fix(python): support blob lists in dict appends

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.38.0-beta.11"
+current_version = "0.38.0-beta.10"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/npm-publish.yml

@@ -40,31 +40,40 @@ jobs:
           - target: aarch64-apple-darwin
             host: macos-latest
             features: fp16kernels
-            # Fat LTO was ~111 of this job's ~113 minutes.
-            lto: thin
-            codegen_units: 16
             pre_build: |-
               brew install protobuf
+              # Fat LTO (the workspace default in .cargo/config.toml) is
+              # single-threaded and is the peak-memory step of the build. On
+              # this runner it accounted for ~111 of the job's ~113 minutes,
+              # making it the critical path of the entire publish pipeline.
+              # ThinLTO parallelizes it across the runner's cores, for a few
+              # percent of runtime performance.
+              export CARGO_PROFILE_RELEASE_LTO=thin
+              export CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16
           - target: x86_64-pc-windows-msvc
             host: windows-2025
             features: ","
-            # The lower peak also keeps this on the standard 4-core runner.
-            lto: thin
-            codegen_units: 16
             pre_build: |-
               choco install --no-progress protoc ninja nasm
               tail -n 1000 /c/ProgramData/chocolatey/logs/chocolatey.log
               # There is an issue where choco doesn't add nasm to the path
               export PATH="$PATH:/c/Program Files/NASM"
               nasm -v
+              # See the ThinLTO note on aarch64-apple-darwin above. Keeping
+              # peak memory down is also what lets this run on the standard
+              # 4-core runner: the 8-core larger runner was only needed to
+              # stop fat LTO from OOMing rustc-LLVM.
+              export CARGO_PROFILE_RELEASE_LTO=thin
+              export CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16
           - target: aarch64-pc-windows-msvc
             host: windows-2025
             features: ","
-            lto: thin
-            codegen_units: 16
             pre_build: |-
                 choco install --no-progress protoc
                 rustup target add aarch64-pc-windows-msvc
+                # See the ThinLTO note on aarch64-apple-darwin above.
+                export CARGO_PROFILE_RELEASE_LTO=thin
+                export CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16
           - target: x86_64-unknown-linux-gnu
             host: ubuntu-latest
             features: fp16kernels
@@ -94,14 +103,6 @@ jobs:
             # https://github.com/napi-rs/napi-rs/blob/main/debian-aarch64.Dockerfile
             docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-debian-aarch64
             features: "fp16kernels"
-            # Fat LTO OOM-killed rustc every nightly; even with lld it peaked
-            # at 31391 MiB of the runner's 32 GiB.
-            lto: thin
-            codegen_units: 16
-            # arm64 Linux links through GNU `ld` where x86_64 defaults to
-            # `rust-lld`, which is why only arm64 OOM'd. lld cut the largest
-            # linker process 7.0 -> 4.0 GiB (lancedb/sophon#7313).
-            linker: /tmp/aarch64-lld-clang
             pre_build: |-
               set -e &&
               apt-get update &&
@@ -111,30 +112,9 @@ jobs:
               # AT_HWCAP2 (added in Linux 3.17). Define it for aws-lc-sys.
               export CFLAGS="$CFLAGS -DAT_HWCAP2=26" &&
               rustup target add aarch64-unknown-linux-gnu
-              # Not `&&`-chained: in dash, errexit does not fire for a
-              # non-final command in an `&&` list, so failures were ignored.
-              #
-              # A wrapper rather than `-C link-arg` because the per-target
-              # rustflags variable does not reach every unit that links, while
-              # the linker variable does. `clang` because GCC silently ignores
-              # `-fuse-ld=lld` unless built with lld support. Two echoes
-              # because printf's newline escape gets rewritten to `;` between
-              # here and the container.
-              echo '#!/bin/sh' > /tmp/aarch64-lld-clang
-              echo 'exec clang --target=aarch64-unknown-linux-gnu --sysroot=/usr/aarch64-unknown-linux-gnu/aarch64-unknown-linux-gnu/sysroot --gcc-toolchain=/usr/aarch64-unknown-linux-gnu -fuse-ld=lld "$@"' >> /tmp/aarch64-lld-clang
-              chmod 0755 /tmp/aarch64-lld-clang
-              # Fail now, not at the cdylib link ~30 minutes later. Linking at
-              # all also proves lld resolved; clang errors out when it cannot.
-              echo 'int main(void){return 0;}' > /tmp/probe.c
-              /tmp/aarch64-lld-clang /tmp/probe.c -o /tmp/probe
-              readelf -h /tmp/probe | grep AArch64
           - target: aarch64-unknown-linux-musl
             host: ubuntu-2404-8x-x64
             features: ","
-            # Fat LTO took the whole runner down. lld cannot help: it died
-            # inside rustc's LLVM, before any linker was spawned.
-            lto: thin
-            codegen_units: 16
             pre_build: |-
               set -e &&
               sudo apt-get update &&
@@ -143,19 +123,6 @@ jobs:
               export EXTRA_ARGS="-x"
     name: build - ${{ matrix.settings.target }}
     runs-on: ${{ matrix.settings.host }}
-    # On the job, not exported from `pre_build`: `Swatinem/rust-cache` hashes
-    # `CARGO_*` into its cache key before any step runs, so a step-local export
-    # leaves the key unchanged while cargo still rebuilds cold. The ThinLTO
-    # legs had been doing that every run.
-    #
-    # Not `RUSTFLAGS`: setting it, even to "", discards every config-file
-    # rustflag, silently dropping .cargo/config.toml's `target-cpu` and
-    # `target-feature` from the published binaries.
-    env:
-      CARGO_PROFILE_RELEASE_LTO: ${{ matrix.settings.lto || 'fat' }}
-      CARGO_PROFILE_RELEASE_CODEGEN_UNITS: ${{ matrix.settings.codegen_units || '1' }}
-      # Empty elsewhere: a per-target variable is only read for that triple.
-      CARGO_TARGET_AARCH64_UNKNOWN_LINUX_GNU_LINKER: ${{ matrix.settings.linker }}
     defaults:
       run:
         working-directory: nodejs
@@ -202,15 +169,19 @@ jobs:
           # creating ref). The nightly cadence also keeps entries inside
           # GitHub's 7-day eviction window, which a tag-only trigger would not.
           save-if: ${{ github.ref == 'refs/heads/main' }}
-      # Docker builds can use rust-cache too: the workspace is bind-mounted, so
-      # `target/` lives on the host and rust-cache's prune keeps the entry
-      # small.
+      # Docker builds can use rust-cache too. `target/` already lives on the
+      # host because the whole workspace is bind-mounted into the container, and
+      # rust-cache's prune and save run host-side, so they can manage it -- which
+      # is what keeps the entry to dependency artifacts rather than a multi-GB
+      # copy of everything.
       #
       # Two differences from the native builds. The container's CARGO_HOME is
-      # bind-mounted from `.cargo-cache` rather than ~/.cargo, so that is cached
-      # explicitly. And the key uses the *host* rustc version, not the compiler
-      # that built these artifacts -- safe, since cargo fingerprints the real
-      # one; a base-image bump just costs one cold build.
+      # bind-mounted from `.cargo-cache` rather than the host's ~/.cargo, so that
+      # has to be cached explicitly. And the key is derived from the *host* rustc
+      # version, which is not the compiler that produced these artifacts; that is
+      # safe because cargo fingerprints the real compiler and rebuilds on a
+      # mismatch, it just means a base-image toolchain bump costs one cold build
+      # instead of invalidating the key.
       - name: Cache cargo (docker builds)
         uses: Swatinem/rust-cache@v2
         if: ${{ matrix.settings.docker }}
@@ -239,14 +210,9 @@ jobs:
           # cache step above saves. Previously the registry mounts pointed at
           # `.cargo/...`, a path nothing cached, so the container re-downloaded
           # the whole crate registry on every run.
-          #
-          # `docker run` inherits nothing; `-e NAME` carries the job's `env:` in.
           options: "--user 0:0 -v ${{ github.workspace }}/.cargo-cache/git/db:/usr/local/cargo/git/db \
                     -v ${{ github.workspace }}/.cargo-cache/registry/cache:/usr/local/cargo/registry/cache \
                     -v ${{ github.workspace }}/.cargo-cache/registry/index:/usr/local/cargo/registry/index \
-                    -e CARGO_PROFILE_RELEASE_LTO \
-                    -e CARGO_PROFILE_RELEASE_CODEGEN_UNITS \
-                    -e CARGO_TARGET_AARCH64_UNKNOWN_LINUX_GNU_LINKER \
                     -v ${{ github.workspace }}:/build -w /build/nodejs"
           run: |
             set -e
@@ -290,18 +256,6 @@ jobs:
         if: always()
         run: df -h
         shell: bash
-      - name: Report peak memory
-        if: always() && runner.os == 'Linux'
-        shell: bash
-        run: |
-          peak=$(find /sys/fs/cgroup -name memory.peak -readable \
-                 -exec cat {} + 2>/dev/null | sort -n | tail -1)
-          if [ -n "$peak" ]; then
-            echo "peak memory: $((peak / 1024 / 1024)) MiB"
-          else
-            echo "peak memory: unavailable (no readable cgroup v2 memory.peak)"
-          fi
-          free -g || true
       - name: Upload artifact
         uses: actions/upload-artifact@v7
         with:

Cargo.lock

@@ -5402,7 +5402,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb"
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.10"
 dependencies = [
  "ahash",
  "anyhow",
@@ -5490,7 +5490,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-nodejs"
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.10"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -5515,7 +5515,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-python"
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.10"
 dependencies = [
  "arrow",
  "async-trait",

docs/src/java/java.md

@@ -14,7 +14,7 @@ Add the following dependency to your `pom.xml`:
 <dependency>
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-core</artifactId>
-    <version>0.38.0-beta.11</version>
+    <version>0.38.0-beta.10</version>
 </dependency>
 ```
 

docs/src/python/python.md

@@ -223,14 +223,10 @@ tokens = list(
 Blob columns store large binary values out of line so they can be read lazily
 instead of being materialized with the rest of the row.
 
-`lancedb.BlobType` is `lance.blob.BlobType` when pylance is installed. Without
-pylance, LanceDB uses a matching `lance.blob.v2` extension type so blob columns
-still work. Queries return descriptors. Call
-[`fetch_blob_files`][lancedb.table.Table.fetch_blob_files] for lazy reads or
-[`fetch_blobs`][lancedb.table.Table.fetch_blobs] for eager bytes.
-
 ::: lancedb.blob
 
+::: lancedb.BlobType
+
 ::: lancedb._blob.BlobFile
     options:
       show_root_full_path: false

java/lancedb-core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
       <groupId>com.lancedb</groupId>
       <artifactId>lancedb-parent</artifactId>
-      <version>0.38.0-beta.11</version>
+      <version>0.38.0-beta.10</version>
       <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.38.0-beta.11</version>
+    <version>0.38.0-beta.10</version>
     <packaging>pom</packaging>
     <name>${project.artifactId}</name>
     <description>LanceDB Java SDK Parent POM</description>

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.10"
 publish = false
 license.workspace = true
 description.workspace = true

nodejs/__test__/arrow.test.ts

@@ -1,16 +1,11 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import * as fs from "node:fs";
-import * as vm from "node:vm";
 import * as arrow15 from "apache-arrow-15";
 import * as arrow16 from "apache-arrow-16";
 import * as arrow17 from "apache-arrow-17";
 import * as arrow18 from "apache-arrow-18";
 
 import {
-  Field as CurrentField,
-  LargeBinary as CurrentLargeBinary,
-  Schema as CurrentSchema,
   Vector as CurrentVector,
   convertToTable,
   tableFromIPC as currentTableFromIPC,
@@ -41,59 +36,6 @@ function sampleRecords(): Array<Record<string, any>> {
     },
   ];
 }
-
-it("serializes an Arrow Table created in another JavaScript realm", async () => {
-  const context = vm.createContext({
-    TextDecoder,
-    TextEncoder,
-    console,
-    setTimeout,
-    clearTimeout,
-  });
-  vm.runInContext(
-    fs.readFileSync(
-      require.resolve("apache-arrow-15/Arrow.es2015.min"),
-      "utf8",
-    ),
-    context,
-  );
-  const foreignTable: unknown = vm.runInContext(
-    "Arrow.tableFromArrays({ id: new Int32Array([1, 2, 3]), text: ['foo', 'bar', 'baz'] })",
-    context,
-  );
-
-  const foreignMetadata = (
-    foreignTable as { schema: { metadata: Map<string, string> } }
-  ).schema.metadata;
-  expect(foreignMetadata).not.toBeInstanceOf(Map);
-
-  const buf = await fromDataToBuffer(
-    foreignTable as Parameters<typeof fromDataToBuffer>[0],
-  );
-  const actual = currentTableFromIPC(buf);
-
-  expect(actual.numRows).toBe(3);
-  expect(actual.getChild("id")?.toJSON()).toEqual([1, 2, 3]);
-  expect(actual.getChild("text")?.toJSON()).toEqual(["foo", "bar", "baz"]);
-});
-
-it("preserves field metadata from a provided schema", async function () {
-  const jsonMetadata = new Map([["ARROW:extension:name", "lance.json"]]);
-  const schema = new CurrentSchema([
-    new CurrentField("meta", new CurrentLargeBinary(), true, jsonMetadata),
-  ]);
-
-  const table = makeArrowTable(
-    [{ meta: Buffer.from(JSON.stringify({ source: "test" })) }],
-    { schema },
-  );
-
-  expect(table.schema.fields[0].metadata).toEqual(jsonMetadata);
-
-  const roundTripped = currentTableFromIPC(await fromTableToBuffer(table));
-  expect(roundTripped.schema.fields[0].metadata).toEqual(jsonMetadata);
-});
-
 describe.each([arrow15, arrow16, arrow17, arrow18])(
   "Arrow",
   (

nodejs/__test__/embedding.test.ts

@@ -187,58 +187,6 @@ describe("embedding functions", () => {
     const vector0 = JSON.parse(JSON.stringify(arr[0].vector));
     expect(vector0).toEqual([1, 2, 3]);
   });
-  it("should append multiple Python embeddings with the same alias", async () => {
-    @register("python-mock")
-    // biome-ignore lint/correctness/noUnusedVariables: the decorator registers this class
-    class MockEmbeddingFunction extends EmbeddingFunction<string> {
-      ndims() {
-        return 3;
-      }
-      embeddingDataType(): Float {
-        return new Float32();
-      }
-      async computeQueryEmbeddings(_data: string) {
-        return [1, 2, 3];
-      }
-      async computeSourceEmbeddings(data: string[]) {
-        return data.map((value) =>
-          value === "hello world" ? [1, 2, 3] : [4, 5, 6],
-        );
-      }
-    }
-
-    const metadata = new Map([
-      [
-        "embedding_functions",
-        '[{"source_column":"text1","vector_column":"vector1","name":"python-mock","model":{}},{"source_column":"text2","vector_column":"vector2","name":"python-mock","model":{}}]',
-      ],
-    ]);
-    const schema = new Schema(
-      [
-        new Field("text1", new Utf8(), true),
-        new Field("text2", new Utf8(), true),
-        new Field(
-          "vector1",
-          new FixedSizeList(3, new Field("item", new Float32(), true)),
-          true,
-        ),
-        new Field(
-          "vector2",
-          new FixedSizeList(3, new Field("item", new Float32(), true)),
-          true,
-        ),
-      ],
-      metadata,
-    );
-
-    const db = await connect(tmpDir.name);
-    const table = await db.createEmptyTable("test", schema);
-    await table.add([{ text1: "hello world", text2: "goodbye world" }]);
-
-    const rows = await table.query().toArray();
-    expect(JSON.parse(JSON.stringify(rows[0].vector1))).toEqual([1, 2, 3]);
-    expect(JSON.parse(JSON.stringify(rows[0].vector2))).toEqual([4, 5, 6]);
-  });
 
   it("should append generated vectors to a non-nullable schema", async () => {
     @register("non_nullable_schema_test")

nodejs/__test__/table.test.ts

@@ -3561,27 +3561,6 @@ describe("when creating an empty table", () => {
     expect((actualSchema.fields[1].type as Float64).precision).toBe(2);
   });
 
-  it("can add and query JSON data", async () => {
-    const schema = new Schema([
-      new Field("id", new Int32(), true),
-      new Field(
-        "meta",
-        new Utf8(),
-        true,
-        new Map([["ARROW:extension:name", "arrow.json"]]),
-      ),
-    ]);
-    const table = await con.createEmptyTable("json", schema);
-    const meta = JSON.stringify({ x: 1 });
-
-    await table.add([{ id: 1, meta }]);
-
-    const rows = await table.query().toArray();
-    expect(rows).toHaveLength(1);
-    expect(rows[0].id).toBe(1);
-    expect(rows[0].meta).toBe(meta);
-  });
-
   it("can create an empty table from schema that specifies field types by name", async () => {
     const schemaLike = {
       fields: [

nodejs/lancedb/arrow.ts

@@ -72,7 +72,8 @@ export type FieldLike =
     };
 
 export type DataLike =
-  | import("apache-arrow").Data
+  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+  | import("apache-arrow").Data<Struct<any>>
   | {
       // biome-ignore lint/suspicious/noExplicitAny: <explanation>
       type: any;
@@ -81,7 +82,6 @@ export type DataLike =
       stride: number;
       nullable: boolean;
       children: DataLike[];
-      dictionary?: { data: readonly DataLike[] };
       get nullCount(): number;
       // biome-ignore lint/suspicious/noExplicitAny: <explanation>
       values: Buffers<any>[BufferType.DATA];

nodejs/lancedb/sanitize.ts

@@ -94,24 +94,17 @@ export function sanitizeMetadata(
   if (metadataLike === undefined || metadataLike === null) {
     return undefined;
   }
-
-  let entries: IterableIterator<[unknown, unknown]>;
-  try {
-    entries = Map.prototype.entries.call(metadataLike);
-  } catch {
+  if (!(metadataLike instanceof Map)) {
     throw Error("Expected metadata, if present, to be a Map<string, string>");
   }
-
-  const metadata = new Map<string, string>();
-  for (const [key, value] of entries) {
-    if (typeof key !== "string" || typeof value !== "string") {
+  for (const item of metadataLike) {
+    if (typeof item[0] !== "string" || typeof item[1] !== "string") {
       throw Error(
         "Expected metadata, if present, to be a Map<string, string> but it had non-string keys or values",
       );
     }
-    metadata.set(key, value);
   }
-  return metadata;
+  return metadataLike as Map<string, string>;
 }
 
 export function sanitizeInt(typeLike: object) {

nodejs/lancedb/schema.ts

@@ -406,11 +406,10 @@ function matchingFields(fields: Field[], tree: FieldTree): Field[] {
           field.name,
           new Struct(matchingFields(struct.children, value)),
           field.nullable,
-          field.metadata,
         ),
       );
     } else {
-      matches.push(field);
+      matches.push(new Field(field.name, value as DataType, field.nullable));
     }
   }
   return matches;

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.38.0-beta.11",
+	"version": "0.38.0-beta.10",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.38.0-beta.11",
+	"version": "0.38.0-beta.10",
 	"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": "0.38.0-beta.11",
+	"version": "0.38.0-beta.10",
 	"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": "0.38.0-beta.11",
+	"version": "0.38.0-beta.10",
 	"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": "0.38.0-beta.11",
+	"version": "0.38.0-beta.10",
 	"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": "0.38.0-beta.11",
+  "version": "0.38.0-beta.10",
   "os": [
     "win32"
   ],

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.38.0-beta.11",
+	"version": "0.38.0-beta.10",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.38.0-beta.11",
+  "version": "0.38.0-beta.10",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.38.0-beta.11",
+      "version": "0.38.0-beta.10",
       "cpu": [
         "x64",
         "arm64"

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.38.0-beta.11",
+  "version": "0.38.0-beta.10",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.10"
 publish = false
 edition.workspace = true
 description = "Python bindings for LanceDB"

python/python/lancedb/__init__.py

@@ -6,7 +6,7 @@ import importlib.metadata
 import os
 from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
-from typing import Dict, Optional, Union, Any, List, Iterable, TYPE_CHECKING
+from typing import Dict, Optional, Union, Any, List, Iterable
 
 __version__ = importlib.metadata.version("lancedb")
 
@@ -20,7 +20,7 @@ from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .remote import ClientConfig
 from .remote.db import RemoteDBConnection
 from .expr import Expr, col, lit, func
-from .schema import blob, vector
+from .schema import blob, vector, BlobType
 from .job import AsyncJob, Job
 from .functions import (
     FunctionArtifactRequest as FunctionArtifactRequest,
@@ -49,19 +49,6 @@ from .namespace import (
 )
 
 
-if TYPE_CHECKING:
-    from lance.blob import BlobType as BlobType
-
-
-def __getattr__(name: str):
-    if name == "BlobType":
-        from .schema import BlobType
-
-        globals()["BlobType"] = BlobType
-        return BlobType
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-
 def _check_s3_bucket_with_dots(
     uri: str, storage_options: Optional[Dict[str, str]]
 ) -> None:

python/python/lancedb/_blob.py

@@ -12,7 +12,7 @@ from typing import TYPE_CHECKING, Optional, Union
 import pyarrow as pa
 
 from .expr import Expr
-from .schema import row_addressable_blob_v2_paths
+from .schema import blob_v2_column_paths
 from .types import BlobMode, QueryProjection, QueryProjectionSpec
 
 if TYPE_CHECKING:
@@ -119,7 +119,7 @@ def blob_v2_projection_sources(
     schema: pa.Schema,
     projection: QueryProjection,
 ) -> dict[str, str]:
-    blob_columns = row_addressable_blob_v2_paths(schema)
+    blob_columns = blob_v2_column_paths(schema)
     if not blob_columns:
         return {}
     columns = set(blob_columns)
@@ -140,9 +140,7 @@ def v2_projection_needs_row_id(
 ) -> bool:
     if with_row_id:
         return False
-    return projection_includes_blob_column(
-        projection, row_addressable_blob_v2_paths(schema)
-    )
+    return projection_includes_blob_column(projection, blob_v2_column_paths(schema))
 
 
 def blob_auto_row_id_for_scan(
@@ -272,8 +270,7 @@ def _iter_projection_pairs(
             if isinstance(expr, str):
                 yield name, expr
             elif isinstance(expr, Expr):
-                source = expr._column_name()
-                yield name, source if source is not None else expr.to_sql()
+                yield name, expr.to_sql()
         return
     for column in projection:
         if isinstance(column, str):
@@ -283,8 +280,7 @@ def _iter_projection_pairs(
             if isinstance(expr, str):
                 yield name, expr
             elif isinstance(expr, Expr):
-                source = expr._column_name()
-                yield name, source if source is not None else expr.to_sql()
+                yield name, expr.to_sql()
 
 
 def _set_blob_column(tbl: pa.Table, output_name: str, blobs: pa.Array) -> pa.Table:

python/python/lancedb/_lancedb.pyi

@@ -87,7 +87,6 @@ class PyExpr:
     def contains(self, substr: "PyExpr") -> "PyExpr": ...
     def isin(self, values: List["PyExpr"]) -> "PyExpr": ...
     def cast(self, data_type: pa.DataType) -> "PyExpr": ...
-    def column_name(self) -> Optional[str]: ...
     def to_sql(self) -> str: ...
 
 def expr_col(name: str) -> PyExpr: ...
@@ -609,7 +608,6 @@ class PyQueryRequest:
     filter: Optional[Union[str, bytes]]
     full_text_search: Optional[FullTextQuery]
     select: Optional[Union[str, List[str]]]
-    select_source_columns: Optional[Dict[str, str]]
     fast_search: Optional[bool]
     with_row_id: Optional[bool]
     use_lsm: Optional[bool]

python/python/lancedb/expr.py

@@ -249,10 +249,6 @@ class Expr:
 
     # ── utilities ────────────────────────────────────────────────────────────
 
-    def _column_name(self) -> str | None:
-        """Return the source name when this is a bare column expression."""
-        return self._inner.column_name()
-
     def to_sql(self) -> str:
         """Render the expression as a SQL string (useful for debugging)."""
         return self._inner.to_sql()
@@ -316,7 +312,7 @@ def func(name: str, *args: ExprLike) -> Expr:
     --------
     >>> from lancedb.expr import col, func
     >>> func("lower", col("name"))
-    Expr(lower(`name`))
+    Expr(lower(name))
     """
     inner_args = [_coerce(a)._inner for a in args]
     return Expr(expr_func(name, inner_args))

python/python/lancedb/query.py

@@ -167,12 +167,6 @@ def _projection_to_scanner_kwargs(columns: QueryProjection) -> Dict[str, Any]:
     return {"columns": projection}
 
 
-def _query_request_projection(req: "PyQueryRequest") -> QueryProjection:
-    if req.select_source_columns is not None:
-        return req.select_source_columns
-    return req.select
-
-
 def _scanner_kwargs_for_query(
     query: Query,
     blob_mode: BlobMode,
@@ -2805,16 +2799,15 @@ class AsyncQueryBase(object):
 
         req = self._inner.to_query_request()
         schema = await self._table.schema()
-        projection = _query_request_projection(req)
         self._blob_auto_row_id = blob_auto_row_id_for_scan(
             schema,
-            projection,
+            req.select,
             with_row_id=self._with_row_id,
         )
         if not self._blob_auto_row_id:
             self._blob_paths = ()
             return
-        self._blob_paths = tuple(blob_v2_projection_sources(schema, projection).keys())
+        self._blob_paths = tuple(blob_v2_projection_sources(schema, req.select).keys())
         self._inner.with_row_id()
 
     def select(self, columns: Union[List[str], dict[str, str]]) -> Self:
@@ -3901,15 +3894,14 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
         blob_paths: tuple[str, ...] = ()
         if self._table is not None:
             schema = await self._table.schema()
-            projection = _query_request_projection(req)
             blob_auto_row_id = blob_auto_row_id_for_scan(
                 schema,
-                projection,
+                req.select,
                 with_row_id=self._with_row_id,
             )
             if blob_auto_row_id:
                 blob_paths = tuple(
-                    blob_v2_projection_sources(schema, projection).keys()
+                    blob_v2_projection_sources(schema, req.select).keys()
                 )
         self._blob_auto_row_id = blob_auto_row_id
         self._blob_paths = blob_paths

python/python/lancedb/remote/table.py

@@ -36,7 +36,6 @@ from lancedb._lancedb import (
     UpdateResult,
 )
 from lancedb.embeddings.base import EmbeddingFunctionConfig
-from lancedb.expr import Expr
 from lancedb.index import (
     FTS,
     BTree,
@@ -864,7 +863,7 @@ class RemoteTable(Table):
 
     def update(
         self,
-        where: Optional[Union[str, Expr]] = None,
+        where: Optional[str] = None,
         values: Optional[dict] = None,
         *,
         values_sql: Optional[Dict[str, str]] = None,
@@ -875,11 +874,9 @@ class RemoteTable(Table):
 
         Parameters
         ----------
-        where: str or [Expr][lancedb.expr.Expr], optional
-            The filter condition. Can be a SQL string or a type-safe
-            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
-            [lit][lancedb.expr.lit]. The filter must not be empty, or it will
-            error.
+        where: str, optional
+            The SQL where clause to use when updating rows. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
         values: dict, optional
             The values to update. The keys are the column names and the values
             are the values to set.

python/python/lancedb/schema.py

@@ -4,34 +4,30 @@
 
 """Schema helpers for Lance blob columns."""
 
-import importlib
-from typing import TYPE_CHECKING
-
 import pyarrow as pa
-import pyarrow.ipc
-
-if TYPE_CHECKING:
-    from lance.blob import BlobType as BlobType
 
 _BLOB_EXTENSION_NAME = "lance.blob.v2"
 _BLOB_V1_KEY = "lance-encoding:blob"
 _ARROW_EXT_NAME_KEY = "ARROW:extension:name"
-_BLOB_V2_STORAGE_TYPE = pa.struct(
-    [
-        pa.field("data", pa.large_binary(), nullable=True),
-        pa.field("uri", pa.utf8(), nullable=True),
-        pa.field("position", pa.uint64(), nullable=True),
-        pa.field("size", pa.uint64(), nullable=True),
-    ]
-)
-_resolved_blob_type = None
 
 
-class _FallbackBlobType(pa.ExtensionType):
-    """lance.blob.v2 extension type used when pylance is not installed."""
+class BlobType(pa.ExtensionType):
+    """PyArrow extension type for a Lance blob v2 column.
+
+    Queries return descriptors; call :meth:`~lancedb.table.Table.fetch_blob_files`
+    for lazy reads or :meth:`~lancedb.table.Table.fetch_blobs` for eager bytes.
+    """
 
     def __init__(self) -> None:
-        pa.ExtensionType.__init__(self, _BLOB_V2_STORAGE_TYPE, _BLOB_EXTENSION_NAME)
+        storage_type = pa.struct(
+            [
+                pa.field("data", pa.large_binary(), nullable=True),
+                pa.field("uri", pa.utf8(), nullable=True),
+                pa.field("position", pa.uint64(), nullable=True),
+                pa.field("size", pa.uint64(), nullable=True),
+            ]
+        )
+        super().__init__(storage_type, _BLOB_EXTENSION_NAME)
 
     def __arrow_ext_serialize__(self) -> bytes:
         return b""
@@ -39,16 +35,23 @@ class _FallbackBlobType(pa.ExtensionType):
     @classmethod
     def __arrow_ext_deserialize__(
         cls, storage_type: pa.DataType, serialized: bytes
-    ) -> "_FallbackBlobType":
+    ) -> "BlobType":
         return cls()
 
     def __reduce__(self):
+        # Ensure pickle round-trips on older pyarrow (apache/arrow#35599).
         return type(self).__arrow_ext_deserialize__, (
             self.storage_type,
             self.__arrow_ext_serialize__(),
         )
 
 
+try:
+    pa.register_extension_type(BlobType())  # type: ignore[arg-type]
+except pa.ArrowKeyError:
+    pass
+
+
 def _metadata_value(metadata: dict, key: str):
     return metadata.get(key.encode()) or metadata.get(key)
 
@@ -89,105 +92,43 @@ def is_blob_like_field(field: pa.Field) -> bool:
     return is_blob_v2_field(field) or _metadata_marks_legacy_blob(field.metadata or {})
 
 
-def _collect_blob_paths(schema: pa.Schema, is_blob) -> list[tuple[str, bool]]:
-    """Walk the schema and return (path, has_list_ancestor) for each blob field."""
-    paths: list[tuple[str, bool]] = []
+def _collect_blob_paths(schema: pa.Schema, is_blob) -> list[str]:
+    paths: list[str] = []
 
-    def walk(fields, prefix: str, has_list_ancestor: bool) -> None:
+    def walk(fields, prefix: str) -> None:
         for field in fields:
             path = f"{prefix}.{field.name}" if prefix else field.name
             if is_blob(field):
-                paths.append((path, has_list_ancestor))
+                paths.append(path)
             elif pa.types.is_struct(field.type):
-                walk(field.type, path, has_list_ancestor)
+                walk(field.type, path)
             elif (
                 pa.types.is_list(field.type)
                 or pa.types.is_large_list(field.type)
                 or pa.types.is_fixed_size_list(field.type)
             ):
-                walk([field.type.value_field], path, True)
+                walk([field.type.value_field], path)
 
-    walk(schema, "", False)
+    walk(schema, "")
     return paths
 
 
 def blob_column_paths(schema: pa.Schema) -> list[str]:
     """Dotted paths of blob-like columns (v2 extension or legacy metadata)."""
-    return [path for path, _ in _collect_blob_paths(schema, is_blob_like_field)]
+    return _collect_blob_paths(schema, is_blob_like_field)
 
 
 def blob_v2_column_paths(schema: pa.Schema) -> list[str]:
-    return [path for path, _ in _collect_blob_paths(schema, is_blob_v2_field)]
-
-
-def row_addressable_blob_v2_paths(schema: pa.Schema) -> list[str]:
-    """Blob v2 paths with one blob addressable by table row id.
-
-    ``fetch_blobs`` and the descriptor row-id ride-along address one blob per
-    row, so a blob inside a list container has no row-id slot and no fetch
-    path. Those columns still store and query as raw descriptors.
-    """
-    return [
-        path
-        for path, has_list_ancestor in _collect_blob_paths(schema, is_blob_v2_field)
-        if not has_list_ancestor
-    ]
+    return _collect_blob_paths(schema, is_blob_v2_field)
 
 
 def schema_has_blob_field(schema: pa.Schema) -> bool:
     return bool(blob_column_paths(schema))
 
 
-def _deserialize_registered_type(extension_type: pa.ExtensionType) -> pa.DataType:
-    """Return the type Arrow reconstructs for this extension name."""
-    schema = pa.schema([pa.field("value", extension_type)])
-    restored = pa.ipc.read_schema(schema.serialize())
-    return restored.field("value").type
-
-
-def _resolve_blob_type():
-    """Return the BlobType class this process should use.
-
-    pylance's class when it owns the lance.blob.v2 registry entry,
-    otherwise LanceDB's fallback. A different registered class is an error.
-    """
-    global _resolved_blob_type
-    if _resolved_blob_type is not None:
-        return _resolved_blob_type
-    try:
-        blob_module = importlib.import_module("lance.blob")
-    except ModuleNotFoundError as err:
-        if err.name not in ("lance", "lance.blob"):
-            raise
-    else:
-        blob_type = getattr(blob_module, "BlobType", None)
-        if blob_type is not None:
-            registered_type = _deserialize_registered_type(blob_type())
-            if type(registered_type) is not blob_type:
-                registered_cls = type(registered_type)
-                raise ValueError(
-                    "lance.blob.v2 is already registered by "
-                    f"{registered_cls.__module__}.{registered_cls.__qualname__}"
-                )
-            _resolved_blob_type = blob_type
-            return blob_type
-    try:
-        pa.register_extension_type(_FallbackBlobType())  # type: ignore[arg-type]
-    except pa.ArrowKeyError as err:
-        raise ValueError(
-            "lance.blob.v2 is already registered by another extension class"
-        ) from err
-    _resolved_blob_type = _FallbackBlobType
-    return _resolved_blob_type
-
-
 def blob(name: str, nullable: bool = True) -> pa.Field:
-    """Create a Lance blob v2 column field.
-
-    When pylance is installed this is ``lance.blob.BlobType``.
-    """
-    blob_type = _resolve_blob_type()
-    return pa.field(name, blob_type(), nullable=nullable)
+    """Create a Lance blob v2 column field."""
+    return pa.field(name, BlobType(), nullable=nullable)
 
 
 def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataType:
@@ -214,11 +155,3 @@ def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataTyp
     ... ])
     """
     return pa.list_(value_type, dimension)
-
-
-def __getattr__(name: str):
-    if name == "BlobType":
-        blob_type = _resolve_blob_type()
-        globals()["BlobType"] = blob_type
-        return blob_type
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

python/python/lancedb/table.py

@@ -104,12 +104,7 @@ from .util import (
     value_to_sql,
 )
 from .index import lang_mapping
-from .schema import (
-    blob_v2_column_paths,
-    is_blob_v2_field,
-    row_addressable_blob_v2_paths,
-    schema_has_blob_field,
-)
+from .schema import blob_v2_column_paths, schema_has_blob_field
 
 
 def _should_push_down_query_table(
@@ -431,7 +426,6 @@ def _cast_to_target_schema(
 
     def gen():
         for batch in reader:
-            batch = _coerce_blob_write_columns(batch, reordered_schema)
             # Table but not RecordBatch has cast.
             cast_batches = (
                 pa.Table.from_batches([batch]).cast(reordered_schema).to_batches()
@@ -444,166 +438,6 @@ def _cast_to_target_schema(
     return pa.RecordBatchReader.from_batches(reordered_schema, gen())
 
 
-def _coerce_blob_write_columns(
-    batch: pa.RecordBatch, target_schema: pa.Schema
-) -> pa.RecordBatch:
-    """Materialize blob storage structs before the stream leaves Python.
-
-    merge_insert requires its source reader to already match the table's
-    physical schema. Unlike add and insert, it does not pass through
-    LanceDB's Rust blob coercion, so preserving binary input here would
-    reach Lance as binary and fail the schema check.
-    """
-    columns = []
-    fields = []
-    changed = False
-    for field, column in zip(batch.schema, batch.columns):
-        target_field = target_schema.field(field.name)
-        coerced = _coerce_blob_value(column, target_field)
-        if coerced is not column:
-            column = coerced
-            field = pa.field(
-                field.name,
-                coerced.type,
-                field.nullable,
-                target_field.metadata,
-            )
-            changed = True
-        columns.append(column)
-        fields.append(field)
-    if not changed:
-        return batch
-    return pa.RecordBatch.from_arrays(
-        columns, schema=pa.schema(fields, metadata=batch.schema.metadata)
-    )
-
-
-def _coerce_blob_value(column: pa.Array, target_field: pa.Field) -> pa.Array:
-    if is_blob_v2_field(target_field) and _can_coerce_to_blob(column.type):
-        return _coerce_value_to_blob(column, target_field)
-
-    target_type = target_field.type
-    if pa.types.is_struct(target_type) and pa.types.is_struct(column.type):
-        children = []
-        fields = []
-        changed = False
-        for source_field in column.type:
-            source_column = column.field(source_field.name)
-            nested_target = next(
-                (field for field in target_type if field.name == source_field.name),
-                None,
-            )
-            if nested_target is None:
-                children.append(source_column)
-                fields.append(source_field)
-                continue
-            coerced = _coerce_blob_value(source_column, nested_target)
-            if coerced is not source_column:
-                changed = True
-            child_array, child_type = _physical_array_and_type(coerced)
-            children.append(child_array)
-            fields.append(
-                pa.field(
-                    source_field.name,
-                    child_type,
-                    source_field.nullable,
-                    nested_target.metadata,
-                )
-            )
-        if not changed:
-            return column
-        return pa.StructArray.from_arrays(
-            children,
-            fields=fields,
-            mask=column.is_null() if column.null_count else None,
-        )
-
-    if _is_list_like(target_type) and _is_list_like(column.type):
-        return _coerce_blob_list_values(column, target_type.value_field)
-
-    return column
-
-
-def _coerce_blob_list_values(
-    column: pa.Array, target_value_field: pa.Field
-) -> pa.Array:
-    """Coerce blob values inside a list column, preserving offsets and nulls.
-
-    Works on the raw child values window instead of ``pc.list_flatten`` because
-    flatten drops values spanned by null slots, which would misalign offsets.
-    """
-    mask = column.is_null() if column.null_count else None
-    if pa.types.is_fixed_size_list(column.type):
-        list_size = column.type.list_size
-        values = column.values.slice(column.offset * list_size, len(column) * list_size)
-        coerced = _coerce_blob_value(values, target_value_field)
-        if coerced is values:
-            return column
-        physical_values, _ = _physical_array_and_type(coerced)
-        return pa.FixedSizeListArray.from_arrays(physical_values, list_size, mask=mask)
-    offsets = column.offsets
-    first_offset = offsets[0].as_py()
-    values = column.values.slice(
-        first_offset,
-        offsets[-1].as_py() - first_offset,
-    )
-    coerced = _coerce_blob_value(values, target_value_field)
-    if coerced is values:
-        return column
-    physical_values, _ = _physical_array_and_type(coerced)
-    if first_offset:
-        offsets = pc.subtract(offsets, pa.scalar(first_offset, offsets.type))
-    if pa.types.is_large_list(column.type):
-        return pa.LargeListArray.from_arrays(offsets, physical_values, mask=mask)
-    return pa.ListArray.from_arrays(offsets, physical_values, mask=mask)
-
-
-def _coerce_value_to_blob(values: pa.Array, target_field: pa.Field) -> pa.Array:
-    if pa.types.is_null(values.type):
-        data = pa.nulls(len(values), type=pa.large_binary())
-    elif pa.types.is_large_binary(values.type):
-        data = values
-    else:
-        data = values.cast(pa.large_binary())
-    length = len(values)
-    storage_type = target_field.type
-    if isinstance(storage_type, pa.ExtensionType):
-        storage_type = storage_type.storage_type
-    storage_fields = list(storage_type)
-    children = []
-    for storage_field in storage_fields:
-        if storage_field.name == "data":
-            children.append(data)
-        else:
-            children.append(pa.nulls(length, type=storage_field.type))
-    storage = pa.StructArray.from_arrays(
-        children,
-        fields=storage_fields,
-        mask=values.is_null() if values.null_count else None,
-    )
-    if isinstance(target_field.type, pa.ExtensionType):
-        return pa.ExtensionArray.from_storage(target_field.type, storage)
-    return storage
-
-
-def _physical_array_and_type(array: pa.Array) -> tuple[pa.Array, pa.DataType]:
-    if isinstance(array.type, pa.ExtensionType):
-        return array.storage, array.type.storage_type
-    return array, array.type
-
-
-def _can_coerce_to_blob(data_type: pa.DataType) -> bool:
-    return _is_binary_like(data_type) or pa.types.is_null(data_type)
-
-
-def _is_binary_like(data_type: pa.DataType) -> bool:
-    return (
-        pa.types.is_binary(data_type)
-        or pa.types.is_large_binary(data_type)
-        or pa.types.is_binary_view(data_type)
-    )
-
-
 def _field_extension_name(field: pa.Field) -> Optional[str]:
     extension_name = getattr(field.type, "extension_name", None)
     if extension_name is not None:
@@ -618,6 +452,210 @@ def _field_extension_name(field: pa.Field) -> Optional[str]:
     return extension_name
 
 
+_JSON_EXTENSION_NAMES = {"arrow.json", "lance.json"}
+_BLOB_EXTENSION_NAME = "lance.blob.v2"
+
+
+def _field_contains_write_extension(field: pa.Field) -> bool:
+    extension_name = _field_extension_name(field)
+    if (
+        extension_name in _JSON_EXTENSION_NAMES
+        or extension_name == _BLOB_EXTENSION_NAME
+    ):
+        return True
+    if pa.types.is_struct(field.type):
+        return any(_field_contains_write_extension(child) for child in field.type)
+    if (
+        pa.types.is_list(field.type)
+        or pa.types.is_large_list(field.type)
+        or pa.types.is_fixed_size_list(field.type)
+    ):
+        return _field_contains_write_extension(field.type.value_field)
+    return False
+
+
+def _with_field_type(
+    field: pa.Field,
+    data_type: pa.DataType,
+    *,
+    name: Optional[str] = None,
+    metadata: Optional[dict] = None,
+) -> pa.Field:
+    return pa.field(
+        name or field.name,
+        data_type,
+        nullable=field.nullable,
+        metadata=field.metadata if metadata is None else metadata,
+    )
+
+
+def _with_list_value_field(
+    data_type: pa.DataType, value_field: pa.Field
+) -> pa.DataType:
+    if pa.types.is_list(data_type):
+        return pa.list_(value_field)
+    if pa.types.is_large_list(data_type):
+        return pa.large_list(value_field)
+    return pa.list_(value_field, data_type.list_size)
+
+
+def _extension_storage_field(field: pa.Field) -> pa.Field:
+    """Return a from-pylist-compatible field for nested write extensions."""
+    extension_name = _field_extension_name(field)
+    if extension_name in _JSON_EXTENSION_NAMES:
+        metadata = dict(field.metadata or {})
+        metadata[b"ARROW:extension:name"] = b"arrow.json"
+        return _with_field_type(field, pa.string(), metadata=metadata)
+    if extension_name == _BLOB_EXTENSION_NAME:
+        metadata = dict(field.metadata or {})
+        metadata[b"ARROW:extension:name"] = _BLOB_EXTENSION_NAME.encode()
+        metadata[b"ARROW:extension:metadata"] = b""
+        storage_type = getattr(field.type, "storage_type", field.type)
+        return _with_field_type(field, storage_type, metadata=metadata)
+    if pa.types.is_struct(field.type):
+        children = [_extension_storage_field(child) for child in field.type]
+        return _with_field_type(field, pa.struct(children))
+    if _is_list_like(field.type):
+        value_field = _extension_storage_field(field.type.value_field)
+        return _with_field_type(field, _with_list_value_field(field.type, value_field))
+    return field
+
+
+def _prepare_extension_field(
+    field: pa.Field, target_field: pa.Field
+) -> Tuple[pa.Field, bool]:
+    extension_name = _field_extension_name(target_field)
+    if extension_name in _JSON_EXTENSION_NAMES:
+        metadata = dict(field.metadata or {})
+        metadata[b"ARROW:extension:name"] = b"arrow.json"
+        return _with_field_type(field, pa.string(), metadata=metadata), True
+    if extension_name == _BLOB_EXTENSION_NAME and pa.types.is_null(field.type):
+        return _with_field_type(field, pa.large_binary()), True
+
+    if pa.types.is_struct(field.type) and pa.types.is_struct(target_field.type):
+        target_children = {child.name: child for child in target_field.type}
+        children = []
+        changed = False
+        for child in field.type:
+            target_child = target_children.get(child.name)
+            if target_child is None:
+                children.append(child)
+                continue
+            prepared, child_changed = _prepare_extension_field(child, target_child)
+            children.append(prepared)
+            changed = changed or child_changed
+        if changed:
+            return _with_field_type(field, pa.struct(children)), True
+
+    if _is_list_like(field.type) and _is_list_like(target_field.type):
+        target_value_field = target_field.type.value_field
+        if _field_contains_write_extension(target_value_field):
+            prepared = _extension_storage_field(target_value_field)
+            data_type = _with_list_value_field(target_field.type, prepared)
+            return _with_field_type(field, data_type), True
+
+    return field, False
+
+
+def _prepare_extension_value(
+    value: Any, target_field: pa.Field, *, within_list: bool = False
+) -> Any:
+    """Shape raw nested blob values for PyArrow's struct construction."""
+    if value is None:
+        return None
+
+    extension_name = _field_extension_name(target_field)
+    if extension_name == _BLOB_EXTENSION_NAME and within_list:
+        if isinstance(value, (bytes, bytearray, memoryview)):
+            return {"data": value}
+        if isinstance(value, str):
+            return {"uri": value}
+        return value
+
+    if pa.types.is_struct(target_field.type) and isinstance(value, dict):
+        target_children = {child.name: child for child in target_field.type}
+        return {
+            name: _prepare_extension_value(
+                child_value, target_children[name], within_list=within_list
+            )
+            if name in target_children
+            else child_value
+            for name, child_value in value.items()
+        }
+
+    if _is_list_like(target_field.type) and isinstance(value, (list, tuple)):
+        return [
+            _prepare_extension_value(
+                item, target_field.type.value_field, within_list=True
+            )
+            for item in value
+        ]
+
+    return value
+
+
+def _prepare_extension_list(data: DATA, target_schema: pa.Schema) -> DATA:
+    """Give inferred list columns the logical type required by extensions."""
+    if not isinstance(data, list) or not data or not isinstance(data[0], dict):
+        return data
+
+    target_fields = {field.name: field for field in target_schema}
+    if not any(
+        _field_contains_write_extension(field) for field in target_fields.values()
+    ):
+        return data
+
+    inferred = pa.Table.from_pylist(data)
+    fields = []
+    changed = False
+    for field in inferred.schema:
+        target_field = target_fields.get(field.name)
+        if target_field is None:
+            fields.append(field)
+            continue
+        prepared, field_changed = _prepare_extension_field(field, target_field)
+        fields.append(prepared)
+        changed = changed or field_changed
+
+    if not changed:
+        return inferred
+
+    insert_schema = pa.schema(fields, metadata=inferred.schema.metadata)
+    prepared_data = [
+        {
+            name: _prepare_extension_value(value, target_fields[name])
+            if name in target_fields
+            else value
+            for name, value in row.items()
+        }
+        for row in data
+    ]
+    return pa.Table.from_pylist(prepared_data, schema=insert_schema)
+
+
+def _is_blob_source_field(field: pa.Field) -> bool:
+    if _field_extension_name(field) == _BLOB_EXTENSION_NAME:
+        return True
+
+    predicates = (
+        "is_binary",
+        "is_large_binary",
+        "is_binary_view",
+        "is_string",
+        "is_large_string",
+        "is_string_view",
+    )
+    if any(
+        predicate(field.type)
+        for name in predicates
+        if (predicate := getattr(pa.types, name, None)) is not None
+    ):
+        return True
+    return pa.types.is_struct(field.type) and any(
+        child.name in {"data", "uri"} for child in field.type
+    )
+
+
 def _align_field_types(
     fields: List[pa.Field],
     target_fields: List[pa.Field],
@@ -630,73 +668,73 @@ def _align_field_types(
         target_field = next((f for f in target_fields if f.name == field.name), None)
         if target_field is None:
             raise ValueError(f"Field '{field.name}' not found in target schema")
-        new_fields.append(_align_field(field, target_field))
+        target_extension_name = _field_extension_name(target_field)
+        # Preserve accepted blob carriers so Lance can construct the declared
+        # blob struct after optional Python preprocessing.
+        if target_extension_name == _BLOB_EXTENSION_NAME and _is_blob_source_field(
+            field
+        ):
+            new_fields.append(field)
+            continue
+        # Preserve arrow.json input until it reaches Lance. LanceDB exposes stored
+        # JSON columns as lance.json (JSONB-backed LargeBinary), but casting the
+        # input to that storage type here merely relabels the raw JSON bytes as
+        # JSONB. Lance must see arrow.json so it can perform the JSONB encoding.
+        if (
+            _field_extension_name(field) == "arrow.json"
+            and target_extension_name in _JSON_EXTENSION_NAMES
+        ):
+            new_fields.append(field)
+            continue
+        if pa.types.is_struct(target_field.type):
+            if pa.types.is_struct(field.type):
+                new_type = pa.struct(
+                    _align_field_types(
+                        field.type.fields,
+                        target_field.type.fields,
+                    )
+                )
+            else:
+                new_type = target_field.type
+        elif pa.types.is_list(target_field.type):
+            if _is_list_like(field.type):
+                new_type = pa.list_(
+                    _align_field_types(
+                        [field.type.value_field],
+                        [target_field.type.value_field],
+                    )[0]
+                )
+            else:
+                new_type = target_field.type
+        elif pa.types.is_large_list(target_field.type):
+            if _is_list_like(field.type):
+                new_type = pa.large_list(
+                    _align_field_types(
+                        [field.type.value_field],
+                        [target_field.type.value_field],
+                    )[0]
+                )
+            else:
+                new_type = target_field.type
+        elif pa.types.is_fixed_size_list(target_field.type):
+            if _is_list_like(field.type):
+                new_type = pa.list_(
+                    _align_field_types(
+                        [field.type.value_field],
+                        [target_field.type.value_field],
+                    )[0],
+                    target_field.type.list_size,
+                )
+            else:
+                new_type = target_field.type
+        else:
+            new_type = target_field.type
+        new_fields.append(
+            pa.field(field.name, new_type, field.nullable, target_field.metadata)
+        )
     return new_fields
 
 
-def _align_list_value_field(
-    value_field: pa.Field, target_value_field: pa.Field
-) -> pa.Field:
-    # A list has exactly one child, so the inferred child name ("item") aligns
-    # positionally and adopts the table's child name; pa.Table.cast renames it.
-    return _align_field(value_field, target_value_field).with_name(
-        target_value_field.name
-    )
-
-
-def _align_field(field: pa.Field, target_field: pa.Field) -> pa.Field:
-    # Preserve arrow.json input until it reaches Lance. LanceDB exposes stored
-    # JSON columns as lance.json (JSONB-backed LargeBinary), but casting the
-    # input to that storage type here merely relabels the raw JSON bytes as
-    # JSONB. Lance must see arrow.json so it can perform the JSONB encoding.
-    if (
-        _field_extension_name(field) == "arrow.json"
-        and _field_extension_name(target_field) == "lance.json"
-    ):
-        return field
-    if pa.types.is_struct(target_field.type):
-        if pa.types.is_struct(field.type):
-            new_type = pa.struct(
-                _align_field_types(
-                    field.type.fields,
-                    target_field.type.fields,
-                )
-            )
-        else:
-            new_type = target_field.type
-    elif pa.types.is_list(target_field.type):
-        if _is_list_like(field.type):
-            new_type = pa.list_(
-                _align_list_value_field(
-                    field.type.value_field, target_field.type.value_field
-                )
-            )
-        else:
-            new_type = target_field.type
-    elif pa.types.is_large_list(target_field.type):
-        if _is_list_like(field.type):
-            new_type = pa.large_list(
-                _align_list_value_field(
-                    field.type.value_field, target_field.type.value_field
-                )
-            )
-        else:
-            new_type = target_field.type
-    elif pa.types.is_fixed_size_list(target_field.type):
-        if _is_list_like(field.type):
-            new_type = pa.list_(
-                _align_list_value_field(
-                    field.type.value_field, target_field.type.value_field
-                ),
-                target_field.type.list_size,
-            )
-        else:
-            new_type = target_field.type
-    else:
-        new_type = target_field.type
-    return pa.field(field.name, new_type, field.nullable, target_field.metadata)
-
-
 def _infer_subschema(
     schema: List[pa.Field],
     reference_fields: List[pa.Field],
@@ -763,7 +801,7 @@ def sanitize_create_table(
         schema = data.schema
     else:
         if schema is not None:
-            data = pa.Table.from_batches([], schema=schema)
+            data = pa.Table.from_pylist([], schema)
     if schema is None:
         if data is None:
             raise ValueError("Either data or schema must be provided")
@@ -1918,7 +1956,7 @@ class Table(ABC):
     @abstractmethod
     def update(
         self,
-        where: Optional[Union[str, Expr]] = None,
+        where: Optional[str] = None,
         values: Optional[dict] = None,
         *,
         values_sql: Optional[Dict[str, str]] = None,
@@ -1933,11 +1971,9 @@ class Table(ABC):
 
         Parameters
         ----------
-        where: str or [Expr][lancedb.expr.Expr], optional
-            The filter condition. Can be a SQL string or a type-safe
-            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
-            [lit][lancedb.expr.lit]. The filter must not be empty, or it will
-            error.
+        where: str, optional
+            The SQL where clause to use when updating rows. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
         values: dict, optional
             The values to update. The keys are the column names and the values
             are the values to set.
@@ -1955,7 +1991,6 @@ class Table(ABC):
         Examples
         --------
         >>> import lancedb
-        >>> from lancedb.expr import col
         >>> import pandas as pd
         >>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1.0, 2], [3, 4], [5, 6]]})
         >>> db = lancedb.connect("./.lancedb")
@@ -1965,7 +2000,7 @@ class Table(ABC):
         0  1  [1.0, 2.0]
         1  2  [3.0, 4.0]
         2  3  [5.0, 6.0]
-        >>> table.update(where=col("x") == 2, values={"vector": [10.0, 10]})
+        >>> table.update(where="x = 2", values={"vector": [10.0, 10]})
         UpdateResult(rows_updated=1, version=2)
         >>> table.to_pandas()
            x        vector
@@ -2872,7 +2907,7 @@ class LanceTable(Table):
             arrow_tbl = self.to_arrow()
             if blob_mode == "descriptions":
                 arrow_tbl = strip_auto_row_ids(
-                    arrow_tbl, row_addressable_blob_v2_paths(self.schema)
+                    arrow_tbl, blob_v2_column_paths(self.schema)
                 )
             return arrow_tbl.to_pandas(**kwargs)
 
@@ -4018,7 +4053,7 @@ class LanceTable(Table):
 
     def update(
         self,
-        where: Optional[Union[str, Expr]] = None,
+        where: Optional[str] = None,
         values: Optional[dict] = None,
         *,
         values_sql: Optional[Dict[str, str]] = None,
@@ -4029,11 +4064,9 @@ class LanceTable(Table):
 
         Parameters
         ----------
-        where: str or [Expr][lancedb.expr.Expr], optional
-            The filter condition. Can be a SQL string or a type-safe
-            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
-            [lit][lancedb.expr.lit]. The filter must not be empty, or it will
-            error.
+        where: str, optional
+            The SQL where clause to use when updating rows. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
         values: dict, optional
             The values to update. The keys are the column names and the values
             are the values to set.
@@ -4051,7 +4084,6 @@ class LanceTable(Table):
         Examples
         --------
         >>> import lancedb
-        >>> from lancedb.expr import col
         >>> import pandas as pd
         >>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1.0, 2], [3, 4], [5, 6]]})
         >>> db = lancedb.connect("./.lancedb")
@@ -4061,7 +4093,7 @@ class LanceTable(Table):
         0  1  [1.0, 2.0]
         1  2  [3.0, 4.0]
         2  3  [5.0, 6.0]
-        >>> table.update(where=col("x") == 2, values={"vector": [10.0, 10]})
+        >>> table.update(where="x = 2", values={"vector": [10.0, 10]})
         UpdateResult(rows_updated=1, version=2)
         >>> table.to_pandas()
            x        vector
@@ -5276,9 +5308,7 @@ class AsyncTable:
         if blob_mode == "descriptions" or not schema_has_blob_field(schema):
             arrow_tbl = await self.to_arrow()
             if blob_mode == "descriptions":
-                arrow_tbl = strip_auto_row_ids(
-                    arrow_tbl, row_addressable_blob_v2_paths(schema)
-                )
+                arrow_tbl = strip_auto_row_ids(arrow_tbl, blob_v2_column_paths(schema))
             return arrow_tbl.to_pandas(**kwargs)
 
         if blob_mode == "lazy" and get_uri_scheme(await self.uri()) == "memory":
@@ -5624,6 +5654,9 @@ class AsyncTable:
         if fill_value is None:
             fill_value = 0.0
 
+        if mode != "overwrite":
+            data = _prepare_extension_list(data, schema)
+
         # _santitize_data is an old code path, but we will use it until the
         # new code path is ready.
         if mode == "overwrite":
@@ -6177,7 +6210,7 @@ class AsyncTable:
         self,
         updates: Optional[Dict[str, Any]] = None,
         *,
-        where: Optional[Union[str, Expr]] = None,
+        where: Optional[str] = None,
         updates_sql: Optional[Dict[str, str]] = None,
     ) -> UpdateResult:
         """
@@ -6192,11 +6225,9 @@ class AsyncTable:
             The updates to apply.  The keys should be the name of the column to
             update.  The values should be the new values to assign.  This is
             required unless updates_sql is supplied.
-        where: str or [Expr][lancedb.expr.Expr], optional
-            The filter condition. Can be a SQL string or a type-safe
-            [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and
-            [lit][lancedb.expr.lit]. Only rows that satisfy this filter will
-            be updated.
+        where: str, optional
+            An SQL filter that controls which rows are updated. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'.  Only rows that satisfy this filter will be udpated.
         updates_sql: dict, optional
             The updates to apply, expressed as SQL expression strings.  The keys should
             be column names. The values should be SQL expressions.  These can be SQL
@@ -6214,14 +6245,13 @@ class AsyncTable:
         --------
         >>> import asyncio
         >>> import lancedb
-        >>> from lancedb.expr import col
         >>> import pandas as pd
         >>> async def demo_update():
         ...     data = pd.DataFrame({"x": [1, 2], "vector": [[1, 2], [3, 4]]})
         ...     db = await lancedb.connect_async("./.lancedb")
         ...     table = await db.create_table("my_table", data)
         ...     # x is [1, 2], vector is [[1, 2], [3, 4]]
-        ...     await table.update({"vector": [10, 10]}, where=col("x") == 2)
+        ...     await table.update({"vector": [10, 10]}, where="x = 2")
         ...     # x is [1, 2], vector is [[1, 2], [10, 10]]
         ...     await table.update(updates_sql={"x": "x + 1"})
         ...     # x is [2, 3], vector is [[1, 2], [10, 10]]
@@ -6235,8 +6265,7 @@ class AsyncTable:
         if updates is not None:
             updates_sql = {k: value_to_sql(v) for k, v in updates.items()}
 
-        predicate = where.to_sql() if isinstance(where, Expr) else where
-        return await self._inner.update(updates_sql, predicate)
+        return await self._inner.update(updates_sql, where)
 
     async def add_columns(
         self,

python/python/tests/test_blob.py

@@ -2,41 +2,17 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 import io
-import subprocess
-import sys
-import textwrap
 
-import lance
 import pyarrow as pa
 import pyarrow.compute as pc
 import pytest
-from lance.blob import BlobType as LanceBlobType
 
 import lancedb
-from lancedb._blob import (
-    blob_v2_projection_sources,
-    read_row_ids_from_hits,
-    stash_auto_row_ids,
-)
-from lancedb.expr import col
+from lancedb._blob import read_row_ids_from_hits, stash_auto_row_ids
 from lancedb.index import FTS
 from lancedb.schema import blob_column_paths, blob_v2_column_paths
 
 
-_HIDE_LANCE_BLOB = """\
-import importlib.abc
-import sys
-
-class _MissingLanceBlob(importlib.abc.MetaPathFinder):
-    def find_spec(self, fullname, path, target=None):
-        if fullname == "lance.blob" or fullname.startswith("lance.blob."):
-            raise ModuleNotFoundError(fullname, name="lance.blob")
-
-sys.modules.pop("lance.blob", None)
-sys.meta_path.insert(0, _MissingLanceBlob())
-"""
-
-
 def _blob_table(name, rows):
     db = lancedb.connect("memory:///")
     schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
@@ -70,181 +46,6 @@ def test_blob_factory_declares_v2_field():
     field = lancedb.blob("image")
     assert isinstance(field.type, pa.ExtensionType)
     assert field.type.extension_name == "lance.blob.v2"
-    assert lancedb.BlobType is LanceBlobType
-    assert type(field.type) is LanceBlobType
-
-
-def test_blob_type_works_without_pylance():
-    script = _HIDE_LANCE_BLOB + textwrap.dedent(
-        """\
-        import lancedb
-        import pyarrow as pa
-
-        field = lancedb.blob("image")
-        if not isinstance(field.type, pa.ExtensionType):
-            raise SystemExit("expected an extension type")
-        if field.type.extension_name != "lance.blob.v2":
-            raise SystemExit(field.type.extension_name)
-        if lancedb.BlobType is not type(field.type):
-            raise SystemExit("BlobType is not the field type class")
-        if lancedb.BlobType.__module__ != "lancedb.schema":
-            raise SystemExit(lancedb.BlobType.__module__)
-
-        db = lancedb.connect("memory:///")
-        table = db.create_table(
-            "images",
-            schema=pa.schema([pa.field("id", pa.int64()), field]),
-        )
-        table.add([{"id": 1, "image": b"hello"}])
-        result = (
-            table.merge_insert("id")
-            .when_matched_update_all()
-            .when_not_matched_insert_all()
-            .execute([{"id": 1, "image": b"updated"}, {"id": 2, "image": b"inserted"}])
-        )
-        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
-            raise SystemExit(
-                f"merge_insert rows updated={result.num_updated_rows} "
-                f"inserted={result.num_inserted_rows}"
-            )
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_blob_resolves_pylance_type_without_eager_import():
-    script = textwrap.dedent(
-        """\
-        import sys
-        import lancedb
-
-        if "lance.blob" in sys.modules:
-            raise SystemExit("import lancedb imported lance.blob")
-        field = lancedb.blob("image")
-        from lance.blob import BlobType
-
-        if type(field.type) is not BlobType:
-            raise SystemExit(f"{type(field.type)} is not {BlobType}")
-        import lance
-
-        image = lance.blob_array([b"x"])
-        if type(image.type) is not BlobType:
-            raise SystemExit("blob_array used a different class")
-        if type(image.type) is not type(field.type):
-            raise SystemExit("field and array classes differ")
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_blob_fallback_fails_if_name_already_registered():
-    script = _HIDE_LANCE_BLOB + textwrap.dedent(
-        """\
-        import pyarrow as pa
-
-        class OtherBlobType(pa.ExtensionType):
-            def __init__(self):
-                super().__init__(
-                    pa.struct([pa.field("data", pa.large_binary())]),
-                    "lance.blob.v2",
-                )
-
-            def __arrow_ext_serialize__(self):
-                return b""
-
-            @classmethod
-            def __arrow_ext_deserialize__(cls, storage_type, serialized):
-                return cls()
-
-        pa.register_extension_type(OtherBlobType())
-        import lancedb
-
-        try:
-            lancedb.blob("image")
-        except ValueError as err:
-            if "already registered" not in str(err):
-                raise SystemExit(err)
-        else:
-            raise SystemExit("expected ValueError")
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_blob_type_rejects_competing_registration_with_pylance():
-    script = textwrap.dedent(
-        """\
-        import pyarrow as pa
-        import pyarrow.ipc
-
-        class OtherBlobType(pa.ExtensionType):
-            def __init__(self):
-                super().__init__(
-                    pa.struct(
-                        [
-                            pa.field("data", pa.large_binary()),
-                            pa.field("uri", pa.utf8()),
-                            pa.field("position", pa.uint64()),
-                            pa.field("size", pa.uint64()),
-                        ]
-                    ),
-                    "lance.blob.v2",
-                )
-
-            def __arrow_ext_serialize__(self):
-                return b""
-
-            @classmethod
-            def __arrow_ext_deserialize__(cls, storage_type, serialized):
-                return cls()
-
-        pa.register_extension_type(OtherBlobType())
-
-        from lance.blob import BlobType
-
-        if BlobType is OtherBlobType:
-            raise SystemExit("pylance BlobType was replaced")
-        schema = pa.schema([pa.field("value", BlobType())])
-        restored = pa.ipc.read_schema(schema.serialize())
-        if type(restored.field("value").type) is not OtherBlobType:
-            raise SystemExit(type(restored.field("value").type))
-
-        import lancedb
-
-        try:
-            lancedb.blob("image")
-        except ValueError as err:
-            if "__main__.OtherBlobType" not in str(err):
-                raise SystemExit(err)
-        else:
-            raise SystemExit("expected ValueError")
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
 
 
 def test_blob_v2_column_paths_include_list_children():
@@ -269,14 +70,6 @@ def test_blob_v2_column_paths_include_list_children():
     ]
 
 
-def test_blob_v2_projection_sources_use_typed_column_name():
-    schema = pa.schema([lancedb.blob("blob")])
-
-    assert blob_v2_projection_sources(schema, {"blob_alias": col("blob")}) == {
-        "blob_alias": "blob"
-    }
-
-
 def _legacy_v1_table(name):
     db = lancedb.connect("memory:///")
     schema = pa.schema(
@@ -373,20 +166,6 @@ async def test_async_table_to_pandas_descriptions_mode_omits_row_id():
     assert set(descriptor.keys()) == {"kind", "position", "size", "blob_id", "blob_uri"}
 
 
-@pytest.mark.asyncio
-async def test_async_typed_blob_projection_preserves_source_column():
-    db = await lancedb.connect_async("memory:///typed_blob_projection")
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("blob")])
-    table = await db.create_table("typed_blob_projection", schema=schema)
-    await table.add([{"id": 1, "blob": b"alpha"}])
-
-    hits = await table.query().select({"blob_alias": col("blob")}).to_arrow()
-
-    assert "_lance_row_id" in hits.schema.field("blob_alias").type.names
-    blobs = await table.fetch_blobs("blob", hits)
-    assert blobs.to_pylist() == [b"alpha"]
-
-
 def test_fetch_blobs_round_trip():
     table = _blob_table(
         "round_trip",
@@ -397,292 +176,6 @@ def test_fetch_blobs_round_trip():
     assert [blobs[0].as_py(), blobs[1].as_py()] == [b"alpha", b"beta"]
 
 
-def test_merge_insert_writes_python_bytes():
-    table = _blob_table("merge_bytes", [{"id": 1, "image": b"before"}])
-    result = (
-        table.merge_insert("id")
-        .when_matched_update_all()
-        .when_not_matched_insert_all()
-        .execute([{"id": 1, "image": b"updated"}, {"id": 2, "image": b"inserted"}])
-    )
-    assert result.num_updated_rows == 1
-    assert result.num_inserted_rows == 1
-    by_id = _row_ids_by_id(table)
-    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-    assert blobs.to_pylist() == [b"updated", b"inserted"]
-
-
-def test_merge_insert_bytes_after_reopen_without_touching_blob_type(tmp_path):
-    db = lancedb.connect(tmp_path)
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table("images", schema=schema)
-    table.add([{"id": 1, "image": b"hello"}])
-
-    script = textwrap.dedent(
-        f"""\
-        import lancedb
-
-        db = lancedb.connect({str(tmp_path)!r})
-        table = db.open_table("images")
-        image_type = table.schema.field("image").type
-        if type(image_type).__name__ != "StructType":
-            raise SystemExit(f"expected StructType, got {{type(image_type)}}")
-        result = (
-            table.merge_insert("id")
-            .when_matched_update_all()
-            .when_not_matched_insert_all()
-            .execute(
-                [{{"id": 1, "image": b"updated"}}, {{"id": 2, "image": b"inserted"}}]
-            )
-        )
-        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
-            raise SystemExit(
-                f"rows updated={{result.num_updated_rows}} "
-                f"inserted={{result.num_inserted_rows}}"
-            )
-        hits = table.search().with_row_id(True).limit(10).to_arrow()
-        by_id = dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
-        blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-        if blobs.to_pylist() != [b"updated", b"inserted"]:
-            raise SystemExit(blobs.to_pylist())
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_merge_insert_bytes_after_reopen_without_pylance(tmp_path):
-    db = lancedb.connect(tmp_path)
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table("images", schema=schema)
-    table.add([{"id": 1, "image": b"hello"}])
-
-    script = _HIDE_LANCE_BLOB + textwrap.dedent(
-        f"""\
-        import lancedb
-
-        db = lancedb.connect({str(tmp_path)!r})
-        table = db.open_table("images")
-        image_type = table.schema.field("image").type
-        if type(image_type).__name__ != "StructType":
-            raise SystemExit(f"expected StructType, got {{type(image_type)}}")
-        result = (
-            table.merge_insert("id")
-            .when_matched_update_all()
-            .when_not_matched_insert_all()
-            .execute(
-                [{{"id": 1, "image": b"updated"}}, {{"id": 2, "image": b"inserted"}}]
-            )
-        )
-        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
-            raise SystemExit(
-                f"rows updated={{result.num_updated_rows}} "
-                f"inserted={{result.num_inserted_rows}}"
-            )
-        hits = table.search().with_row_id(True).limit(10).to_arrow()
-        by_id = dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
-        blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-        if blobs.to_pylist() != [b"updated", b"inserted"]:
-            raise SystemExit(blobs.to_pylist())
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_merge_insert_blob_array_into_reopened_unregistered_table(tmp_path):
-    db = lancedb.connect(tmp_path)
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table("images", schema=schema)
-    table.add([{"id": 1, "image": b"before"}])
-
-    script = textwrap.dedent(
-        f"""\
-        import pyarrow as pa
-        import lancedb
-
-        db = lancedb.connect({str(tmp_path)!r})
-        table = db.open_table("images")
-        image_type = table.schema.field("image").type
-        if type(image_type).__name__ != "StructType":
-            raise SystemExit(
-                f"expected StructType before lance import, got {{type(image_type)}}"
-            )
-
-        import lance
-
-        updates = pa.Table.from_arrays(
-            [
-                pa.array([1, 2], type=pa.int64()),
-                lance.blob_array([b"updated", b"inserted"]),
-            ],
-            names=["id", "image"],
-        )
-        result = (
-            table.merge_insert("id")
-            .when_matched_update_all()
-            .when_not_matched_insert_all()
-            .execute(updates)
-        )
-        if result.num_updated_rows != 1 or result.num_inserted_rows != 1:
-            raise SystemExit(
-                f"rows updated={{result.num_updated_rows}} "
-                f"inserted={{result.num_inserted_rows}}"
-            )
-        hits = table.search().with_row_id(True).limit(10).to_arrow()
-        by_id = dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
-        blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-        if blobs.to_pylist() != [b"updated", b"inserted"]:
-            raise SystemExit(blobs.to_pylist())
-        """
-    )
-    result = subprocess.run(
-        [sys.executable, "-c", script],
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    assert result.returncode == 0, result.stderr
-
-
-def test_add_all_null_blob_column():
-    db = lancedb.connect("memory:///")
-    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
-    table = db.create_table("all_null", schema=schema)
-    table.add([{"id": 1, "image": None}, {"id": 2, "image": None}])
-    by_id = _row_ids_by_id(table)
-    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-    assert blobs.to_pylist() == [None, None]
-
-
-def test_create_table_nested_blob_schema_without_rows():
-    db = lancedb.connect("memory:///")
-    schema = pa.schema(
-        [
-            pa.field("id", pa.int64()),
-            pa.field("info", pa.struct([lancedb.blob("blob")])),
-            pa.field("images", pa.list_(lancedb.blob("image"))),
-        ]
-    )
-    table = db.create_table("nested_empty", schema=schema)
-    assert table.count_rows() == 0
-
-
-def test_merge_insert_nested_blob_dicts():
-    db = lancedb.connect("memory:///")
-    info = pa.StructArray.from_arrays(
-        [
-            pa.array(["first"], type=pa.string()),
-            _blob_array("blob", [b"before"]),
-        ],
-        names=["name", "blob"],
-    )
-    data = pa.Table.from_arrays(
-        [pa.array([1], type=pa.int64()), info],
-        names=["id", "info"],
-    )
-    table = db.create_table("nested_merge", data=data)
-    result = (
-        table.merge_insert("id")
-        .when_matched_update_all()
-        .execute([{"id": 1, "info": {"name": "first", "blob": b"after"}}])
-    )
-    assert result.num_updated_rows == 1
-    by_id = _row_ids_by_id(table)
-    blobs = table.fetch_blobs("info.blob", [by_id[1]])
-    assert blobs.to_pylist() == [b"after"]
-
-
-def _list_blob_table(name):
-    db = lancedb.connect("memory:///")
-    blob_field = lancedb.blob("image")
-    images = pa.ListArray.from_arrays(
-        pa.array([0, 1], type=pa.int32()), _blob_array("image", [b"before"])
-    )
-    data = pa.Table.from_arrays(
-        [pa.array([1], type=pa.int64()), images],
-        schema=pa.schema(
-            [pa.field("id", pa.int64()), pa.field("images", pa.list_(blob_field))]
-        ),
-    )
-    return db.create_table(name, data=data)
-
-
-def test_merge_insert_list_blob_dicts():
-    table = _list_blob_table("list_merge")
-    result = (
-        table.merge_insert("id")
-        .when_matched_update_all()
-        .when_not_matched_insert_all()
-        .execute([{"id": 1, "images": [b"one", b"two"]}, {"id": 2, "images": None}])
-    )
-    assert result.num_updated_rows == 1
-    assert result.num_inserted_rows == 1
-    hits = table.search().limit(10).to_arrow()
-    sizes = {
-        row["id"]: None if row["images"] is None else [d["size"] for d in row["images"]]
-        for row in hits.to_pylist()
-    }
-    assert sizes == {1: [3, 3], 2: None}
-
-
-def test_list_blob_column_queries_as_raw_descriptors():
-    table = _list_blob_table("list_query")
-    hits = table.search().limit(10).to_arrow()
-    element = hits.schema.field("images").type.value_type
-    assert pa.types.is_struct(element)
-    assert "_lance_row_id" not in element.names
-    with pytest.raises(ValueError, match="expected struct before segment"):
-        table.fetch_blobs("images.image", [0])
-
-
-def test_row_addressable_paths_exclude_list_children():
-    from lancedb.schema import row_addressable_blob_v2_paths
-
-    schema = pa.schema(
-        [
-            pa.field("id", pa.int64()),
-            pa.field("info", pa.struct([lancedb.blob("blob")])),
-            pa.field("images", pa.list_(lancedb.blob("image"))),
-        ]
-    )
-    assert blob_v2_column_paths(schema) == ["info.blob", "images.image"]
-    assert row_addressable_blob_v2_paths(schema) == ["info.blob"]
-
-
-def test_merge_insert_writes_pylance_blob_array():
-    table = _blob_table("merge_pylance", [{"id": 1, "image": b"before"}])
-    image = lance.blob_array([b"updated", b"inserted"])
-    assert type(image.type) is LanceBlobType
-    assert type(image.type) is type(lancedb.BlobType())
-    updates = pa.Table.from_arrays(
-        [pa.array([1, 2], type=pa.int64()), image], names=["id", "image"]
-    )
-
-    result = (
-        table.merge_insert("id")
-        .when_matched_update_all()
-        .when_not_matched_insert_all()
-        .execute(updates)
-    )
-
-    assert result.num_updated_rows == 1
-    assert result.num_inserted_rows == 1
-    by_id = _row_ids_by_id(table)
-    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
-    assert blobs.to_pylist() == [b"updated", b"inserted"]
-
-
 def test_fetch_blobs_accepts_query_result():
     table = _blob_table("from_result", [{"id": 1, "image": b"gamma"}])
     hits = table.search().limit(10).to_arrow()
@@ -710,6 +203,80 @@ def test_fetch_blobs_preserves_null_and_empty_values():
     assert blobs[3].as_py() == b"present"
 
 
+def test_add_all_null_list_to_blob_column():
+    table = _blob_table("all_null_add", [{"id": 1, "image": None}])
+
+    hits = table.search().to_arrow()
+    blobs = table.fetch_blobs("image", hits)
+    assert len(blobs) == 1
+    assert blobs[0].as_py() is None
+
+
+def test_add_all_null_list_to_blob_column_with_sanitizer():
+    db = lancedb.connect("memory:///")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("all_null_sanitized_add", schema=schema)
+
+    table.add([{"id": 1, "image": None}], on_bad_vectors="fill")
+
+    hits = table.search().to_arrow()
+    blobs = table.fetch_blobs("image", hits)
+    assert len(blobs) == 1
+    assert blobs[0].as_py() is None
+
+
+def test_add_all_null_list_to_nested_blob_column():
+    db = lancedb.connect("memory:///")
+    blob_field = lancedb.blob("image")
+    info_field = pa.field("info", pa.struct([blob_field]))
+    info = pa.StructArray.from_arrays(
+        [_blob_array("image", [b"seed"])], fields=[blob_field]
+    )
+    seed = pa.Table.from_arrays(
+        [pa.array([0], type=pa.int64()), info],
+        schema=pa.schema([pa.field("id", pa.int64()), info_field]),
+    )
+    table = db.create_table("nested_null_add", data=seed)
+
+    table.add([{"id": 1, "info": {"image": None}}])
+    table.add([{"id": 2, "info": {"image": None}}], on_bad_vectors="fill")
+
+    hits = table.search().where("id > 0").to_arrow()
+    blobs = table.fetch_blobs("info.image", hits)
+    assert len(blobs) == 2
+    assert all(blob.as_py() is None for blob in blobs)
+
+
+@pytest.mark.parametrize("large_list", [False, True], ids=["list", "large_list"])
+def test_add_list_of_dicts_to_blob_list_column(large_list):
+    db = lancedb.connect("memory:///")
+    blob_field = lancedb.blob("image")
+    blob_values = _blob_array("image", [b"seed"])
+    if large_list:
+        items_field = pa.field("items", pa.large_list(blob_field))
+        items = pa.LargeListArray.from_arrays(
+            pa.array([0, 1], type=pa.int64()), blob_values
+        )
+    else:
+        items_field = pa.field("items", pa.list_(blob_field))
+        items = pa.ListArray.from_arrays(pa.array([0, 1], type=pa.int32()), blob_values)
+    seed = pa.Table.from_arrays(
+        [pa.array([0], type=pa.int64()), items],
+        schema=pa.schema([pa.field("id", pa.int64()), items_field]),
+    )
+    table = db.create_table(f"blob_{large_list}_list_add", data=seed)
+
+    table.add([{"id": 1, "items": [None]}])
+    table.add(
+        [{"id": 2, "items": [b"a", None]}],
+        on_bad_vectors="fill",
+    )
+
+    ids = table.search().select(["id"]).to_arrow()["id"].to_pylist()
+    assert sorted(ids) == [0, 1, 2]
+    assert pa.types.is_large_list(table.schema.field("items").type) is large_list
+
+
 def test_fetch_blob_ranges_aligns_repeated_ranges_and_nulls():
     table = _blob_table(
         "range_alignment",
@@ -910,50 +477,6 @@ async def test_blob_v2_hybrid_fetch_blobs_async():
     assert {blobs[i].as_py() for i in range(len(blobs))} == {b"alpha", b"beta"}
 
 
-@pytest.mark.asyncio
-async def test_async_hybrid_typed_blob_projection_preserves_source_column():
-    db = await lancedb.connect_async("memory:///hybrid_typed_blob")
-    schema = pa.schema(
-        [
-            pa.field("id", pa.int64()),
-            pa.field("text", pa.utf8()),
-            pa.field("vector", pa.list_(pa.float32(), list_size=2)),
-            lancedb.blob("blob"),
-        ]
-    )
-    table = await db.create_table("hybrid_typed_blob", schema=schema)
-    await table.add(
-        [
-            {
-                "id": 1,
-                "text": "hello alpha",
-                "vector": [1.0, 0.0],
-                "blob": b"alpha",
-            },
-            {
-                "id": 2,
-                "text": "hello beta",
-                "vector": [0.9, 0.1],
-                "blob": b"beta",
-            },
-        ]
-    )
-    await table.create_index("text", config=FTS(with_position=False))
-
-    hits = await (
-        table.query()
-        .nearest_to([1.0, 0.0])
-        .nearest_to_text("hello")
-        .select({"blob_alias": col("blob")})
-        .limit(2)
-        .to_arrow()
-    )
-
-    assert "_lance_row_id" in hits.schema.field("blob_alias").type.names
-    blobs = await table.fetch_blobs("blob", hits)
-    assert {blobs[i].as_py() for i in range(len(blobs))} == {b"alpha", b"beta"}
-
-
 def test_blob_file_seek_read_and_read_range():
     payload = _identifiable_payload(1024)
     table = _blob_table("seek_read", [{"id": 1, "image": payload}])
@@ -1230,6 +753,7 @@ def test_add_external_uri_string_round_trips_with_flag(tmp_path):
     table = db.create_table("external_string", schema=schema)
     table.add(
         [{"id": 1, "image": blob_path.as_uri()}],
+        on_bad_vectors="fill",
         allow_external_blob_outside_bases=True,
     )
 

python/python/tests/test_expr.py

@@ -52,7 +52,7 @@ class TestExprConstruction:
     def test_func(self):
         e = func("lower", col("name"))
         assert isinstance(e, Expr)
-        assert e.to_sql() == "lower(`name`)"
+        assert e.to_sql() == "lower(name)"
 
     def test_func_unknown_raises(self):
         with pytest.raises(Exception):
@@ -115,7 +115,7 @@ class TestExprOperators:
     def test_and_operator(self):
         e = (col("age") > lit(18)) & (col("status") == lit("active"))
         assert isinstance(e, Expr)
-        assert e.to_sql() == "((age > 18) AND (`status` = 'active'))"
+        assert e.to_sql() == "((age > 18) AND (status = 'active'))"
 
     def test_or_operator(self):
         e = (col("a") == lit(1)) | (col("b") == lit(2))
@@ -166,7 +166,7 @@ class TestExprOperators:
     def test_coerce_plain_str(self):
         e = col("name") == "alice"
         assert isinstance(e, Expr)
-        assert e.to_sql() == "(`name` = 'alice')"
+        assert e.to_sql() == "(name = 'alice')"
 
     def test_reflexive_comparisons(self):
         # 10 < col("age") swaps to col("age") > 10
@@ -198,85 +198,85 @@ class TestExprBytesLiteral:
 
     def test_bytes_equality_expr_sql(self):
         e = col("data") == lit(b"\xca\xfe")
-        assert e.to_sql() == "(`data` = X'CAFE')"
+        assert e.to_sql() == "(data = X'CAFE')"
 
     def test_bytes_ne_expr_sql(self):
         e = col("data") != lit(b"\xff")
-        assert e.to_sql() == "(`data` <> X'FF')"
+        assert e.to_sql() == "(data <> X'FF')"
 
     def test_bytes_compound_expr_sql(self):
         e = (col("data") == lit(b"\x01")) & (col("id") > lit(5))
-        assert e.to_sql() == "((`data` = X'01') AND (id > 5))"
+        assert e.to_sql() == "((data = X'01') AND (id > 5))"
 
     def test_bytes_in_function_call(self):
         # Regression test: binary literals inside scalar function calls
         # used to fail because DataFusion's unparser does not support Binary
         # scalars.  Now handled via a placeholder-substitution rewrite.
         e = func("contains", col("data"), lit(b"\xff"))
-        assert e.to_sql() == "contains(`data`, X'FF')"
+        assert e.to_sql() == "contains(data, X'FF')"
 
     def test_bytes_in_not(self):
         e = ~(col("data") == lit(b"\xff"))
-        assert e.to_sql() == "NOT (`data` = X'FF')"
+        assert e.to_sql() == "NOT (data = X'FF')"
 
 
 class TestExprStringMethods:
     def test_lower(self):
         e = col("name").lower()
         assert isinstance(e, Expr)
-        assert e.to_sql() == "lower(`name`)"
+        assert e.to_sql() == "lower(name)"
 
     def test_upper(self):
         e = col("name").upper()
         assert isinstance(e, Expr)
-        assert e.to_sql() == "upper(`name`)"
+        assert e.to_sql() == "upper(name)"
 
     def test_contains(self):
         e = col("text").contains(lit("hello"))
         assert isinstance(e, Expr)
-        assert e.to_sql() == "contains(`text`, 'hello')"
+        assert e.to_sql() == "contains(text, 'hello')"
 
     def test_contains_with_str_coerce(self):
         e = col("text").contains("hello")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "contains(`text`, 'hello')"
+        assert e.to_sql() == "contains(text, 'hello')"
 
     def test_chained_lower_eq(self):
         e = col("name").lower() == lit("alice")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "(lower(`name`) = 'alice')"
+        assert e.to_sql() == "(lower(name) = 'alice')"
 
 
 class TestExprCast:
     def test_cast_string(self):
         e = col("id").cast("string")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(id, 'Utf8')"
+        assert e.to_sql() == "CAST(id AS VARCHAR)"
 
     def test_cast_int32(self):
         e = col("score").cast("int32")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(score, 'Int32')"
+        assert e.to_sql() == "CAST(score AS INTEGER)"
 
     def test_cast_float64(self):
         e = col("val").cast("float64")
         assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(val, 'Float64')"
+        assert e.to_sql() == "CAST(val AS DOUBLE)"
 
     def test_cast_pyarrow_type(self):
         e = col("score").cast(pa.int32())
         assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(score, 'Int32')"
+        assert e.to_sql() == "CAST(score AS INTEGER)"
 
     def test_cast_pyarrow_float64(self):
         e = col("val").cast(pa.float64())
         assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(val, 'Float64')"
+        assert e.to_sql() == "CAST(val AS DOUBLE)"
 
     def test_cast_pyarrow_string(self):
         e = col("id").cast(pa.string())
         assert isinstance(e, Expr)
-        assert e.to_sql() == "arrow_cast(id, 'Utf8')"
+        assert e.to_sql() == "CAST(id AS VARCHAR)"
 
     def test_cast_pyarrow_and_string_equivalent(self):
         # pa.int32() and "int32" should produce equivalent SQL
@@ -597,14 +597,14 @@ class TestExprIsin:
     def test_isin_strs(self):
         assert (
             col("status").isin(["active", "pending"]).to_sql()
-            == "`status` IN ('active', 'pending')"
+            == "status IN ('active', 'pending')"
         )
 
     def test_isin_coerces_and_mixes(self):
         assert col("id").isin([lit(1), 2]).to_sql() == "id IN (1, 2)"
 
     def test_isin_empty(self):
-        assert col("id").isin([]).to_sql() == "false"
+        assert col("id").isin([]).to_sql() == "id IN ()"
 
     def test_isin_filter(self, simple_table):
         result = simple_table.search().where(col("id").isin([1, 3, 5])).to_arrow()

python/python/tests/test_query.py

@@ -675,21 +675,6 @@ def test_distance_range(table: lancedb.table.Table):
     assert res["_distance"].to_pylist() == [min_dist, max_dist]
 
 
-@pytest.mark.parametrize("expression", ["1 - _distance", "1.0 - _distance"])
-def test_select_arithmetic_with_distance(table, expression):
-    result = (
-        table.search([10, 10])
-        .select({"similarity": expression, "_distance": "_distance"})
-        .distance_type("cosine")
-        .to_arrow()
-    )
-
-    assert result.schema.field("similarity").type == pa.float32()
-    assert result["similarity"].to_pylist() == pytest.approx(
-        [1 - distance for distance in result["_distance"].to_pylist()]
-    )
-
-
 @pytest.mark.asyncio
 async def test_distance_range_async(table_async: AsyncTable):
     q = [0, 0]

python/python/tests/test_table.py

@@ -11,7 +11,6 @@ import warnings
 import weakref
 from concurrent.futures import ThreadPoolExecutor
 from datetime import date, datetime, timedelta
-from decimal import Decimal
 from time import sleep
 from typing import List
 from unittest.mock import patch
@@ -337,21 +336,6 @@ async def test_update_async(mem_db_async: AsyncConnection):
     assert await table.count_rows("id == 10") == 1
 
 
-@pytest.mark.asyncio
-async def test_update_expr_filter_literals_async(mem_db_async: AsyncConnection):
-    values = ["5", "4.66e-84", "it's"]
-    table = await mem_db_async.create_table(
-        "update_expr_literals",
-        data=[{"field": value, "result": "original"} for value in values],
-    )
-
-    for value in values:
-        update_res = await table.update({"result": value}, where=col("field") == value)
-        assert update_res.rows_updated == 1
-
-    assert (await table.to_arrow())["result"].to_pylist() == values
-
-
 def test_create_table(mem_db: DBConnection):
     schema = pa.schema(
         {
@@ -786,6 +770,55 @@ async def test_add_async(mem_db_async: AsyncConnection):
     assert await table.count_rows() == 3
 
 
+@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type")
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    ("values", "expected"),
+    [
+        ([None], [None]),
+        ([None, '{"k": 1}'], [None, '{"k":1}']),
+        (['{"k": 2}'], ['{"k":2}']),
+    ],
+)
+async def test_add_list_of_dicts_to_json_column(
+    mem_db_async: AsyncConnection, values, expected
+):
+    schema = pa.schema([pa.field("id", pa.int64()), pa.field("value", pa.json_())])
+    table = await mem_db_async.create_table("json_list_add", schema=schema)
+
+    await table.add([{"id": idx, "value": value} for idx, value in enumerate(values)])
+
+    rows = (await table.to_arrow()).sort_by("id").to_pylist()
+    assert [row["value"] for row in rows] == expected
+
+
+@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type")
+@pytest.mark.asyncio
+async def test_add_list_of_dicts_to_nested_json_column(
+    mem_db_async: AsyncConnection,
+):
+    json_field = pa.field("value", pa.json_())
+    info_field = pa.field("info", pa.struct([json_field]))
+    info = pa.StructArray.from_arrays(
+        [pa.array(['{"seed": 0}'], type=pa.json_())], fields=[json_field]
+    )
+    seed = pa.Table.from_arrays(
+        [pa.array([0], type=pa.int64()), info],
+        schema=pa.schema([pa.field("id", pa.int64()), info_field]),
+    )
+    table = await mem_db_async.create_table("nested_json_list_add", data=seed)
+
+    await table.add([{"id": 1, "info": {"value": '{"k": 1}'}}])
+    await table.add([{"id": 2, "info": {"value": '{"k": 2}'}}], on_bad_vectors="fill")
+
+    rows = (await table.to_arrow()).sort_by("id").to_pylist()
+    assert rows == [
+        {"id": 0, "info": {"value": '{"seed":0}'}},
+        {"id": 1, "info": {"value": '{"k":1}'}},
+        {"id": 2, "info": {"value": '{"k":2}'}},
+    ]
+
+
 def test_add_overwrite_infers_vector_schema(mem_db: DBConnection):
     """Overwrite should infer vector columns the same way create_table does.
 
@@ -2359,148 +2392,6 @@ def test_update(mem_db: DBConnection):
     assert np.allclose(v, np.array([[1.2, 1.9], [1.1, 1.1]]))
 
 
-def test_update_expr_filter_literals(mem_db: DBConnection):
-    values = ["5", "4.66e-84", "it's"]
-    table = mem_db.create_table(
-        "update_expr_literals",
-        data=[{"field": value, "result": "original"} for value in values],
-    )
-
-    for value in values:
-        update_res = table.update(where=col("field") == value, values={"result": value})
-        assert update_res.rows_updated == 1
-
-    assert table.to_arrow()["result"].to_pylist() == values
-
-
-def test_update_expr_filter_preserves_typed_semantics(mem_db: DBConnection):
-    low = Decimal("1.234567890123456789")
-    high = Decimal("1.234567890123456790")
-    decimal_schema = pa.schema(
-        [("val", pa.decimal128(19, 18)), ("result", pa.string())]
-    )
-    decimal_table = mem_db.create_table(
-        "update_expr_decimal",
-        pa.table(
-            {"val": [low, high], "result": ["old", "old"]},
-            schema=decimal_schema,
-        ),
-    )
-    predicate = col("val") < lit(high)
-    assert decimal_table.search().where(predicate).to_arrow().num_rows == 1
-    result = decimal_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    keyword_table = mem_db.create_table(
-        "update_expr_keyword", [{"null": 1, "result": "old"}]
-    )
-    predicate = col("null") == 1
-    assert keyword_table.search().where(predicate).to_arrow().num_rows == 1
-    result = keyword_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    empty_in_table = mem_db.create_table(
-        "update_expr_empty_in", [{"id": 1, "result": "old"}]
-    )
-    predicate = col("id").isin([])
-    assert empty_in_table.search().where(predicate).to_arrow().num_rows == 0
-    result = empty_in_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 0
-
-    marker = "__lancedb_binary_placeholder_0__"
-    binary_schema = pa.schema(
-        [("payload", pa.binary()), ("text", pa.string()), ("result", pa.string())]
-    )
-    binary_table = mem_db.create_table(
-        "update_expr_binary",
-        pa.table(
-            {
-                "payload": [b"\x01", b"\x02"],
-                "text": ["other", marker],
-                "result": ["old", "old"],
-            },
-            schema=binary_schema,
-        ),
-    )
-    predicate = (col("payload") == lit(b"\x01")) | (col("text") == marker)
-    assert binary_table.search().where(predicate).to_arrow().num_rows == 2
-    result = binary_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 2
-
-    nonfinite_table = mem_db.create_table(
-        "update_expr_nonfinite",
-        [{"x": 1.0, "result": "old"}, {"x": 2.0, "result": "old"}],
-    )
-    predicate = col("x") < float("inf")
-    assert nonfinite_table.search().where(predicate).to_arrow().num_rows == 2
-    result = nonfinite_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 2
-
-    float16_table = mem_db.create_table(
-        "update_expr_float16",
-        [{"x": 1.0, "result": "old"}, {"x": 3.0, "result": "old"}],
-    )
-    predicate = col("x").cast(pa.float16()) < 2.0
-    assert float16_table.search().where(predicate).to_arrow().num_rows == 1
-    result = float16_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    string_cast_table = mem_db.create_table(
-        "update_expr_string_cast",
-        [{"x": 1, "result": "old"}, {"x": 2, "result": "old"}],
-    )
-    predicate = col("x").cast("string") == "1"
-    assert string_cast_table.search().where(predicate).to_arrow().num_rows == 1
-    result = string_cast_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    quoted_identifier_schema = pa.schema(
-        [("payload", pa.binary()), ("odd'name", pa.int64()), ("result", pa.string())]
-    )
-    quoted_identifier_table = mem_db.create_table(
-        "update_expr_quoted_identifier",
-        pa.table(
-            {"payload": [b"\x01"], "odd'name": [1], "result": ["old"]},
-            schema=quoted_identifier_schema,
-        ),
-    )
-    predicate = (col("payload") == lit(b"\x01")) & (col("odd'name") == 1)
-    assert quoted_identifier_table.search().where(predicate).to_arrow().num_rows == 1
-    result = quoted_identifier_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    decimal256_schema = pa.schema(
-        [("val", pa.decimal256(40, 2)), ("result", pa.string())]
-    )
-    decimal256_table = mem_db.create_table(
-        "update_expr_decimal256",
-        pa.table(
-            {
-                "val": [Decimal("1.00"), Decimal("3.00")],
-                "result": ["old", "old"],
-            },
-            schema=decimal256_schema,
-        ),
-    )
-    predicate = col("val") < lit(Decimal("2.00")).cast(pa.decimal256(40, 2))
-    assert decimal256_table.search().where(predicate).to_arrow().num_rows == 1
-    result = decimal256_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 1
-
-    binary_empty_table = mem_db.create_table(
-        "update_expr_binary_empty",
-        pa.table(
-            {"payload": [b"\x01", b"\x02"], "result": ["old", "old"]},
-            schema=pa.schema([("payload", pa.binary()), ("result", pa.string())]),
-        ),
-    )
-    predicate = (col("payload") == lit(b"\x01")).isin([])
-    assert binary_empty_table.search().where(predicate).to_arrow().num_rows == 0
-    assert predicate.to_sql() == "false"
-    result = binary_empty_table.update(where=predicate, values={"result": "new"})
-    assert result.rows_updated == 0
-
-
 def test_update_with_arrow_scalar(mem_db: DBConnection):
     schema = pa.schema({"id": pa.int64(), "vector": pa.list_(pa.float32(), 4)})
     table = mem_db.create_table("my_table", schema=schema)

python/python/tests/test_util.py

@@ -7,7 +7,6 @@ import pathlib
 from typing import Optional
 
 import lance
-from lance.blob import BlobType as LanceBlobType
 from lancedb.conftest import MockTextEmbeddingFunction
 from lancedb.embeddings.base import EmbeddingFunctionConfig
 from lancedb.embeddings.registry import EmbeddingFunctionRegistry
@@ -908,165 +907,6 @@ def test_cast_to_target_schema():
     assert output == expected
 
 
-def test_cast_to_target_schema_coerces_binary_to_blob_v2():
-    data = pa.table({"image": pa.array([b"hello", None], type=pa.binary())})
-    target = pa.schema([lancedb.blob("image")])
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    image = output["image"].chunk(0)
-    assert type(image.type) is lancedb.BlobType
-    assert image.storage.to_pylist() == [
-        {"data": b"hello", "uri": None, "position": None, "size": None},
-        None,
-    ]
-
-
-def test_cast_to_target_schema_coerces_binary_to_metadata_blob_struct():
-    storage = lancedb.blob("image").type.storage_type
-    target = pa.schema(
-        [
-            pa.field(
-                "image",
-                storage,
-                metadata={
-                    b"ARROW:extension:name": b"lance.blob.v2",
-                    b"ARROW:extension:metadata": b"",
-                },
-            )
-        ]
-    )
-    data = pa.table({"image": pa.array([b"hello", None], type=pa.binary())})
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    image = output["image"].chunk(0)
-    assert not isinstance(image.type, pa.ExtensionType)
-    assert image.to_pylist() == [
-        {"data": b"hello", "uri": None, "position": None, "size": None},
-        None,
-    ]
-
-
-def test_cast_to_target_schema_coerces_nested_binary_blob():
-    data = pa.table(
-        {
-            "info": pa.array(
-                [{"blob": b"hello"}, {"blob": None}],
-                type=pa.struct([pa.field("blob", pa.binary())]),
-            )
-        }
-    )
-    target = pa.schema([pa.field("info", pa.struct([lancedb.blob("blob")]))])
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    blob = output["info"].chunk(0).field("blob")
-    assert type(blob.type) is lancedb.BlobType
-    assert blob.storage.to_pylist() == [
-        {"data": b"hello", "uri": None, "position": None, "size": None},
-        None,
-    ]
-
-
-def test_cast_to_target_schema_coerces_list_binary_blob_with_inferred_child_name():
-    data = pa.table(
-        {"images": pa.array([[b"a", b"b"], None], type=pa.list_(pa.binary()))}
-    )
-    target = pa.schema([pa.field("images", pa.list_(lancedb.blob("image")))])
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    images = output["images"].chunk(0)
-    assert images.type.value_field.name == "image"
-    assert type(images.type.value_type) is lancedb.BlobType
-    assert images.to_pylist()[1] is None
-    assert images.values.storage.to_pylist() == [
-        {"data": b"a", "uri": None, "position": None, "size": None},
-        {"data": b"b", "uri": None, "position": None, "size": None},
-    ]
-
-
-def test_list_blob_coercion_preserves_null_slots_with_nonzero_extent():
-    child = pa.field("image", pa.binary())
-    source = pa.ListArray.from_arrays(
-        pa.array([0, 2, 4], type=pa.int32()),
-        pa.array([b"a", b"b", b"dead", b"beef"], type=pa.binary()),
-        mask=pa.array([False, True]),
-    ).cast(pa.list_(child))
-    target = pa.schema([pa.field("images", pa.list_(lancedb.blob("image")))])
-
-    output = _cast_to_target_schema(
-        pa.table({"images": source}).to_reader(), target
-    ).read_all()
-
-    images = output["images"].chunk(0)
-    assert images.to_pylist()[1] is None
-    assert [b["data"] for b in images.to_pylist()[0]] == [b"a", b"b"]
-
-
-def test_fixed_size_list_blob_coercion_keeps_null_rows():
-    child = pa.field("frame", pa.binary())
-    source = (
-        pa.FixedSizeListArray.from_arrays(
-            pa.array([b"a", b"b", b"c", b"d"], type=pa.binary()), 2
-        )
-        .take(pa.array([0, None], type=pa.int32()))
-        .cast(pa.list_(child, 2))
-    )
-    target = pa.schema([pa.field("frames", pa.list_(lancedb.blob("frame"), 2))])
-
-    output = _cast_to_target_schema(
-        pa.table({"frames": source}).to_reader(), target
-    ).read_all()
-
-    frames = output["frames"].chunk(0)
-    assert frames.to_pylist()[1] is None
-    assert [b["data"] for b in frames.to_pylist()[0]] == [b"a", b"b"]
-
-
-def test_cast_to_target_schema_accepts_pylance_blob_v2():
-    target_type = lancedb.BlobType()
-    source = lance.blob_array([b"hello", None])
-    assert type(source.type) is LanceBlobType
-    assert type(source.type) is type(target_type)
-    data = pa.table({"image": source})
-    target = pa.schema([pa.field("image", target_type)])
-
-    output = _cast_to_target_schema(data.to_reader(), target).read_all()
-
-    image = output["image"].chunk(0)
-    assert type(image.type) is LanceBlobType
-    assert image.type == target_type
-    assert image.storage.to_pylist() == [
-        {"data": b"hello", "uri": None, "position": None, "size": None},
-        None,
-    ]
-
-
-def test_cast_to_target_schema_rejects_different_blob_v2_class():
-    class OtherBlobType(pa.ExtensionType):
-        def __init__(self):
-            super().__init__(lancedb.BlobType().storage_type, "lance.blob.v2")
-
-        def __arrow_ext_serialize__(self) -> bytes:
-            return b""
-
-        @classmethod
-        def __arrow_ext_deserialize__(
-            cls, storage_type: pa.DataType, serialized: bytes
-        ) -> "OtherBlobType":
-            return cls()
-
-    storage = lance.blob_array([b"hello"]).storage
-    source = pa.ExtensionArray.from_storage(OtherBlobType(), storage)
-    data = pa.table({"image": source})
-    target = pa.schema([lancedb.blob("image")])
-
-    with pytest.raises(pa.ArrowTypeError, match="different extension type"):
-        _cast_to_target_schema(data.to_reader(), target).read_all()
-
-
 def test_sanitize_data_stream():
     # Make sure we don't collect the whole stream when running sanitize_data
     schema = pa.schema({"a": pa.int32()})

python/src/expr.rs

@@ -130,14 +130,6 @@ impl PyExpr {
 
     // ── utilities ────────────────────────────────────────────────────────────
 
-    /// Return the referenced column name for a bare column expression.
-    fn column_name(&self) -> Option<String> {
-        match &self.0 {
-            DfExpr::Column(column) if column.relation.is_none() => Some(column.name.clone()),
-            _ => None,
-        }
-    }
-
     /// Render the expression as a SQL string (useful for debugging).
     fn to_sql(&self) -> PyResult<String> {
         lancedb::expr::expr_to_sql_string(&self.0).map_err(|e| PyValueError::new_err(e.to_string()))

python/src/query.rs

@@ -1,7 +1,6 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-use std::collections::HashMap;
 use std::sync::Arc;
 use std::time::Duration;
 
@@ -326,7 +325,6 @@ pub struct PyQueryRequest {
     pub filter: Option<PyQueryFilter>,
     pub full_text_search: Option<PyLanceDB<FtsQuery>>,
     pub select: PySelect,
-    pub select_source_columns: Option<HashMap<String, String>>,
     pub fast_search: Option<bool>,
     pub with_row_id: Option<bool>,
     pub use_lsm: Option<bool>,
@@ -357,7 +355,6 @@ impl From<AnyQuery> for PyQueryRequest {
                 full_text_search: query_request
                     .full_text_search
                     .map(|fts| PyLanceDB(fts.query)),
-                select_source_columns: PySelect::source_columns(&query_request.select),
                 select: PySelect(query_request.select),
                 fast_search: Some(query_request.fast_search),
                 with_row_id: Some(query_request.with_row_id),
@@ -383,7 +380,6 @@ impl From<AnyQuery> for PyQueryRequest {
                 offset: vector_query.base.offset,
                 filter: vector_query.base.filter.map(PyQueryFilter),
                 full_text_search: None,
-                select_source_columns: PySelect::source_columns(&vector_query.base.select),
                 select: PySelect(vector_query.base.select),
                 fast_search: Some(vector_query.base.fast_search),
                 with_row_id: Some(vector_query.base.with_row_id),
@@ -416,25 +412,6 @@ impl From<AnyQuery> for PyQueryRequest {
 #[derive(Clone)]
 pub struct PySelect(Select);
 
-impl PySelect {
-    fn source_columns(select: &Select) -> Option<HashMap<String, String>> {
-        match select {
-            Select::Expr(pairs) => Some(
-                pairs
-                    .iter()
-                    .filter_map(|(output, expr)| match expr {
-                        lancedb::expr::DfExpr::Column(column) if column.relation.is_none() => {
-                            Some((output.clone(), column.name.clone()))
-                        }
-                        _ => None,
-                    })
-                    .collect(),
-            ),
-            _ => None,
-        }
-    }
-}
-
 impl<'py> IntoPyObject<'py> for PySelect {
     type Target = PyAny;
     type Output = Bound<'py, Self::Target>;

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.38.0-beta.11"
+version = "0.38.0-beta.10"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true

rust/lancedb/src/database/listing.rs

@@ -13,7 +13,7 @@ use lance::dataset::{ReadParams, WriteMode, builder::DatasetBuilder};
 use lance::io::{ObjectStore, ObjectStoreParams, WrappingObjectStore};
 use lance_datafusion::utils::StreamingWriteSource;
 use lance_file::version::LanceFileVersion;
-use lance_io::object_store::{ReadDirOptions, StorageOptionsAccessor, StorageOptionsProvider};
+use lance_io::object_store::{StorageOptionsAccessor, StorageOptionsProvider};
 use lance_table::io::commit::commit_handler_from_url;
 use object_store::local::LocalFileSystem;
 use snafu::ResultExt;
@@ -281,22 +281,6 @@ impl std::fmt::Display for ListingDatabase {
 }
 
 const LANCE_EXTENSION: &str = "lance";
-
-/// The table a listed child of the database names, or `None` if the child is not a table.
-///
-/// A table is the directory `<name>.lance`; a loose file or any other directory under the
-/// database prefix belongs to something else. `dir_suffix` is `.lance`, built once by the
-/// caller rather than per child.
-/// The table a listed child directory holds, or `None` if it is not a table at all.
-///
-/// Only directories are considered, so a loose object named like a table is not one.
-fn table_name(location: &object_store::path::Path, dir_suffix: &str) -> Option<String> {
-    location
-        .filename()?
-        .strip_suffix(dir_suffix)
-        .map(String::from)
-        .filter(|name| !name.is_empty())
-}
 const ENGINE: &str = "engine";
 const MIRRORED_STORE: &str = "mirroredStore";
 
@@ -960,72 +944,51 @@ impl Database for ListingDatabase {
         Ok(f)
     }
 
-    /// List the tables in the database, a page at a time.
-    ///
-    /// The page_token is opaque, unlike the `start_after` parameter of [`Self::table_names()`].
-    ///
-    /// When there are no more results, the returned page_token will be None.
-    ///
-    /// `limit` is the maximum number of tables to return in the response. But it is possible
-    /// for the response to contain fewer than `limit` tables, even when there are more tables
-    /// to return. Clients should check the returned page_token to determine if there are
-    /// more results, rather than relying on the number of tables returned.
-    ///
-    /// The order that results are returned in not guaranteed to be stable across calls,
-    /// so clients should not rely on it.
     async fn list_tables(&self, request: ListTablesRequest) -> Result<ListTablesResponse> {
         if request.id.as_ref().map(|v| !v.is_empty()).unwrap_or(false) {
             return self.namespace_database().list_tables(request).await;
         }
-        let limit = request.limit.map(|limit| limit.max(0) as usize);
-        let dir_suffix = format!(".{LANCE_EXTENSION}");
-        let mut tables = Vec::new();
-        let mut page_token = request.page_token.filter(|token| !token.is_empty());
+        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();
 
-        // A page of nothing: the store rejects a limit of zero, and no table was handed over
-        // for a token to resume after.
-        if limit == Some(0) {
-            return Ok(ListTablesResponse {
-                context: None,
-                tables,
-                page_token: None,
-            });
+        // Handle pagination with page_token
+        if let Some(ref page_token) = request.page_token {
+            let index = f
+                .iter()
+                .position(|name| name.as_str() > page_token.as_str())
+                .unwrap_or(f.len());
+            f.drain(0..index);
         }
 
-        loop {
-            // Ask only for what the page still has room for, so a database holding more
-            // than one page costs one request per page rather than one per table.
-            let listing = self
-                .object_store
-                .read_dir_page(
-                    self.base_path.clone(),
-                    ReadDirOptions {
-                        page_token: page_token.take(),
-                        limit: limit.map(|limit| limit - tables.len()),
-                    },
-                )
-                .await?;
-            page_token = listing.page_token;
-            // Only child directories can be tables, and the store already separates them
-            // out, so the objects in the page are not looked at.
-            tables.extend(
-                listing
-                    .result
-                    .common_prefixes
-                    .iter()
-                    .filter_map(|location| table_name(location, &dir_suffix)),
-            );
-            // Children that are not tables leave the page short of the limit, so keep
-            // going until the page is full or the database runs out.
-            if page_token.is_none() || limit.is_none_or(|limit| tables.len() >= limit) {
-                break;
+        // Determine if there's a next page. The token is the last name of this page,
+        // not the first of the next one: the next page resumes strictly after the
+        // token, so naming the next page's first entry would skip it.
+        let next_page_token = match request.limit {
+            Some(limit) if f.len() > limit as usize => {
+                f.truncate(limit as usize);
+                f.last().cloned()
             }
-        }
+            _ => None,
+        };
 
         Ok(ListTablesResponse {
             context: None,
-            tables,
-            page_token,
+            tables: f,
+            page_token: next_page_token,
         })
     }
 
@@ -1521,182 +1484,6 @@ mod tests {
     use tokio::sync::Barrier;
     use tokio::time::timeout;
 
-    async fn create_tables(db: &ListingDatabase, names: &[&str]) {
-        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
-        for name in names {
-            db.create_table(CreateTableRequest {
-                name: name.to_string(),
-                namespace_path: vec![],
-                data: Box::new(RecordBatch::new_empty(schema.clone())) as Box<dyn Scannable>,
-                mode: CreateTableMode::Create,
-                write_options: Default::default(),
-                location: None,
-                namespace_client: None,
-            })
-            .await
-            .unwrap();
-        }
-    }
-
-    /// Every table in the database, taken `limit` at a time, which is how a caller walks a
-    /// listing: the token ends the walk, never a short page.
-    async fn walk(db: &ListingDatabase, limit: Option<i32>) -> Vec<String> {
-        let mut seen = Vec::new();
-        let mut page_token = None;
-        loop {
-            let page = db
-                .list_tables(ListTablesRequest {
-                    limit,
-                    page_token,
-                    ..Default::default()
-                })
-                .await
-                .unwrap();
-            seen.extend(page.tables);
-            page_token = page.page_token;
-            if page_token.is_none() {
-                return seen;
-            }
-            assert!(
-                seen.len() < 100,
-                "the walk is serving tables more than once"
-            );
-        }
-    }
-
-    /// Paging with the returned token has to visit every table exactly once, whatever the
-    /// page size, with nothing lost or repeated at a boundary.
-    #[rstest::rstest]
-    #[tokio::test]
-    async fn test_list_tables_pages_over_every_table_once(#[values(1, 2, 3, 5, 10)] limit: i32) {
-        let (_tempdir, db) = setup_database().await;
-        create_tables(&db, &["a", "b", "c", "d", "e"]).await;
-
-        assert_eq!(walk(&db, Some(limit)).await, vec!["a", "b", "c", "d", "e"]);
-    }
-
-    /// The token is opaque: it is whatever resumes the store the database sits on, not a
-    /// table name. Callers hand it back and nothing else.
-    ///
-    /// Nothing validates a token, so one invented by a caller is read as a position rather
-    /// than refused — which is why the token has to come back from a previous page.
-    #[tokio::test]
-    async fn test_the_page_token_is_not_a_table_name() {
-        let (_tempdir, db) = setup_database().await;
-        create_tables(&db, &["a", "b", "c"]).await;
-
-        let page = db
-            .list_tables(ListTablesRequest {
-                limit: Some(1),
-                ..Default::default()
-            })
-            .await
-            .unwrap();
-
-        assert_eq!(page.tables, vec!["a"]);
-        let token = page.page_token.expect("two tables are still to come");
-        assert_ne!(token, "a");
-
-        // Handing it back is the only thing a caller does with it, and it resumes.
-        let rest = db
-            .list_tables(ListTablesRequest {
-                page_token: Some(token),
-                ..Default::default()
-            })
-            .await
-            .unwrap();
-        assert_eq!(rest.tables, vec!["b", "c"]);
-    }
-
-    /// A limit the listing does not fill leaves no token behind, so a caller paging by token
-    /// stops without asking for an empty page.
-    #[tokio::test]
-    async fn test_a_listing_that_runs_out_has_no_token() {
-        let (_tempdir, db) = setup_database().await;
-        create_tables(&db, &["a", "b"]).await;
-
-        let page = db
-            .list_tables(ListTablesRequest {
-                limit: Some(10),
-                ..Default::default()
-            })
-            .await
-            .unwrap();
-
-        assert_eq!(page.tables, vec!["a", "b"]);
-        assert_eq!(page.page_token, None);
-    }
-
-    /// An empty page token means "from the start", which is how a client looping on a token
-    /// spells its first request.
-    #[tokio::test]
-    async fn test_an_empty_page_token_lists_from_the_start() {
-        let (_tempdir, db) = setup_database().await;
-        create_tables(&db, &["a", "b"]).await;
-
-        let page = db
-            .list_tables(ListTablesRequest {
-                page_token: Some(String::new()),
-                ..Default::default()
-            })
-            .await
-            .unwrap();
-
-        assert_eq!(page.tables, vec!["a", "b"]);
-    }
-
-    /// Listing follows the order the object store lists directories in, so a name that
-    /// extends another comes first: the `-` of `users-archive.lance` sorts below the `.` of
-    /// `users.lance`. Pagination pushes its cursor into the list request, so it cannot report
-    /// an order other than the one it resumes in.
-    #[tokio::test]
-    async fn test_listing_order_follows_the_store_not_the_table_name() {
-        let (_tempdir, db) = setup_database().await;
-        create_tables(&db, &["users", "users-archive", "users.old"]).await;
-
-        assert_eq!(
-            walk(&db, None).await,
-            vec!["users-archive", "users", "users.old"]
-        );
-        // And paging reports the same order, so a walk sees each table once.
-        assert_eq!(
-            walk(&db, Some(1)).await,
-            vec!["users-archive", "users", "users.old"]
-        );
-    }
-
-    /// Only directories named `<name>.lance` are tables; loose files and other directories
-    /// under the database prefix are not. A page spent on them is filled from the next one,
-    /// so a page holding only non-tables does not read as an empty database.
-    #[tokio::test]
-    async fn test_listing_ignores_non_table_children() {
-        let (tempdir, db) = setup_database().await;
-        create_tables(&db, &["real"]).await;
-        std::fs::write(tempdir.path().join("aaa-loose.lance"), b"not a table").unwrap();
-        create_dir_all(tempdir.path().join("aaa-scratch")).unwrap();
-
-        let page = db
-            .list_tables(ListTablesRequest {
-                limit: Some(1),
-                ..Default::default()
-            })
-            .await
-            .unwrap();
-
-        assert_eq!(page.tables, vec!["real"]);
-    }
-
-    #[tokio::test]
-    async fn listing_ignores_empty_table_name() {
-        let (tempdir, db) = setup_database().await;
-        create_dir_all(tempdir.path().join(".lance")).unwrap();
-        let page = db.list_tables(ListTablesRequest::default()).await.unwrap();
-        assert!(
-            page.tables.is_empty(),
-            "invalid empty table name was listed"
-        );
-    }
-
     async fn setup_database() -> (tempfile::TempDir, ListingDatabase) {
         let tempdir = tempdir().unwrap();
         let uri = tempdir.path().to_str().unwrap();

rust/lancedb/src/expr.rs

@@ -157,7 +157,7 @@ mod tests {
         use datafusion_common::ScalarValue;
         let expr = col("data").eq(lit(ScalarValue::Binary(Some(vec![0xca, 0xfe]))));
         let sql = expr_to_sql_string(&expr).unwrap();
-        assert_eq!(sql, "(`data` = X'CAFE')");
+        assert_eq!(sql, "(data = X'CAFE')");
     }
 
     #[test]
@@ -167,7 +167,7 @@ mod tests {
         let int_expr = col("id").gt(lit(5i64));
         let combined = bin_expr.and(int_expr);
         let sql = expr_to_sql_string(&combined).unwrap();
-        assert_eq!(sql, "((`data` = X'01') AND (id > 5))");
+        assert_eq!(sql, "((data = X'01') AND (id > 5))");
     }
 
     #[test]
@@ -185,7 +185,7 @@ mod tests {
         // serialized correctly (regression test for placeholder rewrite path).
         let expr = contains(col("data"), lit(ScalarValue::Binary(Some(vec![0xff]))));
         let sql = expr_to_sql_string(&expr).unwrap();
-        assert_eq!(sql, "contains(`data`, X'FF')");
+        assert_eq!(sql, "contains(data, X'FF')");
     }
 
     #[test]
@@ -196,7 +196,7 @@ mod tests {
             .eq(lit(ScalarValue::Binary(Some(vec![0xab, 0xcd]))))
             .not();
         let sql = expr_to_sql_string(&expr).unwrap();
-        assert_eq!(sql, "NOT (`data` = X'ABCD')");
+        assert_eq!(sql, "NOT (data = X'ABCD')");
     }
 
     #[test]
@@ -206,122 +206,6 @@ mod tests {
         assert!(sql.contains("IN"), "expected IN in: {}", sql);
     }
 
-    #[test]
-    fn test_empty_is_in() {
-        let expr = is_in(col("id"), vec![]);
-        assert_eq!(expr_to_sql_string(&expr).unwrap(), "false");
-    }
-
-    #[test]
-    fn test_empty_is_in_discards_binary_children() {
-        use datafusion_common::ScalarValue;
-
-        let expr = is_in(
-            col("payload").eq(lit(ScalarValue::Binary(Some(vec![0x01])))),
-            vec![],
-        );
-        assert_eq!(expr_to_sql_string(&expr).unwrap(), "false");
-    }
-
-    #[test]
-    fn test_keyword_identifier() {
-        let expr = col("null").eq(lit(1i64));
-        assert_eq!(expr_to_sql_string(&expr).unwrap(), "(`null` = 1)");
-    }
-
-    #[test]
-    fn test_decimal_literal_preserves_type() {
-        use datafusion_common::ScalarValue;
-
-        let expr = col("val").lt(lit(ScalarValue::Decimal128(
-            Some(1_234_567_890_123_456_790),
-            19,
-            18,
-        )));
-        let sql = expr_to_sql_string(&expr).unwrap();
-        assert_eq!(
-            sql,
-            "(val < arrow_cast('1.234567890123456790', 'Decimal128(19, 18)'))"
-        );
-    }
-
-    #[test]
-    fn test_non_finite_float_literal_preserves_type() {
-        let expr = col("x").lt(lit(f64::INFINITY));
-        assert_eq!(
-            expr_to_sql_string(&expr).unwrap(),
-            "(x < arrow_cast('inf', 'Float64'))"
-        );
-    }
-
-    #[test]
-    fn test_cast_uses_arrow_type_name() {
-        let string = expr_cast(col("x"), DataType::Utf8);
-        assert_eq!(
-            expr_to_sql_string(&string).unwrap(),
-            "arrow_cast(x, 'Utf8')"
-        );
-
-        let int32 = expr_cast(col("x"), DataType::Int32);
-        assert_eq!(
-            expr_to_sql_string(&int32).unwrap(),
-            "arrow_cast(x, 'Int32')"
-        );
-
-        let expr = expr_cast(col("x"), DataType::Float16).lt(lit(2.0));
-        assert_eq!(
-            expr_to_sql_string(&expr).unwrap(),
-            "(arrow_cast(x, 'Float16') < 2.0)"
-        );
-
-        let decimal = expr_cast(lit("2.00"), DataType::Decimal256(40, 2));
-        assert_eq!(
-            expr_to_sql_string(&decimal).unwrap(),
-            "arrow_cast('2.00', 'Decimal256(40, 2)')"
-        );
-    }
-
-    #[test]
-    fn test_binary_placeholder_does_not_rewrite_user_string() {
-        use datafusion_common::ScalarValue;
-
-        let marker = "__lancedb_binary_placeholder_0__";
-        let expr = col("payload")
-            .eq(lit(ScalarValue::Binary(Some(vec![0x01]))))
-            .or(col("text").eq(lit(marker)));
-        assert_eq!(
-            expr_to_sql_string(&expr).unwrap(),
-            "((payload = X'01') OR (`text` = '__lancedb_binary_placeholder_0__'))"
-        );
-    }
-
-    #[test]
-    fn test_binary_binding_skips_quoted_identifiers() {
-        use datafusion_common::ScalarValue;
-
-        let expr = col("payload")
-            .eq(lit(ScalarValue::Binary(Some(vec![0x01]))))
-            .and(col("odd'name").eq(lit(1i64)))
-            .and(col("odd`'name").eq(lit(2i64)));
-        assert_eq!(
-            expr_to_sql_string(&expr).unwrap(),
-            "(((payload = X'01') AND (`odd'name` = 1)) AND (`odd``'name` = 2))"
-        );
-    }
-
-    #[test]
-    fn test_binary_placeholder_collision_search_is_linear() {
-        use datafusion_common::ScalarValue;
-
-        let collision_shaped = format!("__lancedb_binary_placeholder_0__{}", "_".repeat(64_000));
-        let expr = col("payload")
-            .eq(lit(ScalarValue::Binary(Some(vec![0x01]))))
-            .and(col("text").eq(lit(collision_shaped.clone())));
-        let sql = expr_to_sql_string(&expr).unwrap();
-        assert!(sql.contains("X'01'"));
-        assert!(sql.contains(&format!("'{collision_shaped}'")));
-    }
-
     #[test]
     fn test_multiple_binary_literals() {
         use datafusion_common::ScalarValue;

rust/lancedb/src/expr/sql.rs

@@ -1,24 +1,13 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-use std::{
-    any::TypeId,
-    collections::{HashMap, HashSet},
-};
+use std::any::TypeId;
 
-use arrow_array::types::{
-    Decimal32Type, Decimal64Type, Decimal128Type, Decimal256Type, DecimalType,
-};
-use arrow_schema::DataType;
 use datafusion_common::ScalarValue;
 use datafusion_common::tree_node::{Transformed, TreeNode, TreeNodeRecursion};
 use datafusion_expr::Expr;
-use datafusion_functions::core::expr_fn::{
-    arrow_cast as datafusion_arrow_cast, arrow_try_cast as datafusion_arrow_try_cast,
-};
 use datafusion_sql::sqlparser::{
     dialect::{Dialect as SqlParserDialect, GenericDialect},
-    keywords::ALL_KEYWORDS,
     tokenizer::{Token, Tokenizer},
 };
 use datafusion_sql::unparser::{self, dialect::Dialect as UnparserDialect};
@@ -38,13 +27,11 @@ struct LanceSqlDialect;
 
 impl UnparserDialect for LanceSqlDialect {
     fn identifier_quote_style(&self, identifier: &str) -> Option<char> {
-        let identifier_upper = identifier.to_ascii_uppercase();
-        let needs_quote =
-            (identifier_upper != "ID" && ALL_KEYWORDS.contains(&identifier_upper.as_str()))
-                || identifier.chars().any(|c| c.is_ascii_uppercase())
-                || !identifier.chars().enumerate().all(|(i, c)| {
-                    c == '_' || c.is_ascii_alphabetic() || (i > 0 && c.is_ascii_digit())
-                });
+        let needs_quote = identifier.chars().any(|c| c.is_ascii_uppercase())
+            || !identifier
+                .chars()
+                .enumerate()
+                .all(|(i, c)| c == '_' || c.is_ascii_alphabetic() || (i > 0 && c.is_ascii_digit()));
         if needs_quote { Some('`') } else { None }
     }
 }
@@ -113,128 +100,24 @@ fn bytes_to_hex_sql(bytes: &[u8]) -> String {
     format!("X'{hex}'")
 }
 
-fn string_literals(expr: &Expr) -> HashSet<String> {
-    let mut literals = HashSet::new();
+/// Returns true if *expr* contains a `Binary` or `LargeBinary` scalar literal
+/// anywhere in its subtree.  DataFusion's SQL unparser cannot serialize those
+/// variants, so we route such expressions through a placeholder-substitution
+/// path that emits SQL `X'...'` byte-string literals.
+fn has_binary_literal(expr: &Expr) -> bool {
+    let mut found = false;
     let _ = expr.apply(&mut |e: &Expr| {
-        if let Expr::Literal(
-            ScalarValue::Utf8(Some(value))
-            | ScalarValue::LargeUtf8(Some(value))
-            | ScalarValue::Utf8View(Some(value)),
-            _,
-        ) = e
-        {
-            literals.insert(value.clone());
-        }
-        Ok(TreeNodeRecursion::Continue)
-    });
-    literals
-}
-
-fn typed_string_literal(value: String, data_type: DataType) -> Expr {
-    datafusion_arrow_cast(
-        Expr::Literal(ScalarValue::Utf8(Some(value)), None),
-        Expr::Literal(ScalarValue::Utf8(Some(data_type.to_string())), None),
-    )
-}
-
-fn next_binary_placeholder(user_strings: &HashSet<String>, next_id: &mut usize) -> String {
-    loop {
-        let placeholder = format!("{BINARY_PLACEHOLDER_PREFIX}{}__", *next_id);
-        *next_id += 1;
-        if !user_strings.contains(&placeholder) {
-            return placeholder;
-        }
-    }
-}
-
-fn bind_binary_literals(
-    sql: &str,
-    mut bindings: HashMap<String, Vec<u8>>,
-) -> crate::Result<String> {
-    let bytes = sql.as_bytes();
-    let mut output = Vec::with_capacity(bytes.len());
-    let mut index = 0;
-
-    // Walk SQL string tokens once. Placeholders are plain, unescaped string
-    // literals, so this remains linear even when user strings are large or
-    // deliberately resemble the placeholder prefix.
-    while index < bytes.len() {
-        if bytes[index] == b'`' {
-            let identifier_start = index;
-            index += 1;
-            let mut identifier_end = None;
-            while index < bytes.len() {
-                if bytes[index] == b'`' {
-                    if index + 1 < bytes.len() && bytes[index + 1] == b'`' {
-                        index += 2;
-                    } else {
-                        index += 1;
-                        identifier_end = Some(index);
-                        break;
-                    }
-                } else {
-                    index += 1;
-                }
-            }
-
-            let Some(identifier_end) = identifier_end else {
-                return Err(crate::Error::InvalidInput {
-                    message: "unterminated identifier while binding binary literal".to_string(),
-                });
-            };
-            output.extend_from_slice(&bytes[identifier_start..identifier_end]);
-            continue;
-        }
-
-        if bytes[index] != b'\'' {
-            output.push(bytes[index]);
-            index += 1;
-            continue;
-        }
-
-        let literal_start = index;
-        index += 1;
-        let content_start = index;
-        let mut escaped = false;
-        let mut content_end = None;
-        while index < bytes.len() {
-            if bytes[index] == b'\'' {
-                if index + 1 < bytes.len() && bytes[index + 1] == b'\'' {
-                    escaped = true;
-                    index += 2;
-                } else {
-                    content_end = Some(index);
-                    index += 1;
-                    break;
-                }
-            } else {
-                index += 1;
-            }
-        }
-
-        let Some(content_end) = content_end else {
-            return Err(crate::Error::InvalidInput {
-                message: "unterminated string while binding binary literal".to_string(),
-            });
-        };
-
-        let placeholder = &sql[content_start..content_end];
-        if !escaped && let Some(value) = bindings.remove(placeholder) {
-            output.extend_from_slice(bytes_to_hex_sql(&value).as_bytes());
+        if matches!(
+            e,
+            Expr::Literal(ScalarValue::Binary(_) | ScalarValue::LargeBinary(_), _)
+        ) {
+            found = true;
+            Ok(TreeNodeRecursion::Stop)
         } else {
-            output.extend_from_slice(&bytes[literal_start..index]);
+            Ok(TreeNodeRecursion::Continue)
         }
-    }
-
-    if !bindings.is_empty() {
-        return Err(crate::Error::InvalidInput {
-            message: "failed to bind binary literal while serializing expression".to_string(),
-        });
-    }
-
-    String::from_utf8(output).map_err(|e| crate::Error::InvalidInput {
-        message: format!("failed to bind binary literal: {e}"),
-    })
+    });
+    found
 }
 
 fn run_unparser(expr: &Expr) -> crate::Result<String> {
@@ -247,37 +130,25 @@ fn run_unparser(expr: &Expr) -> crate::Result<String> {
 }
 
 pub fn expr_to_sql_string(expr: &Expr) -> crate::Result<String> {
-    // DataFusion's unparser needs a few adaptations before its SQL can be
-    // reparsed by Lance without changing the typed expression's semantics:
-    //
-    // * decimal literals need an explicit cast to preserve precision and scale;
-    // * casts need exact Arrow type names rather than SQL type aliases;
-    // * an empty IN list is valid in DataFusion but invalid SQL;
-    // * binary literals are unsupported by the unparser and need placeholders.
-    // Eliminate empty membership expressions before visiting their children.
-    // Otherwise a discarded binary child could leave behind a stale binding.
+    // Fast path: no binary literals — DataFusion's unparser handles everything.
+    if !has_binary_literal(expr) {
+        return run_unparser(expr);
+    }
+
+    // Slow path: DataFusion's unparser cannot serialize `Binary`/`LargeBinary`
+    // scalars, so we rewrite each one to a unique string-literal placeholder,
+    // let the unparser do the rest of the work, then substitute the SQL
+    // `X'...'` byte-string literal back in.  This keeps the operator/function
+    // serialization logic centralized in DataFusion and works for every
+    // expression node type the unparser supports.
+    let mut bindings: Vec<Vec<u8>> = Vec::new();
     let rewritten = expr
         .clone()
-        .transform(|e: Expr| match e {
-            Expr::InList(in_list) if in_list.list.is_empty() => Ok(Transformed::yes(
-                Expr::Literal(ScalarValue::Boolean(Some(in_list.negated)), None),
-            )),
-            other => Ok(Transformed::no(other)),
-        })
-        .map_err(|e| crate::Error::InvalidInput {
-            message: format!("failed to rewrite expression: {e}"),
-        })?
-        .data;
-
-    let user_strings = string_literals(&rewritten);
-    let mut next_placeholder_id = 0;
-    let mut binary_bindings = HashMap::new();
-    let rewritten = rewritten
         .transform(|e: Expr| match e {
             Expr::Literal(ScalarValue::Binary(Some(bytes)), m)
             | Expr::Literal(ScalarValue::LargeBinary(Some(bytes)), m) => {
-                let placeholder = next_binary_placeholder(&user_strings, &mut next_placeholder_id);
-                binary_bindings.insert(placeholder.clone(), bytes);
+                let placeholder = format!("{}{}__", BINARY_PLACEHOLDER_PREFIX, bindings.len());
+                bindings.push(bytes);
                 Ok(Transformed::yes(Expr::Literal(
                     ScalarValue::Utf8(Some(placeholder)),
                     m,
@@ -287,57 +158,6 @@ pub fn expr_to_sql_string(expr: &Expr) -> crate::Result<String> {
             | Expr::Literal(ScalarValue::LargeBinary(None), m) => {
                 Ok(Transformed::yes(Expr::Literal(ScalarValue::Null, m)))
             }
-            Expr::Literal(ScalarValue::Decimal32(Some(value), precision, scale), _m) => {
-                let value = Decimal32Type::format_decimal(value, precision, scale);
-                Ok(Transformed::yes(typed_string_literal(
-                    value,
-                    DataType::Decimal32(precision, scale),
-                )))
-            }
-            Expr::Literal(ScalarValue::Decimal64(Some(value), precision, scale), _m) => {
-                let value = Decimal64Type::format_decimal(value, precision, scale);
-                Ok(Transformed::yes(typed_string_literal(
-                    value,
-                    DataType::Decimal64(precision, scale),
-                )))
-            }
-            Expr::Literal(ScalarValue::Decimal128(Some(value), precision, scale), _m) => {
-                let value = Decimal128Type::format_decimal(value, precision, scale);
-                Ok(Transformed::yes(typed_string_literal(
-                    value,
-                    DataType::Decimal128(precision, scale),
-                )))
-            }
-            Expr::Literal(ScalarValue::Decimal256(Some(value), precision, scale), _m) => {
-                let value = Decimal256Type::format_decimal(value, precision, scale);
-                Ok(Transformed::yes(typed_string_literal(
-                    value,
-                    DataType::Decimal256(precision, scale),
-                )))
-            }
-            Expr::Literal(ScalarValue::Float16(Some(value)), _m) if !value.is_finite() => Ok(
-                Transformed::yes(typed_string_literal(value.to_string(), DataType::Float16)),
-            ),
-            Expr::Literal(ScalarValue::Float32(Some(value)), _m) if !value.is_finite() => Ok(
-                Transformed::yes(typed_string_literal(value.to_string(), DataType::Float32)),
-            ),
-            Expr::Literal(ScalarValue::Float64(Some(value)), _m) if !value.is_finite() => Ok(
-                Transformed::yes(typed_string_literal(value.to_string(), DataType::Float64)),
-            ),
-            Expr::Cast(cast) => Ok(Transformed::yes(datafusion_arrow_cast(
-                *cast.expr,
-                Expr::Literal(
-                    ScalarValue::Utf8(Some(cast.field.data_type().to_string())),
-                    None,
-                ),
-            ))),
-            Expr::TryCast(cast) => Ok(Transformed::yes(datafusion_arrow_try_cast(
-                *cast.expr,
-                Expr::Literal(
-                    ScalarValue::Utf8(Some(cast.field.data_type().to_string())),
-                    None,
-                ),
-            ))),
             other => Ok(Transformed::no(other)),
         })
         .map_err(|e| crate::Error::InvalidInput {
@@ -345,12 +165,14 @@ pub fn expr_to_sql_string(expr: &Expr) -> crate::Result<String> {
         })?
         .data;
 
-    let sql = run_unparser(&rewritten)?;
-    if binary_bindings.is_empty() {
-        Ok(sql)
-    } else {
-        bind_binary_literals(&sql, binary_bindings)
+    let mut sql = run_unparser(&rewritten)?;
+    for (i, bytes) in bindings.iter().enumerate() {
+        // The unparser quotes string literals with single quotes, so the
+        // placeholder appears as `'__lancedb_binary_placeholder_<i>__'`.
+        let quoted = format!("'{}{}__'", BINARY_PLACEHOLDER_PREFIX, i);
+        sql = sql.replace(&quoted, &bytes_to_hex_sql(bytes));
     }
+    Ok(sql)
 }
 
 #[cfg(test)]

rust/lancedb/src/table/computed_columns.rs

@@ -22,7 +22,7 @@
 use std::collections::{BTreeSet, HashMap};
 use std::sync::Arc;
 
-use arrow_schema::{DataType, Field as ArrowField, Fields, Schema as ArrowSchema, SchemaRef};
+use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema, SchemaRef};
 use datafusion_common::tree_node::TreeNode;
 use datafusion_physical_plan::PhysicalExpr;
 use lance::dataset::NewColumnTransform;
@@ -1273,11 +1273,6 @@ pub(crate) fn bind(schema: SchemaRef, column: &str, expression: &str) -> Result<
 /// refresh time: that the expression parses, that every column it reads
 /// exists, and that the target name is free. A declaration that survives this
 /// is one a refresh can always act on.
-///
-/// Each accepted column joins the schema the next one resolves against, so a
-/// batch may declare `a` and then `b = a + 1` in one commit. Refresh order
-/// then matters, and refresh enforces it: `b` is refused while `a` still has
-/// unfilled rows.
 pub(crate) fn plan(schema: SchemaRef, columns: &[(String, String)]) -> Result<Vec<ArrowField>> {
     if columns.is_empty() {
         return Err(Error::InvalidInput {
@@ -1285,11 +1280,11 @@ pub(crate) fn plan(schema: SchemaRef, columns: &[(String, String)]) -> Result<Ve
         });
     }
 
-    let mut schema = schema;
     let mut fields = Vec::with_capacity(columns.len());
+    let mut declared: Vec<&str> = Vec::with_capacity(columns.len());
 
     for (name, expression) in columns {
-        if schema.field_with_name(name).is_ok() {
+        if schema.field_with_name(name).is_ok() || declared.contains(&name.as_str()) {
             return Err(Error::ColumnAlreadyExists { name: name.clone() });
         }
 
@@ -1297,50 +1292,16 @@ pub(crate) fn plan(schema: SchemaRef, columns: &[(String, String)]) -> Result<Ve
 
         // Declared columns start entirely null, so nullability is a property
         // of the declaration rather than of what the expression yields.
-        let field = ArrowField::new(name, bound.data_type, true)
-            .with_metadata(computed_column_metadata(expression, &bound.inputs));
-        schema = Arc::new(ArrowSchema::new_with_metadata(
-            schema
-                .fields()
-                .iter()
-                .cloned()
-                .chain(std::iter::once(Arc::new(field.clone())))
-                .collect::<Fields>(),
-            schema.metadata().clone(),
-        ));
-        fields.push(field);
+        fields.push(
+            ArrowField::new(name, bound.data_type, true)
+                .with_metadata(computed_column_metadata(expression, &bound.inputs)),
+        );
+        declared.push(name);
     }
 
     Ok(fields)
 }
 
-/// Run the schema-level checks of
-/// [`AddColumnsBuilder::computed`](super::AddColumnsBuilder::computed) against
-/// `schema` without committing: the Function-binding guard and the planning of
-/// every declaration. For callers that stage declarations behind other work
-/// and need those rejections before any of it lands.
-///
-/// Only the schema is consulted. Declaring also refuses a table with an LSM
-/// write spec or retained SSTables; that is table state, checked at commit.
-///
-/// ```
-/// # use std::sync::Arc;
-/// # use arrow_schema::{DataType, Field, Schema};
-/// use lancedb::table::computed_columns::validate_declarations;
-///
-/// let schema = Arc::new(Schema::new(vec![Field::new("x", DataType::Int32, false)]));
-/// let declarations = vec![
-///     ("a".to_string(), "x + 1".to_string()),
-///     ("b".to_string(), "a * 2".to_string()),
-/// ];
-/// assert!(validate_declarations(schema.clone(), &declarations).is_ok());
-/// assert!(validate_declarations(schema, &[("c".into(), "random()".into())]).is_err());
-/// ```
-pub fn validate_declarations(schema: SchemaRef, columns: &[(String, String)]) -> Result<()> {
-    ensure_no_function_bindings_for_mutation(schema.as_ref(), "schema evolution")?;
-    plan(schema, columns).map(drop)
-}
-
 /// Build the transform that declares `columns` against `schema`.
 ///
 /// An all-null column is how a binding with no values yet is carried into a
@@ -1379,22 +1340,6 @@ pub(super) async fn add_foreign_kind(table: &crate::Table, name: &str, kind: &st
 
 #[cfg(test)]
 mod tests {
-    /// The gate's reproducer: the validator applies the same schema-level
-    /// guard declaring does, so a staging caller is refused before it commits
-    /// anything else.
-    #[test]
-    fn test_validate_declarations_matches_schema_admission_barriers() {
-        let schema = Arc::new(ArrowSchema::new_with_metadata(
-            vec![ArrowField::new("x", DataType::Int32, true)],
-            HashMap::from([(
-                FUNCTION_BINDINGS_META_KEY.to_string(),
-                "not valid binding metadata".to_string(),
-            )]),
-        ));
-        let declarations = vec![("a".to_string(), "x + 1".to_string())];
-        assert!(super::validate_declarations(schema, &declarations).is_err());
-    }
-
     #[test]
     fn output_arrow_type_grammar_matches_the_shared_golden() {
         let golden: serde_json::Value = serde_json::from_str(include_str!(
@@ -1637,40 +1582,6 @@ mod tests {
         assert!(declared(&table).await.is_empty());
     }
 
-    /// A batch may build on itself: one commit, and the later entry's inputs
-    /// name the earlier one.
-    #[tokio::test]
-    async fn test_a_declaration_may_read_one_declared_before_it() {
-        let table = table_with_ints("chain").await;
-        let before = table.version().await.unwrap();
-        add_computed(
-            &table,
-            &[("a".into(), "x + 1".into()), ("b".into(), "a * 2".into())],
-        )
-        .await
-        .unwrap();
-        assert_eq!(table.version().await.unwrap(), before + 1);
-        let declared = declared(&table).await;
-        assert_eq!(declared[1].name, "b");
-        assert_eq!(declared[1].inputs, vec!["a".to_string()]);
-
-        // Order is the dependency order; reading ahead is still unknown.
-        let err = add_computed(
-            &table,
-            &[("c".into(), "d + 1".into()), ("d".into(), "x + 1".into())],
-        )
-        .await
-        .unwrap_err();
-        assert!(matches!(err, Error::InvalidExpression { column, .. } if column == "c"));
-        assert!(
-            validate_declarations(
-                table.schema().await.unwrap(),
-                &[("e".into(), "random()".into())]
-            )
-            .is_err()
-        );
-    }
-
     /// A column added by an ordinary transform is materialized, not bound, so
     /// it carries no declaration to report.
     #[tokio::test]

rust/lancedb/src/table/datafusion/blob_coerce.rs

@@ -36,14 +36,6 @@ pub(super) fn coerce_blob_expr(
     };
 
     let input_shape = match input_field.data_type() {
-        DataType::Null => {
-            let expr: Arc<dyn PhysicalExpr> = Arc::new(CastExpr::new(
-                input_expr,
-                table_field.data_type().clone(),
-                None,
-            ));
-            return Ok((expr, table_field.clone()));
-        }
         DataType::Binary | DataType::LargeBinary | DataType::BinaryView => BlobInputShape::Bytes,
         DataType::Utf8 | DataType::LargeUtf8 | DataType::Utf8View => BlobInputShape::String,
         DataType::Struct(children) => {
@@ -163,7 +155,7 @@ mod tests {
     use crate::blob::blob;
     use arrow_array::{
         Array, ArrayRef, BinaryArray, BinaryViewArray, Int32Array, Int64Array, LargeBinaryArray,
-        NullArray, RecordBatch, StringArray, StringViewArray, StructArray, UInt8Array, UInt64Array,
+        RecordBatch, StringArray, StringViewArray, StructArray, UInt8Array, UInt64Array,
     };
     use arrow_schema::Schema;
     use datafusion::prelude::SessionContext;
@@ -287,18 +279,6 @@ mod tests {
         assert_eq!(data.value(0), b"view");
     }
 
-    #[tokio::test]
-    async fn null_column_coerces_to_all_null_blob_struct() {
-        let batch = batch_with_image(
-            Field::new("image", DataType::Null, true),
-            Arc::new(NullArray::new(2)),
-        );
-        let coerced = coerce(batch, &blob_table_schema()).await;
-        let image = image_struct(&coerced);
-        assert!(image.is_null(0));
-        assert!(image.is_null(1));
-    }
-
     #[tokio::test]
     async fn binary_nulls_stay_null_after_coercion() {
         let batch = batch_with_image(

rust/lancedb/src/table/query.rs

@@ -17,7 +17,6 @@ use arrow::array::{AsArray, FixedSizeListBuilder, Float32Builder};
 use arrow::datatypes::{Float32Type, UInt8Type};
 use arrow_array::Array;
 use arrow_schema::{DataType, Schema};
-use datafusion_common::{Column, DataFusionError, SchemaError};
 use datafusion_physical_plan::ExecutionPlan;
 use datafusion_physical_plan::projection::ProjectionExec;
 use datafusion_physical_plan::repartition::RepartitionExec;
@@ -192,7 +191,7 @@ pub async fn create_plan(
     if query.query_vector.len() > 1 {
         if column.is_none() {
             // Infer a vector column with the same dimension of the query vector.
-            let arrow_schema = Schema::from(schema);
+            let arrow_schema = Schema::from(ds_ref.schema());
             column = Some(default_vector_column(
                 &arrow_schema,
                 Some(query.query_vector[0].len() as i32),
@@ -269,7 +268,7 @@ pub async fn create_plan(
         let column = if let Some(col) = column {
             col
         } else {
-            let arrow_schema = Schema::from(schema);
+            let arrow_schema = Schema::from(ds_ref.schema());
             default_vector_column(&arrow_schema, Some(query_vector.len() as i32))?
         };
 
@@ -375,97 +374,7 @@ pub async fn create_plan(
         scanner.order_by(Some(order_by.clone()))?;
     }
 
-    scanner
-        .create_plan()
-        .await
-        .map_err(|error| enrich_lance_field_not_found(error, schema))
-}
-
-/// Replace DataFusion's top-level field candidates with qualified leaf paths.
-///
-/// DataFusion resolves nested fields but its `FieldNotFound` error only lists the
-/// top-level Arrow fields. This makes a missing leaf look unavailable even when it
-/// exists below a struct. Keep every other Lance/DataFusion error unchanged and
-/// enrich only this one schema error at the LanceDB query boundary.
-fn enrich_lance_field_not_found(
-    error: lance::Error,
-    schema: &lance_core::datatypes::Schema,
-) -> Error {
-    let Some(field) = find_missing_field(&error) else {
-        return error.into();
-    };
-    field_not_found_error(field, &Schema::from(schema))
-}
-
-fn field_not_found_diagnostic(
-    error: &(dyn std::error::Error + 'static),
-    schema: &Schema,
-) -> Option<Error> {
-    let field = find_missing_field(error)?;
-    Some(field_not_found_error(field, schema))
-}
-
-fn field_not_found_error(field: &Column, schema: &Schema) -> Error {
-    let valid_fields = leaf_field_paths(schema);
-    let mut message = format!("Schema error: No field named {}", field.quoted_flat_name());
-    if !valid_fields.is_empty() {
-        message.push_str(". Valid fields are ");
-        message.push_str(&valid_fields.join(", "));
-    }
-    message.push('.');
-
-    Error::InvalidInput { message }
-}
-
-fn find_missing_field<'a>(error: &'a (dyn std::error::Error + 'static)) -> Option<&'a Column> {
-    if let Some(DataFusionError::SchemaError(schema_error, _)) =
-        error.downcast_ref::<DataFusionError>()
-        && let SchemaError::FieldNotFound { field, .. } = schema_error.as_ref()
-    {
-        return Some(field);
-    }
-
-    error.source().and_then(find_missing_field)
-}
-
-fn leaf_field_paths(schema: &Schema) -> Vec<String> {
-    fn format_segment(segment: &str) -> String {
-        // Quote every segment instead of maintaining a SQL keyword list. Bare
-        // lowercase names such as `true` can be parsed as expressions rather
-        // than identifiers, while backticks preserve all field names in both
-        // local SQL parsers.
-        format!("`{}`", segment.replace('`', "``"))
-    }
-
-    fn visit(fields: &arrow_schema::Fields, path: &mut Vec<String>, paths: &mut Vec<String>) {
-        for field in fields {
-            // Neither local planner can address an empty field-path segment,
-            // even when it is backtick-quoted. Do not advertise leaves beneath
-            // such a segment as valid filter fields.
-            if field.name().is_empty() {
-                continue;
-            }
-            path.push(field.name().clone());
-            match field.data_type() {
-                DataType::Struct(children) if !children.is_empty() => {
-                    visit(children, path, paths);
-                }
-                _ => {
-                    paths.push(
-                        path.iter()
-                            .map(|segment| format_segment(segment))
-                            .collect::<Vec<_>>()
-                            .join("."),
-                    );
-                }
-            }
-            path.pop();
-        }
-    }
-
-    let mut paths = Vec::new();
-    visit(schema.fields(), &mut Vec::new(), &mut paths);
-    paths
+    Ok(scanner.create_plan().await?)
 }
 
 //Helper functions below
@@ -825,10 +734,7 @@ async fn parse_arrow_ipc_response(bytes: bytes::Bytes) -> Result<DatasetRecordBa
 #[cfg(test)]
 #[allow(deprecated)]
 mod tests {
-    use arrow_array::{
-        ArrayRef, FixedSizeListArray, Float32Array, Int32Array, RecordBatch, StringArray,
-        StructArray,
-    };
+    use arrow_array::{ArrayRef, FixedSizeListArray, Float32Array};
     use futures::TryStreamExt;
     use lance_arrow::FixedSizeListArrayExt;
     use std::sync::{
@@ -837,7 +743,7 @@ mod tests {
     };
 
     use super::*;
-    use crate::query::{ExecutableQuery, QueryBase, QueryExecutionOptions, QueryRequest};
+    use crate::query::{QueryExecutionOptions, QueryRequest};
     use crate::table::BaseTable;
 
     fn fixed_size_list_array(values: Vec<f32>, dimension: i32) -> FixedSizeListArray {
@@ -978,6 +884,7 @@ mod tests {
     async fn test_execute_query_local_routing() {
         use crate::connect;
         use crate::table::query::execute_query;
+        use arrow_array::{Int32Array, RecordBatch};
         use arrow_schema::{DataType, Field, Schema};
 
         let conn = connect("memory://").execute().await.unwrap();
@@ -1017,164 +924,6 @@ mod tests {
         assert_eq!(count, 2); // 4 and 5
     }
 
-    #[tokio::test]
-    async fn test_missing_filter_field_lists_nested_fields_in_local_planners() {
-        use crate::connect;
-        use arrow_schema::{DataType, Field, Schema};
-
-        let conn = connect("memory://").execute().await.unwrap();
-        let metadata = Arc::new(StructArray::from(vec![
-            (
-                Arc::new(Field::new("year", DataType::Int32, false)),
-                Arc::new(Int32Array::from(vec![2024])) as ArrayRef,
-            ),
-            (
-                Arc::new(Field::new("genre", DataType::Utf8, false)),
-                Arc::new(StringArray::from(vec!["fiction"])) as ArrayRef,
-            ),
-            (
-                Arc::new(Field::new("Title", DataType::Int32, false)),
-                Arc::new(Int32Array::from(vec![7])) as ArrayRef,
-            ),
-            (
-                Arc::new(Field::new("true", DataType::Int32, false)),
-                Arc::new(Int32Array::from(vec![8])) as ArrayRef,
-            ),
-            (
-                Arc::new(Field::new("", DataType::Int32, false)),
-                Arc::new(Int32Array::from(vec![10])) as ArrayRef,
-            ),
-        ]));
-        let vector = Arc::new(fixed_size_list_array(vec![0.0, 1.0], 2));
-        let schema = Arc::new(Schema::new(vec![
-            Field::new("id", DataType::Int32, false),
-            Field::new("vector", vector.data_type().clone(), false),
-            Field::new("content", DataType::Utf8, false),
-            Field::new("metadata", metadata.data_type().clone(), false),
-        ]));
-        let batch = RecordBatch::try_new(
-            schema,
-            vec![
-                Arc::new(Int32Array::from(vec![1])),
-                vector,
-                Arc::new(StringArray::from(vec!["example"])),
-                metadata,
-            ],
-        )
-        .unwrap();
-        let table = conn
-            .create_table("nested_error", batch)
-            .execute()
-            .await
-            .unwrap();
-
-        let error = table
-            .query()
-            .only_if("year = 2024")
-            .execute()
-            .await
-            .err()
-            .expect("query should reject the unqualified nested field");
-        let case_sensitive_path = "`metadata`.`Title`";
-        let keyword_path = "`metadata`.`true`";
-        let expected = format!(
-            "No field named year. Valid fields are `id`, `vector`, `content`, `metadata`.`year`, `metadata`.`genre`, {case_sensitive_path}, {keyword_path}."
-        );
-
-        assert!(
-            error.to_string().contains(&expected),
-            "unexpected error: {error}"
-        );
-        for (path, value) in [(case_sensitive_path, 7), (keyword_path, 8)] {
-            table
-                .query()
-                .only_if(format!("{path} = {value}"))
-                .execute()
-                .await
-                .expect("the path advertised by the diagnostic should be reusable");
-        }
-
-        table.set_unenforced_primary_key(["id"]).await.unwrap();
-        table
-            .set_lsm_write_spec(crate::table::LsmWriteSpec::unsharded())
-            .await
-            .unwrap();
-        let lsm_error = table
-            .query()
-            .only_if("year = 2024")
-            .execute()
-            .await
-            .err()
-            .expect("LSM query should reject the unqualified nested field");
-
-        assert!(
-            lsm_error.to_string().contains(&expected),
-            "unexpected LSM error: {lsm_error}"
-        );
-        for (path, value) in [(case_sensitive_path, 7), (keyword_path, 8)] {
-            table
-                .query()
-                .only_if(format!("{path} = {value}"))
-                .execute()
-                .await
-                .expect("the path advertised by the diagnostic should be reusable in LSM queries");
-        }
-    }
-
-    #[test]
-    fn test_leaf_field_paths_preserve_arbitrary_depth() {
-        use arrow_schema::{DataType, Field, Schema};
-
-        fn nested_field(path: &[&str]) -> Field {
-            let mut segments = path.iter().rev();
-            let mut field = Field::new(
-                *segments.next().expect("path must have a leaf"),
-                DataType::Int32,
-                false,
-            );
-            for segment in segments {
-                field = Field::new(*segment, DataType::Struct(vec![field].into()), false);
-            }
-            field
-        }
-
-        let schema = Schema::new(vec![
-            nested_field(&["a", "b", "c", "d", "e"]),
-            nested_field(&["metadata", "child.with.dot"]),
-            nested_field(&["metadata", "Title"]),
-            nested_field(&["metadata", "123child"]),
-            nested_field(&["metadata", "child`tick"]),
-            nested_field(&["metadata", ""]),
-            nested_field(&["", "child"]),
-        ]);
-
-        assert_eq!(
-            leaf_field_paths(&schema),
-            vec![
-                "`a`.`b`.`c`.`d`.`e`",
-                "`metadata`.`child.with.dot`",
-                "`metadata`.`Title`",
-                "`metadata`.`123child`",
-                "`metadata`.`child``tick`",
-            ]
-        );
-
-        let source = DataFusionError::SchemaError(
-            Box::new(SchemaError::FieldNotFound {
-                field: Box::new(Column::from_name("missing")),
-                valid_fields: Vec::new(),
-            }),
-            Box::new(None),
-        );
-        let error = field_not_found_diagnostic(&source, &schema).unwrap();
-        assert!(
-            error.to_string().contains(
-                "Valid fields are `a`.`b`.`c`.`d`.`e`, `metadata`.`child.with.dot`, `metadata`.`Title`, `metadata`.`123child`, `metadata`.`child``tick`"
-            ),
-            "unexpected error: {error}"
-        );
-    }
-
     #[derive(Debug, Default)]
     struct CountingNamespaceClient {
         query_table_calls: AtomicUsize,

rust/lancedb/src/table/query/lsm.rs

@@ -27,8 +27,6 @@ use std::sync::Arc;
 
 use arrow_array::Array;
 use arrow_schema::{DataType, Schema as ArrowSchema};
-use datafusion::common::{DataFusionError, ToDFSchema};
-use datafusion::prelude::SessionContext;
 use datafusion_physical_plan::expressions::Column;
 use datafusion_physical_plan::projection::ProjectionExec;
 use datafusion_physical_plan::{ExecutionPlan, PhysicalExpr};
@@ -393,21 +391,7 @@ fn base_scanner(
     }
     if let Some(filter) = &query.base.filter {
         scanner = match filter {
-            QueryFilter::Sql(sql) => {
-                // Parse here instead of inside `LsmScanner::filter` so the typed
-                // DataFusion `FieldNotFound` error is still available for the
-                // same nested-field enrichment used by the ordinary scanner.
-                let schema = ArrowSchema::from(dataset.schema());
-                let df_schema = schema.clone().to_dfschema().map_err(|error| {
-                    enrich_filter_error(error, &schema, "Failed to create DFSchema")
-                })?;
-                let expr = SessionContext::new()
-                    .parse_sql_expr(sql, &df_schema)
-                    .map_err(|error| {
-                        enrich_filter_error(error, &schema, "Failed to parse filter expression")
-                    })?;
-                scanner.filter_expr(expr)
-            }
+            QueryFilter::Sql(sql) => scanner.filter(sql)?,
             QueryFilter::Datafusion(expr) => scanner.filter_expr(expr.clone()),
             QueryFilter::Substrait(_) => {
                 return Err(Error::NotSupported {
@@ -419,12 +403,6 @@ fn base_scanner(
     Ok(scanner)
 }
 
-fn enrich_filter_error(error: DataFusionError, schema: &ArrowSchema, context: &str) -> Error {
-    super::field_not_found_diagnostic(&error, schema).unwrap_or_else(|| Error::InvalidInput {
-        message: format!("{context}: {error}"),
-    })
-}
-
 /// Plain scan: filter / projection / limit over base ∪ SSTables ∪ in-memory.
 /// The plain scan applies limit and offset inside the planner.
 async fn plain_plan(

rust/lancedb/src/table/refresh.rs

@@ -7,16 +7,6 @@
 //! therefore idempotent and does not observe input mutation -- once a row is
 //! filled, changing what the expression reads leaves the stored result alone.
 //!
-//! A column's computed inputs are filled first -- the dependency graph is
-//! walked once, each reachable column filled once in dependency order, each
-//! fill its own commit. Every fill in the pass, the requested column's
-//! included, covers only the fragments of the snapshot the pass started
-//! from: a commit may rebase over a concurrent append, and the fragment that
-//! admits carries placeholder nulls no earlier fill covered, so it waits for
-//! a later refresh rather than being read as values. Two concurrent fills of
-//! one input collide on its field in lance's conflict check, so a dependent
-//! fill can only commit over inputs that were durable when it read them.
-//!
 //! Two passes per fragment. The first scans only the unfilled live rows and
 //! evaluates the expression over them, which yields the exact fill count and
 //! decides whether the fragment is staged at all -- a fragment where nothing
@@ -51,8 +41,7 @@ use crate::{Error, Result};
 /// The result of refreshing a computed column.
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
 pub struct RefreshColumnResult {
-    /// Rows that had a value computed, in the requested column only; inputs
-    /// filled on its behalf are not counted.
+    /// Rows that had a value computed.
     #[serde(default)]
     pub rows_filled: u64,
     /// The commit version associated with the operation.
@@ -63,7 +52,6 @@ pub struct RefreshColumnResult {
 struct RefreshExecution {
     result: RefreshColumnResult,
     source_version: u64,
-    published_version: Option<u64>,
 }
 
 /// Internal implementation of the refresh logic.
@@ -86,12 +74,7 @@ async fn execute_refresh_column_with_source(
 
     let expression = declared_expression(&dataset, column)?;
     let schema = Arc::new(ArrowSchema::from(dataset.schema()));
-    let bound = Arc::new(super::computed_columns::bind(
-        schema.clone(),
-        column,
-        &expression,
-    )?);
-    ensure_inputs_filled(&dataset, &schema, column, &bound).await?;
+    let bound = Arc::new(super::computed_columns::bind(schema, column, &expression)?);
     let field = dataset
         .schema()
         .field(column)
@@ -117,25 +100,25 @@ async fn execute_refresh_column_with_source(
         replacements.push(fragment.write_columns(values, &column_schema).await?);
     }
 
-    let source_version = dataset.version().version;
     if replacements.is_empty() {
+        let source_version = dataset.version().version;
         return Ok(RefreshExecution {
             result: RefreshColumnResult {
                 rows_filled: 0,
                 version: source_version,
             },
             source_version,
-            published_version: None,
         });
     }
 
+    let read_version = dataset.version().version;
     // The dataset's own session, so registrations and caches survive the
     // commit being installed on the handle.
     let session = dataset.session();
     let new_dataset = Dataset::commit(
         WriteDestination::Dataset(dataset.clone()),
         Operation::DataReplacement { replacements },
-        Some(source_version),
+        Some(read_version),
         None,
         None,
         session,
@@ -150,52 +133,10 @@ async fn execute_refresh_column_with_source(
             rows_filled,
             version,
         },
-        source_version,
-        published_version: Some(version),
+        source_version: read_version,
     })
 }
 
-/// Refuse while a computed input still has rows a refresh of it would fill:
-/// read now, its placeholder null would be evaluated as a value and kept.
-async fn ensure_inputs_filled(
-    dataset: &Dataset,
-    schema: &Arc<ArrowSchema>,
-    column: &str,
-    bound: &BoundExpression,
-) -> Result<()> {
-    for input in &bound.roots {
-        let Some(declaration) = schema
-            .field_with_name(input)
-            .ok()
-            .and_then(computed_column_from_field)
-        else {
-            continue;
-        };
-        let ComputedColumnKind::Sql { expression } = &declaration.kind else {
-            return Err(Error::NotSupported {
-                message: format!(
-                    "computed column '{column}' reads '{input}', whose fill state this \
-                     refresh cannot check; refresh '{input}' first"
-                ),
-            });
-        };
-        let input_bound = super::computed_columns::bind(schema.clone(), input, expression)?;
-        let mut unfilled = 0u64;
-        for fragment in dataset.get_fragments() {
-            unfilled += count_fragment_gains(dataset, &fragment, &input_bound, input).await?;
-        }
-        if unfilled > 0 {
-            return Err(Error::InvalidInput {
-                message: format!(
-                    "computed column '{column}' reads '{input}', which has {unfilled} unfilled \
-                     rows; refresh '{input}' first"
-                ),
-            });
-        }
-    }
-    Ok(())
-}
-
 /// Run the refresh as a [`Job`] in this process.
 pub(crate) async fn execute_refresh_column_async(
     table: &NativeTable,
@@ -219,7 +160,8 @@ pub(crate) async fn execute_refresh_column_async(
             rows_failed: 0,
             rows_remaining: 0,
             source_version: execution.source_version,
-            published_version: execution.published_version,
+            published_version: (execution.result.rows_filled > 0)
+                .then_some(execution.result.version),
         })
     })))
 }
@@ -442,8 +384,7 @@ mod tests {
             .version)
     }
 
-    async fn read(table: &Table, column: &str) -> Vec<Option<i64>> {
-        use arrow_array::{Array, Int64Array};
+    async fn read(table: &Table, column: &str) -> Vec<Option<i32>> {
         let batches = table
             .query()
             .select(Select::columns(&[column]))
@@ -453,19 +394,15 @@ mod tests {
             .try_collect::<Vec<_>>()
             .await
             .unwrap();
-        let mut values: Vec<Option<i64>> = batches
+        let mut values: Vec<Option<i32>> = batches
             .iter()
             .flat_map(|batch| {
-                let array = &batch[column];
-                match array.as_any().downcast_ref::<Int32Array>() {
-                    Some(ints) => ints.iter().map(|v| v.map(i64::from)).collect::<Vec<_>>(),
-                    None => array
-                        .as_any()
-                        .downcast_ref::<Int64Array>()
-                        .unwrap()
-                        .iter()
-                        .collect::<Vec<_>>(),
-                }
+                batch[column]
+                    .as_any()
+                    .downcast_ref::<Int32Array>()
+                    .unwrap()
+                    .iter()
+                    .collect::<Vec<_>>()
             })
             .collect();
         values.sort();
@@ -477,98 +414,6 @@ mod tests {
         table.add(batch).execute().await.unwrap();
     }
 
-    /// The gate's reproducer: `b = coalesce(a, 0)` refreshed before `a`
-    /// must not bake zeros from `a`'s placeholder null. It is refused, and
-    /// names the input, until `a` is filled -- after every append too.
-    #[tokio::test]
-    async fn test_dependent_refresh_refuses_an_unfilled_input() {
-        let table = table_with("dependent_refresh_order", vec![1, 2, 3]).await;
-        table
-            .add_columns()
-            .computed("a", "x + 1")
-            .computed("b", "coalesce(a, 0)")
-            .execute()
-            .await
-            .unwrap();
-
-        let err = table.refresh_column("b").await.unwrap_err();
-        assert!(
-            matches!(&err, Error::InvalidInput { message } if message.contains("refresh 'a' first")),
-            "{err}"
-        );
-        assert_eq!(read(&table, "b").await, vec![None, None, None]);
-
-        assert_eq!(table.refresh_column("a").await.unwrap().rows_filled, 3);
-        assert_eq!(table.refresh_column("b").await.unwrap().rows_filled, 3);
-        assert_eq!(read(&table, "b").await, vec![Some(2), Some(3), Some(4)]);
-
-        append(&table, vec![10]).await;
-        assert!(table.refresh_column("b").await.is_err());
-        table.refresh_column("a").await.unwrap();
-        assert_eq!(table.refresh_column("b").await.unwrap().rows_filled, 1);
-        assert_eq!(
-            table.count_rows(Some("b = 0".to_string())).await.unwrap(),
-            0
-        );
-    }
-
-    /// Names that need quoting, and a nested input, survive the trip through
-    /// declaration metadata and the dependency check: the recorded inputs
-    /// are matched by name, never re-parsed as SQL.
-    #[tokio::test]
-    async fn test_dependent_refresh_handles_awkward_column_names() {
-        use arrow_array::{Int32Array, StructArray};
-        use arrow_schema::{DataType, Field, Fields};
-
-        let conn = connect("memory://").execute().await.unwrap();
-        let age_fields = Fields::from(vec![Field::new("age", DataType::Int32, true)]);
-        let meta = StructArray::new(
-            age_fields.clone(),
-            vec![Arc::new(Int32Array::from(vec![10, 20])) as _],
-            None,
-        );
-        let schema = Arc::new(arrow_schema::Schema::new(vec![
-            Field::new("camelCase", DataType::Int32, true),
-            Field::new("with-hyphen", DataType::Int32, true),
-            Field::new("meta", DataType::Struct(age_fields), true),
-        ]));
-        let batch = arrow_array::RecordBatch::try_new(
-            schema,
-            vec![
-                Arc::new(Int32Array::from(vec![1, 2])) as _,
-                Arc::new(Int32Array::from(vec![100, 200])) as _,
-                Arc::new(meta) as _,
-            ],
-        )
-        .unwrap();
-        let table = conn
-            .create_table("awkward_names", batch)
-            .execute()
-            .await
-            .unwrap();
-
-        table
-            .add_columns()
-            .computed("y", "`camelCase` * 2")
-            .computed("z", "coalesce(y, 0) + `with-hyphen` + meta.age")
-            .execute()
-            .await
-            .unwrap();
-        let z = crate::table::computed_columns::computed_columns(
-            table.schema().await.unwrap().as_ref(),
-        )
-        .into_iter()
-        .find(|c| c.name == "z")
-        .unwrap();
-        assert_eq!(z.inputs, vec!["meta.age", "with-hyphen", "y"]);
-
-        let err = table.refresh_column("z").await.unwrap_err();
-        assert!(err.to_string().contains("refresh 'y' first"), "{err}");
-        assert_eq!(table.refresh_column("y").await.unwrap().rows_filled, 2);
-        assert_eq!(table.refresh_column("z").await.unwrap().rows_filled, 2);
-        assert_eq!(read(&table, "z").await, vec![Some(112), Some(224)]);
-    }
-
     #[tokio::test]
     async fn test_refresh_fills_a_declared_column() {
         let table = table_with("refresh_fills", vec![1, 2, 3]).await;
@@ -806,8 +651,7 @@ mod tests {
 
         let read_back = read(&table, "doubled").await;
         assert_eq!(read_back.len(), 20_000);
-        let mut expected: Vec<Option<i64>> =
-            values.iter().map(|v| Some(i64::from(v * 2))).collect();
+        let mut expected: Vec<Option<i32>> = values.iter().map(|v| Some(v * 2)).collect();
         expected.sort();
         assert_eq!(read_back, expected);
     }