             );
         }
 
+        for (key, value) in &config.extra_headers {
+            let key_parsed = HeaderName::from_str(key).map_err(|_| Error::InvalidInput {
+                message: format!("non-ascii value for header '{}' provided", key),
+            })?;
+            headers.insert(
+                key_parsed,
+                HeaderValue::from_str(value).map_err(|_| Error::InvalidInput {
+                    message: format!("non-ascii value for header '{}' provided", key),
+                })?,
+            );
+        }
+
         Ok(headers)
     }
 

rust/lancedb/src/remote/db.rs

@@ -4,7 +4,7 @@
 use std::collections::HashMap;
 use std::sync::Arc;
 
-use arrow_array::RecordBatchReader;
+use arrow_array::RecordBatchIterator;
 use async_trait::async_trait;
 use http::StatusCode;
 use lance_io::object_store::StorageOptions;
@@ -13,13 +13,12 @@ use reqwest::header::CONTENT_TYPE;
 use serde::Deserialize;
 use tokio::task::spawn_blocking;
 
-use crate::connection::{
-    ConnectionInternal, CreateTableBuilder, CreateTableMode, NoData, OpenTableBuilder,
-    TableNamesBuilder,
+use crate::database::{
+    CreateTableData, CreateTableMode, CreateTableRequest, Database, OpenTableRequest,
+    TableNamesRequest,
 };
-use crate::embeddings::EmbeddingRegistry;
 use crate::error::Result;
-use crate::Table;
+use crate::table::BaseTable;
 
 use super::client::{ClientConfig, HttpSend, RequestResultExt, RestfulLanceDbClient, Sender};
 use super::table::RemoteTable;
@@ -105,13 +104,13 @@ impl From<&CreateTableMode> for &'static str {
 }
 
 #[async_trait]
-impl<S: HttpSend> ConnectionInternal for RemoteDatabase<S> {
-    async fn table_names(&self, options: TableNamesBuilder) -> Result<Vec<String>> {
+impl<S: HttpSend> Database for RemoteDatabase<S> {
+    async fn table_names(&self, request: TableNamesRequest) -> Result<Vec<String>> {
         let mut req = self.client.get("/v1/table/");
-        if let Some(limit) = options.limit {
+        if let Some(limit) = request.limit {
             req = req.query(&[("limit", limit)]);
         }
-        if let Some(start_after) = options.start_after {
+        if let Some(start_after) = request.start_after {
             req = req.query(&[("page_token", start_after)]);
         }
         let (request_id, rsp) = self.client.send(req, true).await?;
@@ -127,11 +126,15 @@ impl<S: HttpSend> ConnectionInternal for RemoteDatabase<S> {
         Ok(tables)
     }
 
-    async fn do_create_table(
-        &self,
-        options: CreateTableBuilder<false, NoData>,
-        data: Box<dyn RecordBatchReader + Send>,
-    ) -> Result<Table> {
+    async fn create_table(&self, request: CreateTableRequest) -> Result<Arc<dyn BaseTable>> {
+        let data = match request.data {
+            CreateTableData::Data(data) => data,
+            CreateTableData::Empty(table_definition) => {
+                let schema = table_definition.schema.clone();
+                Box::new(RecordBatchIterator::new(vec![], schema))
+            }
+        };
+
         // TODO: https://github.com/lancedb/lancedb/issues/1026
         // We should accept data from an async source.  In the meantime, spawn this as blocking
         // to make sure we don't block the tokio runtime if the source is slow.
@@ -141,8 +144,8 @@ impl<S: HttpSend> ConnectionInternal for RemoteDatabase<S> {
 
         let req = self
             .client
-            .post(&format!("/v1/table/{}/create/", options.name))
-            .query(&[("mode", Into::<&str>::into(&options.mode))])
+            .post(&format!("/v1/table/{}/create/", request.name))
+            .query(&[("mode", Into::<&str>::into(&request.mode))])
             .body(data_buffer)
             .header(CONTENT_TYPE, ARROW_STREAM_CONTENT_TYPE);
 
@@ -151,14 +154,18 @@ impl<S: HttpSend> ConnectionInternal for RemoteDatabase<S> {
         if rsp.status() == StatusCode::BAD_REQUEST {
             let body = rsp.text().await.err_to_http(request_id.clone())?;
             if body.contains("already exists") {
-                return match options.mode {
+                return match request.mode {
                     CreateTableMode::Create => {
-                        Err(crate::Error::TableAlreadyExists { name: options.name })
+                        Err(crate::Error::TableAlreadyExists { name: request.name })
                     }
                     CreateTableMode::ExistOk(callback) => {
-                        let builder = OpenTableBuilder::new(options.parent, options.name);
-                        let builder = (callback)(builder);
-                        builder.execute().await
+                        let req = OpenTableRequest {
+                            name: request.name.clone(),
+                            index_cache_size: None,
+                            lance_read_params: None,
+                        };
+                        let req = (callback)(req);
+                        self.open_table(req).await
                     }
 
                     // This should not happen, as we explicitly set the mode to overwrite and the server
@@ -183,31 +190,31 @@ impl<S: HttpSend> ConnectionInternal for RemoteDatabase<S> {
 
         self.client.check_response(&request_id, rsp).await?;
 
-        self.table_cache.insert(options.name.clone(), ()).await;
+        self.table_cache.insert(request.name.clone(), ()).await;
 
-        Ok(Table::new(Arc::new(RemoteTable::new(
+        Ok(Arc::new(RemoteTable::new(
             self.client.clone(),
-            options.name,
-        ))))
+            request.name,
+        )))
     }
 
-    async fn do_open_table(&self, options: OpenTableBuilder) -> Result<Table> {
+    async fn open_table(&self, request: OpenTableRequest) -> Result<Arc<dyn BaseTable>> {
         // We describe the table to confirm it exists before moving on.
-        if self.table_cache.get(&options.name).is_none() {
+        if self.table_cache.get(&request.name).await.is_none() {
             let req = self
                 .client
-                .post(&format!("/v1/table/{}/describe/", options.name));
+                .post(&format!("/v1/table/{}/describe/", request.name));
             let (request_id, resp) = self.client.send(req, true).await?;
             if resp.status() == StatusCode::NOT_FOUND {
-                return Err(crate::Error::TableNotFound { name: options.name });
+                return Err(crate::Error::TableNotFound { name: request.name });
             }
             self.client.check_response(&request_id, resp).await?;
         }
 
-        Ok(Table::new(Arc::new(RemoteTable::new(
+        Ok(Arc::new(RemoteTable::new(
             self.client.clone(),
-            options.name,
-        ))))
+            request.name,
+        )))
     }
 
     async fn rename_table(&self, current_name: &str, new_name: &str) -> Result<()> {
@@ -230,14 +237,14 @@ impl<S: HttpSend> ConnectionInternal for RemoteDatabase<S> {
         Ok(())
     }
 
-    async fn drop_db(&self) -> Result<()> {
+    async fn drop_all_tables(&self) -> Result<()> {
         Err(crate::Error::NotSupported {
             message: "Dropping databases is not supported in the remote API".to_string(),
         })
     }
 
-    fn embedding_registry(&self) -> &dyn EmbeddingRegistry {
-        todo!()
+    fn as_any(&self) -> &dyn std::any::Any {
+        self
     }
 }
 
@@ -273,7 +280,7 @@ mod tests {
 
     use crate::connection::ConnectBuilder;
     use crate::{
-        connection::CreateTableMode,
+        database::CreateTableMode,
         remote::{ARROW_STREAM_CONTENT_TYPE, JSON_CONTENT_TYPE},
         Connection, Error,
     };

rust/lancedb/src/remote/table.rs

@@ -2,21 +2,22 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 use std::io::Cursor;
+use std::pin::Pin;
 use std::sync::{Arc, Mutex};
 
 use crate::index::Index;
 use crate::index::IndexStatistics;
-use crate::query::Select;
-use crate::table::AddDataMode;
+use crate::query::{QueryRequest, Select, VectorQueryRequest};
+use crate::table::{AddDataMode, AnyQuery, Filter};
 use crate::utils::{supported_btree_data_type, supported_vector_data_type};
-use crate::{DistanceType, Error, Table};
+use crate::{DistanceType, Error};
 use arrow_array::RecordBatchReader;
 use arrow_ipc::reader::FileReader;
 use arrow_schema::{DataType, SchemaRef};
 use async_trait::async_trait;
 use datafusion_common::DataFusionError;
 use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
-use datafusion_physical_plan::{ExecutionPlan, SendableRecordBatchStream};
+use datafusion_physical_plan::{ExecutionPlan, RecordBatchStream, SendableRecordBatchStream};
 use futures::TryStreamExt;
 use http::header::CONTENT_TYPE;
 use http::StatusCode;
@@ -31,16 +32,16 @@ use crate::{
     connection::NoData,
     error::Result,
     index::{IndexBuilder, IndexConfig},
-    query::{Query, QueryExecutionOptions, VectorQuery},
+    query::QueryExecutionOptions,
     table::{
-        merge::MergeInsertBuilder, AddDataBuilder, NativeTable, OptimizeAction, OptimizeStats,
-        TableDefinition, TableInternal, UpdateBuilder,
+        merge::MergeInsertBuilder, AddDataBuilder, BaseTable, OptimizeAction, OptimizeStats,
+        TableDefinition, UpdateBuilder,
     },
 };
 
 use super::client::RequestResultExt;
 use super::client::{HttpSend, RestfulLanceDbClient, Sender};
-use super::{ARROW_STREAM_CONTENT_TYPE, JSON_CONTENT_TYPE};
+use super::ARROW_STREAM_CONTENT_TYPE;
 
 #[derive(Debug)]
 pub struct RemoteTable<S: HttpSend = Sender> {
@@ -147,7 +148,7 @@ impl<S: HttpSend> RemoteTable<S> {
         Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
     }
 
-    fn apply_query_params(body: &mut serde_json::Value, params: &Query) -> Result<()> {
+    fn apply_query_params(body: &mut serde_json::Value, params: &QueryRequest) -> Result<()> {
         if let Some(offset) = params.offset {
             body["offset"] = serde_json::Value::Number(serde_json::Number::from(offset));
         }
@@ -204,10 +205,10 @@ impl<S: HttpSend> RemoteTable<S> {
     }
 
     fn apply_vector_query_params(
-        mut body: serde_json::Value,
-        query: &VectorQuery,
-    ) -> Result<Vec<serde_json::Value>> {
-        Self::apply_query_params(&mut body, &query.base)?;
+        body: &mut serde_json::Value,
+        query: &VectorQueryRequest,
+    ) -> Result<()> {
+        Self::apply_query_params(body, &query.base)?;
 
         // Apply general parameters, before we dispatch based on number of query vectors.
         body["prefilter"] = query.base.prefilter.into();
@@ -253,22 +254,21 @@ impl<S: HttpSend> RemoteTable<S> {
             0 => {
                 // Server takes empty vector, not null or undefined.
                 body["vector"] = serde_json::Value::Array(Vec::new());
-                Ok(vec![body])
             }
             1 => {
                 body["vector"] = vector_to_json(&query.query_vector[0])?;
-                Ok(vec![body])
             }
             _ => {
-                let mut bodies = Vec::with_capacity(query.query_vector.len());
-                for vector in &query.query_vector {
-                    let mut body = body.clone();
-                    body["vector"] = vector_to_json(vector)?;
-                    bodies.push(body);
-                }
-                Ok(bodies)
+                let vectors = query
+                    .query_vector
+                    .iter()
+                    .map(vector_to_json)
+                    .collect::<Result<Vec<_>>>()?;
+                body["vector"] = serde_json::Value::Array(vectors);
             }
         }
+
+        Ok(())
     }
 
     async fn check_mutable(&self) -> Result<()> {
@@ -288,6 +288,33 @@ impl<S: HttpSend> RemoteTable<S> {
         let read_guard = self.version.read().await;
         *read_guard
     }
+
+    async fn execute_query(
+        &self,
+        query: &AnyQuery,
+        _options: QueryExecutionOptions,
+    ) -> Result<Pin<Box<dyn RecordBatchStream + Send>>> {
+        let request = self.client.post(&format!("/v1/table/{}/query/", self.name));
+
+        let version = self.current_version().await;
+        let mut body = serde_json::json!({ "version": version });
+
+        match query {
+            AnyQuery::Query(query) => {
+                Self::apply_query_params(&mut body, query)?;
+                // Empty vector can be passed if no vector search is performed.
+                body["vector"] = serde_json::Value::Array(Vec::new());
+            }
+            AnyQuery::VectorQuery(query) => {
+                Self::apply_vector_query_params(&mut body, query)?;
+            }
+        }
+
+        let request = request.json(&body);
+        let (request_id, response) = self.client.send(request, true).await?;
+        let stream = self.read_arrow_stream(&request_id, response).await?;
+        Ok(stream)
+    }
 }
 
 #[derive(Deserialize)]
@@ -325,13 +352,10 @@ mod test_utils {
 }
 
 #[async_trait]
-impl<S: HttpSend> TableInternal for RemoteTable<S> {
+impl<S: HttpSend> BaseTable for RemoteTable<S> {
     fn as_any(&self) -> &dyn std::any::Any {
         self
     }
-    fn as_native(&self) -> Option<&NativeTable> {
-        None
-    }
     fn name(&self) -> &str {
         &self.name
     }
@@ -398,7 +422,7 @@ impl<S: HttpSend> TableInternal for RemoteTable<S> {
         let schema = self.describe().await?.schema;
         Ok(Arc::new(schema.try_into()?))
     }
-    async fn count_rows(&self, filter: Option<String>) -> Result<usize> {
+    async fn count_rows(&self, filter: Option<Filter>) -> Result<usize> {
         let mut request = self
             .client
             .post(&format!("/v1/table/{}/count_rows/", self.name));
@@ -406,6 +430,11 @@ impl<S: HttpSend> TableInternal for RemoteTable<S> {
         let version = self.current_version().await;
 
         if let Some(filter) = filter {
+            let Filter::Sql(filter) = filter else {
+                return Err(Error::NotSupported {
+                    message: "querying a remote table with a datafusion filter".to_string(),
+                });
+            };
             request = request.json(&serde_json::json!({ "predicate": filter, "version": version }));
         } else {
             let body = serde_json::json!({ "version": version });
@@ -453,59 +482,19 @@ impl<S: HttpSend> TableInternal for RemoteTable<S> {
 
     async fn create_plan(
         &self,
-        query: &VectorQuery,
-        _options: QueryExecutionOptions,
+        query: &AnyQuery,
+        options: QueryExecutionOptions,
     ) -> Result<Arc<dyn ExecutionPlan>> {
-        let request = self.client.post(&format!("/v1/table/{}/query/", self.name));
-
-        let version = self.current_version().await;
-        let body = serde_json::json!({ "version": version });
-        let bodies = Self::apply_vector_query_params(body, query)?;
-
-        let mut futures = Vec::with_capacity(bodies.len());
-        for body in bodies {
-            let request = request.try_clone().unwrap().json(&body);
-            let future = async move {
-                let (request_id, response) = self.client.send(request, true).await?;
-                self.read_arrow_stream(&request_id, response).await
-            };
-            futures.push(future);
-        }
-        let streams = futures::future::try_join_all(futures).await?;
-        if streams.len() == 1 {
-            let stream = streams.into_iter().next().unwrap();
-            Ok(Arc::new(OneShotExec::new(stream)))
-        } else {
-            let stream_execs = streams
-                .into_iter()
-                .map(|stream| Arc::new(OneShotExec::new(stream)) as Arc<dyn ExecutionPlan>)
-                .collect();
-            Table::multi_vector_plan(stream_execs)
-        }
+        let stream = self.execute_query(query, options).await?;
+        Ok(Arc::new(OneShotExec::new(stream)))
     }
 
-    async fn plain_query(
+    async fn query(
         &self,
-        query: &Query,
+        query: &AnyQuery,
         _options: QueryExecutionOptions,
     ) -> Result<DatasetRecordBatchStream> {
-        let request = self
-            .client
-            .post(&format!("/v1/table/{}/query/", self.name))
-            .header(CONTENT_TYPE, JSON_CONTENT_TYPE);
-
-        let version = self.current_version().await;
-        let mut body = serde_json::json!({ "version": version });
-        Self::apply_query_params(&mut body, query)?;
-        // Empty vector can be passed if no vector search is performed.
-        body["vector"] = serde_json::Value::Array(Vec::new());
-
-        let request = request.json(&body);
-
-        let (request_id, response) = self.client.send(request, true).await?;
-
-        let stream = self.read_arrow_stream(&request_id, response).await?;
-
+        let stream = self.execute_query(query, _options).await?;
         Ok(DatasetRecordBatchStream::new(stream))
     }
     async fn update(&self, update: UpdateBuilder) -> Result<u64> {
@@ -820,11 +809,14 @@ impl<S: HttpSend> TableInternal for RemoteTable<S> {
         Ok(Some(stats))
     }
 
-    /// Not yet supported on LanceDB Cloud.
-    async fn drop_index(&self, _name: &str) -> Result<()> {
-        Err(Error::NotSupported {
-            message: "Drop index is not yet supported on LanceDB Cloud.".into(),
-        })
+    async fn drop_index(&self, index_name: &str) -> Result<()> {
+        let request = self.client.post(&format!(
+            "/v1/table/{}/index/{}/drop/",
+            self.name, index_name
+        ));
+        let (request_id, response) = self.client.send(request, true).await?;
+        self.check_table_response(&request_id, response).await?;
+        Ok(())
     }
 
     async fn table_definition(&self) -> Result<TableDefinition> {
@@ -888,6 +880,7 @@ mod tests {
     use reqwest::Body;
 
     use crate::index::vector::IvfFlatIndexBuilder;
+    use crate::remote::JSON_CONTENT_TYPE;
     use crate::{
         index::{vector::IvfPqIndexBuilder, Index, IndexStatistics, IndexType},
         query::{ExecutableQuery, QueryBase},
@@ -1468,9 +1461,21 @@ mod tests {
                 request.headers().get("Content-Type").unwrap(),
                 JSON_CONTENT_TYPE
             );
+            let body: serde_json::Value =
+                serde_json::from_slice(request.body().unwrap().as_bytes().unwrap()).unwrap();
+            let query_vectors = body["vector"].as_array().unwrap();
+            assert_eq!(query_vectors.len(), 2);
+            assert_eq!(query_vectors[0].as_array().unwrap().len(), 3);
+            assert_eq!(query_vectors[1].as_array().unwrap().len(), 3);
             let data = RecordBatch::try_new(
-                Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)])),
-                vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
+                Arc::new(Schema::new(vec![
+                    Field::new("a", DataType::Int32, false),
+                    Field::new("query_index", DataType::Int32, false),
+                ])),
+                vec![
+                    Arc::new(Int32Array::from(vec![1, 2, 3, 4, 5, 6])),
+                    Arc::new(Int32Array::from(vec![0, 0, 0, 1, 1, 1])),
+                ],
             )
             .unwrap();
             let response_body = write_ipc_file(&data);
@@ -1487,8 +1492,6 @@ mod tests {
             .unwrap()
             .add_query_vector(vec![0.4, 0.5, 0.6])
             .unwrap();
-        let plan = query.explain_plan(true).await.unwrap();
-        assert!(plan.contains("UnionExec"), "Plan: {}", plan);
 
         let results = query
             .execute()
@@ -2022,4 +2025,17 @@ mod tests {
 
         table.drop_columns(&["a", "b"]).await.unwrap();
     }
+
+    #[tokio::test]
+    async fn test_drop_index() {
+        let table = Table::new_with_handler("my_table", |request| {
+            assert_eq!(request.method(), "POST");
+            assert_eq!(
+                request.url().path(),
+                "/v1/table/my_table/index/my_index/drop/"
+            );
+            http::Response::builder().status(200).body("{}").unwrap()
+        });
+        table.drop_index("my_index").await.unwrap();
+    }
 }

rust/lancedb/src/table.rs

@@ -12,6 +12,7 @@ use arrow::datatypes::{Float32Type, UInt8Type};
 use arrow_array::{RecordBatchIterator, RecordBatchReader};
 use arrow_schema::{DataType, Field, Schema, SchemaRef};
 use async_trait::async_trait;
+use datafusion_expr::Expr;
 use datafusion_physical_plan::display::DisplayableExecutionPlan;
 use datafusion_physical_plan::projection::ProjectionExec;
 use datafusion_physical_plan::repartition::RepartitionExec;
@@ -21,12 +22,13 @@ use futures::{StreamExt, TryStreamExt};
 use lance::dataset::builder::DatasetBuilder;
 use lance::dataset::cleanup::RemovalStats;
 use lance::dataset::optimize::{compact_files, CompactionMetrics, IndexRemapperOptions};
-use lance::dataset::scanner::{DatasetRecordBatchStream, Scanner};
+use lance::dataset::scanner::Scanner;
 pub use lance::dataset::ColumnAlteration;
 pub use lance::dataset::NewColumnTransform;
 pub use lance::dataset::ReadParams;
+pub use lance::dataset::Version;
 use lance::dataset::{
-    Dataset, InsertBuilder, UpdateBuilder as LanceUpdateBuilder, Version, WhenMatched, WriteMode,
+    Dataset, InsertBuilder, UpdateBuilder as LanceUpdateBuilder, WhenMatched, WriteMode,
     WriteParams,
 };
 use lance::dataset::{MergeInsertBuilder as LanceMergeInsertBuilder, WhenNotMatchedBySource};
@@ -60,7 +62,8 @@ use crate::index::{
 };
 use crate::index::{IndexConfig, IndexStatisticsImpl};
 use crate::query::{
-    IntoQueryVector, Query, QueryExecutionOptions, Select, VectorQuery, DEFAULT_TOP_K,
+    IntoQueryVector, Query, QueryExecutionOptions, QueryRequest, Select, VectorQuery,
+    VectorQueryRequest, DEFAULT_TOP_K,
 };
 use crate::utils::{
     default_vector_column, supported_bitmap_data_type, supported_btree_data_type,
@@ -71,11 +74,13 @@ use crate::utils::{
 use self::dataset::DatasetConsistencyWrapper;
 use self::merge::MergeInsertBuilder;
 
+pub mod datafusion;
 pub(crate) mod dataset;
 pub mod merge;
 
 pub use chrono::Duration;
 pub use lance::dataset::optimize::CompactionOptions;
+pub use lance::dataset::scanner::DatasetRecordBatchStream;
 pub use lance_index::optimize::OptimizeOptions;
 
 /// Defines the type of column
@@ -230,6 +235,24 @@ pub struct OptimizeStats {
     pub prune: Option<RemovalStats>,
 }
 
+/// Describes what happens when a vector either contains NaN or
+/// does not have enough values
+#[derive(Clone, Debug, Default)]
+enum BadVectorHandling {
+    /// An error is returned
+    #[default]
+    Error,
+    #[allow(dead_code)] // https://github.com/lancedb/lancedb/issues/992
+    /// The offending row is droppped
+    Drop,
+    #[allow(dead_code)] // https://github.com/lancedb/lancedb/issues/992
+    /// The invalid/missing items are replaced by fill_value
+    Fill(f32),
+    #[allow(dead_code)] // https://github.com/lancedb/lancedb/issues/992
+    /// The invalid items are replaced by NULL
+    None,
+}
+
 /// Options to use when writing data
 #[derive(Clone, Debug, Default)]
 pub struct WriteOptions {
@@ -255,7 +278,7 @@ pub enum AddDataMode {
 /// A builder for configuring a [`crate::connection::Connection::create_table`] or [`Table::add`]
 /// operation
 pub struct AddDataBuilder<T: IntoArrow> {
-    parent: Arc<dyn TableInternal>,
+    parent: Arc<dyn BaseTable>,
     pub(crate) data: T,
     pub(crate) mode: AddDataMode,
     pub(crate) write_options: WriteOptions,
@@ -300,13 +323,13 @@ impl<T: IntoArrow> AddDataBuilder<T> {
 /// A builder for configuring an [`Table::update`] operation
 #[derive(Debug, Clone)]
 pub struct UpdateBuilder {
-    parent: Arc<dyn TableInternal>,
+    parent: Arc<dyn BaseTable>,
     pub(crate) filter: Option<String>,
     pub(crate) columns: Vec<(String, String)>,
 }
 
 impl UpdateBuilder {
-    fn new(parent: Arc<dyn TableInternal>) -> Self {
+    fn new(parent: Arc<dyn BaseTable>) -> Self {
         Self {
             parent,
             filter: None,
@@ -363,64 +386,102 @@ impl UpdateBuilder {
     }
 }
 
+/// Filters that can be used to limit the rows returned by a query
+pub enum Filter {
+    /// A SQL filter string
+    Sql(String),
+    /// A Datafusion logical expression
+    Datafusion(Expr),
+}
+
+/// A query that can be used to search a LanceDB table
+pub enum AnyQuery {
+    Query(QueryRequest),
+    VectorQuery(VectorQueryRequest),
+}
+
+/// A trait for anything "table-like".  This is used for both native tables (which target
+/// Lance datasets) and remote tables (which target LanceDB cloud)
+///
+/// This trait is still EXPERIMENTAL and subject to change in the future
 #[async_trait]
-pub(crate) trait TableInternal: std::fmt::Display + std::fmt::Debug + Send + Sync {
-    #[allow(dead_code)]
+pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
+    /// Get a reference to std::any::Any
     fn as_any(&self) -> &dyn std::any::Any;
-    /// Cast as [`NativeTable`], or return None it if is not a [`NativeTable`].
-    fn as_native(&self) -> Option<&NativeTable>;
     /// Get the name of the table.
     fn name(&self) -> &str;
     /// Get the arrow [Schema] of the table.
     async fn schema(&self) -> Result<SchemaRef>;
     /// Count the number of rows in this table.
-    async fn count_rows(&self, filter: Option<String>) -> Result<usize>;
+    async fn count_rows(&self, filter: Option<Filter>) -> Result<usize>;
+    /// Create a physical plan for the query.
     async fn create_plan(
         &self,
-        query: &VectorQuery,
+        query: &AnyQuery,
         options: QueryExecutionOptions,
     ) -> Result<Arc<dyn ExecutionPlan>>;
-    async fn plain_query(
+    /// Execute a query and return the results as a stream of RecordBatches.
+    async fn query(
         &self,
-        query: &Query,
+        query: &AnyQuery,
         options: QueryExecutionOptions,
     ) -> Result<DatasetRecordBatchStream>;
-    async fn explain_plan(&self, query: &VectorQuery, verbose: bool) -> Result<String> {
+    /// Explain the plan for a query.
+    async fn explain_plan(&self, query: &AnyQuery, verbose: bool) -> Result<String> {
         let plan = self.create_plan(query, Default::default()).await?;
         let display = DisplayableExecutionPlan::new(plan.as_ref());
 
         Ok(format!("{}", display.indent(verbose)))
     }
+    /// Add new records to the table.
     async fn add(
         &self,
         add: AddDataBuilder<NoData>,
         data: Box<dyn arrow_array::RecordBatchReader + Send>,
     ) -> Result<()>;
+    /// Delete rows from the table.
     async fn delete(&self, predicate: &str) -> Result<()>;
+    /// Update rows in the table.
     async fn update(&self, update: UpdateBuilder) -> Result<u64>;
+    /// Create an index on the provided column(s).
     async fn create_index(&self, index: IndexBuilder) -> Result<()>;
+    /// List the indices on the table.
     async fn list_indices(&self) -> Result<Vec<IndexConfig>>;
+    /// Drop an index from the table.
     async fn drop_index(&self, name: &str) -> Result<()>;
+    /// Get statistics about the index.
     async fn index_stats(&self, index_name: &str) -> Result<Option<IndexStatistics>>;
+    /// Merge insert new records into the table.
     async fn merge_insert(
         &self,
         params: MergeInsertBuilder,
         new_data: Box<dyn RecordBatchReader + Send>,
     ) -> Result<()>;
+    /// Optimize the dataset.
     async fn optimize(&self, action: OptimizeAction) -> Result<OptimizeStats>;
+    /// Add columns to the table.
     async fn add_columns(
         &self,
         transforms: NewColumnTransform,
         read_columns: Option<Vec<String>>,
     ) -> Result<()>;
+    /// Alter columns in the table.
     async fn alter_columns(&self, alterations: &[ColumnAlteration]) -> Result<()>;
+    /// Drop columns from the table.
     async fn drop_columns(&self, columns: &[&str]) -> Result<()>;
+    /// Get the version of the table.
     async fn version(&self) -> Result<u64>;
+    /// Checkout a specific version of the table.
     async fn checkout(&self, version: u64) -> Result<()>;
+    /// Checkout the latest version of the table.
     async fn checkout_latest(&self) -> Result<()>;
+    /// Restore the table to the currently checked out version.
     async fn restore(&self) -> Result<()>;
+    /// List the versions of the table.
     async fn list_versions(&self) -> Result<Vec<Version>>;
+    /// Get the table definition.
     async fn table_definition(&self) -> Result<TableDefinition>;
+    /// Get the table URI
     fn dataset_uri(&self) -> &str;
 }
 
@@ -429,7 +490,7 @@ pub(crate) trait TableInternal: std::fmt::Display + std::fmt::Debug + Send + Syn
 /// The type of the each row is defined in Apache Arrow [Schema].
 #[derive(Clone)]
 pub struct Table {
-    inner: Arc<dyn TableInternal>,
+    inner: Arc<dyn BaseTable>,
     embedding_registry: Arc<dyn EmbeddingRegistry>,
 }
 
@@ -465,15 +526,19 @@ impl std::fmt::Display for Table {
 }
 
 impl Table {
-    pub(crate) fn new(inner: Arc<dyn TableInternal>) -> Self {
+    pub fn new(inner: Arc<dyn BaseTable>) -> Self {
         Self {
             inner,
             embedding_registry: Arc::new(MemoryRegistry::new()),
         }
     }
 
+    pub fn base_table(&self) -> &Arc<dyn BaseTable> {
+        &self.inner
+    }
+
     pub(crate) fn new_with_embedding_registry(
-        inner: Arc<dyn TableInternal>,
+        inner: Arc<dyn BaseTable>,
         embedding_registry: Arc<dyn EmbeddingRegistry>,
     ) -> Self {
         Self {
@@ -506,7 +571,7 @@ impl Table {
     ///
     /// * `filter` if present, only count rows matching the filter
     pub async fn count_rows(&self, filter: Option<String>) -> Result<usize> {
-        self.inner.count_rows(filter).await
+        self.inner.count_rows(filter.map(Filter::Sql)).await
     }
 
     /// Insert new records into this Table
@@ -1045,6 +1110,17 @@ impl From<NativeTable> for Table {
     }
 }
 
+pub trait NativeTableExt {
+    /// Cast as [`NativeTable`], or return None it if is not a [`NativeTable`].
+    fn as_native(&self) -> Option<&NativeTable>;
+}
+
+impl NativeTableExt for Arc<dyn BaseTable> {
+    fn as_native(&self) -> Option<&NativeTable> {
+        self.as_any().downcast_ref::<NativeTable>()
+    }
+}
+
 /// A table in a LanceDB database.
 #[derive(Debug, Clone)]
 pub struct NativeTable {
@@ -1164,7 +1240,7 @@ impl NativeTable {
     /// # Returns
     ///
     /// * A [TableImpl] object.
-    pub(crate) async fn create(
+    pub async fn create(
         uri: &str,
         name: &str,
         batches: impl RecordBatchReader + Send + 'static,
@@ -1304,10 +1380,11 @@ impl NativeTable {
 
     pub async fn load_indices(&self) -> Result<Vec<VectorIndex>> {
         let dataset = self.dataset.get().await?;
-        let (indices, mf) = futures::try_join!(dataset.load_indices(), dataset.latest_manifest())?;
+        let mf = dataset.manifest();
+        let indices = dataset.load_indices().await?;
         Ok(indices
             .iter()
-            .map(|i| VectorIndex::new_from_format(&(mf.0), i))
+            .map(|i| VectorIndex::new_from_format(mf, i))
             .collect())
     }
 
@@ -1658,7 +1735,7 @@ impl NativeTable {
 
     async fn generic_query(
         &self,
-        query: &VectorQuery,
+        query: &AnyQuery,
         options: QueryExecutionOptions,
     ) -> Result<DatasetRecordBatchStream> {
         let plan = self.create_plan(query, options).await?;
@@ -1748,15 +1825,11 @@ impl NativeTable {
 }
 
 #[async_trait::async_trait]
-impl TableInternal for NativeTable {
+impl BaseTable for NativeTable {
     fn as_any(&self) -> &dyn std::any::Any {
         self
     }
 
-    fn as_native(&self) -> Option<&NativeTable> {
-        Some(self)
-    }
-
     fn name(&self) -> &str {
         self.name.as_str()
     }
@@ -1812,8 +1885,15 @@ impl TableInternal for NativeTable {
         TableDefinition::try_from_rich_schema(schema)
     }
 
-    async fn count_rows(&self, filter: Option<String>) -> Result<usize> {
-        Ok(self.dataset.get().await?.count_rows(filter).await?)
+    async fn count_rows(&self, filter: Option<Filter>) -> Result<usize> {
+        let dataset = self.dataset.get().await?;
+        match filter {
+            None => Ok(dataset.count_rows(None).await?),
+            Some(Filter::Sql(sql)) => Ok(dataset.count_rows(Some(sql)).await?),
+            Some(Filter::Datafusion(_)) => Err(Error::NotSupported {
+                message: "Datafusion filters are not yet supported".to_string(),
+            }),
+        }
     }
 
     async fn add(
@@ -1907,12 +1987,17 @@ impl TableInternal for NativeTable {
 
     async fn create_plan(
         &self,
-        query: &VectorQuery,
+        query: &AnyQuery,
         options: QueryExecutionOptions,
     ) -> Result<Arc<dyn ExecutionPlan>> {
+        let query = match query {
+            AnyQuery::VectorQuery(query) => query.clone(),
+            AnyQuery::Query(query) => VectorQueryRequest::from_plain_query(query.clone()),
+        };
+
         let ds_ref = self.dataset.get().await?;
-        let mut column = query.column.clone();
         let schema = ds_ref.schema();
+        let mut column = query.column.clone();
 
         let mut query_vector = query.query_vector.first().cloned();
         if query.query_vector.len() > 1 {
@@ -1957,7 +2042,10 @@ impl TableInternal for NativeTable {
                         let mut sub_query = query.clone();
                         sub_query.query_vector = vec![query_vector];
                         let options_ref = options.clone();
-                        async move { self.create_plan(&sub_query, options_ref).await }
+                        async move {
+                            self.create_plan(&AnyQuery::VectorQuery(sub_query), options_ref)
+                                .await
+                        }
                     })
                     .collect::<Vec<_>>();
                 let plans = futures::future::try_join_all(plan_futures).await?;
@@ -2055,13 +2143,12 @@ impl TableInternal for NativeTable {
         Ok(scanner.create_plan().await?)
     }
 
-    async fn plain_query(
+    async fn query(
         &self,
-        query: &Query,
+        query: &AnyQuery,
         options: QueryExecutionOptions,
     ) -> Result<DatasetRecordBatchStream> {
-        self.generic_query(&query.clone().into_vector(), options)
-            .await
+        self.generic_query(query, options).await
     }
 
     async fn merge_insert(
@@ -2330,7 +2417,10 @@ mod tests {
 
         assert_eq!(table.count_rows(None).await.unwrap(), 10);
         assert_eq!(
-            table.count_rows(Some("i >= 5".to_string())).await.unwrap(),
+            table
+                .count_rows(Some(Filter::Sql("i >= 5".to_string())))
+                .await
+                .unwrap(),
             5
         );
     }

rust/lancedb/src/table/datafusion.rs

@@ -0,0 +1,263 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! This module contains adapters to allow LanceDB tables to be used as DataFusion table providers.
+use std::{collections::HashMap, sync::Arc};
+
+use arrow_schema::Schema as ArrowSchema;
+use async_trait::async_trait;
+use datafusion_catalog::{Session, TableProvider};
+use datafusion_common::{DataFusionError, Result as DataFusionResult, Statistics};
+use datafusion_execution::{SendableRecordBatchStream, TaskContext};
+use datafusion_expr::{Expr, TableProviderFilterPushDown, TableType};
+use datafusion_physical_plan::{
+    stream::RecordBatchStreamAdapter, DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties,
+};
+use futures::{TryFutureExt, TryStreamExt};
+
+use super::{AnyQuery, BaseTable};
+use crate::{
+    query::{QueryExecutionOptions, QueryRequest, Select},
+    Result,
+};
+
+/// Datafusion attempts to maintain batch metadata
+///
+/// This is needless and it triggers bugs in DF.  This operator erases metadata from the batches.
+#[derive(Debug)]
+struct MetadataEraserExec {
+    input: Arc<dyn ExecutionPlan>,
+    schema: Arc<ArrowSchema>,
+    properties: PlanProperties,
+}
+
+impl MetadataEraserExec {
+    fn compute_properties_from_input(
+        input: &Arc<dyn ExecutionPlan>,
+        schema: &Arc<ArrowSchema>,
+    ) -> PlanProperties {
+        let input_properties = input.properties();
+        let eq_properties = input_properties
+            .eq_properties
+            .clone()
+            .with_new_schema(schema.clone())
+            .unwrap();
+        input_properties.clone().with_eq_properties(eq_properties)
+    }
+
+    fn new(input: Arc<dyn ExecutionPlan>) -> Self {
+        let schema = Arc::new(
+            input
+                .schema()
+                .as_ref()
+                .clone()
+                .with_metadata(HashMap::new()),
+        );
+        Self {
+            properties: Self::compute_properties_from_input(&input, &schema),
+            input,
+            schema,
+        }
+    }
+}
+
+impl DisplayAs for MetadataEraserExec {
+    fn fmt_as(&self, _: DisplayFormatType, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "MetadataEraserExec")
+    }
+}
+
+impl ExecutionPlan for MetadataEraserExec {
+    fn name(&self) -> &str {
+        "MetadataEraserExec"
+    }
+
+    fn as_any(&self) -> &dyn std::any::Any {
+        self
+    }
+
+    fn properties(&self) -> &PlanProperties {
+        &self.properties
+    }
+
+    fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
+        vec![&self.input]
+    }
+
+    fn with_new_children(
+        self: Arc<Self>,
+        children: Vec<Arc<dyn ExecutionPlan>>,
+    ) -> DataFusionResult<Arc<dyn ExecutionPlan>> {
+        assert_eq!(children.len(), 1);
+        let new_properties = Self::compute_properties_from_input(&children[0], &self.schema);
+        Ok(Arc::new(Self {
+            input: children[0].clone(),
+            schema: self.schema.clone(),
+            properties: new_properties,
+        }))
+    }
+
+    fn execute(
+        &self,
+        partition: usize,
+        context: Arc<TaskContext>,
+    ) -> DataFusionResult<SendableRecordBatchStream> {
+        let stream = self.input.execute(partition, context)?;
+        let schema = self.schema.clone();
+        let stream = stream.map_ok(move |batch| batch.with_schema(schema.clone()).unwrap());
+        Ok(
+            Box::pin(RecordBatchStreamAdapter::new(self.schema.clone(), stream))
+                as SendableRecordBatchStream,
+        )
+    }
+}
+
+#[derive(Debug)]
+pub struct BaseTableAdapter {
+    table: Arc<dyn BaseTable>,
+    schema: Arc<ArrowSchema>,
+}
+
+impl BaseTableAdapter {
+    pub async fn try_new(table: Arc<dyn BaseTable>) -> Result<Self> {
+        let schema = Arc::new(
+            table
+                .schema()
+                .await?
+                .as_ref()
+                .clone()
+                .with_metadata(HashMap::default()),
+        );
+        Ok(Self { table, schema })
+    }
+}
+
+#[async_trait]
+impl TableProvider for BaseTableAdapter {
+    fn as_any(&self) -> &dyn std::any::Any {
+        self
+    }
+
+    fn schema(&self) -> Arc<ArrowSchema> {
+        self.schema.clone()
+    }
+
+    fn table_type(&self) -> TableType {
+        TableType::Base
+    }
+
+    async fn scan(
+        &self,
+        _state: &dyn Session,
+        projection: Option<&Vec<usize>>,
+        filters: &[Expr],
+        limit: Option<usize>,
+    ) -> DataFusionResult<Arc<dyn ExecutionPlan>> {
+        let mut query = QueryRequest::default();
+        if let Some(projection) = projection {
+            let field_names = projection
+                .iter()
+                .map(|i| self.schema.field(*i).name().to_string())
+                .collect();
+            query.select = Select::Columns(field_names);
+        }
+        assert!(filters.is_empty());
+        if let Some(limit) = limit {
+            query.limit = Some(limit);
+        } else {
+            // Need to override the default of 10
+            query.limit = None;
+        }
+        let plan = self
+            .table
+            .create_plan(&AnyQuery::Query(query), QueryExecutionOptions::default())
+            .map_err(|err| DataFusionError::External(err.into()))
+            .await?;
+        Ok(Arc::new(MetadataEraserExec::new(plan)))
+    }
+
+    fn supports_filters_pushdown(
+        &self,
+        filters: &[&Expr],
+    ) -> DataFusionResult<Vec<TableProviderFilterPushDown>> {
+        // TODO: Pushdown unsupported until we can support datafusion filters in BaseTable::create_plan
+        Ok(vec![
+            TableProviderFilterPushDown::Unsupported;
+            filters.len()
+        ])
+    }
+
+    fn statistics(&self) -> Option<Statistics> {
+        // TODO
+        None
+    }
+}
+
+#[cfg(test)]
+pub mod tests {
+    use std::{collections::HashMap, sync::Arc};
+
+    use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator, RecordBatchReader};
+    use arrow_schema::{DataType, Field, Schema};
+    use datafusion::{datasource::provider_as_source, prelude::SessionContext};
+    use datafusion_catalog::TableProvider;
+    use datafusion_expr::LogicalPlanBuilder;
+    use futures::TryStreamExt;
+    use tempfile::tempdir;
+
+    use crate::{connect, table::datafusion::BaseTableAdapter};
+
+    fn make_test_batches() -> impl RecordBatchReader + Send + Sync + 'static {
+        let metadata = HashMap::from_iter(vec![("foo".to_string(), "bar".to_string())]);
+        let schema = Arc::new(
+            Schema::new(vec![Field::new("i", DataType::Int32, false)]).with_metadata(metadata),
+        );
+        RecordBatchIterator::new(
+            vec![RecordBatch::try_new(
+                schema.clone(),
+                vec![Arc::new(Int32Array::from_iter_values(0..10))],
+            )],
+            schema,
+        )
+    }
+
+    #[tokio::test]
+    async fn test_metadata_erased() {
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+
+        let db = connect(uri).execute().await.unwrap();
+
+        let tbl = db
+            .create_table("foo", make_test_batches())
+            .execute()
+            .await
+            .unwrap();
+
+        let provider = Arc::new(
+            BaseTableAdapter::try_new(tbl.base_table().clone())
+                .await
+                .unwrap(),
+        );
+
+        assert!(provider.schema().metadata().is_empty());
+
+        let plan = LogicalPlanBuilder::scan("foo", provider_as_source(provider), None)
+            .unwrap()
+            .build()
+            .unwrap();
+
+        let mut stream = SessionContext::new()
+            .execute_logical_plan(plan)
+            .await
+            .unwrap()
+            .execute_stream()
+            .await
+            .unwrap();
+
+        while let Some(batch) = stream.try_next().await.unwrap() {
+            assert!(batch.schema().metadata().is_empty());
+        }
+    }
+}

rust/lancedb/src/table/merge.rs

@@ -7,14 +7,14 @@ use arrow_array::RecordBatchReader;
 
 use crate::Result;
 
-use super::TableInternal;
+use super::BaseTable;
 
 /// A builder used to create and run a merge insert operation
 ///
 /// See [`super::Table::merge_insert`] for more context
 #[derive(Debug, Clone)]
 pub struct MergeInsertBuilder {
-    table: Arc<dyn TableInternal>,
+    table: Arc<dyn BaseTable>,
     pub(crate) on: Vec<String>,
     pub(crate) when_matched_update_all: bool,
     pub(crate) when_matched_update_all_filt: Option<String>,
@@ -24,7 +24,7 @@ pub struct MergeInsertBuilder {
 }
 
 impl MergeInsertBuilder {
-    pub(super) fn new(table: Arc<dyn TableInternal>, on: Vec<String>) -> Self {
+    pub(super) fn new(table: Arc<dyn BaseTable>, on: Vec<String>) -> Self {
         Self {
             table,
             on,