feat: pause and resume jobs from the Python SDK

Cancellation is the only job control the SDK exposes, and it is terminal, so a long-running server-side job cannot be parked and picked up again. This adds pause_job and resume_job to the Rust core connection and the Python bindings (sync and async), posting to the server's /v1/jobs/pause and /v1/jobs/resume endpoints. A pause parks the job until it is resumed: its workers drain and stop. The outcome strings mirror the server's answers -- a job finalizing its results reports "committing" and cannot be parked, and a resume before the drain is confirmed reports "still_pausing"; both are retried rather than failed. Resuming re-queues the job and its workers pick their work back up from checkpoints. Local connections report the operations as unsupported, like the rest of the jobs API.
fix: preserve duplicate take offsets (#4024 )
2026-09-01 19:18:38 +00:00 · 2026-09-01 13:23:13 +00:00 · 2026-09-01 20:08:47 +08:00 · 2026-08-31 14:02:40 -07:00 · 2026-08-31 10:40:11 -07:00 · 2026-08-31 22:34:34 +08:00
68 changed files with 4173 additions and 12019 deletions
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.38.0-beta.12"
+current_version = "0.38.0"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
@@ -44,3 +44,27 @@ updates:
      python-deps:
        patterns:
          - "*"
+
+  # The npm ecosystem covers pnpm lockfiles. There are two separate installs:
+  # the bindings themselves and the examples, which have their own lockfile.
+  # As with cargo and pip above, only bump the lockfile — the version ranges
+  # in package.json are our consumers' constraints, not ours.
+  - package-ecosystem: npm
+    directory: /nodejs
+    schedule:
+      interval: weekly
+    versioning-strategy: lockfile-only
+    groups:
+      nodejs-deps:
+        patterns:
+          - "*"
+
+  - package-ecosystem: npm
+    directory: /nodejs/examples
+    schedule:
+      interval: weekly
+    versioning-strategy: lockfile-only
+    groups:
+      nodejs-examples-deps:
+        patterns:
+          - "*"
@@ -29,12 +29,14 @@ jobs:
    steps:
      - uses: actions/setup-node@v6
        with:
-          node-version: "18"
+          node-version: "24"
+      - uses: pnpm/action-setup@v6
+        with:
+          version: 11.1.1
      # These rules are disabled because Github will always ensure there
      # is a blank line between the title and the body and Github will
      # word wrap the description field to ensure a reasonable max line
      # length.
-      - run: npm install @commitlint/config-conventional
      - run: >
          echo 'module.exports = {
            "rules": {
@@ -43,7 +45,11 @@ jobs:
              "body-leading-blank": [0, "always"]
            }
          }' > .commitlintrc.js
-      - run: npx commitlint --extends @commitlint/config-conventional --verbose <<< $COMMIT_MSG
+      - run: >
+          pnpm dlx
+          --package @commitlint/cli@21.2.2
+          --package @commitlint/config-conventional@21.2.2
+          commitlint --extends @commitlint/config-conventional --verbose <<< $COMMIT_MSG
        env:
          COMMIT_MSG: >
            ${{ github.event.pull_request.title }}
@@ -54,7 +60,7 @@ jobs:
        with:
          script: |
            const message = `**ACTION NEEDED**
-              
+
              Lance follows the [Conventional Commits specification](https://www.conventionalcommits.org/en/v1.0.0/) for release automation.

              The PR title and description are used as the merge commit message.\
@@ -56,7 +56,7 @@ jobs:
        uses: lycheeverse/lychee-action@e7477775783ea5526144ba13e8db5eec57747ce8 # v2.9.0
        with:
          # Restricted to http(s) on purpose. Much of docs/src is generated
-          # API reference (the js/ tree comes from `npm run docs` in nodejs)
+          # API reference (the js/ tree comes from `pnpm run docs` in nodejs)
          # and the hand-written pages use mkdocstrings cross-references and
          # nav-relative paths that only resolve in the site mkdocs builds,
          # not in this checkout, so relative links would be reported as
@@ -55,9 +55,7 @@ jobs:
      - name: Set up node
        uses: actions/setup-node@v6
        with:
-          node-version: 20
-          cache: 'npm'
-          cache-dependency-path: docs/package-lock.json
+          node-version: 24
      - name: Install node dependencies
        working-directory: nodejs
        run: |
@@ -47,9 +47,8 @@ jobs:
        version: 11.1.1
    - uses: actions/setup-node@v6
      with:
-        # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
-        # in October. The library itself still supports Node >= 18
-        # (see test matrix below).
+        # Build on a supported LTS; the matrix job below covers every
+        # Node version the library claims to support.
        node-version: 24
        cache: 'pnpm'
        cache-dependency-path: nodejs/pnpm-lock.yaml
@@ -84,7 +83,7 @@ jobs:
    timeout-minutes: 30
    strategy:
      matrix:
-        node-version: [ "18", "20" ]
+        node-version: [ "22", "24", "26" ]
    runs-on: "ubuntu-22.04"
    defaults:
      run:
@@ -101,9 +100,9 @@ jobs:
    - uses: actions/setup-node@v6
      name: Setup Node.js 24 for build
      with:
-        # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
-        # in October. Build/install runs on Node 24; tests run on the
-        # matrix version below using direct jest invocation.
+        # Build and install once on a fixed version so the generated docs
+        # are identical across matrix legs; the tests below then run on each
+        # supported Node version.
        node-version: 24
        cache: 'pnpm'
        cache-dependency-path: nodejs/pnpm-lock.yaml
@@ -152,9 +151,9 @@ jobs:
        S3_TEST: "1"
        # Newer @smithy/core uses dynamic ESM imports.
        NODE_OPTIONS: "--experimental-vm-modules"
-      # Invoke jest directly because pnpm 11 itself requires Node 22+
-      # while the matrix tests on older Node versions.
-      run: npx jest --verbose
+      # Invoke the installed jest binary directly; the pnpm shim is set up
+      # against the build-phase Node, not the version selected above.
+      run: node_modules/.bin/jest --verbose
    - name: Test examples
      working-directory: ./
      env:
@@ -164,7 +163,7 @@ jobs:
      run: |
        python ci/mock_openai.py &
        cd nodejs/examples
-        npx jest --testEnvironment jest-environment-node-single-context --verbose
+        node_modules/.bin/jest --testEnvironment jest-environment-node-single-context --verbose
  macos:
    timeout-minutes: 30
    # macos-15 ships a newer linker; the older macos-14 linker fails to insert
@@ -185,8 +184,7 @@ jobs:
        version: 11.1.1
    - uses: actions/setup-node@v6
      with:
-        # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
-        # in October.
+        # pnpm 11 requires Node >= 22.13.
        node-version: 24
        cache: 'pnpm'
        cache-dependency-path: nodejs/pnpm-lock.yaml
@@ -168,8 +168,7 @@ jobs:
      - name: Setup node
        uses: actions/setup-node@v6
        with:
-          # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
-          # in October.
+          # pnpm 11 requires Node >= 22.13.
          node-version: 24
          cache: pnpm
          cache-dependency-path: nodejs/pnpm-lock.yaml
@@ -251,7 +250,7 @@ jobs:
          run: |
            set -e
            ${{ matrix.settings.pre_build }}
-            npx napi build --platform --release \
+            node_modules/.bin/napi build --platform --release \
              --features ${{ matrix.settings.features }} \
              --target ${{ matrix.settings.target }} \
              --dts ../lancedb/native.d.ts \
@@ -271,7 +270,7 @@ jobs:
      - name: Build
        run: |
          ${{ matrix.settings.pre_build }}
-          npx napi build --platform --release \
+          node_modules/.bin/napi build --platform --release \
              --features ${{ matrix.settings.features }} \
              --target ${{ matrix.settings.target }} \
              --dts ../lancedb/native.d.ts \
@@ -339,7 +338,7 @@ jobs:
          - target: aarch64-unknown-linux-gnu
            host: ubuntu-2404-8x-arm64
        node:
-          - '20'
+          - '22'
    runs-on: ${{ matrix.settings.host }}
    defaults:
      run:
@@ -385,9 +384,9 @@ jobs:
      - name: Move built files
        run: cp dist/native.d.ts dist/native.js dist/*.node lancedb/
      - name: Test bindings
-        # Invoke jest directly because pnpm 11 itself requires Node 22+
-        # while the matrix tests on older Node versions.
-        run: npx jest --verbose
+        # Invoke the installed jest binary directly; the pnpm shim is set up
+        # against the install-phase Node, not the version selected above.
+        run: node_modules/.bin/jest --verbose
  publish:
    name: Publish
    runs-on: ubuntu-latest
@@ -1,22 +0,0 @@
-name: Update package-lock.json
-
-on:
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-        with:
-          ref: main
-          persist-credentials: false
-          fetch-depth: 0
-          lfs: true
-      - uses: ./.github/workflows/update_package_lock
-        with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
@@ -1,22 +0,0 @@
-name: Update NodeJs package-lock.json
-
-on:
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-        with:
-          ref: main
-          persist-credentials: false
-          fetch-depth: 0
-          lfs: true
-      - uses: ./.github/workflows/update_package_lock_nodejs
-        with:
-          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
@@ -20,7 +20,10 @@ repos:
    hooks:
      - id: local-biome-check
        name: biome check
-        entry: npx @biomejs/biome@1.8.3 check --config-path nodejs/biome.json nodejs/
+        # Use the biome from nodejs/package.json rather than a separately
+        # pinned one: the two drifted apart and disagreed on formatting, so
+        # this hook rejected code that `pnpm lint` accepted.
+        entry: nodejs/node_modules/.bin/biome check --config-path nodejs/biome.json nodejs/
        language: system
        types: [text]
        files: "nodejs/.*"
@@ -38,7 +38,7 @@ Before committing changes, run formatting for every language you touched. At min
 * Rust changes: run `cargo fmt --all`.
 * Python changes: run `ruff format .` and `ruff check .` from the repository root,
  and run targeted tests through `cd python && uv run ...`.
-* TypeScript changes: run the relevant `npm`/`pnpm` lint, format, build, and docs commands in `nodejs`.
+* TypeScript changes: run the relevant `pnpm` lint, format, build, and docs commands in `nodejs`.

 Before creating a PR, the exact value passed to `gh pr create --title` must follow
 Conventional Commits, such as `fix: support nested field paths in native index creation`
@@ -101,12 +101,12 @@ Python bindings changes:
 TypeScript bindings changes:

 1. Add napi-rs method binding on `Table` in `nodejs/src/table.rs`.
-2. Run `npm run build` to generate TypeScript definitions.
+2. Run `pnpm build` to generate TypeScript definitions.
 3. Add typescript method on abstract class `Table` in `nodejs/src/table.ts`.
 4. Add concrete method on `LocalTable` class in `nodejs/src/native_table.ts`.
    * Note: despite the name, this class is also used for remote tables.
 5. Add test in `nodejs/__test__/table.test.ts`.
-6. Run `npm run docs` to generate TypeScript documentation.
+6. Run `pnpm run docs` to generate TypeScript documentation.

 ## Python API reference

@@ -535,9 +535,9 @@ dependencies = [

 [[package]]
 name = "async-trait"
-version = "0.1.91"
+version = "0.1.92"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ae36dc4177970ef04fde5178d3e2429882def40e57a451f919c098f72baa6cec"
+checksum = "82f6aeea286b8eb4dd3431a1be1b59d290ace00f5bfd8e2a159bc2a05e2c1667"
 dependencies = [
 "proc-macro2",
 "quote",
@@ -1443,9 +1443,9 @@ checksum = "175812e0be2bccb6abe50bb8d566126198344f707e304f45c648fd8f2cc0365e"

 [[package]]
 name = "bytemuck"
-version = "1.25.0"
+version = "1.25.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c8efb64bd706a16a1bdde310ae86b351e4d21550d98d056f22f8a7f7a2183fec"
+checksum = "95832e849adfb21180ccb6826a99da14e5d266ae5c2e668e1602cf234f153797"
 dependencies = [
 "bytemuck_derive",
 ]
@@ -1597,9 +1597,9 @@ checksum = "613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724"

 [[package]]
 name = "chacha20"
-version = "0.10.0"
+version = "0.10.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6f8d983286843e49675a4b7a2d174efe136dc93a18d69130dd18198a6c167601"
+checksum = "65c35e4b699c7e15ccbe7ee35c005e4fc0a278d22238a2857e6ce2dadeda1b06"
 dependencies = [
 "cfg-if 1.0.4",
 "cpufeatures 0.3.0",
@@ -3455,8 +3455,8 @@ checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"

 [[package]]
 name = "fsst"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-array",
 "rand 0.9.5",
@@ -4815,8 +4815,8 @@ checksum = "e037a2e1d8d5fdbd49b16a4ea09d5d6401c1f29eca5ff29d03d3824dba16256a"

 [[package]]
 name = "lance"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arc-swap",
 "arrow",
@@ -4888,8 +4888,8 @@ dependencies = [

 [[package]]
 name = "lance-arrow"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4911,7 +4911,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-scalar"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4925,7 +4925,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-stats"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -4934,8 +4934,8 @@ dependencies = [

 [[package]]
 name = "lance-bitpacking"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrayref",
 "crunchy",
@@ -4945,8 +4945,8 @@ dependencies = [

 [[package]]
 name = "lance-core"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4983,8 +4983,8 @@ dependencies = [

 [[package]]
 name = "lance-datafusion"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5013,8 +5013,8 @@ dependencies = [

 [[package]]
 name = "lance-datagen"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5031,8 +5031,8 @@ dependencies = [

 [[package]]
 name = "lance-derive"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "proc-macro2",
 "quote",
@@ -5041,8 +5041,8 @@ dependencies = [

 [[package]]
 name = "lance-encoding"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-arith",
 "arrow-array",
@@ -5075,8 +5075,8 @@ dependencies = [

 [[package]]
 name = "lance-file"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-arith",
 "arrow-array",
@@ -5107,8 +5107,8 @@ dependencies = [

 [[package]]
 name = "lance-index"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arc-swap",
 "arrow",
@@ -5172,8 +5172,8 @@ dependencies = [

 [[package]]
 name = "lance-index-core"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5195,8 +5195,8 @@ dependencies = [

 [[package]]
 name = "lance-io"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5236,8 +5236,8 @@ dependencies = [

 [[package]]
 name = "lance-linalg"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5251,8 +5251,8 @@ dependencies = [

 [[package]]
 name = "lance-namespace"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow",
 "async-trait",
@@ -5264,8 +5264,8 @@ dependencies = [

 [[package]]
 name = "lance-namespace-impls"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow",
 "arrow-ipc",
@@ -5318,8 +5318,8 @@ dependencies = [

 [[package]]
 name = "lance-select"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -5333,8 +5333,8 @@ dependencies = [

 [[package]]
 name = "lance-table"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5374,8 +5374,8 @@ dependencies = [

 [[package]]
 name = "lance-testing"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5388,8 +5388,8 @@ dependencies = [

 [[package]]
 name = "lance-tokenizer"
-version = "12.0.0-beta.2"
-source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
+version = "12.0.0-beta.5"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.5#556637791d0048c2b4f1342dd84b67c8bbd65259"
 dependencies = [
 "frostem",
 "icu_segmenter",
@@ -5402,7 +5402,7 @@ dependencies = [

 [[package]]
 name = "lancedb"
-version = "0.38.0-beta.12"
+version = "0.38.0"
 dependencies = [
 "ahash",
 "anyhow",
@@ -5490,7 +5490,7 @@ dependencies = [

 [[package]]
 name = "lancedb-nodejs"
-version = "0.38.0-beta.12"
+version = "0.38.0"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -5515,7 +5515,7 @@ dependencies = [

 [[package]]
 name = "lancedb-python"
-version = "0.38.0-beta.12"
+version = "0.38.0"
 dependencies = [
 "arrow",
 "async-trait",
@@ -5748,9 +5748,9 @@ dependencies = [

 [[package]]
 name = "log"
-version = "0.4.33"
+version = "0.4.34"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0ceec5bc11778974d1bcb055b18002eba7f4b3518b6a0081b3af5f21666da9ad"
+checksum = "f9f8bd3e56ce4dfc153cf470fffbfa98c7620958b312ca5c3a4b8d5181fd13c6"

 [[package]]
 name = "loom"
@@ -6001,9 +6001,9 @@ dependencies = [

 [[package]]
 name = "moka"
-version = "0.12.15"
+version = "0.12.16"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "957228ad12042ee839f93c8f257b62b4c0ab5eaae1d4fa60de53b27c9d7c5046"
+checksum = "4293f18e7567a1caf3c584855554377025c65e0aa445344d04171f5ad63d19b9"
 dependencies = [
 "async-lock",
 "crossbeam-channel",
@@ -6097,14 +6097,15 @@ dependencies = [

 [[package]]
 name = "napi"
-version = "3.11.0"
+version = "3.12.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "de33522036981030a75c231829566bc63414e08101a6f5ff4ac6cef19c8e0941"
+checksum = "58c5f4d5375213fdb7be2655e152386e82f026f9a5ba36a75556e11359aafe09"
 dependencies = [
 "bitflags 2.11.1",
 "chrono",
 "ctor 1.0.12",
 "futures",
+ "libc",
 "napi-build",
 "napi-sys",
 "nohash-hasher",
@@ -6116,15 +6117,15 @@ dependencies = [

 [[package]]
 name = "napi-build"
-version = "2.4.0"
+version = "2.4.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5282704fbe8d49b0cf8b08e3f33233416a528658f205c7e5ace63b582de0b11c"
+checksum = "60fdf9b392c50e7c4170fa633bd909490ed7835cea4c046776d1a4dd8d2ae0ab"

 [[package]]
 name = "napi-derive"
-version = "3.6.1"
+version = "3.6.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4d5c9c02556ea6dc99dffd36c1ce60141411657438501a125b675776d011ce92"
+checksum = "0fa55ea69990c90b888e9e77044410e304ce7f35de599dc6d0b5c1923d2e59af"
 dependencies = [
 "convert_case",
 "ctor 1.0.12",
@@ -6136,9 +6137,9 @@ dependencies = [

 [[package]]
 name = "napi-derive-backend"
-version = "6.1.1"
+version = "6.1.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d60b5d773ad46c698c8cc2cd9fde0b283d39cbb7f71c04bee633c7bdba4423bd"
+checksum = "df4056ac7c18e4438ccf0edaed4340ca0d269278c8ec19284f7b23cb039fd0ae"
 dependencies = [
 "convert_case",
 "proc-macro2",
@@ -8601,9 +8602,9 @@ dependencies = [

 [[package]]
 name = "roaring"
-version = "0.11.4"
+version = "0.11.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1dedc5658c6ecb3bdb5ef5f3295bb9253f42dcf3fd1402c03f6b1f7659c3c4a9"
+checksum = "18bd8a37d17a58532776dcdf6041ce64929adca78e8489d5cacbafe99229d3e1"
 dependencies = [
 "bytemuck",
 "byteorder",
@@ -9063,9 +9064,9 @@ dependencies = [

 [[package]]
 name = "serde_with"
-version = "3.21.0"
+version = "3.22.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "76a5c54c7310e7b8b9577c286d7e399ddd876c3e12b3ed917a8aabc4b96e9e8c"
+checksum = "ee78f1fbe43ac4a0e47aadb3dbd357b69eb0d3793e948624cd03dd2750ab1c0a"
 dependencies = [
 "base64 0.22.1",
 "bs58",
@@ -9073,6 +9074,7 @@ dependencies = [
 "hex",
 "indexmap 1.9.3",
 "indexmap 2.14.0",
+ "jiff",
 "schemars 0.9.0",
 "schemars 1.2.1",
 "serde_core",
@@ -9083,9 +9085,9 @@ dependencies = [

 [[package]]
 name = "serde_with_macros"
-version = "3.21.0"
+version = "3.22.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "84d57bc0c8b9a17920c178daa6bb924850d54a9c97ab45194bb8c17ad66bb660"
+checksum = "8705578779c2b6bd90d84d66eb2e206b708b1a4d7b9f17641b293545bf1c7e46"
 dependencies = [
 "darling 0.23.0",
 "proc-macro2",
@@ -10452,9 +10454,9 @@ checksum = "06abde3611657adf66d383f00b093d7faecc7fa57071cce2578660c9f1010821"

 [[package]]
 name = "uuid"
-version = "1.24.0"
+version = "1.26.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bf3923a6f5c4c6382e0b653c4117f48d631ea17f38ed86e2a828e6f7412f5239"
+checksum = "b5772d71c9be8a8a6ac2117d949c5b224c1b72241bb611d9a3012edcf8af7812"
 dependencies = [
 "getrandom 0.4.2",
 "js-sys",
@@ -13,20 +13,20 @@ categories = ["database-implementations"]
 rust-version = "1.91.0"

 [workspace.dependencies]
-lance = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=12.0.0-beta.5", default-features = false, "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=12.0.0-beta.5", default-features = false, "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=12.0.0-beta.5", default-features = false, "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=12.0.0-beta.5", "tag" = "v12.0.0-beta.5", "git" = "https://github.com/lance-format/lance.git" }
 lancedb = { path = "rust/lancedb", default-features = false }
 ahash = "0.8"
 # Note that this one does not include pyarrow
@@ -5,5 +5,5 @@ licenses:
 	cd python && cargo about generate ../about.hbs -o RUST_THIRD_PARTY_LICENSES.html -c ../about.toml
 	cd python && uv sync --all-extras && uv tool run pip-licenses --python .venv/bin/python --format=markdown --with-urls --output-file=PYTHON_THIRD_PARTY_LICENSES.md
 	cd nodejs && cargo about generate ../about.hbs -o RUST_THIRD_PARTY_LICENSES.html -c ../about.toml
-	cd nodejs && npx license-checker --markdown --out NODEJS_THIRD_PARTY_LICENSES.md
+	cd nodejs && pnpm dlx license-checker@25 --markdown --out NODEJS_THIRD_PARTY_LICENSES.md
 	cd java && ./mvnw license:aggregate-add-third-party -q
@@ -12,16 +12,12 @@ done
 # This updates the lockfile without building
 cargo metadata --quiet > /dev/null

-pushd nodejs || exit 1
-npm install --package-lock-only --silent
-popd
-
 if git diff --quiet --exit-code; then
  echo "No lockfile changes to commit; skipping amend."
 elif $AMEND; then
-  git add Cargo.lock nodejs/package-lock.json
+  git add Cargo.lock
  git commit --amend --no-edit
 else
-  git add Cargo.lock nodejs/package-lock.json
+  git add Cargo.lock
  git commit -m "Update lockfiles"
 fi
@@ -131,18 +131,13 @@ allow = [
    "BSD-3-Clause",
    "ISC",
    "Unicode-3.0",
-    "Unicode-DFS-2016",
    "Zlib",
    "CC0-1.0",
    "MPL-2.0",
    "BSL-1.0",
-    "OpenSSL",
    # 0BSD ("BSD Zero Clause") is effectively public domain — no attribution
    # required. Pulled in by `mock_instant`.
    "0BSD",
-    # bzip2-1.0.6 is the permissive upstream bzip2 license (BSD-like). Pulled
-    # in by `libbz2-rs-sys`, the pure-Rust bzip2 implementation.
-    "bzip2-1.0.6",
    # CDLA-Permissive-2.0 is a permissive data license used by `webpki-roots`
    # for the Mozilla CA root bundle. Data-only, distribution-compatible.
    "CDLA-Permissive-2.0",
@@ -150,12 +145,7 @@ allow = [
 confidence-threshold = 0.8
 # Per-crate license exceptions: allow a license for a specific crate only,
 # rather than globally via the `allow` list above.
-exceptions = [
-    # CDDL-1.0 (copyleft) is pulled in only as a dev/profiling dependency via
-    # `inferno` -> `pprof` -> `lance-testing`; it is a test dependency that we
-    # do not distribute, so scope the allowance to `inferno` alone.
-    { allow = ["CDDL-1.0"], crate = "inferno" },
-]
+exceptions = []
 # Crates whose license cannot be determined from Cargo metadata but whose
 # license we've manually confirmed from upstream. Keep this list minimal.
 [[licenses.clarify]]
@@ -47,22 +47,24 @@ pytest -vv python/tests/docs

 ### Checking typescript examples

-The `@lancedb/lancedb` package must be built before running the tests:
+The examples depend on `@lancedb/lancedb` at `file:../dist`, so the package must be
+built before running the tests. This uses pnpm; see the
+[Typescript contributing guide](../nodejs/CONTRIBUTING.md) for the toolchain setup.

 ```shell
 pushd nodejs
-npm ci
-npm run build
+pnpm install
+pnpm build
 popd
 ```

-Then you can run the examples by going to the `nodejs/examples` directory and
-running the tests like a normal npm package:
+Then you can run the examples by going to the `nodejs/examples` directory, which is a
+separate pnpm package with its own lockfile:

 ```shell
 pushd nodejs/examples
-npm ci
-npm test
+pnpm install
+pnpm test
 popd
 ```

@@ -84,6 +86,7 @@ The new files should be checked into the repository.

 ```shell
 pushd nodejs
-npm run docs
+# `pnpm docs` would invoke pnpm's built-in `docs` command, not the script.
+pnpm run docs
 popd
 ```
@@ -1,135 +0,0 @@
-{
-  "name": "lancedb-docs-test",
-  "version": "1.0.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "lancedb-docs-test",
-      "version": "1.0.0",
-      "license": "Apache 2",
-      "dependencies": {
-        "apache-arrow": "file:../node/node_modules/apache-arrow",
-        "vectordb": "file:../node"
-      },
-      "devDependencies": {
-        "@types/node": "^20.11.8",
-        "typescript": "^5.3.3"
-      }
-    },
-    "../node": {
-      "name": "vectordb",
-      "version": "0.21.2-beta.0",
-      "cpu": [
-        "x64",
-        "arm64"
-      ],
-      "license": "Apache-2.0",
-      "os": [
-        "darwin",
-        "linux",
-        "win32"
-      ],
-      "dependencies": {
-        "@neon-rs/load": "^0.0.74",
-        "axios": "^1.4.0"
-      },
-      "devDependencies": {
-        "@neon-rs/cli": "^0.0.160",
-        "@types/chai": "^4.3.4",
-        "@types/chai-as-promised": "^7.1.5",
-        "@types/mocha": "^10.0.1",
-        "@types/node": "^18.16.2",
-        "@types/sinon": "^10.0.15",
-        "@types/temp": "^0.9.1",
-        "@types/uuid": "^9.0.3",
-        "@typescript-eslint/eslint-plugin": "^5.59.1",
-        "apache-arrow-old": "npm:apache-arrow@13.0.0",
-        "cargo-cp-artifact": "^0.1",
-        "chai": "^4.3.7",
-        "chai-as-promised": "^7.1.1",
-        "eslint": "^8.39.0",
-        "eslint-config-standard-with-typescript": "^34.0.1",
-        "eslint-plugin-import": "^2.26.0",
-        "eslint-plugin-n": "^15.7.0",
-        "eslint-plugin-promise": "^6.1.1",
-        "mocha": "^10.2.0",
-        "openai": "^4.24.1",
-        "sinon": "^15.1.0",
-        "temp": "^0.9.4",
-        "ts-node": "^10.9.1",
-        "ts-node-dev": "^2.0.0",
-        "typedoc": "^0.24.7",
-        "typedoc-plugin-markdown": "^3.15.3",
-        "typescript": "^5.1.0",
-        "uuid": "^9.0.0"
-      },
-      "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.21.2-beta.0",
-        "@lancedb/vectordb-darwin-x64": "0.21.2-beta.0",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.21.2-beta.0",
-        "@lancedb/vectordb-linux-x64-gnu": "0.21.2-beta.0",
-        "@lancedb/vectordb-win32-x64-msvc": "0.21.2-beta.0"
-      },
-      "peerDependencies": {
-        "@apache-arrow/ts": "^14.0.2",
-        "apache-arrow": "^14.0.2"
-      }
-    },
-    "../node/node_modules/apache-arrow": {
-      "version": "14.0.2",
-      "license": "Apache-2.0",
-      "dependencies": {
-        "@types/command-line-args": "5.2.0",
-        "@types/command-line-usage": "5.0.2",
-        "@types/node": "20.3.0",
-        "@types/pad-left": "2.1.1",
-        "command-line-args": "5.2.1",
-        "command-line-usage": "7.0.1",
-        "flatbuffers": "23.5.26",
-        "json-bignum": "^0.0.3",
-        "pad-left": "^2.1.0",
-        "tslib": "^2.5.3"
-      },
-      "bin": {
-        "arrow2csv": "bin/arrow2csv.js"
-      }
-    },
-    "node_modules/@types/node": {
-      "version": "20.11.8",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.11.8.tgz",
-      "integrity": "sha512-i7omyekpPTNdv4Jb/Rgqg0RU8YqLcNsI12quKSDkRXNfx7Wxdm6HhK1awT3xTgEkgxPn3bvnSpiEAc7a7Lpyow==",
-      "dev": true,
-      "dependencies": {
-        "undici-types": "~5.26.4"
-      }
-    },
-    "node_modules/apache-arrow": {
-      "resolved": "../node/node_modules/apache-arrow",
-      "link": true
-    },
-    "node_modules/typescript": {
-      "version": "5.3.3",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.3.3.tgz",
-      "integrity": "sha512-pXWcraxM0uxAS+tN0AG/BF2TyqmHO014Z070UsJ+pFvYuRSq8KH8DmWpnbXe0pEPDHXZV3FcAbJkijJ5oNEnWw==",
-      "dev": true,
-      "bin": {
-        "tsc": "bin/tsc",
-        "tsserver": "bin/tsserver"
-      },
-      "engines": {
-        "node": ">=14.17"
-      }
-    },
-    "node_modules/undici-types": {
-      "version": "5.26.5",
-      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
-      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
-      "dev": true
-    },
-    "node_modules/vectordb": {
-      "resolved": "../node",
-      "link": true
-    }
-  }
-}
@@ -1,20 +0,0 @@
-{
-  "name": "lancedb-docs-test",
-  "version": "1.0.0",
-  "description": "auto-generated tests from doc",
-  "author": "dev@lancedb.com",
-  "license": "Apache 2",
-  "dependencies": {
-    "apache-arrow": "file:../node/node_modules/apache-arrow",
-    "vectordb": "file:../node"
-  },
-  "scripts": {
-    "build": "tsc -b && cd ../node && npm run build-release",
-    "example": "npm run build && node",
-    "test": "npm run build && ls dist/*.js | xargs -n 1 node"
-  },
-  "devDependencies": {
-    "@types/node": "^20.11.8",
-    "typescript": "^5.3.3"
-  }
-}
@@ -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.12</version>
+    <version>0.38.0</version>
 </dependency>
 ```

@@ -1,17 +0,0 @@
-{
-  "include": [
-    "src/*.ts",
-  ],
-  "compilerOptions": {
-    "target": "es2022",
-    "module": "nodenext",
-    "declaration": true,
-    "outDir": "./dist",
-    "strict": true,
-    "allowJs": true,
-    "resolveJsonModule": true,
-  },
-  "exclude": [
-    "./dist/*",
-  ]
-}
@@ -8,7 +8,7 @@
    <parent>
      <groupId>com.lancedb</groupId>
      <artifactId>lancedb-parent</artifactId>
-      <version>0.38.0-beta.12</version>
+      <version>0.38.0-final.0</version>
      <relativePath>../pom.xml</relativePath>
    </parent>

@@ -6,7 +6,7 @@

    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-parent</artifactId>
-    <version>0.38.0-beta.12</version>
+    <version>0.38.0-final.0</version>
    <packaging>pom</packaging>
    <name>${project.artifactId}</name>
    <description>LanceDB Java SDK Parent POM</description>
@@ -28,7 +28,7 @@
    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>12.0.0-beta.2</lance-core.version>
+        <lance-core.version>12.0.0-beta.5</lance-core.version>
        <spotless.skip>false</spotless.skip>
        <spotless.version>2.30.0</spotless.version>
        <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>
@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.38.0-beta.12"
+version = "0.38.0"
 publish = false
 license.workspace = true
 description.workspace = true
@@ -5,8 +5,8 @@ import packageJson = require("../package.json");

 describe("package metadata", () => {
  it("requires Node.js type declarations compatible with the runtime", () => {
-    expect(packageJson.engines.node).toBe(">= 18");
-    expect(packageJson.peerDependencies["@types/node"]).toBe(">=18");
+    expect(packageJson.engines.node).toBe(">= 22");
+    expect(packageJson.peerDependencies["@types/node"]).toBe(">=22");
    expect(packageJson.peerDependenciesMeta["@types/node"]).toEqual({
      optional: true,
    });
@@ -3,6 +3,7 @@

 import * as http from "http";
 import { RequestListener } from "http";
+import packageJson = require("../package.json");
 import {
  ClientConfig,
  Connection,
@@ -70,7 +71,13 @@ async function withMockDatabase(
  try {
    await callback(db);
  } finally {
-    server.close();
+    // `close()` alone leaves the port bound until keep-alive sockets drain, so
+    // a single failing test would cascade into EADDRINUSE for every test after
+    // it. Destroy the connections and wait for the port to actually be free.
+    await new Promise<void>((resolve) => {
+      server.closeAllConnections();
+      server.close(() => resolve());
+    });
  }
 }

@@ -131,7 +138,7 @@ describe("remote connection", () => {
      (req, res) => {
        expect(req.headers["x-api-key"]).toEqual("fake");
        expect(req.headers["user-agent"]).toEqual(
-          `LanceDB-Node-Client/${process.env.npm_package_version}`,
+          `LanceDB-Node-Client/${packageJson.version}`,
        );

        const body = JSON.stringify({ tables: [] });
@@ -8,7 +8,8 @@
    "//1": "--experimental-vm-modules is needed to run jest with sentence-transformers",
    "//2": "--testEnvironment is needed to run jest with sentence-transformers",
    "//3": "See: https://github.com/huggingface/transformers.js/issues/57",
-    "test": "node --experimental-vm-modules node_modules/.bin/jest --testEnvironment jest-environment-node-single-context --verbose",
+    "//4": "jest is invoked by its JS entry, not node_modules/.bin/jest: under pnpm that path is a shell shim, which `node` cannot execute",
+    "test": "node --experimental-vm-modules node_modules/jest/bin/jest.js --testEnvironment jest-environment-node-single-context --verbose",
    "lint": "biome check *.ts && biome format *.ts",
    "lint-ci": "biome ci .",
    "lint-fix": "biome check --write *.ts && pnpm format",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.38.0-beta.12",
+	"version": "0.38.0",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.38.0-beta.12",
+	"version": "0.38.0",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.38.0-beta.12",
+	"version": "0.38.0",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.38.0-beta.12",
+	"version": "0.38.0",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.38.0-beta.12",
+	"version": "0.38.0",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",
@@ -1,6 +1,6 @@
 {
  "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.38.0-beta.12",
+  "version": "0.38.0",
  "os": [
    "win32"
  ],
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.38.0-beta.12",
+	"version": "0.38.0",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",
@@ -11,7 +11,7 @@
    "ann"
  ],
  "private": false,
-  "version": "0.38.0-beta.12",
+  "version": "0.38.0",
  "main": "dist/index.js",
  "exports": {
    ".": "./dist/index.js",
@@ -67,7 +67,7 @@
    "timeout": "3m"
  },
  "engines": {
-    "node": ">= 18"
+    "node": ">= 22"
  },
  "packageManager": "pnpm@11.1.1",
  "cpu": ["x64", "arm64"],
@@ -101,7 +101,7 @@
    "openai": "4.29.2"
  },
  "peerDependencies": {
-    "@types/node": ">=18",
+    "@types/node": ">=22",
    "apache-arrow": ">=15.0.0 <=18.1.0"
  },
  "peerDependenciesMeta": {
@@ -664,7 +664,11 @@ impl JsFullTextQuery {
 }

 fn parse_fts_query(query: Object) -> napi::Result<FullTextSearchQuery> {
-    if let Ok(Some(query)) = query.get::<&JsFullTextQuery>("query") {
+    // `&JsFullTextQuery` recovers a native class reference through napi's borrow-tracked
+    // path, which is only usable from generated `#[napi]` argument conversion. This is a
+    // manual lookup on a nested `Object` property instead, so use `ClassInstance`, which
+    // unwraps the class without requiring a borrow scope.
+    if let Ok(Some(query)) = query.get::<ClassInstance<JsFullTextQuery>>("query") {
        Ok(FullTextSearchQuery::new_query(query.inner.clone()))
    } else if let Ok(Some(query_text)) = query.get::<String>("query") {
        let mut query_text = query_text;
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.38.0-beta.12"
+version = "0.38.0"
 publish = false
 edition.workspace = true
 description = "Python bindings for LanceDB"
@@ -150,9 +150,12 @@ class Connection(object):
    def job(self, job_id: str) -> Job: ...
    async def create_function_async(self, request_json: str) -> Job: ...
    async def get_function(self, name: str, version: str) -> str: ...
+    async def drop_function(self, name: str, version: str) -> bool: ...
    async def list_jobs(self) -> List[JobInfo]: ...
    async def get_job(self, job_id: str) -> Optional[JobDescription]: ...
    async def cancel_job(self, job_id: str) -> bool: ...
+    async def pause_job(self, job_id: str) -> str: ...
+    async def resume_job(self, job_id: str) -> str: ...
    async def job_history(
        self, job_id: Optional[str] = None
    ) -> List[pa.RecordBatch]: ...
@@ -606,6 +609,7 @@ class FullTextQuery:
 class PyQueryRequest:
    limit: Optional[int]
    offset: Optional[int]
+    take_offsets: Optional[List[int]]
    filter: Optional[Union[str, bytes]]
    full_text_search: Optional[FullTextQuery]
    select: Optional[Union[str, List[str]]]
@@ -712,6 +712,16 @@ class DBConnection(EnforceOverrides):
            "Function catalog operations are not supported for this connection type"
        )

+    def drop_function(self, name: str, *, version: str) -> bool:
+        """Drop one exact immutable Function version from the remote catalog.
+
+        Returns True when the version changed to Dropped and False for an
+        idempotent replay. Local connections raise NotImplementedError.
+        """
+        raise NotImplementedError(
+            "Function catalog operations are not supported for this connection type"
+        )
+
    def job(self, job_id: str) -> Job:
        """A [Job][lancedb.job.Job] handle for a server-side job by id.

@@ -743,6 +753,26 @@ class DBConnection(EnforceOverrides):
            "cancel_job is not supported for this connection type"
        )

+    def pause_job(self, job_id: str) -> str:
+        """Pause a server-side job by id.
+
+        The job's workers drain and it stays parked until resumed. Returns
+        "pausing", "already_paused", or "committing" -- a job finalizing its
+        results cannot be parked; retry shortly.
+        """
+        raise NotImplementedError("pause_job is not supported for this connection type")
+
+    def resume_job(self, job_id: str) -> str:
+        """Resume a paused server-side job by id.
+
+        Its workers pick their work back up from checkpoints. Returns
+        "resumed", "still_pausing" -- the pause's worker drain is not
+        confirmed yet; retry shortly -- or "not_paused".
+        """
+        raise NotImplementedError(
+            "resume_job is not supported for this connection type"
+        )
+
    def job_history(self, job_id: Optional[str] = None) -> List[pa.RecordBatch]:
        """The lifecycle event history of a server-side job, as Arrow batches.

@@ -1413,6 +1443,10 @@ class LanceDBConnection(DBConnection):
    def get_function(self, name: str, *, version: str) -> FunctionVersion:
        return LOOP.run(self._conn.get_function(name, version=version))

+    @override
+    def drop_function(self, name: str, *, version: str) -> bool:
+        return LOOP.run(self._conn.drop_function(name, version=version))
+
    @override
    def list_jobs(self) -> List[JobInfo]:
        """List server-side jobs across the database's tables."""
@@ -1436,6 +1470,22 @@ class LanceDBConnection(DBConnection):
        """
        return LOOP.run(self._conn.cancel_job(job_id))

+    @override
+    def pause_job(self, job_id: str) -> str:
+        """Pause a server-side job by id.
+
+        Returns "pausing", "already_paused", or "committing".
+        """
+        return LOOP.run(self._conn.pause_job(job_id))
+
+    @override
+    def resume_job(self, job_id: str) -> str:
+        """Resume a paused server-side job by id.
+
+        Returns "resumed", "still_pausing", or "not_paused".
+        """
+        return LOOP.run(self._conn.resume_job(job_id))
+
    @override
    def job_history(self, job_id: Optional[str] = None) -> List[pa.RecordBatch]:
        """The lifecycle event history of a server-side job, as Arrow batches.
@@ -2243,6 +2293,10 @@ class AsyncConnection(object):
        """Open one exact immutable Function version from the remote catalog."""
        return FunctionVersion.from_json(await self._inner.get_function(name, version))

+    async def drop_function(self, name: str, *, version: str) -> bool:
+        """Drop one exact immutable Function version from the remote catalog."""
+        return await self._inner.drop_function(name, version)
+
    async def list_jobs(self) -> List[JobInfo]:
        """List server-side jobs across the database's tables."""
        return await self._inner.list_jobs()
@@ -2263,6 +2317,23 @@ class AsyncConnection(object):
        """
        return await self._inner.cancel_job(job_id)

+    async def pause_job(self, job_id: str) -> str:
+        """Pause a server-side job by id.
+
+        The job's workers drain and it stays parked until resumed. Returns
+        "pausing", "already_paused", or "committing" -- a job finalizing its
+        results cannot be parked; retry shortly.
+        """
+        return await self._inner.pause_job(job_id)
+
+    async def resume_job(self, job_id: str) -> str:
+        """Resume a paused server-side job by id.
+
+        Its workers pick their work back up from checkpoints. Returns
+        "resumed", "still_pausing" -- retry shortly -- or "not_paused".
+        """
+        return await self._inner.resume_job(job_id)
+
    async def job_history(self, job_id: Optional[str] = None) -> List[pa.RecordBatch]:
        """The lifecycle event history of a server-side job, as Arrow batches.

@@ -49,11 +49,25 @@ from pydantic import (
    model_validator,
 )

+from .schema import is_blob_v2_field as _is_blob_v2_field
+
 _Int32 = conint(strict=True, ge=-(2**31), le=2**31 - 1)
 _UInt32 = conint(strict=True, ge=0, le=2**32 - 1)
 _UInt64 = conint(strict=True, ge=0, le=2**64 - 1)


+def _validate_gpu_wire_marker(value: Any) -> bool:
+    if value is not True:
+        raise ValueError("runtime.gpu must be true")
+    return True
+
+
+def _normalize_gpu_marker(value: bool) -> Optional[bool]:
+    if not isinstance(value, bool):
+        raise ValueError("gpu must be a boolean")
+    return True if value else None
+
+
 class _FrozenDict(dict):
    def _immutable(self, *args, **kwargs):
        raise TypeError("remote canonical values are immutable")
@@ -239,6 +253,23 @@ class PythonRuntimeSpec(_RemoteValue):
    python_version: Optional[str] = None
    environment: Optional[PythonEnvironmentSpec] = None
    env: Optional[Mapping[str, str]] = None
+    gpu: Optional[bool] = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def _discard_unknown_runtime_payload(cls, value):
+        if isinstance(value, Mapping):
+            kind = value.get("kind")
+            if isinstance(kind, str) and kind not in {"python", "python_v2"}:
+                return {"kind": kind}
+        return value
+
+    @field_validator("gpu", mode="before")
+    @classmethod
+    def _validate_gpu_marker(cls, value):
+        if value is None:
+            return None
+        return _validate_gpu_wire_marker(value)

    @model_validator(mode="after")
    def _validate_runtime_kind(self):
@@ -247,18 +278,28 @@ class PythonRuntimeSpec(_RemoteValue):
                raise ValueError("python runtime requires python_version")
            if self.environment is None:
                raise ValueError("python runtime requires environment")
+            if self.gpu is not None:
+                raise ValueError("python runtime with gpu requires kind='python_v2'")
+        elif self.kind == "python_v2":
+            if self.python_version is None:
+                raise ValueError("python_v2 runtime requires python_version")
+            if self.environment is None:
+                raise ValueError("python_v2 runtime requires environment")
+            if self.gpu is None:
+                raise ValueError("python_v2 runtime requires gpu")
        else:
            object.__setattr__(self, "python_version", None)
            object.__setattr__(self, "environment", None)
            object.__setattr__(self, "env", None)
+            object.__setattr__(self, "gpu", None)
        return self


 class FunctionVersion(_RemoteValue):
    """An exact immutable Function version returned by Enterprise.

-    Scheduling resources, priority, concurrency, and retry policy belong to
-    the submitting Job and are not part of this identity.
+    The GPU execution requirement is part of this identity. CPU and memory sizing,
+    priority, concurrency, and retry policy belong to the execution platform.
    """

    name: str
@@ -479,6 +520,7 @@ class RefreshColumnResult(_RemoteValue):


 _FUNCTION_NAME = re.compile(r"^[A-Za-z_][A-Za-z0-9_.-]*$")
+_FUNCTION_BLOB_V2_TYPE = "blob_v2"


 _GRAMMAR_PRIMITIVES = (
@@ -495,6 +537,7 @@ _GRAMMAR_PRIMITIVES = (
    (pa.float32(), "float32"),
    (pa.float64(), "float64"),
    (pa.string(), "utf8"),
+    (pa.large_string(), "large_utf8"),
    (pa.binary(), "binary"),
    (pa.date32(), "date32"),
    (pa.date64(), "date64"),
@@ -502,31 +545,177 @@ _GRAMMAR_PRIMITIVES = (


 def _canonical_arrow_type(data_type: pa.DataType) -> str:
-    """The server's V1 Function type grammar. Anything outside it is rejected
-    here rather than at registration."""
+    """The compact Function grammar, or canonical exact JSON for nested types."""
+    grammar = _grammar_arrow_type(data_type)
+    if grammar is not None:
+        return grammar
+    exact = _exact_arrow_type(data_type)
+    return json.dumps(exact, ensure_ascii=False, sort_keys=True, separators=(",", ":"))
+
+
+def _grammar_arrow_type(data_type: pa.DataType) -> Optional[str]:
    for candidate, name in _GRAMMAR_PRIMITIVES:
        if data_type == candidate:
            return name
    if pa.types.is_list(data_type) or pa.types.is_large_list(data_type):
+        item = _grammar_list_item(data_type)
+        if item is None:
+            return None
        prefix = "list" if pa.types.is_list(data_type) else "large_list"
-        return f"{prefix}<{_canonical_list_item(data_type)}>"
+        return f"{prefix}<{item}>"
    if pa.types.is_fixed_size_list(data_type) and data_type.list_size > 0:
-        return (
-            f"fixed_size_list<{_canonical_list_item(data_type)}, {data_type.list_size}>"
-        )
-    raise TypeError(f"unsupported Arrow type for Function signature: {data_type}")
+        item = _grammar_list_item(data_type)
+        if item is not None:
+            return f"fixed_size_list<{item}, {data_type.list_size}>"
+    return None


-def _canonical_list_item(data_type: pa.DataType) -> str:
+def _grammar_list_item(data_type: pa.DataType) -> Optional[str]:
    """The grammar names only the item type; it always means a non-nullable
-    child called `item`, so any other child metadata cannot be represented."""
+    child called `item`, so other child properties require exact JSON."""
    child = data_type.value_field
    if child.name != "item" or child.nullable or child.metadata:
+        return None
+    return _grammar_arrow_type(child.type)
+
+
+def _validate_exact_arrow_field(field: pa.Field) -> None:
+    if not field.name:
        raise TypeError(
-            "unsupported Arrow type for Function signature: list items must be a "
-            f"non-nullable field named 'item', got {child}"
+            "unsupported Arrow type for Function signature: field names "
+            "must not be empty"
        )
-    return _canonical_arrow_type(child.type)
+    if _is_blob_v2_field(field):
+        if not _has_supported_blob_v2_layout(field):
+            raise TypeError(
+                "unsupported Arrow type for Function signature: lance.blob.v2 "
+                f"requires a supported Blob storage layout, got {field}"
+            )
+    elif field.metadata:
+        raise TypeError(
+            "unsupported Arrow type for Function signature: field metadata "
+            f"is not supported, got {field}"
+        )
+
+
+def _has_supported_blob_v2_layout(field: pa.Field) -> bool:
+    data_type = field.type
+    if isinstance(data_type, pa.ExtensionType):
+        data_type = data_type.storage_type
+    if not pa.types.is_struct(data_type):
+        return False
+
+    fields = tuple(data_type)
+
+    def matches(spec, compare_nullable) -> bool:
+        return len(fields) == len(spec) and all(
+            actual.name == name
+            and actual.type == expected_type
+            and (not check_nullable or actual.nullable == nullable)
+            for actual, (name, expected_type, nullable), check_nullable in zip(
+                fields, spec, compare_nullable
+            )
+        )
+
+    logical_minimal = (
+        ("data", pa.large_binary(), True),
+        ("uri", pa.utf8(), True),
+    )
+    logical_full = logical_minimal + (
+        ("position", pa.uint64(), True),
+        ("size", pa.uint64(), True),
+    )
+    prepared = (
+        ("kind", pa.uint8(), True),
+        ("data", pa.large_binary(), True),
+        ("uri", pa.utf8(), True),
+        ("blob_id", pa.uint32(), True),
+        ("blob_size", pa.uint64(), True),
+        ("position", pa.uint64(), True),
+    )
+    descriptor = (
+        ("kind", pa.uint8(), False),
+        ("position", pa.uint64(), False),
+        ("size", pa.uint64(), False),
+        ("blob_id", pa.uint32(), False),
+        ("blob_uri", pa.utf8(), False),
+    )
+    return (
+        matches(logical_minimal, (True, True))
+        or matches(logical_full, (True, True, False, False))
+        or matches(prepared, (True,) * len(prepared))
+        or matches(descriptor, (False,) * len(descriptor))
+    )
+
+
+def _canonical_arrow_field(field: pa.Field) -> str:
+    _validate_exact_arrow_field(field)
+    if _is_blob_v2_field(field):
+        return _FUNCTION_BLOB_V2_TYPE
+    return _canonical_arrow_type(field.type)
+
+
+def _exact_arrow_field(field: pa.Field) -> dict[str, Any]:
+    _validate_exact_arrow_field(field)
+    if _is_blob_v2_field(field):
+        raise TypeError(
+            "unsupported Arrow type for Function signature: nested Blob v2 "
+            "fields are not supported; declare Blob parameters or named result "
+            "fields directly"
+        )
+    value = {
+        "name": field.name,
+        "nullable": field.nullable,
+        "type": _exact_arrow_type(field.type),
+    }
+    return value
+
+
+def _exact_arrow_type(data_type: pa.DataType) -> dict[str, Any]:
+    for candidate, name in _GRAMMAR_PRIMITIVES:
+        if data_type == candidate:
+            return {"type": name}
+    if pa.types.is_struct(data_type):
+        fields = list(data_type)
+        names = [field.name for field in fields]
+        if not fields or len(set(names)) != len(names):
+            raise TypeError(
+                "unsupported Arrow type for Function signature: structs must have "
+                "non-empty, uniquely named fields"
+            )
+        return {
+            "type": "struct",
+            "fields": [_exact_arrow_field(field) for field in fields],
+        }
+    if (
+        pa.types.is_list(data_type)
+        or pa.types.is_large_list(data_type)
+        or pa.types.is_fixed_size_list(data_type)
+    ):
+        if pa.types.is_fixed_size_list(data_type):
+            if data_type.value_field.name != "item":
+                raise TypeError(
+                    "unsupported Arrow type for Function signature: fixed-size list "
+                    "items must be named 'item'"
+                )
+            if data_type.list_size <= 0:
+                raise TypeError(
+                    f"unsupported Arrow type for Function signature: {data_type}"
+                )
+        value: dict[str, Any] = {
+            "type": (
+                "list"
+                if pa.types.is_list(data_type)
+                else "large_list"
+                if pa.types.is_large_list(data_type)
+                else "fixed_size_list"
+            ),
+            "fields": [_exact_arrow_field(data_type.value_field)],
+        }
+        if pa.types.is_fixed_size_list(data_type):
+            value["length"] = data_type.list_size
+        return value
+    raise TypeError(f"unsupported Arrow type for Function signature: {data_type}")


 def _list_of(item: pa.DataType) -> pa.DataType:
@@ -600,8 +789,15 @@ def _callable_parameters(function: Callable[..., Any]) -> tuple[inspect.Paramete

 def _function_output(output: pa.DataType | pa.Field | pa.Schema) -> FunctionOutput:
    if isinstance(output, pa.Schema):
+        if output.metadata:
+            raise TypeError("Function output schema metadata is not supported")
        fields = tuple(output)
-    elif isinstance(output, pa.Field) and pa.types.is_struct(output.type):
+    elif (
+        isinstance(output, pa.Field)
+        and not _is_blob_v2_field(output)
+        and pa.types.is_struct(output.type)
+    ):
+        _validate_exact_arrow_field(output)
        if output.nullable:
            raise ValueError("Function output must be non-nullable")
        fields = tuple(output.type)
@@ -617,11 +813,12 @@ def _function_output(output: pa.DataType | pa.Field | pa.Schema) -> FunctionOutp
            raise TypeError(
                "output_schema must be a PyArrow DataType, Field, or Schema"
            )
+        _validate_exact_arrow_field(field)
        if field.nullable:
            raise ValueError("Function output must be non-nullable")
        return FunctionOutput(
            kind="scalar",
-            arrow_type=_canonical_arrow_type(field.type),
+            arrow_type=_canonical_arrow_field(field),
            nullable=False,
        )

@@ -629,6 +826,8 @@ def _function_output(output: pa.DataType | pa.Field | pa.Schema) -> FunctionOutp
        raise ValueError("named-struct Function output must contain at least one field")
    if any(field.nullable for field in fields):
        raise ValueError("Function output fields must be non-nullable")
+    for field in fields:
+        _validate_exact_arrow_field(field)
    names = [field.name for field in fields]
    if len(set(names)) != len(names):
        raise ValueError("Function output field names must be unique")
@@ -637,7 +836,7 @@ def _function_output(output: pa.DataType | pa.Field | pa.Schema) -> FunctionOutp
        fields=tuple(
            FunctionResultField(
                name=field.name,
-                arrow_type=_canonical_arrow_type(field.type),
+                arrow_type=_canonical_arrow_field(field),
                nullable=False,
            )
            for field in fields
@@ -657,6 +856,10 @@ def _infer_signature(
    if input_schema is not None:
        if not isinstance(input_schema, pa.Schema):
            raise TypeError("input_schema must be a PyArrow Schema")
+        if input_schema.metadata:
+            raise TypeError("Function input schema metadata is not supported")
+        for field in input_schema:
+            _validate_exact_arrow_field(field)
        expected = tuple(parameter.name for parameter in parameters)
        actual = tuple(input_schema.names)
        if actual != expected:
@@ -667,7 +870,7 @@ def _infer_signature(
        inputs = tuple(
            FunctionParameter(
                name=field.name,
-                arrow_type=_canonical_arrow_type(field.type),
+                arrow_type=_canonical_arrow_field(field),
                nullable=field.nullable,
            )
            for field in input_schema
@@ -690,7 +893,9 @@ def _infer_signature(
        inputs.append(
            FunctionParameter(
                name=parameter.name,
-                arrow_type=_canonical_arrow_type(data_type),
+                arrow_type=_canonical_arrow_field(
+                    pa.field(parameter.name, data_type, nullable=nullable)
+                ),
                nullable=nullable,
            )
        )
@@ -910,6 +1115,7 @@ class UdfDefinition:
        pip: tuple[str, ...],
        env: Mapping[str, str],
        python_version: Optional[str],
+        gpu: bool = False,
        conda: tuple[str, ...] = (),
        conda_channels: tuple[str, ...] = (),
    ):
@@ -938,12 +1144,14 @@ class UdfDefinition:
        signature = _infer_signature(function, input_schema, output_schema)
        source = _package_source(function)
        digest = f"sha256:{hashlib.sha256(source).hexdigest()}"
+        gpu_marker = _normalize_gpu_marker(gpu)
        runtime = PythonRuntimeSpec(
-            kind="python",
+            kind="python_v2" if gpu_marker is not None else "python",
            python_version=python_version
            or f"{sys.version_info.major}.{sys.version_info.minor}",
            environment=environment_spec,
            env=environment,
+            gpu=gpu_marker,
        )
        self._function = function
        self._request = FunctionRegistrationRequest(
@@ -989,6 +1197,7 @@ def udf(
    pip: tuple[str, ...] | list[str] = (),
    env: Optional[Mapping[str, str]] = None,
    python_version: Optional[str] = None,
+    gpu: bool = False,
    conda: tuple[str, ...] | list[str] = (),
    conda_channels: tuple[str, ...] | list[str] = (),
 ) -> Callable[[Callable[..., Any]], UdfDefinition]: ...
@@ -1003,6 +1212,7 @@ def udf(
    pip: tuple[str, ...] | list[str] = (),
    env: Optional[Mapping[str, str]] = None,
    python_version: Optional[str] = None,
+    gpu: bool = False,
    conda: tuple[str, ...] | list[str] = (),
    conda_channels: tuple[str, ...] | list[str] = (),
 ):
@@ -1035,6 +1245,10 @@ def udf(
        Environment variables included in the Function definition.
    python_version : str, optional
        Remote Python major/minor version. Defaults to the client version.
+    gpu : bool, default False
+        Whether every remote execution requires a GPU. The execution platform
+        selects one compatible GPU for each worker. The requirement is part of
+        the immutable Function version.

    The packaged artifact is a snapshot: the function source plus exactly
    the module-level names it references (modules as imports, importable
@@ -1059,6 +1273,11 @@ def udf(
    ...     return value * 2
    >>> score(1.5)
    3.0
+    >>> @udf(pip=["cupy-cuda12x"], gpu=True)
+    ... def gpu_score(value: int) -> int:
+    ...     return value * 2
+    >>> gpu_score.registration_request.runtime.gpu
+    True
    """

    def decorate(target: Callable[..., Any]) -> UdfDefinition:
@@ -1070,6 +1289,7 @@ def udf(
            pip=tuple(pip),
            env={} if env is None else env,
            python_version=python_version,
+            gpu=gpu,
            conda=tuple(conda),
            conda_channels=tuple(conda_channels),
        )
@@ -109,6 +109,7 @@ def _query_is_plain_scan(query: Query) -> bool:
    return (
        query.vector is None
        and query.full_text_query is None
+        and query.take_offsets is None
        and not query.postfilter
        and not query.order_by
    )
@@ -804,6 +805,10 @@ class Query(pydantic.BaseModel):
    # offset to start fetching results from
    offset: Optional[int] = None

+    # Dataset offsets whose duplicate occurrences must be restored after lookup.
+    # This is populated when a take query is converted to this serializable form.
+    take_offsets: Optional[List[int]] = None
+
    # if true, will only search the indexed data
    fast_search: Optional[bool] = None

@@ -825,6 +830,7 @@ class Query(pydantic.BaseModel):
        query = cls()
        query.limit = req.limit
        query.offset = req.offset
+        query.take_offsets = req.take_offsets
        query.filter = req.filter
        query.full_text_query = req.full_text_search
        query.columns = req.select
@@ -749,6 +749,10 @@ class RemoteDBConnection(DBConnection):
    def get_function(self, name: str, *, version: str) -> FunctionVersion:
        return LOOP.run(self._conn.get_function(name, version=version))

+    @override
+    def drop_function(self, name: str, *, version: str) -> bool:
+        return LOOP.run(self._conn.drop_function(name, version=version))
+
    @override
    def list_jobs(self) -> List["JobInfo"]:
        """List server-side jobs across the database's tables."""
@@ -772,6 +776,22 @@ class RemoteDBConnection(DBConnection):
        """
        return LOOP.run(self._conn.cancel_job(job_id))

+    @override
+    def pause_job(self, job_id: str) -> str:
+        """Pause a server-side job by id.
+
+        Returns "pausing", "already_paused", or "committing".
+        """
+        return LOOP.run(self._conn.pause_job(job_id))
+
+    @override
+    def resume_job(self, job_id: str) -> str:
+        """Resume a paused server-side job by id.
+
+        Returns "resumed", "still_pausing", or "not_paused".
+        """
+        return LOOP.run(self._conn.resume_job(job_id))
+
    @override
    def job_history(self, job_id: Optional[str] = None) -> List[pa.RecordBatch]:
        """The lifecycle event history of a server-side job, as Arrow batches.
@@ -67,7 +67,15 @@ from ..query import (
    LanceTakeQueryBuilder,
    LanceVectorQueryBuilder,
 )
-from ..table import AsyncTable, BlobMode, Branches, IndexStatistics, Query, Table, Tags
+from ..table import (
+    AsyncTable,
+    BlobMode,
+    Branches,
+    IndexStatistics,
+    Query,
+    Table,
+    Tags,
+)
 from ..types import BaseTokenizerType


@@ -559,18 +559,12 @@ def _coerce_blob_list_values(


 def _coerce_value_to_blob(values: pa.Array, target_field: pa.Field) -> pa.Array:
-    if _is_string_like(values.type):
-        carrier_name = "uri"
-        carrier = values
-    elif pa.types.is_null(values.type):
-        carrier_name = None
-        carrier = None
+    if pa.types.is_null(values.type):
+        data = pa.nulls(len(values), type=pa.large_binary())
    elif pa.types.is_large_binary(values.type):
-        carrier_name = "data"
-        carrier = values
+        data = values
    else:
-        carrier_name = "data"
-        carrier = values.cast(pa.large_binary())
+        data = values.cast(pa.large_binary())
    length = len(values)
    storage_type = target_field.type
    if isinstance(storage_type, pa.ExtensionType):
@@ -578,8 +572,8 @@ def _coerce_value_to_blob(values: pa.Array, target_field: pa.Field) -> pa.Array:
    storage_fields = list(storage_type)
    children = []
    for storage_field in storage_fields:
-        if storage_field.name == carrier_name:
-            children.append(carrier.cast(storage_field.type))
+        if storage_field.name == "data":
+            children.append(data)
        else:
            children.append(pa.nulls(length, type=storage_field.type))
    storage = pa.StructArray.from_arrays(
@@ -599,11 +593,7 @@ def _physical_array_and_type(array: pa.Array) -> tuple[pa.Array, pa.DataType]:


 def _can_coerce_to_blob(data_type: pa.DataType) -> bool:
-    return (
-        _is_binary_like(data_type)
-        or _is_string_like(data_type)
-        or pa.types.is_null(data_type)
-    )
+    return _is_binary_like(data_type) or pa.types.is_null(data_type)


 def _is_binary_like(data_type: pa.DataType) -> bool:
@@ -614,15 +604,6 @@ def _is_binary_like(data_type: pa.DataType) -> bool:
    )


-def _is_string_like(data_type: pa.DataType) -> bool:
-    predicates = ("is_string", "is_large_string", "is_string_view")
-    return any(
-        predicate(data_type)
-        for name in predicates
-        if (predicate := getattr(pa.types, name, None)) is not None
-    )
-
-
 def _field_extension_name(field: pa.Field) -> Optional[str]:
    extension_name = getattr(field.type, "extension_name", None)
    if extension_name is not None:
@@ -637,187 +618,6 @@ 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 _align_field_types(
    fields: List[pa.Field],
    target_fields: List[pa.Field],
@@ -1878,9 +1678,9 @@ class Table(ABC):
        Offsets are mostly useful for sampling as the set of all valid offsets is easily
        known in advance to be [0, len(table)).

-        No guarantees are made regarding the order in which results are returned.  If
-        you desire an output order that matches the order of the given offsets, you will
-        need to add the row offset column to the output and align it yourself.
+        No guarantees are made regarding the order in which results are returned.
+        Repeated offsets produce repeated rows, which makes this method suitable for
+        sampling with replacement.

        Parameters
        ----------
@@ -2365,9 +2165,11 @@ class Table(ABC):
            Function columns are supported only on LanceDB Cloud and
            Enterprise.
        computed: Dict[str, str], optional
-            A map of column name to a SQL expression defining the column. The
-            column's type and inputs are derived from the expression, so no
-            data type is supplied.
+            A mapping from output column names to SQL expressions derives each
+            output field from its expression. A direct projection of a Blob v2
+            field inherits Blob v2 semantics; other expressions derive their
+            ordinary Arrow type. Mapping order is declaration and dependency
+            order.

            Unlike ``transforms``, the expression is stored rather than
            evaluated now: the column is committed with no values, and rows get
@@ -4288,6 +4090,7 @@ class LanceTable(Table):
            )
            and not self._route_pushdown_to_rust
            and self.current_branch() is None
+            and query.take_offsets is None
        ):
            from lancedb.namespace import _execute_server_side_query

@@ -5824,9 +5627,6 @@ 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":
@@ -6184,7 +5984,23 @@ class AsyncTable:

    def _sync_query_to_async(
        self, query: Query
-    ) -> AsyncHybridQuery | AsyncFTSQuery | AsyncVectorQuery | AsyncQuery:
+    ) -> (
+        AsyncHybridQuery
+        | AsyncFTSQuery
+        | AsyncVectorQuery
+        | AsyncQuery
+        | AsyncTakeQuery
+    ):
+        if query.take_offsets is not None:
+            take_query = self.take_offsets(query.take_offsets)
+            if query.columns:
+                take_query = take_query.select(query.columns)
+            if query.use_lsm is not None:
+                take_query = take_query.use_lsm(query.use_lsm)
+            if query.with_row_id:
+                take_query = take_query.with_row_id()
+            return take_query
+
        async_query = self.query()
        if query.limit is not None:
            async_query = async_query.limit(query.limit)
@@ -6249,6 +6065,7 @@ class AsyncTable:
                self._namespace_client, self._pushdown_operations
            )
            and not self._route_pushdown_to_rust
+            and query.take_offsets is None
        ):
            from lancedb.namespace import _execute_server_side_query

@@ -6471,8 +6288,11 @@ class AsyncTable:
            Function columns are supported only on LanceDB Cloud and
            Enterprise.
        computed: Dict[str, str], optional
-            A map of column name to a SQL expression defining the column. The
-            column's type and inputs are derived from the expression.
+            A mapping from output column names to SQL expressions derives each
+            output field from its expression. A direct projection of a Blob v2
+            field inherits Blob v2 semantics; other expressions derive their
+            ordinary Arrow type. Mapping order is declaration and dependency
+            order.

            Unlike ``transforms``, the expression is stored rather than
            evaluated now: the column is committed with no values, and rows get
@@ -6743,6 +6563,9 @@ class AsyncTable:
        Offsets are mostly useful for sampling as the set of all valid offsets is easily
        known in advance to be [0, len(table)).

+        No guarantees are made regarding the order in which results are returned.
+        Repeated offsets produce repeated rows.
+
        Parameters
        ----------
        offsets: list[int]
@@ -710,80 +710,6 @@ 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",
@@ -1304,7 +1230,6 @@ 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,
    )

@@ -12,6 +12,8 @@ from datetime import date
 import http.server
 import json
 from pathlib import Path
+import subprocess
+import sys
 import threading
 from typing import Optional

@@ -19,7 +21,13 @@ import pyarrow as pa
 import pytest

 import lancedb
-from lancedb.functions import UdfDefinition, udf
+from lancedb.functions import (
+    PythonRuntimeSpec,
+    UdfDefinition,
+    _canonical_arrow_type,
+    _GRAMMAR_PRIMITIVES,
+    udf,
+)

 THRESHOLD = 20
 _CACHE = None
@@ -61,6 +69,80 @@ def test_scalar_udf_matches_shared_registration_golden_and_remains_callable():
    }


+def _main_udf_source(
+    *, threshold: int = 20, input_annotation: str = "int", comparison: str = ">="
+) -> str:
+    return (
+        "from __future__ import annotations\n"
+        "from lancedb.functions import udf\n"
+        f"THRESHOLD = {threshold}\n"
+        "\n"
+        "@udf\n"
+        f"def label(value: {input_annotation}) -> str:\n"
+        f"    return 'big' if value {comparison} THRESHOLD else 'small'\n"
+        "\n"
+        "assert label.__module__ == '__main__'\n"
+        "print(label.registration_request.to_canonical_json())\n"
+    )
+
+
+def _run_main_udf(path: Path, source: str) -> dict:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(source)
+    result = subprocess.run(
+        [sys.executable, str(path)],
+        check=True,
+        capture_output=True,
+        text=True,
+    )
+    return json.loads(result.stdout)
+
+
+def test_main_udf_registration_identity_is_stable_across_processes_and_paths(
+    tmp_path,
+):
+    source = _main_udf_source()
+    original_path = tmp_path / "original" / "job.py"
+    moved_path = tmp_path / "moved" / "renamed_job.py"
+
+    original_runs = [_run_main_udf(original_path, source) for _ in range(2)]
+    moved_run = _run_main_udf(moved_path, source)
+
+    assert len({run["artifact"]["digest"] for run in [*original_runs, moved_run]}) == 1
+    assert all(
+        run["signature"] == original_runs[0]["signature"]
+        for run in [original_runs[1], moved_run]
+    )
+    assert original_runs[0] == original_runs[1] == moved_run
+
+    body_change = _run_main_udf(
+        tmp_path / "changes" / "body.py", _main_udf_source(comparison=">")
+    )
+    global_change = _run_main_udf(
+        tmp_path / "changes" / "global.py", _main_udf_source(threshold=21)
+    )
+    annotation_change = _run_main_udf(
+        tmp_path / "changes" / "annotation.py",
+        _main_udf_source(input_annotation="float"),
+    )
+
+    baseline = original_runs[0]
+    assert baseline["signature"] == body_change["signature"]
+    assert baseline["signature"] == global_change["signature"]
+    assert baseline["signature"] != annotation_change["signature"]
+    assert (
+        len(
+            {
+                baseline["artifact"]["digest"],
+                body_change["artifact"]["digest"],
+                global_change["artifact"]["digest"],
+                annotation_change["artifact"]["digest"],
+            }
+        )
+        == 4
+    )
+
+
 def _run_packaged(definition, *args):
    """Execute the shipped artifact in a fresh namespace, as a worker would."""
    source = base64.b64decode(definition.registration_request.artifact.content.data)
@@ -89,6 +171,58 @@ def test_udf_conda_environment():
        udf(name="channels", conda_channels=["conda-forge"])(lambda value: value)


+def test_udf_gpu_marker_uses_gpu_runtime():
+    @udf(pip=["cupy-cuda12x"], gpu=True)
+    def double_on_gpu(value: int) -> int:
+        return value * 2
+
+    request = json.loads(double_on_gpu.registration_request.to_canonical_json())
+    assert request["runtime"]["kind"] == "python_v2"
+    assert request["runtime"]["gpu"] is True
+
+    @udf(pip=["pyarrow"])
+    def cpu_function(value: int) -> int:
+        return value
+
+    cpu_runtime = json.loads(cpu_function.registration_request.to_canonical_json())[
+        "runtime"
+    ]
+    assert cpu_runtime["kind"] == "python"
+    assert "gpu" not in cpu_runtime
+
+    def identity(value: int) -> int:
+        return value
+
+    for invalid in [None, 0, 1, -1, 1.5, "", "true", "1", "H100"]:
+        with pytest.raises(ValueError, match="gpu must be a boolean"):
+            udf(name="invalid_gpu", gpu=invalid)(identity)
+
+    base_runtime = {
+        "kind": "python_v2",
+        "python_version": "3.12",
+        "environment": {"kind": "pip"},
+    }
+    runtime = PythonRuntimeSpec.model_validate({**base_runtime, "gpu": True})
+    assert runtime.gpu is True
+    for invalid in [False, 1, 0, "", "true", "1", "H100"]:
+        with pytest.raises(ValueError, match="runtime.gpu must be true"):
+            PythonRuntimeSpec.model_validate({**base_runtime, "gpu": invalid})
+
+
+def test_unknown_runtime_discards_payload_before_known_field_validation():
+    for payload in [
+        {"kind": "python_v3", "gpu": {"model": "H100"}},
+        {"kind": "python_v3", "resources": []},
+        {
+            "kind": "python_v3",
+            "environment": {"kind": []},
+            "python_version": 3.15,
+        },
+    ]:
+        runtime = PythonRuntimeSpec.model_validate(payload)
+        assert runtime.to_canonical_json() == '{"kind":"python_v3"}'
+
+
 def test_udf_packages_attribute_access_and_body_imports():
    @udf
    def word_norm(body: str) -> float:
@@ -168,9 +302,7 @@ def test_udf_resolves_module_globals_before_builtins(tmp_path):
        udf(module.uses_callable_shadow)


-def test_canonical_arrow_type_is_exactly_the_grammar():
-    from lancedb.functions import _GRAMMAR_PRIMITIVES, _canonical_arrow_type
-
+def test_canonical_arrow_type_prefers_the_compact_grammar():
    golden = json.loads(
        (
            Path(__file__).parents[3]
@@ -181,14 +313,19 @@ def test_canonical_arrow_type_is_exactly_the_grammar():
        case["arrow_type"] for case in golden["valid"] if "<" not in case["arrow_type"]
    ]
    assert [name for _, name in _GRAMMAR_PRIMITIVES] == primitives
+    assert _canonical_arrow_type(pa.list_(pa.field("item", pa.float32(), False))) == (
+        "list<float32>"
+    )
+    assert (
+        _canonical_arrow_type(pa.large_list(pa.field("item", pa.float32(), False)))
+        == "large_list<float32>"
+    )
    for outside in [
        pa.timestamp("us"),
        pa.decimal128(10, 2),
-        pa.large_string(),
        pa.large_binary(),
        pa.binary(4),
        pa.duration("s"),
-        pa.struct([pa.field("a", pa.int32())]),
        pa.list_(pa.float32(), 0),
        pa.list_(pa.timestamp("us")),
    ]:
@@ -378,14 +515,27 @@ def test_udf_recursion_versus_a_rebound_module_name(tmp_path):
        udf(raw_fact)


-def test_canonical_arrow_type_rejects_unrepresentable_list_children():
-    from lancedb.functions import _canonical_arrow_type
-
+def test_canonical_arrow_type_uses_exact_json_for_list_child_properties():
+    nullable = pa.list_(pa.float32())
+    assert json.loads(_canonical_arrow_type(nullable)) == {
+        "type": "list",
+        "fields": [
+            {
+                "name": "item",
+                "nullable": True,
+                "type": {"type": "float32"},
+            }
+        ],
+    }
+    named = pa.list_(pa.field("custom", pa.float32(), nullable=False))
+    assert json.loads(_canonical_arrow_type(named))["fields"][0]["name"] == "custom"
    for outside in [
-        pa.list_(pa.float32()),  # pyarrow default: nullable child
-        pa.list_(pa.field("custom", pa.float32(), nullable=False)),
        pa.list_(pa.field("item", pa.float32(), nullable=False, metadata={"k": "v"})),
        pa.list_(pa.field("item", pa.float32(), nullable=False), 0),
+        pa.list_(
+            pa.field("item", pa.float32(), nullable=False, metadata={"k": "v"}), 3
+        ),
+        pa.list_(pa.field("custom", pa.float32(), nullable=False), 3),
    ]:
        with pytest.raises(TypeError, match="unsupported Arrow type"):
            _canonical_arrow_type(outside)
@@ -395,6 +545,29 @@ def test_canonical_arrow_type_rejects_unrepresentable_list_children():
        )
        == "fixed_size_list<float32, 3>"
    )
+    fixed = json.loads(_canonical_arrow_type(pa.list_(pa.float32(), 3)))
+    assert fixed == {
+        "type": "fixed_size_list",
+        "fields": [
+            {
+                "name": "item",
+                "nullable": True,
+                "type": {"type": "float32"},
+            }
+        ],
+        "length": 3,
+    }
+    large = json.loads(_canonical_arrow_type(pa.large_list(pa.float32())))
+    assert large["type"] == "large_list"
+    assert large["fields"][0]["nullable"] is True
+
+    for invalid_struct in [
+        pa.struct([]),
+        pa.struct([pa.field("a", pa.int32()), pa.field("a", pa.int64())]),
+        pa.struct([pa.field("", pa.int32())]),
+    ]:
+        with pytest.raises(TypeError, match="unsupported Arrow type"):
+            _canonical_arrow_type(invalid_struct)


 def _calls_missing(value: int) -> int:
@@ -432,6 +605,7 @@ def _arrow_type_from_golden(spec: dict) -> pa.DataType:
        "null": pa.null(),
        "bool": pa.bool_(),
        "utf8": pa.string(),
+        "large_utf8": pa.large_string(),
        "binary": pa.binary(),
        "float16": pa.float16(),
        "float32": pa.float32(),
@@ -448,8 +622,6 @@ def test_arrow_type_grammar_matches_the_shared_golden():
            / "rust/lancedb/tests/fixtures/first_class_functions/v1/arrow_types.json"
        ).read_text()
    )
-    from lancedb.functions import _canonical_arrow_type
-
    emitted = {
        case["arrow_type"]: _canonical_arrow_type(_arrow_type_from_golden(case["json"]))
        for case in golden["valid"]
@@ -482,6 +654,250 @@ def test_explicit_arrow_schema_is_deterministic():
    assert signature.output.nullable is False


+def test_blob_fields_use_the_scalar_function_semantic_type():
+    @udf(
+        input_schema=pa.schema([lancedb.blob("image", nullable=False)]),
+        output_schema=lancedb.blob("result", nullable=False),
+    )
+    def copy_blob(image):
+        return image
+
+    signature = copy_blob.registration_request.signature
+    assert signature.inputs[0].arrow_type == "blob_v2"
+    assert signature.output.kind == "scalar"
+    assert signature.output.arrow_type == "blob_v2"
+
+
+def test_named_struct_function_can_include_a_blob_result_field():
+    @udf(
+        input_schema=pa.schema([lancedb.blob("image", nullable=False)]),
+        output_schema=pa.schema(
+            [
+                lancedb.blob("thumbnail", nullable=False),
+                pa.field("width", pa.int32(), nullable=False),
+            ]
+        ),
+    )
+    def inspect_blob(image):
+        return {"thumbnail": image, "width": 1}
+
+    output = inspect_blob.registration_request.signature.output
+    assert output.kind == "named_struct"
+    assert [(field.name, field.arrow_type) for field in output.fields] == [
+        ("thumbnail", "blob_v2"),
+        ("width", "int32"),
+    ]
+
+
+def test_metadata_marked_blob_field_uses_the_semantic_type():
+    extension = lancedb.blob("image", nullable=False).type
+    storage = (
+        extension.storage_type if isinstance(extension, pa.ExtensionType) else extension
+    )
+    metadata_blob = pa.field(
+        "image",
+        storage,
+        nullable=False,
+        metadata={"ARROW:extension:name": "lance.blob.v2"},
+    )
+
+    @udf(
+        input_schema=pa.schema([metadata_blob]),
+        output_schema=pa.field("size", pa.int64(), nullable=False),
+    )
+    def blob_size(image):
+        return len(image)
+
+    assert blob_size.registration_request.signature.inputs[0].arrow_type == "blob_v2"
+
+
+def test_blob_marker_rejects_invalid_storage_layout():
+    malformed = pa.field(
+        "image",
+        pa.int64(),
+        nullable=False,
+        metadata={"ARROW:extension:name": "lance.blob.v2"},
+    )
+
+    with pytest.raises(TypeError, match="requires a supported Blob storage layout"):
+
+        @udf(
+            input_schema=pa.schema([malformed]),
+            output_schema=pa.field("size", pa.int64(), nullable=False),
+        )
+        def blob_size(image):
+            return len(image)
+
+
+def test_nested_blob_signature_field_has_a_clear_error():
+    nested = pa.field(
+        "value",
+        pa.struct([lancedb.blob("image", nullable=False)]),
+        nullable=False,
+    )
+    with pytest.raises(TypeError, match="nested Blob v2 fields are not supported"):
+
+        @udf(
+            input_schema=pa.schema([nested]),
+            output_schema=pa.field("size", pa.int64(), nullable=False),
+        )
+        def blob_size(value):
+            return len(value["image"])
+
+
+def test_nested_non_blob_extension_is_not_silently_unwrapped():
+    class TestExtension(pa.ExtensionType):
+        def __init__(self):
+            super().__init__(pa.int64(), "test.function.extension")
+
+        def __arrow_ext_serialize__(self):
+            return b""
+
+        @classmethod
+        def __arrow_ext_deserialize__(cls, storage_type, serialized):
+            return cls()
+
+    nested = pa.field(
+        "value",
+        pa.struct([pa.field("extended", TestExtension(), nullable=False)]),
+        nullable=False,
+    )
+    with pytest.raises(TypeError, match="unsupported Arrow type"):
+
+        @udf(
+            input_schema=pa.schema([nested]),
+            output_schema=pa.field("result", pa.int64(), nullable=False),
+        )
+        def extension_value(value):
+            return value["extended"]
+
+
+def test_explicit_large_utf8_schemas_use_the_canonical_function_name():
+    input_schema = pa.schema([pa.field("text", pa.large_string(), nullable=True)])
+    output_schema = pa.field("result", pa.large_string(), nullable=False)
+
+    @udf(input_schema=input_schema, output_schema=output_schema)
+    def preserve(text):
+        return text
+
+    signature = preserve.registration_request.signature
+    assert signature.inputs[0].arrow_type == "large_utf8"
+    assert signature.inputs[0].nullable is True
+    assert signature.output.arrow_type == "large_utf8"
+    assert signature.output.nullable is False
+
+    nested = pa.struct([pa.field("text", pa.large_string(), nullable=True)])
+    assert json.loads(_canonical_arrow_type(nested)) == {
+        "type": "struct",
+        "fields": [
+            {
+                "name": "text",
+                "nullable": True,
+                "type": {"type": "large_utf8"},
+            }
+        ],
+    }
+
+
+def test_nested_struct_output_uses_canonical_exact_json():
+    token = pa.struct(
+        [
+            pa.field("position", pa.int32(), nullable=False),
+            pa.field("value", pa.string(), nullable=False),
+            pa.field("length", pa.int32(), nullable=False),
+        ]
+    )
+    analysis = pa.struct(
+        [
+            pa.field("normalized_text", pa.string(), nullable=False),
+            pa.field("has_content", pa.bool_(), nullable=False),
+            pa.field(
+                "metrics",
+                pa.struct(
+                    [
+                        pa.field("character_count", pa.int64(), nullable=False),
+                        pa.field("word_count", pa.int32(), nullable=False),
+                        pa.field("average_word_length", pa.float64(), nullable=False),
+                    ]
+                ),
+                nullable=False,
+            ),
+            pa.field(
+                "diagnostics",
+                pa.struct(
+                    [
+                        pa.field("status", pa.string(), nullable=False),
+                        pa.field(
+                            "normalization",
+                            pa.struct(
+                                [
+                                    pa.field("changed", pa.bool_(), nullable=False),
+                                    pa.field(
+                                        "original_length", pa.int64(), nullable=False
+                                    ),
+                                ]
+                            ),
+                            nullable=False,
+                        ),
+                    ]
+                ),
+                nullable=False,
+            ),
+            pa.field(
+                "token_preview",
+                pa.list_(pa.field("item", token, nullable=False)),
+                nullable=False,
+            ),
+        ]
+    )
+
+    @udf(
+        input_schema=pa.schema([pa.field("text", pa.string(), nullable=False)]),
+        output_schema=pa.field("analysis", analysis, nullable=False),
+    )
+    def analyze(text):
+        return {"normalized_text": text}
+
+    output = analyze.registration_request.signature.output
+    assert output.kind == "named_struct"
+    assert [field.name for field in output.fields] == [
+        "normalized_text",
+        "has_content",
+        "metrics",
+        "diagnostics",
+        "token_preview",
+    ]
+    metrics = json.loads(output.fields[2].arrow_type)
+    assert metrics == {
+        "type": "struct",
+        "fields": [
+            {
+                "name": "character_count",
+                "nullable": False,
+                "type": {"type": "int64"},
+            },
+            {
+                "name": "word_count",
+                "nullable": False,
+                "type": {"type": "int32"},
+            },
+            {
+                "name": "average_word_length",
+                "nullable": False,
+                "type": {"type": "float64"},
+            },
+        ],
+    }
+    preview = json.loads(output.fields[4].arrow_type)
+    assert preview["type"] == "list"
+    assert preview["fields"][0]["type"]["type"] == "struct"
+    assert [field["name"] for field in preview["fields"][0]["type"]["fields"]] == [
+        "position",
+        "value",
+        "length",
+    ]
+
+
 def test_annotation_and_explicit_schema_validation_fail_closed():
    with pytest.raises(TypeError, match="missing Function annotations"):

@@ -525,6 +941,72 @@ def test_annotation_and_explicit_schema_validation_fail_closed():
        def nullable_explicit(value):
            return value

+    for invalid_field in [
+        pa.field("", pa.int32(), nullable=False),
+        pa.field("result", pa.int32(), nullable=False, metadata={"k": "v"}),
+    ]:
+        with pytest.raises(TypeError, match="unsupported Arrow type"):
+
+            @udf(
+                input_schema=pa.schema([pa.field("value", pa.int64())]),
+                output_schema=pa.schema([invalid_field]),
+            )
+            def invalid_explicit_field(value):
+                return value
+
+    with pytest.raises(TypeError, match="unsupported Arrow type"):
+
+        @udf(
+            input_schema=pa.schema(
+                [pa.field("value", pa.int64(), metadata={"k": "v"})]
+            ),
+            output_schema=pa.int64(),
+        )
+        def input_field_metadata(value):
+            return value
+
+    with pytest.raises(TypeError, match="unsupported Arrow type"):
+
+        @udf(
+            input_schema=pa.schema([pa.field("value", pa.int64())]),
+            output_schema=pa.field(
+                "result", pa.int64(), nullable=False, metadata={"k": "v"}
+            ),
+        )
+        def scalar_output_field_metadata(value):
+            return value
+
+    struct_type = pa.struct([pa.field("value", pa.int64(), nullable=False)])
+    with pytest.raises(TypeError, match="unsupported Arrow type"):
+
+        @udf(
+            input_schema=pa.schema([pa.field("value", pa.int64())]),
+            output_schema=pa.field(
+                "result", struct_type, nullable=False, metadata={"k": "v"}
+            ),
+        )
+        def struct_output_field_metadata(value):
+            return {"value": value}
+
+    for input_schema, output_schema in [
+        (
+            pa.schema([pa.field("value", pa.int64())], metadata={"k": "v"}),
+            pa.int64(),
+        ),
+        (
+            pa.schema([pa.field("value", pa.int64())]),
+            pa.schema(
+                [pa.field("result", pa.int64(), nullable=False)],
+                metadata={"k": "v"},
+            ),
+        ),
+    ]:
+        with pytest.raises(TypeError, match="schema metadata"):
+
+            @udf(input_schema=input_schema, output_schema=output_schema)
+            def schema_metadata(value):
+                return value
+

 def test_local_function_catalog_operations_are_not_supported(tmp_path):
    db = lancedb.connect(tmp_path)
@@ -535,6 +1017,8 @@ def test_local_function_catalog_operations_are_not_supported(tmp_path):
        db.create_function_async(normalize_score)
    with pytest.raises(NotImplementedError, match=message):
        db.get_function("normalize_score", version="fv_exact")
+    with pytest.raises(NotImplementedError, match=message):
+        db.drop_function("normalize_score", version="fv_exact")


@contextlib.contextmanager
@@ -580,6 +1064,12 @@ def _mock_remote_function_catalog():
                    "version": "fv_exact",
                }
                response = state["version"]
+            elif self.path == "/v1/functions/drop":
+                assert body == {
+                    "name": "normalize_score",
+                    "version": "fv_exact",
+                }
+                response = {"dropped": True}
            else:
                status = 404
                response = {"error": "not found"}
@@ -638,3 +1128,40 @@ def test_blocking_remote_registration_returns_function_version():
        "/v1/functions/create",
        "/v1/jobs/describe",
    ]
+
+
+def test_remote_drop_function_sends_exact_version():
+    with _mock_remote_function_catalog() as (host, state):
+        db = lancedb.connect(
+            "db://dev",
+            api_key="fake",
+            host_override=host,
+            client_config={"retry_config": {"retries": 0}},
+        )
+        assert db.drop_function("normalize_score", version="fv_exact") is True
+
+    assert state["requests"] == [
+        (
+            "/v1/functions/drop",
+            {"name": "normalize_score", "version": "fv_exact"},
+        )
+    ]
+
+
+@pytest.mark.asyncio
+async def test_async_remote_drop_function_sends_exact_version():
+    with _mock_remote_function_catalog() as (host, state):
+        db = await lancedb.connect_async(
+            "db://dev",
+            api_key="fake",
+            host_override=host,
+            client_config={"retry_config": {"retries": 0}},
+        )
+        assert await db.drop_function("normalize_score", version="fv_exact") is True
+
+    assert state["requests"] == [
+        (
+            "/v1/functions/drop",
+            {"name": "normalize_score", "version": "fv_exact"},
+        )
+    ]
@@ -1923,6 +1923,21 @@ def test_take_queries(tmp_path):
        17,
    ]

+    # Duplicate offsets are occurrences, not set members. Ordering is unspecified.
+    assert sorted(table.take_offsets([5, 2, 5, 17]).to_pandas()["idx"].to_list()) == [
+        2,
+        5,
+        5,
+        17,
+    ]
+
+    # Converting a take builder to its serializable query representation must
+    # retain occurrence metadata and execute with the same multiplicity.
+    query = table.take_offsets([5, 2, 5, 17]).select(["idx"]).to_query_object()
+    assert query.take_offsets == [5, 2, 5, 17]
+    converted = table._execute_query(query).read_all()
+    assert sorted(converted["idx"].to_pylist()) == [2, 5, 5, 17]
+
    # Take by row id
    assert list(
        sorted(table.take_row_ids([5, 2, 17]).to_pandas()["idx"].to_list())
@@ -479,24 +479,49 @@ def test_remote_permutation_is_picklable():
                match = re.search(
                    r"_rowoffset\s+in\s+\((.*?)\)", body["filter"], re.IGNORECASE
                )
-                offsets = [int(o.strip()) for o in match.group(1).split(",")]
+                offsets = list(
+                    dict.fromkeys(int(o.strip()) for o in match.group(1).split(","))
+                )
            else:
                offsets = list(range(len(rows)))
-            table = pa.table({"a": [rows[offset] for offset in offsets]})
+            columns = body.get("columns") or ["a"]
+            table = pa.table(
+                {
+                    column: (
+                        [rows[offset] for offset in offsets]
+                        if column == "a"
+                        else offsets
+                    )
+                    for column in columns
+                }
+            )

            request.send_response(200)
            request.send_header("Content-Type", "application/vnd.apache.arrow.file")
            request.end_headers()
            with pa.ipc.new_file(request.wfile, schema=table.schema) as writer:
-                writer.write_table(table)
+                writer.write_table(table, max_chunksize=2)
        else:
            request.send_response(404)
            request.end_headers()

    with mock_lancedb_connection(handler) as db:
-        permutation = Permutation.identity(db.open_table("test"))
+        table = db.open_table("test")
+        assert table.take_offsets([0, 2, 0, 4]).to_list() == [
+            {"a": 0},
+            {"a": 0},
+            {"a": 2},
+            {"a": 4},
+        ]
+
+        permutation = Permutation.identity(table)
        restored = pickle.loads(pickle.dumps(permutation))
-        assert restored.__getitems__([0, 2, 4]) == [{"a": 0}, {"a": 2}, {"a": 4}]
+        assert restored.__getitems__([0, 2, 0, 4]) == [
+            {"a": 0},
+            {"a": 2},
+            {"a": 0},
+            {"a": 4},
+        ]


 def test_create_table_exist_ok():
@@ -2509,6 +2534,26 @@ def test_remote_connection_jobs_surface():
            request.send_header("Content-Type", "application/json")
            request.end_headers()
            request.wfile.write(b'{"job_id": "job-1"}')
+        elif request.path == "/v1/jobs/pause":
+            if payload["job_id"] != "job-1":
+                request.send_response(404)
+                request.end_headers()
+                return
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b'{"job_id": "job-1", "paused": true}')
+        elif request.path == "/v1/jobs/resume":
+            if payload["job_id"] != "job-1":
+                request.send_response(404)
+                request.end_headers()
+                return
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(
+                b'{"job_id": "job-1", "resumed": false, "still_pausing": true}'
+            )
        elif request.path == "/v1/jobs/query_events":
            assert payload["job_id"] == "job-1"
            request.send_response(200)
@@ -2537,6 +2582,9 @@ def test_remote_connection_jobs_surface():
        assert db.cancel_job("job-1") is True
        assert db.cancel_job("missing") is False

+        assert db.pause_job("job-1") == "pausing"
+        assert db.resume_job("job-1") == "still_pausing"
+
        batches = db.job_history("job-1")
        assert len(batches) == 1
        assert batches[0].num_rows == 2
@@ -786,55 +786,6 @@ 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.

@@ -4136,6 +4087,29 @@ def test_computed_column_rejects_transforms_and_computed_together(tmp_path):
        table.add_columns({"a": "x + 1"}, computed={"b": "x * 2"})


+def test_computed_column_blob_projection_inherits_semantics(tmp_path):
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("computed_column_blob", schema=schema)
+    table.add(
+        [
+            {"id": 1, "image": b"hello"},
+            {"id": 2, "image": b""},
+            {"id": 3, "image": None},
+        ]
+    )
+
+    table.add_columns(computed={"image_copy": "image", "second_copy": "image_copy"})
+    assert table.refresh_column("image_copy").rows_filled == 2
+    assert table.refresh_column("second_copy").rows_filled == 2
+    assert table.blob_columns() == ["image", "image_copy", "second_copy"]
+
+    hits = table.search().with_row_id(True).limit(10).to_arrow()
+    rows = sorted(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
+    copied = table.fetch_blobs("second_copy", [row_id for _, row_id in rows])
+    assert copied.to_pylist() == [b"hello", b"", None]
+
+
@pytest.mark.asyncio
 async def test_computed_column_async(tmp_path):
    db = await lancedb.connect_async(tmp_path)
@@ -629,6 +629,17 @@ impl Connection {
        })
    }

+    pub fn drop_function(
+        self_: PyRef<'_, Self>,
+        name: String,
+        version: String,
+    ) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.get_inner()?.clone();
+        future_into_py(self_.py(), async move {
+            inner.drop_function(name, version).await.infer_error()
+        })
+    }
+
    pub fn list_jobs(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.get_inner()?.clone();
        future_into_py(self_.py(), async move {
@@ -655,6 +666,30 @@ impl Connection {
        })
    }

+    pub fn pause_job(self_: PyRef<'_, Self>, job_id: String) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.get_inner()?.clone();
+        future_into_py(self_.py(), async move {
+            let status = inner.pause_job(&job_id).await.infer_error()?;
+            Ok(match status {
+                lancedb::database::PauseJobStatus::Pausing => "pausing",
+                lancedb::database::PauseJobStatus::AlreadyPaused => "already_paused",
+                lancedb::database::PauseJobStatus::Committing => "committing",
+            })
+        })
+    }
+
+    pub fn resume_job(self_: PyRef<'_, Self>, job_id: String) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.get_inner()?.clone();
+        future_into_py(self_.py(), async move {
+            let status = inner.resume_job(&job_id).await.infer_error()?;
+            Ok(match status {
+                lancedb::database::ResumeJobStatus::Resumed => "resumed",
+                lancedb::database::ResumeJobStatus::StillPausing => "still_pausing",
+                lancedb::database::ResumeJobStatus::NotPaused => "not_paused",
+            })
+        })
+    }
+
    #[pyo3(signature = (job_id=None))]
    pub fn job_history(
        self_: PyRef<'_, Self>,
@@ -323,6 +323,7 @@ impl<'py> IntoPyObject<'py> for PyQueryVectors {
 pub struct PyQueryRequest {
    pub limit: Option<usize>,
    pub offset: Option<usize>,
+    pub take_offsets: Option<Vec<u64>>,
    pub filter: Option<PyQueryFilter>,
    pub full_text_search: Option<PyLanceDB<FtsQuery>>,
    pub select: PySelect,
@@ -353,6 +354,7 @@ impl From<AnyQuery> for PyQueryRequest {
            AnyQuery::Query(query_request) => Self {
                limit: query_request.limit,
                offset: query_request.offset,
+                take_offsets: query_request.take_offsets,
                filter: query_request.filter.map(PyQueryFilter),
                full_text_search: query_request
                    .full_text_search
@@ -381,6 +383,7 @@ impl From<AnyQuery> for PyQueryRequest {
            AnyQuery::VectorQuery(vector_query) => Self {
                limit: vector_query.base.limit,
                offset: vector_query.base.offset,
+                take_offsets: vector_query.base.take_offsets,
                filter: vector_query.base.filter.map(PyQueryFilter),
                full_text_search: None,
                select_source_columns: PySelect::source_columns(&vector_query.base.select),
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.38.0-beta.12"
+version = "0.38.0"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
@@ -24,7 +24,7 @@ use crate::data::scannable::Scannable;
 use crate::database::listing::ListingDatabase;
 use crate::database::{
    CloneTableRequest, Database, DatabaseOptions, JobDescription, JobInfo, OpenTableRequest,
-    ReadConsistency, TableNamesRequest,
+    PauseJobStatus, ReadConsistency, ResumeJobStatus, TableNamesRequest,
 };
 use crate::embeddings::{EmbeddingRegistry, MemoryRegistry};
 use crate::error::{Error, Result};
@@ -523,6 +523,21 @@ impl Connection {
            .await
    }

+    /// Drop one exact immutable Function version from the remote catalog.
+    ///
+    /// Returns `true` when the server appended a Dropped transition and
+    /// `false` for an idempotent replay. Local databases return
+    /// [`Error::NotSupported`].
+    pub async fn drop_function(
+        &self,
+        name: impl AsRef<str>,
+        version: impl AsRef<str>,
+    ) -> Result<bool> {
+        self.internal
+            .drop_function(name.as_ref(), version.as_ref())
+            .await
+    }
+
    /// Rename a table in the database.
    ///
    /// This is only supported in LanceDB Cloud.
@@ -575,6 +590,18 @@ impl Connection {
        self.internal.cancel_job(job_id.as_ref()).await
    }

+    /// Pause a server-side job by id. Its workers drain and it stays parked
+    /// until resumed; see [`PauseJobStatus`] for the outcomes.
+    pub async fn pause_job(&self, job_id: impl AsRef<str>) -> Result<PauseJobStatus> {
+        self.internal.pause_job(job_id.as_ref()).await
+    }
+
+    /// Resume a paused server-side job by id. Its workers pick their work
+    /// back up from checkpoints; see [`ResumeJobStatus`] for the outcomes.
+    pub async fn resume_job(&self, job_id: impl AsRef<str>) -> Result<ResumeJobStatus> {
+        self.internal.resume_job(job_id.as_ref()).await
+    }
+
    /// The lifecycle event history of a server-side job (all jobs when
    /// `job_id` is `None`), as recorded Arrow batches.
    pub async fn job_history(&self, job_id: Option<&str>) -> Result<Vec<RecordBatch>> {
@@ -235,6 +235,29 @@ pub struct JobDescription {
    pub failure: Option<crate::error::JobFailure>,
 }

+/// The server's answer to a pause request.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum PauseJobStatus {
+    /// The pause was accepted; workers drain and the job stays parked.
+    Pausing,
+    /// The job was already paused, so a repeated pause changed nothing.
+    AlreadyPaused,
+    /// The job is finalizing its results and cannot be parked right now.
+    /// The commit is the short tail of a long job; retry shortly.
+    Committing,
+}
+
+/// The server's answer to a resume request.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum ResumeJobStatus {
+    /// The job re-entered the queue and will run again.
+    Resumed,
+    /// The pause's worker drain is not confirmed yet; retry shortly.
+    StillPausing,
+    /// The job was not paused, so there was nothing to resume.
+    NotPaused,
+}
+
 fn job_op_not_supported<T>(what: &str) -> Result<T> {
    Err(crate::error::Error::NotSupported {
        message: format!("{} is not supported by this database", what),
@@ -307,6 +330,10 @@ pub trait Database:
    ) -> Result<crate::function::FunctionVersion> {
        function_catalog_not_supported()
    }
+    /// Drop one exact immutable Function version from the remote catalog.
+    async fn drop_function(&self, _name: &str, _version: &str) -> Result<bool> {
+        function_catalog_not_supported()
+    }
    /// A [`crate::job::Job`] handle for a server-side job by id, suitable for
    /// waiting on or cancelling the job. The handle is constructed without a
    /// server round trip; an unknown id surfaces when the handle is used.
@@ -327,6 +354,16 @@ pub trait Database:
    async fn cancel_job(&self, _job_id: &str) -> Result<bool> {
        job_op_not_supported("cancel_job")
    }
+    /// Pause a job by id. The job's workers drain and it stays parked until
+    /// resumed; see [`PauseJobStatus`] for the outcomes.
+    async fn pause_job(&self, _job_id: &str) -> Result<PauseJobStatus> {
+        job_op_not_supported("pause_job")
+    }
+    /// Resume a paused job by id. It re-enters the queue and its workers pick
+    /// their work back up from checkpoints; see [`ResumeJobStatus`].
+    async fn resume_job(&self, _job_id: &str) -> Result<ResumeJobStatus> {
+        job_op_not_supported("resume_job")
+    }
    /// The lifecycle event history of a job (all jobs when `job_id` is
    /// `None`), as recorded Arrow batches.
    async fn job_history(&self, _job_id: Option<&str>) -> Result<Vec<RecordBatch>> {
@@ -539,9 +539,7 @@ impl Database for LanceNamespaceDatabase {
        self.namespace
            .drop_table(drop_request)
            .await
-            .map_err(|e| Error::Runtime {
-                message: format!("Failed to drop table: {}", e),
-            })?;
+            .map_err(|e| map_namespace_lance_error(e, name))?;

        Ok(())
    }
@@ -1495,6 +1493,15 @@ mod tests {
            .expect("Failed to list tables");
        assert!(!table_names_after.contains(&"drop_test".to_string()));

+        let error = conn
+            .drop_table("drop_test", &["test_ns".into()])
+            .await
+            .expect_err("dropping a missing table should fail");
+        assert!(
+            matches!(error, Error::TableNotFound { ref name, .. } if name == "drop_test"),
+            "expected TableNotFound, got: {error:?}"
+        );
+
        // Verify: Cannot open dropped table
        let open_result = conn.open_table("drop_test").execute().await;
        assert!(open_result.is_err());
@@ -31,7 +31,7 @@ use lance::io::RecordBatchStream;
 use lance_arrow::RecordBatchExt;
 use lance_core::ROW_ID;
 use lance_core::error::LanceOptionExt;
-use std::collections::HashMap;
+use std::collections::{HashMap, HashSet};
 use std::sync::Arc;

 /// Reads a permutation of a source table based on row IDs stored in a separate table
@@ -234,7 +234,14 @@ impl PermutationReader {
            .expect_ok()?
            .values();

-        let in_list: Vec<Expr> = row_ids.iter().map(|id| lit(*id)).collect();
+        let mut unique_row_ids = HashSet::with_capacity(num_rows);
+        let in_list: Vec<Expr> = row_ids
+            .iter()
+            .copied()
+            .filter(|row_id| unique_row_ids.insert(*row_id))
+            .map(lit)
+            .collect();
+        let num_unique_row_ids = unique_row_ids.len();

        let base_query = QueryRequest {
            filter: Some(QueryFilter::Datafusion(col(ROW_ID).in_list(in_list, false))),
@@ -247,7 +254,7 @@ impl PermutationReader {
            .query(
                &AnyQuery::Query(base_query),
                QueryExecutionOptions {
-                    max_batch_length: num_rows as u32,
+                    max_batch_length: num_unique_row_ids as u32,
                    ..Default::default()
                },
            )
@@ -262,9 +269,9 @@ impl PermutationReader {
            });
        }

-        if batches.iter().map(|b| b.num_rows()).sum::<usize>() != num_rows {
+        if batches.iter().map(|b| b.num_rows()).sum::<usize>() != num_unique_row_ids {
            return Err(Error::InvalidInput {
-                message: "Base table returned different number of rows than the number of row IDs"
+                message: "Base table returned a different number of rows than the number of unique row IDs"
                    .to_string(),
            });
        }
@@ -504,6 +511,7 @@ impl PermutationReader {
            let table = Table::from(self.base_table.clone());
            let batches = table
                .take_offsets(offsets.to_vec())
+                .preserve_order()
                .select(selection.clone())
                .execute()
                .await?
@@ -803,10 +811,10 @@ mod tests {
        .unwrap();

        // Take offsets in reverse order and verify returned rows match that order
-        let offsets = vec![5, 3, 1, 0];
+        let offsets = vec![5, 3, 5, 1, 0];
        let batch = reader.take_offsets(&offsets, Select::All).await.unwrap();

-        assert_eq!(batch.num_rows(), 4);
+        assert_eq!(batch.num_rows(), 5);

        let idx_values = batch
            .column(0)
@@ -820,6 +828,52 @@ mod tests {
        assert_eq!(idx_values, expected);
    }

+    #[tokio::test]
+    async fn test_take_offsets_preserves_repeated_rows_in_permutation() {
+        let base_table = lance_datagen::gen_batch()
+            .col("idx", lance_datagen::array::step::<Int32Type>())
+            .into_mem_table("tbl", RowCount::from(5), BatchCount::from(1))
+            .await;
+        let base_row_ids = collect_column::<UInt64Type>(&base_table, "_rowid").await;
+        let permutation_row_ids = vec![
+            base_row_ids[3],
+            base_row_ids[1],
+            base_row_ids[3],
+            base_row_ids[2],
+        ];
+        let permutation_batch = RecordBatch::try_new(
+            Arc::new(Schema::new(vec![
+                Field::new("row_id", DataType::UInt64, false),
+                Field::new(SPLIT_ID_COLUMN, DataType::UInt64, false),
+            ])),
+            vec![
+                Arc::new(UInt64Array::from(permutation_row_ids)),
+                Arc::new(UInt64Array::from(vec![0; 4])),
+            ],
+        )
+        .unwrap();
+        let permutation_table = virtual_table("row_ids", &permutation_batch).await;
+        let reader = PermutationReader::try_from_tables(
+            base_table.base_table().clone(),
+            permutation_table.base_table().clone(),
+            0,
+        )
+        .await
+        .unwrap();
+
+        let batch = reader
+            .take_offsets(&[0, 1, 2, 3], Select::All)
+            .await
+            .unwrap();
+        let idx_values = batch
+            .column(0)
+            .as_primitive::<Int32Type>()
+            .values()
+            .to_vec();
+
+        assert_eq!(idx_values, vec![3, 1, 3, 2]);
+    }
+
    #[tokio::test]
    async fn test_take_offsets_with_column_selection() {
        let (base_table, row_ids_table, row_ids) = setup_permutation_tables(10).await;
@@ -883,17 +937,17 @@ mod tests {
            .unwrap();

        // With no permutation table, take_offsets uses the base table directly
-        let offsets = vec![0, 2, 4, 6];
+        let offsets = vec![0, 2, 0, 4, 6];
        let batch = reader.take_offsets(&offsets, Select::All).await.unwrap();

-        assert_eq!(batch.num_rows(), 4);
+        assert_eq!(batch.num_rows(), 5);

        let idx_values = batch
            .column(0)
            .as_primitive::<Int32Type>()
            .values()
            .to_vec();
-        assert_eq!(idx_values, vec![0, 2, 4, 6]);
+        assert_eq!(idx_values, vec![0, 2, 0, 4, 6]);
    }

    #[tokio::test]
@@ -15,6 +15,9 @@ use serde_json::Value;

 use crate::{Error, Result};

+/// Semantic Function type for a Blob v2 value.
+pub const FUNCTION_BLOB_V2_TYPE: &str = "blob_v2";
+
 fn invalid_json(error: impl std::fmt::Display) -> Error {
    Error::InvalidInput {
        message: format!("invalid remote Function JSON: {error}"),
@@ -207,6 +210,33 @@ pub enum PythonRuntimeSpec {
        environment: PythonEnvironmentSpec,
        env: BTreeMap<String, String>,
    },
+    /// The GPU-enabled Sophon-managed Python runtime.
+    ///
+    /// # Examples
+    ///
+    /// ```
+    /// use std::collections::BTreeMap;
+    /// use lancedb::function::{PythonEnvironmentSpec, PythonRuntimeSpec};
+    ///
+    /// let runtime = PythonRuntimeSpec::PythonV2 {
+    ///     python_version: "3.12".to_string(),
+    ///     environment: PythonEnvironmentSpec {
+    ///         kind: "pip".to_string(),
+    ///         packages: vec!["cupy-cuda12x".to_string()],
+    ///         channels: Vec::new(),
+    ///         path: None,
+    ///         modules: Vec::new(),
+    ///         image: None,
+    ///     },
+    ///     env: BTreeMap::new(),
+    /// };
+    /// assert!(runtime.requires_gpu());
+    /// ```
+    PythonV2 {
+        python_version: String,
+        environment: PythonEnvironmentSpec,
+        env: BTreeMap<String, String>,
+    },
    /// A runtime kind introduced by a newer server.
    ///
    /// Unknown payload fields are intentionally not retained because the
@@ -219,22 +249,27 @@ impl PythonRuntimeSpec {
    pub fn kind(&self) -> &str {
        match self {
            Self::Python { .. } => "python",
+            Self::PythonV2 { .. } => "python_v2",
            Self::Unrecognized { kind } => kind,
        }
    }

-    /// The Python version for the V1 runtime, or `None` for an unknown kind.
+    /// The Python version for a known Python runtime, or `None` for an unknown kind.
    pub fn python_version(&self) -> Option<&str> {
        match self {
-            Self::Python { python_version, .. } => Some(python_version),
+            Self::Python { python_version, .. } | Self::PythonV2 { python_version, .. } => {
+                Some(python_version)
+            }
            Self::Unrecognized { .. } => None,
        }
    }

-    /// The Python environment for the V1 runtime, or `None` for an unknown kind.
+    /// The Python environment for a known Python runtime, or `None` for an unknown kind.
    pub fn environment(&self) -> Option<&PythonEnvironmentSpec> {
        match self {
-            Self::Python { environment, .. } => Some(environment),
+            Self::Python { environment, .. } | Self::PythonV2 { environment, .. } => {
+                Some(environment)
+            }
            Self::Unrecognized { .. } => None,
        }
    }
@@ -242,38 +277,73 @@ impl PythonRuntimeSpec {
    /// Environment variables, or `None` for an unknown kind.
    pub fn env(&self) -> Option<&BTreeMap<String, String>> {
        match self {
-            Self::Python { env, .. } => Some(env),
+            Self::Python { env, .. } | Self::PythonV2 { env, .. } => Some(env),
            Self::Unrecognized { .. } => None,
        }
    }
+
+    /// Whether the runtime requires a GPU selected by the execution platform.
+    pub fn requires_gpu(&self) -> bool {
+        matches!(self, Self::PythonV2 { .. })
+    }
 }

 #[derive(Deserialize)]
-struct PythonRuntimeWire {
-    kind: String,
-    #[serde(default)]
-    python_version: Option<String>,
-    #[serde(default)]
-    environment: Option<PythonEnvironmentSpec>,
+struct PythonRuntimeV1Wire {
+    python_version: String,
+    environment: PythonEnvironmentSpec,
    #[serde(default)]
    env: BTreeMap<String, String>,
+    #[serde(default)]
+    gpu: Option<Value>,
+}
+
+#[derive(Deserialize)]
+struct PythonRuntimeV2Wire {
+    python_version: String,
+    environment: PythonEnvironmentSpec,
+    #[serde(default)]
+    env: BTreeMap<String, String>,
+    gpu: bool,
 }

 impl<'de> Deserialize<'de> for PythonRuntimeSpec {
    fn deserialize<D: Deserializer<'de>>(deserializer: D) -> std::result::Result<Self, D::Error> {
-        let wire = PythonRuntimeWire::deserialize(deserializer)?;
-        if wire.kind == "python" {
-            Ok(Self::Python {
-                python_version: wire
-                    .python_version
-                    .ok_or_else(|| de::Error::missing_field("python_version"))?,
-                environment: wire
-                    .environment
-                    .ok_or_else(|| de::Error::missing_field("environment"))?,
-                env: wire.env,
-            })
-        } else {
-            Ok(Self::Unrecognized { kind: wire.kind })
+        let value = Value::deserialize(deserializer)?;
+        let kind = value
+            .get("kind")
+            .ok_or_else(|| de::Error::missing_field("kind"))?
+            .as_str()
+            .ok_or_else(|| de::Error::custom("runtime.kind must be a string"))?
+            .to_string();
+        match kind.as_str() {
+            "python" => {
+                let wire: PythonRuntimeV1Wire =
+                    serde_json::from_value(value).map_err(de::Error::custom)?;
+                if wire.gpu.is_some() {
+                    return Err(de::Error::custom(
+                        "python runtime with gpu requires kind='python_v2'",
+                    ));
+                }
+                Ok(Self::Python {
+                    python_version: wire.python_version,
+                    environment: wire.environment,
+                    env: wire.env,
+                })
+            }
+            "python_v2" => {
+                let wire: PythonRuntimeV2Wire =
+                    serde_json::from_value(value).map_err(de::Error::custom)?;
+                if !wire.gpu {
+                    return Err(de::Error::custom("runtime.gpu must be true"));
+                }
+                Ok(Self::PythonV2 {
+                    python_version: wire.python_version,
+                    environment: wire.environment,
+                    env: wire.env,
+                })
+            }
+            _ => Ok(Self::Unrecognized { kind }),
        }
    }
 }
@@ -287,6 +357,8 @@ impl Serialize for PythonRuntimeSpec {
            environment: &'a PythonEnvironmentSpec,
            #[serde(skip_serializing_if = "BTreeMap::is_empty")]
            env: &'a BTreeMap<String, String>,
+            #[serde(skip_serializing_if = "Option::is_none")]
+            gpu: Option<bool>,
        }

        #[derive(Serialize)]
@@ -304,6 +376,19 @@ impl Serialize for PythonRuntimeSpec {
                python_version,
                environment,
                env,
+                gpu: None,
+            }
+            .serialize(serializer),
+            Self::PythonV2 {
+                python_version,
+                environment,
+                env,
+            } => PythonRuntimeRef {
+                kind: "python_v2",
+                python_version,
+                environment,
+                env,
+                gpu: Some(true),
            }
            .serialize(serializer),
            Self::Unrecognized { kind } => UnrecognizedRuntimeRef { kind }.serialize(serializer),
@@ -313,8 +398,8 @@ impl Serialize for PythonRuntimeSpec {

 /// Immutable Function version returned by the Enterprise catalog.
 ///
-/// Scheduling resources, priority, concurrency, and retry policy belong to
-/// the submitting Job and are not part of this identity.
+/// The GPU execution requirement is part of this identity. CPU and memory sizing,
+/// priority, concurrency, and retry policy belong to the execution platform.
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
 pub struct FunctionVersion {
    name: String,
@@ -589,7 +674,7 @@ impl_json!(RefreshColumnResult);

 #[cfg(test)]
 mod conda_environment_tests {
-    use super::PythonEnvironmentSpec;
+    use super::{PythonEnvironmentSpec, PythonRuntimeSpec};

    #[test]
    fn conda_channels_round_trip_and_pip_stays_bare() {
@@ -608,4 +693,45 @@ mod conda_environment_tests {
            serde_json::from_str(r#"{"kind":"pip","packages":["numpy"]}"#).unwrap();
        assert!(!serde_json::to_string(&pip).unwrap().contains("channels"));
    }
+
+    #[test]
+    fn gpu_python_runtime_marker_round_trips_and_validates() {
+        let runtime: PythonRuntimeSpec = serde_json::from_str(
+            r#"{"kind":"python_v2","python_version":"3.12","environment":{"kind":"pip"},"gpu":true}"#,
+        )
+        .unwrap();
+        assert_eq!(runtime.kind(), "python_v2");
+        assert!(runtime.requires_gpu());
+        assert_eq!(
+            super::canonical_json(&runtime).unwrap(),
+            r#"{"environment":{"kind":"pip"},"gpu":true,"kind":"python_v2","python_version":"3.12"}"#
+        );
+
+        for invalid in [
+            r#"{"kind":"python","python_version":"3.12","environment":{"kind":"pip"},"gpu":true}"#,
+            r#"{"kind":"python_v2","python_version":"3.12","environment":{"kind":"pip"}}"#,
+            r#"{"kind":"python_v2","python_version":"3.12","environment":{"kind":"pip"},"gpu":1}"#,
+            r#"{"kind":"python_v2","python_version":"3.12","environment":{"kind":"pip"},"gpu":false}"#,
+            r#"{"kind":"python_v2","python_version":"3.12","environment":{"kind":"pip"},"gpu":"true"}"#,
+            r#"{"kind":"python_v2","python_version":"3.12","environment":{"kind":"pip"},"gpu":"H100"}"#,
+        ] {
+            assert!(serde_json::from_str::<PythonRuntimeSpec>(invalid).is_err());
+        }
+    }
+
+    #[test]
+    fn unknown_runtime_discards_payload_before_known_field_validation() {
+        for encoded in [
+            r#"{"kind":"python_v3","gpu":{"model":"H100"}}"#,
+            r#"{"kind":"python_v3","resources":[]}"#,
+            r#"{"kind":"python_v3","python_version":3.15,"environment":{"kind":[]}}"#,
+        ] {
+            let runtime: PythonRuntimeSpec = serde_json::from_str(encoded).unwrap();
+            assert_eq!(runtime.kind(), "python_v3");
+            assert_eq!(
+                super::canonical_json(&runtime).unwrap(),
+                r#"{"kind":"python_v3"}"#
+            );
+        }
+    }
 }
@@ -1,21 +1,37 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors

+use std::collections::{HashMap, HashSet};
+use std::pin::Pin;
 use std::sync::Arc;
 use std::{future::Future, time::Duration};

 use arrow::compute::concat_batches;
-use arrow_array::{Array, Float16Array, Float32Array, Float64Array, RecordBatch, make_array};
+use arrow_array::{
+    Array, Float16Array, Float32Array, Float64Array, RecordBatch, UInt64Array,
+    cast::AsArray,
+    make_array,
+    types::{Int64Type, UInt64Type},
+};
 use arrow_schema::{DataType, SchemaRef};
+use datafusion_common::{DataFusionError, Result as DataFusionResult};
+use datafusion_execution::TaskContext;
 use datafusion_expr::{Expr, col, lit};
-use datafusion_physical_plan::ExecutionPlan;
-use futures::{FutureExt, TryFutureExt, TryStreamExt, stream, try_join};
+use datafusion_physical_expr::{EquivalenceProperties, Partitioning};
+use datafusion_physical_plan::{
+    DisplayAs, DisplayFormatType, ExecutionPlan, ExecutionPlanProperties, PlanProperties,
+    coalesce_partitions::CoalescePartitionsExec,
+    execution_plan::{Boundedness, EmissionType},
+    limit::GlobalLimitExec,
+    stream::RecordBatchStreamAdapter,
+};
+use futures::{FutureExt, StreamExt, TryFutureExt, TryStreamExt, stream, try_join};
 use half::f16;
 /// Re-export Lance ColumnOrdering type for use in query ordering
 pub use lance::dataset::scanner::ColumnOrdering;
 use lance::dataset::{ROW_ID, scanner::DatasetRecordBatchStream};
 use lance_arrow::RecordBatchExt;
-use lance_datafusion::exec::execute_plan;
+use lance_datafusion::exec::{execute_plan, format_plan as format_analyzed_plan};
 use lance_index::scalar::FullTextSearchQuery;
 use lance_index::scalar::inverted::SCORE_COL;
 use lance_index::vector::DIST_COL;
@@ -825,6 +841,14 @@ pub struct QueryRequest {
    /// Offset of the query.
    pub offset: Option<usize>,

+    /// Dataset offsets whose occurrence multiplicity must be restored after
+    /// executing the physical lookup represented by this request.
+    ///
+    /// This is client-side execution metadata used when a [`TakeQuery`] is
+    /// converted into a request. It is not sent to remote services.
+    #[doc(hidden)]
+    pub take_offsets: Option<Vec<u64>>,
+
    /// Apply filter to the returned rows.
    pub filter: Option<QueryFilter>,

@@ -893,6 +917,7 @@ impl Default for QueryRequest {
        Self {
            limit: None,
            offset: None,
+            take_offsets: None,
            filter: None,
            filter_error: None,
            full_text_search: None,
@@ -1529,6 +1554,302 @@ impl HasQuery for VectorQuery {
    }
 }

+fn take_occurrences(offsets: &[u64]) -> HashMap<u64, usize> {
+    let mut occurrences = HashMap::with_capacity(offsets.len());
+    for offset in offsets {
+        *occurrences.entry(*offset).or_insert(0) += 1;
+    }
+    occurrences
+}
+
+fn restore_take_batch_with_occurrences(
+    batch: RecordBatch,
+    offsets: &[u64],
+    occurrences: &HashMap<u64, usize>,
+    ordering_column: &str,
+    drop_ordering_column: bool,
+    preserve_order: bool,
+) -> Result<RecordBatch> {
+    let actual_offsets = batch
+        .column_by_name(ordering_column)
+        .ok_or_else(|| Error::Schema {
+            message: format!(
+                "take query result did not include ordering column '{ordering_column}'"
+            ),
+        })?;
+    let actual_offsets = match actual_offsets.data_type() {
+        DataType::UInt64 => actual_offsets
+            .as_primitive::<UInt64Type>()
+            .values()
+            .to_vec(),
+        DataType::Int64 => actual_offsets
+            .as_primitive::<Int64Type>()
+            .values()
+            .iter()
+            .map(|offset| {
+                u64::try_from(*offset).map_err(|_| Error::Schema {
+                    message: format!(
+                        "take query ordering column '{ordering_column}' contained a negative offset"
+                    ),
+                })
+            })
+            .collect::<Result<Vec<_>>>()?,
+        data_type => {
+            return Err(Error::Schema {
+                message: format!(
+                    "take query ordering column '{ordering_column}' had unsupported type {data_type}"
+                ),
+            });
+        }
+    };
+
+    let mut desired_order = Vec::with_capacity(offsets.len());
+    if preserve_order {
+        let ordering = actual_offsets
+            .iter()
+            .copied()
+            .enumerate()
+            .map(|(index, offset)| (offset, index as u64))
+            .collect::<HashMap<_, _>>();
+        // Missing offsets retain the filter-based behavior of returning no row.
+        desired_order.extend(
+            offsets
+                .iter()
+                .filter_map(|offset| ordering.get(offset).copied()),
+        );
+    } else {
+        // Public take queries do not guarantee output order. Preserve the lookup's
+        // existing order and only restore the multiplicity of each matching row.
+        for (index, offset) in actual_offsets.iter().enumerate() {
+            if let Some(count) = occurrences.get(offset) {
+                desired_order.extend(std::iter::repeat_n(index as u64, *count));
+            }
+        }
+    }
+
+    let mut ordered_batch = if desired_order.len() == batch.num_rows()
+        && desired_order
+            .iter()
+            .enumerate()
+            .all(|(index, desired)| *desired == index as u64)
+    {
+        batch
+    } else {
+        arrow_select::take::take_record_batch(&batch, &UInt64Array::from(desired_order))?
+    };
+
+    if drop_ordering_column {
+        ordered_batch = ordered_batch.drop_column(ordering_column)?;
+    }
+
+    Ok(ordered_batch)
+}
+
+#[cfg(test)]
+fn restore_take_batch(
+    batch: RecordBatch,
+    offsets: &[u64],
+    ordering_column: &str,
+    drop_ordering_column: bool,
+    preserve_order: bool,
+) -> Result<RecordBatch> {
+    restore_take_batch_with_occurrences(
+        batch,
+        offsets,
+        &take_occurrences(offsets),
+        ordering_column,
+        drop_ordering_column,
+        preserve_order,
+    )
+}
+
+/// Restores the logical offset occurrence sequence above the physical lookup plan.
+///
+/// The lookup plan returns each matching row at most once. For ordinary unordered
+/// takes this operator expands each input batch incrementally and preserves the
+/// lookup's partitioning. The explicitly ordered reader path collects one coalesced
+/// input before restoring requested order. Pagination must remain above this operator
+/// so it applies to occurrences.
+#[derive(Debug)]
+struct TakeRestoreExec {
+    input: Arc<dyn ExecutionPlan>,
+    offsets: Vec<u64>,
+    occurrences: Arc<HashMap<u64, usize>>,
+    ordering_column: String,
+    drop_ordering_column: bool,
+    preserve_order: bool,
+    schema: SchemaRef,
+    properties: Arc<PlanProperties>,
+}
+
+impl TakeRestoreExec {
+    fn try_new(
+        input: Arc<dyn ExecutionPlan>,
+        offsets: Vec<u64>,
+        ordering_column: String,
+        drop_ordering_column: bool,
+        preserve_order: bool,
+    ) -> Result<Self> {
+        let schema = if drop_ordering_column {
+            RecordBatch::new_empty(input.schema())
+                .drop_column(&ordering_column)?
+                .schema()
+        } else {
+            input.schema()
+        };
+        let partition_count = if preserve_order {
+            1
+        } else {
+            input.output_partitioning().partition_count()
+        };
+        let emission_type = if preserve_order {
+            EmissionType::Final
+        } else {
+            EmissionType::Incremental
+        };
+        let properties = Arc::new(PlanProperties::new(
+            EquivalenceProperties::new(schema.clone()),
+            Partitioning::UnknownPartitioning(partition_count),
+            emission_type,
+            Boundedness::Bounded,
+        ));
+
+        Ok(Self {
+            input,
+            occurrences: Arc::new(take_occurrences(&offsets)),
+            offsets,
+            ordering_column,
+            drop_ordering_column,
+            preserve_order,
+            schema,
+            properties,
+        })
+    }
+}
+
+impl DisplayAs for TakeRestoreExec {
+    fn fmt_as(
+        &self,
+        _display_type: DisplayFormatType,
+        formatter: &mut std::fmt::Formatter<'_>,
+    ) -> std::fmt::Result {
+        write!(
+            formatter,
+            "TakeRestoreExec: occurrences={}",
+            self.offsets.len()
+        )
+    }
+}
+
+impl ExecutionPlan for TakeRestoreExec {
+    fn name(&self) -> &str {
+        "TakeRestoreExec"
+    }
+
+    fn properties(&self) -> &Arc<PlanProperties> {
+        &self.properties
+    }
+
+    fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
+        vec![&self.input]
+    }
+
+    fn maintains_input_order(&self) -> Vec<bool> {
+        vec![!self.preserve_order]
+    }
+
+    fn benefits_from_input_partitioning(&self) -> Vec<bool> {
+        vec![false]
+    }
+
+    fn with_new_children(
+        self: Arc<Self>,
+        children: Vec<Arc<dyn ExecutionPlan>>,
+    ) -> DataFusionResult<Arc<dyn ExecutionPlan>> {
+        if children.len() != 1 {
+            return Err(DataFusionError::Internal(format!(
+                "TakeRestoreExec expected one child, got {}",
+                children.len()
+            )));
+        }
+        let child = children.into_iter().next().unwrap();
+        let plan = Self::try_new(
+            child,
+            self.offsets.clone(),
+            self.ordering_column.clone(),
+            self.drop_ordering_column,
+            self.preserve_order,
+        )
+        .map_err(|error| DataFusionError::External(Box::new(error)))?;
+        Ok(Arc::new(plan))
+    }
+
+    fn execute(
+        &self,
+        partition: usize,
+        context: Arc<TaskContext>,
+    ) -> DataFusionResult<datafusion_physical_plan::SendableRecordBatchStream> {
+        let partition_count = self.input.output_partitioning().partition_count();
+        if partition >= partition_count || (self.preserve_order && partition != 0) {
+            return Err(DataFusionError::Internal(format!(
+                "TakeRestoreExec cannot execute partition {partition}; input has {partition_count} partitions"
+            )));
+        }
+
+        let input = self.input.execute(partition, context)?;
+        let output_schema = self.schema.clone();
+        let offsets = self.offsets.clone();
+        let occurrences = self.occurrences.clone();
+        let ordering_column = self.ordering_column.clone();
+        let drop_ordering_column = self.drop_ordering_column;
+        let preserve_order = self.preserve_order;
+        let stream: Pin<Box<dyn futures::Stream<Item = DataFusionResult<RecordBatch>> + Send>> =
+            if preserve_order {
+                let input_schema = input.schema();
+                Box::pin(stream::once(async move {
+                    let batches = input.try_collect::<Vec<_>>().await?;
+                    let batch = if batches.is_empty() {
+                        RecordBatch::new_empty(input_schema.clone())
+                    } else {
+                        concat_batches(&input_schema, &batches)?
+                    };
+                    restore_take_batch_with_occurrences(
+                        batch,
+                        &offsets,
+                        &occurrences,
+                        &ordering_column,
+                        drop_ordering_column,
+                        true,
+                    )
+                    .map_err(|error| DataFusionError::External(Box::new(error)))
+                }))
+            } else {
+                Box::pin(input.map(move |batch| {
+                    batch.and_then(|batch| {
+                        restore_take_batch_with_occurrences(
+                            batch,
+                            &offsets,
+                            &occurrences,
+                            &ordering_column,
+                            drop_ordering_column,
+                            false,
+                        )
+                        .map_err(|error| DataFusionError::External(Box::new(error)))
+                    })
+                }))
+            };
+
+        Ok(Box::pin(RecordBatchStreamAdapter::new(
+            output_schema,
+            stream,
+        )))
+    }
+
+    fn supports_limit_pushdown(&self) -> bool {
+        false
+    }
+}
+
 /// A builder for LanceDB take queries.
 ///
 /// See [`crate::Table::query`] for more details on queries
@@ -1545,6 +1866,8 @@ impl HasQuery for VectorQuery {
 pub struct TakeQuery {
    parent: Arc<dyn BaseTable>,
    request: QueryRequest,
+    offsets: Option<Vec<u64>>,
+    preserve_order: bool,
 }

 impl TakeQuery {
@@ -1552,15 +1875,24 @@ impl TakeQuery {
    ///
    /// See [`crate::Table::take_offsets`] for more details.
    pub fn from_offsets(parent: Arc<dyn BaseTable>, offsets: Vec<u64>) -> Self {
-        let in_list: Vec<Expr> = offsets.iter().map(|o| lit(*o)).collect();
+        let mut seen = HashSet::with_capacity(offsets.len());
+        let in_list: Vec<Expr> = offsets
+            .iter()
+            .copied()
+            .filter(|offset| seen.insert(*offset))
+            .map(lit)
+            .collect();
        Self {
            parent,
            request: QueryRequest {
                filter: Some(QueryFilter::Datafusion(
                    col("_rowoffset").in_list(in_list, false),
                )),
+                take_offsets: Some(offsets.clone()),
                ..Default::default()
            },
+            offsets: Some(offsets),
+            preserve_order: false,
        }
    }

@@ -1575,9 +1907,181 @@ impl TakeQuery {
                filter: Some(QueryFilter::Datafusion(col(ROW_ID).in_list(in_list, false))),
                ..Default::default()
            },
+            offsets: None,
+            preserve_order: false,
        }
    }

+    /// Preserve the requested offset order when restoring duplicate occurrences.
+    ///
+    /// This is reserved for readers whose API explicitly guarantees ordering.
+    pub(crate) fn preserve_order(mut self) -> Self {
+        debug_assert!(self.offsets.is_some());
+        self.preserve_order = true;
+        self
+    }
+
+    async fn request_with_row_offset(
+        parent: &dyn BaseTable,
+        request: &QueryRequest,
+    ) -> Result<(QueryRequest, String, bool)> {
+        const ROW_OFFSET: &str = "_rowoffset";
+        const INTERNAL_ROW_OFFSET: &str = "__lancedb_take_row_offset";
+
+        let mut request = request.clone();
+        // The physical lookup must not recursively restore occurrences. The
+        // wrapper above this request owns that logical operation.
+        request.take_offsets = None;
+        let (ordering_column, drop_ordering_column) = match &mut request.select {
+            Select::All => {
+                let mut columns = parent
+                    .schema()
+                    .await?
+                    .fields()
+                    .iter()
+                    .map(|field| field.name().clone())
+                    .collect::<Vec<_>>();
+                columns.push(ROW_OFFSET.to_string());
+                request.select = Select::Columns(columns);
+                (ROW_OFFSET.to_string(), true)
+            }
+            Select::Columns(columns) => {
+                if columns.iter().any(|column| column == ROW_OFFSET) {
+                    (ROW_OFFSET.to_string(), false)
+                } else {
+                    columns.push(ROW_OFFSET.to_string());
+                    (ROW_OFFSET.to_string(), true)
+                }
+            }
+            Select::Dynamic(columns) => {
+                let mut ordering_column = INTERNAL_ROW_OFFSET.to_string();
+                while columns.iter().any(|(name, _)| name == &ordering_column) {
+                    ordering_column.push('_');
+                }
+                columns.push((ordering_column.clone(), ROW_OFFSET.to_string()));
+                (ordering_column, true)
+            }
+            Select::Expr(columns) => {
+                let mut ordering_column = INTERNAL_ROW_OFFSET.to_string();
+                while columns.iter().any(|(name, _)| name == &ordering_column) {
+                    ordering_column.push('_');
+                }
+                columns.push((ordering_column.clone(), col(ROW_OFFSET)));
+                (ordering_column, true)
+            }
+        };
+
+        Ok((request, ordering_column, drop_ordering_column))
+    }
+
+    async fn prepare_offsets_lookup(
+        parent: &dyn BaseTable,
+        request: &QueryRequest,
+    ) -> Result<(QueryRequest, String, bool, usize, Option<usize>)> {
+        let (mut request, ordering_column, drop_ordering_column) =
+            Self::request_with_row_offset(parent, request).await?;
+        // The lookup operates on distinct physical rows. Pagination is a logical
+        // operation over occurrences and must be applied only after restoration.
+        let output_offset = request.offset.take().unwrap_or_default();
+        let output_limit = request.limit.take();
+
+        Ok((
+            request,
+            ordering_column,
+            drop_ordering_column,
+            output_offset,
+            output_limit,
+        ))
+    }
+
+    fn wrap_offsets_plan(
+        lookup: Arc<dyn ExecutionPlan>,
+        offsets: &[u64],
+        ordering_column: String,
+        drop_ordering_column: bool,
+        output_offset: usize,
+        output_limit: Option<usize>,
+        preserve_order: bool,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        let lookup = if preserve_order {
+            Arc::new(CoalescePartitionsExec::new(lookup)) as Arc<dyn ExecutionPlan>
+        } else {
+            lookup
+        };
+        let restored: Arc<dyn ExecutionPlan> = Arc::new(TakeRestoreExec::try_new(
+            lookup,
+            offsets.to_vec(),
+            ordering_column,
+            drop_ordering_column,
+            preserve_order,
+        )?);
+
+        if output_offset > 0 || output_limit.is_some() {
+            Ok(Arc::new(GlobalLimitExec::new(
+                restored,
+                output_offset,
+                output_limit,
+            )))
+        } else {
+            Ok(restored)
+        }
+    }
+
+    fn wrap_offsets_explanation(
+        lookup: &str,
+        occurrence_count: usize,
+        output_offset: usize,
+        output_limit: Option<usize>,
+        preserve_order: bool,
+    ) -> String {
+        fn indent(plan: &str, spaces: usize) -> String {
+            let indentation = " ".repeat(spaces);
+            plan.lines()
+                .map(|line| format!("{indentation}{line}"))
+                .collect::<Vec<_>>()
+                .join("\n")
+        }
+
+        let restored = if preserve_order {
+            format!(
+                "TakeRestoreExec: occurrences={occurrence_count}\n  CoalescePartitionsExec\n{}",
+                indent(lookup, 4)
+            )
+        } else {
+            format!(
+                "TakeRestoreExec: occurrences={occurrence_count}\n{}",
+                indent(lookup, 2)
+            )
+        };
+
+        if output_offset > 0 || output_limit.is_some() {
+            let fetch = output_limit
+                .map(|limit| limit.to_string())
+                .unwrap_or_else(|| "None".to_string());
+            format!(
+                "GlobalLimitExec: skip={output_offset}, fetch={fetch}\n{}",
+                indent(&restored, 2)
+            )
+        } else {
+            restored
+        }
+    }
+
+    async fn create_offsets_plan(
+        &self,
+        offsets: &[u64],
+        options: QueryExecutionOptions,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        create_take_offsets_plan(
+            self.parent.as_ref(),
+            &self.request,
+            offsets,
+            options,
+            self.preserve_order,
+        )
+        .await
+    }
+
    /// Convert the `TakeQuery` into a `QueryRequest`.
    pub fn into_request(self) -> QueryRequest {
        self.request
@@ -1622,6 +2126,63 @@ impl TakeQuery {
    }
 }

+pub(crate) async fn create_take_offsets_plan(
+    parent: &dyn BaseTable,
+    request: &QueryRequest,
+    offsets: &[u64],
+    options: QueryExecutionOptions,
+    preserve_order: bool,
+) -> Result<Arc<dyn ExecutionPlan>> {
+    let (request, ordering_column, drop_ordering_column, output_offset, output_limit) =
+        TakeQuery::prepare_offsets_lookup(parent, request).await?;
+    let lookup_options = if preserve_order {
+        options.without_output_batch_length_limit()
+    } else {
+        options
+    };
+    let lookup = parent
+        .create_plan(&AnyQuery::Query(request), lookup_options)
+        .await?;
+
+    TakeQuery::wrap_offsets_plan(
+        lookup,
+        offsets,
+        ordering_column,
+        drop_ordering_column,
+        output_offset,
+        output_limit,
+        preserve_order,
+    )
+}
+
+pub(crate) async fn explain_take_offsets_plan(
+    parent: &dyn BaseTable,
+    request: &QueryRequest,
+    offsets: &[u64],
+    verbose: bool,
+) -> Result<String> {
+    let (request, _, _, output_offset, output_limit) =
+        TakeQuery::prepare_offsets_lookup(parent, request).await?;
+    let lookup = parent
+        .explain_plan(&AnyQuery::Query(request), verbose)
+        .await?;
+    Ok(TakeQuery::wrap_offsets_explanation(
+        &lookup,
+        offsets.len(),
+        output_offset,
+        output_limit,
+        false,
+    ))
+}
+
+pub(crate) async fn prepare_take_offsets_request(
+    parent: &dyn BaseTable,
+    request: &QueryRequest,
+) -> Result<QueryRequest> {
+    let (request, _, _, _, _) = TakeQuery::prepare_offsets_lookup(parent, request).await?;
+    Ok(request)
+}
+
 impl HasQuery for TakeQuery {
    fn mut_query(&mut self) -> &mut QueryRequest {
        &mut self.request
@@ -1630,6 +2191,10 @@ impl HasQuery for TakeQuery {

 impl ExecutableQuery for TakeQuery {
    async fn create_plan(&self, options: QueryExecutionOptions) -> Result<Arc<dyn ExecutionPlan>> {
+        if let Some(offsets) = &self.offsets {
+            return self.create_offsets_plan(offsets, options).await;
+        }
+
        let req = AnyQuery::Query(self.request.clone());
        self.parent.clone().create_plan(&req, options).await
    }
@@ -1638,6 +2203,18 @@ impl ExecutableQuery for TakeQuery {
        &self,
        options: QueryExecutionOptions,
    ) -> Result<SendableRecordBatchStream> {
+        if self.offsets.is_some() {
+            let plan = self.create_plan(options.clone()).await?;
+            let inner = execute_plan(plan, Default::default())?;
+            let inner = MaxBatchLengthStream::new_boxed(inner, options.max_batch_length as usize);
+            let inner = if let Some(timeout) = options.timeout {
+                TimeoutStream::new_boxed(inner, timeout)
+            } else {
+                inner
+            };
+            return Ok(DatasetRecordBatchStream::new(inner).into());
+        }
+
        let query = AnyQuery::Query(self.request.clone());
        Ok(SendableRecordBatchStream::from(
            self.parent.clone().query(&query, options).await?,
@@ -1645,11 +2222,51 @@ impl ExecutableQuery for TakeQuery {
    }

    async fn explain_plan(&self, verbose: bool) -> Result<String> {
+        if let Some(offsets) = &self.offsets {
+            let (request, _, _, output_offset, output_limit) =
+                Self::prepare_offsets_lookup(self.parent.as_ref(), &self.request).await?;
+            // Ask the backend to explain only the distinct-row lookup. This keeps
+            // remote explanation non-executing while still showing the client-side
+            // operators that create_plan and execution place above that lookup.
+            let lookup = self
+                .parent
+                .explain_plan(&AnyQuery::Query(request), verbose)
+                .await?;
+            return Ok(Self::wrap_offsets_explanation(
+                &lookup,
+                offsets.len(),
+                output_offset,
+                output_limit,
+                self.preserve_order,
+            ));
+        }
+
        let query = AnyQuery::Query(self.request.clone());
        self.parent.explain_plan(&query, verbose).await
    }

    async fn analyze_plan_with_options(&self, options: QueryExecutionOptions) -> Result<String> {
+        if self.offsets.is_some() {
+            if self.parent.analyze_plan_is_remote() {
+                let (request, _, _, _, _) =
+                    Self::prepare_offsets_lookup(self.parent.as_ref(), &self.request).await?;
+                // Remote analysis is owned by the service. The current wire
+                // request represents only the distinct-row lookup, so return
+                // the service report unchanged instead of fabricating metrics
+                // for client-side restoration operators.
+                return self
+                    .parent
+                    .analyze_plan(&AnyQuery::Query(request), options)
+                    .await;
+            }
+
+            let plan = self.create_plan(options).await?;
+            execute_plan(plan.clone(), Default::default())?
+                .try_collect::<Vec<_>>()
+                .await?;
+            return Ok(format_analyzed_plan(plan));
+        }
+
        let query = AnyQuery::Query(self.request.clone());
        self.parent.analyze_plan(&query, options).await
    }
@@ -1670,6 +2287,7 @@ mod tests {
        StringArray, cast::AsArray, types::Float32Type,
    };
    use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema};
+    use datafusion_physical_plan::display::DisplayableExecutionPlan;
    use futures::{StreamExt, TryStreamExt};
    use lance_testing::datagen::{BatchGenerator, IncrementingInt32, RandomVector};
    use rand::seq::IndexedRandom;
@@ -2924,6 +3542,218 @@ mod tests {
        assert_eq!(results[0].num_columns(), 1);
    }

+    #[tokio::test]
+    async fn test_take_offsets_preserves_duplicate_multiplicity() {
+        let tmp_dir = tempdir().unwrap();
+        let table = make_test_table(&tmp_dir).await;
+
+        let results = table
+            .take_offsets(vec![5, 1, 5, 17])
+            .select(Select::Columns(vec!["id".to_string()]))
+            .execute_with_options(QueryExecutionOptions {
+                max_batch_length: 2,
+                ..Default::default()
+            })
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+
+        assert_eq!(results.len(), 2);
+        assert!(results.iter().all(|batch| batch.num_columns() == 1));
+        let mut ids = results
+            .iter()
+            .flat_map(|batch| {
+                batch
+                    .column_by_name("id")
+                    .unwrap()
+                    .as_primitive::<Int32Type>()
+                    .values()
+                    .to_vec()
+            })
+            .collect::<Vec<_>>();
+        ids.sort_unstable();
+        assert_eq!(ids, vec![1, 5, 5, 17]);
+    }
+
+    #[tokio::test]
+    async fn test_take_offsets_plan_is_incremental() {
+        let tmp_dir = tempdir().unwrap();
+        let table = make_test_table(&tmp_dir).await;
+
+        let plan = table
+            .take_offsets(vec![5, 1, 17])
+            .create_plan(QueryExecutionOptions {
+                max_batch_length: 1,
+                ..Default::default()
+            })
+            .await
+            .unwrap();
+
+        assert_eq!(plan.properties().emission_type, EmissionType::Incremental);
+        let displayed = DisplayableExecutionPlan::new(plan.as_ref())
+            .indent(false)
+            .to_string();
+        assert!(displayed.contains("TakeRestoreExec"));
+        assert!(!displayed.contains("CoalescePartitionsExec"));
+    }
+
+    #[tokio::test]
+    async fn test_take_into_request_preserves_duplicate_multiplicity() {
+        let tmp_dir = tempdir().unwrap();
+        let table = make_test_table(&tmp_dir).await;
+        let request = table.take_offsets(vec![5, 5]).into_request();
+        assert_eq!(request.take_offsets, Some(vec![5, 5]));
+
+        let batches = table
+            .base_table()
+            .query(&AnyQuery::Query(request), QueryExecutionOptions::default())
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+
+        assert_eq!(batches.iter().map(RecordBatch::num_rows).sum::<usize>(), 2);
+    }
+
+    #[test]
+    fn test_restore_take_batch_only_reorders_when_requested() {
+        let batch = RecordBatch::try_from_iter([
+            (
+                "id",
+                Arc::new(Int32Array::from(vec![17, 5, 1])) as Arc<dyn Array>,
+            ),
+            (
+                "_rowoffset",
+                Arc::new(UInt64Array::from(vec![17, 5, 1])) as Arc<dyn Array>,
+            ),
+        ])
+        .unwrap();
+
+        let restored =
+            restore_take_batch(batch.clone(), &[5, 1, 5, 17], "_rowoffset", true, false).unwrap();
+        assert_eq!(
+            restored
+                .column_by_name("id")
+                .unwrap()
+                .as_primitive::<Int32Type>()
+                .values(),
+            &[17, 5, 5, 1]
+        );
+
+        let ordered = restore_take_batch(batch, &[5, 1, 5, 17], "_rowoffset", true, true).unwrap();
+        assert_eq!(
+            ordered
+                .column_by_name("id")
+                .unwrap()
+                .as_primitive::<Int32Type>()
+                .values(),
+            &[5, 1, 5, 17]
+        );
+    }
+
+    #[tokio::test]
+    async fn test_take_offsets_applies_pagination_after_restoration() {
+        let tmp_dir = tempdir().unwrap();
+        let table = make_test_table(&tmp_dir).await;
+
+        let limited = table
+            .take_offsets(vec![0, 1, 0, 2])
+            .select(Select::Columns(vec!["id".to_string()]))
+            .limit(3)
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let limited = concat_batches(&limited[0].schema(), &limited).unwrap();
+        assert_eq!(limited.num_rows(), 3);
+        assert!(
+            limited
+                .column_by_name("id")
+                .unwrap()
+                .as_primitive::<Int32Type>()
+                .values()
+                .iter()
+                .all(|id| [0, 1, 2].contains(id))
+        );
+
+        let offset = table
+            .take_offsets(vec![5, 1, 5, 17])
+            .select(Select::Columns(vec!["id".to_string()]))
+            .offset(1)
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let offset = concat_batches(&offset[0].schema(), &offset).unwrap();
+        assert_eq!(offset.num_rows(), 3);
+        assert!(
+            offset
+                .column_by_name("id")
+                .unwrap()
+                .as_primitive::<Int32Type>()
+                .values()
+                .iter()
+                .all(|id| [1, 5, 17].contains(id))
+        );
+    }
+
+    #[tokio::test]
+    async fn test_take_offsets_create_plan_restores_occurrences() {
+        let tmp_dir = tempdir().unwrap();
+        let table = make_test_table(&tmp_dir).await;
+        let take = table
+            .take_offsets(vec![5, 1, 5, 17])
+            .select(Select::Columns(vec!["id".to_string()]));
+
+        let plan = take
+            .create_plan(QueryExecutionOptions::default())
+            .await
+            .unwrap();
+        assert_eq!(plan.schema().fields().len(), 1);
+        assert_eq!(plan.schema().field(0).name(), "id");
+        let planned = execute_plan(plan, Default::default())
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let planned = concat_batches(&planned[0].schema(), &planned).unwrap();
+        let mut ids = planned
+            .column_by_name("id")
+            .unwrap()
+            .as_primitive::<Int32Type>()
+            .values()
+            .to_vec();
+        ids.sort_unstable();
+        assert_eq!(ids, vec![1, 5, 5, 17]);
+    }
+
+    #[tokio::test]
+    async fn test_take_offsets_plan_introspection_shows_restoration() {
+        let tmp_dir = tempdir().unwrap();
+        let table = make_test_table(&tmp_dir).await;
+        let take = table
+            .take_offsets(vec![0, 1, 0, 2])
+            .select(Select::Columns(vec!["id".to_string()]))
+            .limit(3);
+
+        let explained = take.explain_plan(false).await.unwrap();
+        assert!(explained.contains("GlobalLimitExec"));
+        assert!(explained.contains("TakeRestoreExec"));
+        assert!(!explained.contains("CoalescePartitionsExec"));
+
+        let analyzed = take.analyze_plan().await.unwrap();
+        assert!(analyzed.contains("GlobalLimitExec"));
+        assert!(analyzed.contains("TakeRestoreExec"));
+        assert!(!analyzed.contains("CoalescePartitionsExec"));
+    }
+
    #[tokio::test]
    async fn test_take_row_ids() {
        let tmp_dir = tempdir().unwrap();
@@ -26,7 +26,9 @@ use crate::database::{
 use crate::error::Result;
 use crate::function::{FunctionRegistrationRequest, FunctionVersion};
 use crate::job::Job;
-use crate::remote::job::{DescribeJobResponse, RemoteJob, job_state_to_client};
+use crate::remote::job::{
+    DescribeJobResponse, PauseJobResponse, RemoteJob, ResumeJobResponse, job_state_to_client,
+};
 use crate::remote::util::stream_as_body;
 use crate::table::BaseTable;

@@ -533,6 +535,11 @@ struct RemoteListJobsResponse {
    page_token: Option<String>,
 }

+#[derive(serde::Deserialize)]
+struct RemoteDropFunctionResponse {
+    dropped: bool,
+}
+
 /// Bound on `list_jobs` page walking; a warning is logged when the listing
 /// is truncated at this many pages.
 const MAX_LIST_JOBS_PAGES: usize = 100;
@@ -583,6 +590,20 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
        response.json().await.err_to_http(request_id)
    }

+    async fn drop_function(&self, name: &str, version: &str) -> Result<bool> {
+        let req = self
+            .client
+            .post("/v1/functions/drop")
+            .json(&serde_json::json!({
+                "name": name,
+                "version": version,
+            }));
+        let (request_id, response) = self.client.send(req).await?;
+        let response = self.client.check_response(&request_id, response).await?;
+        let response: RemoteDropFunctionResponse = response.json().await.err_to_http(request_id)?;
+        Ok(response.dropped)
+    }
+
    fn job(&self, job_id: &str) -> Result<crate::job::Job> {
        Ok(crate::job::Job::new(Box::new(super::job::RemoteJob::new(
            self.client.clone(),
@@ -665,6 +686,40 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
        }
    }

+    async fn pause_job(&self, job_id: &str) -> Result<crate::database::PauseJobStatus> {
+        let req = self
+            .client
+            .post("/v1/jobs/pause")
+            .json(&serde_json::json!({ "job_id": job_id }));
+        let (request_id, rsp) = self.client.send(req).await?;
+        let rsp = self.client.check_response(&request_id, rsp).await?;
+        let body: PauseJobResponse = rsp.json().await.err_to_http(request_id)?;
+        Ok(if body.paused {
+            crate::database::PauseJobStatus::Pausing
+        } else if body.committing {
+            crate::database::PauseJobStatus::Committing
+        } else {
+            crate::database::PauseJobStatus::AlreadyPaused
+        })
+    }
+
+    async fn resume_job(&self, job_id: &str) -> Result<crate::database::ResumeJobStatus> {
+        let req = self
+            .client
+            .post("/v1/jobs/resume")
+            .json(&serde_json::json!({ "job_id": job_id }));
+        let (request_id, rsp) = self.client.send(req).await?;
+        let rsp = self.client.check_response(&request_id, rsp).await?;
+        let body: ResumeJobResponse = rsp.json().await.err_to_http(request_id)?;
+        Ok(if body.resumed {
+            crate::database::ResumeJobStatus::Resumed
+        } else if body.still_pausing {
+            crate::database::ResumeJobStatus::StillPausing
+        } else {
+            crate::database::ResumeJobStatus::NotPaused
+        })
+    }
+
    async fn job_history(&self, job_id: Option<&str>) -> Result<Vec<arrow_array::RecordBatch>> {
        let mut body = serde_json::json!({});
        if let Some(job_id) = job_id {
@@ -2600,6 +2655,45 @@ mod tests {
        assert!(!conn.cancel_job("nope").await.unwrap());
    }

+    #[tokio::test]
+    async fn test_pause_and_resume_job() {
+        use crate::database::{PauseJobStatus, ResumeJobStatus};
+        let conn = Connection::new_with_handler(|request| {
+            assert_eq!(request.url().path(), "/v1/jobs/pause");
+            http::Response::builder()
+                .status(200)
+                .body(r#"{"job_id": "job-1", "paused": true}"#)
+                .unwrap()
+        });
+        assert_eq!(
+            conn.pause_job("job-1").await.unwrap(),
+            PauseJobStatus::Pausing
+        );
+
+        let conn = Connection::new_with_handler(|_| {
+            http::Response::builder()
+                .status(200)
+                .body(r#"{"job_id": "job-1", "paused": false, "committing": true}"#)
+                .unwrap()
+        });
+        assert_eq!(
+            conn.pause_job("job-1").await.unwrap(),
+            PauseJobStatus::Committing
+        );
+
+        let conn = Connection::new_with_handler(|request| {
+            assert_eq!(request.url().path(), "/v1/jobs/resume");
+            http::Response::builder()
+                .status(200)
+                .body(r#"{"job_id": "job-1", "resumed": false, "still_pausing": true}"#)
+                .unwrap()
+        });
+        assert_eq!(
+            conn.resume_job("job-1").await.unwrap(),
+            ResumeJobStatus::StillPausing
+        );
+    }
+
    #[tokio::test]
    async fn test_job_history_parses_arrow_stream() {
        let schema = Arc::new(Schema::new(vec![Field::new(
@@ -2689,6 +2783,25 @@ mod tests {
        assert_eq!(version.version(), "fv_01K3EXACT");
    }

+    #[tokio::test]
+    async fn test_drop_function_sends_exact_version_and_decodes_replay() {
+        let conn = Connection::new_with_handler(|request| {
+            assert_eq!(request.method(), &reqwest::Method::POST);
+            assert_eq!(request.url().path(), "/v1/functions/drop");
+            let body: serde_json::Value =
+                serde_json::from_slice(request.body().unwrap().as_bytes().unwrap()).unwrap();
+            assert_eq!(
+                body,
+                serde_json::json!({"name": "embed", "version": "fv_01K3EXACT"})
+            );
+            http::Response::builder()
+                .status(200)
+                .body(r#"{"dropped":false}"#)
+                .unwrap()
+        });
+        assert!(!conn.drop_function("embed", "fv_01K3EXACT").await.unwrap());
+    }
+
    #[tokio::test]
    async fn test_conn_job_waits_to_done() {
        let polls = Arc::new(AtomicUsize::new(0));
@@ -73,6 +73,28 @@ pub(super) struct ReportedFailure {
    retryable: Option<bool>,
 }

+/// Forward-compatible `/v1/jobs/pause` wire envelope.
+#[derive(Deserialize)]
+pub(super) struct PauseJobResponse {
+    /// False when the job was already paused, so a repeated pause changed
+    /// nothing.
+    #[serde(default)]
+    pub(super) paused: bool,
+    /// The job is finalizing its results and cannot be parked right now.
+    #[serde(default)]
+    pub(super) committing: bool,
+}
+
+/// Forward-compatible `/v1/jobs/resume` wire envelope.
+#[derive(Deserialize)]
+pub(super) struct ResumeJobResponse {
+    #[serde(default)]
+    pub(super) resumed: bool,
+    /// The pause's worker drain is not confirmed yet.
+    #[serde(default)]
+    pub(super) still_pausing: bool,
+}
+
 /// Forward-compatible `/v1/jobs/describe` wire envelope.
 #[derive(Deserialize)]
 pub(super) struct DescribeJobResponse {
@@ -40,8 +40,8 @@ use crate::table::{
 use crate::table::{AnyQuery, Filter, Predicate, PreprocessingOutput, TableStatistics};
 use crate::utils::background_cache::BackgroundCache;
 use crate::utils::{
-    resolve_arrow_field_path, resolve_arrow_fts_field_path, supported_btree_data_type,
-    supported_vector_data_type,
+    MaxBatchLengthStream, TimeoutStream, resolve_arrow_field_path, resolve_arrow_fts_field_path,
+    supported_btree_data_type, supported_vector_data_type,
 };
 use crate::{DistanceType, Error};
 use crate::{
@@ -2022,6 +2022,9 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
    fn as_any(&self) -> &dyn std::any::Any {
        self
    }
+    fn analyze_plan_is_remote(&self) -> bool {
+        true
+    }
    fn name(&self) -> &str {
        &self.name
    }
@@ -2594,6 +2597,13 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
        query: &AnyQuery,
        options: QueryExecutionOptions,
    ) -> Result<Arc<dyn ExecutionPlan>> {
+        if let AnyQuery::Query(request) = query
+            && let Some(offsets) = &request.take_offsets
+        {
+            return crate::query::create_take_offsets_plan(self, request, offsets, options, false)
+                .await;
+        }
+
        let streams = self.execute_query(query, &options).await?;
        if streams.len() == 1 {
            let stream = streams.into_iter().next().unwrap();
@@ -2612,6 +2622,27 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
        query: &AnyQuery,
        options: QueryExecutionOptions,
    ) -> Result<DatasetRecordBatchStream> {
+        if let AnyQuery::Query(request) = query
+            && let Some(offsets) = &request.take_offsets
+        {
+            let plan = crate::query::create_take_offsets_plan(
+                self,
+                request,
+                offsets,
+                options.clone(),
+                false,
+            )
+            .await?;
+            let inner = execute_plan(plan, Default::default())?;
+            let inner = MaxBatchLengthStream::new_boxed(inner, options.max_batch_length as usize);
+            let inner = if let Some(timeout) = options.timeout {
+                TimeoutStream::new_boxed(inner, timeout)
+            } else {
+                inner
+            };
+            return Ok(DatasetRecordBatchStream::new(inner));
+        }
+
        let streams = self.execute_query(query, &options).await?;

        if streams.len() == 1 {
@@ -2649,6 +2680,12 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
    }

    async fn explain_plan(&self, query: &AnyQuery, verbose: bool) -> Result<String> {
+        if let AnyQuery::Query(request) = query
+            && let Some(offsets) = &request.take_offsets
+        {
+            return crate::query::explain_take_offsets_plan(self, request, offsets, verbose).await;
+        }
+
        let base_request = self
            .client
            .post(&format!("/v1/table/{}/explain_plan/", self.identifier));
@@ -2701,6 +2738,17 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
        query: &AnyQuery,
        options: QueryExecutionOptions,
    ) -> Result<String> {
+        let prepared_query = if let AnyQuery::Query(request) = query
+            && request.take_offsets.is_some()
+        {
+            Some(AnyQuery::Query(
+                crate::query::prepare_take_offsets_request(self, request).await?,
+            ))
+        } else {
+            None
+        };
+        let query = prepared_query.as_ref().unwrap_or(query);
+
        let mut request = self
            .client
            .post(&format!("/v1/table/{}/analyze_plan/", self.identifier));
@@ -3180,8 +3228,8 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
            self.schema().await?.as_ref(),
            "schema evolution",
        )?;
-        // The server plans the declaration: expression validation, type
-        // inference and the persisted binding all happen there.
+        // The server plans the declaration against its table schema, including
+        // Blob v2 semantics inherited by a direct field projection.
        let entries = columns
            .iter()
            .map(
@@ -3690,7 +3738,7 @@ mod tests {
    };
    use arrow_schema::{DataType, Field, Schema};
    use chrono::{DateTime, Utc};
-    use futures::{StreamExt, TryFutureExt, future::BoxFuture};
+    use futures::{StreamExt, TryFutureExt, TryStreamExt, future::BoxFuture};
    use lance_index::scalar::inverted::{DocumentGranularity, query::MatchQuery};
    use lance_index::scalar::{FullTextSearchQuery, InvertedIndexParams};
    use reqwest::Body;
@@ -5611,6 +5659,114 @@ mod tests {
        assert_eq!(result, "analyzed plan");
    }

+    #[tokio::test]
+    async fn test_take_offsets_explain_plan_does_not_execute_query() {
+        let table = Table::new_with_handler("my_table", |request| {
+            assert_eq!(request.method(), "POST");
+            assert_eq!(request.url().path(), "/v1/table/my_table/explain_plan/");
+
+            http::Response::builder()
+                .status(200)
+                .body(r#""RemoteLookupExec""#)
+                .unwrap()
+        });
+
+        let explained = table
+            .take_offsets(vec![0, 1, 0, 2])
+            .select(crate::query::Select::columns(&["id"]))
+            .limit(3)
+            .explain_plan(false)
+            .await
+            .unwrap();
+
+        assert!(explained.contains("GlobalLimitExec"));
+        assert!(explained.contains("TakeRestoreExec"));
+        assert!(!explained.contains("CoalescePartitionsExec"));
+        assert!(explained.contains("RemoteLookupExec"));
+    }
+
+    #[tokio::test]
+    async fn test_converted_take_request_restores_remote_occurrences() {
+        let table = Table::new_with_handler("my_table", |request| {
+            assert_eq!(request.method(), "POST");
+            assert_eq!(request.url().path(), "/v1/table/my_table/query/");
+
+            let body: serde_json::Value =
+                serde_json::from_slice(request.body().unwrap().as_bytes().unwrap()).unwrap();
+            assert_eq!(body["columns"], json!(["id", "_rowoffset"]));
+
+            let data = RecordBatch::try_new(
+                Arc::new(Schema::new(vec![
+                    Field::new("id", DataType::Int32, false),
+                    Field::new("_rowoffset", DataType::UInt64, false),
+                ])),
+                vec![
+                    Arc::new(Int32Array::from(vec![5])),
+                    Arc::new(arrow_array::UInt64Array::from(vec![5])),
+                ],
+            )
+            .unwrap();
+            http::Response::builder()
+                .status(200)
+                .header(CONTENT_TYPE, ARROW_FILE_CONTENT_TYPE)
+                .body(write_ipc_file(&data))
+                .unwrap()
+        });
+
+        let request = table
+            .take_offsets(vec![5, 5])
+            .select(crate::query::Select::columns(&["id"]))
+            .into_request();
+        let batches = table
+            .base_table()
+            .query(&AnyQuery::Query(request), QueryExecutionOptions::default())
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+
+        assert_eq!(batches.iter().map(RecordBatch::num_rows).sum::<usize>(), 2);
+        assert!(
+            batches
+                .iter()
+                .all(|batch| batch.schema().fields().len() == 1)
+        );
+    }
+
+    #[tokio::test]
+    async fn test_take_offsets_analyze_plan_delegates_to_remote() {
+        let table = Table::new_with_handler("my_table", |request| {
+            assert_eq!(request.method(), "POST");
+            assert_eq!(request.url().path(), "/v1/table/my_table/analyze_plan/");
+            assert_eq!(
+                request
+                    .url()
+                    .query_pairs()
+                    .find(|(key, _)| key == "distributed_metrics"),
+                Some(("distributed_metrics".into(), "per_worker".into()))
+            );
+
+            http::Response::builder()
+                .status(200)
+                .body(r#""Remote analyzed plan: worker metrics""#)
+                .unwrap()
+        });
+
+        let analyzed = table
+            .take_offsets(vec![0, 1, 0, 2])
+            .select(crate::query::Select::columns(&["id"]))
+            .limit(3)
+            .analyze_plan_with_options(QueryExecutionOptions {
+                analyze_plan_distributed_metrics: AnalyzePlanDistributedMetrics::PerWorker,
+                ..Default::default()
+            })
+            .await
+            .unwrap();
+
+        assert_eq!(analyzed, "Remote analyzed plan: worker metrics");
+    }
+
    #[tokio::test]
    async fn test_query_structured_fts() {
        let table =
@@ -5734,9 +5890,8 @@ mod tests {
            ))
            .execute()
            .await;
-        let err = match result {
-            Ok(_) => panic!("legacy remote query unexpectedly succeeded"),
-            Err(err) => err,
+        let Err(err) = result else {
+            panic!("legacy remote query unexpectedly succeeded")
        };

        assert!(
@@ -7388,8 +7543,8 @@ mod tests {
        assert_eq!(result.version, if old_server { 0 } else { 43 });
    }

-    /// A declaration is sent as `{name, computed}` entries for the server to
-    /// plan; the client never types the expression itself.
+    /// A declaration is sent as `{name, computed}` for the server to plan; the
+    /// client never types the expression itself.
    #[tokio::test]
    async fn test_add_computed_columns_sends_the_expression() {
        let table = Table::new_with_handler("my_table", |request| match request.url().path() {
@@ -7465,6 +7620,93 @@ mod tests {
        assert_eq!(result.version, 8);
    }

+    #[tokio::test]
+    async fn test_add_function_column_allows_an_existing_binding() {
+        let binding = crate::function::FunctionBinding::from_json(include_str!(
+            "../../tests/fixtures/first_class_functions/v1/remote_function_binding.json"
+        ))
+        .unwrap();
+        let binding_metadata = crate::table::computed_columns::function_bindings_metadata(
+            std::slice::from_ref(&binding),
+        )
+        .unwrap();
+        let mut fields = vec![
+            Field::new("title", DataType::Utf8, true),
+            Field::new("body", DataType::Utf8, true),
+        ];
+        fields.extend(binding.outputs().iter().map(|output| {
+            let data_type = match output.arrow_type.as_str() {
+                "utf8" => DataType::Utf8,
+                "int64" => DataType::Int64,
+                other => panic!("unexpected fixture output type {other}"),
+            };
+            Field::new(&output.output_name, data_type, true).with_metadata(
+                crate::table::computed_columns::function_computed_column_metadata(
+                    binding.binding_id(),
+                    output.output_ordinal,
+                    &["title".into(), "body".into()],
+                ),
+            )
+        }));
+        let schema = Schema::new_with_metadata(
+            fields,
+            HashMap::from([(
+                crate::table::computed_columns::FUNCTION_BINDINGS_META_KEY.to_string(),
+                binding_metadata,
+            )]),
+        );
+        let table =
+            Table::new_with_handler("my_table", move |request| match request.url().path() {
+                "/v1/table/my_table/describe/" => http::Response::builder()
+                    .status(200)
+                    .body(describe_response(&schema))
+                    .unwrap(),
+                "/v1/table/my_table/add_columns/" => {
+                    let actual: serde_json::Value =
+                        serde_json::from_slice(request.body().unwrap().as_bytes().unwrap())
+                            .unwrap();
+                    assert_eq!(
+                        actual["new_columns"],
+                        serde_json::json!([
+                            {"name":"secondary_text","all_null":true},
+                            {"name":"secondary_token_count","all_null":true}
+                        ])
+                    );
+                    http::Response::builder()
+                        .status(200)
+                        .body(r#"{"version":10}"#.to_string())
+                        .unwrap()
+                }
+                path => panic!("Unexpected path: {path}"),
+            });
+        let application = crate::function::FunctionApplication::from_json(
+            r#"{
+                "function":{"name":"text_features","version":"fv_01K3TEXT"},
+                "inputs":[
+                    {"parameter":"title","kind":"column","value":{"path":"title"}},
+                    {"parameter":"body","kind":"column","value":{"path":"body"}}
+                ],
+                "output":{"kind":"named_struct","fields":[
+                    {"name":"normalized_text","arrow_type":"utf8","nullable":false},
+                    {"name":"token_count","arrow_type":"int64","nullable":false}
+                ]},
+                "columns":{
+                    "normalized_text":"secondary_text",
+                    "token_count":"secondary_token_count"
+                }
+            }"#,
+        )
+        .unwrap();
+
+        let result = table
+            .add_columns()
+            .function(application)
+            .execute()
+            .await
+            .unwrap();
+        assert_eq!(result.version, 10);
+    }
+
    #[tokio::test]
    async fn test_add_fixed_size_list_function_column_declares_the_vector_type() {
        let table = Table::new_with_handler("my_table", |request| {
@@ -595,6 +595,14 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
        query: &AnyQuery,
        options: QueryExecutionOptions,
    ) -> Result<String>;
+    /// Whether [`BaseTable::analyze_plan`] is provided by a remote service.
+    ///
+    /// Client-side query wrappers use this to preserve backend metrics and
+    /// distributed-analysis options instead of replacing them with a local plan.
+    #[doc(hidden)]
+    fn analyze_plan_is_remote(&self) -> bool {
+        false
+    }

    /// Add new records to the table.
    async fn add(&self, add: AddDataBuilder) -> Result<AddResult>;
@@ -750,8 +758,8 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
    /// Declare computed columns, each defined by a SQL expression.
    ///
    /// Where the declaration is planned depends on the backend: a local table
-    /// validates and types the expression itself, a remote one sends the text
-    /// for the server to plan.
+    /// validates and types the expression itself, while a remote one sends the
+    /// expression for the server to plan.
    async fn add_computed_columns(
        &self,
        _columns: &[(String, String)],
@@ -1652,9 +1660,9 @@ impl Table {
    /// Offsets are useful for sampling as the set of all valid offsets is easily
    /// known in advance to be [0, len(table)).
    ///
-    /// No guarantees are made regarding the order in which results are returned.  If you
-    /// desire an output order that matches the order of the given offsets, you will need
-    /// to add the row offset column to the output and align it yourself.
+    /// No guarantees are made regarding the order in which results are returned.
+    /// Repeated offsets produce repeated rows, which makes this method suitable for
+    /// sampling with replacement.
    ///
    /// Parameters
    /// ----------
@@ -5763,6 +5771,13 @@ mod tests {
        assert!(index_bytes > 0);
        assert_eq!(with_index, data_only + index_bytes);

+        // Release builds reject unstable overlay datasets unless explicitly opted in.
+        if !lance_table::feature_flags::can_read_dataset(
+            lance_table::feature_flags::FLAG_UNSTABLE_DATA_OVERLAY_FILES,
+        ) {
+            return;
+        }
+
        // Commit an overlay file supplying new `foo` values for the first three
        // rows of fragment 0. There is no high-level API that writes overlays
        // yet, so write the overlay's data file and commit the `DataOverlay`
@@ -110,7 +110,7 @@ fn requires_local_namespace_execution(query: &AnyQuery) -> bool {
    // pushing these down would silently ignore the user's setting. For use_lsm that
    // is worse than a tuning miss: MemWAL read routing lives only in `create_plan`,
    // so a pushed-down query would return stale base-only data with no error.
-    if query.base().use_lsm.is_some() {
+    if query.base().use_lsm.is_some() || query.base().take_offsets.is_some() {
        return true;
    }
    matches!(
@@ -154,6 +154,13 @@ pub async fn create_plan(
    options: QueryExecutionOptions,
 ) -> Result<Arc<dyn ExecutionPlan>> {
    let query = query.canonicalized()?;
+    if let AnyQuery::Query(request) = &query
+        && let Some(offsets) = &request.take_offsets
+    {
+        return crate::query::create_take_offsets_plan(table, request, offsets, options, false)
+            .await;
+    }
+
    let query = match query {
        AnyQuery::VectorQuery(query) => query,
        AnyQuery::Query(query) => VectorQueryRequest::from_plain_query(query),
@@ -29,10 +29,14 @@
 //! inputs masked to null first, so a poison value in a row nobody is filling
 //! cannot fail the refresh.

+use std::collections::HashSet;
 use std::sync::Arc;

-use arrow_array::{ArrayRef, BooleanArray, RecordBatch, RecordBatchOptions};
-use arrow_schema::Schema as ArrowSchema;
+use arrow_array::{
+    Array, ArrayRef, BooleanArray, LargeBinaryArray, RecordBatch, RecordBatchOptions, StructArray,
+    new_null_array,
+};
+use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema};
 use datafusion_expr::ColumnarValue;
 use futures::{Stream, StreamExt, TryStreamExt};
 use lance::Dataset;
@@ -40,7 +44,7 @@ use lance::dataset::WriteDestination;
 use lance::dataset::fragment::FileFragment;
 use lance::dataset::transaction::Operation;
 use lance_core::ROW_ID;
-use lance_core::datatypes::Schema as LanceSchema;
+use lance_core::datatypes::{BlobHandling, Schema as LanceSchema};
 use serde::{Deserialize, Serialize};

 use super::computed_columns::{BoundExpression, ComputedColumnKind, computed_column_from_field};
@@ -104,6 +108,7 @@ async fn execute_refresh_column_with_source(
        fields: vec![field.clone()],
        metadata: Default::default(),
    };
+    let output_is_blob = field.is_blob_v2();

    let mut rows_filled = 0u64;
    let mut replacements = Vec::new();
@@ -113,7 +118,8 @@ async fn execute_refresh_column_with_source(
            continue;
        }
        rows_filled += gained;
-        let values = fill_stream(&dataset, &fragment, bound.clone(), column).await?;
+        let values =
+            fill_stream(&dataset, &fragment, bound.clone(), column, output_is_blob).await?;
        replacements.push(fragment.write_columns(values, &column_schema).await?);
    }

@@ -294,12 +300,15 @@ fn evaluation_batch(
    mask_out: Option<&BooleanArray>,
 ) -> lance_core::Result<RecordBatch> {
    let mut columns = Vec::with_capacity(bound.roots.len());
+    let mut fields = Vec::with_capacity(bound.roots.len());
    for name in &bound.roots {
-        let column = batch.column_by_name(name).ok_or_else(|| {
+        let index = batch.schema_ref().index_of(name).map_err(|_| {
            lance_core::Error::invalid_input(format!(
                "refreshing a computed column read no {name} column"
            ))
        })?;
+        let column = batch.column(index);
+        fields.push(batch.schema_ref().field(index).clone());
        // Rows outside the mask must not reach the expression: a value in a
        // deleted or already-filled row can be one it would choke on.
        columns.push(match mask_out {
@@ -308,7 +317,7 @@ fn evaluation_batch(
        });
    }
    Ok(RecordBatch::try_new_with_options(
-        bound.read_schema.clone(),
+        Arc::new(ArrowSchema::new(fields)),
        columns,
        &RecordBatchOptions::new().with_row_count(Some(batch.num_rows())),
    )?)
@@ -329,6 +338,99 @@ fn evaluate(bound: &BoundExpression, batch: &RecordBatch) -> lance_core::Result<
    }
 }

+fn materialized_blob_ids(schema: &LanceSchema, paths: &[String]) -> Result<HashSet<u32>> {
+    paths
+        .iter()
+        .map(|path| {
+            let field = schema
+                .resolve(path)
+                .and_then(|fields| fields.last().copied())
+                .ok_or_else(|| Error::InvalidInput {
+                    message: format!("computed Blob input '{path}' no longer exists"),
+                })?;
+            if !field.is_blob_v2() {
+                return Err(Error::InvalidInput {
+                    message: format!("computed Blob input '{path}' is no longer Blob v2"),
+                });
+            }
+            u32::try_from(field.id).map_err(|_| Error::InvalidInput {
+                message: format!(
+                    "computed Blob input '{path}' has invalid field id {}",
+                    field.id
+                ),
+            })
+        })
+        .collect()
+}
+
+fn configure_blob_inputs(
+    scanner: &mut lance::dataset::scanner::Scanner,
+    schema: &LanceSchema,
+    bound: &BoundExpression,
+    extra_blob_id: Option<u32>,
+) -> Result<()> {
+    let mut ids = materialized_blob_ids(schema, &bound.blob_paths)?;
+    ids.extend(extra_blob_id);
+    scanner.blob_handling(BlobHandling::SomeBlobsBinary(ids));
+    Ok(())
+}
+
+fn blob_array_from_binary(
+    array: &ArrayRef,
+    target_field: &ArrowField,
+) -> lance_core::Result<ArrayRef> {
+    let values = array
+        .as_any()
+        .downcast_ref::<LargeBinaryArray>()
+        .ok_or_else(|| {
+            lance_core::Error::invalid_input(format!(
+                "a Blob v2 computed output produced {}, expected LargeBinary",
+                array.data_type()
+            ))
+        })?;
+    let mut builder = lance::blob::BlobArrayBuilder::new(values.len());
+    for index in 0..values.len() {
+        if values.is_null(index) {
+            builder.push_null()?;
+        } else {
+            builder.push_bytes(values.value(index))?;
+        }
+    }
+    let minimal = builder.finish()?;
+    let minimal = minimal
+        .as_any()
+        .downcast_ref::<StructArray>()
+        .ok_or_else(|| lance_core::Error::internal("Blob builder returned a non-struct array"))?;
+    let DataType::Struct(target_fields) = target_field.data_type() else {
+        return Err(lance_core::Error::invalid_input(format!(
+            "Blob v2 output field '{}' has non-struct type {}",
+            target_field.name(),
+            target_field.data_type()
+        )));
+    };
+    let columns = target_fields
+        .iter()
+        .map(|field| match field.name().as_str() {
+            "data" | "uri" => minimal
+                .column_by_name(field.name())
+                .cloned()
+                .ok_or_else(|| {
+                    lance_core::Error::internal(format!("Blob builder omitted '{}'", field.name()))
+                }),
+            "position" | "size" => Ok(new_null_array(field.data_type(), minimal.len())),
+            name => Err(lance_core::Error::invalid_input(format!(
+                "Blob v2 output field '{}' has unsupported logical child '{name}'",
+                target_field.name()
+            ))),
+        })
+        .collect::<lance_core::Result<Vec<_>>>()?;
+    Ok(Arc::new(StructArray::try_new(
+        target_fields.clone(),
+        columns,
+        minimal.nulls().cloned(),
+    )?))
+}
+
 /// How many rows of one fragment would gain a value.
 ///
 /// Scans only the unfilled live rows -- deleted rows never reach the
@@ -347,6 +449,7 @@ async fn count_fragment_gains(
        .with_row_id()
        .filter(&format!("{} IS NULL", quote_identifier(column)))?
        .project(&bound.roots)?;
+    configure_blob_inputs(&mut scanner, dataset.schema(), bound, None)?;

    let mut gained = 0u64;
    let mut batches = scanner.try_into_stream().await?;
@@ -368,6 +471,7 @@ async fn fill_stream(
    fragment: &FileFragment,
    bound: Arc<BoundExpression>,
    column: &str,
+    output_is_blob: bool,
 ) -> Result<impl Stream<Item = lance_core::Result<RecordBatch>> + Send + use<>> {
    let mut projection: Vec<String> = bound.roots.clone();
    projection.push(column.to_string());
@@ -377,6 +481,20 @@ async fn fill_stream(
        .with_row_id()
        .include_deleted_rows()
        .project(&projection)?;
+    let output_blob_id = output_is_blob
+        .then(|| {
+            dataset
+                .schema()
+                .field(column)
+                .and_then(|field| u32::try_from(field.id).ok())
+        })
+        .flatten();
+    configure_blob_inputs(
+        &mut scanner,
+        dataset.schema(),
+        bound.as_ref(),
+        output_blob_id,
+    )?;

    let projected = Arc::new(ArrowSchema::new(vec![
        ArrowSchema::from(dataset.schema())
@@ -412,6 +530,11 @@ async fn fill_stream(

        let computed = evaluate(&bound, &evaluation_batch(&batch, &bound, Some(&keep))?)?;
        let merged = arrow_select::zip::zip(&fill, &computed, existing)?;
+        let merged = if output_is_blob {
+            blob_array_from_binary(&merged, projected.field(0))?
+        } else {
+            merged
+        };
        Ok(RecordBatch::try_new(projected.clone(), vec![merged])?)
    }))
 }
@@ -420,8 +543,12 @@ async fn fill_stream(
 mod tests {
    use std::sync::Arc;

-    use arrow_array::{Int32Array, record_batch};
+    use arrow_array::{
+        Array, ArrayRef, Int32Array, LargeBinaryArray, RecordBatch, StructArray, record_batch,
+    };
+    use arrow_schema::Field as ArrowField;
    use futures::TryStreamExt;
+    use lance_core::ROW_ID;

    use crate::connect;
    use crate::query::{ExecutableQuery, QueryBase, Select};
@@ -477,6 +604,25 @@ mod tests {
        table.add(batch).execute().await.unwrap();
    }

+    #[test]
+    fn test_blob_output_matches_complete_logical_field() {
+        let values: ArrayRef = Arc::new(LargeBinaryArray::from(vec![
+            Some(b"hello".as_slice()),
+            None,
+        ]));
+        let field = ArrowField::new(
+            "image",
+            lance_core::datatypes::BLOB_V2_LOGICAL_TYPE.clone(),
+            true,
+        );
+
+        let output = super::blob_array_from_binary(&values, &field).unwrap();
+        assert_eq!(output.data_type(), field.data_type());
+        let output = output.as_any().downcast_ref::<StructArray>().unwrap();
+        assert_eq!(output.column_by_name("position").unwrap().null_count(), 2);
+        assert_eq!(output.column_by_name("size").unwrap().null_count(), 2);
+    }
+
    /// 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.
@@ -1164,4 +1310,366 @@ mod tests {
        let err = table.refresh_column("embedding").await.unwrap_err();
        assert!(matches!(err, Error::NotSupported { message } if message.contains("udf")));
    }
+
+    fn blob_batch(ids: Vec<i32>, payloads: Vec<Option<&[u8]>>) -> RecordBatch {
+        use arrow_array::Int32Array;
+        use arrow_schema::{Field, Schema};
+
+        let mut builder = lance::blob::BlobArrayBuilder::new(payloads.len());
+        for payload in payloads {
+            match payload {
+                Some(payload) => builder.push_bytes(payload).unwrap(),
+                None => builder.push_null().unwrap(),
+            }
+        }
+        RecordBatch::try_new(
+            Arc::new(Schema::new(vec![
+                Field::new("id", arrow_schema::DataType::Int32, false),
+                crate::blob("image", true),
+            ])),
+            vec![Arc::new(Int32Array::from(ids)), builder.finish().unwrap()],
+        )
+        .unwrap()
+    }
+
+    async fn create_blob_table(path: &std::path::Path, batch: RecordBatch) -> Table {
+        let conn = connect(path.to_str().unwrap()).execute().await.unwrap();
+        conn.create_table("blobs", batch).execute().await.unwrap()
+    }
+
+    #[tokio::test]
+    async fn test_refresh_inherits_and_publishes_blob_output() {
+        use arrow_array::UInt64Array;
+        use lance_arrow::{
+            BLOB_DEDICATED_SIZE_THRESHOLD_META_KEY, BLOB_INLINE_SIZE_THRESHOLD_META_KEY,
+        };
+        use lance_core::datatypes::BlobKind;
+
+        use crate::table::schema_evolution::FieldMetadataUpdate;
+
+        let tmp = tempfile::tempdir().unwrap();
+        let table = create_blob_table(
+            tmp.path(),
+            blob_batch(
+                vec![1, 2, 3, 4],
+                vec![Some(b"hello"), Some(b"ab"), Some(b""), None],
+            ),
+        )
+        .await;
+        table
+            .add_columns()
+            .computed("image_copy", "image")
+            .execute()
+            .await
+            .unwrap();
+        table
+            .update_field_metadata(&[FieldMetadataUpdate::new("image_copy")
+                .set(BLOB_INLINE_SIZE_THRESHOLD_META_KEY, "1")
+                .set(BLOB_DEDICATED_SIZE_THRESHOLD_META_KEY, "4")])
+            .await
+            .unwrap();
+
+        let first_refresh = table.refresh_column("image_copy").await.unwrap();
+        assert_eq!(first_refresh.rows_filled, 3);
+        assert_eq!(
+            table.blob_columns().await.unwrap(),
+            vec!["image".to_string(), "image_copy".to_string()]
+        );
+
+        let batches = table
+            .query()
+            .with_row_id()
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let batch = arrow_select::concat::concat_batches(&batches[0].schema(), &batches).unwrap();
+        assert!(
+            batch
+                .column_by_name("image_copy")
+                .unwrap()
+                .as_any()
+                .is::<arrow_array::StructArray>()
+        );
+        let row_ids = batch
+            .column_by_name(ROW_ID)
+            .unwrap()
+            .as_any()
+            .downcast_ref::<UInt64Array>()
+            .unwrap()
+            .values()
+            .to_vec();
+        let original = table.fetch_blobs("image", &row_ids).await.unwrap();
+        let copied = table.fetch_blobs("image_copy", &row_ids).await.unwrap();
+        assert_eq!(original, copied);
+        let ids = batch
+            .column_by_name("id")
+            .unwrap()
+            .as_any()
+            .downcast_ref::<Int32Array>()
+            .unwrap();
+        let files = table
+            .fetch_blob_files("image_copy", &row_ids)
+            .await
+            .unwrap();
+        let mut layouts = ids
+            .values()
+            .iter()
+            .copied()
+            .zip(files)
+            .map(|(id, file)| (id, file.and_then(|file| file.kind())))
+            .collect::<Vec<_>>();
+        layouts.sort_by_key(|(id, _)| *id);
+        assert_eq!(
+            layouts,
+            vec![
+                (1, Some(BlobKind::Dedicated)),
+                (2, Some(BlobKind::Packed)),
+                (3, Some(BlobKind::Inline)),
+                (4, None),
+            ]
+        );
+
+        table
+            .add(blob_batch(vec![5], vec![Some(b"appended")]))
+            .execute()
+            .await
+            .unwrap();
+        table
+            .optimize(crate::table::OptimizeAction::Compact {
+                options: crate::table::CompactionOptions::default(),
+                remap_options: None,
+            })
+            .await
+            .unwrap();
+        assert_eq!(
+            table
+                .refresh_column("image_copy")
+                .await
+                .unwrap()
+                .rows_filled,
+            1
+        );
+        assert_eq!(
+            table
+                .refresh_column("image_copy")
+                .await
+                .unwrap()
+                .rows_filled,
+            0
+        );
+
+        table.checkout(first_refresh.version).await.unwrap();
+        assert_eq!(table.count_rows(None).await.unwrap(), 4);
+        assert_eq!(
+            table.blob_columns().await.unwrap(),
+            vec!["image".to_string(), "image_copy".to_string()]
+        );
+        table.checkout_latest().await.unwrap();
+    }
+
+    #[tokio::test]
+    async fn test_refresh_inherits_nested_struct_blob_input() {
+        use arrow_array::{Int32Array, StructArray, UInt64Array};
+        use arrow_schema::{DataType, Field, Fields, Schema};
+
+        let tmp = tempfile::tempdir().unwrap();
+        let mut blob_builder = lance::blob::BlobArrayBuilder::new(2);
+        blob_builder.push_bytes(b"nested").unwrap();
+        blob_builder.push_null().unwrap();
+        let blob_field = crate::blob("image", true);
+        let metadata_fields = Fields::from(vec![blob_field.clone()]);
+        let metadata = StructArray::new(
+            metadata_fields.clone(),
+            vec![blob_builder.finish().unwrap()],
+            None,
+        );
+        let batch = RecordBatch::try_new(
+            Arc::new(Schema::new(vec![
+                Field::new("id", DataType::Int32, false),
+                Field::new("metadata", DataType::Struct(metadata_fields), true),
+            ])),
+            vec![Arc::new(Int32Array::from(vec![1, 2])), Arc::new(metadata)],
+        )
+        .unwrap();
+        let table = create_blob_table(tmp.path(), batch).await;
+        table
+            .add_columns()
+            .computed("payload_copy", "metadata.image")
+            .execute()
+            .await
+            .unwrap();
+
+        assert_eq!(
+            table
+                .refresh_column("payload_copy")
+                .await
+                .unwrap()
+                .rows_filled,
+            1
+        );
+        assert_eq!(
+            table.blob_columns().await.unwrap(),
+            vec!["metadata.image".to_string(), "payload_copy".to_string()]
+        );
+        let batches = table
+            .query()
+            .with_row_id()
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let row_ids = batches[0]
+            .column_by_name(ROW_ID)
+            .unwrap()
+            .as_any()
+            .downcast_ref::<UInt64Array>()
+            .unwrap()
+            .values();
+        let payloads = table.fetch_blobs("payload_copy", row_ids).await.unwrap();
+        assert_eq!(payloads.value(0), b"nested");
+        assert!(payloads.is_null(1));
+    }
+
+    #[tokio::test]
+    async fn test_refresh_preserves_list_shape_when_materializing_blob_input() {
+        use arrow_array::{Int32Array, ListArray};
+        use arrow_buffer::{OffsetBuffer, ScalarBuffer};
+        use arrow_schema::{DataType, Field, Schema};
+
+        let tmp = tempfile::tempdir().unwrap();
+        let mut blob_builder = lance::blob::BlobArrayBuilder::new(3);
+        blob_builder.push_bytes(b"a").unwrap();
+        blob_builder.push_bytes(b"bb").unwrap();
+        blob_builder.push_null().unwrap();
+        let item = Arc::new(crate::blob("item", true));
+        let images = ListArray::new(
+            item.clone(),
+            OffsetBuffer::new(ScalarBuffer::from(vec![0, 2, 3])),
+            blob_builder.finish().unwrap(),
+            None,
+        );
+        let batch = RecordBatch::try_new(
+            Arc::new(Schema::new(vec![
+                Field::new("id", DataType::Int32, false),
+                Field::new("images", DataType::List(item), true),
+            ])),
+            vec![Arc::new(Int32Array::from(vec![1, 2])), Arc::new(images)],
+        )
+        .unwrap();
+        let table = create_blob_table(tmp.path(), batch).await;
+        table
+            .add_columns()
+            .computed("image_payloads", "images")
+            .execute()
+            .await
+            .unwrap();
+
+        assert_eq!(
+            table
+                .refresh_column("image_payloads")
+                .await
+                .unwrap()
+                .rows_filled,
+            2
+        );
+        let batches = table
+            .query()
+            .select(Select::columns(&["image_payloads"]))
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let output = batches[0]
+            .column_by_name("image_payloads")
+            .unwrap()
+            .as_any()
+            .downcast_ref::<ListArray>()
+            .unwrap();
+        assert_eq!(output.value_offsets(), &[0, 2, 3]);
+        assert!(output.values().as_any().is::<LargeBinaryArray>());
+    }
+
+    #[tokio::test]
+    async fn test_refresh_inherits_external_blob_input() {
+        use arrow_array::{Int32Array, StringArray, UInt64Array};
+        use arrow_schema::{DataType, Field, Schema};
+
+        let tmp = tempfile::tempdir().unwrap();
+        let payload = b"external-payload";
+        let path = tmp.path().join("payload.bin");
+        std::fs::write(&path, payload).unwrap();
+        let uri = url::Url::from_file_path(path).unwrap().to_string();
+        let conn = connect(tmp.path().join("db").to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+        let table = conn
+            .create_empty_table(
+                "external",
+                Arc::new(Schema::new(vec![
+                    Field::new("id", DataType::Int32, false),
+                    crate::blob("image", true),
+                ])),
+            )
+            .execute()
+            .await
+            .unwrap();
+        let batch = RecordBatch::try_new(
+            Arc::new(Schema::new(vec![
+                Field::new("id", DataType::Int32, false),
+                Field::new("image", DataType::Utf8, true),
+            ])),
+            vec![
+                Arc::new(Int32Array::from(vec![1])),
+                Arc::new(StringArray::from(vec![Some(uri)])),
+            ],
+        )
+        .unwrap();
+        table
+            .add(batch)
+            .allow_external_blob_outside_bases(true)
+            .execute()
+            .await
+            .unwrap();
+        table
+            .add_columns()
+            .computed("payload_copy", "image")
+            .execute()
+            .await
+            .unwrap();
+
+        assert_eq!(
+            table
+                .refresh_column("payload_copy")
+                .await
+                .unwrap()
+                .rows_filled,
+            1
+        );
+        let batches = table
+            .query()
+            .with_row_id()
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let row_ids = batches[0]
+            .column_by_name(ROW_ID)
+            .unwrap()
+            .as_any()
+            .downcast_ref::<UInt64Array>()
+            .unwrap()
+            .values();
+        let payloads = table.fetch_blobs("payload_copy", row_ids).await.unwrap();
+        assert_eq!(payloads.value(0), payload);
+    }
 }
@@ -45,7 +45,11 @@ async fn local_function_catalog_operations_return_stable_not_supported() {
        .get_function("normalize_score", "fv_exact")
        .await
        .unwrap_err();
-    for error in [create_error, lookup_error] {
+    let drop_error = connection
+        .drop_function("normalize_score", "fv_exact")
+        .await
+        .unwrap_err();
+    for error in [create_error, lookup_error, drop_error] {
        assert!(matches!(
            error,
            Error::NotSupported { message }
@@ -78,6 +78,12 @@
        "type": "utf8"
      }
    },
+    {
+      "arrow_type": "large_utf8",
+      "json": {
+        "type": "large_utf8"
+      }
+    },
    {
      "arrow_type": "binary",
      "json": {
@@ -171,6 +177,21 @@
        ]
      }
    },
+    {
+      "arrow_type": "list<large_utf8>",
+      "json": {
+        "type": "list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "large_utf8"
+            }
+          }
+        ]
+      }
+    },
    {
      "arrow_type": "large_list<utf8>",
      "json": {
@@ -186,6 +207,21 @@
        ]
      }
    },
+    {
+      "arrow_type": "large_list<large_utf8>",
+      "json": {
+        "type": "large_list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "large_utf8"
+            }
+          }
+        ]
+      }
+    },
    {
      "arrow_type": "fixed_size_list<float32, 384>",
      "json": {
@@ -330,4 +366,4 @@
    "timestamp[us]",
    "struct<a: int32>"
  ]
-}
+}