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Author SHA1 Message Date
Lance Release
24a8ccd832 Bump version: 0.26.2-beta.0 → 0.26.2 2026-02-09 06:06:08 +00:00
80 changed files with 2230 additions and 56331 deletions

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@@ -1,173 +0,0 @@
name: Codex Fix CI
on:
workflow_dispatch:
inputs:
workflow_run_url:
description: "Failing CI workflow run URL (e.g., https://github.com/lancedb/lancedb/actions/runs/12345678)"
required: true
type: string
branch:
description: "Branch to fix (e.g., main, release/v2.0, or feature-branch)"
required: true
type: string
guidelines:
description: "Additional guidelines for the fix (optional)"
required: false
type: string
permissions:
contents: write
pull-requests: write
actions: read
jobs:
fix-ci:
runs-on: warp-ubuntu-latest-x64-4x
timeout-minutes: 60
env:
CC: clang
CXX: clang++
steps:
- name: Show inputs
run: |
echo "workflow_run_url = ${{ inputs.workflow_run_url }}"
echo "branch = ${{ inputs.branch }}"
echo "guidelines = ${{ inputs.guidelines }}"
- name: Checkout Repo
uses: actions/checkout@v4
with:
ref: ${{ inputs.branch }}
fetch-depth: 0
persist-credentials: true
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: 20
- name: Install Codex CLI
run: npm install -g @openai/codex
- name: Install Rust toolchain
uses: dtolnay/rust-toolchain@stable
with:
toolchain: stable
components: clippy, rustfmt
- uses: Swatinem/rust-cache@v2
- name: Install system dependencies
run: |
sudo apt-get update
sudo apt-get install -y protobuf-compiler libssl-dev
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install Python dependencies
run: |
pip install maturin ruff pytest pyarrow pandas polars
- name: Set up Java
uses: actions/setup-java@v4
with:
distribution: temurin
java-version: '11'
cache: maven
- name: Install Node.js dependencies for TypeScript bindings
run: |
cd nodejs
npm ci
- name: Configure git user
run: |
git config user.name "lancedb automation"
git config user.email "robot@lancedb.com"
- name: Run Codex to fix CI failure
env:
WORKFLOW_RUN_URL: ${{ inputs.workflow_run_url }}
BRANCH: ${{ inputs.branch }}
GUIDELINES: ${{ inputs.guidelines }}
GITHUB_TOKEN: ${{ secrets.ROBOT_TOKEN }}
GH_TOKEN: ${{ secrets.ROBOT_TOKEN }}
OPENAI_API_KEY: ${{ secrets.CODEX_TOKEN }}
run: |
set -euo pipefail
cat <<EOF >/tmp/codex-prompt.txt
You are running inside the lancedb repository on a GitHub Actions runner. Your task is to fix a CI failure.
Input parameters:
- Failing workflow run URL: ${WORKFLOW_RUN_URL}
- Branch to fix: ${BRANCH}
- Additional guidelines: ${GUIDELINES:-"None provided"}
Follow these steps exactly:
1. Extract the run ID from the workflow URL. The URL format is https://github.com/lancedb/lancedb/actions/runs/<run_id>.
2. Use "gh run view <run_id> --json jobs,conclusion,name" to get information about the failed run.
3. Identify which jobs failed. For each failed job, use "gh run view <run_id> --job <job_id> --log-failed" to get the failure logs.
4. Analyze the failure logs to understand what went wrong. Common failures include:
- Compilation errors
- Test failures
- Clippy warnings treated as errors
- Formatting issues
- Dependency issues
5. Based on the analysis, fix the issues in the codebase:
- For compilation errors: Fix the code that doesn't compile
- For test failures: Fix the failing tests or the code they test
- For clippy warnings: Apply the suggested fixes
- For formatting issues: Run "cargo fmt --all"
- For other issues: Apply appropriate fixes
6. After making fixes, verify them locally:
- Run "cargo fmt --all" to ensure formatting is correct
- Run "cargo clippy --workspace --tests --all-features -- -D warnings" to check for issues
- Run ONLY the specific failing tests to confirm they pass now:
- For Rust test failures: Run the specific test with "cargo test -p <crate> <test_name>"
- For Python test failures: Build with "cd python && maturin develop" then run "pytest <specific_test_file>::<test_name>"
- For Java test failures: Run "cd java && mvn test -Dtest=<TestClass>#<testMethod>"
- For TypeScript test failures: Run "cd nodejs && npm run build && npm test -- --testNamePattern='<test_name>'"
- Do NOT run the full test suite - only run the tests that were failing
7. If the additional guidelines are provided, follow them as well.
8. Inspect "git status --short" and "git diff" to review your changes.
9. Create a fix branch: "git checkout -b codex/fix-ci-<run_id>".
10. Stage all changes with "git add -A" and commit with message "fix: resolve CI failures from run <run_id>".
11. Push the branch: "git push origin codex/fix-ci-<run_id>". If the remote branch exists, delete it first with "gh api -X DELETE repos/lancedb/lancedb/git/refs/heads/codex/fix-ci-<run_id>" then push. Do NOT use "git push --force" or "git push -f".
12. Create a pull request targeting "${BRANCH}":
- Title: "ci: <short summary describing the fix>" (e.g., "ci: fix clippy warnings in lancedb" or "ci: resolve test flakiness in vector search")
- First, write the PR body to /tmp/pr-body.md using a heredoc (cat <<'PREOF' > /tmp/pr-body.md). The body should include:
- Link to the failing workflow run
- Summary of what failed
- Description of the fixes applied
- Then run "gh pr create --base ${BRANCH} --body-file /tmp/pr-body.md".
13. Display the new PR URL, "git status --short", and a summary of what was fixed.
Constraints:
- Use bash commands for all operations.
- Do not merge the PR.
- Do not modify GitHub workflow files unless they are the cause of the failure.
- If any command fails, diagnose and attempt to fix the issue instead of aborting immediately.
- If you cannot fix the issue automatically, create the PR anyway with a clear explanation of what you tried and what remains to be fixed.
- env "GH_TOKEN" is available, use "gh" tools for GitHub-related operations.
EOF
printenv OPENAI_API_KEY | codex login --with-api-key
codex --config shell_environment_policy.ignore_default_excludes=true exec --dangerously-bypass-approvals-and-sandbox "$(cat /tmp/codex-prompt.txt)"

74
Cargo.lock generated
View File

@@ -3072,9 +3072,9 @@ checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"
[[package]]
name = "fsst"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "5f9e5c0b1c67a38cb92b41535d44623483beb9511592ae23a3bf42ddec758690"
checksum = "0f03a771ab914e207dd26bd2f12666839555ec8ecc7e1770e1ed6f9900d899a4"
dependencies = [
"arrow-array",
"rand 0.9.2",
@@ -4405,9 +4405,9 @@ dependencies = [
[[package]]
name = "lance"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "2b7f07b905df393a5554eba19055c620f9ea25a3e40a013bda4bd8dc4ca66f01"
checksum = "47b685aca3f97ee02997c83ded16f59c747ccb69e74c8abbbae4aa3d22cf1301"
dependencies = [
"arrow",
"arrow-arith",
@@ -4472,9 +4472,9 @@ dependencies = [
[[package]]
name = "lance-arrow"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "100e076cb81c8f0c24cd2881c706fc53e037c7d6e81eb320e929e265d157effb"
checksum = "daf00c7537df524cc518a089f0d156a036d95ca3f5bc2bc1f0a9f9293e9b62ef"
dependencies = [
"arrow-array",
"arrow-buffer",
@@ -4493,9 +4493,9 @@ dependencies = [
[[package]]
name = "lance-bitpacking"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "588318d3d1ba0f97162fab39a323a0a49866bb35b32af42572c6b6a12296fa27"
checksum = "46752e4ac8fc5590a445e780b63a8800adc7a770bd74770a8dc66963778e4e77"
dependencies = [
"arrayref",
"paste",
@@ -4504,9 +4504,9 @@ dependencies = [
[[package]]
name = "lance-core"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "6fa01d1cf490ccfd3b8eaeee2781415d0419e6be8366040e57e43677abf2644e"
checksum = "3d13d87d07305c6d4b4dc7780fb1107babf782a0e5b1dc7872e17ae1f8fd11ca"
dependencies = [
"arrow-array",
"arrow-buffer",
@@ -4543,9 +4543,9 @@ dependencies = [
[[package]]
name = "lance-datafusion"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ef89a39e3284eef76f79e63f23de8881a0583ad6feb20ed39f47eadd847a2b88"
checksum = "6451b5af876eaef8bec4b38a39dadac9d44621e1ecf85d0cdf6097a5d0aa8721"
dependencies = [
"arrow",
"arrow-array",
@@ -4575,9 +4575,9 @@ dependencies = [
[[package]]
name = "lance-datagen"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "fc2a60eef5c47e65d91e2ffa8e7e1629c52e7190c8b88a371a1a60601dc49371"
checksum = "e1736708dd7867dfbab8fcc930b21c96717c6c00be73b7d9a240336a4ed80375"
dependencies = [
"arrow",
"arrow-array",
@@ -4595,9 +4595,9 @@ dependencies = [
[[package]]
name = "lance-encoding"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "95ce4a6631308aa681b2671af8f2a845ff781f8d4e755a2a7ccd012379467094"
checksum = "d6b6ca4ff94833240d5ba4a94a742cba786d1949b3c3fa7e11d6f0050443432a"
dependencies = [
"arrow-arith",
"arrow-array",
@@ -4634,9 +4634,9 @@ dependencies = [
[[package]]
name = "lance-file"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "e2d4d82357cbfaa1a18494226c15b1cb3c8ed0b6c84b91146323c82047ede419"
checksum = "55fbe959bffe185543aed3cbeb14484f1aa2e55886034fdb1ea3d8cc9b70aad8"
dependencies = [
"arrow-arith",
"arrow-array",
@@ -4668,9 +4668,9 @@ dependencies = [
[[package]]
name = "lance-geo"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a7183fc870da62826f0f97df8007b634da053eb310157856efe1dc74f446951c"
checksum = "a52b0adabc953d457f336a784a3b37353a180e6a79905f544949746e0d4c6483"
dependencies = [
"datafusion",
"geo-traits",
@@ -4684,9 +4684,9 @@ dependencies = [
[[package]]
name = "lance-index"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "20e9c5aa7024a63af9ae89ee8c0f23c8421b7896742e5cd4a271a60f9956cb80"
checksum = "6b67654bf86fd942dd2cf08294ee7e91053427cd148225f49c9ff398ff9a40fd"
dependencies = [
"arrow",
"arrow-arith",
@@ -4753,9 +4753,9 @@ dependencies = [
[[package]]
name = "lance-io"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "c7d2af0b17fb374a8181bcf1a10bce5703ae3ee4373c1587ce4bba23e15e45c8"
checksum = "8eb0ccc1c414e31687d83992d546af0a0237c8d2f4bf2ae3d347d539fd0fc141"
dependencies = [
"arrow",
"arrow-arith",
@@ -4795,9 +4795,9 @@ dependencies = [
[[package]]
name = "lance-linalg"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "5125aa62696e75a7475807564b4921f252d8815be606b84bc00e6def0f5c24bb"
checksum = "083404cf12dcdb1a7df98fb58f9daf626b6e43a2f794b37b6b89b4012a0e1f78"
dependencies = [
"arrow-array",
"arrow-buffer",
@@ -4813,9 +4813,9 @@ dependencies = [
[[package]]
name = "lance-namespace"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "70545c2676ce954dfd801da5c6a631a70bba967826cd3a8f31b47d1f04bbfed3"
checksum = "c12778d2aabf9c2bfd16e2509ebe120e562a288d8ae630ec6b6b204868df41b2"
dependencies = [
"arrow",
"async-trait",
@@ -4827,9 +4827,9 @@ dependencies = [
[[package]]
name = "lance-namespace-impls"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "92519f9f27d62655030aac62ea0db9614b65f086ebe651c1b0a96e351b668022"
checksum = "8863aababdd13a6d2c8d6179dc6981f4f8f49d8b66a00c5dd75115aec4cadc99"
dependencies = [
"arrow",
"arrow-ipc",
@@ -4872,9 +4872,9 @@ dependencies = [
[[package]]
name = "lance-table"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "b06ad37bd90045de8ef533df170c6098e6ff6ecb427aade47d7db8e2c86f2678"
checksum = "f0fcc83f197ce2000c4abe4f5e0873490ab1f41788fa76571c4209b87d4daf50"
dependencies = [
"arrow",
"arrow-array",
@@ -4913,9 +4913,9 @@ dependencies = [
[[package]]
name = "lance-testing"
version = "2.0.1"
version = "2.0.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "cd7f13b0f2b6337af015dcb1519645388dca08c970037aa77aff517687c4019f"
checksum = "7fb1f7c7e06f91360e141ecee1cf2110f858c231705f69f2cd2fda9e30c1e9f4"
dependencies = [
"arrow-array",
"arrow-schema",
@@ -4926,7 +4926,7 @@ dependencies = [
[[package]]
name = "lancedb"
version = "0.26.2"
version = "0.26.1"
dependencies = [
"ahash",
"anyhow",
@@ -5006,7 +5006,7 @@ dependencies = [
[[package]]
name = "lancedb-nodejs"
version = "0.26.2"
version = "0.26.1"
dependencies = [
"arrow-array",
"arrow-ipc",
@@ -5026,7 +5026,7 @@ dependencies = [
[[package]]
name = "lancedb-python"
version = "0.29.2"
version = "0.29.1"
dependencies = [
"arrow",
"async-trait",

View File

@@ -15,20 +15,20 @@ categories = ["database-implementations"]
rust-version = "1.88.0"
[workspace.dependencies]
lance = { "version" = "=2.0.1", default-features = false }
lance-core = "=2.0.1"
lance-datagen = "=2.0.1"
lance-file = "=2.0.1"
lance-io = { "version" = "=2.0.1", default-features = false }
lance-index = "=2.0.1"
lance-linalg = "=2.0.1"
lance-namespace = "=2.0.1"
lance-namespace-impls = { "version" = "=2.0.1", default-features = false }
lance-table = "=2.0.1"
lance-testing = "=2.0.1"
lance-datafusion = "=2.0.1"
lance-encoding = "=2.0.1"
lance-arrow = "=2.0.1"
lance = { "version" = "=2.0.0", default-features = false }
lance-core = "=2.0.0"
lance-datagen = "=2.0.0"
lance-file = "=2.0.0"
lance-io = { "version" = "=2.0.0", default-features = false }
lance-index = "=2.0.0"
lance-linalg = "=2.0.0"
lance-namespace = "=2.0.0"
lance-namespace-impls = { "version" = "=2.0.0", default-features = false }
lance-table = "=2.0.0"
lance-testing = "=2.0.0"
lance-datafusion = "=2.0.0"
lance-encoding = "=2.0.0"
lance-arrow = "=2.0.0"
ahash = "0.8"
# Note that this one does not include pyarrow
arrow = { version = "57.2", optional = false }

View File

@@ -1,9 +0,0 @@
.PHONY: licenses
licenses:
cargo about generate about.hbs -o RUST_THIRD_PARTY_LICENSES.html -c about.toml
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 java && ./mvnw license:aggregate-add-third-party -q

File diff suppressed because it is too large Load Diff

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@@ -1,70 +0,0 @@
<html>
<head>
<style>
@media (prefers-color-scheme: dark) {
body {
background: #333;
color: white;
}
a {
color: skyblue;
}
}
.container {
font-family: sans-serif;
max-width: 800px;
margin: 0 auto;
}
.intro {
text-align: center;
}
.licenses-list {
list-style-type: none;
margin: 0;
padding: 0;
}
.license-used-by {
margin-top: -10px;
}
.license-text {
max-height: 200px;
overflow-y: scroll;
white-space: pre-wrap;
}
</style>
</head>
<body>
<main class="container">
<div class="intro">
<h1>Third Party Licenses</h1>
<p>This page lists the licenses of the projects used in cargo-about.</p>
</div>
<h2>Overview of licenses:</h2>
<ul class="licenses-overview">
{{#each overview}}
<li><a href="#{{id}}">{{name}}</a> ({{count}})</li>
{{/each}}
</ul>
<h2>All license text:</h2>
<ul class="licenses-list">
{{#each licenses}}
<li class="license">
<h3 id="{{id}}">{{name}}</h3>
<h4>Used by:</h4>
<ul class="license-used-by">
{{#each used_by}}
<li><a href="{{#if crate.repository}} {{crate.repository}} {{else}} https://crates.io/crates/{{crate.name}} {{/if}}">{{crate.name}} {{crate.version}}</a></li>
{{/each}}
</ul>
<pre class="license-text">{{text}}</pre>
</li>
{{/each}}
</ul>
</main>
</body>
</html>

View File

@@ -1,18 +0,0 @@
accepted = [
"0BSD",
"Apache-2.0",
"Apache-2.0 WITH LLVM-exception",
"BSD-2-Clause",
"BSD-3-Clause",
"BSL-1.0",
"bzip2-1.0.6",
"CC0-1.0",
"CDDL-1.0",
"CDLA-Permissive-2.0",
"ISC",
"MIT",
"MPL-2.0",
"OpenSSL",
"Unicode-3.0",
"Zlib",
]

View File

@@ -1,71 +0,0 @@
List of third-party dependencies grouped by their license type.
Apache 2.0:
* error-prone annotations (com.google.errorprone:error_prone_annotations:2.28.0 - https://errorprone.info/error_prone_annotations)
Apache License 2.0:
* JsonNullable Jackson module (org.openapitools:jackson-databind-nullable:0.2.6 - https://github.com/OpenAPITools/jackson-databind-nullable)
Apache License V2.0:
* FlatBuffers Java API (com.google.flatbuffers:flatbuffers-java:23.5.26 - https://github.com/google/flatbuffers)
Apache License, Version 2.0:
* Apache Commons Codec (commons-codec:commons-codec:1.15 - https://commons.apache.org/proper/commons-codec/)
* Apache HttpClient (org.apache.httpcomponents.client5:httpclient5:5.2.1 - https://hc.apache.org/httpcomponents-client-5.0.x/5.2.1/httpclient5/)
* Apache HttpComponents Core HTTP/1.1 (org.apache.httpcomponents.core5:httpcore5:5.2 - https://hc.apache.org/httpcomponents-core-5.2.x/5.2/httpcore5/)
* Apache HttpComponents Core HTTP/2 (org.apache.httpcomponents.core5:httpcore5-h2:5.2 - https://hc.apache.org/httpcomponents-core-5.2.x/5.2/httpcore5-h2/)
* Arrow Format (org.apache.arrow:arrow-format:15.0.0 - https://arrow.apache.org/arrow-format/)
* Arrow Java C Data Interface (org.apache.arrow:arrow-c-data:15.0.0 - https://arrow.apache.org/arrow-c-data/)
* Arrow Java Dataset (org.apache.arrow:arrow-dataset:15.0.0 - https://arrow.apache.org/arrow-dataset/)
* Arrow Memory - Core (org.apache.arrow:arrow-memory-core:15.0.0 - https://arrow.apache.org/arrow-memory/arrow-memory-core/)
* Arrow Memory - Netty (org.apache.arrow:arrow-memory-netty:15.0.0 - https://arrow.apache.org/arrow-memory/arrow-memory-netty/)
* Arrow Vectors (org.apache.arrow:arrow-vector:15.0.0 - https://arrow.apache.org/arrow-vector/)
* Guava: Google Core Libraries for Java (com.google.guava:guava:33.3.1-jre - https://github.com/google/guava)
* J2ObjC Annotations (com.google.j2objc:j2objc-annotations:3.0.0 - https://github.com/google/j2objc/)
* Netty/Buffer (io.netty:netty-buffer:4.1.104.Final - https://netty.io/netty-buffer/)
* Netty/Common (io.netty:netty-common:4.1.104.Final - https://netty.io/netty-common/)
Apache-2.0:
* Apache Commons Lang (org.apache.commons:commons-lang3:3.18.0 - https://commons.apache.org/proper/commons-lang/)
* lance-namespace-apache-client (org.lance:lance-namespace-apache-client:0.4.5 - https://github.com/openapitools/openapi-generator)
* lance-namespace-core (org.lance:lance-namespace-core:0.4.5 - https://lance.org/format/namespace/lance-namespace-core/)
EDL 1.0:
* Jakarta Activation API jar (jakarta.activation:jakarta.activation-api:1.2.2 - https://github.com/eclipse-ee4j/jaf/jakarta.activation-api)
Eclipse Distribution License - v 1.0:
* Eclipse Collections API (org.eclipse.collections:eclipse-collections-api:11.1.0 - https://github.com/eclipse/eclipse-collections/eclipse-collections-api)
* Eclipse Collections Main Library (org.eclipse.collections:eclipse-collections:11.1.0 - https://github.com/eclipse/eclipse-collections/eclipse-collections)
* Jakarta XML Binding API (jakarta.xml.bind:jakarta.xml.bind-api:2.3.3 - https://github.com/eclipse-ee4j/jaxb-api/jakarta.xml.bind-api)
Eclipse Public License - v 1.0:
* Eclipse Collections API (org.eclipse.collections:eclipse-collections-api:11.1.0 - https://github.com/eclipse/eclipse-collections/eclipse-collections-api)
* Eclipse Collections Main Library (org.eclipse.collections:eclipse-collections:11.1.0 - https://github.com/eclipse/eclipse-collections/eclipse-collections)
The Apache Software License, Version 2.0:
* FindBugs-jsr305 (com.google.code.findbugs:jsr305:3.0.2 - http://findbugs.sourceforge.net/)
* Guava InternalFutureFailureAccess and InternalFutures (com.google.guava:failureaccess:1.0.2 - https://github.com/google/guava/failureaccess)
* Guava ListenableFuture only (com.google.guava:listenablefuture:9999.0-empty-to-avoid-conflict-with-guava - https://github.com/google/guava/listenablefuture)
* Jackson datatype: JSR310 (com.fasterxml.jackson.datatype:jackson-datatype-jsr310:2.16.0 - https://github.com/FasterXML/jackson-modules-java8/jackson-datatype-jsr310)
* Jackson module: Old JAXB Annotations (javax.xml.bind) (com.fasterxml.jackson.module:jackson-module-jaxb-annotations:2.17.1 - https://github.com/FasterXML/jackson-modules-base)
* Jackson-annotations (com.fasterxml.jackson.core:jackson-annotations:2.16.0 - https://github.com/FasterXML/jackson)
* Jackson-core (com.fasterxml.jackson.core:jackson-core:2.16.0 - https://github.com/FasterXML/jackson-core)
* jackson-databind (com.fasterxml.jackson.core:jackson-databind:2.15.2 - https://github.com/FasterXML/jackson)
* Jackson-JAXRS: base (com.fasterxml.jackson.jaxrs:jackson-jaxrs-base:2.17.1 - https://github.com/FasterXML/jackson-jaxrs-providers/jackson-jaxrs-base)
* Jackson-JAXRS: JSON (com.fasterxml.jackson.jaxrs:jackson-jaxrs-json-provider:2.17.1 - https://github.com/FasterXML/jackson-jaxrs-providers/jackson-jaxrs-json-provider)
* JAR JNI Loader (org.questdb:jar-jni:1.1.1 - https://github.com/questdb/rust-maven-plugin)
* Lance Core (org.lance:lance-core:2.0.0 - https://lance.org/)
The MIT License:
* Checker Qual (org.checkerframework:checker-qual:3.43.0 - https://checkerframework.org/)

View File

@@ -1,71 +0,0 @@
List of third-party dependencies grouped by their license type.
Apache 2.0:
* error-prone annotations (com.google.errorprone:error_prone_annotations:2.28.0 - https://errorprone.info/error_prone_annotations)
Apache License 2.0:
* JsonNullable Jackson module (org.openapitools:jackson-databind-nullable:0.2.6 - https://github.com/OpenAPITools/jackson-databind-nullable)
Apache License V2.0:
* FlatBuffers Java API (com.google.flatbuffers:flatbuffers-java:23.5.26 - https://github.com/google/flatbuffers)
Apache License, Version 2.0:
* Apache Commons Codec (commons-codec:commons-codec:1.15 - https://commons.apache.org/proper/commons-codec/)
* Apache HttpClient (org.apache.httpcomponents.client5:httpclient5:5.2.1 - https://hc.apache.org/httpcomponents-client-5.0.x/5.2.1/httpclient5/)
* Apache HttpComponents Core HTTP/1.1 (org.apache.httpcomponents.core5:httpcore5:5.2 - https://hc.apache.org/httpcomponents-core-5.2.x/5.2/httpcore5/)
* Apache HttpComponents Core HTTP/2 (org.apache.httpcomponents.core5:httpcore5-h2:5.2 - https://hc.apache.org/httpcomponents-core-5.2.x/5.2/httpcore5-h2/)
* Arrow Format (org.apache.arrow:arrow-format:15.0.0 - https://arrow.apache.org/arrow-format/)
* Arrow Java C Data Interface (org.apache.arrow:arrow-c-data:15.0.0 - https://arrow.apache.org/arrow-c-data/)
* Arrow Java Dataset (org.apache.arrow:arrow-dataset:15.0.0 - https://arrow.apache.org/arrow-dataset/)
* Arrow Memory - Core (org.apache.arrow:arrow-memory-core:15.0.0 - https://arrow.apache.org/arrow-memory/arrow-memory-core/)
* Arrow Memory - Netty (org.apache.arrow:arrow-memory-netty:15.0.0 - https://arrow.apache.org/arrow-memory/arrow-memory-netty/)
* Arrow Vectors (org.apache.arrow:arrow-vector:15.0.0 - https://arrow.apache.org/arrow-vector/)
* Guava: Google Core Libraries for Java (com.google.guava:guava:33.3.1-jre - https://github.com/google/guava)
* J2ObjC Annotations (com.google.j2objc:j2objc-annotations:3.0.0 - https://github.com/google/j2objc/)
* Netty/Buffer (io.netty:netty-buffer:4.1.104.Final - https://netty.io/netty-buffer/)
* Netty/Common (io.netty:netty-common:4.1.104.Final - https://netty.io/netty-common/)
Apache-2.0:
* Apache Commons Lang (org.apache.commons:commons-lang3:3.18.0 - https://commons.apache.org/proper/commons-lang/)
* lance-namespace-apache-client (org.lance:lance-namespace-apache-client:0.4.5 - https://github.com/openapitools/openapi-generator)
* lance-namespace-core (org.lance:lance-namespace-core:0.4.5 - https://lance.org/format/namespace/lance-namespace-core/)
EDL 1.0:
* Jakarta Activation API jar (jakarta.activation:jakarta.activation-api:1.2.2 - https://github.com/eclipse-ee4j/jaf/jakarta.activation-api)
Eclipse Distribution License - v 1.0:
* Eclipse Collections API (org.eclipse.collections:eclipse-collections-api:11.1.0 - https://github.com/eclipse/eclipse-collections/eclipse-collections-api)
* Eclipse Collections Main Library (org.eclipse.collections:eclipse-collections:11.1.0 - https://github.com/eclipse/eclipse-collections/eclipse-collections)
* Jakarta XML Binding API (jakarta.xml.bind:jakarta.xml.bind-api:2.3.3 - https://github.com/eclipse-ee4j/jaxb-api/jakarta.xml.bind-api)
Eclipse Public License - v 1.0:
* Eclipse Collections API (org.eclipse.collections:eclipse-collections-api:11.1.0 - https://github.com/eclipse/eclipse-collections/eclipse-collections-api)
* Eclipse Collections Main Library (org.eclipse.collections:eclipse-collections:11.1.0 - https://github.com/eclipse/eclipse-collections/eclipse-collections)
The Apache Software License, Version 2.0:
* FindBugs-jsr305 (com.google.code.findbugs:jsr305:3.0.2 - http://findbugs.sourceforge.net/)
* Guava InternalFutureFailureAccess and InternalFutures (com.google.guava:failureaccess:1.0.2 - https://github.com/google/guava/failureaccess)
* Guava ListenableFuture only (com.google.guava:listenablefuture:9999.0-empty-to-avoid-conflict-with-guava - https://github.com/google/guava/listenablefuture)
* Jackson datatype: JSR310 (com.fasterxml.jackson.datatype:jackson-datatype-jsr310:2.16.0 - https://github.com/FasterXML/jackson-modules-java8/jackson-datatype-jsr310)
* Jackson module: Old JAXB Annotations (javax.xml.bind) (com.fasterxml.jackson.module:jackson-module-jaxb-annotations:2.17.1 - https://github.com/FasterXML/jackson-modules-base)
* Jackson-annotations (com.fasterxml.jackson.core:jackson-annotations:2.16.0 - https://github.com/FasterXML/jackson)
* Jackson-core (com.fasterxml.jackson.core:jackson-core:2.16.0 - https://github.com/FasterXML/jackson-core)
* jackson-databind (com.fasterxml.jackson.core:jackson-databind:2.15.2 - https://github.com/FasterXML/jackson)
* Jackson-JAXRS: base (com.fasterxml.jackson.jaxrs:jackson-jaxrs-base:2.17.1 - https://github.com/FasterXML/jackson-jaxrs-providers/jackson-jaxrs-base)
* Jackson-JAXRS: JSON (com.fasterxml.jackson.jaxrs:jackson-jaxrs-json-provider:2.17.1 - https://github.com/FasterXML/jackson-jaxrs-providers/jackson-jaxrs-json-provider)
* JAR JNI Loader (org.questdb:jar-jni:1.1.1 - https://github.com/questdb/rust-maven-plugin)
* Lance Core (org.lance:lance-core:2.0.0 - https://lance.org/)
The MIT License:
* Checker Qual (org.checkerframework:checker-qual:3.43.0 - https://checkerframework.org/)

View File

@@ -28,7 +28,7 @@
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<arrow.version>15.0.0</arrow.version>
<lance-core.version>2.0.1</lance-core.version>
<lance-core.version>2.0.0</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>
@@ -160,19 +160,6 @@
<groupId>com.diffplug.spotless</groupId>
<artifactId>spotless-maven-plugin</artifactId>
</plugin>
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>license-maven-plugin</artifactId>
<version>2.4.0</version>
<configuration>
<outputDirectory>${project.basedir}</outputDirectory>
<thirdPartyFilename>JAVA_THIRD_PARTY_LICENSES.md</thirdPartyFilename>
<fileTemplate>/org/codehaus/mojo/license/third-party-file-groupByLicense.ftl</fileTemplate>
<includedScopes>compile,runtime</includedScopes>
<excludedScopes>test,provided</excludedScopes>
<sortArtifactByName>true</sortArtifactByName>
</configuration>
</plugin>
</plugins>
<pluginManagement>
<plugins>

View File

@@ -1,668 +0,0 @@
[@75lb/deep-merge@1.1.2](https://github.com/75lb/deep-merge) - MIT
[@aashutoshrathi/word-wrap@1.2.6](https://github.com/aashutoshrathi/word-wrap) - MIT
[@ampproject/remapping@2.2.1](https://github.com/ampproject/remapping) - Apache-2.0
[@aws-crypto/crc32@3.0.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/crc32c@3.0.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/ie11-detection@3.0.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/sha1-browser@3.0.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/sha256-browser@3.0.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/sha256-browser@5.2.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/sha256-js@3.0.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/sha256-js@5.2.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/supports-web-crypto@3.0.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/supports-web-crypto@5.2.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/util@3.0.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-crypto/util@5.2.0](https://github.com/aws/aws-sdk-js-crypto-helpers) - Apache-2.0
[@aws-sdk/client-dynamodb@3.602.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/client-kms@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/client-s3@3.550.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/client-sso-oidc@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/client-sso-oidc@3.600.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/client-sso@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/client-sso@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/client-sts@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/client-sts@3.600.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/core@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/core@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-env@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-env@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-http@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-http@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-ini@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-ini@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-node@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-node@3.600.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-process@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-process@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-sso@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-sso@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-web-identity@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/credential-provider-web-identity@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/endpoint-cache@3.572.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-bucket-endpoint@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-endpoint-discovery@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-expect-continue@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-flexible-checksums@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-host-header@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-host-header@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-location-constraint@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-logger@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-logger@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-recursion-detection@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-recursion-detection@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-sdk-s3@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-signing@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-ssec@3.537.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-user-agent@3.540.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/middleware-user-agent@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/region-config-resolver@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/region-config-resolver@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/signature-v4-multi-region@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/token-providers@3.549.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/token-providers@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/types@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/types@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/util-arn-parser@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/util-endpoints@3.540.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/util-endpoints@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/util-locate-window@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/util-user-agent-browser@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/util-user-agent-browser@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/util-user-agent-node@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/util-user-agent-node@3.598.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/util-utf8-browser@3.259.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@aws-sdk/xml-builder@3.535.0](https://github.com/aws/aws-sdk-js-v3) - Apache-2.0
[@babel/code-frame@7.26.2](https://github.com/babel/babel) - MIT
[@babel/compat-data@7.23.5](https://github.com/babel/babel) - MIT
[@babel/core@7.23.7](https://github.com/babel/babel) - MIT
[@babel/generator@7.23.6](https://github.com/babel/babel) - MIT
[@babel/helper-compilation-targets@7.23.6](https://github.com/babel/babel) - MIT
[@babel/helper-environment-visitor@7.22.20](https://github.com/babel/babel) - MIT
[@babel/helper-function-name@7.23.0](https://github.com/babel/babel) - MIT
[@babel/helper-hoist-variables@7.22.5](https://github.com/babel/babel) - MIT
[@babel/helper-module-imports@7.22.15](https://github.com/babel/babel) - MIT
[@babel/helper-module-transforms@7.23.3](https://github.com/babel/babel) - MIT
[@babel/helper-plugin-utils@7.22.5](https://github.com/babel/babel) - MIT
[@babel/helper-simple-access@7.22.5](https://github.com/babel/babel) - MIT
[@babel/helper-split-export-declaration@7.22.6](https://github.com/babel/babel) - MIT
[@babel/helper-string-parser@7.25.9](https://github.com/babel/babel) - MIT
[@babel/helper-validator-identifier@7.25.9](https://github.com/babel/babel) - MIT
[@babel/helper-validator-option@7.23.5](https://github.com/babel/babel) - MIT
[@babel/helpers@7.27.0](https://github.com/babel/babel) - MIT
[@babel/parser@7.27.0](https://github.com/babel/babel) - MIT
[@babel/plugin-syntax-async-generators@7.8.4](https://github.com/babel/babel/tree/master/packages/babel-plugin-syntax-async-generators) - MIT
[@babel/plugin-syntax-bigint@7.8.3](https://github.com/babel/babel/tree/master/packages/babel-plugin-syntax-bigint) - MIT
[@babel/plugin-syntax-class-properties@7.12.13](https://github.com/babel/babel) - MIT
[@babel/plugin-syntax-import-meta@7.10.4](https://github.com/babel/babel) - MIT
[@babel/plugin-syntax-json-strings@7.8.3](https://github.com/babel/babel/tree/master/packages/babel-plugin-syntax-json-strings) - MIT
[@babel/plugin-syntax-jsx@7.23.3](https://github.com/babel/babel) - MIT
[@babel/plugin-syntax-logical-assignment-operators@7.10.4](https://github.com/babel/babel) - MIT
[@babel/plugin-syntax-nullish-coalescing-operator@7.8.3](https://github.com/babel/babel/tree/master/packages/babel-plugin-syntax-nullish-coalescing-operator) - MIT
[@babel/plugin-syntax-numeric-separator@7.10.4](https://github.com/babel/babel) - MIT
[@babel/plugin-syntax-object-rest-spread@7.8.3](https://github.com/babel/babel/tree/master/packages/babel-plugin-syntax-object-rest-spread) - MIT
[@babel/plugin-syntax-optional-catch-binding@7.8.3](https://github.com/babel/babel/tree/master/packages/babel-plugin-syntax-optional-catch-binding) - MIT
[@babel/plugin-syntax-optional-chaining@7.8.3](https://github.com/babel/babel/tree/master/packages/babel-plugin-syntax-optional-chaining) - MIT
[@babel/plugin-syntax-top-level-await@7.14.5](https://github.com/babel/babel) - MIT
[@babel/plugin-syntax-typescript@7.23.3](https://github.com/babel/babel) - MIT
[@babel/template@7.27.0](https://github.com/babel/babel) - MIT
[@babel/traverse@7.23.7](https://github.com/babel/babel) - MIT
[@babel/types@7.27.0](https://github.com/babel/babel) - MIT
[@bcoe/v8-coverage@0.2.3](https://github.com/demurgos/v8-coverage) - MIT
[@biomejs/biome@1.8.3](https://github.com/biomejs/biome) - MIT OR Apache-2.0
[@biomejs/cli-darwin-arm64@1.8.3](https://github.com/biomejs/biome) - MIT OR Apache-2.0
[@eslint-community/eslint-utils@4.4.0](https://github.com/eslint-community/eslint-utils) - MIT
[@eslint-community/regexpp@4.10.0](https://github.com/eslint-community/regexpp) - MIT
[@eslint/eslintrc@2.1.4](https://github.com/eslint/eslintrc) - MIT
[@eslint/js@8.57.0](https://github.com/eslint/eslint) - MIT
[@huggingface/jinja@0.3.2](https://github.com/huggingface/huggingface.js) - MIT
[@huggingface/transformers@3.0.2](https://github.com/huggingface/transformers.js) - Apache-2.0
[@humanwhocodes/config-array@0.11.14](https://github.com/humanwhocodes/config-array) - Apache-2.0
[@humanwhocodes/module-importer@1.0.1](https://github.com/humanwhocodes/module-importer) - Apache-2.0
[@humanwhocodes/object-schema@2.0.2](https://github.com/humanwhocodes/object-schema) - BSD-3-Clause
[@img/sharp-darwin-arm64@0.33.5](https://github.com/lovell/sharp) - Apache-2.0
[@img/sharp-libvips-darwin-arm64@1.0.4](https://github.com/lovell/sharp-libvips) - LGPL-3.0-or-later
[@isaacs/cliui@8.0.2](https://github.com/yargs/cliui) - ISC
[@isaacs/fs-minipass@4.0.1](https://github.com/npm/fs-minipass) - ISC
[@istanbuljs/load-nyc-config@1.1.0](https://github.com/istanbuljs/load-nyc-config) - ISC
[@istanbuljs/schema@0.1.3](https://github.com/istanbuljs/schema) - MIT
[@jest/console@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/core@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/environment@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/expect-utils@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/expect@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/fake-timers@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/globals@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/reporters@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/schemas@29.6.3](https://github.com/jestjs/jest) - MIT
[@jest/source-map@29.6.3](https://github.com/jestjs/jest) - MIT
[@jest/test-result@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/test-sequencer@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/transform@29.7.0](https://github.com/jestjs/jest) - MIT
[@jest/types@29.6.3](https://github.com/jestjs/jest) - MIT
[@jridgewell/gen-mapping@0.3.3](https://github.com/jridgewell/gen-mapping) - MIT
[@jridgewell/resolve-uri@3.1.1](https://github.com/jridgewell/resolve-uri) - MIT
[@jridgewell/set-array@1.1.2](https://github.com/jridgewell/set-array) - MIT
[@jridgewell/sourcemap-codec@1.4.15](https://github.com/jridgewell/sourcemap-codec) - MIT
[@jridgewell/trace-mapping@0.3.22](https://github.com/jridgewell/trace-mapping) - MIT
[@lancedb/lancedb@0.26.2](https://github.com/lancedb/lancedb) - Apache-2.0
[@napi-rs/cli@2.18.3](https://github.com/napi-rs/napi-rs) - MIT
[@nodelib/fs.scandir@2.1.5](https://github.com/nodelib/nodelib/tree/master/packages/fs/fs.scandir) - MIT
[@nodelib/fs.stat@2.0.5](https://github.com/nodelib/nodelib/tree/master/packages/fs/fs.stat) - MIT
[@nodelib/fs.walk@1.2.8](https://github.com/nodelib/nodelib/tree/master/packages/fs/fs.walk) - MIT
[@pkgjs/parseargs@0.11.0](https://github.com/pkgjs/parseargs) - MIT
[@protobufjs/aspromise@1.1.2](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@protobufjs/base64@1.1.2](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@protobufjs/codegen@2.0.4](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@protobufjs/eventemitter@1.1.0](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@protobufjs/fetch@1.1.0](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@protobufjs/float@1.0.2](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@protobufjs/inquire@1.1.0](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@protobufjs/path@1.1.2](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@protobufjs/pool@1.1.0](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@protobufjs/utf8@1.1.0](https://github.com/dcodeIO/protobuf.js) - BSD-3-Clause
[@shikijs/core@1.10.3](https://github.com/shikijs/shiki) - MIT
[@sinclair/typebox@0.27.8](https://github.com/sinclairzx81/typebox) - MIT
[@sinonjs/commons@3.0.1](https://github.com/sinonjs/commons) - BSD-3-Clause
[@sinonjs/fake-timers@10.3.0](https://github.com/sinonjs/fake-timers) - BSD-3-Clause
[@smithy/abort-controller@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/abort-controller@3.1.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/chunked-blob-reader-native@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/chunked-blob-reader@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/config-resolver@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/config-resolver@3.0.3](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/core@1.4.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/core@2.2.3](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/credential-provider-imds@2.3.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/credential-provider-imds@3.1.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/eventstream-codec@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/eventstream-serde-browser@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/eventstream-serde-config-resolver@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/eventstream-serde-node@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/eventstream-serde-universal@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/fetch-http-handler@2.5.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/fetch-http-handler@3.1.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/hash-blob-browser@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/hash-node@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/hash-node@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/hash-stream-node@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/invalid-dependency@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/invalid-dependency@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/is-array-buffer@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/is-array-buffer@3.0.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/md5-js@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-content-length@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-content-length@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-endpoint@2.5.1](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-endpoint@3.0.3](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-retry@2.3.1](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-retry@3.0.6](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-serde@2.3.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-serde@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-stack@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/middleware-stack@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/node-config-provider@2.3.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/node-config-provider@3.1.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/node-http-handler@2.5.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/node-http-handler@3.1.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/property-provider@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/property-provider@3.1.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/protocol-http@3.3.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/protocol-http@4.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/querystring-builder@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/querystring-builder@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/querystring-parser@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/querystring-parser@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/service-error-classification@2.1.5](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/service-error-classification@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/shared-ini-file-loader@2.4.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/shared-ini-file-loader@3.1.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/signature-v4@2.2.1](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/signature-v4@3.1.1](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/smithy-client@2.5.1](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/smithy-client@3.1.4](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/types@2.12.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/types@3.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/url-parser@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/url-parser@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-base64@2.3.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-base64@3.0.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-body-length-browser@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-body-length-browser@3.0.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-body-length-node@2.3.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-body-length-node@3.0.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-buffer-from@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-buffer-from@3.0.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-config-provider@2.3.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-config-provider@3.0.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-defaults-mode-browser@2.2.1](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-defaults-mode-browser@3.0.6](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-defaults-mode-node@2.3.1](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-defaults-mode-node@3.0.6](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-endpoints@1.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-endpoints@2.0.3](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-hex-encoding@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-hex-encoding@3.0.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-middleware@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-middleware@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-retry@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-retry@3.0.2](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-stream@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-stream@3.0.4](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-uri-escape@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-uri-escape@3.0.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-utf8@2.3.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-utf8@3.0.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-waiter@2.2.0](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@smithy/util-waiter@3.1.1](https://github.com/awslabs/smithy-typescript) - Apache-2.0
[@swc/helpers@0.5.12](https://github.com/swc-project/swc) - Apache-2.0
[@types/axios@0.14.0](https://github.com/mzabriskie/axios) - MIT
[@types/babel__core@7.20.5](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/babel__generator@7.6.8](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/babel__template@7.4.4](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/babel__traverse@7.20.5](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/command-line-args@5.2.3](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/command-line-usage@5.0.2](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/command-line-usage@5.0.4](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/graceful-fs@4.1.9](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/hast@3.0.4](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/istanbul-lib-coverage@2.0.6](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/istanbul-lib-report@3.0.3](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/istanbul-reports@3.0.4](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/jest@29.5.12](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/json-schema@7.0.15](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/node-fetch@2.6.11](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/node@18.19.26](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/node@20.16.10](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/node@20.17.9](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/node@22.7.4](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/semver@7.5.6](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/stack-utils@2.0.3](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/tmp@0.2.6](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/unist@3.0.2](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/yargs-parser@21.0.3](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@types/yargs@17.0.32](https://github.com/DefinitelyTyped/DefinitelyTyped) - MIT
[@typescript-eslint/eslint-plugin@7.1.0](https://github.com/typescript-eslint/typescript-eslint) - MIT
[@typescript-eslint/parser@7.1.0](https://github.com/typescript-eslint/typescript-eslint) - BSD-2-Clause
[@typescript-eslint/scope-manager@7.1.0](https://github.com/typescript-eslint/typescript-eslint) - MIT
[@typescript-eslint/type-utils@7.1.0](https://github.com/typescript-eslint/typescript-eslint) - MIT
[@typescript-eslint/types@7.1.0](https://github.com/typescript-eslint/typescript-eslint) - MIT
[@typescript-eslint/typescript-estree@7.1.0](https://github.com/typescript-eslint/typescript-eslint) - BSD-2-Clause
[@typescript-eslint/utils@7.1.0](https://github.com/typescript-eslint/typescript-eslint) - MIT
[@typescript-eslint/visitor-keys@7.1.0](https://github.com/typescript-eslint/typescript-eslint) - MIT
[@ungap/structured-clone@1.2.0](https://github.com/ungap/structured-clone) - ISC
[abort-controller@3.0.0](https://github.com/mysticatea/abort-controller) - MIT
[acorn-jsx@5.3.2](https://github.com/acornjs/acorn-jsx) - MIT
[acorn@8.11.3](https://github.com/acornjs/acorn) - MIT
[agentkeepalive@4.5.0](https://github.com/node-modules/agentkeepalive) - MIT
[ajv@6.12.6](https://github.com/ajv-validator/ajv) - MIT
[ansi-escapes@4.3.2](https://github.com/sindresorhus/ansi-escapes) - MIT
[ansi-regex@5.0.1](https://github.com/chalk/ansi-regex) - MIT
[ansi-regex@6.1.0](https://github.com/chalk/ansi-regex) - MIT
[ansi-styles@4.3.0](https://github.com/chalk/ansi-styles) - MIT
[ansi-styles@5.2.0](https://github.com/chalk/ansi-styles) - MIT
[ansi-styles@6.2.1](https://github.com/chalk/ansi-styles) - MIT
[anymatch@3.1.3](https://github.com/micromatch/anymatch) - ISC
[apache-arrow@15.0.0](https://github.com/apache/arrow) - Apache-2.0
[apache-arrow@16.0.0](https://github.com/apache/arrow) - Apache-2.0
[apache-arrow@17.0.0](https://github.com/apache/arrow) - Apache-2.0
[apache-arrow@18.0.0](https://github.com/apache/arrow) - Apache-2.0
[argparse@1.0.10](https://github.com/nodeca/argparse) - MIT
[argparse@2.0.1](https://github.com/nodeca/argparse) - Python-2.0
[array-back@3.1.0](https://github.com/75lb/array-back) - MIT
[array-back@6.2.2](https://github.com/75lb/array-back) - MIT
[array-union@2.1.0](https://github.com/sindresorhus/array-union) - MIT
[asynckit@0.4.0](https://github.com/alexindigo/asynckit) - MIT
[axios@1.8.4](https://github.com/axios/axios) - MIT
[babel-jest@29.7.0](https://github.com/jestjs/jest) - MIT
[babel-plugin-istanbul@6.1.1](https://github.com/istanbuljs/babel-plugin-istanbul) - BSD-3-Clause
[babel-plugin-jest-hoist@29.6.3](https://github.com/jestjs/jest) - MIT
[babel-preset-current-node-syntax@1.0.1](https://github.com/nicolo-ribaudo/babel-preset-current-node-syntax) - MIT
[babel-preset-jest@29.6.3](https://github.com/jestjs/jest) - MIT
[balanced-match@1.0.2](https://github.com/juliangruber/balanced-match) - MIT
[base-64@0.1.0](https://github.com/mathiasbynens/base64) - MIT
[bowser@2.11.0](https://github.com/lancedikson/bowser) - MIT
[brace-expansion@1.1.11](https://github.com/juliangruber/brace-expansion) - MIT
[brace-expansion@2.0.1](https://github.com/juliangruber/brace-expansion) - MIT
[braces@3.0.3](https://github.com/micromatch/braces) - MIT
[browserslist@4.22.2](https://github.com/browserslist/browserslist) - MIT
[bs-logger@0.2.6](https://github.com/huafu/bs-logger) - MIT
[bser@2.1.1](https://github.com/facebook/watchman) - Apache-2.0
[buffer-from@1.1.2](https://github.com/LinusU/buffer-from) - MIT
[callsites@3.1.0](https://github.com/sindresorhus/callsites) - MIT
[camelcase@5.3.1](https://github.com/sindresorhus/camelcase) - MIT
[camelcase@6.3.0](https://github.com/sindresorhus/camelcase) - MIT
[caniuse-lite@1.0.30001579](https://github.com/browserslist/caniuse-lite) - CC-BY-4.0
[chalk-template@0.4.0](https://github.com/chalk/chalk-template) - MIT
[chalk@4.1.2](https://github.com/chalk/chalk) - MIT
[char-regex@1.0.2](https://github.com/Richienb/char-regex) - MIT
[charenc@0.0.2](https://github.com/pvorb/node-charenc) - BSD-3-Clause
[chownr@3.0.0](https://github.com/isaacs/chownr) - BlueOak-1.0.0
[ci-info@3.9.0](https://github.com/watson/ci-info) - MIT
[cjs-module-lexer@1.2.3](https://github.com/nodejs/cjs-module-lexer) - MIT
[cliui@8.0.1](https://github.com/yargs/cliui) - ISC
[co@4.6.0](https://github.com/tj/co) - MIT
[collect-v8-coverage@1.0.2](https://github.com/SimenB/collect-v8-coverage) - MIT
[color-convert@2.0.1](https://github.com/Qix-/color-convert) - MIT
[color-name@1.1.4](https://github.com/colorjs/color-name) - MIT
[color-string@1.9.1](https://github.com/Qix-/color-string) - MIT
[color@4.2.3](https://github.com/Qix-/color) - MIT
[combined-stream@1.0.8](https://github.com/felixge/node-combined-stream) - MIT
[command-line-args@5.2.1](https://github.com/75lb/command-line-args) - MIT
[command-line-usage@7.0.1](https://github.com/75lb/command-line-usage) - MIT
[concat-map@0.0.1](https://github.com/substack/node-concat-map) - MIT
[convert-source-map@2.0.0](https://github.com/thlorenz/convert-source-map) - MIT
[create-jest@29.7.0](https://github.com/jestjs/jest) - MIT
[cross-spawn@7.0.6](https://github.com/moxystudio/node-cross-spawn) - MIT
[crypt@0.0.2](https://github.com/pvorb/node-crypt) - BSD-3-Clause
[debug@4.3.4](https://github.com/debug-js/debug) - MIT
[dedent@1.5.1](https://github.com/dmnd/dedent) - MIT
[deep-is@0.1.4](https://github.com/thlorenz/deep-is) - MIT
[deepmerge@4.3.1](https://github.com/TehShrike/deepmerge) - MIT
[delayed-stream@1.0.0](https://github.com/felixge/node-delayed-stream) - MIT
[detect-libc@2.0.3](https://github.com/lovell/detect-libc) - Apache-2.0
[detect-newline@3.1.0](https://github.com/sindresorhus/detect-newline) - MIT
[diff-sequences@29.6.3](https://github.com/jestjs/jest) - MIT
[digest-fetch@1.3.0](https://github.com/devfans/digest-fetch) - ISC
[dir-glob@3.0.1](https://github.com/kevva/dir-glob) - MIT
[doctrine@3.0.0](https://github.com/eslint/doctrine) - Apache-2.0
[eastasianwidth@0.2.0](https://github.com/komagata/eastasianwidth) - MIT
[electron-to-chromium@1.4.642](https://github.com/kilian/electron-to-chromium) - ISC
[emittery@0.13.1](https://github.com/sindresorhus/emittery) - MIT
[emoji-regex@8.0.0](https://github.com/mathiasbynens/emoji-regex) - MIT
[emoji-regex@9.2.2](https://github.com/mathiasbynens/emoji-regex) - MIT
[entities@4.5.0](https://github.com/fb55/entities) - BSD-2-Clause
[error-ex@1.3.2](https://github.com/qix-/node-error-ex) - MIT
[escalade@3.1.1](https://github.com/lukeed/escalade) - MIT
[escape-string-regexp@2.0.0](https://github.com/sindresorhus/escape-string-regexp) - MIT
[escape-string-regexp@4.0.0](https://github.com/sindresorhus/escape-string-regexp) - MIT
[eslint-scope@7.2.2](https://github.com/eslint/eslint-scope) - BSD-2-Clause
[eslint-visitor-keys@3.4.3](https://github.com/eslint/eslint-visitor-keys) - Apache-2.0
[eslint@8.57.0](https://github.com/eslint/eslint) - MIT
[espree@9.6.1](https://github.com/eslint/espree) - BSD-2-Clause
[esprima@4.0.1](https://github.com/jquery/esprima) - BSD-2-Clause
[esquery@1.5.0](https://github.com/estools/esquery) - BSD-3-Clause
[esrecurse@4.3.0](https://github.com/estools/esrecurse) - BSD-2-Clause
[estraverse@5.3.0](https://github.com/estools/estraverse) - BSD-2-Clause
[esutils@2.0.3](https://github.com/estools/esutils) - BSD-2-Clause
[event-target-shim@5.0.1](https://github.com/mysticatea/event-target-shim) - MIT
[execa@5.1.1](https://github.com/sindresorhus/execa) - MIT
[exit@0.1.2](https://github.com/cowboy/node-exit) - MIT
[expect@29.7.0](https://github.com/jestjs/jest) - MIT
[fast-deep-equal@3.1.3](https://github.com/epoberezkin/fast-deep-equal) - MIT
[fast-glob@3.3.2](https://github.com/mrmlnc/fast-glob) - MIT
[fast-json-stable-stringify@2.1.0](https://github.com/epoberezkin/fast-json-stable-stringify) - MIT
[fast-levenshtein@2.0.6](https://github.com/hiddentao/fast-levenshtein) - MIT
[fast-xml-parser@4.2.5](https://github.com/NaturalIntelligence/fast-xml-parser) - MIT
[fastq@1.16.0](https://github.com/mcollina/fastq) - ISC
[fb-watchman@2.0.2](https://github.com/facebook/watchman) - Apache-2.0
[file-entry-cache@6.0.1](https://github.com/royriojas/file-entry-cache) - MIT
[fill-range@7.1.1](https://github.com/jonschlinkert/fill-range) - MIT
[find-replace@3.0.0](https://github.com/75lb/find-replace) - MIT
[find-up@4.1.0](https://github.com/sindresorhus/find-up) - MIT
[find-up@5.0.0](https://github.com/sindresorhus/find-up) - MIT
[flat-cache@3.2.0](https://github.com/jaredwray/flat-cache) - MIT
[flatbuffers@1.12.0](https://github.com/google/flatbuffers) - Apache*
[flatbuffers@23.5.26](https://github.com/google/flatbuffers) - Apache*
[flatbuffers@24.3.25](https://github.com/google/flatbuffers) - Apache-2.0
[flatted@3.2.9](https://github.com/WebReflection/flatted) - ISC
[follow-redirects@1.15.6](https://github.com/follow-redirects/follow-redirects) - MIT
[foreground-child@3.3.0](https://github.com/tapjs/foreground-child) - ISC
[form-data-encoder@1.7.2](https://github.com/octet-stream/form-data-encoder) - MIT
[form-data@4.0.0](https://github.com/form-data/form-data) - MIT
[formdata-node@4.4.1](https://github.com/octet-stream/form-data) - MIT
[fs.realpath@1.0.0](https://github.com/isaacs/fs.realpath) - ISC
[fsevents@2.3.3](https://github.com/fsevents/fsevents) - MIT
[function-bind@1.1.2](https://github.com/Raynos/function-bind) - MIT
[gensync@1.0.0-beta.2](https://github.com/loganfsmyth/gensync) - MIT
[get-caller-file@2.0.5](https://github.com/stefanpenner/get-caller-file) - ISC
[get-package-type@0.1.0](https://github.com/cfware/get-package-type) - MIT
[get-stream@6.0.1](https://github.com/sindresorhus/get-stream) - MIT
[glob-parent@5.1.2](https://github.com/gulpjs/glob-parent) - ISC
[glob-parent@6.0.2](https://github.com/gulpjs/glob-parent) - ISC
[glob@10.4.5](https://github.com/isaacs/node-glob) - ISC
[glob@7.2.3](https://github.com/isaacs/node-glob) - ISC
[globals@11.12.0](https://github.com/sindresorhus/globals) - MIT
[globals@13.24.0](https://github.com/sindresorhus/globals) - MIT
[globby@11.1.0](https://github.com/sindresorhus/globby) - MIT
[graceful-fs@4.2.11](https://github.com/isaacs/node-graceful-fs) - ISC
[graphemer@1.4.0](https://github.com/flmnt/graphemer) - MIT
[guid-typescript@1.0.9](https://github.com/NicolasDeveloper/guid-typescript) - ISC
[has-flag@4.0.0](https://github.com/sindresorhus/has-flag) - MIT
[hasown@2.0.0](https://github.com/inspect-js/hasOwn) - MIT
[html-escaper@2.0.2](https://github.com/WebReflection/html-escaper) - MIT
[human-signals@2.1.0](https://github.com/ehmicky/human-signals) - Apache-2.0
[humanize-ms@1.2.1](https://github.com/node-modules/humanize-ms) - MIT
[ignore@5.3.0](https://github.com/kaelzhang/node-ignore) - MIT
[import-fresh@3.3.0](https://github.com/sindresorhus/import-fresh) - MIT
[import-local@3.1.0](https://github.com/sindresorhus/import-local) - MIT
[imurmurhash@0.1.4](https://github.com/jensyt/imurmurhash-js) - MIT
[inflight@1.0.6](https://github.com/npm/inflight) - ISC
[inherits@2.0.4](https://github.com/isaacs/inherits) - ISC
[interpret@1.4.0](https://github.com/gulpjs/interpret) - MIT
[is-arrayish@0.2.1](https://github.com/qix-/node-is-arrayish) - MIT
[is-arrayish@0.3.2](https://github.com/qix-/node-is-arrayish) - MIT
[is-buffer@1.1.6](https://github.com/feross/is-buffer) - MIT
[is-core-module@2.13.1](https://github.com/inspect-js/is-core-module) - MIT
[is-extglob@2.1.1](https://github.com/jonschlinkert/is-extglob) - MIT
[is-fullwidth-code-point@3.0.0](https://github.com/sindresorhus/is-fullwidth-code-point) - MIT
[is-generator-fn@2.1.0](https://github.com/sindresorhus/is-generator-fn) - MIT
[is-glob@4.0.3](https://github.com/micromatch/is-glob) - MIT
[is-number@7.0.0](https://github.com/jonschlinkert/is-number) - MIT
[is-path-inside@3.0.3](https://github.com/sindresorhus/is-path-inside) - MIT
[is-stream@2.0.1](https://github.com/sindresorhus/is-stream) - MIT
[isexe@2.0.0](https://github.com/isaacs/isexe) - ISC
[istanbul-lib-coverage@3.2.2](https://github.com/istanbuljs/istanbuljs) - BSD-3-Clause
[istanbul-lib-instrument@5.2.1](https://github.com/istanbuljs/istanbuljs) - BSD-3-Clause
[istanbul-lib-instrument@6.0.1](https://github.com/istanbuljs/istanbuljs) - BSD-3-Clause
[istanbul-lib-report@3.0.1](https://github.com/istanbuljs/istanbuljs) - BSD-3-Clause
[istanbul-lib-source-maps@4.0.1](https://github.com/istanbuljs/istanbuljs) - BSD-3-Clause
[istanbul-reports@3.1.6](https://github.com/istanbuljs/istanbuljs) - BSD-3-Clause
[jackspeak@3.4.3](https://github.com/isaacs/jackspeak) - BlueOak-1.0.0
[jest-changed-files@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-circus@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-cli@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-config@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-diff@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-docblock@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-each@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-environment-node@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-get-type@29.6.3](https://github.com/jestjs/jest) - MIT
[jest-haste-map@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-leak-detector@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-matcher-utils@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-message-util@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-mock@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-pnp-resolver@1.2.3](https://github.com/arcanis/jest-pnp-resolver) - MIT
[jest-regex-util@29.6.3](https://github.com/jestjs/jest) - MIT
[jest-resolve-dependencies@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-resolve@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-runner@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-runtime@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-snapshot@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-util@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-validate@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-watcher@29.7.0](https://github.com/jestjs/jest) - MIT
[jest-worker@29.7.0](https://github.com/jestjs/jest) - MIT
[jest@29.7.0](https://github.com/jestjs/jest) - MIT
[js-tokens@4.0.0](https://github.com/lydell/js-tokens) - MIT
[js-yaml@3.14.1](https://github.com/nodeca/js-yaml) - MIT
[js-yaml@4.1.0](https://github.com/nodeca/js-yaml) - MIT
[jsesc@2.5.2](https://github.com/mathiasbynens/jsesc) - MIT
[json-bignum@0.0.3](https://github.com/datalanche/json-bignum) - MIT
[json-buffer@3.0.1](https://github.com/dominictarr/json-buffer) - MIT
[json-parse-even-better-errors@2.3.1](https://github.com/npm/json-parse-even-better-errors) - MIT
[json-schema-traverse@0.4.1](https://github.com/epoberezkin/json-schema-traverse) - MIT
[json-stable-stringify-without-jsonify@1.0.1](https://github.com/samn/json-stable-stringify) - MIT
[json5@2.2.3](https://github.com/json5/json5) - MIT
[keyv@4.5.4](https://github.com/jaredwray/keyv) - MIT
[kleur@3.0.3](https://github.com/lukeed/kleur) - MIT
[leven@3.1.0](https://github.com/sindresorhus/leven) - MIT
[levn@0.4.1](https://github.com/gkz/levn) - MIT
[lines-and-columns@1.2.4](https://github.com/eventualbuddha/lines-and-columns) - MIT
[linkify-it@5.0.0](https://github.com/markdown-it/linkify-it) - MIT
[locate-path@5.0.0](https://github.com/sindresorhus/locate-path) - MIT
[locate-path@6.0.0](https://github.com/sindresorhus/locate-path) - MIT
[lodash.camelcase@4.3.0](https://github.com/lodash/lodash) - MIT
[lodash.memoize@4.1.2](https://github.com/lodash/lodash) - MIT
[lodash.merge@4.6.2](https://github.com/lodash/lodash) - MIT
[lodash@4.17.21](https://github.com/lodash/lodash) - MIT
[long@5.2.3](https://github.com/dcodeIO/long.js) - Apache-2.0
[lru-cache@10.4.3](https://github.com/isaacs/node-lru-cache) - ISC
[lru-cache@5.1.1](https://github.com/isaacs/node-lru-cache) - ISC
[lunr@2.3.9](https://github.com/olivernn/lunr.js) - MIT
[make-dir@4.0.0](https://github.com/sindresorhus/make-dir) - MIT
[make-error@1.3.6](https://github.com/JsCommunity/make-error) - ISC
[makeerror@1.0.12](https://github.com/daaku/nodejs-makeerror) - BSD-3-Clause
[markdown-it@14.1.0](https://github.com/markdown-it/markdown-it) - MIT
[md5@2.3.0](https://github.com/pvorb/node-md5) - BSD-3-Clause
[mdurl@2.0.0](https://github.com/markdown-it/mdurl) - MIT
[merge-stream@2.0.0](https://github.com/grncdr/merge-stream) - MIT
[merge2@1.4.1](https://github.com/teambition/merge2) - MIT
[micromatch@4.0.8](https://github.com/micromatch/micromatch) - MIT
[mime-db@1.52.0](https://github.com/jshttp/mime-db) - MIT
[mime-types@2.1.35](https://github.com/jshttp/mime-types) - MIT
[mimic-fn@2.1.0](https://github.com/sindresorhus/mimic-fn) - MIT
[minimatch@3.1.2](https://github.com/isaacs/minimatch) - ISC
[minimatch@9.0.3](https://github.com/isaacs/minimatch) - ISC
[minimatch@9.0.5](https://github.com/isaacs/minimatch) - ISC
[minimist@1.2.8](https://github.com/minimistjs/minimist) - MIT
[minipass@7.1.2](https://github.com/isaacs/minipass) - ISC
[minizlib@3.0.1](https://github.com/isaacs/minizlib) - MIT
[mkdirp@3.0.1](https://github.com/isaacs/node-mkdirp) - MIT
[mnemonist@0.38.3](https://github.com/yomguithereal/mnemonist) - MIT
[ms@2.1.2](https://github.com/zeit/ms) - MIT
[ms@2.1.3](https://github.com/vercel/ms) - MIT
[natural-compare@1.4.0](https://github.com/litejs/natural-compare-lite) - MIT
[node-domexception@1.0.0](https://github.com/jimmywarting/node-domexception) - MIT
[node-fetch@2.7.0](https://github.com/bitinn/node-fetch) - MIT
[node-int64@0.4.0](https://github.com/broofa/node-int64) - MIT
[node-releases@2.0.14](https://github.com/chicoxyzzy/node-releases) - MIT
[normalize-path@3.0.0](https://github.com/jonschlinkert/normalize-path) - MIT
[npm-run-path@4.0.1](https://github.com/sindresorhus/npm-run-path) - MIT
[obliterator@1.6.1](https://github.com/yomguithereal/obliterator) - MIT
[once@1.4.0](https://github.com/isaacs/once) - ISC
[onetime@5.1.2](https://github.com/sindresorhus/onetime) - MIT
[onnxruntime-common@1.19.2](https://github.com/Microsoft/onnxruntime) - MIT
[onnxruntime-common@1.20.0-dev.20241016-2b8fc5529b](https://github.com/Microsoft/onnxruntime) - MIT
[onnxruntime-node@1.19.2](https://github.com/Microsoft/onnxruntime) - MIT
[onnxruntime-web@1.21.0-dev.20241024-d9ca84ef96](https://github.com/Microsoft/onnxruntime) - MIT
[openai@4.29.2](https://github.com/openai/openai-node) - Apache-2.0
[optionator@0.9.3](https://github.com/gkz/optionator) - MIT
[p-limit@2.3.0](https://github.com/sindresorhus/p-limit) - MIT
[p-limit@3.1.0](https://github.com/sindresorhus/p-limit) - MIT
[p-locate@4.1.0](https://github.com/sindresorhus/p-locate) - MIT
[p-locate@5.0.0](https://github.com/sindresorhus/p-locate) - MIT
[p-try@2.2.0](https://github.com/sindresorhus/p-try) - MIT
[package-json-from-dist@1.0.1](https://github.com/isaacs/package-json-from-dist) - BlueOak-1.0.0
[parent-module@1.0.1](https://github.com/sindresorhus/parent-module) - MIT
[parse-json@5.2.0](https://github.com/sindresorhus/parse-json) - MIT
[path-exists@4.0.0](https://github.com/sindresorhus/path-exists) - MIT
[path-is-absolute@1.0.1](https://github.com/sindresorhus/path-is-absolute) - MIT
[path-key@3.1.1](https://github.com/sindresorhus/path-key) - MIT
[path-parse@1.0.7](https://github.com/jbgutierrez/path-parse) - MIT
[path-scurry@1.11.1](https://github.com/isaacs/path-scurry) - BlueOak-1.0.0
[path-type@4.0.0](https://github.com/sindresorhus/path-type) - MIT
[picocolors@1.0.0](https://github.com/alexeyraspopov/picocolors) - ISC
[picomatch@2.3.1](https://github.com/micromatch/picomatch) - MIT
[pirates@4.0.6](https://github.com/danez/pirates) - MIT
[pkg-dir@4.2.0](https://github.com/sindresorhus/pkg-dir) - MIT
[platform@1.3.6](https://github.com/bestiejs/platform.js) - MIT
[prelude-ls@1.2.1](https://github.com/gkz/prelude-ls) - MIT
[pretty-format@29.7.0](https://github.com/jestjs/jest) - MIT
[prompts@2.4.2](https://github.com/terkelg/prompts) - MIT
[protobufjs@7.4.0](https://github.com/protobufjs/protobuf.js) - BSD-3-Clause
[proxy-from-env@1.1.0](https://github.com/Rob--W/proxy-from-env) - MIT
[punycode.js@2.3.1](https://github.com/mathiasbynens/punycode.js) - MIT
[punycode@2.3.1](https://github.com/mathiasbynens/punycode.js) - MIT
[pure-rand@6.0.4](https://github.com/dubzzz/pure-rand) - MIT
[queue-microtask@1.2.3](https://github.com/feross/queue-microtask) - MIT
[react-is@18.2.0](https://github.com/facebook/react) - MIT
[rechoir@0.6.2](https://github.com/tkellen/node-rechoir) - MIT
[reflect-metadata@0.2.2](https://github.com/rbuckton/reflect-metadata) - Apache-2.0
[require-directory@2.1.1](https://github.com/troygoode/node-require-directory) - MIT
[resolve-cwd@3.0.0](https://github.com/sindresorhus/resolve-cwd) - MIT
[resolve-from@4.0.0](https://github.com/sindresorhus/resolve-from) - MIT
[resolve-from@5.0.0](https://github.com/sindresorhus/resolve-from) - MIT
[resolve.exports@2.0.2](https://github.com/lukeed/resolve.exports) - MIT
[resolve@1.22.8](https://github.com/browserify/resolve) - MIT
[reusify@1.0.4](https://github.com/mcollina/reusify) - MIT
[rimraf@3.0.2](https://github.com/isaacs/rimraf) - ISC
[rimraf@5.0.10](https://github.com/isaacs/rimraf) - ISC
[run-parallel@1.2.0](https://github.com/feross/run-parallel) - MIT
[semver@6.3.1](https://github.com/npm/node-semver) - ISC
[semver@7.6.3](https://github.com/npm/node-semver) - ISC
[sharp@0.33.5](https://github.com/lovell/sharp) - Apache-2.0
[shebang-command@2.0.0](https://github.com/kevva/shebang-command) - MIT
[shebang-regex@3.0.0](https://github.com/sindresorhus/shebang-regex) - MIT
[shelljs@0.8.5](https://github.com/shelljs/shelljs) - BSD-3-Clause
[shiki@1.10.3](https://github.com/shikijs/shiki) - MIT
[shx@0.3.4](https://github.com/shelljs/shx) - MIT
[signal-exit@3.0.7](https://github.com/tapjs/signal-exit) - ISC
[signal-exit@4.1.0](https://github.com/tapjs/signal-exit) - ISC
[simple-swizzle@0.2.2](https://github.com/qix-/node-simple-swizzle) - MIT
[sisteransi@1.0.5](https://github.com/terkelg/sisteransi) - MIT
[slash@3.0.0](https://github.com/sindresorhus/slash) - MIT
[source-map-support@0.5.13](https://github.com/evanw/node-source-map-support) - MIT
[source-map@0.6.1](https://github.com/mozilla/source-map) - BSD-3-Clause
[sprintf-js@1.0.3](https://github.com/alexei/sprintf.js) - BSD-3-Clause
[stack-utils@2.0.6](https://github.com/tapjs/stack-utils) - MIT
[stream-read-all@3.0.1](https://github.com/75lb/stream-read-all) - MIT
[string-length@4.0.2](https://github.com/sindresorhus/string-length) - MIT
[string-width@4.2.3](https://github.com/sindresorhus/string-width) - MIT
[string-width@5.1.2](https://github.com/sindresorhus/string-width) - MIT
[strip-ansi@6.0.1](https://github.com/chalk/strip-ansi) - MIT
[strip-ansi@7.1.0](https://github.com/chalk/strip-ansi) - MIT
[strip-bom@4.0.0](https://github.com/sindresorhus/strip-bom) - MIT
[strip-final-newline@2.0.0](https://github.com/sindresorhus/strip-final-newline) - MIT
[strip-json-comments@3.1.1](https://github.com/sindresorhus/strip-json-comments) - MIT
[strnum@1.0.5](https://github.com/NaturalIntelligence/strnum) - MIT
[supports-color@7.2.0](https://github.com/chalk/supports-color) - MIT
[supports-color@8.1.1](https://github.com/chalk/supports-color) - MIT
[supports-preserve-symlinks-flag@1.0.0](https://github.com/inspect-js/node-supports-preserve-symlinks-flag) - MIT
[table-layout@3.0.2](https://github.com/75lb/table-layout) - MIT
[tar@7.4.3](https://github.com/isaacs/node-tar) - ISC
[test-exclude@6.0.0](https://github.com/istanbuljs/test-exclude) - ISC
[text-table@0.2.0](https://github.com/substack/text-table) - MIT
[tmp@0.2.3](https://github.com/raszi/node-tmp) - MIT
[tmpl@1.0.5](https://github.com/daaku/nodejs-tmpl) - BSD-3-Clause
[to-regex-range@5.0.1](https://github.com/micromatch/to-regex-range) - MIT
[tr46@0.0.3](https://github.com/Sebmaster/tr46.js) - MIT
[ts-api-utils@1.0.3](https://github.com/JoshuaKGoldberg/ts-api-utils) - MIT
[ts-jest@29.1.2](https://github.com/kulshekhar/ts-jest) - MIT
[tslib@1.14.1](https://github.com/Microsoft/tslib) - 0BSD
[tslib@2.6.2](https://github.com/Microsoft/tslib) - 0BSD
[type-check@0.4.0](https://github.com/gkz/type-check) - MIT
[type-detect@4.0.8](https://github.com/chaijs/type-detect) - MIT
[type-fest@0.20.2](https://github.com/sindresorhus/type-fest) - (MIT OR CC0-1.0)
[type-fest@0.21.3](https://github.com/sindresorhus/type-fest) - (MIT OR CC0-1.0)
[typedoc-plugin-markdown@4.2.1](https://github.com/typedoc2md/typedoc-plugin-markdown) - MIT
[typedoc@0.26.4](https://github.com/TypeStrong/TypeDoc) - Apache-2.0
[typescript-eslint@7.1.0](https://github.com/typescript-eslint/typescript-eslint) - MIT
[typescript@5.5.4](https://github.com/Microsoft/TypeScript) - Apache-2.0
[typical@4.0.0](https://github.com/75lb/typical) - MIT
[typical@7.1.1](https://github.com/75lb/typical) - MIT
[uc.micro@2.1.0](https://github.com/markdown-it/uc.micro) - MIT
[undici-types@5.26.5](https://github.com/nodejs/undici) - MIT
[undici-types@6.19.8](https://github.com/nodejs/undici) - MIT
[update-browserslist-db@1.0.13](https://github.com/browserslist/update-db) - MIT
[uri-js@4.4.1](https://github.com/garycourt/uri-js) - BSD-2-Clause
[uuid@9.0.1](https://github.com/uuidjs/uuid) - MIT
[v8-to-istanbul@9.2.0](https://github.com/istanbuljs/v8-to-istanbul) - ISC
[walker@1.0.8](https://github.com/daaku/nodejs-walker) - Apache-2.0
[web-streams-polyfill@3.3.3](https://github.com/MattiasBuelens/web-streams-polyfill) - MIT
[web-streams-polyfill@4.0.0-beta.3](https://github.com/MattiasBuelens/web-streams-polyfill) - MIT
[webidl-conversions@3.0.1](https://github.com/jsdom/webidl-conversions) - BSD-2-Clause
[whatwg-url@5.0.0](https://github.com/jsdom/whatwg-url) - MIT
[which@2.0.2](https://github.com/isaacs/node-which) - ISC
[wordwrapjs@5.1.0](https://github.com/75lb/wordwrapjs) - MIT
[wrap-ansi@7.0.0](https://github.com/chalk/wrap-ansi) - MIT
[wrap-ansi@8.1.0](https://github.com/chalk/wrap-ansi) - MIT
[wrappy@1.0.2](https://github.com/npm/wrappy) - ISC
[write-file-atomic@4.0.2](https://github.com/npm/write-file-atomic) - ISC
[y18n@5.0.8](https://github.com/yargs/y18n) - ISC
[yallist@3.1.1](https://github.com/isaacs/yallist) - ISC
[yallist@5.0.0](https://github.com/isaacs/yallist) - BlueOak-1.0.0
[yaml@2.4.5](https://github.com/eemeli/yaml) - ISC
[yargs-parser@21.1.1](https://github.com/yargs/yargs-parser) - ISC
[yargs@17.7.2](https://github.com/yargs/yargs) - MIT
[yocto-queue@0.1.0](https://github.com/sindresorhus/yocto-queue) - MIT

File diff suppressed because it is too large Load Diff

View File

@@ -1,12 +1,12 @@
{
"name": "@lancedb/lancedb",
"version": "0.26.2",
"version": "0.26.1",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "@lancedb/lancedb",
"version": "0.26.2",
"version": "0.26.1",
"cpu": [
"x64",
"arm64"

View File

@@ -13,7 +13,6 @@ use crate::header::JsHeaderProvider;
use crate::table::Table;
use crate::ConnectionOptions;
use lancedb::connection::{ConnectBuilder, Connection as LanceDBConnection};
use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};
#[napi]

View File

@@ -1,5 +1,5 @@
[tool.bumpversion]
current_version = "0.30.0-beta.0"
current_version = "0.29.2"
parse = """(?x)
(?P<major>0|[1-9]\\d*)\\.
(?P<minor>0|[1-9]\\d*)\\.

View File

@@ -1,6 +1,6 @@
[package]
name = "lancedb-python"
version = "0.30.0-beta.0"
version = "0.29.2"
edition.workspace = true
description = "Python bindings for LanceDB"
license.workspace = true

View File

@@ -1,206 +0,0 @@
| Name | Version | License | URL |
|--------------------------------|-----------------|--------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|
| InstructorEmbedding | 1.0.1 | Apache License 2.0 | https://github.com/HKUNLP/instructor-embedding |
| Jinja2 | 3.1.6 | BSD License | https://github.com/pallets/jinja/ |
| Markdown | 3.10.2 | BSD-3-Clause | https://Python-Markdown.github.io/ |
| MarkupSafe | 3.0.3 | BSD-3-Clause | https://github.com/pallets/markupsafe/ |
| PyJWT | 2.11.0 | MIT | https://github.com/jpadilla/pyjwt |
| PyYAML | 6.0.3 | MIT License | https://pyyaml.org/ |
| Pygments | 2.19.2 | BSD License | https://pygments.org |
| accelerate | 1.12.0 | Apache Software License | https://github.com/huggingface/accelerate |
| adlfs | 2026.2.0 | BSD License | UNKNOWN |
| aiohappyeyeballs | 2.6.1 | Python Software Foundation License | https://github.com/aio-libs/aiohappyeyeballs |
| aiohttp | 3.13.3 | Apache-2.0 AND MIT | https://github.com/aio-libs/aiohttp |
| aiosignal | 1.4.0 | Apache Software License | https://github.com/aio-libs/aiosignal |
| annotated-types | 0.7.0 | MIT License | https://github.com/annotated-types/annotated-types |
| anyio | 4.12.1 | MIT | https://anyio.readthedocs.io/en/stable/versionhistory.html |
| appnope | 0.1.4 | BSD License | http://github.com/minrk/appnope |
| asttokens | 3.0.1 | Apache 2.0 | https://github.com/gristlabs/asttokens |
| attrs | 25.4.0 | MIT | https://www.attrs.org/en/stable/changelog.html |
| awscli | 1.44.35 | Apache Software License | http://aws.amazon.com/cli/ |
| azure-core | 1.38.0 | MIT License | https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/core/azure-core |
| azure-datalake-store | 0.0.53 | MIT License | https://github.com/Azure/azure-data-lake-store-python |
| azure-identity | 1.25.1 | MIT | https://github.com/Azure/azure-sdk-for-python |
| azure-storage-blob | 12.28.0 | MIT License | https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-blob |
| babel | 2.18.0 | BSD License | https://babel.pocoo.org/ |
| backrefs | 6.1 | MIT | https://github.com/facelessuser/backrefs |
| beautifulsoup4 | 4.14.3 | MIT License | https://www.crummy.com/software/BeautifulSoup/bs4/ |
| bleach | 6.3.0 | Apache Software License | https://github.com/mozilla/bleach |
| boto3 | 1.42.45 | Apache-2.0 | https://github.com/boto/boto3 |
| botocore | 1.42.45 | Apache-2.0 | https://github.com/boto/botocore |
| cachetools | 7.0.0 | MIT | https://github.com/tkem/cachetools/ |
| certifi | 2026.1.4 | Mozilla Public License 2.0 (MPL 2.0) | https://github.com/certifi/python-certifi |
| cffi | 2.0.0 | MIT | https://cffi.readthedocs.io/en/latest/whatsnew.html |
| cfgv | 3.5.0 | MIT | https://github.com/asottile/cfgv |
| charset-normalizer | 3.4.4 | MIT | https://github.com/jawah/charset_normalizer/blob/master/CHANGELOG.md |
| click | 8.3.1 | BSD-3-Clause | https://github.com/pallets/click/ |
| cohere | 5.20.4 | MIT License | https://github.com/cohere-ai/cohere-python |
| colorama | 0.4.6 | BSD License | https://github.com/tartley/colorama |
| colpali_engine | 0.3.13 | MIT License | https://github.com/illuin-tech/colpali |
| comm | 0.2.3 | BSD License | https://github.com/ipython/comm |
| cryptography | 46.0.4 | Apache-2.0 OR BSD-3-Clause | https://github.com/pyca/cryptography |
| datafusion | 51.0.0 | Apache Software License | https://datafusion.apache.org/python |
| debugpy | 1.8.20 | MIT License | https://aka.ms/debugpy |
| decorator | 5.2.1 | BSD License | UNKNOWN |
| defusedxml | 0.7.1 | Python Software Foundation License | https://github.com/tiran/defusedxml |
| deprecation | 2.1.0 | Apache Software License | http://deprecation.readthedocs.io/ |
| distlib | 0.4.0 | Python Software Foundation License | https://github.com/pypa/distlib |
| distro | 1.9.0 | Apache Software License | https://github.com/python-distro/distro |
| docutils | 0.19 | BSD License; GNU General Public License (GPL); Public Domain; Python Software Foundation License | https://docutils.sourceforge.io/ |
| duckdb | 1.4.4 | MIT License | https://github.com/duckdb/duckdb-python |
| executing | 2.2.1 | MIT License | https://github.com/alexmojaki/executing |
| fastavro | 1.12.1 | MIT | https://github.com/fastavro/fastavro |
| fastjsonschema | 2.21.2 | BSD License | https://github.com/horejsek/python-fastjsonschema |
| filelock | 3.20.3 | Unlicense | https://github.com/tox-dev/py-filelock |
| frozenlist | 1.8.0 | Apache-2.0 | https://github.com/aio-libs/frozenlist |
| fsspec | 2026.2.0 | BSD-3-Clause | https://github.com/fsspec/filesystem_spec |
| ftfy | 6.3.1 | Apache-2.0 | https://ftfy.readthedocs.io/en/latest/ |
| ghp-import | 2.1.0 | Apache Software License | https://github.com/c-w/ghp-import |
| google-ai-generativelanguage | 0.6.15 | Apache Software License | https://github.com/googleapis/google-cloud-python/tree/main/packages/google-ai-generativelanguage |
| google-api-core | 2.25.2 | Apache Software License | https://github.com/googleapis/python-api-core |
| google-api-python-client | 2.189.0 | Apache Software License | https://github.com/googleapis/google-api-python-client/ |
| google-auth | 2.48.0 | Apache Software License | https://github.com/googleapis/google-auth-library-python |
| google-auth-httplib2 | 0.3.0 | Apache Software License | https://github.com/GoogleCloudPlatform/google-auth-library-python-httplib2 |
| google-generativeai | 0.8.6 | Apache Software License | https://github.com/google/generative-ai-python |
| googleapis-common-protos | 1.72.0 | Apache Software License | https://github.com/googleapis/google-cloud-python/tree/main/packages/googleapis-common-protos |
| griffe | 2.0.0 | ISC | https://mkdocstrings.github.io/griffe |
| griffecli | 2.0.0 | ISC | UNKNOWN |
| griffelib | 2.0.0 | ISC | UNKNOWN |
| grpcio | 1.78.0 | Apache-2.0 | https://grpc.io |
| grpcio-status | 1.71.2 | Apache Software License | https://grpc.io |
| h11 | 0.16.0 | MIT License | https://github.com/python-hyper/h11 |
| hf-xet | 1.2.0 | Apache-2.0 | https://github.com/huggingface/xet-core |
| httpcore | 1.0.9 | BSD-3-Clause | https://www.encode.io/httpcore/ |
| httplib2 | 0.31.2 | MIT License | https://github.com/httplib2/httplib2 |
| httpx | 0.28.1 | BSD License | https://github.com/encode/httpx |
| huggingface_hub | 0.36.2 | Apache Software License | https://github.com/huggingface/huggingface_hub |
| ibm-cos-sdk | 2.14.3 | Apache Software License | https://github.com/ibm/ibm-cos-sdk-python |
| ibm-cos-sdk-core | 2.14.3 | Apache Software License | https://github.com/ibm/ibm-cos-sdk-python-core |
| ibm-cos-sdk-s3transfer | 2.14.3 | Apache Software License | https://github.com/IBM/ibm-cos-sdk-python-s3transfer |
| ibm_watsonx_ai | 1.5.1 | BSD License | https://ibm.github.io/watsonx-ai-python-sdk/changelog.html |
| identify | 2.6.16 | MIT | https://github.com/pre-commit/identify |
| idna | 3.11 | BSD-3-Clause | https://github.com/kjd/idna |
| iniconfig | 2.3.0 | MIT | https://github.com/pytest-dev/iniconfig |
| ipykernel | 6.31.0 | BSD-3-Clause | https://ipython.org |
| ipython | 9.10.0 | BSD-3-Clause | https://ipython.org |
| ipython_pygments_lexers | 1.1.1 | BSD License | https://github.com/ipython/ipython-pygments-lexers |
| isodate | 0.7.2 | BSD License | https://github.com/gweis/isodate/ |
| jedi | 0.19.2 | MIT License | https://github.com/davidhalter/jedi |
| jiter | 0.13.0 | MIT License | https://github.com/pydantic/jiter/ |
| jmespath | 1.0.1 | MIT License | https://github.com/jmespath/jmespath.py |
| joblib | 1.5.3 | BSD-3-Clause | https://joblib.readthedocs.io |
| jsonschema | 4.26.0 | MIT | https://github.com/python-jsonschema/jsonschema |
| jsonschema-specifications | 2025.9.1 | MIT | https://github.com/python-jsonschema/jsonschema-specifications |
| jupyter_client | 8.8.0 | BSD License | https://jupyter.org |
| jupyter_core | 5.9.1 | BSD-3-Clause | https://jupyter.org |
| jupyterlab_pygments | 0.3.0 | BSD License | https://github.com/jupyterlab/jupyterlab_pygments |
| jupytext | 1.19.1 | MIT License | https://github.com/mwouts/jupytext |
| lance-namespace | 0.4.5 | Apache-2.0 | https://github.com/lance-format/lance-namespace |
| lance-namespace-urllib3-client | 0.4.5 | Apache-2.0 | https://github.com/lance-format/lance-namespace |
| lancedb | 0.29.2 | Apache Software License | https://github.com/lancedb/lancedb |
| lomond | 0.3.3 | BSD License | https://github.com/wildfoundry/dataplicity-lomond |
| markdown-it-py | 4.0.0 | MIT License | https://github.com/executablebooks/markdown-it-py |
| matplotlib-inline | 0.2.1 | UNKNOWN | https://github.com/ipython/matplotlib-inline |
| mdit-py-plugins | 0.5.0 | MIT License | https://github.com/executablebooks/mdit-py-plugins |
| mdurl | 0.1.2 | MIT License | https://github.com/executablebooks/mdurl |
| mergedeep | 1.3.4 | MIT License | https://github.com/clarketm/mergedeep |
| mistune | 3.2.0 | BSD License | https://github.com/lepture/mistune |
| mkdocs | 1.6.1 | BSD-2-Clause | https://github.com/mkdocs/mkdocs |
| mkdocs-autorefs | 1.4.3 | ISC | https://mkdocstrings.github.io/autorefs |
| mkdocs-get-deps | 0.2.0 | MIT | https://github.com/mkdocs/get-deps |
| mkdocs-jupyter | 0.25.1 | Apache-2.0 | https://github.com/danielfrg/mkdocs-jupyter |
| mkdocs-material | 9.7.1 | MIT | https://github.com/squidfunk/mkdocs-material |
| mkdocs-material-extensions | 1.3.1 | MIT | https://github.com/facelessuser/mkdocs-material-extensions |
| mkdocstrings | 1.0.3 | ISC | https://mkdocstrings.github.io |
| mkdocstrings-python | 2.0.2 | ISC | https://mkdocstrings.github.io/python |
| mpmath | 1.3.0 | BSD License | http://mpmath.org/ |
| msal | 1.34.0 | MIT License | https://github.com/AzureAD/microsoft-authentication-library-for-python |
| msal-extensions | 1.3.1 | MIT License | https://github.com/AzureAD/microsoft-authentication-extensions-for-python/releases |
| multidict | 6.7.1 | Apache License 2.0 | https://github.com/aio-libs/multidict |
| nbclient | 0.10.4 | BSD License | https://jupyter.org |
| nbconvert | 7.17.0 | BSD License | https://jupyter.org |
| nbformat | 5.10.4 | BSD License | https://jupyter.org |
| nest-asyncio | 1.6.0 | BSD License | https://github.com/erdewit/nest_asyncio |
| networkx | 3.6.1 | BSD-3-Clause | https://networkx.org/ |
| nodeenv | 1.10.0 | BSD License | https://github.com/ekalinin/nodeenv |
| numpy | 2.4.2 | BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0 | https://numpy.org |
| ollama | 0.6.1 | MIT | https://ollama.com |
| open_clip_torch | 3.2.0 | MIT License | https://github.com/mlfoundations/open_clip |
| openai | 2.18.0 | Apache Software License | https://github.com/openai/openai-python |
| packaging | 26.0 | Apache-2.0 OR BSD-2-Clause | https://github.com/pypa/packaging |
| paginate | 0.5.7 | MIT License | https://github.com/Signum/paginate |
| pandas | 2.3.3 | BSD License | https://pandas.pydata.org |
| pandocfilters | 1.5.1 | BSD License | http://github.com/jgm/pandocfilters |
| parso | 0.8.6 | MIT License | https://github.com/davidhalter/parso |
| pathspec | 1.0.4 | Mozilla Public License 2.0 (MPL 2.0) | UNKNOWN |
| peft | 0.17.1 | Apache Software License | https://github.com/huggingface/peft |
| pexpect | 4.9.0 | ISC License (ISCL) | https://pexpect.readthedocs.io/ |
| pillow | 12.1.0 | MIT-CMU | https://python-pillow.github.io |
| platformdirs | 4.5.1 | MIT | https://github.com/tox-dev/platformdirs |
| pluggy | 1.6.0 | MIT License | UNKNOWN |
| polars | 1.3.0 | MIT License | https://www.pola.rs/ |
| pre_commit | 4.5.1 | MIT | https://github.com/pre-commit/pre-commit |
| prompt_toolkit | 3.0.52 | BSD License | https://github.com/prompt-toolkit/python-prompt-toolkit |
| propcache | 0.4.1 | Apache Software License | https://github.com/aio-libs/propcache |
| proto-plus | 1.27.1 | Apache Software License | https://github.com/googleapis/proto-plus-python |
| protobuf | 5.29.6 | 3-Clause BSD License | https://developers.google.com/protocol-buffers/ |
| psutil | 7.2.2 | BSD-3-Clause | https://github.com/giampaolo/psutil |
| ptyprocess | 0.7.0 | ISC License (ISCL) | https://github.com/pexpect/ptyprocess |
| pure_eval | 0.2.3 | MIT License | http://github.com/alexmojaki/pure_eval |
| pyarrow | 23.0.0 | Apache-2.0 | https://arrow.apache.org/ |
| pyarrow-stubs | 20.0.0.20251215 | BSD-2-Clause | https://github.com/zen-xu/pyarrow-stubs |
| pyasn1 | 0.6.2 | BSD-2-Clause | https://github.com/pyasn1/pyasn1 |
| pyasn1_modules | 0.4.2 | BSD License | https://github.com/pyasn1/pyasn1-modules |
| pycparser | 3.0 | BSD-3-Clause | https://github.com/eliben/pycparser |
| pydantic | 2.12.5 | MIT | https://github.com/pydantic/pydantic |
| pydantic_core | 2.41.5 | MIT | https://github.com/pydantic/pydantic-core |
| pylance | 2.0.0 | Apache Software License | UNKNOWN |
| pymdown-extensions | 10.20.1 | MIT | https://github.com/facelessuser/pymdown-extensions |
| pyparsing | 3.3.2 | MIT | https://github.com/pyparsing/pyparsing/ |
| pyright | 1.1.408 | MIT | https://github.com/RobertCraigie/pyright-python |
| pytest | 9.0.2 | MIT | https://docs.pytest.org/en/latest/ |
| pytest-asyncio | 1.3.0 | Apache-2.0 | https://github.com/pytest-dev/pytest-asyncio |
| pytest-mock | 3.15.1 | MIT License | https://github.com/pytest-dev/pytest-mock/ |
| python-dateutil | 2.9.0.post0 | Apache Software License; BSD License | https://github.com/dateutil/dateutil |
| pytz | 2025.2 | MIT License | http://pythonhosted.org/pytz |
| pyyaml_env_tag | 1.1 | MIT | https://github.com/waylan/pyyaml-env-tag |
| pyzmq | 27.1.0 | BSD License | https://pyzmq.readthedocs.org |
| referencing | 0.37.0 | MIT | https://github.com/python-jsonschema/referencing |
| regex | 2026.1.15 | Apache-2.0 AND CNRI-Python | https://github.com/mrabarnett/mrab-regex |
| requests | 2.32.5 | Apache Software License | https://requests.readthedocs.io |
| rpds-py | 0.30.0 | MIT | https://github.com/crate-py/rpds |
| rsa | 4.7.2 | Apache Software License | https://stuvel.eu/rsa |
| ruff | 0.15.0 | MIT License | https://docs.astral.sh/ruff |
| s3transfer | 0.16.0 | Apache Software License | https://github.com/boto/s3transfer |
| safetensors | 0.7.0 | Apache Software License | https://github.com/huggingface/safetensors |
| scikit-learn | 1.8.0 | BSD-3-Clause | https://scikit-learn.org |
| scipy | 1.17.0 | BSD License | https://scipy.org/ |
| sentence-transformers | 5.2.2 | Apache Software License | https://www.SBERT.net |
| sentencepiece | 0.2.1 | UNKNOWN | https://github.com/google/sentencepiece |
| six | 1.17.0 | MIT License | https://github.com/benjaminp/six |
| sniffio | 1.3.1 | Apache Software License; MIT License | https://github.com/python-trio/sniffio |
| soupsieve | 2.8.3 | MIT | https://github.com/facelessuser/soupsieve |
| stack-data | 0.6.3 | MIT License | http://github.com/alexmojaki/stack_data |
| sympy | 1.14.0 | BSD License | https://sympy.org |
| tabulate | 0.9.0 | MIT License | https://github.com/astanin/python-tabulate |
| tantivy | 0.25.1 | UNKNOWN | UNKNOWN |
| threadpoolctl | 3.6.0 | BSD License | https://github.com/joblib/threadpoolctl |
| timm | 1.0.24 | Apache Software License | https://github.com/huggingface/pytorch-image-models |
| tinycss2 | 1.4.0 | BSD License | https://www.courtbouillon.org/tinycss2 |
| tokenizers | 0.22.2 | Apache Software License | https://github.com/huggingface/tokenizers |
| torch | 2.8.0 | BSD License | https://pytorch.org/ |
| torchvision | 0.23.0 | BSD | https://github.com/pytorch/vision |
| tornado | 6.5.4 | Apache Software License | http://www.tornadoweb.org/ |
| tqdm | 4.67.3 | MPL-2.0 AND MIT | https://tqdm.github.io |
| traitlets | 5.14.3 | BSD License | https://github.com/ipython/traitlets |
| transformers | 4.57.6 | Apache Software License | https://github.com/huggingface/transformers |
| types-requests | 2.32.4.20260107 | Apache-2.0 | https://github.com/python/typeshed |
| typing-inspection | 0.4.2 | MIT | https://github.com/pydantic/typing-inspection |
| typing_extensions | 4.15.0 | PSF-2.0 | https://github.com/python/typing_extensions |
| tzdata | 2025.3 | Apache-2.0 | https://github.com/python/tzdata |
| uritemplate | 4.2.0 | BSD 3-Clause OR Apache-2.0 | https://uritemplate.readthedocs.org |
| urllib3 | 2.6.3 | MIT | https://github.com/urllib3/urllib3/blob/main/CHANGES.rst |
| virtualenv | 20.36.1 | MIT | https://github.com/pypa/virtualenv |
| watchdog | 6.0.0 | Apache Software License | https://github.com/gorakhargosh/watchdog |
| webencodings | 0.5.1 | BSD License | https://github.com/SimonSapin/python-webencodings |
| yarl | 1.22.0 | Apache Software License | https://github.com/aio-libs/yarl |

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@@ -9,7 +9,7 @@ import json
from ._lancedb import async_permutation_builder, PermutationReader
from .table import LanceTable
from .background_loop import LOOP
from .util import batch_to_tensor, batch_to_tensor_rows
from .util import batch_to_tensor
from typing import Any, Callable, Iterator, Literal, Optional, TYPE_CHECKING, Union
if TYPE_CHECKING:
@@ -333,11 +333,7 @@ class Transforms:
"""
@staticmethod
def arrow2python(batch: pa.RecordBatch) -> list[dict[str, Any]]:
return batch.to_pylist()
@staticmethod
def arrow2pythoncol(batch: pa.RecordBatch) -> dict[str, list[Any]]:
def arrow2python(batch: pa.RecordBatch) -> dict[str, list[Any]]:
return batch.to_pydict()
@staticmethod
@@ -691,17 +687,7 @@ class Permutation:
return
def with_format(
self,
format: Literal[
"numpy",
"python",
"python_col",
"pandas",
"arrow",
"torch",
"torch_col",
"polars",
],
self, format: Literal["numpy", "python", "pandas", "arrow", "torch", "polars"]
) -> "Permutation":
"""
Set the format for batches
@@ -710,18 +696,16 @@ class Permutation:
The format can be one of:
- "numpy" - the batch will be a dict of numpy arrays (one per column)
- "python" - the batch will be a list of dicts (one per row)
- "python_col" - the batch will be a dict of lists (one entry per column)
- "python" - the batch will be a dict of lists (one per column)
- "pandas" - the batch will be a pandas DataFrame
- "arrow" - the batch will be a pyarrow RecordBatch
- "torch" - the batch will be a list of tensors, one per row
- "torch_col" - the batch will be a 2D torch tensor (first dim indexes columns)
- "torch" - the batch will be a two dimensional torch tensor
- "polars" - the batch will be a polars DataFrame
Conversion may or may not involve a data copy. Lance uses Arrow internally
and so it is able to zero-copy to the arrow and polars formats.
and so it is able to zero-copy to the arrow and polars.
Conversion to torch_col will be zero-copy but will only support a subset of data
Conversion to torch will be zero-copy but will only support a subset of data
types (numeric types).
Conversion to numpy and/or pandas will typically be zero-copy for numeric
@@ -734,8 +718,6 @@ class Permutation:
assert format is not None, "format is required"
if format == "python":
return self.with_transform(Transforms.arrow2python)
if format == "python_col":
return self.with_transform(Transforms.arrow2pythoncol)
elif format == "numpy":
return self.with_transform(Transforms.arrow2numpy)
elif format == "pandas":
@@ -743,8 +725,6 @@ class Permutation:
elif format == "arrow":
return self.with_transform(Transforms.arrow2arrow)
elif format == "torch":
return self.with_transform(batch_to_tensor_rows)
elif format == "torch_col":
return self.with_transform(batch_to_tensor)
elif format == "polars":
return self.with_transform(Transforms.arrow2polars())
@@ -766,20 +746,15 @@ class Permutation:
def __getitem__(self, index: int) -> Any:
"""
Returns a single row from the permutation by offset
"""
return self.__getitems__([index])
Return a single row from the permutation
def __getitems__(self, indices: list[int]) -> Any:
"""
Returns rows from the permutation by offset
"""
The output will always be a python dictionary regardless of the format.
async def do_getitems():
return await self.reader.take_offsets(indices, selection=self.selection)
batch = LOOP.run(do_getitems())
return self.transform_fn(batch)
This method is mostly useful for debugging and exploration. For actual
processing use [iter](#iter) or a torch data loader to perform batched
processing.
"""
pass
@deprecated(details="Use with_skip instead")
def skip(self, skip: int) -> "Permutation":

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@@ -2118,17 +2118,19 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
""" # noqa: E501
self._create_query_builders()
reranker_label = str(self._reranker) if self._reranker else "No reranker"
vector_plan = self._table._explain_plan(
self._vector_query.to_query_object(), verbose=verbose
results = ["Vector Search Plan:"]
results.append(
self._table._explain_plan(
self._vector_query.to_query_object(), verbose=verbose
)
)
fts_plan = self._table._explain_plan(
self._fts_query.to_query_object(), verbose=verbose
results.append("FTS Search Plan:")
results.append(
self._table._explain_plan(
self._fts_query.to_query_object(), verbose=verbose
)
)
# Indent sub-plans under the reranker
indented_vector = "\n".join(" " + line for line in vector_plan.splitlines())
indented_fts = "\n".join(" " + line for line in fts_plan.splitlines())
return f"{reranker_label}\n {indented_vector}\n {indented_fts}"
return "\n".join(results)
def analyze_plan(self):
"""Execute the query and display with runtime metrics.
@@ -3162,20 +3164,23 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
... plan = await table.query().nearest_to([1.0, 2.0]).nearest_to_text("hello").explain_plan(True)
... print(plan)
>>> asyncio.run(doctest_example()) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
RRFReranker(K=60)
ProjectionExec: expr=[vector@0 as vector, text@3 as text, _distance@2 as _distance]
Take: columns="vector, _rowid, _distance, (text)"
CoalesceBatchesExec: target_batch_size=1024
GlobalLimitExec: skip=0, fetch=10
FilterExec: _distance@2 IS NOT NULL
SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST, _rowid@1 ASC NULLS LAST], preserve_partitioning=[false]
KNNVectorDistance: metric=l2
LanceRead: uri=..., projection=[vector], ...
ProjectionExec: expr=[vector@2 as vector, text@3 as text, _score@1 as _score]
Take: columns="_rowid, _score, (vector), (text)"
CoalesceBatchesExec: target_batch_size=1024
GlobalLimitExec: skip=0, fetch=10
MatchQuery: column=text, query=hello
Vector Search Plan:
ProjectionExec: expr=[vector@0 as vector, text@3 as text, _distance@2 as _distance]
Take: columns="vector, _rowid, _distance, (text)"
CoalesceBatchesExec: target_batch_size=1024
GlobalLimitExec: skip=0, fetch=10
FilterExec: _distance@2 IS NOT NULL
SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST, _rowid@1 ASC NULLS LAST], preserve_partitioning=[false]
KNNVectorDistance: metric=l2
LanceRead: uri=..., projection=[vector], ...
<BLANKLINE>
FTS Search Plan:
ProjectionExec: expr=[vector@2 as vector, text@3 as text, _score@1 as _score]
Take: columns="_rowid, _score, (vector), (text)"
CoalesceBatchesExec: target_batch_size=1024
GlobalLimitExec: skip=0, fetch=10
MatchQuery: column=text, query=hello
<BLANKLINE>
Parameters
----------
@@ -3187,12 +3192,12 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
plan : str
""" # noqa: E501
vector_plan = await self._inner.to_vector_query().explain_plan(verbose)
fts_plan = await self._inner.to_fts_query().explain_plan(verbose)
# Indent sub-plans under the reranker
indented_vector = "\n".join(" " + line for line in vector_plan.splitlines())
indented_fts = "\n".join(" " + line for line in fts_plan.splitlines())
return f"{self._reranker}\n {indented_vector}\n {indented_fts}"
results = ["Vector Search Plan:"]
results.append(await self._inner.to_vector_query().explain_plan(verbose))
results.append("FTS Search Plan:")
results.append(await self._inner.to_fts_query().explain_plan(verbose))
return "\n".join(results)
async def analyze_plan(self):
"""

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@@ -42,18 +42,10 @@ class AnswerdotaiRerankers(Reranker):
rerankers = attempt_import_or_raise(
"rerankers"
) # import here for faster ops later
self.model_name = model_name
self.model_type = model_type
self.reranker = rerankers.Reranker(
model_name=model_name, model_type=model_type, **kwargs
)
def __str__(self):
return (
f"AnswerdotaiRerankers(model_type={self.model_type}, "
f"model_name={self.model_name})"
)
def _rerank(self, result_set: pa.Table, query: str):
result_set = self._handle_empty_results(result_set)
if len(result_set) == 0:

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@@ -40,9 +40,6 @@ class Reranker(ABC):
if ARROW_VERSION.major <= 13:
self._concat_tables_args = {"promote": True}
def __str__(self):
return self.__class__.__name__
def rerank_vector(
self,
query: str,

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@@ -44,9 +44,6 @@ class CohereReranker(Reranker):
self.top_n = top_n
self.api_key = api_key
def __str__(self):
return f"CohereReranker(model_name={self.model_name})"
@cached_property
def _client(self):
cohere = attempt_import_or_raise("cohere")

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@@ -50,9 +50,6 @@ class CrossEncoderReranker(Reranker):
if self.device is None:
self.device = "cuda" if torch.cuda.is_available() else "cpu"
def __str__(self):
return f"CrossEncoderReranker(model_name={self.model_name})"
@cached_property
def model(self):
sbert = attempt_import_or_raise("sentence_transformers")

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@@ -45,9 +45,6 @@ class JinaReranker(Reranker):
self.top_n = top_n
self.api_key = api_key
def __str__(self):
return f"JinaReranker(model_name={self.model_name})"
@cached_property
def _client(self):
import requests

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@@ -38,9 +38,6 @@ class LinearCombinationReranker(Reranker):
self.weight = weight
self.fill = fill
def __str__(self):
return f"LinearCombinationReranker(weight={self.weight}, fill={self.fill})"
def rerank_hybrid(
self,
query: str, # noqa: F821

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@@ -54,12 +54,6 @@ class MRRReranker(Reranker):
self.weight_vector = weight_vector
self.weight_fts = weight_fts
def __str__(self):
return (
f"MRRReranker(weight_vector={self.weight_vector}, "
f"weight_fts={self.weight_fts})"
)
def rerank_hybrid(
self,
query: str, # noqa: F821

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@@ -43,9 +43,6 @@ class OpenaiReranker(Reranker):
self.column = column
self.api_key = api_key
def __str__(self):
return f"OpenaiReranker(model_name={self.model_name})"
def _rerank(self, result_set: pa.Table, query: str):
result_set = self._handle_empty_results(result_set)
if len(result_set) == 0:

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@@ -36,9 +36,6 @@ class RRFReranker(Reranker):
super().__init__(return_score)
self.K = K
def __str__(self):
return f"RRFReranker(K={self.K})"
def rerank_hybrid(
self,
query: str, # noqa: F821

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@@ -52,9 +52,6 @@ class VoyageAIReranker(Reranker):
self.api_key = api_key
self.truncation = truncation
def __str__(self):
return f"VoyageAIReranker(model_name={self.model_name})"
@cached_property
def _client(self):
voyageai = attempt_import_or_raise("voyageai")

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@@ -904,9 +904,7 @@ class Table(ABC):
----------
field_names: str or list of str
The name(s) of the field to index.
If ``use_tantivy`` is False (default), only a single field name
(str) is supported. To index multiple fields, create a separate
FTS index for each field.
can be only str if use_tantivy=True for now.
replace: bool, default False
If True, replace the existing index if it exists. Note that this is
not yet an atomic operation; the index will be temporarily
@@ -2300,11 +2298,7 @@ class LanceTable(Table):
):
if not use_tantivy:
if not isinstance(field_names, str):
raise ValueError(
"Native FTS indexes can only be created on a single field "
"at a time. To search over multiple text fields, create a "
"separate FTS index for each field."
)
raise ValueError("field_names must be a string when use_tantivy=False")
if tokenizer_name is None:
tokenizer_configs = {

View File

@@ -419,22 +419,3 @@ def batch_to_tensor(batch: pa.RecordBatch):
"""
torch = attempt_import_or_raise("torch", "torch")
return torch.stack([torch.from_dlpack(col) for col in batch.columns])
def batch_to_tensor_rows(batch: pa.RecordBatch):
"""
Convert a PyArrow RecordBatch to a list of PyTorch Tensor, one per row
Each column is converted to a tensor (using zero-copy via DLPack)
and the columns are then stacked into a single tensor. The 2D tensor
is then converted to a list of tensors, one per row
Fails if torch or numpy is not installed.
Fails if a column's data type is not supported by PyTorch.
"""
torch = attempt_import_or_raise("torch", "torch")
numpy = attempt_import_or_raise("numpy", "numpy")
columns = [col.to_numpy(zero_copy_only=False) for col in batch.columns]
stacked = torch.tensor(numpy.column_stack(columns))
rows = list(stacked.unbind(dim=0))
return rows

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@@ -163,7 +163,9 @@ async def test_explain_plan(table: AsyncTable):
table.query().nearest_to_text("dog").nearest_to([0.1, 0.1]).explain_plan(True)
)
assert "Vector Search Plan" in plan
assert "KNNVectorDistance" in plan
assert "FTS Search Plan" in plan
assert "LanceRead" in plan

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@@ -664,20 +664,23 @@ def test_iter_basic(some_permutation: Permutation):
expected_batches = (950 + batch_size - 1) // batch_size # ceiling division
assert len(batches) == expected_batches
# Check that all batches are lists of dicts (default python format)
assert all(isinstance(batch, list) for batch in batches)
# Check that all batches are dicts (default python format)
assert all(isinstance(batch, dict) for batch in batches)
# Check that batches have the correct structure
for batch in batches:
assert "id" in batch[0]
assert "value" in batch[0]
assert "id" in batch
assert "value" in batch
assert isinstance(batch["id"], list)
assert isinstance(batch["value"], list)
# Check that all batches except the last have the correct size
for batch in batches[:-1]:
assert len(batch) == batch_size
assert len(batch["id"]) == batch_size
assert len(batch["value"]) == batch_size
# Last batch might be smaller
assert len(batches[-1]) <= batch_size
assert len(batches[-1]["id"]) <= batch_size
def test_iter_skip_last_batch(some_permutation: Permutation):
@@ -696,11 +699,11 @@ def test_iter_skip_last_batch(some_permutation: Permutation):
if 950 % batch_size != 0:
assert len(batches_without_skip) == num_full_batches + 1
# Last batch should be smaller
assert len(batches_without_skip[-1]) == 950 % batch_size
assert len(batches_without_skip[-1]["id"]) == 950 % batch_size
# All batches with skip_last_batch should be full size
for batch in batches_with_skip:
assert len(batch) == batch_size
assert len(batch["id"]) == batch_size
def test_iter_different_batch_sizes(some_permutation: Permutation):
@@ -717,12 +720,12 @@ def test_iter_different_batch_sizes(some_permutation: Permutation):
# Test with batch size equal to total rows
single_batch = list(some_permutation.iter(950, skip_last_batch=False))
assert len(single_batch) == 1
assert len(single_batch[0]) == 950
assert len(single_batch[0]["id"]) == 950
# Test with batch size larger than total rows
oversized_batch = list(some_permutation.iter(10000, skip_last_batch=False))
assert len(oversized_batch) == 1
assert len(oversized_batch[0]) == 950
assert len(oversized_batch[0]["id"]) == 950
def test_dunder_iter(some_permutation: Permutation):
@@ -735,13 +738,15 @@ def test_dunder_iter(some_permutation: Permutation):
# All batches should be full size
for batch in batches:
assert len(batch) == 100
assert len(batch["id"]) == 100
assert len(batch["value"]) == 100
some_permutation = some_permutation.with_batch_size(400)
batches = list(some_permutation)
assert len(batches) == 2 # floor(950 / 400) since skip_last_batch=True
for batch in batches:
assert len(batch) == 400
assert len(batch["id"]) == 400
assert len(batch["value"]) == 400
def test_iter_with_different_formats(some_permutation: Permutation):
@@ -756,7 +761,7 @@ def test_iter_with_different_formats(some_permutation: Permutation):
# Test with python format (default)
python_perm = some_permutation.with_format("python")
python_batches = list(python_perm.iter(batch_size, skip_last_batch=False))
assert all(isinstance(batch, list) for batch in python_batches)
assert all(isinstance(batch, dict) for batch in python_batches)
# Test with pandas format
pandas_perm = some_permutation.with_format("pandas")
@@ -775,8 +780,8 @@ def test_iter_with_column_selection(some_permutation: Permutation):
# Check that batches only contain the id column
for batch in batches:
assert "id" in batch[0]
assert "value" not in batch[0]
assert "id" in batch
assert "value" not in batch
def test_iter_with_column_rename(some_permutation: Permutation):
@@ -786,9 +791,9 @@ def test_iter_with_column_rename(some_permutation: Permutation):
# Check that batches have the renamed column
for batch in batches:
assert "id" in batch[0]
assert "data" in batch[0]
assert "value" not in batch[0]
assert "id" in batch
assert "data" in batch
assert "value" not in batch
def test_iter_with_limit_offset(some_permutation: Permutation):
@@ -807,14 +812,14 @@ def test_iter_with_limit_offset(some_permutation: Permutation):
assert len(limit_batches) == 5
no_skip = some_permutation.iter(101, skip_last_batch=False)
row_100 = next(no_skip)[100]["id"]
row_100 = next(no_skip)["id"][100]
# Test with both limit and offset
limited_perm = some_permutation.with_skip(100).with_take(300)
limited_batches = list(limited_perm.iter(100, skip_last_batch=False))
# Should have 3 batches (300 / 100)
assert len(limited_batches) == 3
assert limited_batches[0][0]["id"] == row_100
assert limited_batches[0]["id"][0] == row_100
def test_iter_empty_permutation(mem_db):
@@ -837,7 +842,7 @@ def test_iter_single_row(mem_db):
# With skip_last_batch=False, should get one batch
batches = list(perm.iter(10, skip_last_batch=False))
assert len(batches) == 1
assert len(batches[0]) == 1
assert len(batches[0]["id"]) == 1
# With skip_last_batch=True, should skip the single row (since it's < batch_size)
batches_skip = list(perm.iter(10, skip_last_batch=True))
@@ -855,7 +860,8 @@ def test_identity_permutation(mem_db):
batches = list(permutation.iter(10, skip_last_batch=False))
assert len(batches) == 1
assert len(batches[0]) == 10
assert len(batches[0]["id"]) == 10
assert len(batches[0]["value"]) == 10
permutation = permutation.remove_columns(["value"])
assert permutation.num_columns == 1
@@ -898,10 +904,10 @@ def test_transform_fn(mem_db):
py_result = list(permutation.with_format("python").iter(10, skip_last_batch=False))[
0
]
assert len(py_result) == 10
assert "id" in py_result[0]
assert "value" in py_result[0]
assert isinstance(py_result, list)
assert len(py_result) == 2
assert len(py_result["id"]) == 10
assert len(py_result["value"]) == 10
assert isinstance(py_result, dict)
try:
import torch
@@ -909,11 +915,9 @@ def test_transform_fn(mem_db):
torch_result = list(
permutation.with_format("torch").iter(10, skip_last_batch=False)
)[0]
assert isinstance(torch_result, list)
assert len(torch_result) == 10
assert isinstance(torch_result[0], torch.Tensor)
assert torch_result[0].shape == (2,)
assert torch_result[0].dtype == torch.int64
assert torch_result.shape == (2, 10)
assert torch_result.dtype == torch.int64
assert isinstance(torch_result, torch.Tensor)
except ImportError:
# Skip check if torch is not installed
pass
@@ -941,113 +945,3 @@ def test_custom_transform(mem_db):
batch = batches[0]
assert batch == pa.record_batch([range(10)], ["id"])
def test_getitems_basic(some_permutation: Permutation):
"""Test __getitems__ returns correct rows by offset."""
result = some_permutation.__getitems__([0, 1, 2])
assert isinstance(result, list)
assert "id" in result[0]
assert "value" in result[0]
assert len(result) == 3
def test_getitems_single_index(some_permutation: Permutation):
"""Test __getitems__ with a single index."""
result = some_permutation.__getitems__([0])
assert len(result) == 1
def test_getitems_preserves_order(some_permutation: Permutation):
"""Test __getitems__ returns rows in the requested order."""
# Get rows in forward order
forward = some_permutation.__getitems__([0, 1, 2, 3, 4])
# Get the same rows in reverse order
reverse = some_permutation.__getitems__([4, 3, 2, 1, 0])
assert [r["id"] for r in forward] == list(reversed([r["id"] for r in reverse]))
assert [r["value"] for r in forward] == list(
reversed([r["value"] for r in reverse])
)
def test_getitems_non_contiguous(some_permutation: Permutation):
"""Test __getitems__ with non-contiguous indices."""
result = some_permutation.__getitems__([0, 10, 50, 100, 500])
assert len(result) == 5
# Each id/value pair should match what we'd get individually
for i, offset in enumerate([0, 10, 50, 100, 500]):
single = some_permutation.__getitems__([offset])
assert result[i]["id"] == single[0]["id"]
assert result[i]["value"] == single[0]["value"]
def test_getitems_with_column_selection(some_permutation: Permutation):
"""Test __getitems__ respects column selection."""
id_only = some_permutation.select_columns(["id"])
result = id_only.__getitems__([0, 1, 2])
assert "id" in result[0]
assert "value" not in result[0]
assert len(result) == 3
def test_getitems_with_column_rename(some_permutation: Permutation):
"""Test __getitems__ respects column renames."""
renamed = some_permutation.rename_column("value", "data")
result = renamed.__getitems__([0, 1])
assert "data" in result[0]
assert "value" not in result[0]
assert len(result) == 2
def test_getitems_with_format(some_permutation: Permutation):
"""Test __getitems__ applies the transform function."""
arrow_perm = some_permutation.with_format("arrow")
result = arrow_perm.__getitems__([0, 1, 2])
assert isinstance(result, pa.RecordBatch)
assert result.num_rows == 3
def test_getitems_with_custom_transform(some_permutation: Permutation):
"""Test __getitems__ with a custom transform."""
def transform(batch: pa.RecordBatch) -> list:
return batch.column("id").to_pylist()
custom = some_permutation.with_transform(transform)
result = custom.__getitems__([0, 1, 2])
assert isinstance(result, list)
assert len(result) == 3
def test_getitems_identity_permutation(mem_db):
"""Test __getitems__ on an identity permutation."""
tbl = mem_db.create_table(
"test_table", pa.table({"id": range(10), "value": range(10)})
)
perm = Permutation.identity(tbl)
result = perm.__getitems__([0, 5, 9])
assert [r["id"] for r in result] == [0, 5, 9]
assert [r["value"] for r in result] == [0, 5, 9]
def test_getitems_with_limit_offset(some_permutation: Permutation):
"""Test __getitems__ on a permutation with skip/take applied."""
limited = some_permutation.with_skip(100).with_take(200)
# Should be able to access offsets within the limited range
result = limited.__getitems__([0, 1, 199])
assert len(result) == 3
# The first item of the limited permutation should match offset 100 of original
full_result = some_permutation.__getitems__([100])
limited_result = limited.__getitems__([0])
assert limited_result[0]["id"] == full_result[0]["id"]
def test_getitems_invalid_offset(some_permutation: Permutation):
"""Test __getitems__ with an out-of-range offset raises an error."""
with pytest.raises(Exception):
some_permutation.__getitems__([999999])

View File

@@ -4,7 +4,6 @@
import pyarrow as pa
import pytest
from lancedb.util import tbl_to_tensor
from lancedb.permutation import Permutation
torch = pytest.importorskip("torch")
@@ -17,26 +16,3 @@ def test_table_dataloader(mem_db):
for batch in dataloader:
assert batch.size(0) == 1
assert batch.size(1) == 10
def test_permutation_dataloader(mem_db):
table = mem_db.create_table("test_table", pa.table({"a": range(1000)}))
permutation = Permutation.identity(table)
dataloader = torch.utils.data.DataLoader(permutation, batch_size=10, shuffle=True)
for batch in dataloader:
assert batch["a"].size(0) == 10
permutation = permutation.with_format("torch")
dataloader = torch.utils.data.DataLoader(permutation, batch_size=10, shuffle=True)
for batch in dataloader:
assert batch.size(0) == 10
assert batch.size(1) == 1
permutation = permutation.with_format("torch_col")
dataloader = torch.utils.data.DataLoader(
permutation, collate_fn=lambda x: x, batch_size=10, shuffle=True
)
for batch in dataloader:
assert batch.size(0) == 1
assert batch.size(1) == 10

View File

@@ -121,8 +121,7 @@ impl Connection {
let mode = Self::parse_create_mode_str(mode)?;
let batches: Box<dyn arrow::array::RecordBatchReader + Send> =
Box::new(ArrowArrayStreamReader::from_pyarrow_bound(&data)?);
let batches = ArrowArrayStreamReader::from_pyarrow_bound(&data)?;
let mut builder = inner.create_table(name, batches).mode(mode);

View File

@@ -6,7 +6,7 @@ use std::sync::{Arc, Mutex};
use crate::{
arrow::RecordBatchStream, connection::Connection, error::PythonErrorExt, table::Table,
};
use arrow::pyarrow::{PyArrowType, ToPyArrow};
use arrow::pyarrow::ToPyArrow;
use lancedb::{
dataloader::permutation::{
builder::{PermutationBuilder as LancePermutationBuilder, ShuffleStrategy},
@@ -328,21 +328,4 @@ impl PyPermutationReader {
Ok(RecordBatchStream::new(stream))
})
}
#[pyo3(signature = (indices, *, selection=None))]
pub fn take_offsets<'py>(
slf: PyRef<'py, Self>,
indices: Vec<u64>,
selection: Option<Bound<'py, PyAny>>,
) -> PyResult<Bound<'py, PyAny>> {
let selection = Self::parse_selection(selection)?;
let reader = slf.reader.clone();
future_into_py(slf.py(), async move {
let batch = reader
.take_offsets(&indices, selection)
.await
.infer_error()?;
Ok(PyArrowType(batch))
})
}
}

View File

@@ -296,8 +296,7 @@ impl Table {
data: Bound<'_, PyAny>,
mode: String,
) -> PyResult<Bound<'a, PyAny>> {
let batches: Box<dyn arrow::array::RecordBatchReader + Send> =
Box::new(ArrowArrayStreamReader::from_pyarrow_bound(&data)?);
let batches = ArrowArrayStreamReader::from_pyarrow_bound(&data)?;
let mut op = self_.inner_ref()?.add(batches);
if mode == "append" {
op = op.mode(AddDataMode::Append);

5349
python/uv.lock generated

File diff suppressed because it is too large Load Diff

View File

@@ -3,12 +3,13 @@
use std::{iter::once, sync::Arc};
use arrow_array::{Float64Array, Int32Array, RecordBatch, StringArray};
use arrow_array::{Float64Array, Int32Array, RecordBatch, RecordBatchIterator, StringArray};
use arrow_schema::{DataType, Field, Schema};
use aws_config::Region;
use aws_sdk_bedrockruntime::Client;
use futures::StreamExt;
use lancedb::{
arrow::IntoArrow,
connect,
embeddings::{bedrock::BedrockEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},
query::{ExecutableQuery, QueryBase},
@@ -66,7 +67,7 @@ async fn main() -> Result<()> {
Ok(())
}
fn make_data() -> RecordBatch {
fn make_data() -> impl IntoArrow {
let schema = Schema::new(vec![
Field::new("id", DataType::Int32, true),
Field::new("text", DataType::Utf8, false),
@@ -82,9 +83,10 @@ fn make_data() -> RecordBatch {
]);
let price = Float64Array::from(vec![10.0, 50.0, 100.0, 30.0]);
let schema = Arc::new(schema);
RecordBatch::try_new(
let rb = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(id), Arc::new(text), Arc::new(price)],
)
.unwrap()
.unwrap();
Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema))
}

View File

@@ -3,13 +3,12 @@
use std::sync::Arc;
use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator, StringArray};
use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray};
use arrow_schema::{DataType, Field, Schema};
use futures::TryStreamExt;
use lance_index::scalar::FullTextSearchQuery;
use lancedb::connection::Connection;
use lancedb::index::scalar::FtsIndexBuilder;
use lancedb::index::Index;
use lancedb::query::{ExecutableQuery, QueryBase};
@@ -30,7 +29,7 @@ async fn main() -> Result<()> {
Ok(())
}
fn create_some_records() -> Result<Box<dyn arrow_array::RecordBatchReader + Send>> {
fn create_some_records() -> Result<Box<dyn RecordBatchReader + Send>> {
const TOTAL: usize = 1000;
let schema = Arc::new(Schema::new(vec![
@@ -67,7 +66,7 @@ fn create_some_records() -> Result<Box<dyn arrow_array::RecordBatchReader + Send
}
async fn create_table(db: &Connection) -> Result<Table> {
let initial_data = create_some_records()?;
let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
let tbl = db.create_table("my_table", initial_data).execute().await?;
Ok(tbl)
}

View File

@@ -1,13 +1,14 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
use arrow_array::{RecordBatch, StringArray};
use arrow_array::{RecordBatch, RecordBatchIterator, StringArray};
use arrow_schema::{DataType, Field, Schema};
use futures::TryStreamExt;
use lance_index::scalar::FullTextSearchQuery;
use lancedb::index::scalar::FtsIndexBuilder;
use lancedb::index::Index;
use lancedb::{
arrow::IntoArrow,
connect,
embeddings::{
sentence_transformers::SentenceTransformersEmbeddings, EmbeddingDefinition,
@@ -69,7 +70,7 @@ async fn main() -> Result<()> {
Ok(())
}
fn make_data() -> RecordBatch {
fn make_data() -> impl IntoArrow {
let schema = Schema::new(vec![Field::new("facts", DataType::Utf8, false)]);
let facts = StringArray::from_iter_values(vec![
@@ -100,7 +101,8 @@ fn make_data() -> RecordBatch {
"The first chatbot was ELIZA, created in the 1960s.",
]);
let schema = Arc::new(schema);
RecordBatch::try_new(schema.clone(), vec![Arc::new(facts)]).unwrap()
let rb = RecordBatch::try_new(schema.clone(), vec![Arc::new(facts)]).unwrap();
Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema))
}
async fn create_index(table: &Table) -> Result<()> {

View File

@@ -8,12 +8,13 @@
use std::sync::Arc;
use arrow_array::types::Float32Type;
use arrow_array::{FixedSizeListArray, Int32Array, RecordBatch, RecordBatchIterator};
use arrow_array::{
FixedSizeListArray, Int32Array, RecordBatch, RecordBatchIterator, RecordBatchReader,
};
use arrow_schema::{DataType, Field, Schema};
use futures::TryStreamExt;
use lancedb::connection::Connection;
use lancedb::index::vector::IvfPqIndexBuilder;
use lancedb::index::Index;
use lancedb::query::{ExecutableQuery, QueryBase};
@@ -33,7 +34,7 @@ async fn main() -> Result<()> {
Ok(())
}
fn create_some_records() -> Result<Box<dyn arrow_array::RecordBatchReader + Send>> {
fn create_some_records() -> Result<Box<dyn RecordBatchReader + Send>> {
const TOTAL: usize = 1000;
const DIM: usize = 128;
@@ -72,9 +73,9 @@ fn create_some_records() -> Result<Box<dyn arrow_array::RecordBatchReader + Send
}
async fn create_table(db: &Connection) -> Result<Table> {
let initial_data = create_some_records()?;
let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
let tbl = db
.create_table("my_table", initial_data)
.create_table("my_table", Box::new(initial_data))
.execute()
.await
.unwrap();

View File

@@ -5,9 +5,11 @@
use std::{iter::once, sync::Arc};
use arrow_array::{RecordBatch, StringArray};
use arrow_array::{Float64Array, Int32Array, RecordBatch, RecordBatchIterator, StringArray};
use arrow_schema::{DataType, Field, Schema};
use futures::StreamExt;
use lancedb::{
arrow::IntoArrow,
connect,
embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},
query::{ExecutableQuery, QueryBase},
@@ -62,20 +64,26 @@ async fn main() -> Result<()> {
}
// --8<-- [end:openai_embeddings]
fn make_data() -> RecordBatch {
arrow_array::record_batch!(
("id", Int32, [1, 2, 3, 4]),
(
"text",
Utf8,
[
"Black T-Shirt",
"Leather Jacket",
"Winter Parka",
"Hooded Sweatshirt"
]
),
("price", Float64, [10.0, 50.0, 100.0, 30.0])
fn make_data() -> impl IntoArrow {
let schema = Schema::new(vec![
Field::new("id", DataType::Int32, true),
Field::new("text", DataType::Utf8, false),
Field::new("price", DataType::Float64, false),
]);
let id = Int32Array::from(vec![1, 2, 3, 4]);
let text = StringArray::from_iter_values(vec![
"Black T-Shirt",
"Leather Jacket",
"Winter Parka",
"Hooded Sweatshirt",
]);
let price = Float64Array::from(vec![10.0, 50.0, 100.0, 30.0]);
let schema = Arc::new(schema);
let rb = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(id), Arc::new(text), Arc::new(price)],
)
.unwrap()
.unwrap();
Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema))
}

View File

@@ -3,10 +3,11 @@
use std::{iter::once, sync::Arc};
use arrow_array::{RecordBatch, StringArray};
use arrow_array::{RecordBatch, RecordBatchIterator, StringArray};
use arrow_schema::{DataType, Field, Schema};
use futures::StreamExt;
use lancedb::{
arrow::IntoArrow,
connect,
embeddings::{
sentence_transformers::SentenceTransformersEmbeddings, EmbeddingDefinition,
@@ -58,7 +59,7 @@ async fn main() -> Result<()> {
Ok(())
}
fn make_data() -> RecordBatch {
fn make_data() -> impl IntoArrow {
let schema = Schema::new(vec![Field::new("facts", DataType::Utf8, false)]);
let facts = StringArray::from_iter_values(vec![
@@ -89,5 +90,6 @@ fn make_data() -> RecordBatch {
"The first chatbot was ELIZA, created in the 1960s.",
]);
let schema = Arc::new(schema);
RecordBatch::try_new(schema.clone(), vec![Arc::new(facts)]).unwrap()
let rb = RecordBatch::try_new(schema.clone(), vec![Arc::new(facts)]).unwrap();
Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema))
}

View File

@@ -8,9 +8,11 @@
use std::sync::Arc;
use arrow_array::types::Float32Type;
use arrow_array::{FixedSizeListArray, Int32Array, RecordBatch};
use arrow_array::{FixedSizeListArray, Int32Array, RecordBatch, RecordBatchIterator};
use arrow_schema::{DataType, Field, Schema};
use futures::TryStreamExt;
use lancedb::arrow::IntoArrow;
use lancedb::connection::Connection;
use lancedb::index::Index;
use lancedb::query::{ExecutableQuery, QueryBase};
@@ -57,7 +59,7 @@ async fn open_with_existing_tbl() -> Result<()> {
Ok(())
}
fn create_some_records() -> Result<RecordBatch> {
fn create_some_records() -> Result<impl IntoArrow> {
const TOTAL: usize = 1000;
const DIM: usize = 128;
@@ -74,18 +76,25 @@ fn create_some_records() -> Result<RecordBatch> {
]));
// Create a RecordBatch stream.
Ok(RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
Arc::new(
FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
(0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])),
DIM as i32,
let batches = RecordBatchIterator::new(
vec![RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
Arc::new(
FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
(0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])),
DIM as i32,
),
),
),
],
)?)
],
)
.unwrap()]
.into_iter()
.map(Ok),
schema.clone(),
);
Ok(Box::new(batches))
}
async fn create_table(db: &Connection) -> Result<LanceDbTable> {

View File

@@ -6,8 +6,8 @@
use std::collections::HashMap;
use std::sync::Arc;
use arrow_array::RecordBatch;
use arrow_schema::SchemaRef;
use arrow_array::RecordBatchReader;
use arrow_schema::{Field, SchemaRef};
use lance::dataset::ReadParams;
use lance_namespace::models::{
CreateNamespaceRequest, CreateNamespaceResponse, DescribeNamespaceRequest,
@@ -17,20 +17,24 @@ use lance_namespace::models::{
#[cfg(feature = "aws")]
use object_store::aws::AwsCredential;
use crate::connection::create_table::CreateTableBuilder;
use crate::data::scannable::Scannable;
use crate::database::listing::ListingDatabase;
use crate::database::{
CloneTableRequest, Database, DatabaseOptions, OpenTableRequest, ReadConsistency,
TableNamesRequest,
use crate::arrow::{IntoArrow, IntoArrowStream, SendableRecordBatchStream};
use crate::database::listing::{
ListingDatabase, OPT_NEW_TABLE_STORAGE_VERSION, OPT_NEW_TABLE_V2_MANIFEST_PATHS,
};
use crate::database::{
CloneTableRequest, CreateTableData, CreateTableMode, CreateTableRequest, Database,
DatabaseOptions, OpenTableRequest, ReadConsistency, TableNamesRequest,
};
use crate::embeddings::{
EmbeddingDefinition, EmbeddingFunction, EmbeddingRegistry, MemoryRegistry, WithEmbeddings,
};
use crate::embeddings::{EmbeddingRegistry, MemoryRegistry};
use crate::error::{Error, Result};
#[cfg(feature = "remote")]
use crate::remote::{
client::ClientConfig,
db::{OPT_REMOTE_API_KEY, OPT_REMOTE_HOST_OVERRIDE, OPT_REMOTE_REGION},
};
use crate::table::{TableDefinition, WriteOptions};
use crate::Table;
use lance::io::ObjectStoreParams;
pub use lance_encoding::version::LanceFileVersion;
@@ -38,8 +42,6 @@ pub use lance_encoding::version::LanceFileVersion;
use lance_io::object_store::StorageOptions;
use lance_io::object_store::{StorageOptionsAccessor, StorageOptionsProvider};
mod create_table;
fn merge_storage_options(
store_params: &mut ObjectStoreParams,
pairs: impl IntoIterator<Item = (String, String)>,
@@ -114,6 +116,337 @@ impl TableNamesBuilder {
}
}
pub struct NoData {}
impl IntoArrow for NoData {
fn into_arrow(self) -> Result<Box<dyn arrow_array::RecordBatchReader + Send>> {
unreachable!("NoData should never be converted to Arrow")
}
}
// Stores the value given from the initial CreateTableBuilder::new call
// and defers errors until `execute` is called
enum CreateTableBuilderInitialData {
None,
Iterator(Result<Box<dyn RecordBatchReader + Send>>),
Stream(Result<SendableRecordBatchStream>),
}
/// A builder for configuring a [`Connection::create_table`] operation
pub struct CreateTableBuilder<const HAS_DATA: bool> {
parent: Arc<dyn Database>,
embeddings: Vec<(EmbeddingDefinition, Arc<dyn EmbeddingFunction>)>,
embedding_registry: Arc<dyn EmbeddingRegistry>,
request: CreateTableRequest,
// This is a bit clumsy but we defer errors until `execute` is called
// to maintain backwards compatibility
data: CreateTableBuilderInitialData,
}
// Builder methods that only apply when we have initial data
impl CreateTableBuilder<true> {
fn new<T: IntoArrow>(
parent: Arc<dyn Database>,
name: String,
data: T,
embedding_registry: Arc<dyn EmbeddingRegistry>,
) -> Self {
let dummy_schema = Arc::new(arrow_schema::Schema::new(Vec::<Field>::default()));
Self {
parent,
request: CreateTableRequest::new(
name,
CreateTableData::Empty(TableDefinition::new_from_schema(dummy_schema)),
),
embeddings: Vec::new(),
embedding_registry,
data: CreateTableBuilderInitialData::Iterator(data.into_arrow()),
}
}
fn new_streaming<T: IntoArrowStream>(
parent: Arc<dyn Database>,
name: String,
data: T,
embedding_registry: Arc<dyn EmbeddingRegistry>,
) -> Self {
let dummy_schema = Arc::new(arrow_schema::Schema::new(Vec::<Field>::default()));
Self {
parent,
request: CreateTableRequest::new(
name,
CreateTableData::Empty(TableDefinition::new_from_schema(dummy_schema)),
),
embeddings: Vec::new(),
embedding_registry,
data: CreateTableBuilderInitialData::Stream(data.into_arrow()),
}
}
/// Execute the create table operation
pub async fn execute(self) -> Result<Table> {
let embedding_registry = self.embedding_registry.clone();
let parent = self.parent.clone();
let request = self.into_request()?;
Ok(Table::new_with_embedding_registry(
parent.create_table(request).await?,
parent,
embedding_registry,
))
}
fn into_request(self) -> Result<CreateTableRequest> {
if self.embeddings.is_empty() {
match self.data {
CreateTableBuilderInitialData::Iterator(maybe_iter) => {
let data = maybe_iter?;
Ok(CreateTableRequest {
data: CreateTableData::Data(data),
..self.request
})
}
CreateTableBuilderInitialData::None => {
unreachable!("No data provided for CreateTableBuilder<true>")
}
CreateTableBuilderInitialData::Stream(maybe_stream) => {
let data = maybe_stream?;
Ok(CreateTableRequest {
data: CreateTableData::StreamingData(data),
..self.request
})
}
}
} else {
let CreateTableBuilderInitialData::Iterator(maybe_iter) = self.data else {
return Err(Error::NotSupported { message: "Creating a table with embeddings is currently not support when the input is streaming".to_string() });
};
let data = maybe_iter?;
let data = Box::new(WithEmbeddings::new(data, self.embeddings));
Ok(CreateTableRequest {
data: CreateTableData::Data(data),
..self.request
})
}
}
}
// Builder methods that only apply when we do not have initial data
impl CreateTableBuilder<false> {
fn new(
parent: Arc<dyn Database>,
name: String,
schema: SchemaRef,
embedding_registry: Arc<dyn EmbeddingRegistry>,
) -> Self {
let table_definition = TableDefinition::new_from_schema(schema);
Self {
parent,
request: CreateTableRequest::new(name, CreateTableData::Empty(table_definition)),
data: CreateTableBuilderInitialData::None,
embeddings: Vec::default(),
embedding_registry,
}
}
/// Execute the create table operation
pub async fn execute(self) -> Result<Table> {
let parent = self.parent.clone();
let embedding_registry = self.embedding_registry.clone();
let request = self.into_request()?;
Ok(Table::new_with_embedding_registry(
parent.create_table(request).await?,
parent,
embedding_registry,
))
}
fn into_request(self) -> Result<CreateTableRequest> {
if self.embeddings.is_empty() {
return Ok(self.request);
}
let CreateTableData::Empty(table_def) = self.request.data else {
unreachable!("CreateTableBuilder<false> should always have Empty data")
};
let schema = table_def.schema.clone();
let empty_batch = arrow_array::RecordBatch::new_empty(schema.clone());
let reader = Box::new(std::iter::once(Ok(empty_batch)).collect::<Vec<_>>());
let reader = arrow_array::RecordBatchIterator::new(reader.into_iter(), schema);
let with_embeddings = WithEmbeddings::new(reader, self.embeddings);
let table_definition = with_embeddings.table_definition()?;
Ok(CreateTableRequest {
data: CreateTableData::Empty(table_definition),
..self.request
})
}
}
impl<const HAS_DATA: bool> CreateTableBuilder<HAS_DATA> {
/// Set the mode for creating the table
///
/// This controls what happens if a table with the given name already exists
pub fn mode(mut self, mode: CreateTableMode) -> Self {
self.request.mode = mode;
self
}
/// Apply the given write options when writing the initial data
pub fn write_options(mut self, write_options: WriteOptions) -> Self {
self.request.write_options = write_options;
self
}
/// Set an option for the storage layer.
///
/// Options already set on the connection will be inherited by the table,
/// but can be overridden here.
///
/// See available options at <https://lancedb.com/docs/storage/>
pub fn storage_option(mut self, key: impl Into<String>, value: impl Into<String>) -> Self {
let store_params = self
.request
.write_options
.lance_write_params
.get_or_insert(Default::default())
.store_params
.get_or_insert(Default::default());
merge_storage_options(store_params, [(key.into(), value.into())]);
self
}
/// Set multiple options for the storage layer.
///
/// Options already set on the connection will be inherited by the table,
/// but can be overridden here.
///
/// See available options at <https://lancedb.com/docs/storage/>
pub fn storage_options(
mut self,
pairs: impl IntoIterator<Item = (impl Into<String>, impl Into<String>)>,
) -> Self {
let store_params = self
.request
.write_options
.lance_write_params
.get_or_insert(Default::default())
.store_params
.get_or_insert(Default::default());
let updates = pairs
.into_iter()
.map(|(key, value)| (key.into(), value.into()));
merge_storage_options(store_params, updates);
self
}
/// Add an embedding definition to the table.
///
/// The `embedding_name` must match the name of an embedding function that
/// was previously registered with the connection's [`EmbeddingRegistry`].
pub fn add_embedding(mut self, definition: EmbeddingDefinition) -> Result<Self> {
// Early verification of the embedding name
let embedding_func = self
.embedding_registry
.get(&definition.embedding_name)
.ok_or_else(|| Error::EmbeddingFunctionNotFound {
name: definition.embedding_name.clone(),
reason: "No embedding function found in the connection's embedding_registry"
.to_string(),
})?;
self.embeddings.push((definition, embedding_func));
Ok(self)
}
/// Set whether to use V2 manifest paths for the table. (default: false)
///
/// These paths provide more efficient opening of tables with many
/// versions on object stores.
///
/// <div class="warning">Turning this on will make the dataset unreadable
/// for older versions of LanceDB (prior to 0.10.0).</div>
///
/// To migrate an existing dataset, instead use the
/// [[NativeTable::migrate_manifest_paths_v2]].
///
/// This has no effect in LanceDB Cloud.
#[deprecated(since = "0.15.1", note = "Use `database_options` instead")]
pub fn enable_v2_manifest_paths(mut self, use_v2_manifest_paths: bool) -> Self {
let store_params = self
.request
.write_options
.lance_write_params
.get_or_insert_with(Default::default)
.store_params
.get_or_insert_with(Default::default);
let value = if use_v2_manifest_paths {
"true".to_string()
} else {
"false".to_string()
};
merge_storage_options(
store_params,
[(OPT_NEW_TABLE_V2_MANIFEST_PATHS.to_string(), value)],
);
self
}
/// Set the data storage version.
///
/// The default is `LanceFileVersion::Stable`.
#[deprecated(since = "0.15.1", note = "Use `database_options` instead")]
pub fn data_storage_version(mut self, data_storage_version: LanceFileVersion) -> Self {
let store_params = self
.request
.write_options
.lance_write_params
.get_or_insert_with(Default::default)
.store_params
.get_or_insert_with(Default::default);
merge_storage_options(
store_params,
[(
OPT_NEW_TABLE_STORAGE_VERSION.to_string(),
data_storage_version.to_string(),
)],
);
self
}
/// Set the namespace for the table
pub fn namespace(mut self, namespace: Vec<String>) -> Self {
self.request.namespace = namespace;
self
}
/// Set a custom location for the table.
///
/// If not set, the database will derive a location from its URI and the table name.
/// This is useful when integrating with namespace systems that manage table locations.
pub fn location(mut self, location: impl Into<String>) -> Self {
self.request.location = Some(location.into());
self
}
/// Set a storage options provider for automatic credential refresh.
///
/// This allows tables to automatically refresh cloud storage credentials
/// when they expire, enabling long-running operations on remote storage.
pub fn storage_options_provider(mut self, provider: Arc<dyn StorageOptionsProvider>) -> Self {
let store_params = self
.request
.write_options
.lance_write_params
.get_or_insert(Default::default())
.store_params
.get_or_insert(Default::default());
set_storage_options_provider(store_params, provider);
self
}
}
#[derive(Clone, Debug)]
pub struct OpenTableBuilder {
parent: Arc<dyn Database>,
@@ -351,17 +684,35 @@ impl Connection {
///
/// * `name` - The name of the table
/// * `initial_data` - The initial data to write to the table
pub fn create_table<T: Scannable + 'static>(
pub fn create_table<T: IntoArrow>(
&self,
name: impl Into<String>,
initial_data: T,
) -> CreateTableBuilder {
let initial_data = Box::new(initial_data);
CreateTableBuilder::new(
) -> CreateTableBuilder<true> {
CreateTableBuilder::<true>::new(
self.internal.clone(),
self.embedding_registry.clone(),
name.into(),
initial_data,
self.embedding_registry.clone(),
)
}
/// Create a new table from a stream of data
///
/// # Parameters
///
/// * `name` - The name of the table
/// * `initial_data` - The initial data to write to the table
pub fn create_table_streaming<T: IntoArrowStream>(
&self,
name: impl Into<String>,
initial_data: T,
) -> CreateTableBuilder<true> {
CreateTableBuilder::<true>::new_streaming(
self.internal.clone(),
name.into(),
initial_data,
self.embedding_registry.clone(),
)
}
@@ -375,9 +726,13 @@ impl Connection {
&self,
name: impl Into<String>,
schema: SchemaRef,
) -> CreateTableBuilder {
let empty_batch = RecordBatch::new_empty(schema);
self.create_table(name, empty_batch)
) -> CreateTableBuilder<false> {
CreateTableBuilder::<false>::new(
self.internal.clone(),
name.into(),
schema,
self.embedding_registry.clone(),
)
}
/// Open an existing table in the database
@@ -994,11 +1349,20 @@ mod test_utils {
#[cfg(test)]
mod tests {
use crate::database::listing::{ListingDatabaseOptions, NewTableConfig};
use crate::query::QueryBase;
use crate::query::{ExecutableQuery, QueryExecutionOptions};
use crate::test_utils::connection::new_test_connection;
use arrow::compute::concat_batches;
use arrow_array::RecordBatchReader;
use arrow_schema::{DataType, Field, Schema};
use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
use futures::{stream, TryStreamExt};
use lance_core::error::{ArrowResult, DataFusionResult};
use lance_testing::datagen::{BatchGenerator, IncrementingInt32};
use tempfile::tempdir;
use crate::test_utils::connection::new_test_connection;
use crate::arrow::SimpleRecordBatchStream;
use super::*;
@@ -1114,6 +1478,139 @@ mod tests {
assert_eq!(tables, vec!["table1".to_owned()]);
}
fn make_data() -> Box<dyn RecordBatchReader + Send + 'static> {
let id = Box::new(IncrementingInt32::new().named("id".to_string()));
Box::new(BatchGenerator::new().col(id).batches(10, 2000))
}
#[tokio::test]
async fn test_create_table_v2() {
let tmp_dir = tempdir().unwrap();
let uri = tmp_dir.path().to_str().unwrap();
let db = connect(uri)
.database_options(&ListingDatabaseOptions {
new_table_config: NewTableConfig {
data_storage_version: Some(LanceFileVersion::Legacy),
..Default::default()
},
..Default::default()
})
.execute()
.await
.unwrap();
let tbl = db
.create_table("v1_test", make_data())
.execute()
.await
.unwrap();
// In v1 the row group size will trump max_batch_length
let batches = tbl
.query()
.limit(20000)
.execute_with_options(QueryExecutionOptions {
max_batch_length: 50000,
..Default::default()
})
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
assert_eq!(batches.len(), 20);
let db = connect(uri)
.database_options(&ListingDatabaseOptions {
new_table_config: NewTableConfig {
data_storage_version: Some(LanceFileVersion::Stable),
..Default::default()
},
..Default::default()
})
.execute()
.await
.unwrap();
let tbl = db
.create_table("v2_test", make_data())
.execute()
.await
.unwrap();
// In v2 the page size is much bigger than 50k so we should get a single batch
let batches = tbl
.query()
.execute_with_options(QueryExecutionOptions {
max_batch_length: 50000,
..Default::default()
})
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
assert_eq!(batches.len(), 1);
}
#[tokio::test]
async fn test_create_table_streaming() {
let tmp_dir = tempdir().unwrap();
let uri = tmp_dir.path().to_str().unwrap();
let db = connect(uri).execute().await.unwrap();
let batches = make_data().collect::<ArrowResult<Vec<_>>>().unwrap();
let schema = batches.first().unwrap().schema();
let one_batch = concat_batches(&schema, batches.iter()).unwrap();
let ldb_stream = stream::iter(batches.clone().into_iter().map(Result::Ok));
let ldb_stream: SendableRecordBatchStream =
Box::pin(SimpleRecordBatchStream::new(ldb_stream, schema.clone()));
let tbl1 = db
.create_table_streaming("one", ldb_stream)
.execute()
.await
.unwrap();
let df_stream = stream::iter(batches.into_iter().map(DataFusionResult::Ok));
let df_stream: datafusion_physical_plan::SendableRecordBatchStream =
Box::pin(RecordBatchStreamAdapter::new(schema.clone(), df_stream));
let tbl2 = db
.create_table_streaming("two", df_stream)
.execute()
.await
.unwrap();
let tbl1_data = tbl1
.query()
.execute()
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
let tbl1_data = concat_batches(&schema, tbl1_data.iter()).unwrap();
assert_eq!(tbl1_data, one_batch);
let tbl2_data = tbl2
.query()
.execute()
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
let tbl2_data = concat_batches(&schema, tbl2_data.iter()).unwrap();
assert_eq!(tbl2_data, one_batch);
}
#[tokio::test]
async fn drop_table() {
let tc = new_test_connection().await.unwrap();
@@ -1143,6 +1640,41 @@ mod tests {
assert_eq!(tables.len(), 0);
}
#[tokio::test]
async fn test_create_table_already_exists() {
let tmp_dir = tempdir().unwrap();
let uri = tmp_dir.path().to_str().unwrap();
let db = connect(uri).execute().await.unwrap();
let schema = Arc::new(Schema::new(vec![Field::new("x", DataType::Int32, false)]));
db.create_empty_table("test", schema.clone())
.execute()
.await
.unwrap();
// TODO: None of the open table options are "inspectable" right now but once one is we
// should assert we are passing these options in correctly
db.create_empty_table("test", schema)
.mode(CreateTableMode::exist_ok(|mut req| {
req.index_cache_size = Some(16);
req
}))
.execute()
.await
.unwrap();
let other_schema = Arc::new(Schema::new(vec![Field::new("y", DataType::Int32, false)]));
assert!(db
.create_empty_table("test", other_schema.clone())
.execute()
.await
.is_err());
let overwritten = db
.create_empty_table("test", other_schema.clone())
.mode(CreateTableMode::Overwrite)
.execute()
.await
.unwrap();
assert_eq!(other_schema, overwritten.schema().await.unwrap());
}
#[tokio::test]
async fn test_clone_table() {
let tmp_dir = tempdir().unwrap();
@@ -1153,8 +1685,7 @@ mod tests {
let mut batch_gen = BatchGenerator::new()
.col(Box::new(IncrementingInt32::new().named("id")))
.col(Box::new(IncrementingInt32::new().named("value")));
let reader: Box<dyn arrow_array::RecordBatchReader + Send> =
Box::new(batch_gen.batches(5, 100));
let reader = batch_gen.batches(5, 100);
let source_table = db
.create_table("source_table", reader)
@@ -1189,4 +1720,128 @@ mod tests {
let cloned_count = cloned_table.count_rows(None).await.unwrap();
assert_eq!(source_count, cloned_count);
}
#[tokio::test]
async fn test_create_empty_table_with_embeddings() {
use crate::embeddings::{EmbeddingDefinition, EmbeddingFunction};
use arrow_array::{
Array, FixedSizeListArray, Float32Array, RecordBatch, RecordBatchIterator, StringArray,
};
use std::borrow::Cow;
#[derive(Debug, Clone)]
struct MockEmbedding {
dim: usize,
}
impl EmbeddingFunction for MockEmbedding {
fn name(&self) -> &str {
"test_embedding"
}
fn source_type(&self) -> Result<Cow<'_, DataType>> {
Ok(Cow::Owned(DataType::Utf8))
}
fn dest_type(&self) -> Result<Cow<'_, DataType>> {
Ok(Cow::Owned(DataType::new_fixed_size_list(
DataType::Float32,
self.dim as i32,
true,
)))
}
fn compute_source_embeddings(&self, source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
let len = source.len();
let values = vec![1.0f32; len * self.dim];
let values = Arc::new(Float32Array::from(values));
let field = Arc::new(Field::new("item", DataType::Float32, true));
Ok(Arc::new(FixedSizeListArray::new(
field,
self.dim as i32,
values,
None,
)))
}
fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
unimplemented!()
}
}
let tmp_dir = tempdir().unwrap();
let uri = tmp_dir.path().to_str().unwrap();
let db = connect(uri).execute().await.unwrap();
let embed_func = Arc::new(MockEmbedding { dim: 128 });
db.embedding_registry()
.register("test_embedding", embed_func.clone())
.unwrap();
let schema = Arc::new(Schema::new(vec![Field::new("name", DataType::Utf8, true)]));
let ed = EmbeddingDefinition {
source_column: "name".to_owned(),
dest_column: Some("name_embedding".to_owned()),
embedding_name: "test_embedding".to_owned(),
};
let table = db
.create_empty_table("test", schema)
.mode(CreateTableMode::Overwrite)
.add_embedding(ed)
.unwrap()
.execute()
.await
.unwrap();
let table_schema = table.schema().await.unwrap();
assert!(table_schema.column_with_name("name").is_some());
assert!(table_schema.column_with_name("name_embedding").is_some());
let embedding_field = table_schema.field_with_name("name_embedding").unwrap();
assert_eq!(
embedding_field.data_type(),
&DataType::new_fixed_size_list(DataType::Float32, 128, true)
);
let input_schema = Arc::new(Schema::new(vec![Field::new("name", DataType::Utf8, true)]));
let input_batch = RecordBatch::try_new(
input_schema.clone(),
vec![Arc::new(StringArray::from(vec![
Some("Alice"),
Some("Bob"),
Some("Charlie"),
]))],
)
.unwrap();
let input_reader = Box::new(RecordBatchIterator::new(
vec![Ok(input_batch)].into_iter(),
input_schema,
));
table.add(input_reader).execute().await.unwrap();
let results = table
.query()
.execute()
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
assert_eq!(results.len(), 1);
let batch = &results[0];
assert_eq!(batch.num_rows(), 3);
assert!(batch.column_by_name("name_embedding").is_some());
let embedding_col = batch
.column_by_name("name_embedding")
.unwrap()
.as_any()
.downcast_ref::<FixedSizeListArray>()
.unwrap();
assert_eq!(embedding_col.len(), 3);
}
}

View File

@@ -1,612 +0,0 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
use std::sync::Arc;
use lance_io::object_store::StorageOptionsProvider;
use crate::{
connection::{merge_storage_options, set_storage_options_provider},
data::scannable::{Scannable, WithEmbeddingsScannable},
database::{CreateTableMode, CreateTableRequest, Database},
embeddings::{EmbeddingDefinition, EmbeddingFunction, EmbeddingRegistry},
table::WriteOptions,
Error, Result, Table,
};
pub struct CreateTableBuilder {
parent: Arc<dyn Database>,
embeddings: Vec<(EmbeddingDefinition, Arc<dyn EmbeddingFunction>)>,
embedding_registry: Arc<dyn EmbeddingRegistry>,
request: CreateTableRequest,
}
impl CreateTableBuilder {
pub(super) fn new(
parent: Arc<dyn Database>,
embedding_registry: Arc<dyn EmbeddingRegistry>,
name: String,
data: Box<dyn Scannable>,
) -> Self {
Self {
parent,
embeddings: Vec::new(),
embedding_registry,
request: CreateTableRequest::new(name, data),
}
}
/// Set the mode for creating the table
///
/// This controls what happens if a table with the given name already exists
pub fn mode(mut self, mode: CreateTableMode) -> Self {
self.request.mode = mode;
self
}
/// Apply the given write options when writing the initial data
pub fn write_options(mut self, write_options: WriteOptions) -> Self {
self.request.write_options = write_options;
self
}
/// Set an option for the storage layer.
///
/// Options already set on the connection will be inherited by the table,
/// but can be overridden here.
///
/// See available options at <https://lancedb.com/docs/storage/>
pub fn storage_option(mut self, key: impl Into<String>, value: impl Into<String>) -> Self {
let store_params = self
.request
.write_options
.lance_write_params
.get_or_insert(Default::default())
.store_params
.get_or_insert(Default::default());
merge_storage_options(store_params, [(key.into(), value.into())]);
self
}
/// Set multiple options for the storage layer.
///
/// Options already set on the connection will be inherited by the table,
/// but can be overridden here.
///
/// See available options at <https://lancedb.com/docs/storage/>
pub fn storage_options(
mut self,
pairs: impl IntoIterator<Item = (impl Into<String>, impl Into<String>)>,
) -> Self {
let store_params = self
.request
.write_options
.lance_write_params
.get_or_insert(Default::default())
.store_params
.get_or_insert(Default::default());
let updates = pairs
.into_iter()
.map(|(key, value)| (key.into(), value.into()));
merge_storage_options(store_params, updates);
self
}
/// Add an embedding definition to the table.
///
/// The `embedding_name` must match the name of an embedding function that
/// was previously registered with the connection's [`EmbeddingRegistry`].
pub fn add_embedding(mut self, definition: EmbeddingDefinition) -> Result<Self> {
// Early verification of the embedding name
let embedding_func = self
.embedding_registry
.get(&definition.embedding_name)
.ok_or_else(|| Error::EmbeddingFunctionNotFound {
name: definition.embedding_name.clone(),
reason: "No embedding function found in the connection's embedding_registry"
.to_string(),
})?;
self.embeddings.push((definition, embedding_func));
Ok(self)
}
/// Set the namespace for the table
pub fn namespace(mut self, namespace: Vec<String>) -> Self {
self.request.namespace = namespace;
self
}
/// Set a custom location for the table.
///
/// If not set, the database will derive a location from its URI and the table name.
/// This is useful when integrating with namespace systems that manage table locations.
pub fn location(mut self, location: impl Into<String>) -> Self {
self.request.location = Some(location.into());
self
}
/// Set a storage options provider for automatic credential refresh.
///
/// This allows tables to automatically refresh cloud storage credentials
/// when they expire, enabling long-running operations on remote storage.
pub fn storage_options_provider(mut self, provider: Arc<dyn StorageOptionsProvider>) -> Self {
let store_params = self
.request
.write_options
.lance_write_params
.get_or_insert(Default::default())
.store_params
.get_or_insert(Default::default());
set_storage_options_provider(store_params, provider);
self
}
/// Execute the create table operation
pub async fn execute(mut self) -> Result<Table> {
let embedding_registry = self.embedding_registry.clone();
let parent = self.parent.clone();
// If embeddings were configured via add_embedding(), wrap the data
if !self.embeddings.is_empty() {
let wrapped_data: Box<dyn Scannable> = Box::new(WithEmbeddingsScannable::try_new(
self.request.data,
self.embeddings,
)?);
self.request.data = wrapped_data;
}
Ok(Table::new_with_embedding_registry(
parent.create_table(self.request).await?,
parent,
embedding_registry,
))
}
}
#[cfg(test)]
mod tests {
use arrow_array::{
record_batch, Array, FixedSizeListArray, Float32Array, RecordBatch, RecordBatchIterator,
};
use arrow_schema::{ArrowError, DataType, Field, Schema};
use futures::TryStreamExt;
use lance_file::version::LanceFileVersion;
use tempfile::tempdir;
use crate::{
arrow::{SendableRecordBatchStream, SimpleRecordBatchStream},
connect,
database::listing::{ListingDatabaseOptions, NewTableConfig},
embeddings::{EmbeddingDefinition, EmbeddingFunction, MemoryRegistry},
query::{ExecutableQuery, QueryBase, Select},
test_utils::embeddings::MockEmbed,
};
use std::borrow::Cow;
use super::*;
#[tokio::test]
async fn create_empty_table() {
let db = connect("memory://").execute().await.unwrap();
let schema = Arc::new(Schema::new(vec![
Field::new("id", DataType::Int64, false),
Field::new("value", DataType::Float64, false),
]));
db.create_empty_table("name", schema.clone())
.execute()
.await
.unwrap();
let table = db.open_table("name").execute().await.unwrap();
assert_eq!(table.schema().await.unwrap(), schema);
assert_eq!(table.count_rows(None).await.unwrap(), 0);
}
async fn test_create_table_with_data<T>(data: T)
where
T: Scannable + 'static,
{
let db = connect("memory://").execute().await.unwrap();
let schema = data.schema();
db.create_table("data_table", data).execute().await.unwrap();
let table = db.open_table("data_table").execute().await.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 3);
assert_eq!(table.schema().await.unwrap(), schema);
}
#[tokio::test]
async fn create_table_with_batch() {
let batch = record_batch!(("id", Int64, [1, 2, 3])).unwrap();
test_create_table_with_data(batch).await;
}
#[tokio::test]
async fn test_create_table_with_vec_batch() {
let data = vec![
record_batch!(("id", Int64, [1, 2])).unwrap(),
record_batch!(("id", Int64, [3])).unwrap(),
];
test_create_table_with_data(data).await;
}
#[tokio::test]
async fn test_create_table_with_record_batch_reader() {
let data = vec![
record_batch!(("id", Int64, [1, 2])).unwrap(),
record_batch!(("id", Int64, [3])).unwrap(),
];
let schema = data[0].schema();
let reader: Box<dyn arrow_array::RecordBatchReader + Send> = Box::new(
RecordBatchIterator::new(data.into_iter().map(Ok), schema.clone()),
);
test_create_table_with_data(reader).await;
}
#[tokio::test]
async fn test_create_table_with_stream() {
let data = vec![
record_batch!(("id", Int64, [1, 2])).unwrap(),
record_batch!(("id", Int64, [3])).unwrap(),
];
let schema = data[0].schema();
let inner = futures::stream::iter(data.into_iter().map(Ok));
let stream: SendableRecordBatchStream = Box::pin(SimpleRecordBatchStream {
schema,
stream: inner,
});
test_create_table_with_data(stream).await;
}
#[derive(Debug)]
struct MyError;
impl std::fmt::Display for MyError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "MyError occurred")
}
}
impl std::error::Error for MyError {}
#[tokio::test]
async fn test_create_preserves_reader_error() {
let first_batch = record_batch!(("id", Int64, [1, 2])).unwrap();
let schema = first_batch.schema();
let iterator = vec![
Ok(first_batch),
Err(ArrowError::ExternalError(Box::new(MyError))),
];
let reader: Box<dyn arrow_array::RecordBatchReader + Send> = Box::new(
RecordBatchIterator::new(iterator.into_iter(), schema.clone()),
);
let db = connect("memory://").execute().await.unwrap();
let result = db.create_table("failing_table", reader).execute().await;
assert!(result.is_err());
// TODO: when we upgrade to Lance 2.0.0, this should pass
// assert!(matches!(result, Err(Error::External { source})
// if source.downcast_ref::<MyError>().is_some()
// ));
}
#[tokio::test]
async fn test_create_preserves_stream_error() {
let first_batch = record_batch!(("id", Int64, [1, 2])).unwrap();
let schema = first_batch.schema();
let iterator = vec![
Ok(first_batch),
Err(Error::External {
source: Box::new(MyError),
}),
];
let stream = futures::stream::iter(iterator);
let stream: SendableRecordBatchStream = Box::pin(SimpleRecordBatchStream {
schema: schema.clone(),
stream,
});
let db = connect("memory://").execute().await.unwrap();
let result = db
.create_table("failing_stream_table", stream)
.execute()
.await;
assert!(result.is_err());
// TODO: when we upgrade to Lance 2.0.0, this should pass
// assert!(matches!(result, Err(Error::External { source})
// if source.downcast_ref::<MyError>().is_some()
// ));
}
#[tokio::test]
#[allow(deprecated)]
async fn test_create_table_with_storage_options() {
let batch = record_batch!(("id", Int64, [1, 2, 3])).unwrap();
let db = connect("memory://").execute().await.unwrap();
let table = db
.create_table("options_table", batch)
.storage_option("timeout", "30s")
.storage_options([("retry_count", "3")])
.execute()
.await
.unwrap();
let final_options = table.storage_options().await.unwrap();
assert_eq!(final_options.get("timeout"), Some(&"30s".to_string()));
assert_eq!(final_options.get("retry_count"), Some(&"3".to_string()));
}
#[tokio::test]
async fn test_create_table_unregistered_embedding() {
let db = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("text", Utf8, ["hello", "world"])).unwrap();
// Try to add an embedding that doesn't exist in the registry
let result = db
.create_table("embed_table", batch)
.add_embedding(EmbeddingDefinition::new(
"text",
"nonexistent_embedding_function",
None::<&str>,
));
match result {
Err(Error::EmbeddingFunctionNotFound { name, .. }) => {
assert_eq!(name, "nonexistent_embedding_function");
}
Err(other) => panic!("Expected EmbeddingFunctionNotFound error, got: {:?}", other),
Ok(_) => panic!("Expected error, but got Ok"),
}
}
#[tokio::test]
async fn test_create_table_already_exists() {
let tmp_dir = tempdir().unwrap();
let uri = tmp_dir.path().to_str().unwrap();
let db = connect(uri).execute().await.unwrap();
let schema = Arc::new(Schema::new(vec![Field::new("x", DataType::Int32, false)]));
db.create_empty_table("test", schema.clone())
.execute()
.await
.unwrap();
db.create_empty_table("test", schema)
.mode(CreateTableMode::exist_ok(|mut req| {
req.index_cache_size = Some(16);
req
}))
.execute()
.await
.unwrap();
let other_schema = Arc::new(Schema::new(vec![Field::new("y", DataType::Int32, false)]));
assert!(db
.create_empty_table("test", other_schema.clone())
.execute()
.await
.is_err()); // TODO: assert what this error is
let overwritten = db
.create_empty_table("test", other_schema.clone())
.mode(CreateTableMode::Overwrite)
.execute()
.await
.unwrap();
assert_eq!(other_schema, overwritten.schema().await.unwrap());
}
#[tokio::test]
#[rstest::rstest]
#[case(LanceFileVersion::Legacy)]
#[case(LanceFileVersion::Stable)]
async fn test_create_table_with_storage_version(
#[case] data_storage_version: LanceFileVersion,
) {
let db = connect("memory://")
.database_options(&ListingDatabaseOptions {
new_table_config: NewTableConfig {
data_storage_version: Some(data_storage_version),
..Default::default()
},
..Default::default()
})
.execute()
.await
.unwrap();
let batch = record_batch!(("id", Int64, [1, 2, 3])).unwrap();
let table = db
.create_table("legacy_table", batch)
.execute()
.await
.unwrap();
let native_table = table.as_native().unwrap();
let storage_format = native_table
.manifest()
.await
.unwrap()
.data_storage_format
.lance_file_version()
.unwrap();
// Compare resolved versions since Stable/Next are aliases that resolve at storage time
assert_eq!(storage_format.resolve(), data_storage_version.resolve());
}
#[tokio::test]
async fn test_create_table_with_embedding() {
// Register the mock embedding function
let registry = Arc::new(MemoryRegistry::new());
let mock_embedding: Arc<dyn EmbeddingFunction> = Arc::new(MockEmbed::new("mock", 4));
registry.register("mock", mock_embedding).unwrap();
// Connect with the custom registry
let conn = connect("memory://")
.embedding_registry(registry)
.execute()
.await
.unwrap();
// Create data without the embedding column
let batch = record_batch!(("text", Utf8, ["hello", "world", "test"])).unwrap();
// Create table with add_embedding - embeddings should be computed automatically
let table = conn
.create_table("embed_test", batch)
.add_embedding(EmbeddingDefinition::new(
"text",
"mock",
Some("text_embedding"),
))
.unwrap()
.execute()
.await
.unwrap();
// Verify row count
assert_eq!(table.count_rows(None).await.unwrap(), 3);
// Verify the schema includes the embedding column
let result_schema = table.schema().await.unwrap();
assert_eq!(result_schema.fields().len(), 2);
assert_eq!(result_schema.field(0).name(), "text");
assert_eq!(result_schema.field(1).name(), "text_embedding");
// Verify the embedding column has the correct type
assert!(matches!(
result_schema.field(1).data_type(),
DataType::FixedSizeList(_, 4)
));
// Query to verify the embeddings were computed
let results: Vec<RecordBatch> = table
.query()
.select(Select::columns(&["text", "text_embedding"]))
.execute()
.await
.unwrap()
.try_collect()
.await
.unwrap();
let total_rows: usize = results.iter().map(|b| b.num_rows()).sum();
assert_eq!(total_rows, 3);
// Check that all rows have embedding values (not null)
for batch in &results {
let embedding_col = batch.column(1);
assert_eq!(embedding_col.null_count(), 0);
assert_eq!(embedding_col.len(), batch.num_rows());
}
// Verify the schema metadata contains the column definitions
assert!(
result_schema
.metadata
.contains_key("lancedb::column_definitions"),
"Schema metadata should contain column definitions"
);
}
#[tokio::test]
async fn test_create_empty_table_with_embeddings() {
#[derive(Debug, Clone)]
struct MockEmbedding {
dim: usize,
}
impl EmbeddingFunction for MockEmbedding {
fn name(&self) -> &str {
"test_embedding"
}
fn source_type(&self) -> Result<Cow<'_, DataType>> {
Ok(Cow::Owned(DataType::Utf8))
}
fn dest_type(&self) -> Result<Cow<'_, DataType>> {
Ok(Cow::Owned(DataType::new_fixed_size_list(
DataType::Float32,
self.dim as i32,
true,
)))
}
fn compute_source_embeddings(&self, source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
let len = source.len();
let values = vec![1.0f32; len * self.dim];
let values = Arc::new(Float32Array::from(values));
let field = Arc::new(Field::new("item", DataType::Float32, true));
Ok(Arc::new(FixedSizeListArray::new(
field,
self.dim as i32,
values,
None,
)))
}
fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
unimplemented!()
}
}
let tmp_dir = tempdir().unwrap();
let uri = tmp_dir.path().to_str().unwrap();
let db = connect(uri).execute().await.unwrap();
let embed_func = Arc::new(MockEmbedding { dim: 128 });
db.embedding_registry()
.register("test_embedding", embed_func.clone())
.unwrap();
let schema = Arc::new(Schema::new(vec![Field::new("name", DataType::Utf8, true)]));
let ed = EmbeddingDefinition {
source_column: "name".to_owned(),
dest_column: Some("name_embedding".to_owned()),
embedding_name: "test_embedding".to_owned(),
};
let table = db
.create_empty_table("test", schema)
.mode(CreateTableMode::Overwrite)
.add_embedding(ed)
.unwrap()
.execute()
.await
.unwrap();
let table_schema = table.schema().await.unwrap();
assert!(table_schema.column_with_name("name").is_some());
assert!(table_schema.column_with_name("name_embedding").is_some());
let embedding_field = table_schema.field_with_name("name_embedding").unwrap();
assert_eq!(
embedding_field.data_type(),
&DataType::new_fixed_size_list(DataType::Float32, 128, true)
);
let input_batch = record_batch!(("name", Utf8, ["Alice", "Bob", "Charlie"])).unwrap();
table.add(input_batch).execute().await.unwrap();
let results = table
.query()
.execute()
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
assert_eq!(results.len(), 1);
let batch = &results[0];
assert_eq!(batch.num_rows(), 3);
assert!(batch.column_by_name("name_embedding").is_some());
let embedding_col = batch
.column_by_name("name_embedding")
.unwrap()
.as_any()
.downcast_ref::<FixedSizeListArray>()
.unwrap();
assert_eq!(embedding_col.len(), 3);
}
}

View File

@@ -5,4 +5,3 @@
pub mod inspect;
pub mod sanitize;
pub mod scannable;

View File

@@ -1,580 +0,0 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
//! Data source abstraction for LanceDB.
//!
//! This module provides a [`Scannable`] trait that allows input data sources to express
//! capabilities (row count, rescannability) so the insert pipeline can make
//! better decisions about write parallelism and retry strategies.
use std::sync::Arc;
use arrow_array::{RecordBatch, RecordBatchIterator, RecordBatchReader};
use arrow_schema::{ArrowError, SchemaRef};
use async_trait::async_trait;
use futures::stream::once;
use futures::StreamExt;
use lance_datafusion::utils::StreamingWriteSource;
use crate::arrow::{
SendableRecordBatchStream, SendableRecordBatchStreamExt, SimpleRecordBatchStream,
};
use crate::embeddings::{
compute_embeddings_for_batch, compute_output_schema, EmbeddingDefinition, EmbeddingFunction,
EmbeddingRegistry,
};
use crate::table::{ColumnDefinition, ColumnKind, TableDefinition};
use crate::{Error, Result};
pub trait Scannable: Send {
/// Returns the schema of the data.
fn schema(&self) -> SchemaRef;
/// Read data as a stream of record batches.
///
/// For rescannable sources (in-memory data like RecordBatch, Vec<RecordBatch>),
/// this can be called multiple times and returns cloned data each time.
///
/// For non-rescannable sources (streams, readers), this can only be called once.
/// Calling it a second time returns a stream whose first item is an error.
fn scan_as_stream(&mut self) -> SendableRecordBatchStream;
/// Optional hint about the number of rows.
///
/// When available, this allows the pipeline to estimate total data size
/// and choose appropriate partitioning.
fn num_rows(&self) -> Option<usize> {
None
}
/// Whether the source can be re-read from the beginning.
///
/// `true` for in-memory data (Tables, DataFrames) and disk-based sources (Datasets).
/// `false` for streaming sources (DuckDB results, network streams).
///
/// When true, the pipeline can retry failed writes by rescanning.
fn rescannable(&self) -> bool {
false
}
}
impl std::fmt::Debug for dyn Scannable {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("Scannable")
.field("schema", &self.schema())
.field("num_rows", &self.num_rows())
.field("rescannable", &self.rescannable())
.finish()
}
}
impl Scannable for RecordBatch {
fn schema(&self) -> SchemaRef {
Self::schema(self)
}
fn scan_as_stream(&mut self) -> SendableRecordBatchStream {
let batch = self.clone();
let schema = batch.schema();
Box::pin(SimpleRecordBatchStream {
schema,
stream: once(async move { Ok(batch) }),
})
}
fn num_rows(&self) -> Option<usize> {
Some(Self::num_rows(self))
}
fn rescannable(&self) -> bool {
true
}
}
impl Scannable for Vec<RecordBatch> {
fn schema(&self) -> SchemaRef {
if self.is_empty() {
Arc::new(arrow_schema::Schema::empty())
} else {
self[0].schema()
}
}
fn scan_as_stream(&mut self) -> SendableRecordBatchStream {
if self.is_empty() {
let schema = Scannable::schema(self);
return Box::pin(SimpleRecordBatchStream {
schema,
stream: once(async {
Err(Error::InvalidInput {
message: "Cannot scan an empty Vec<RecordBatch>".to_string(),
})
}),
});
}
let schema = Scannable::schema(self);
let batches = self.clone();
let stream = futures::stream::iter(batches.into_iter().map(Ok));
Box::pin(SimpleRecordBatchStream { schema, stream })
}
fn num_rows(&self) -> Option<usize> {
Some(self.iter().map(|b| b.num_rows()).sum())
}
fn rescannable(&self) -> bool {
true
}
}
impl Scannable for Box<dyn RecordBatchReader + Send> {
fn schema(&self) -> SchemaRef {
RecordBatchReader::schema(self.as_ref())
}
fn scan_as_stream(&mut self) -> SendableRecordBatchStream {
let schema = Scannable::schema(self);
// Swap self with a reader that errors on iteration, so a second call
// produces a clear error instead of silently returning empty data.
let err_reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(
vec![Err(ArrowError::InvalidArgumentError(
"Reader has already been consumed".into(),
))],
schema.clone(),
));
let reader = std::mem::replace(self, err_reader);
// Bridge the blocking RecordBatchReader to an async stream via a channel.
let (tx, rx) = tokio::sync::mpsc::channel::<crate::Result<RecordBatch>>(2);
tokio::task::spawn_blocking(move || {
for batch_result in reader {
let result = batch_result.map_err(Into::into);
if tx.blocking_send(result).is_err() {
break;
}
}
});
let stream = futures::stream::unfold(rx, |mut rx| async move {
rx.recv().await.map(|batch| (batch, rx))
})
.fuse();
Box::pin(SimpleRecordBatchStream { schema, stream })
}
}
impl Scannable for SendableRecordBatchStream {
fn schema(&self) -> SchemaRef {
self.as_ref().schema()
}
fn scan_as_stream(&mut self) -> SendableRecordBatchStream {
let schema = Scannable::schema(self);
// Swap self with an error stream so a second call produces a clear error.
let error_stream = Box::pin(SimpleRecordBatchStream {
schema: schema.clone(),
stream: once(async {
Err(Error::InvalidInput {
message: "Stream has already been consumed".to_string(),
})
}),
});
std::mem::replace(self, error_stream)
}
}
#[async_trait]
impl StreamingWriteSource for Box<dyn Scannable> {
fn arrow_schema(&self) -> SchemaRef {
self.schema()
}
fn into_stream(mut self) -> datafusion_physical_plan::SendableRecordBatchStream {
self.scan_as_stream().into_df_stream()
}
}
/// A scannable that applies embeddings to the stream.
pub struct WithEmbeddingsScannable {
inner: Box<dyn Scannable>,
embeddings: Vec<(EmbeddingDefinition, Arc<dyn EmbeddingFunction>)>,
output_schema: SchemaRef,
}
impl WithEmbeddingsScannable {
/// Create a new WithEmbeddingsScannable.
///
/// The embeddings are applied to the inner scannable's data as new columns.
pub fn try_new(
inner: Box<dyn Scannable>,
embeddings: Vec<(EmbeddingDefinition, Arc<dyn EmbeddingFunction>)>,
) -> Result<Self> {
let output_schema = compute_output_schema(&inner.schema(), &embeddings)?;
// Build column definitions: Physical for base columns, Embedding for new ones
let base_col_count = inner.schema().fields().len();
let column_definitions: Vec<ColumnDefinition> = (0..base_col_count)
.map(|_| ColumnDefinition {
kind: ColumnKind::Physical,
})
.chain(embeddings.iter().map(|(ed, _)| ColumnDefinition {
kind: ColumnKind::Embedding(ed.clone()),
}))
.collect();
let table_definition = TableDefinition::new(output_schema, column_definitions);
let output_schema = table_definition.into_rich_schema();
Ok(Self {
inner,
embeddings,
output_schema,
})
}
}
impl Scannable for WithEmbeddingsScannable {
fn schema(&self) -> SchemaRef {
self.output_schema.clone()
}
fn scan_as_stream(&mut self) -> SendableRecordBatchStream {
let inner_stream = self.inner.scan_as_stream();
let embeddings = self.embeddings.clone();
let output_schema = self.output_schema.clone();
let mapped_stream = inner_stream.then(move |batch_result| {
let embeddings = embeddings.clone();
async move {
let batch = batch_result?;
let result = tokio::task::spawn_blocking(move || {
compute_embeddings_for_batch(batch, &embeddings)
})
.await
.map_err(|e| Error::Runtime {
message: format!("Task panicked during embedding computation: {}", e),
})??;
Ok(result)
}
});
Box::pin(SimpleRecordBatchStream {
schema: output_schema,
stream: mapped_stream,
})
}
fn num_rows(&self) -> Option<usize> {
self.inner.num_rows()
}
fn rescannable(&self) -> bool {
self.inner.rescannable()
}
}
pub fn scannable_with_embeddings(
inner: Box<dyn Scannable>,
table_definition: &TableDefinition,
registry: Option<&Arc<dyn EmbeddingRegistry>>,
) -> Result<Box<dyn Scannable>> {
if let Some(registry) = registry {
let mut embeddings = Vec::with_capacity(table_definition.column_definitions.len());
for cd in table_definition.column_definitions.iter() {
if let ColumnKind::Embedding(embedding_def) = &cd.kind {
match registry.get(&embedding_def.embedding_name) {
Some(func) => {
embeddings.push((embedding_def.clone(), func));
}
None => {
return Err(Error::EmbeddingFunctionNotFound {
name: embedding_def.embedding_name.clone(),
reason: format!(
"Table was defined with an embedding column `{}` but no embedding function was found with that name within the registry.",
embedding_def.embedding_name
),
});
}
}
}
}
if !embeddings.is_empty() {
return Ok(Box::new(WithEmbeddingsScannable::try_new(
inner, embeddings,
)?));
}
}
Ok(inner)
}
#[cfg(test)]
mod tests {
use super::*;
use arrow_array::record_batch;
use futures::TryStreamExt;
#[tokio::test]
async fn test_record_batch_rescannable() {
let mut batch = record_batch!(("id", Int64, [0, 1, 2])).unwrap();
let stream1 = batch.scan_as_stream();
let batches1: Vec<RecordBatch> = stream1.try_collect().await.unwrap();
assert_eq!(batches1.len(), 1);
assert_eq!(batches1[0], batch);
assert!(batch.rescannable());
let stream2 = batch.scan_as_stream();
let batches2: Vec<RecordBatch> = stream2.try_collect().await.unwrap();
assert_eq!(batches2.len(), 1);
assert_eq!(batches2[0], batch);
}
#[tokio::test]
async fn test_vec_batch_rescannable() {
let mut batches = vec![
record_batch!(("id", Int64, [0, 1])).unwrap(),
record_batch!(("id", Int64, [2, 3, 4])).unwrap(),
];
let stream1 = batches.scan_as_stream();
let result1: Vec<RecordBatch> = stream1.try_collect().await.unwrap();
assert_eq!(result1.len(), 2);
assert_eq!(result1[0], batches[0]);
assert_eq!(result1[1], batches[1]);
assert!(batches.rescannable());
let stream2 = batches.scan_as_stream();
let result2: Vec<RecordBatch> = stream2.try_collect().await.unwrap();
assert_eq!(result2.len(), 2);
assert_eq!(result2[0], batches[0]);
assert_eq!(result2[1], batches[1]);
}
#[tokio::test]
async fn test_vec_batch_empty_errors() {
let mut empty: Vec<RecordBatch> = vec![];
let mut stream = empty.scan_as_stream();
let result = stream.next().await;
assert!(result.is_some());
assert!(result.unwrap().is_err());
}
#[tokio::test]
async fn test_reader_not_rescannable() {
let batch = record_batch!(("id", Int64, [0, 1, 2])).unwrap();
let schema = batch.schema();
let mut reader: Box<dyn arrow_array::RecordBatchReader + Send> = Box::new(
RecordBatchIterator::new(vec![Ok(batch.clone())], schema.clone()),
);
let stream1 = reader.scan_as_stream();
let result1: Vec<RecordBatch> = stream1.try_collect().await.unwrap();
assert_eq!(result1.len(), 1);
assert_eq!(result1[0], batch);
assert!(!reader.rescannable());
// Second call returns a stream whose first item is an error
let mut stream2 = reader.scan_as_stream();
let result2 = stream2.next().await;
assert!(result2.is_some());
assert!(result2.unwrap().is_err());
}
#[tokio::test]
async fn test_stream_not_rescannable() {
let batch = record_batch!(("id", Int64, [0, 1, 2])).unwrap();
let schema = batch.schema();
let inner_stream = futures::stream::iter(vec![Ok(batch.clone())]);
let mut stream: SendableRecordBatchStream = Box::pin(SimpleRecordBatchStream {
schema: schema.clone(),
stream: inner_stream,
});
let stream1 = stream.scan_as_stream();
let result1: Vec<RecordBatch> = stream1.try_collect().await.unwrap();
assert_eq!(result1.len(), 1);
assert_eq!(result1[0], batch);
assert!(!stream.rescannable());
// Second call returns a stream whose first item is an error
let mut stream2 = stream.scan_as_stream();
let result2 = stream2.next().await;
assert!(result2.is_some());
assert!(result2.unwrap().is_err());
}
mod embedding_tests {
use super::*;
use crate::embeddings::MemoryRegistry;
use crate::table::{ColumnDefinition, ColumnKind};
use crate::test_utils::embeddings::MockEmbed;
use arrow_array::Array as _;
use arrow_array::{ArrayRef, StringArray};
use arrow_schema::{DataType, Field, Schema};
#[tokio::test]
async fn test_with_embeddings_scannable() {
let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)]));
let text_array = StringArray::from(vec!["hello", "world", "test"]);
let batch =
RecordBatch::try_new(schema.clone(), vec![Arc::new(text_array) as ArrayRef])
.unwrap();
let mock_embedding: Arc<dyn EmbeddingFunction> = Arc::new(MockEmbed::new("mock", 4));
let embedding_def = EmbeddingDefinition::new("text", "mock", Some("text_embedding"));
let mut scannable = WithEmbeddingsScannable::try_new(
Box::new(batch.clone()),
vec![(embedding_def, mock_embedding)],
)
.unwrap();
// Check that schema has the embedding column
let output_schema = scannable.schema();
assert_eq!(output_schema.fields().len(), 2);
assert_eq!(output_schema.field(0).name(), "text");
assert_eq!(output_schema.field(1).name(), "text_embedding");
// Check num_rows and rescannable are preserved
assert_eq!(scannable.num_rows(), Some(3));
assert!(scannable.rescannable());
// Read the data
let stream = scannable.scan_as_stream();
let results: Vec<RecordBatch> = stream.try_collect().await.unwrap();
assert_eq!(results.len(), 1);
let result_batch = &results[0];
assert_eq!(result_batch.num_rows(), 3);
assert_eq!(result_batch.num_columns(), 2);
// Verify the embedding column is present and has the right shape
let embedding_col = result_batch.column(1);
assert_eq!(embedding_col.len(), 3);
}
#[tokio::test]
async fn test_maybe_embedded_scannable_no_embeddings() {
let batch = record_batch!(("id", Int64, [1, 2, 3])).unwrap();
// Create a table definition with no embedding columns
let table_def = TableDefinition::new_from_schema(batch.schema());
// Even with a registry, if there are no embedding columns, it's a passthrough
let registry: Arc<dyn EmbeddingRegistry> = Arc::new(MemoryRegistry::new());
let mut scannable =
scannable_with_embeddings(Box::new(batch.clone()), &table_def, Some(&registry))
.unwrap();
// Check that data passes through unchanged
let stream = scannable.scan_as_stream();
let results: Vec<RecordBatch> = stream.try_collect().await.unwrap();
assert_eq!(results.len(), 1);
assert_eq!(results[0], batch);
}
#[tokio::test]
async fn test_maybe_embedded_scannable_with_embeddings() {
let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)]));
let text_array = StringArray::from(vec!["hello", "world"]);
let batch =
RecordBatch::try_new(schema.clone(), vec![Arc::new(text_array) as ArrayRef])
.unwrap();
// Create a table definition with an embedding column
let embedding_def = EmbeddingDefinition::new("text", "mock", Some("text_embedding"));
let embedding_schema = Arc::new(Schema::new(vec![
Field::new("text", DataType::Utf8, false),
Field::new(
"text_embedding",
DataType::FixedSizeList(
Arc::new(Field::new("item", DataType::Float32, true)),
4,
),
false,
),
]));
let table_def = TableDefinition::new(
embedding_schema,
vec![
ColumnDefinition {
kind: ColumnKind::Physical,
},
ColumnDefinition {
kind: ColumnKind::Embedding(embedding_def.clone()),
},
],
);
// Register the mock embedding function
let registry: Arc<dyn EmbeddingRegistry> = Arc::new(MemoryRegistry::new());
let mock_embedding: Arc<dyn EmbeddingFunction> = Arc::new(MockEmbed::new("mock", 4));
registry.register("mock", mock_embedding).unwrap();
let mut scannable =
scannable_with_embeddings(Box::new(batch), &table_def, Some(&registry)).unwrap();
// Read and verify the data has embeddings
let stream = scannable.scan_as_stream();
let results: Vec<RecordBatch> = stream.try_collect().await.unwrap();
assert_eq!(results.len(), 1);
let result_batch = &results[0];
assert_eq!(result_batch.num_columns(), 2);
assert_eq!(result_batch.schema().field(1).name(), "text_embedding");
}
#[tokio::test]
async fn test_maybe_embedded_scannable_missing_function() {
let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)]));
let text_array = StringArray::from(vec!["hello"]);
let batch =
RecordBatch::try_new(schema.clone(), vec![Arc::new(text_array) as ArrayRef])
.unwrap();
// Create a table definition with an embedding column
let embedding_def =
EmbeddingDefinition::new("text", "nonexistent", Some("text_embedding"));
let embedding_schema = Arc::new(Schema::new(vec![
Field::new("text", DataType::Utf8, false),
Field::new(
"text_embedding",
DataType::FixedSizeList(
Arc::new(Field::new("item", DataType::Float32, true)),
4,
),
false,
),
]));
let table_def = TableDefinition::new(
embedding_schema,
vec![
ColumnDefinition {
kind: ColumnKind::Physical,
},
ColumnDefinition {
kind: ColumnKind::Embedding(embedding_def),
},
],
);
// Registry has no embedding functions registered
let registry: Arc<dyn EmbeddingRegistry> = Arc::new(MemoryRegistry::new());
let result = scannable_with_embeddings(Box::new(batch), &table_def, Some(&registry));
// Should fail because the embedding function is not found
assert!(result.is_err());
let err = result.err().unwrap();
assert!(
matches!(err, Error::EmbeddingFunctionNotFound { .. }),
"Expected EmbeddingFunctionNotFound"
);
}
}
}

View File

@@ -18,7 +18,12 @@ use std::collections::HashMap;
use std::sync::Arc;
use std::time::Duration;
use arrow_array::RecordBatchReader;
use async_trait::async_trait;
use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
use futures::stream;
use lance::dataset::ReadParams;
use lance_datafusion::utils::StreamingWriteSource;
use lance_namespace::models::{
CreateNamespaceRequest, CreateNamespaceResponse, DescribeNamespaceRequest,
DescribeNamespaceResponse, DropNamespaceRequest, DropNamespaceResponse, ListNamespacesRequest,
@@ -26,9 +31,9 @@ use lance_namespace::models::{
};
use lance_namespace::LanceNamespace;
use crate::data::scannable::Scannable;
use crate::arrow::{SendableRecordBatchStream, SendableRecordBatchStreamExt};
use crate::error::Result;
use crate::table::{BaseTable, WriteOptions};
use crate::table::{BaseTable, TableDefinition, WriteOptions};
pub mod listing;
pub mod namespace;
@@ -110,14 +115,51 @@ impl Default for CreateTableMode {
}
}
/// The data to start a table or a schema to create an empty table
pub enum CreateTableData {
/// Creates a table using an iterator of data, the schema will be obtained from the data
Data(Box<dyn RecordBatchReader + Send>),
/// Creates a table using a stream of data, the schema will be obtained from the data
StreamingData(SendableRecordBatchStream),
/// Creates an empty table, the definition / schema must be provided separately
Empty(TableDefinition),
}
impl CreateTableData {
pub fn schema(&self) -> Arc<arrow_schema::Schema> {
match self {
Self::Data(reader) => reader.schema(),
Self::StreamingData(stream) => stream.schema(),
Self::Empty(definition) => definition.schema.clone(),
}
}
}
#[async_trait]
impl StreamingWriteSource for CreateTableData {
fn arrow_schema(&self) -> Arc<arrow_schema::Schema> {
self.schema()
}
fn into_stream(self) -> datafusion_physical_plan::SendableRecordBatchStream {
match self {
Self::Data(reader) => reader.into_stream(),
Self::StreamingData(stream) => stream.into_df_stream(),
Self::Empty(table_definition) => {
let schema = table_definition.schema.clone();
Box::pin(RecordBatchStreamAdapter::new(schema, stream::empty()))
}
}
}
}
/// A request to create a table
pub struct CreateTableRequest {
/// The name of the new table
pub name: String,
/// The namespace to create the table in. Empty list represents root namespace.
pub namespace: Vec<String>,
/// Initial data to write to the table, can be empty.
pub data: Box<dyn Scannable>,
/// Initial data to write to the table, can be None to create an empty table
pub data: CreateTableData,
/// The mode to use when creating the table
pub mode: CreateTableMode,
/// Options to use when writing data (only used if `data` is not None)
@@ -131,7 +173,7 @@ pub struct CreateTableRequest {
}
impl CreateTableRequest {
pub fn new(name: String, data: Box<dyn Scannable>) -> Self {
pub fn new(name: String, data: CreateTableData) -> Self {
Self {
name,
namespace: vec![],

View File

@@ -922,7 +922,7 @@ impl Database for ListingDatabase {
.with_read_params(read_params.clone())
.load()
.await
.map_err(|e| -> Error { e.into() })?;
.map_err(|e| Error::Lance { source: e })?;
let version_ref = match (request.source_version, request.source_tag) {
(Some(v), None) => Ok(Ref::Version(None, Some(v))),
@@ -937,7 +937,7 @@ impl Database for ListingDatabase {
source_dataset
.shallow_clone(&target_uri, version_ref, Some(storage_params))
.await
.map_err(|e| -> Error { e.into() })?;
.map_err(|e| Error::Lance { source: e })?;
let cloned_table = NativeTable::open_with_params(
&target_uri,
@@ -1098,10 +1098,8 @@ impl Database for ListingDatabase {
mod tests {
use super::*;
use crate::connection::ConnectRequest;
use crate::data::scannable::Scannable;
use crate::database::{CreateTableMode, CreateTableRequest};
use crate::table::WriteOptions;
use crate::Table;
use crate::database::{CreateTableData, CreateTableMode, CreateTableRequest, WriteOptions};
use crate::table::{Table, TableDefinition};
use arrow_array::{Int32Array, RecordBatch, StringArray};
use arrow_schema::{DataType, Field, Schema};
use std::path::PathBuf;
@@ -1141,7 +1139,7 @@ mod tests {
.create_table(CreateTableRequest {
name: "source_table".to_string(),
namespace: vec![],
data: Box::new(RecordBatch::new_empty(schema.clone())) as Box<dyn Scannable>,
data: CreateTableData::Empty(TableDefinition::new_from_schema(schema.clone())),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1198,11 +1196,16 @@ mod tests {
)
.unwrap();
let reader = Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch)],
schema.clone(),
));
let source_table = db
.create_table(CreateTableRequest {
name: "source_with_data".to_string(),
namespace: vec![],
data: Box::new(batch) as Box<dyn Scannable>,
data: CreateTableData::Data(reader),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1261,7 +1264,7 @@ mod tests {
db.create_table(CreateTableRequest {
name: "source".to_string(),
namespace: vec![],
data: Box::new(RecordBatch::new_empty(schema)) as Box<dyn Scannable>,
data: CreateTableData::Empty(TableDefinition::new_from_schema(schema)),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1297,7 +1300,7 @@ mod tests {
db.create_table(CreateTableRequest {
name: "source".to_string(),
namespace: vec![],
data: Box::new(RecordBatch::new_empty(schema)) as Box<dyn Scannable>,
data: CreateTableData::Empty(TableDefinition::new_from_schema(schema)),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1337,7 +1340,7 @@ mod tests {
db.create_table(CreateTableRequest {
name: "source".to_string(),
namespace: vec![],
data: Box::new(RecordBatch::new_empty(schema)) as Box<dyn Scannable>,
data: CreateTableData::Empty(TableDefinition::new_from_schema(schema)),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1377,7 +1380,7 @@ mod tests {
db.create_table(CreateTableRequest {
name: "source".to_string(),
namespace: vec![],
data: Box::new(RecordBatch::new_empty(schema)) as Box<dyn Scannable>,
data: CreateTableData::Empty(TableDefinition::new_from_schema(schema)),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1432,7 +1435,7 @@ mod tests {
db.create_table(CreateTableRequest {
name: "source".to_string(),
namespace: vec![],
data: Box::new(RecordBatch::new_empty(schema)) as Box<dyn Scannable>,
data: CreateTableData::Empty(TableDefinition::new_from_schema(schema)),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1481,11 +1484,16 @@ mod tests {
)
.unwrap();
let reader = Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch1)],
schema.clone(),
));
let source_table = db
.create_table(CreateTableRequest {
name: "versioned_source".to_string(),
namespace: vec![],
data: Box::new(batch1) as Box<dyn Scannable>,
data: CreateTableData::Data(reader),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1509,7 +1517,14 @@ mod tests {
let db = Arc::new(db);
let source_table_obj = Table::new(source_table.clone(), db.clone());
source_table_obj.add(batch2).execute().await.unwrap();
source_table_obj
.add(Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch2)],
schema.clone(),
)))
.execute()
.await
.unwrap();
// Verify source table now has 4 rows
assert_eq!(source_table.count_rows(None).await.unwrap(), 4);
@@ -1555,11 +1570,16 @@ mod tests {
)
.unwrap();
let reader = Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch1)],
schema.clone(),
));
let source_table = db
.create_table(CreateTableRequest {
name: "tagged_source".to_string(),
namespace: vec![],
data: Box::new(batch1),
data: CreateTableData::Data(reader),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1587,7 +1607,14 @@ mod tests {
.unwrap();
let source_table_obj = Table::new(source_table.clone(), db.clone());
source_table_obj.add(batch2).execute().await.unwrap();
source_table_obj
.add(Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch2)],
schema.clone(),
)))
.execute()
.await
.unwrap();
// Source table should have 4 rows
assert_eq!(source_table.count_rows(None).await.unwrap(), 4);
@@ -1630,11 +1657,16 @@ mod tests {
)
.unwrap();
let reader = Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch1)],
schema.clone(),
));
let source_table = db
.create_table(CreateTableRequest {
name: "independent_source".to_string(),
namespace: vec![],
data: Box::new(batch1),
data: CreateTableData::Data(reader),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1674,7 +1706,14 @@ mod tests {
let db = Arc::new(db);
let cloned_table_obj = Table::new(cloned_table.clone(), db.clone());
cloned_table_obj.add(batch_clone).execute().await.unwrap();
cloned_table_obj
.add(Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch_clone)],
schema.clone(),
)))
.execute()
.await
.unwrap();
// Add different data to the source table
let batch_source = RecordBatch::try_new(
@@ -1687,7 +1726,14 @@ mod tests {
.unwrap();
let source_table_obj = Table::new(source_table.clone(), db);
source_table_obj.add(batch_source).execute().await.unwrap();
source_table_obj
.add(Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch_source)],
schema.clone(),
)))
.execute()
.await
.unwrap();
// Verify they have evolved independently
assert_eq!(source_table.count_rows(None).await.unwrap(), 4); // 2 + 2
@@ -1705,11 +1751,16 @@ mod tests {
RecordBatch::try_new(schema.clone(), vec![Arc::new(Int32Array::from(vec![1, 2]))])
.unwrap();
let reader = Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch1)],
schema.clone(),
));
let source_table = db
.create_table(CreateTableRequest {
name: "latest_version_source".to_string(),
namespace: vec![],
data: Box::new(batch1),
data: CreateTableData::Data(reader),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1728,7 +1779,14 @@ mod tests {
.unwrap();
let source_table_obj = Table::new(source_table.clone(), db.clone());
source_table_obj.add(batch).execute().await.unwrap();
source_table_obj
.add(Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch)],
schema.clone(),
)))
.execute()
.await
.unwrap();
}
// Source should have 8 rows total (2 + 2 + 2 + 2)
@@ -1791,11 +1849,16 @@ mod tests {
)
.unwrap();
let reader = Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch)],
schema.clone(),
));
let table = db
.create_table(CreateTableRequest {
name: "test_stable".to_string(),
namespace: vec![],
data: Box::new(batch),
data: CreateTableData::Data(reader),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -1824,6 +1887,11 @@ mod tests {
)
.unwrap();
let reader = Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch)],
schema.clone(),
));
let mut storage_options = HashMap::new();
storage_options.insert(
OPT_NEW_TABLE_ENABLE_STABLE_ROW_IDS.to_string(),
@@ -1846,7 +1914,7 @@ mod tests {
.create_table(CreateTableRequest {
name: "test_stable_table_level".to_string(),
namespace: vec![],
data: Box::new(batch),
data: CreateTableData::Data(reader),
mode: CreateTableMode::Create,
write_options,
location: None,
@@ -1895,6 +1963,11 @@ mod tests {
)
.unwrap();
let reader = Box::new(arrow_array::RecordBatchIterator::new(
vec![Ok(batch)],
schema.clone(),
));
let mut storage_options = HashMap::new();
storage_options.insert(
OPT_NEW_TABLE_ENABLE_STABLE_ROW_IDS.to_string(),
@@ -1917,7 +1990,7 @@ mod tests {
.create_table(CreateTableRequest {
name: "test_override".to_string(),
namespace: vec![],
data: Box::new(batch),
data: CreateTableData::Data(reader),
mode: CreateTableMode::Create,
write_options,
location: None,
@@ -2035,7 +2108,7 @@ mod tests {
db.create_table(CreateTableRequest {
name: "table1".to_string(),
namespace: vec![],
data: Box::new(RecordBatch::new_empty(schema.clone())) as Box<dyn Scannable>,
data: CreateTableData::Empty(TableDefinition::new_from_schema(schema.clone())),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,
@@ -2047,7 +2120,7 @@ mod tests {
db.create_table(CreateTableRequest {
name: "table2".to_string(),
namespace: vec![],
data: Box::new(RecordBatch::new_empty(schema)) as Box<dyn Scannable>,
data: CreateTableData::Empty(TableDefinition::new_from_schema(schema)),
mode: CreateTableMode::Create,
write_options: Default::default(),
location: None,

View File

@@ -354,13 +354,15 @@ mod tests {
use super::*;
use crate::connect_namespace;
use crate::query::ExecutableQuery;
use arrow_array::{Int32Array, RecordBatch, StringArray};
use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator, StringArray};
use arrow_schema::{DataType, Field, Schema};
use futures::TryStreamExt;
use tempfile::tempdir;
/// Helper function to create test data
fn create_test_data() -> RecordBatch {
fn create_test_data() -> RecordBatchIterator<
std::vec::IntoIter<std::result::Result<RecordBatch, arrow_schema::ArrowError>>,
> {
let schema = Arc::new(Schema::new(vec![
Field::new("id", DataType::Int32, false),
Field::new("name", DataType::Utf8, false),
@@ -369,7 +371,12 @@ mod tests {
let id_array = Int32Array::from(vec![1, 2, 3, 4, 5]);
let name_array = StringArray::from(vec!["Alice", "Bob", "Charlie", "David", "Eve"]);
RecordBatch::try_new(schema, vec![Arc::new(id_array), Arc::new(name_array)]).unwrap()
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(id_array), Arc::new(name_array)],
)
.unwrap();
RecordBatchIterator::new(vec![std::result::Result::Ok(batch)].into_iter(), schema)
}
#[tokio::test]
@@ -611,7 +618,13 @@ mod tests {
// Test: Overwrite the table
let table2 = conn
.create_table("overwrite_test", test_data2)
.create_table(
"overwrite_test",
RecordBatchIterator::new(
vec![std::result::Result::Ok(test_data2)].into_iter(),
schema,
),
)
.namespace(vec!["test_ns".into()])
.mode(CreateTableMode::Overwrite)
.execute()

View File

@@ -13,7 +13,7 @@ use lance_datafusion::exec::SessionContextExt;
use crate::{
arrow::{SendableRecordBatchStream, SendableRecordBatchStreamExt, SimpleRecordBatchStream},
connect,
database::{CreateTableRequest, Database},
database::{CreateTableData, CreateTableRequest, Database},
dataloader::permutation::{
shuffle::{Shuffler, ShufflerConfig},
split::{SplitStrategy, Splitter, SPLIT_ID_COLUMN},
@@ -313,8 +313,10 @@ impl PermutationBuilder {
}
};
let create_table_request =
CreateTableRequest::new(name.to_string(), Box::new(streaming_data));
let create_table_request = CreateTableRequest::new(
name.to_string(),
CreateTableData::StreamingData(streaming_data),
);
let table = database.create_table(create_table_request).await?;
@@ -345,7 +347,7 @@ mod tests {
.col("col_b", lance_datagen::array::step::<Int32Type>())
.into_ldb_stream(RowCount::from(100), BatchCount::from(10));
let data_table = db
.create_table("base_tbl", initial_data)
.create_table_streaming("base_tbl", initial_data)
.execute()
.await
.unwrap();
@@ -385,7 +387,7 @@ mod tests {
.col("some_value", lance_datagen::array::step::<Int32Type>())
.into_ldb_stream(RowCount::from(100), BatchCount::from(10));
let data_table = db
.create_table("mytbl", initial_data)
.create_table_streaming("mytbl", initial_data)
.execute()
.await
.unwrap();

View File

@@ -39,9 +39,6 @@ pub struct PermutationReader {
limit: Option<u64>,
available_rows: u64,
split: u64,
// Cached map of offset to row id for the split
#[allow(clippy::type_complexity)]
offset_map: Arc<tokio::sync::Mutex<Option<Arc<HashMap<u64, u64>>>>>,
}
impl std::fmt::Debug for PermutationReader {
@@ -75,7 +72,6 @@ impl PermutationReader {
limit: None,
available_rows: 0,
split,
offset_map: Arc::new(tokio::sync::Mutex::new(None)),
};
slf.validate().await?;
// Calculate the number of available rows
@@ -161,7 +157,6 @@ impl PermutationReader {
let available_rows = self.verify_limit_offset(self.limit, Some(offset)).await?;
self.offset = Some(offset);
self.available_rows = available_rows;
self.offset_map = Arc::new(tokio::sync::Mutex::new(None));
Ok(self)
}
@@ -169,7 +164,6 @@ impl PermutationReader {
let available_rows = self.verify_limit_offset(Some(limit), self.offset).await?;
self.available_rows = available_rows;
self.limit = Some(limit);
self.offset_map = Arc::new(tokio::sync::Mutex::new(None));
Ok(self)
}
@@ -186,9 +180,8 @@ impl PermutationReader {
base_table: &Arc<dyn BaseTable>,
row_ids: RecordBatch,
selection: Select,
has_row_id: bool,
) -> Result<RecordBatch> {
let has_row_id = Self::has_row_id(&selection)?;
let num_rows = row_ids.num_rows();
let row_ids = row_ids
.column(0)
@@ -289,13 +282,14 @@ impl PermutationReader {
row_ids: DatasetRecordBatchStream,
selection: Select,
) -> Result<SendableRecordBatchStream> {
let has_row_id = Self::has_row_id(&selection)?;
let mut stream = row_ids
.map_err(Error::from)
.try_filter_map(move |batch| {
let selection = selection.clone();
let base_table = base_table.clone();
async move {
Self::load_batch(&base_table, batch, selection)
Self::load_batch(&base_table, batch, selection, has_row_id)
.await
.map(Some)
}
@@ -403,82 +397,6 @@ impl PermutationReader {
Self::row_ids_to_batches(self.base_table.clone(), row_ids, selection).await
}
/// If we are going to use `take` then we load the offset -> row id map once for the split and cache it
///
/// This method fetches the map with find-or-create semantics.
async fn get_offset_map(
&self,
permutation_table: &Arc<dyn BaseTable>,
) -> Result<Arc<HashMap<u64, u64>>> {
let mut offset_map_ref = self.offset_map.lock().await;
if let Some(offset_map) = &*offset_map_ref {
return Ok(offset_map.clone());
}
let mut offset_map = HashMap::new();
let mut row_ids_query = Table::from(permutation_table.clone())
.query()
.select(Select::Columns(vec![SRC_ROW_ID_COL.to_string()]))
.only_if(format!("{} = {}", SPLIT_ID_COLUMN, self.split));
if let Some(offset) = self.offset {
row_ids_query = row_ids_query.offset(offset as usize);
}
if let Some(limit) = self.limit {
row_ids_query = row_ids_query.limit(limit as usize);
}
let mut row_ids = row_ids_query.execute().await?;
while let Some(batch) = row_ids.try_next().await? {
let row_ids = batch
.column(0)
.as_primitive::<UInt64Type>()
.values()
.to_vec();
for (i, row_id) in row_ids.iter().enumerate() {
offset_map.insert(i as u64, *row_id);
}
}
let offset_map = Arc::new(offset_map);
*offset_map_ref = Some(offset_map.clone());
Ok(offset_map)
}
pub async fn take_offsets(&self, offsets: &[u64], selection: Select) -> Result<RecordBatch> {
if let Some(permutation_table) = &self.permutation_table {
let offset_map = self.get_offset_map(permutation_table).await?;
let row_ids = offsets
.iter()
.map(|o| offset_map.get(o).copied().expect_ok().map_err(Error::from))
.collect::<Result<Vec<_>>>()?;
let row_ids = RecordBatch::try_new(
Arc::new(arrow_schema::Schema::new(vec![arrow_schema::Field::new(
"row_id",
arrow_schema::DataType::UInt64,
false,
)])),
vec![Arc::new(UInt64Array::from(row_ids))],
)?;
Self::load_batch(&self.base_table, row_ids, selection).await
} else {
let table = Table::from(self.base_table.clone());
let batches = table
.take_offsets(offsets.to_vec())
.select(selection.clone())
.execute()
.await?
.try_collect::<Vec<_>>()
.await?;
if let Some(first_batch) = batches.first() {
let schema = first_batch.schema();
let batch = arrow::compute::concat_batches(&schema, &batches)?;
Ok(batch)
} else {
Ok(RecordBatch::try_new(
self.output_schema(selection).await?,
vec![],
)?)
}
}
}
pub async fn output_schema(&self, selection: Select) -> Result<SchemaRef> {
let table = Table::from(self.base_table.clone());
table.query().select(selection).output_schema().await
@@ -625,224 +543,4 @@ mod tests {
check_batch(&mut stream, &row_ids[7..9]).await;
assert!(stream.try_next().await.unwrap().is_none());
}
/// Helper to create a base table and permutation table for take_offsets tests.
/// Returns (base_table, row_ids_table, shuffled_row_ids).
async fn setup_permutation_tables(num_rows: usize) -> (Table, Table, Vec<u64>) {
let base_table = lance_datagen::gen_batch()
.col("idx", lance_datagen::array::step::<Int32Type>())
.col("other_col", lance_datagen::array::step::<UInt64Type>())
.into_mem_table("tbl", RowCount::from(num_rows as u64), BatchCount::from(1))
.await;
let mut row_ids = collect_column::<UInt64Type>(&base_table, "_rowid").await;
row_ids.shuffle(&mut rand::rng());
let split_ids = UInt64Array::from_iter_values(std::iter::repeat_n(0u64, row_ids.len()));
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(row_ids.clone())),
Arc::new(split_ids),
],
)
.unwrap();
let row_ids_table = virtual_table("row_ids", &permutation_batch).await;
(base_table, row_ids_table, row_ids)
}
#[tokio::test]
async fn test_take_offsets_with_permutation_table() {
let (base_table, row_ids_table, row_ids) = setup_permutation_tables(10).await;
let reader = PermutationReader::try_from_tables(
base_table.base_table().clone(),
row_ids_table.base_table().clone(),
0,
)
.await
.unwrap();
// Take specific offsets and verify the returned rows match the permutation order
let offsets = vec![0, 2, 4];
let batch = reader.take_offsets(&offsets, Select::All).await.unwrap();
assert_eq!(batch.num_rows(), 3);
let idx_values = batch
.column(0)
.as_primitive::<Int32Type>()
.values()
.to_vec();
let expected: Vec<i32> = offsets
.iter()
.map(|&o| row_ids[o as usize] as i32)
.collect();
assert_eq!(idx_values, expected);
}
#[tokio::test]
async fn test_take_offsets_preserves_order() {
let (base_table, row_ids_table, row_ids) = setup_permutation_tables(10).await;
let reader = PermutationReader::try_from_tables(
base_table.base_table().clone(),
row_ids_table.base_table().clone(),
0,
)
.await
.unwrap();
// Take offsets in reverse order and verify returned rows match that order
let offsets = vec![5, 3, 1, 0];
let batch = reader.take_offsets(&offsets, Select::All).await.unwrap();
assert_eq!(batch.num_rows(), 4);
let idx_values = batch
.column(0)
.as_primitive::<Int32Type>()
.values()
.to_vec();
let expected: Vec<i32> = offsets
.iter()
.map(|&o| row_ids[o as usize] as i32)
.collect();
assert_eq!(idx_values, expected);
}
#[tokio::test]
async fn test_take_offsets_with_column_selection() {
let (base_table, row_ids_table, row_ids) = setup_permutation_tables(10).await;
let reader = PermutationReader::try_from_tables(
base_table.base_table().clone(),
row_ids_table.base_table().clone(),
0,
)
.await
.unwrap();
let offsets = vec![1, 3];
let batch = reader
.take_offsets(&offsets, Select::Columns(vec!["idx".to_string()]))
.await
.unwrap();
assert_eq!(batch.num_rows(), 2);
assert_eq!(batch.num_columns(), 1);
assert_eq!(batch.schema().field(0).name(), "idx");
let idx_values = batch
.column(0)
.as_primitive::<Int32Type>()
.values()
.to_vec();
let expected: Vec<i32> = offsets
.iter()
.map(|&o| row_ids[o as usize] as i32)
.collect();
assert_eq!(idx_values, expected);
}
#[tokio::test]
async fn test_take_offsets_invalid_offset() {
let (base_table, row_ids_table, _) = setup_permutation_tables(5).await;
let reader = PermutationReader::try_from_tables(
base_table.base_table().clone(),
row_ids_table.base_table().clone(),
0,
)
.await
.unwrap();
// Offset 999 doesn't exist in the offset map
let result = reader.take_offsets(&[0, 999], Select::All).await;
assert!(result.is_err());
}
#[tokio::test]
async fn test_take_offsets_identity_reader() {
let base_table = lance_datagen::gen_batch()
.col("idx", lance_datagen::array::step::<Int32Type>())
.into_mem_table("tbl", RowCount::from(10), BatchCount::from(1))
.await;
let reader = PermutationReader::identity(base_table.base_table().clone()).await;
// With no permutation table, take_offsets uses the base table directly
let offsets = vec![0, 2, 4, 6];
let batch = reader.take_offsets(&offsets, Select::All).await.unwrap();
assert_eq!(batch.num_rows(), 4);
let idx_values = batch
.column(0)
.as_primitive::<Int32Type>()
.values()
.to_vec();
assert_eq!(idx_values, vec![0, 2, 4, 6]);
}
#[tokio::test]
async fn test_take_offsets_caches_offset_map() {
let (base_table, row_ids_table, row_ids) = setup_permutation_tables(10).await;
let reader = PermutationReader::try_from_tables(
base_table.base_table().clone(),
row_ids_table.base_table().clone(),
0,
)
.await
.unwrap();
// First call populates the cache
let batch1 = reader.take_offsets(&[0, 1], Select::All).await.unwrap();
// Second call should use the cached offset map and produce consistent results
let batch2 = reader.take_offsets(&[0, 1], Select::All).await.unwrap();
let values1 = batch1
.column(0)
.as_primitive::<Int32Type>()
.values()
.to_vec();
let values2 = batch2
.column(0)
.as_primitive::<Int32Type>()
.values()
.to_vec();
assert_eq!(values1, values2);
let expected: Vec<i32> = vec![row_ids[0] as i32, row_ids[1] as i32];
assert_eq!(values1, expected);
}
#[tokio::test]
async fn test_take_offsets_single_offset() {
let (base_table, row_ids_table, row_ids) = setup_permutation_tables(5).await;
let reader = PermutationReader::try_from_tables(
base_table.base_table().clone(),
row_ids_table.base_table().clone(),
0,
)
.await
.unwrap();
let batch = reader.take_offsets(&[2], Select::All).await.unwrap();
assert_eq!(batch.num_rows(), 1);
let idx_values = batch
.column(0)
.as_primitive::<Int32Type>()
.values()
.to_vec();
assert_eq!(idx_values, vec![row_ids[2] as i32]);
}
}

View File

@@ -18,7 +18,7 @@ use std::{
};
use arrow_array::{Array, RecordBatch, RecordBatchReader};
use arrow_schema::{DataType, Field, SchemaBuilder, SchemaRef};
use arrow_schema::{DataType, Field, SchemaBuilder};
// use async_trait::async_trait;
use serde::{Deserialize, Serialize};
@@ -190,112 +190,6 @@ impl<R: RecordBatchReader> WithEmbeddings<R> {
}
}
/// Compute embedding arrays for a batch.
///
/// When multiple embedding functions are defined, they are computed in parallel using
/// scoped threads. For a single embedding function, computation is done inline.
fn compute_embedding_arrays(
batch: &RecordBatch,
embeddings: &[(EmbeddingDefinition, Arc<dyn EmbeddingFunction>)],
) -> Result<Vec<Arc<dyn Array>>> {
if embeddings.len() == 1 {
let (fld, func) = &embeddings[0];
let src_column =
batch
.column_by_name(&fld.source_column)
.ok_or_else(|| Error::InvalidInput {
message: format!("Source column '{}' not found", fld.source_column),
})?;
return Ok(vec![func.compute_source_embeddings(src_column.clone())?]);
}
// Parallel path: multiple embeddings
std::thread::scope(|s| {
let handles: Vec<_> = embeddings
.iter()
.map(|(fld, func)| {
let src_column = batch.column_by_name(&fld.source_column).ok_or_else(|| {
Error::InvalidInput {
message: format!("Source column '{}' not found", fld.source_column),
}
})?;
let handle = s.spawn(move || func.compute_source_embeddings(src_column.clone()));
Ok(handle)
})
.collect::<Result<_>>()?;
handles
.into_iter()
.map(|h| {
h.join().map_err(|e| Error::Runtime {
message: format!("Thread panicked during embedding computation: {:?}", e),
})?
})
.collect()
})
}
/// Compute the output schema when embeddings are applied to a base schema.
///
/// This returns the schema with embedding columns appended.
pub fn compute_output_schema(
base_schema: &SchemaRef,
embeddings: &[(EmbeddingDefinition, Arc<dyn EmbeddingFunction>)],
) -> Result<SchemaRef> {
let mut sb: SchemaBuilder = base_schema.as_ref().into();
for (ed, func) in embeddings {
let src_field = base_schema
.field_with_name(&ed.source_column)
.map_err(|_| Error::InvalidInput {
message: format!("Source column '{}' not found in schema", ed.source_column),
})?;
let field_name = ed
.dest_column
.clone()
.unwrap_or_else(|| format!("{}_embedding", &ed.source_column));
sb.push(Field::new(
field_name,
func.dest_type()?.into_owned(),
src_field.is_nullable(),
));
}
Ok(Arc::new(sb.finish()))
}
/// Compute embeddings for a batch and append as new columns.
///
/// This function computes embeddings using the provided embedding functions and
/// appends them as new columns to the batch.
pub fn compute_embeddings_for_batch(
batch: RecordBatch,
embeddings: &[(EmbeddingDefinition, Arc<dyn EmbeddingFunction>)],
) -> Result<RecordBatch> {
let embedding_arrays = compute_embedding_arrays(&batch, embeddings)?;
let mut result = batch;
for ((fld, _), embedding) in embeddings.iter().zip(embedding_arrays.iter()) {
let dst_field_name = fld
.dest_column
.clone()
.unwrap_or_else(|| format!("{}_embedding", &fld.source_column));
let dst_field = Field::new(
dst_field_name,
embedding.data_type().clone(),
embedding.nulls().is_some(),
);
result = result.try_with_column(dst_field, embedding.clone())?;
}
Ok(result)
}
impl<R: RecordBatchReader> WithEmbeddings<R> {
fn dest_fields(&self) -> Result<Vec<Field>> {
let schema = self.inner.schema();
@@ -346,6 +240,48 @@ impl<R: RecordBatchReader> WithEmbeddings<R> {
column_definitions,
})
}
fn compute_embeddings_parallel(&self, batch: &RecordBatch) -> Result<Vec<Arc<dyn Array>>> {
if self.embeddings.len() == 1 {
let (fld, func) = &self.embeddings[0];
let src_column =
batch
.column_by_name(&fld.source_column)
.ok_or_else(|| Error::InvalidInput {
message: format!("Source column '{}' not found", fld.source_column),
})?;
return Ok(vec![func.compute_source_embeddings(src_column.clone())?]);
}
// Parallel path: multiple embeddings
std::thread::scope(|s| {
let handles: Vec<_> = self
.embeddings
.iter()
.map(|(fld, func)| {
let src_column = batch.column_by_name(&fld.source_column).ok_or_else(|| {
Error::InvalidInput {
message: format!("Source column '{}' not found", fld.source_column),
}
})?;
let handle =
s.spawn(move || func.compute_source_embeddings(src_column.clone()));
Ok(handle)
})
.collect::<Result<_>>()?;
handles
.into_iter()
.map(|h| {
h.join().map_err(|e| Error::Runtime {
message: format!("Thread panicked during embedding computation: {:?}", e),
})?
})
.collect()
})
}
}
impl<R: RecordBatchReader> Iterator for MaybeEmbedded<R> {
@@ -373,13 +309,37 @@ impl<R: RecordBatchReader> Iterator for WithEmbeddings<R> {
fn next(&mut self) -> Option<Self::Item> {
let batch = self.inner.next()?;
match batch {
Ok(batch) => match compute_embeddings_for_batch(batch, &self.embeddings) {
Ok(batch_with_embeddings) => Some(Ok(batch_with_embeddings)),
Err(e) => Some(Err(arrow_schema::ArrowError::ComputeError(format!(
"Error computing embedding: {}",
e
)))),
},
Ok(batch) => {
let embeddings = match self.compute_embeddings_parallel(&batch) {
Ok(emb) => emb,
Err(e) => {
return Some(Err(arrow_schema::ArrowError::ComputeError(format!(
"Error computing embedding: {}",
e
))))
}
};
let mut batch = batch;
for ((fld, _), embedding) in self.embeddings.iter().zip(embeddings.iter()) {
let dst_field_name = fld
.dest_column
.clone()
.unwrap_or_else(|| format!("{}_embedding", &fld.source_column));
let dst_field = Field::new(
dst_field_name,
embedding.data_type().clone(),
embedding.nulls().is_some(),
);
match batch.try_with_column(dst_field.clone(), embedding.clone()) {
Ok(b) => batch = b,
Err(e) => return Some(Err(e)),
};
}
Some(Ok(batch))
}
Err(e) => Some(Err(e)),
}
}

View File

@@ -6,7 +6,7 @@ use std::sync::PoisonError;
use arrow_schema::ArrowError;
use snafu::Snafu;
pub(crate) type BoxError = Box<dyn std::error::Error + Send + Sync>;
type BoxError = Box<dyn std::error::Error + Send + Sync>;
#[derive(Debug, Snafu)]
#[snafu(visibility(pub(crate)))]
@@ -80,9 +80,6 @@ pub enum Error {
Arrow { source: ArrowError },
#[snafu(display("LanceDBError: not supported: {message}"))]
NotSupported { message: String },
/// External error pass through from user code.
#[snafu(transparent)]
External { source: BoxError },
#[snafu(whatever, display("{message}"))]
Other {
message: String,
@@ -95,26 +92,15 @@ pub type Result<T> = std::result::Result<T, Error>;
impl From<ArrowError> for Error {
fn from(source: ArrowError) -> Self {
match source {
ArrowError::ExternalError(source) => match source.downcast::<Self>() {
Ok(e) => *e,
Err(source) => Self::External { source },
},
_ => Self::Arrow { source },
}
Self::Arrow { source }
}
}
impl From<lance::Error> for Error {
fn from(source: lance::Error) -> Self {
// Try to unwrap external errors that were wrapped by lance
match source {
lance::Error::Wrapped { error, .. } => match error.downcast::<Self>() {
Ok(e) => *e,
Err(source) => Self::External { source },
},
_ => Self::Lance { source },
}
// TODO: Once Lance is changed to preserve ObjectStore, DataFusion, and Arrow errors, we can
// pass those variants through here as well.
Self::Lance { source }
}
}

View File

@@ -218,9 +218,8 @@ mod test {
datagen = datagen.col(Box::<IncrementingInt32>::default());
datagen = datagen.col(Box::new(RandomVector::default().named("vector".into())));
let data: Box<dyn arrow_array::RecordBatchReader + Send> = Box::new(datagen.batch(100));
let res = db
.create_table("test", data)
.create_table("test", Box::new(datagen.batch(100)))
.write_options(WriteOptions {
lance_write_params: Some(param),
})

View File

@@ -12,10 +12,10 @@ use arrow_schema::Schema;
use crate::{Error, Result};
/// Convert a Arrow IPC file to a batch reader
pub fn ipc_file_to_batches(buf: Vec<u8>) -> Result<Box<dyn RecordBatchReader + Send>> {
pub fn ipc_file_to_batches(buf: Vec<u8>) -> Result<impl RecordBatchReader> {
let buf_reader = Cursor::new(buf);
let reader = FileReader::try_new(buf_reader, None)?;
Ok(Box::new(reader))
Ok(reader)
}
/// Convert record batches to Arrow IPC file

View File

@@ -39,6 +39,7 @@
//! #### Connect to a database.
//!
//! ```rust
//! # use arrow_schema::{Field, Schema};
//! # tokio::runtime::Runtime::new().unwrap().block_on(async {
//! let db = lancedb::connect("data/sample-lancedb").execute().await.unwrap();
//! # });
@@ -73,10 +74,7 @@
//!
//! #### Create a table
//!
//! To create a Table, you need to provide an [`arrow_array::RecordBatch`]. The
//! schema of the `RecordBatch` determines the schema of the table.
//!
//! Vector columns should be represented as `FixedSizeList<Float16/Float32>` data type.
//! To create a Table, you need to provide a [`arrow_schema::Schema`] and a [`arrow_array::RecordBatch`] stream.
//!
//! ```rust
//! # use std::sync::Arc;
@@ -87,29 +85,34 @@
//! # tokio::runtime::Runtime::new().unwrap().block_on(async {
//! # let tmpdir = tempfile::tempdir().unwrap();
//! # let db = lancedb::connect(tmpdir.path().to_str().unwrap()).execute().await.unwrap();
//! let ndims = 128;
//! let schema = Arc::new(Schema::new(vec![
//! Field::new("id", DataType::Int32, false),
//! Field::new(
//! "vector",
//! DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), ndims),
//! DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 128),
//! true,
//! ),
//! ]));
//! let data = RecordBatch::try_new(
//! // Create a RecordBatch stream.
//! let batches = RecordBatchIterator::new(
//! vec![RecordBatch::try_new(
//! schema.clone(),
//! vec![
//! Arc::new(Int32Array::from_iter_values(0..256)),
//! Arc::new(
//! FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
//! (0..256).map(|_| Some(vec![Some(1.0); ndims as usize])),
//! ndims,
//! (0..256).map(|_| Some(vec![Some(1.0); 128])),
//! 128,
//! ),
//! ),
//! ],
//! )
//! .unwrap();
//! db.create_table("my_table", data)
//! .unwrap()]
//! .into_iter()
//! .map(Ok),
//! schema.clone(),
//! );
//! db.create_table("my_table", Box::new(batches))
//! .execute()
//! .await
//! .unwrap();
@@ -148,18 +151,42 @@
//! #### Open table and search
//!
//! ```rust
//! # use std::sync::Arc;
//! # use futures::TryStreamExt;
//! # use arrow_schema::{DataType, Schema, Field};
//! # use arrow_array::{RecordBatch, RecordBatchIterator};
//! # use arrow_array::{FixedSizeListArray, Float32Array, Int32Array, types::Float32Type};
//! # use lancedb::query::{ExecutableQuery, QueryBase};
//! # async fn example(table: &lancedb::Table) -> lancedb::Result<()> {
//! # tokio::runtime::Runtime::new().unwrap().block_on(async {
//! # let tmpdir = tempfile::tempdir().unwrap();
//! # let db = lancedb::connect(tmpdir.path().to_str().unwrap()).execute().await.unwrap();
//! # let schema = Arc::new(Schema::new(vec![
//! # Field::new("id", DataType::Int32, false),
//! # Field::new("vector", DataType::FixedSizeList(
//! # Arc::new(Field::new("item", DataType::Float32, true)), 128), true),
//! # ]));
//! # let batches = RecordBatchIterator::new(vec![
//! # RecordBatch::try_new(schema.clone(),
//! # vec![
//! # Arc::new(Int32Array::from_iter_values(0..10)),
//! # Arc::new(FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
//! # (0..10).map(|_| Some(vec![Some(1.0); 128])), 128)),
//! # ]).unwrap()
//! # ].into_iter().map(Ok),
//! # schema.clone());
//! # db.create_table("my_table", Box::new(batches)).execute().await.unwrap();
//! # let table = db.open_table("my_table").execute().await.unwrap();
//! let results = table
//! .query()
//! .nearest_to(&[1.0; 128])?
//! .nearest_to(&[1.0; 128])
//! .unwrap()
//! .execute()
//! .await?
//! .await
//! .unwrap()
//! .try_collect::<Vec<_>>()
//! .await?;
//! # Ok(())
//! # }
//! .await
//! .unwrap();
//! # });
//! ```
pub mod arrow;

View File

@@ -1381,7 +1381,7 @@ mod tests {
use arrow::{array::downcast_array, compute::concat_batches, datatypes::Int32Type};
use arrow_array::{
cast::AsArray, types::Float32Type, FixedSizeListArray, Float32Array, Int32Array,
RecordBatch, StringArray,
RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray,
};
use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema};
use futures::{StreamExt, TryStreamExt};
@@ -1402,7 +1402,7 @@ mod tests {
let batches = make_test_batches();
let conn = connect(uri).execute().await.unwrap();
let table = conn
.create_table("my_table", batches)
.create_table("my_table", Box::new(batches))
.execute()
.await
.unwrap();
@@ -1463,7 +1463,7 @@ mod tests {
let batches = make_non_empty_batches();
let conn = connect(uri).execute().await.unwrap();
let table = conn
.create_table("my_table", batches)
.create_table("my_table", Box::new(batches))
.execute()
.await
.unwrap();
@@ -1525,7 +1525,7 @@ mod tests {
let batches = make_non_empty_batches();
let conn = connect(uri).execute().await.unwrap();
let table = conn
.create_table("my_table", batches)
.create_table("my_table", Box::new(batches))
.execute()
.await
.unwrap();
@@ -1578,7 +1578,7 @@ mod tests {
let batches = make_non_empty_batches();
let conn = connect(uri).execute().await.unwrap();
let table = conn
.create_table("my_table", batches)
.create_table("my_table", Box::new(batches))
.execute()
.await
.unwrap();
@@ -1599,13 +1599,13 @@ mod tests {
assert!(result.is_err());
}
fn make_non_empty_batches() -> Box<dyn arrow_array::RecordBatchReader + Send> {
fn make_non_empty_batches() -> impl RecordBatchReader + Send + 'static {
let vec = Box::new(RandomVector::new().named("vector".to_string()));
let id = Box::new(IncrementingInt32::new().named("id".to_string()));
Box::new(BatchGenerator::new().col(vec).col(id).batch(512))
BatchGenerator::new().col(vec).col(id).batch(512)
}
fn make_test_batches() -> RecordBatch {
fn make_test_batches() -> impl RecordBatchReader + Send + 'static {
let dim: usize = 128;
let schema = Arc::new(ArrowSchema::new(vec![
ArrowField::new("key", DataType::Int32, false),
@@ -1619,7 +1619,12 @@ mod tests {
),
ArrowField::new("uri", DataType::Utf8, true),
]));
RecordBatch::new_empty(schema)
RecordBatchIterator::new(
vec![RecordBatch::new_empty(schema.clone())]
.into_iter()
.map(Ok),
schema,
)
}
async fn make_test_table(tmp_dir: &tempfile::TempDir) -> Table {
@@ -1628,7 +1633,7 @@ mod tests {
let batches = make_non_empty_batches();
let conn = connect(uri).execute().await.unwrap();
conn.create_table("my_table", batches)
conn.create_table("my_table", Box::new(batches))
.execute()
.await
.unwrap()
@@ -1857,8 +1862,10 @@ mod tests {
let record_batch =
RecordBatch::try_new(schema.clone(), vec![Arc::new(text), Arc::new(vector)]).unwrap();
let record_batch_iter =
RecordBatchIterator::new(vec![record_batch].into_iter().map(Ok), schema.clone());
let table = conn
.create_table("my_table", record_batch)
.create_table("my_table", record_batch_iter)
.execute()
.await
.unwrap();
@@ -1942,8 +1949,10 @@ mod tests {
],
)
.unwrap();
let record_batch_iter =
RecordBatchIterator::new(vec![record_batch].into_iter().map(Ok), schema.clone());
let table = conn
.create_table("my_table", record_batch)
.create_table("my_table", record_batch_iter)
.mode(CreateTableMode::Overwrite)
.execute()
.await
@@ -2053,6 +2062,8 @@ mod tests {
async fn test_pagination_with_fts() {
let db = connect("memory://test").execute().await.unwrap();
let data = fts_test_data(400);
let schema = data.schema();
let data = RecordBatchIterator::new(vec![Ok(data)], schema);
let table = db.create_table("test_table", data).execute().await.unwrap();
table

View File

@@ -491,7 +491,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
}
/// Apply dynamic headers from the header provider if configured
pub(crate) async fn apply_dynamic_headers(&self, mut request: Request) -> Result<Request> {
async fn apply_dynamic_headers(&self, mut request: Request) -> Result<Request> {
if let Some(ref provider) = self.header_provider {
let headers = provider.get_headers().await?;
let request_headers = request.headers_mut();
@@ -555,9 +555,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
message: "Attempted to retry a request that cannot be cloned".to_string(),
})?;
let (_, r) = tmp_req.build_split();
let mut r = r.map_err(|e| Error::Runtime {
message: format!("Failed to build request: {}", e),
})?;
let mut r = r.unwrap();
let request_id = self.extract_request_id(&mut r);
let mut retry_counter = RetryCounter::new(retry_config, request_id.clone());
@@ -573,9 +571,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
}
let (c, request) = req_builder.build_split();
let mut request = request.map_err(|e| Error::Runtime {
message: format!("Failed to build request: {}", e),
})?;
let mut request = request.unwrap();
self.set_request_id(&mut request, &request_id.clone());
// Apply dynamic headers before each retry attempt
@@ -625,7 +621,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
}
}
pub(crate) fn log_request(&self, request: &Request, request_id: &String) {
fn log_request(&self, request: &Request, request_id: &String) {
if log::log_enabled!(log::Level::Debug) {
let content_type = request
.headers()

View File

@@ -4,11 +4,13 @@
use std::collections::HashMap;
use std::sync::Arc;
use arrow_array::RecordBatchIterator;
use async_trait::async_trait;
use http::StatusCode;
use lance_io::object_store::StorageOptions;
use moka::future::Cache;
use reqwest::header::CONTENT_TYPE;
use tokio::task::spawn_blocking;
use lance_namespace::models::{
CreateNamespaceRequest, CreateNamespaceResponse, DescribeNamespaceRequest,
@@ -17,17 +19,16 @@ use lance_namespace::models::{
};
use crate::database::{
CloneTableRequest, CreateTableMode, CreateTableRequest, Database, DatabaseOptions,
OpenTableRequest, ReadConsistency, TableNamesRequest,
CloneTableRequest, CreateTableData, CreateTableMode, CreateTableRequest, Database,
DatabaseOptions, OpenTableRequest, ReadConsistency, TableNamesRequest,
};
use crate::error::Result;
use crate::remote::util::stream_as_body;
use crate::table::BaseTable;
use crate::Error;
use super::client::{ClientConfig, HttpSend, RequestResultExt, RestfulLanceDbClient, Sender};
use super::table::RemoteTable;
use super::util::parse_server_version;
use super::util::{batches_to_ipc_bytes, parse_server_version};
use super::ARROW_STREAM_CONTENT_TYPE;
// Request structure for the remote clone table API
@@ -435,8 +436,26 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
Ok(response)
}
async fn create_table(&self, mut request: CreateTableRequest) -> Result<Arc<dyn BaseTable>> {
let body = stream_as_body(request.data.scan_as_stream())?;
async fn create_table(&self, request: CreateTableRequest) -> Result<Arc<dyn BaseTable>> {
let data = match request.data {
CreateTableData::Data(data) => data,
CreateTableData::StreamingData(_) => {
return Err(Error::NotSupported {
message: "Creating a remote table from a streaming source".to_string(),
})
}
CreateTableData::Empty(table_definition) => {
let schema = table_definition.schema.clone();
Box::new(RecordBatchIterator::new(vec![], schema))
}
};
// TODO: https://github.com/lancedb/lancedb/issues/1026
// We should accept data from an async source. In the meantime, spawn this as blocking
// to make sure we don't block the tokio runtime if the source is slow.
let data_buffer = spawn_blocking(move || batches_to_ipc_bytes(data))
.await
.unwrap()?;
let identifier =
build_table_identifier(&request.name, &request.namespace, &self.client.id_delimiter);
@@ -444,7 +463,7 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
.client
.post(&format!("/v1/table/{}/create/", identifier))
.query(&[("mode", Into::<&str>::into(&request.mode))])
.body(body)
.body(data_buffer)
.header(CONTENT_TYPE, ARROW_STREAM_CONTENT_TYPE);
let (request_id, rsp) = self.client.send(req).await?;
@@ -794,7 +813,7 @@ mod tests {
use std::collections::HashMap;
use std::sync::{Arc, OnceLock};
use arrow_array::{Int32Array, RecordBatch};
use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator};
use arrow_schema::{DataType, Field, Schema};
use crate::connection::ConnectBuilder;
@@ -974,7 +993,8 @@ mod tests {
vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
)
.unwrap();
let table = conn.create_table("table1", data).execute().await.unwrap();
let reader = RecordBatchIterator::new([Ok(data.clone())], data.schema());
let table = conn.create_table("table1", reader).execute().await.unwrap();
assert_eq!(table.name(), "table1");
}
@@ -991,7 +1011,8 @@ mod tests {
vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
)
.unwrap();
let result = conn.create_table("table1", data).execute().await;
let reader = RecordBatchIterator::new([Ok(data.clone())], data.schema());
let result = conn.create_table("table1", reader).execute().await;
assert!(result.is_err());
assert!(
matches!(result, Err(crate::Error::TableAlreadyExists { name }) if name == "table1")
@@ -1024,7 +1045,8 @@ mod tests {
vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
)
.unwrap();
let mut builder = conn.create_table("table1", data.clone());
let reader = RecordBatchIterator::new([Ok(data.clone())], data.schema());
let mut builder = conn.create_table("table1", reader);
if let Some(mode) = mode {
builder = builder.mode(mode);
}
@@ -1049,8 +1071,9 @@ mod tests {
.unwrap();
let called: Arc<OnceLock<bool>> = Arc::new(OnceLock::new());
let reader = RecordBatchIterator::new([Ok(data.clone())], data.schema());
let called_in_cb = called.clone();
conn.create_table("table1", data)
conn.create_table("table1", reader)
.mode(CreateTableMode::ExistOk(Box::new(move |b| {
called_in_cb.clone().set(true).unwrap();
b
@@ -1239,8 +1262,9 @@ mod tests {
vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
)
.unwrap();
let reader = RecordBatchIterator::new([Ok(data.clone())], data.schema());
let table = conn
.create_table("table1", data)
.create_table("table1", reader)
.namespace(vec!["ns1".to_string()])
.execute()
.await
@@ -1706,8 +1730,10 @@ mod tests {
vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
)
.unwrap();
let reader = RecordBatchIterator::new([Ok(data.clone())], schema.clone());
let table = conn
.create_table("test_table", data)
.create_table("test_table", reader)
.namespace(namespace.clone())
.execute()
.await;
@@ -1780,7 +1806,9 @@ mod tests {
let data =
RecordBatch::try_new(schema.clone(), vec![Arc::new(Int32Array::from(vec![i]))])
.unwrap();
conn.create_table(format!("table{}", i), data)
let reader = RecordBatchIterator::new([Ok(data.clone())], schema.clone());
conn.create_table(format!("table{}", i), reader)
.namespace(namespace.clone())
.execute()
.await

File diff suppressed because it is too large Load Diff

View File

@@ -1,50 +1,29 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
use arrow_ipc::CompressionType;
use futures::{Stream, StreamExt};
use std::io::Cursor;
use arrow_array::RecordBatchReader;
use reqwest::Response;
use crate::{arrow::SendableRecordBatchStream, Result};
use crate::Result;
use super::db::ServerVersion;
pub fn stream_as_ipc(
data: SendableRecordBatchStream,
) -> Result<impl Stream<Item = Result<bytes::Bytes>>> {
let options = arrow_ipc::writer::IpcWriteOptions::default()
.try_with_compression(Some(CompressionType::LZ4_FRAME))?;
pub fn batches_to_ipc_bytes(batches: impl RecordBatchReader) -> Result<Vec<u8>> {
const WRITE_BUF_SIZE: usize = 4096;
let buf = Vec::with_capacity(WRITE_BUF_SIZE);
let writer =
arrow_ipc::writer::StreamWriter::try_new_with_options(buf, &data.schema(), options)?;
let stream = futures::stream::try_unfold(
(data, writer, false),
move |(mut data, mut writer, finished)| async move {
if finished {
return Ok(None);
}
match data.next().await {
Some(Ok(batch)) => {
writer.write(&batch)?;
let buffer = std::mem::take(writer.get_mut());
Ok(Some((bytes::Bytes::from(buffer), (data, writer, false))))
}
Some(Err(e)) => Err(e),
None => {
writer.finish()?;
let buffer = std::mem::take(writer.get_mut());
Ok(Some((bytes::Bytes::from(buffer), (data, writer, true))))
}
}
},
);
Ok(stream)
}
let mut buf = Cursor::new(buf);
{
let mut writer = arrow_ipc::writer::StreamWriter::try_new(&mut buf, &batches.schema())?;
pub fn stream_as_body(data: SendableRecordBatchStream) -> Result<reqwest::Body> {
let stream = stream_as_ipc(data)?;
Ok(reqwest::Body::wrap_stream(stream))
for batch in batches {
let batch = batch?;
writer.write(&batch)?;
}
writer.finish()?;
}
Ok(buf.into_inner())
}
pub fn parse_server_version(req_id: &str, rsp: &Response) -> Result<ServerVersion> {

View File

@@ -5,7 +5,7 @@
use arrow::array::{AsArray, FixedSizeListBuilder, Float32Builder};
use arrow::datatypes::{Float32Type, UInt8Type};
use arrow_array::{RecordBatch, RecordBatchReader};
use arrow_array::{RecordBatchIterator, RecordBatchReader};
use arrow_schema::{DataType, Field, Schema, SchemaRef};
use async_trait::async_trait;
use datafusion_expr::Expr;
@@ -16,6 +16,8 @@ use datafusion_physical_plan::union::UnionExec;
use datafusion_physical_plan::ExecutionPlan;
use futures::{FutureExt, StreamExt, TryFutureExt};
use lance::dataset::builder::DatasetBuilder;
use lance::dataset::cleanup::RemovalStats;
use lance::dataset::optimize::{compact_files, CompactionMetrics, IndexRemapperOptions};
use lance::dataset::scanner::Scanner;
pub use lance::dataset::ColumnAlteration;
pub use lance::dataset::NewColumnTransform;
@@ -44,15 +46,17 @@ use lance_namespace::models::{
use lance_namespace::LanceNamespace;
use lance_table::format::Manifest;
use lance_table::io::commit::ManifestNamingScheme;
use log::info;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::format;
use std::path::Path;
use std::sync::Arc;
use crate::data::scannable::{scannable_with_embeddings, Scannable};
use crate::arrow::IntoArrow;
use crate::connection::NoData;
use crate::database::Database;
use crate::embeddings::{EmbeddingDefinition, EmbeddingRegistry, MemoryRegistry};
use crate::embeddings::{EmbeddingDefinition, EmbeddingRegistry, MaybeEmbedded, MemoryRegistry};
use crate::error::{Error, Result};
use crate::index::vector::VectorIndex;
use crate::index::IndexStatistics;
@@ -71,27 +75,23 @@ use crate::utils::{
use self::dataset::DatasetConsistencyWrapper;
use self::merge::MergeInsertBuilder;
mod add_data;
pub mod datafusion;
pub(crate) mod dataset;
pub mod delete;
pub mod merge;
pub mod optimize;
pub mod schema_evolution;
pub mod update;
pub use add_data::{AddDataBuilder, AddDataMode, AddResult};
use crate::index::waiter::wait_for_index;
pub use chrono::Duration;
pub use delete::DeleteResult;
use futures::future::{join_all, Either};
pub use lance::dataset::optimize::CompactionOptions;
pub use lance::dataset::refs::{TagContents, Tags as LanceTags};
pub use lance::dataset::scanner::DatasetRecordBatchStream;
use lance::dataset::statistics::DatasetStatisticsExt;
use lance_index::frag_reuse::FRAG_REUSE_INDEX_NAME;
pub use lance_index::optimize::OptimizeOptions;
pub use optimize::{CompactionOptions, OptimizeAction, OptimizeStats};
pub use schema_evolution::{AddColumnsResult, AlterColumnsResult, DropColumnsResult};
use serde_with::skip_serializing_none;
pub use update::{UpdateBuilder, UpdateResult};
@@ -169,6 +169,85 @@ impl TableDefinition {
}
}
/// Optimize the dataset.
///
/// Similar to `VACUUM` in PostgreSQL, it offers different options to
/// optimize different parts of the table on disk.
///
/// By default, it optimizes everything, as [`OptimizeAction::All`].
pub enum OptimizeAction {
/// Run all optimizations with default values
All,
/// Compacts files in the dataset
///
/// LanceDb uses a readonly filesystem for performance and safe concurrency. Every time
/// new data is added it will be added into new files. Small files
/// can hurt both read and write performance. Compaction will merge small files
/// into larger ones.
///
/// All operations that modify data (add, delete, update, merge insert, etc.) will create
/// new files. If these operations are run frequently then compaction should run frequently.
///
/// If these operations are never run (search only) then compaction is not necessary.
Compact {
options: CompactionOptions,
remap_options: Option<Arc<dyn IndexRemapperOptions>>,
},
/// Prune old version of datasets
///
/// Every change in LanceDb is additive. When data is removed from a dataset a new version is
/// created that doesn't contain the removed data. However, the old version, which does contain
/// the removed data, is left in place. This is necessary for consistency and concurrency and
/// also enables time travel functionality like the ability to checkout an older version of the
/// dataset to undo changes.
///
/// Over time, these old versions can consume a lot of disk space. The prune operation will
/// remove versions of the dataset that are older than a certain age. This will free up the
/// space used by that old data.
///
/// Once a version is pruned it can no longer be checked out.
Prune {
/// The duration of time to keep versions of the dataset.
older_than: Option<Duration>,
/// Because they may be part of an in-progress transaction, files newer than 7 days old are not deleted by default.
/// If you are sure that there are no in-progress transactions, then you can set this to True to delete all files older than `older_than`.
delete_unverified: Option<bool>,
/// If true, an error will be returned if there are any old versions that are still tagged.
error_if_tagged_old_versions: Option<bool>,
},
/// Optimize the indices
///
/// This operation optimizes all indices in the table. When new data is added to LanceDb
/// it is not added to the indices. However, it can still turn up in searches because the search
/// function will scan both the indexed data and the unindexed data in parallel. Over time, the
/// unindexed data can become large enough that the search performance is slow. This operation
/// will add the unindexed data to the indices without rerunning the full index creation process.
///
/// Optimizing an index is faster than re-training the index but it does not typically adjust the
/// underlying model relied upon by the index. This can eventually lead to poor search accuracy
/// and so users may still want to occasionally retrain the index after adding a large amount of
/// data.
///
/// For example, when using IVF, an index will create clusters. Optimizing an index assigns unindexed
/// data to the existing clusters, but it does not move the clusters or create new clusters.
Index(OptimizeOptions),
}
impl Default for OptimizeAction {
fn default() -> Self {
Self::All
}
}
/// Statistics about the optimization.
pub struct OptimizeStats {
/// Stats of the file compaction.
pub compaction: Option<CompactionMetrics>,
/// Stats of the version pruning
pub prune: Option<RemovalStats>,
}
/// Describes what happens when a vector either contains NaN or
/// does not have enough values
#[derive(Clone, Debug, Default)]
@@ -198,6 +277,60 @@ pub struct WriteOptions {
pub lance_write_params: Option<WriteParams>,
}
#[derive(Debug, Clone, Default)]
pub enum AddDataMode {
/// Rows will be appended to the table (the default)
#[default]
Append,
/// The existing table will be overwritten with the new data
Overwrite,
}
/// A builder for configuring a [`crate::connection::Connection::create_table`] or [`Table::add`]
/// operation
pub struct AddDataBuilder<T: IntoArrow> {
parent: Arc<dyn BaseTable>,
pub(crate) data: T,
pub(crate) mode: AddDataMode,
pub(crate) write_options: WriteOptions,
embedding_registry: Option<Arc<dyn EmbeddingRegistry>>,
}
impl<T: IntoArrow> std::fmt::Debug for AddDataBuilder<T> {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("AddDataBuilder")
.field("parent", &self.parent)
.field("mode", &self.mode)
.field("write_options", &self.write_options)
.finish()
}
}
impl<T: IntoArrow> AddDataBuilder<T> {
pub fn mode(mut self, mode: AddDataMode) -> Self {
self.mode = mode;
self
}
pub fn write_options(mut self, options: WriteOptions) -> Self {
self.write_options = options;
self
}
pub async fn execute(self) -> Result<AddResult> {
let parent = self.parent.clone();
let data = self.data.into_arrow()?;
let without_data = AddDataBuilder::<NoData> {
data: NoData {},
mode: self.mode,
parent: self.parent,
write_options: self.write_options,
embedding_registry: self.embedding_registry,
};
parent.add(without_data, data).await
}
}
/// Filters that can be used to limit the rows returned by a query
pub enum Filter {
/// A SQL filter string
@@ -231,6 +364,15 @@ pub trait Tags: Send + Sync {
async fn update(&mut self, tag: &str, version: u64) -> Result<()>;
}
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
pub struct AddResult {
// The commit version associated with the operation.
// A version of `0` indicates compatibility with legacy servers that do not return
/// a commit version.
#[serde(default)]
pub version: u64,
}
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
pub struct MergeResult {
// The commit version associated with the operation.
@@ -303,7 +445,11 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
) -> Result<String>;
/// Add new records to the table.
async fn add(&self, add: AddDataBuilder) -> Result<AddResult>;
async fn add(
&self,
add: AddDataBuilder<NoData>,
data: Box<dyn arrow_array::RecordBatchReader + Send>,
) -> Result<AddResult>;
/// Delete rows from the table.
async fn delete(&self, predicate: &str) -> Result<DeleteResult>;
/// Update rows in the table.
@@ -448,30 +594,6 @@ mod test_utils {
embedding_registry: Arc::new(MemoryRegistry::new()),
}
}
pub fn new_with_handler_and_config<T>(
name: impl Into<String>,
handler: impl Fn(reqwest::Request) -> http::Response<T> + Clone + Send + Sync + 'static,
config: crate::remote::ClientConfig,
) -> Self
where
T: Into<reqwest::Body>,
{
let inner = Arc::new(crate::remote::table::RemoteTable::new_mock_with_config(
name.into(),
handler.clone(),
config.clone(),
));
let database = Arc::new(crate::remote::db::RemoteDatabase::new_mock_with_config(
handler, config,
));
Self {
inner,
database: Some(database),
// Registry is unused.
embedding_registry: Arc::new(MemoryRegistry::new()),
}
}
}
}
@@ -572,14 +694,16 @@ impl Table {
///
/// # Arguments
///
/// * `data` data to be added to the Table
/// * `batches` data to be added to the Table
/// * `options` options to control how data is added
pub fn add<T: Scannable + 'static>(&self, data: T) -> AddDataBuilder {
AddDataBuilder::new(
self.inner.clone(),
Box::new(data),
Some(self.embedding_registry.clone()),
)
pub fn add<T: IntoArrow>(&self, batches: T) -> AddDataBuilder<T> {
AddDataBuilder {
parent: self.inner.clone(),
data: batches,
mode: AddDataMode::Append,
write_options: WriteOptions::default(),
embedding_registry: Some(self.embedding_registry.clone()),
}
}
/// Update existing records in the Table
@@ -618,26 +742,31 @@ impl Table {
/// .execute()
/// .await
/// .unwrap();
/// let schema = Arc::new(Schema::new(vec![
/// Field::new("id", DataType::Int32, false),
/// Field::new("vector", DataType::FixedSizeList(
/// Arc::new(Field::new("item", DataType::Float32, true)), 128), true),
/// ]));
/// let data = RecordBatch::try_new(
/// schema.clone(),
/// vec![
/// Arc::new(Int32Array::from_iter_values(0..10)),
/// Arc::new(
/// FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
/// (0..10).map(|_| Some(vec![Some(1.0); 128])),
/// 128,
/// # let schema = Arc::new(Schema::new(vec![
/// # Field::new("id", DataType::Int32, false),
/// # Field::new("vector", DataType::FixedSizeList(
/// # Arc::new(Field::new("item", DataType::Float32, true)), 128), true),
/// # ]));
/// let batches = RecordBatchIterator::new(
/// vec![RecordBatch::try_new(
/// schema.clone(),
/// vec![
/// Arc::new(Int32Array::from_iter_values(0..10)),
/// Arc::new(
/// FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
/// (0..10).map(|_| Some(vec![Some(1.0); 128])),
/// 128,
/// ),
/// ),
/// ),
/// ],
/// )
/// .unwrap();
/// ],
/// )
/// .unwrap()]
/// .into_iter()
/// .map(Ok),
/// schema.clone(),
/// );
/// let tbl = db
/// .create_table("delete_test", data)
/// .create_table("delete_test", Box::new(batches))
/// .execute()
/// .await
/// .unwrap();
@@ -1397,7 +1526,7 @@ impl NativeTable {
name: name.to_string(),
source: Box::new(e),
},
e => e.into(),
source => Error::Lance { source },
})?;
let dataset = DatasetConsistencyWrapper::new_latest(dataset, read_consistency_interval);
@@ -1481,7 +1610,7 @@ impl NativeTable {
lance::Error::Namespace { source, .. } => Error::Runtime {
message: format!("Failed to get table info from namespace: {:?}", source),
},
e => e.into(),
source => Error::Lance { source },
})?;
let dataset = builder
@@ -1493,7 +1622,7 @@ impl NativeTable {
name: name.to_string(),
source: Box::new(e),
},
e => e.into(),
source => Error::Lance { source },
})?;
let uri = dataset.uri().to_string();
@@ -1587,7 +1716,7 @@ impl NativeTable {
lance::Error::DatasetAlreadyExists { .. } => Error::TableAlreadyExists {
name: name.to_string(),
},
e => e.into(),
source => Error::Lance { source },
})?;
let id = Self::build_id(&namespace, name);
@@ -1614,12 +1743,12 @@ impl NativeTable {
read_consistency_interval: Option<std::time::Duration>,
namespace_client: Option<Arc<dyn LanceNamespace>>,
) -> Result<Self> {
let data: Box<dyn Scannable> = Box::new(RecordBatch::new_empty(schema));
let batches = RecordBatchIterator::new(vec![], schema);
Self::create(
uri,
name,
namespace,
data,
batches,
write_store_wrapper,
params,
read_consistency_interval,
@@ -1708,7 +1837,7 @@ impl NativeTable {
lance::Error::DatasetAlreadyExists { .. } => Error::TableAlreadyExists {
name: name.to_string(),
},
e => e.into(),
source => Error::Lance { source },
})?;
let id = Self::build_id(&namespace, name);
@@ -1730,6 +1859,16 @@ impl NativeTable {
})
}
async fn optimize_indices(&self, options: &OptimizeOptions) -> Result<()> {
info!("LanceDB: optimizing indices: {:?}", options);
self.dataset
.get_mut()
.await?
.optimize_indices(options)
.await?;
Ok(())
}
/// Merge new data into this table.
pub async fn merge(
&mut self,
@@ -1745,6 +1884,47 @@ impl NativeTable {
Ok(())
}
/// Remove old versions of the dataset from disk.
///
/// # Arguments
/// * `older_than` - The duration of time to keep versions of the dataset.
/// * `delete_unverified` - Because they may be part of an in-progress
/// transaction, files newer than 7 days old are not deleted by default.
/// If you are sure that there are no in-progress transactions, then you
/// can set this to True to delete all files older than `older_than`.
///
/// This calls into [lance::dataset::Dataset::cleanup_old_versions] and
/// returns the result.
async fn cleanup_old_versions(
&self,
older_than: Duration,
delete_unverified: Option<bool>,
error_if_tagged_old_versions: Option<bool>,
) -> Result<RemovalStats> {
Ok(self
.dataset
.get_mut()
.await?
.cleanup_old_versions(older_than, delete_unverified, error_if_tagged_old_versions)
.await?)
}
/// Compact files in the dataset.
///
/// This can be run after making several small appends to optimize the table
/// for faster reads.
///
/// This calls into [lance::dataset::optimize::compact_files].
async fn compact_files(
&self,
options: CompactionOptions,
remap_options: Option<Arc<dyn IndexRemapperOptions>>,
) -> Result<CompactionMetrics> {
let mut dataset_mut = self.dataset.get_mut().await?;
let metrics = compact_files(&mut dataset_mut, options, remap_options).await?;
Ok(metrics)
}
// TODO: why are these individual methods and not some single "get_stats" method?
pub async fn count_fragments(&self) -> Result<usize> {
Ok(self.dataset.get().await?.count_fragments())
@@ -2490,7 +2670,17 @@ impl BaseTable for NativeTable {
}
}
async fn add(&self, add: AddDataBuilder) -> Result<AddResult> {
async fn add(
&self,
add: AddDataBuilder<NoData>,
data: Box<dyn RecordBatchReader + Send>,
) -> Result<AddResult> {
let data = Box::new(MaybeEmbedded::try_new(
data,
self.table_definition().await?,
add.embedding_registry,
)?) as Box<dyn RecordBatchReader + Send>;
let lance_params = add.write_options.lance_write_params.unwrap_or(WriteParams {
mode: match add.mode {
AddDataMode::Append => WriteMode::Append,
@@ -2499,11 +2689,6 @@ impl BaseTable for NativeTable {
..Default::default()
});
// Apply embeddings if configured
let table_def = self.table_definition().await?;
let data =
scannable_with_embeddings(add.data, &table_def, add.embedding_registry.as_ref())?;
let dataset = {
// Limited scope for the mutable borrow of self.dataset avoids deadlock.
let ds = self.dataset.get_mut().await?;
@@ -2825,8 +3010,55 @@ impl BaseTable for NativeTable {
}
async fn optimize(&self, action: OptimizeAction) -> Result<OptimizeStats> {
// Delegate to the submodule implementation
optimize::execute_optimize(self, action).await
let mut stats = OptimizeStats {
compaction: None,
prune: None,
};
match action {
OptimizeAction::All => {
stats.compaction = self
.optimize(OptimizeAction::Compact {
options: CompactionOptions::default(),
remap_options: None,
})
.await?
.compaction;
stats.prune = self
.optimize(OptimizeAction::Prune {
older_than: None,
delete_unverified: None,
error_if_tagged_old_versions: None,
})
.await?
.prune;
self.optimize(OptimizeAction::Index(OptimizeOptions::default()))
.await?;
}
OptimizeAction::Compact {
options,
remap_options,
} => {
stats.compaction = Some(self.compact_files(options, remap_options).await?);
}
OptimizeAction::Prune {
older_than,
delete_unverified,
error_if_tagged_old_versions,
} => {
stats.prune = Some(
self.cleanup_old_versions(
older_than.unwrap_or(Duration::try_days(7).expect("valid delta")),
delete_unverified,
error_if_tagged_old_versions,
)
.await?,
);
}
OptimizeAction::Index(options) => {
self.optimize_indices(&options).await?;
}
}
Ok(stats)
}
async fn add_columns(
@@ -3110,7 +3342,7 @@ mod tests {
use arrow_array::{BinaryArray, LargeBinaryArray};
use arrow_data::ArrayDataBuilder;
use arrow_schema::{DataType, Field, Schema};
use futures::TryStreamExt;
use lance::dataset::WriteMode;
use lance::io::{ObjectStoreParams, WrappingObjectStore};
use lance::Dataset;
use rand::Rng;
@@ -3121,17 +3353,14 @@ mod tests {
use crate::connection::ConnectBuilder;
use crate::index::scalar::{BTreeIndexBuilder, BitmapIndexBuilder};
use crate::index::vector::{IvfHnswPqIndexBuilder, IvfHnswSqIndexBuilder};
use crate::query::{ExecutableQuery, QueryBase};
use crate::test_utils::connection::new_test_connection;
#[tokio::test]
async fn test_open() {
let tmp_dir = tempdir().unwrap();
let dataset_path = tmp_dir.path().join("test.lance");
let batch = make_test_batches();
let reader = RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema());
Dataset::write(reader, dataset_path.to_str().unwrap(), None)
let batches = make_test_batches();
Dataset::write(batches, dataset_path.to_str().unwrap(), None)
.await
.unwrap();
@@ -3164,12 +3393,9 @@ mod tests {
let tmp_dir = tempdir().unwrap();
let uri = tmp_dir.path().to_str().unwrap();
let batch = make_test_batches();
let reader: Box<dyn RecordBatchReader + Send> = Box::new(RecordBatchIterator::new(
vec![Ok(batch.clone())],
batch.schema(),
));
let table = NativeTable::create(uri, "test", vec![], reader, None, None, None, None)
let batches = make_test_batches();
let batches = Box::new(batches) as Box<dyn RecordBatchReader + Send>;
let table = NativeTable::create(uri, "test", vec![], batches, None, None, None, None)
.await
.unwrap();
@@ -3183,6 +3409,33 @@ mod tests {
);
}
#[tokio::test]
async fn test_add() {
let tmp_dir = tempdir().unwrap();
let uri = tmp_dir.path().to_str().unwrap();
let conn = connect(uri).execute().await.unwrap();
let batches = make_test_batches();
let schema = batches.schema().clone();
let table = conn.create_table("test", batches).execute().await.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 10);
let new_batches = RecordBatchIterator::new(
vec![RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(100..110))],
)
.unwrap()]
.into_iter()
.map(Ok),
schema.clone(),
);
table.add(new_batches).execute().await.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 20);
assert_eq!(table.name(), "test");
}
#[tokio::test]
async fn test_merge_insert() {
let tmp_dir = tempdir().unwrap();
@@ -3199,7 +3452,7 @@ mod tests {
assert_eq!(table.count_rows(None).await.unwrap(), 10);
// Create new data with i=5..15
let new_batches = merge_insert_test_batches(5, 1);
let new_batches = Box::new(merge_insert_test_batches(5, 1));
// Perform a "insert if not exists"
let mut merge_insert_builder = table.merge_insert(&["i"]);
@@ -3213,7 +3466,7 @@ mod tests {
assert_eq!(result.num_attempts, 1);
// Create new data with i=15..25 (no id matches)
let new_batches = merge_insert_test_batches(15, 2);
let new_batches = Box::new(merge_insert_test_batches(15, 2));
// Perform a "bulk update" (should not affect anything)
let mut merge_insert_builder = table.merge_insert(&["i"]);
merge_insert_builder.when_matched_update_all(None);
@@ -3226,7 +3479,7 @@ mod tests {
);
// Conditional update that only replaces the age=0 data
let new_batches = merge_insert_test_batches(5, 3);
let new_batches = Box::new(merge_insert_test_batches(5, 3));
let mut merge_insert_builder = table.merge_insert(&["i"]);
merge_insert_builder.when_matched_update_all(Some("target.age = 0".to_string()));
merge_insert_builder.execute(new_batches).await.unwrap();
@@ -3252,7 +3505,7 @@ mod tests {
assert_eq!(table.count_rows(None).await.unwrap(), 10);
// Test use_index=true (default behavior)
let new_batches = merge_insert_test_batches(5, 1);
let new_batches = Box::new(merge_insert_test_batches(5, 1));
let mut merge_insert_builder = table.merge_insert(&["i"]);
merge_insert_builder.when_not_matched_insert_all();
merge_insert_builder.use_index(true);
@@ -3260,7 +3513,7 @@ mod tests {
assert_eq!(table.count_rows(None).await.unwrap(), 15);
// Test use_index=false (force table scan)
let new_batches = merge_insert_test_batches(15, 2);
let new_batches = Box::new(merge_insert_test_batches(15, 2));
let mut merge_insert_builder = table.merge_insert(&["i"]);
merge_insert_builder.when_not_matched_insert_all();
merge_insert_builder.use_index(false);
@@ -3268,6 +3521,59 @@ mod tests {
assert_eq!(table.count_rows(None).await.unwrap(), 25);
}
#[tokio::test]
async fn test_add_overwrite() {
let tmp_dir = tempdir().unwrap();
let uri = tmp_dir.path().to_str().unwrap();
let conn = connect(uri).execute().await.unwrap();
let batches = make_test_batches();
let schema = batches.schema().clone();
let table = conn.create_table("test", batches).execute().await.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 10);
let batches = vec![RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(100..110))],
)
.unwrap()]
.into_iter()
.map(Ok);
let new_batches = RecordBatchIterator::new(batches.clone(), schema.clone());
// Can overwrite using AddDataOptions::mode
table
.add(new_batches)
.mode(AddDataMode::Overwrite)
.execute()
.await
.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 10);
assert_eq!(table.name(), "test");
// Can overwrite using underlying WriteParams (which
// take precedence over AddDataOptions::mode)
let param: WriteParams = WriteParams {
mode: WriteMode::Overwrite,
..Default::default()
};
let new_batches = RecordBatchIterator::new(batches.clone(), schema.clone());
table
.add(new_batches)
.write_options(WriteOptions {
lance_write_params: Some(param),
})
.mode(AddDataMode::Append)
.execute()
.await
.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 10);
assert_eq!(table.name(), "test");
}
#[derive(Default, Debug)]
struct NoOpCacheWrapper {
called: AtomicBool,
@@ -3323,25 +3629,35 @@ mod tests {
assert!(wrapper.called());
}
fn merge_insert_test_batches(offset: i32, age: i32) -> Box<dyn RecordBatchReader + Send> {
fn merge_insert_test_batches(
offset: i32,
age: i32,
) -> impl RecordBatchReader + Send + Sync + 'static {
let schema = Arc::new(Schema::new(vec![
Field::new("i", DataType::Int32, false),
Field::new("age", DataType::Int32, false),
]));
let batch = RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_iter_values(offset..(offset + 10))),
Arc::new(Int32Array::from_iter_values(std::iter::repeat_n(age, 10))),
],
RecordBatchIterator::new(
vec![RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_iter_values(offset..(offset + 10))),
Arc::new(Int32Array::from_iter_values(std::iter::repeat_n(age, 10))),
],
)],
schema,
)
.unwrap();
Box::new(RecordBatchIterator::new(vec![Ok(batch)], schema))
}
fn make_test_batches() -> RecordBatch {
fn make_test_batches() -> impl RecordBatchReader + Send + Sync + 'static {
let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
RecordBatch::try_new(schema, vec![Arc::new(Int32Array::from_iter_values(0..10))]).unwrap()
RecordBatchIterator::new(
vec![RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..10))],
)],
schema,
)
}
#[tokio::test]
@@ -3429,9 +3745,14 @@ mod tests {
);
let vectors = Arc::new(create_fixed_size_list(float_arr, dimension).unwrap());
let batch = RecordBatch::try_new(schema.clone(), vec![vectors.clone()]).unwrap();
let batches = RecordBatchIterator::new(
vec![RecordBatch::try_new(schema.clone(), vec![vectors.clone()]).unwrap()]
.into_iter()
.map(Ok),
schema,
);
let table = conn.create_table("test", batch).execute().await.unwrap();
let table = conn.create_table("test", batches).execute().await.unwrap();
assert_eq!(table.index_stats("my_index").await.unwrap(), None);
@@ -3462,31 +3783,6 @@ mod tests {
assert_eq!(table.list_indices().await.unwrap().len(), 0);
}
#[tokio::test]
async fn test_dynamic_select() {
let tc = new_test_connection().await.unwrap();
let db = tc.connection;
let table = db
.create_table("test", some_sample_data())
.execute()
.await
.unwrap();
let query = table.query().select(Select::dynamic(&[("i_alias", "i")]));
let result = query.execute().await;
let batches = result
.expect("should have result")
.try_collect::<Vec<_>>()
.await
.unwrap();
for batch in batches {
assert!(batch.column_by_name("i_alias").is_some());
}
}
#[tokio::test]
async fn test_ivf_pq_uses_default_partition_size_for_num_partitions() {
use arrow_array::{Float32Array, RecordBatch};
@@ -3513,9 +3809,14 @@ mod tests {
let float_arr =
Float32Array::from_iter_values((0..(num_rows * dimension)).map(|v| v as f32));
let vectors = Arc::new(create_fixed_size_list(float_arr, dimension as i32).unwrap());
let batch = RecordBatch::try_new(schema.clone(), vec![vectors]).unwrap();
let batches = RecordBatchIterator::new(
vec![RecordBatch::try_new(schema.clone(), vec![vectors]).unwrap()]
.into_iter()
.map(Ok),
schema,
);
let table = conn.create_table("test", batch).execute().await.unwrap();
let table = conn.create_table("test", batches).execute().await.unwrap();
let native_table = table.as_native().unwrap();
let builder = IvfPqIndexBuilder::default();
table
@@ -3585,9 +3886,14 @@ mod tests {
);
let vectors = Arc::new(create_fixed_size_list(float_arr, dimension).unwrap());
let batch = RecordBatch::try_new(schema.clone(), vec![vectors.clone()]).unwrap();
let batches = RecordBatchIterator::new(
vec![RecordBatch::try_new(schema.clone(), vec![vectors.clone()]).unwrap()]
.into_iter()
.map(Ok),
schema,
);
let table = conn.create_table("test", batch).execute().await.unwrap();
let table = conn.create_table("test", batches).execute().await.unwrap();
let stats = table.index_stats("my_index").await.unwrap();
assert!(stats.is_none());
@@ -3645,9 +3951,14 @@ mod tests {
);
let vectors = Arc::new(create_fixed_size_list(float_arr, dimension).unwrap());
let batch = RecordBatch::try_new(schema.clone(), vec![vectors.clone()]).unwrap();
let batches = RecordBatchIterator::new(
vec![RecordBatch::try_new(schema.clone(), vec![vectors.clone()]).unwrap()]
.into_iter()
.map(Ok),
schema,
);
let table = conn.create_table("test", batch).execute().await.unwrap();
let table = conn.create_table("test", batches).execute().await.unwrap();
let stats = table.index_stats("my_index").await.unwrap();
assert!(stats.is_none());
@@ -3690,7 +4001,7 @@ mod tests {
Ok(FixedSizeListArray::from(data))
}
fn some_sample_data() -> Box<dyn arrow_array::RecordBatchReader + Send> {
fn some_sample_data() -> Box<dyn RecordBatchReader + Send> {
let batch = RecordBatch::try_new(
Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)])),
vec![Arc::new(Int32Array::from(vec![1]))],
@@ -3714,7 +4025,10 @@ mod tests {
.unwrap();
let conn = ConnectBuilder::new(uri).execute().await.unwrap();
let table = conn
.create_table("my_table", batch.clone())
.create_table(
"my_table",
RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema()),
)
.execute()
.await
.unwrap();
@@ -3793,7 +4107,10 @@ mod tests {
.unwrap();
let table = conn
.create_table("test_bitmap", batch.clone())
.create_table(
"test_bitmap",
RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema()),
)
.execute()
.await
.unwrap();
@@ -3894,7 +4211,10 @@ mod tests {
.unwrap();
let table = conn
.create_table("test_bitmap", batch.clone())
.create_table(
"test_bitmap",
RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema()),
)
.execute()
.await
.unwrap();
@@ -3954,7 +4274,10 @@ mod tests {
.unwrap();
let table = conn
.create_table("test_bitmap", batch.clone())
.create_table(
"test_bitmap",
RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema()),
)
.execute()
.await
.unwrap();
@@ -3999,7 +4322,7 @@ mod tests {
let conn1 = ConnectBuilder::new(uri).execute().await.unwrap();
let table1 = conn1
.create_empty_table("my_table", RecordBatchReader::schema(&data))
.create_empty_table("my_table", data.schema())
.execute()
.await
.unwrap();
@@ -4269,7 +4592,10 @@ mod tests {
.unwrap();
let table = conn
.create_table("test_stats", batch.clone())
.create_table(
"test_stats",
RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema()),
)
.execute()
.await
.unwrap();
@@ -4282,11 +4608,21 @@ mod tests {
],
)
.unwrap();
table.add(batch.clone()).execute().await.unwrap();
table
.add(RecordBatchIterator::new(
vec![Ok(batch.clone())],
batch.schema(),
))
.execute()
.await
.unwrap();
}
let empty_table = conn
.create_table("test_stats_empty", RecordBatch::new_empty(batch.schema()))
.create_table(
"test_stats_empty",
RecordBatchIterator::new(vec![], batch.schema()),
)
.execute()
.await
.unwrap();
@@ -4360,12 +4696,22 @@ mod tests {
.unwrap();
let table = conn
.create_table("test_list_indices_skip_frag_reuse", batch.clone())
.create_table(
"test_list_indices_skip_frag_reuse",
RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema()),
)
.execute()
.await
.unwrap();
table.add(batch.clone()).execute().await.unwrap();
table
.add(RecordBatchIterator::new(
vec![Ok(batch.clone())],
batch.schema(),
))
.execute()
.await
.unwrap();
table
.create_index(&["id"], Index::Bitmap(BitmapIndexBuilder {}))
@@ -4395,9 +4741,8 @@ mod tests {
let tmp_dir = tempdir().unwrap();
let dataset_path = tmp_dir.path().join("test_ns_query.lance");
let batch = make_test_batches();
let reader = RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema());
Dataset::write(reader, dataset_path.to_str().unwrap(), None)
let batches = make_test_batches();
Dataset::write(batches, dataset_path.to_str().unwrap(), None)
.await
.unwrap();
@@ -4449,9 +4794,8 @@ mod tests {
let tmp_dir = tempdir().unwrap();
let dataset_path = tmp_dir.path().join("test_ns_plain.lance");
let batch = make_test_batches();
let reader = RecordBatchIterator::new(vec![Ok(batch.clone())], batch.schema());
Dataset::write(reader, dataset_path.to_str().unwrap(), None)
let batches = make_test_batches();
Dataset::write(batches, dataset_path.to_str().unwrap(), None)
.await
.unwrap();

View File

@@ -1,343 +0,0 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
use std::sync::Arc;
use serde::{Deserialize, Serialize};
use crate::data::scannable::Scannable;
use crate::embeddings::EmbeddingRegistry;
use crate::Result;
use super::{BaseTable, WriteOptions};
#[derive(Debug, Clone, Default)]
pub enum AddDataMode {
/// Rows will be appended to the table (the default)
#[default]
Append,
/// The existing table will be overwritten with the new data
Overwrite,
}
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
pub struct AddResult {
// The commit version associated with the operation.
// A version of `0` indicates compatibility with legacy servers that do not return
/// a commit version.
#[serde(default)]
pub version: u64,
}
/// A builder for configuring a [`crate::table::Table::add`] operation
pub struct AddDataBuilder {
pub(crate) parent: Arc<dyn BaseTable>,
pub(crate) data: Box<dyn Scannable>,
pub(crate) mode: AddDataMode,
pub(crate) write_options: WriteOptions,
pub(crate) embedding_registry: Option<Arc<dyn EmbeddingRegistry>>,
}
impl std::fmt::Debug for AddDataBuilder {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("AddDataBuilder")
.field("parent", &self.parent)
.field("mode", &self.mode)
.field("write_options", &self.write_options)
.finish()
}
}
impl AddDataBuilder {
pub(crate) fn new(
parent: Arc<dyn BaseTable>,
data: Box<dyn Scannable>,
embedding_registry: Option<Arc<dyn EmbeddingRegistry>>,
) -> Self {
Self {
parent,
data,
mode: AddDataMode::Append,
write_options: WriteOptions::default(),
embedding_registry,
}
}
pub fn mode(mut self, mode: AddDataMode) -> Self {
self.mode = mode;
self
}
pub fn write_options(mut self, options: WriteOptions) -> Self {
self.write_options = options;
self
}
pub async fn execute(self) -> Result<AddResult> {
self.parent.clone().add(self).await
}
}
#[cfg(test)]
mod tests {
use std::sync::Arc;
use arrow_array::{record_batch, RecordBatch, RecordBatchIterator};
use arrow_schema::{ArrowError, DataType, Field, Schema};
use futures::TryStreamExt;
use lance::dataset::{WriteMode, WriteParams};
use crate::arrow::{SendableRecordBatchStream, SimpleRecordBatchStream};
use crate::connect;
use crate::data::scannable::Scannable;
use crate::embeddings::{
EmbeddingDefinition, EmbeddingFunction, EmbeddingRegistry, MemoryRegistry,
};
use crate::query::{ExecutableQuery, QueryBase, Select};
use crate::table::{ColumnDefinition, ColumnKind, Table, TableDefinition, WriteOptions};
use crate::test_utils::embeddings::MockEmbed;
use crate::Error;
use super::AddDataMode;
async fn create_test_table() -> Table {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("id", Int64, [1, 2, 3])).unwrap();
conn.create_table("test", batch).execute().await.unwrap()
}
async fn test_add_with_data<T>(data: T)
where
T: Scannable + 'static,
{
let table = create_test_table().await;
let schema = data.schema();
table.add(data).execute().await.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 5); // 3 initial + 2 added
assert_eq!(table.schema().await.unwrap(), schema);
}
#[tokio::test]
async fn test_add_with_batch() {
let batch = record_batch!(("id", Int64, [4, 5])).unwrap();
test_add_with_data(batch).await;
}
#[tokio::test]
async fn test_add_with_vec_batch() {
let data = vec![
record_batch!(("id", Int64, [4])).unwrap(),
record_batch!(("id", Int64, [5])).unwrap(),
];
test_add_with_data(data).await;
}
#[tokio::test]
async fn test_add_with_record_batch_reader() {
let data = vec![
record_batch!(("id", Int64, [4])).unwrap(),
record_batch!(("id", Int64, [5])).unwrap(),
];
let schema = data[0].schema();
let reader: Box<dyn arrow_array::RecordBatchReader + Send> = Box::new(
RecordBatchIterator::new(data.into_iter().map(Ok), schema.clone()),
);
test_add_with_data(reader).await;
}
#[tokio::test]
async fn test_add_with_stream() {
let data = vec![
record_batch!(("id", Int64, [4])).unwrap(),
record_batch!(("id", Int64, [5])).unwrap(),
];
let schema = data[0].schema();
let inner = futures::stream::iter(data.into_iter().map(Ok));
let stream: SendableRecordBatchStream = Box::pin(SimpleRecordBatchStream {
schema,
stream: inner,
});
test_add_with_data(stream).await;
}
#[derive(Debug)]
struct MyError;
impl std::fmt::Display for MyError {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "MyError occurred")
}
}
impl std::error::Error for MyError {}
#[tokio::test]
async fn test_add_preserves_reader_error() {
let table = create_test_table().await;
let first_batch = record_batch!(("id", Int64, [4])).unwrap();
let schema = first_batch.schema();
let iterator = vec![
Ok(first_batch),
Err(ArrowError::ExternalError(Box::new(MyError))),
];
let reader: Box<dyn arrow_array::RecordBatchReader + Send> = Box::new(
RecordBatchIterator::new(iterator.into_iter(), schema.clone()),
);
let result = table.add(reader).execute().await;
assert!(result.is_err());
}
#[tokio::test]
async fn test_add_preserves_stream_error() {
let table = create_test_table().await;
let first_batch = record_batch!(("id", Int64, [4])).unwrap();
let schema = first_batch.schema();
let iterator = vec![
Ok(first_batch),
Err(Error::External {
source: Box::new(MyError),
}),
];
let stream = futures::stream::iter(iterator);
let stream: SendableRecordBatchStream = Box::pin(SimpleRecordBatchStream {
schema: schema.clone(),
stream,
});
let result = table.add(stream).execute().await;
assert!(result.is_err());
}
#[tokio::test]
async fn test_add() {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("i", Int32, [0, 1, 2])).unwrap();
let table = conn
.create_table("test", batch.clone())
.execute()
.await
.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 3);
let new_batch = record_batch!(("i", Int32, [3])).unwrap();
table.add(new_batch).execute().await.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 4);
assert_eq!(table.schema().await.unwrap(), batch.schema());
}
#[tokio::test]
async fn test_add_overwrite() {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("i", Int32, [0, 1, 2])).unwrap();
let table = conn
.create_table("test", batch.clone())
.execute()
.await
.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), batch.num_rows());
let new_batch = record_batch!(("x", Float32, [0.0, 1.0])).unwrap();
let res = table
.add(new_batch.clone())
.mode(AddDataMode::Overwrite)
.execute()
.await
.unwrap();
assert_eq!(res.version, table.version().await.unwrap());
assert_eq!(table.count_rows(None).await.unwrap(), new_batch.num_rows());
assert_eq!(table.schema().await.unwrap(), new_batch.schema());
// Can overwrite using underlying WriteParams (which
// take precedence over AddDataMode)
let param: WriteParams = WriteParams {
mode: WriteMode::Overwrite,
..Default::default()
};
table
.add(new_batch.clone())
.write_options(WriteOptions {
lance_write_params: Some(param),
})
.mode(AddDataMode::Append)
.execute()
.await
.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), new_batch.num_rows());
}
#[tokio::test]
async fn test_add_with_embeddings() {
let registry = Arc::new(MemoryRegistry::new());
let mock_embedding: Arc<dyn EmbeddingFunction> = Arc::new(MockEmbed::new("mock", 4));
registry.register("mock", mock_embedding).unwrap();
let conn = connect("memory://")
.embedding_registry(registry)
.execute()
.await
.unwrap();
let schema = Arc::new(Schema::new(vec![
Field::new("text", DataType::Utf8, false),
Field::new(
"text_embedding",
DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
false,
),
]));
// Add embedding metadata to the schema
let embedding_def = EmbeddingDefinition::new("text", "mock", Some("text_embedding"));
let table_def = TableDefinition::new(
schema.clone(),
vec![
ColumnDefinition {
kind: ColumnKind::Physical,
},
ColumnDefinition {
kind: ColumnKind::Embedding(embedding_def),
},
],
);
let rich_schema = table_def.into_rich_schema();
let table = conn
.create_empty_table("embed_test", rich_schema)
.execute()
.await
.unwrap();
// Now add new data WITHOUT the embedding column - it should be computed automatically
let new_batch = record_batch!(("text", Utf8, ["hello", "world"])).unwrap();
table.add(new_batch).execute().await.unwrap();
assert_eq!(table.count_rows(None).await.unwrap(), 2);
// Query to verify the embeddings were computed for the new rows
let results: Vec<RecordBatch> = table
.query()
.select(Select::columns(&["text", "text_embedding"]))
.execute()
.await
.unwrap()
.try_collect()
.await
.unwrap();
let total_rows: usize = results.iter().map(|b| b.num_rows()).sum();
assert_eq!(total_rows, 2);
// Check that all rows have embedding values (not null)
for batch in &results {
let embedding_col = batch.column(1);
assert_eq!(embedding_col.null_count(), 0);
}
}
}

View File

@@ -287,7 +287,8 @@ pub mod tests {
use arrow::array::AsArray;
use arrow_array::{
BinaryArray, Float64Array, Int32Array, Int64Array, RecordBatch, StringArray, UInt32Array,
BinaryArray, Float64Array, Int32Array, Int64Array, RecordBatch, RecordBatchIterator,
RecordBatchReader, StringArray, UInt32Array,
};
use arrow_schema::{DataType, Field, Schema};
use datafusion::{
@@ -307,7 +308,7 @@ pub mod tests {
table::datafusion::BaseTableAdapter,
};
fn make_test_batches() -> RecordBatch {
fn make_test_batches() -> impl RecordBatchReader + Send + Sync + 'static {
let metadata = HashMap::from_iter(vec![("foo".to_string(), "bar".to_string())]);
let schema = Arc::new(
Schema::new(vec![
@@ -316,17 +317,19 @@ pub mod tests {
])
.with_metadata(metadata),
);
RecordBatch::try_new(
RecordBatchIterator::new(
vec![RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_iter_values(0..10)),
Arc::new(UInt32Array::from_iter_values(0..10)),
],
)],
schema,
vec![
Arc::new(Int32Array::from_iter_values(0..10)),
Arc::new(UInt32Array::from_iter_values(0..10)),
],
)
.unwrap()
}
fn make_tbl_two_test_batches() -> RecordBatch {
fn make_tbl_two_test_batches() -> impl RecordBatchReader + Send + Sync + 'static {
let metadata = HashMap::from_iter(vec![("foo".to_string(), "bar".to_string())]);
let schema = Arc::new(
Schema::new(vec![
@@ -339,26 +342,28 @@ pub mod tests {
])
.with_metadata(metadata),
);
RecordBatch::try_new(
RecordBatchIterator::new(
vec![RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int64Array::from_iter_values(0..1000)),
Arc::new(StringArray::from_iter_values(
(0..1000).map(|i| i.to_string()),
)),
Arc::new(Float64Array::from_iter_values((0..1000).map(|i| i as f64))),
Arc::new(StringArray::from_iter_values(
(0..1000).map(|i| format!("{{\"i\":{}}}", i)),
)),
Arc::new(BinaryArray::from_iter_values(
(0..1000).map(|i| (i as u32).to_be_bytes().to_vec()),
)),
Arc::new(StringArray::from_iter_values(
(0..1000).map(|i| i.to_string()),
)),
],
)],
schema,
vec![
Arc::new(Int64Array::from_iter_values(0..1000)),
Arc::new(StringArray::from_iter_values(
(0..1000).map(|i| i.to_string()),
)),
Arc::new(Float64Array::from_iter_values((0..1000).map(|i| i as f64))),
Arc::new(StringArray::from_iter_values(
(0..1000).map(|i| format!("{{\"i\":{}}}", i)),
)),
Arc::new(BinaryArray::from_iter_values(
(0..1000).map(|i| (i as u32).to_be_bytes().to_vec()),
)),
Arc::new(StringArray::from_iter_values(
(0..1000).map(|i| i.to_string()),
)),
],
)
.unwrap()
}
struct TestFixture {

View File

@@ -222,7 +222,7 @@ mod tests {
use std::vec;
use super::*;
use arrow_array::{record_batch, RecordBatchIterator};
use arrow_array::{record_batch, Int32Array, RecordBatchIterator};
use datafusion::prelude::SessionContext;
use datafusion_catalog::MemTable;
use tempfile::tempdir;
@@ -238,8 +238,11 @@ mod tests {
// Create initial table
let batch = record_batch!(("id", Int32, [1, 2, 3])).unwrap();
let schema = batch.schema();
let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
let table = db
.create_table("test_insert", batch)
.create_table("test_insert", Box::new(reader))
.execute()
.await
.unwrap();
@@ -276,8 +279,11 @@ mod tests {
// Create initial table with 3 rows
let batch = record_batch!(("id", Int32, [1, 2, 3])).unwrap();
let schema = batch.schema();
let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
let table = db
.create_table("test_overwrite", batch)
.create_table("test_overwrite", Box::new(reader))
.execute()
.await
.unwrap();
@@ -312,9 +318,20 @@ mod tests {
let db = connect(uri).execute().await.unwrap();
// Create initial table
let batch = record_batch!(("id", Int32, [1, 2, 3])).unwrap();
let schema = Arc::new(ArrowSchema::new(vec![Field::new(
"id",
DataType::Int32,
false,
)]));
let batches = vec![RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
)
.unwrap()];
let reader = RecordBatchIterator::new(batches.into_iter().map(Ok), schema.clone());
let table = db
.create_table("test_empty", batch)
.create_table("test_empty", Box::new(reader))
.execute()
.await
.unwrap();
@@ -335,13 +352,12 @@ mod tests {
false,
)]));
// Empty batches
let source_reader: Box<dyn arrow_array::RecordBatchReader + Send> =
Box::new(RecordBatchIterator::new(
std::iter::empty::<Result<RecordBatch, arrow_schema::ArrowError>>(),
source_schema,
));
let source_reader = RecordBatchIterator::new(
std::iter::empty::<Result<RecordBatch, arrow_schema::ArrowError>>(),
source_schema,
);
let source_table = db
.create_table("empty_source", source_reader)
.create_table("empty_source", Box::new(source_reader))
.execute()
.await
.unwrap();
@@ -373,10 +389,20 @@ mod tests {
let db = connect(uri).execute().await.unwrap();
// Create initial table
let batch = record_batch!(("id", Int32, [1])).unwrap();
let schema = batch.schema();
let schema = Arc::new(ArrowSchema::new(vec![Field::new(
"id",
DataType::Int32,
true,
)]));
let batches =
vec![
RecordBatch::try_new(schema.clone(), vec![Arc::new(Int32Array::from(vec![1]))])
.unwrap(),
];
let reader = RecordBatchIterator::new(batches.into_iter().map(Ok), schema.clone());
let table = db
.create_table("test_multi_batch", batch)
.create_table("test_multi_batch", Box::new(reader))
.execute()
.await
.unwrap();

View File

@@ -97,7 +97,7 @@ mod tests {
table::datafusion::BaseTableAdapter,
Connection, Table,
};
use arrow_array::{Int32Array, RecordBatch, StringArray};
use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator, StringArray};
use arrow_schema::{DataType, Field, Schema as ArrowSchema};
use datafusion::prelude::SessionContext;
@@ -173,7 +173,14 @@ mod tests {
// Create LanceDB database and table
let db = crate::connect("memory://test").execute().await.unwrap();
let table = db.create_table("foo", batch).execute().await.unwrap();
let table = db
.create_table(
"foo",
RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema),
)
.execute()
.await
.unwrap();
// Create FTS index
table
@@ -316,7 +323,13 @@ mod tests {
RecordBatch::try_new(metadata_schema.clone(), vec![metadata_col, extra_col]).unwrap();
let _metadata_table = db
.create_table("metadata", metadata_batch.clone())
.create_table(
"metadata",
RecordBatchIterator::new(
vec![Ok(metadata_batch.clone())].into_iter(),
metadata_schema.clone(),
),
)
.execute()
.await
.unwrap();
@@ -380,7 +393,14 @@ mod tests {
let batch =
RecordBatch::try_new(schema.clone(), vec![id_col, text_col, category_col]).unwrap();
let table = db.create_table(table_name, batch).execute().await.unwrap();
let table = db
.create_table(
table_name,
RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema),
)
.execute()
.await
.unwrap();
// Create FTS index
table
@@ -526,7 +546,14 @@ mod tests {
]));
let batch = RecordBatch::try_new(schema.clone(), vec![id_col, text_col]).unwrap();
let table = db.create_table("docs", batch).execute().await.unwrap();
let table = db
.create_table(
"docs",
RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema),
)
.execute()
.await
.unwrap();
// Create FTS index with position information for phrase queries
table
@@ -664,7 +691,14 @@ mod tests {
let batch =
RecordBatch::try_new(schema.clone(), vec![id_col, title_col, content_col]).unwrap();
let table = db.create_table("multi_col", batch).execute().await.unwrap();
let table = db
.create_table(
"multi_col",
RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema),
)
.execute()
.await
.unwrap();
// Create FTS indices on both columns
table
@@ -929,7 +963,13 @@ mod tests {
let metadata_batch =
RecordBatch::try_new(metadata_schema.clone(), vec![metadata_id, extra_info]).unwrap();
let _metadata_table = db
.create_table("metadata", metadata_batch.clone())
.create_table(
"metadata",
RecordBatchIterator::new(
vec![Ok(metadata_batch.clone())].into_iter(),
metadata_schema,
),
)
.execute()
.await
.unwrap();
@@ -1318,7 +1358,14 @@ mod tests {
]));
let batch = RecordBatch::try_new(schema.clone(), vec![id_col, text_col]).unwrap();
let table = db.create_table("docs", batch).execute().await.unwrap();
let table = db
.create_table(
"docs",
RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema),
)
.execute()
.await
.unwrap();
// Create FTS index with position information
table
@@ -1463,7 +1510,14 @@ mod tests {
let batch =
RecordBatch::try_new(schema.clone(), vec![id_col, title_col, content_col]).unwrap();
let table = db.create_table("docs", batch).execute().await.unwrap();
let table = db
.create_table(
"docs",
RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema),
)
.execute()
.await
.unwrap();
// Create FTS indices on both columns
table
@@ -1537,7 +1591,14 @@ mod tests {
let batch =
RecordBatch::try_new(schema.clone(), vec![id_col, title_col, content_col]).unwrap();
let table = db.create_table("docs", batch).execute().await.unwrap();
let table = db
.create_table(
"docs",
RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema),
)
.execute()
.await
.unwrap();
// Create FTS indices
table
@@ -1663,23 +1724,36 @@ mod tests {
.unwrap();
// Create table with simple text for n-gram testing
let data = RecordBatch::try_new(
let data = RecordBatchIterator::new(
vec![RecordBatch::try_new(
Arc::new(ArrowSchema::new(vec![
Field::new("id", DataType::Int32, false),
Field::new("text", DataType::Utf8, false),
])),
vec![
Arc::new(Int32Array::from(vec![1, 2, 3])),
Arc::new(StringArray::from(vec![
"hello world",
"lance database",
"lance is cool",
])),
],
)
.unwrap()]
.into_iter()
.map(Ok),
Arc::new(ArrowSchema::new(vec![
Field::new("id", DataType::Int32, false),
Field::new("text", DataType::Utf8, false),
])),
vec![
Arc::new(Int32Array::from(vec![1, 2, 3])),
Arc::new(StringArray::from(vec![
"hello world",
"lance database",
"lance is cool",
])),
],
)
.unwrap();
);
let table = Arc::new(db.create_table("docs", data).execute().await.unwrap());
let table = Arc::new(
db.create_table("docs", Box::new(data))
.execute()
.await
.unwrap(),
);
// Create FTS index with n-gram tokenizer (default min_ngram_length=3)
table
@@ -1802,29 +1876,43 @@ mod tests {
.unwrap();
// Create table with two text columns
let data = RecordBatch::try_new(
let data = RecordBatchIterator::new(
vec![RecordBatch::try_new(
Arc::new(ArrowSchema::new(vec![
Field::new("id", DataType::Int32, false),
Field::new("title", DataType::Utf8, false),
Field::new("content", DataType::Utf8, false),
])),
vec![
Arc::new(Int32Array::from(vec![1, 2, 3])),
Arc::new(StringArray::from(vec![
"Important Document",
"Another Document",
"Random Text",
])),
Arc::new(StringArray::from(vec![
"This is important information",
"This has details",
"Nothing special here",
])),
],
)
.unwrap()]
.into_iter()
.map(Ok),
Arc::new(ArrowSchema::new(vec![
Field::new("id", DataType::Int32, false),
Field::new("title", DataType::Utf8, false),
Field::new("content", DataType::Utf8, false),
])),
vec![
Arc::new(Int32Array::from(vec![1, 2, 3])),
Arc::new(StringArray::from(vec![
"Important Document",
"Another Document",
"Random Text",
])),
Arc::new(StringArray::from(vec![
"This is important information",
"This has details",
"Nothing special here",
])),
],
)
.unwrap();
);
let table = Arc::new(db.create_table("docs", data).execute().await.unwrap());
let table = Arc::new(
db.create_table("docs", Box::new(data))
.execute()
.await
.unwrap(),
);
// Create FTS indices on both columns
table

View File

@@ -57,6 +57,15 @@ impl DatasetRef {
matches!(self, Self::Latest { .. })
}
async fn need_reload(&self) -> Result<bool> {
Ok(match self {
Self::Latest { dataset, .. } => {
dataset.latest_version_id().await? != dataset.version().version
}
Self::TimeTravel { dataset, version } => dataset.version().version != *version,
})
}
async fn as_latest(&mut self, read_consistency_interval: Option<Duration>) -> Result<()> {
match self {
Self::Latest { .. } => Ok(()),
@@ -109,21 +118,6 @@ impl DatasetRef {
Ok(())
}
fn is_up_to_date(&self) -> bool {
match self {
Self::Latest {
read_consistency_interval,
last_consistency_check,
..
} => match (read_consistency_interval, last_consistency_check) {
(None, _) => true,
(Some(_), None) => false,
(Some(interval), Some(last_check)) => last_check.elapsed() < *interval,
},
Self::TimeTravel { dataset, version } => dataset.version().version == *version,
}
}
fn time_travel_version(&self) -> Option<u64> {
match self {
Self::Latest { .. } => None,
@@ -211,7 +205,18 @@ impl DatasetConsistencyWrapper {
}
pub async fn reload(&self) -> Result<()> {
self.0.write().await.reload().await
if !self.0.read().await.need_reload().await? {
return Ok(());
}
let mut write_guard = self.0.write().await;
// on lock escalation -- check if someone else has already reloaded
if !write_guard.need_reload().await? {
return Ok(());
}
// actually need reloading
write_guard.reload().await
}
/// Returns the version, if in time travel mode, or None otherwise
@@ -230,20 +235,35 @@ impl DatasetConsistencyWrapper {
}
}
async fn is_up_to_date(&self) -> bool {
self.0.read().await.is_up_to_date()
async fn is_up_to_date(&self) -> Result<bool> {
let dataset_ref = self.0.read().await;
match &*dataset_ref {
DatasetRef::Latest {
read_consistency_interval,
last_consistency_check,
..
} => match (read_consistency_interval, last_consistency_check) {
(None, _) => Ok(true),
(Some(_), None) => Ok(false),
(Some(read_consistency_interval), Some(last_consistency_check)) => {
if &last_consistency_check.elapsed() < read_consistency_interval {
Ok(true)
} else {
Ok(false)
}
}
},
DatasetRef::TimeTravel { dataset, version } => {
Ok(dataset.version().version == *version)
}
}
}
/// Ensures that the dataset is loaded and up-to-date with consistency and
/// version parameters.
async fn ensure_up_to_date(&self) -> Result<()> {
if !self.is_up_to_date().await {
// Re-check under write lock — another task may have reloaded
// while we waited for the lock.
let mut write_guard = self.0.write().await;
if !write_guard.is_up_to_date() {
write_guard.reload().await?;
}
if !self.is_up_to_date().await? {
self.reload().await?;
}
Ok(())
}
@@ -331,60 +351,4 @@ mod tests {
let stats = io_stats.incremental_stats();
assert_eq!(stats.read_iops, 1);
}
/// Regression test: before the fix, the reload fast-path (no version change)
/// did not reset `last_consistency_check`, causing a list call on every
/// subsequent query once the interval expired.
#[tokio::test]
async fn test_reload_resets_consistency_timer() {
let db = connect("memory://")
.read_consistency_interval(Duration::from_secs(1))
.execute()
.await
.unwrap();
let io_stats = IoStatsHolder::default();
let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
let table = db
.create_empty_table("test", schema)
.write_options(WriteOptions {
lance_write_params: Some(WriteParams {
store_params: Some(ObjectStoreParams {
object_store_wrapper: Some(Arc::new(io_stats.clone())),
..Default::default()
}),
..Default::default()
}),
})
.execute()
.await
.unwrap();
let start = Instant::now();
io_stats.incremental_stats(); // reset
// Step 1: within interval — no list
table.schema().await.unwrap();
let s = io_stats.incremental_stats();
assert_eq!(s.read_iops, 0, "step 1, elapsed={:?}", start.elapsed());
// Step 2: still within interval — no list
table.schema().await.unwrap();
let s = io_stats.incremental_stats();
assert_eq!(s.read_iops, 0, "step 2, elapsed={:?}", start.elapsed());
// Step 3: sleep past the 1s boundary
tokio::time::sleep(Duration::from_secs(1)).await;
// Step 4: interval expired — exactly 1 list, timer resets
table.schema().await.unwrap();
let s = io_stats.incremental_stats();
assert_eq!(s.read_iops, 1, "step 4, elapsed={:?}", start.elapsed());
// Step 5: 10 more calls — timer just reset, no lists (THIS is the regression test).
for _ in 0..10 {
table.schema().await.unwrap();
}
let s = io_stats.incremental_stats();
assert_eq!(s.read_iops, 0, "step 5, elapsed={:?}", start.elapsed());
}
}

View File

@@ -34,7 +34,7 @@ pub(crate) async fn execute_delete(table: &NativeTable, predicate: &str) -> Resu
#[cfg(test)]
mod tests {
use crate::connect;
use arrow_array::{record_batch, Int32Array, RecordBatch};
use arrow_array::{record_batch, Int32Array, RecordBatch, RecordBatchIterator};
use arrow_schema::{DataType, Field, Schema};
use std::sync::Arc;
@@ -53,7 +53,10 @@ mod tests {
.unwrap();
let table = conn
.create_table("test_delete", batch)
.create_table(
"test_delete",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -99,7 +102,10 @@ mod tests {
let original_schema = batch.schema();
let table = conn
.create_table("test_delete_all", batch)
.create_table(
"test_delete_all",
RecordBatchIterator::new(vec![Ok(batch)], original_schema.clone()),
)
.execute()
.await
.unwrap();
@@ -120,8 +126,13 @@ mod tests {
// Create a table with 5 rows
let batch = record_batch!(("id", Int32, [1, 2, 3, 4, 5])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_delete_noop", batch)
.create_table(
"test_delete_noop",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();

View File

@@ -1,729 +0,0 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
//! Table optimization operations for compaction, pruning, and index optimization.
//!
//! This module contains the implementation of optimization operations that help
//! maintain good performance for LanceDB tables.
use std::sync::Arc;
use lance::dataset::cleanup::RemovalStats;
use lance::dataset::optimize::{compact_files, CompactionMetrics, IndexRemapperOptions};
use lance_index::optimize::OptimizeOptions;
use lance_index::DatasetIndexExt;
use log::info;
pub use chrono::Duration;
pub use lance::dataset::optimize::CompactionOptions;
use super::NativeTable;
use crate::error::Result;
/// Optimize the dataset.
///
/// Similar to `VACUUM` in PostgreSQL, it offers different options to
/// optimize different parts of the table on disk.
///
/// By default, it optimizes everything, as [`OptimizeAction::All`].
pub enum OptimizeAction {
/// Run all optimizations with default values
All,
/// Compacts files in the dataset
///
/// LanceDb uses a readonly filesystem for performance and safe concurrency. Every time
/// new data is added it will be added into new files. Small files
/// can hurt both read and write performance. Compaction will merge small files
/// into larger ones.
///
/// All operations that modify data (add, delete, update, merge insert, etc.) will create
/// new files. If these operations are run frequently then compaction should run frequently.
///
/// If these operations are never run (search only) then compaction is not necessary.
Compact {
options: CompactionOptions,
remap_options: Option<Arc<dyn IndexRemapperOptions>>,
},
/// Prune old version of datasets
///
/// Every change in LanceDb is additive. When data is removed from a dataset a new version is
/// created that doesn't contain the removed data. However, the old version, which does contain
/// the removed data, is left in place. This is necessary for consistency and concurrency and
/// also enables time travel functionality like the ability to checkout an older version of the
/// dataset to undo changes.
///
/// Over time, these old versions can consume a lot of disk space. The prune operation will
/// remove versions of the dataset that are older than a certain age. This will free up the
/// space used by that old data.
///
/// Once a version is pruned it can no longer be checked out.
Prune {
/// The duration of time to keep versions of the dataset.
older_than: Option<Duration>,
/// Because they may be part of an in-progress transaction, files newer than 7 days old are not deleted by default.
/// If you are sure that there are no in-progress transactions, then you can set this to True to delete all files older than `older_than`.
delete_unverified: Option<bool>,
/// If true, an error will be returned if there are any old versions that are still tagged.
error_if_tagged_old_versions: Option<bool>,
},
/// Optimize the indices
///
/// This operation optimizes all indices in the table. When new data is added to LanceDb
/// it is not added to the indices. However, it can still turn up in searches because the search
/// function will scan both the indexed data and the unindexed data in parallel. Over time, the
/// unindexed data can become large enough that the search performance is slow. This operation
/// will add the unindexed data to the indices without rerunning the full index creation process.
///
/// Optimizing an index is faster than re-training the index but it does not typically adjust the
/// underlying model relied upon by the index. This can eventually lead to poor search accuracy
/// and so users may still want to occasionally retrain the index after adding a large amount of
/// data.
///
/// For example, when using IVF, an index will create clusters. Optimizing an index assigns unindexed
/// data to the existing clusters, but it does not move the clusters or create new clusters.
Index(OptimizeOptions),
}
impl Default for OptimizeAction {
fn default() -> Self {
Self::All
}
}
/// Statistics about the optimization.
#[derive(Debug, Default)]
pub struct OptimizeStats {
/// Stats of the file compaction.
pub compaction: Option<CompactionMetrics>,
/// Stats of the version pruning
pub prune: Option<RemovalStats>,
}
/// Internal implementation of optimize_indices
///
/// This logic was moved from NativeTable to keep table.rs clean.
pub(crate) async fn optimize_indices(table: &NativeTable, options: &OptimizeOptions) -> Result<()> {
info!("LanceDB: optimizing indices: {:?}", options);
table
.dataset
.get_mut()
.await?
.optimize_indices(options)
.await?;
Ok(())
}
/// Remove old versions of the dataset from disk.
///
/// # Arguments
/// * `older_than` - The duration of time to keep versions of the dataset.
/// * `delete_unverified` - Because they may be part of an in-progress
/// transaction, files newer than 7 days old are not deleted by default.
/// If you are sure that there are no in-progress transactions, then you
/// can set this to True to delete all files older than `older_than`.
///
/// This calls into [lance::dataset::Dataset::cleanup_old_versions] and
/// returns the result.
pub(crate) async fn cleanup_old_versions(
table: &NativeTable,
older_than: Duration,
delete_unverified: Option<bool>,
error_if_tagged_old_versions: Option<bool>,
) -> Result<RemovalStats> {
Ok(table
.dataset
.get_mut()
.await?
.cleanup_old_versions(older_than, delete_unverified, error_if_tagged_old_versions)
.await?)
}
/// Compact files in the dataset.
///
/// This can be run after making several small appends to optimize the table
/// for faster reads.
///
/// This calls into [lance::dataset::optimize::compact_files].
pub(crate) async fn compact_files_impl(
table: &NativeTable,
options: CompactionOptions,
remap_options: Option<Arc<dyn IndexRemapperOptions>>,
) -> Result<CompactionMetrics> {
let mut dataset_mut = table.dataset.get_mut().await?;
let metrics = compact_files(&mut dataset_mut, options, remap_options).await?;
Ok(metrics)
}
/// Execute the optimize operation on the table.
///
/// This is the main entry point for all optimization operations.
pub(crate) async fn execute_optimize(
table: &NativeTable,
action: OptimizeAction,
) -> Result<OptimizeStats> {
let mut stats = OptimizeStats {
compaction: None,
prune: None,
};
match action {
OptimizeAction::All => {
// Call helper functions directly to avoid async recursion issues
stats.compaction =
Some(compact_files_impl(table, CompactionOptions::default(), None).await?);
stats.prune = Some(
cleanup_old_versions(
table,
Duration::try_days(7).expect("valid delta"),
None,
None,
)
.await?,
);
optimize_indices(table, &OptimizeOptions::default()).await?;
}
OptimizeAction::Compact {
options,
remap_options,
} => {
stats.compaction = Some(compact_files_impl(table, options, remap_options).await?);
}
OptimizeAction::Prune {
older_than,
delete_unverified,
error_if_tagged_old_versions,
} => {
stats.prune = Some(
cleanup_old_versions(
table,
older_than.unwrap_or(Duration::try_days(7).expect("valid delta")),
delete_unverified,
error_if_tagged_old_versions,
)
.await?,
);
}
OptimizeAction::Index(options) => {
optimize_indices(table, &options).await?;
}
}
Ok(stats)
}
#[cfg(test)]
mod tests {
use arrow_array::{Int32Array, RecordBatch, StringArray};
use arrow_schema::{DataType, Field, Schema};
use rstest::rstest;
use std::sync::Arc;
use crate::connect;
use crate::index::{scalar::BTreeIndexBuilder, Index};
use crate::query::ExecutableQuery;
use crate::table::{CompactionOptions, OptimizeAction, OptimizeStats};
use futures::TryStreamExt;
#[tokio::test]
async fn test_optimize_compact_simple() {
let conn = connect("memory://").execute().await.unwrap();
// Create a table with initial data
let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..100))],
)
.unwrap();
let table = conn
.create_table("test_compact", batch)
.execute()
.await
.unwrap();
// Add more data to create multiple fragments
for i in 0..5 {
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(
(i * 100 + 100)..((i + 1) * 100 + 100),
))],
)
.unwrap();
table.add(batch).execute().await.unwrap();
}
// Verify we have multiple fragments before compaction
let initial_row_count = table.count_rows(None).await.unwrap();
assert_eq!(initial_row_count, 600);
// Run compaction
let stats = table
.optimize(OptimizeAction::Compact {
options: CompactionOptions {
target_rows_per_fragment: 1000,
..Default::default()
},
remap_options: None,
})
.await
.unwrap();
// Verify compaction occurred
assert!(stats.compaction.is_some());
let compaction_metrics = stats.compaction.unwrap();
assert!(compaction_metrics.fragments_removed > 0);
// Verify data integrity after compaction
let final_row_count = table.count_rows(None).await.unwrap();
assert_eq!(final_row_count, 600);
// Verify data content is correct
let batches = table
.query()
.execute()
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
let total_rows: usize = batches.iter().map(|b| b.num_rows()).sum();
assert_eq!(total_rows, 600);
// Verify the values are as expected
let mut all_values: Vec<i32> = Vec::new();
for batch in &batches {
let array = batch["i"].as_any().downcast_ref::<Int32Array>().unwrap();
all_values.extend(array.values().iter().copied());
}
all_values.sort();
let expected: Vec<i32> = (0..600).collect();
assert_eq!(all_values, expected);
}
#[tokio::test]
async fn test_optimize_prune_versions() {
let conn = connect("memory://").execute().await.unwrap();
// Create a table
let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..10))],
)
.unwrap();
let table = conn
.create_table("test_prune", batch)
.execute()
.await
.unwrap();
// Make several modifications to create versions
for i in 0..5 {
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(
(i * 10 + 10)..((i + 1) * 10 + 10),
))],
)
.unwrap();
table.add(batch).execute().await.unwrap();
}
// Verify multiple versions exist
let versions = table.list_versions().await.unwrap();
assert!(versions.len() > 1);
// Run prune with a very old cutoff (won't delete recent versions)
let stats = table
.optimize(OptimizeAction::Prune {
older_than: Some(chrono::Duration::try_days(0).unwrap()),
delete_unverified: Some(true),
error_if_tagged_old_versions: None,
})
.await
.unwrap();
// Prune-only operation should not have compaction stats
assert!(stats.compaction.is_none());
// Verify prune stats
let prune_stats = stats.prune.unwrap();
assert!(prune_stats.bytes_removed > 0);
assert_eq!(prune_stats.old_versions, 5);
// Verify data is still intact
let final_row_count = table.count_rows(None).await.unwrap();
assert_eq!(final_row_count, 60);
// Verify data content is correct
let batches = table
.query()
.execute()
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
let mut all_values: Vec<i32> = Vec::new();
for batch in &batches {
let array = batch["i"].as_any().downcast_ref::<Int32Array>().unwrap();
all_values.extend(array.values().iter().copied());
}
all_values.sort();
let expected: Vec<i32> = (0..60).collect();
assert_eq!(all_values, expected);
}
#[tokio::test]
async fn test_optimize_index() {
let conn = connect("memory://").execute().await.unwrap();
// Create a table with data
let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..100))],
)
.unwrap();
let table = conn
.create_table("test_index_optimize", batch)
.execute()
.await
.unwrap();
// Create an index
table
.create_index(&["i"], Index::BTree(BTreeIndexBuilder::default()))
.execute()
.await
.unwrap();
// Add more data (unindexed)
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(100..200))],
)
.unwrap();
table.add(batch).execute().await.unwrap();
// Verify index stats before optimization
let indices = table.list_indices().await.unwrap();
assert_eq!(indices.len(), 1);
let index_name = indices[0].name.clone();
let stats_before = table.index_stats(&index_name).await.unwrap().unwrap();
assert_eq!(stats_before.num_indexed_rows, 100);
assert_eq!(stats_before.num_unindexed_rows, 100);
// Run index optimization
let stats = table
.optimize(OptimizeAction::Index(Default::default()))
.await
.unwrap();
// For index optimization, compaction and prune stats should be None
assert!(stats.compaction.is_none());
assert!(stats.prune.is_none());
// Verify index stats after optimization
let stats_after = table.index_stats(&index_name).await.unwrap().unwrap();
assert_eq!(stats_after.num_indexed_rows, 200);
assert_eq!(stats_after.num_unindexed_rows, 0);
assert!(stats_after.num_indices.is_some());
// Verify data integrity
let final_row_count = table.count_rows(None).await.unwrap();
assert_eq!(final_row_count, 200);
}
#[tokio::test]
async fn test_optimize_all() {
let conn = connect("memory://").execute().await.unwrap();
// Create a table with data
let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..100))],
)
.unwrap();
let table = conn
.create_table("test_optimize_all", batch)
.execute()
.await
.unwrap();
// Add more data
for i in 0..3 {
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(
(i * 100 + 100)..((i + 1) * 100 + 100),
))],
)
.unwrap();
table.add(batch).execute().await.unwrap();
}
// Run all optimizations
let stats = table.optimize(OptimizeAction::All).await.unwrap();
// Verify stats from both compaction and prune
assert!(stats.compaction.is_some());
assert!(stats.prune.is_some());
// Verify data integrity
let final_row_count = table.count_rows(None).await.unwrap();
assert_eq!(final_row_count, 400);
// Verify data content
let batches = table
.query()
.execute()
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
let mut all_values: Vec<i32> = Vec::new();
for batch in &batches {
let array = batch["i"].as_any().downcast_ref::<Int32Array>().unwrap();
all_values.extend(array.values().iter().copied());
}
all_values.sort();
let expected: Vec<i32> = (0..400).collect();
assert_eq!(all_values, expected);
}
#[tokio::test]
async fn test_optimize_default_action() {
// Verify that default action is All
let action: OptimizeAction = Default::default();
assert!(matches!(action, OptimizeAction::All));
}
#[tokio::test]
async fn test_optimize_stats_default() {
// Verify OptimizeStats default values
let stats: OptimizeStats = Default::default();
assert!(stats.compaction.is_none());
assert!(stats.prune.is_none());
}
#[tokio::test]
async fn test_compact_with_deferred_index_remap() {
// Smoke test: verifies compaction with deferred index remap doesn't error.
// We don't currently assert that remap is actually deferred.
let conn = connect("memory://").execute().await.unwrap();
// Create a table with data
let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..100))],
)
.unwrap();
let table = conn
.create_table("test_deferred_remap", batch.clone())
.execute()
.await
.unwrap();
// Add more data
table.add(batch).execute().await.unwrap();
// Create an index
table
.create_index(&["id"], Index::BTree(BTreeIndexBuilder::default()))
.execute()
.await
.unwrap();
// Run compaction with deferred index remap
let stats = table
.optimize(OptimizeAction::Compact {
options: CompactionOptions {
target_rows_per_fragment: 2000,
defer_index_remap: true,
..Default::default()
},
remap_options: None,
})
.await
.unwrap();
assert!(stats.compaction.is_some());
// Verify data integrity after compaction
let final_row_count = table.count_rows(None).await.unwrap();
assert_eq!(final_row_count, 200);
// Verify data content is correct
let batches = table
.query()
.execute()
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
let mut all_values: Vec<i32> = Vec::new();
for batch in &batches {
let array = batch["id"].as_any().downcast_ref::<Int32Array>().unwrap();
all_values.extend(array.values().iter().copied());
}
all_values.sort();
// Since we added the same data twice (0..100 twice), we expect 200 values
// with values 0-99 appearing twice
let mut expected: Vec<i32> = (0..100).chain(0..100).collect();
expected.sort();
assert_eq!(all_values, expected);
}
#[tokio::test]
async fn test_compaction_preserves_schema() {
let conn = connect("memory://").execute().await.unwrap();
// Create a table with multiple columns
let schema = Arc::new(Schema::new(vec![
Field::new("id", DataType::Int32, false),
Field::new("name", DataType::Utf8, true),
]));
let batch = RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_iter_values(0..10)),
Arc::new(StringArray::from(
(0..10).map(|i| format!("name_{}", i)).collect::<Vec<_>>(),
)),
],
)
.unwrap();
let original_schema = batch.schema();
let table = conn
.create_table("test_schema_preserved", batch.clone())
.execute()
.await
.unwrap();
// Add more data
table.add(batch).execute().await.unwrap();
// Run compaction
table
.optimize(OptimizeAction::Compact {
options: CompactionOptions::default(),
remap_options: None,
})
.await
.unwrap();
// Verify schema is preserved
let current_schema = table.schema().await.unwrap();
assert_eq!(current_schema, original_schema);
// Verify data is intact and correct
let batches = table
.query()
.execute()
.await
.unwrap()
.try_collect::<Vec<_>>()
.await
.unwrap();
let total_rows: usize = batches.iter().map(|b| b.num_rows()).sum();
assert_eq!(total_rows, 20);
}
#[tokio::test]
async fn test_optimize_empty_table() {
let conn = connect("memory://").execute().await.unwrap();
// Create a table and delete all data
let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..10))],
)
.unwrap();
let table = conn
.create_table("test_empty_optimize", batch)
.execute()
.await
.unwrap();
// Delete all rows
table.delete("true").await.unwrap();
// Verify table is empty
assert_eq!(table.count_rows(None).await.unwrap(), 0);
// Optimize should work on empty table
let stats = table.optimize(OptimizeAction::All).await.unwrap();
assert!(stats.compaction.is_some());
assert!(stats.prune.is_some());
// Verify table is still empty but schema is preserved
assert_eq!(table.count_rows(None).await.unwrap(), 0);
let current_schema = table.schema().await.unwrap();
assert_eq!(current_schema, schema);
}
#[rstest]
#[case::all(OptimizeAction::All)]
#[case::compact(OptimizeAction::Compact {
options: CompactionOptions::default(),
remap_options: None,
})]
#[case::prune(OptimizeAction::Prune {
older_than: Some(chrono::Duration::try_days(0).unwrap()),
delete_unverified: Some(true),
error_if_tagged_old_versions: None,
})]
#[case::index(OptimizeAction::Index(Default::default()))]
#[tokio::test]
async fn test_optimize_fails_on_checked_out_table(#[case] action: OptimizeAction) {
let conn = connect("memory://").execute().await.unwrap();
let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..10))],
)
.unwrap();
let table = conn
.create_table("test_checkout_optimize", batch.clone())
.execute()
.await
.unwrap();
table.add(batch).execute().await.unwrap();
table.checkout(1).await.unwrap();
let result = table.optimize(action).await;
assert!(result.is_err());
let err_msg = result.unwrap_err().to_string();
assert!(
err_msg.contains("cannot be modified when a specific version is checked out"),
"Expected error message about checked out table, got: {}",
err_msg
);
}
}

View File

@@ -89,7 +89,7 @@ pub(crate) async fn execute_drop_columns(
#[cfg(test)]
mod tests {
use arrow_array::{record_batch, Int32Array, StringArray};
use arrow_array::{record_batch, Int32Array, RecordBatchIterator, StringArray};
use arrow_schema::DataType;
use futures::TryStreamExt;
use lance::dataset::ColumnAlteration;
@@ -105,9 +105,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("id", Int32, [1, 2, 3, 4, 5])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_add_columns", batch)
.create_table(
"test_add_columns",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -165,9 +169,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("x", Int32, [10, 20, 30])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_add_multi_columns", batch)
.create_table(
"test_add_multi_columns",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -197,9 +205,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("id", Int32, [1, 2, 3])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_add_const_column", batch)
.create_table(
"test_add_const_column",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -243,9 +255,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("old_name", Int32, [1, 2, 3])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_alter_rename", batch)
.create_table(
"test_alter_rename",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -288,7 +304,10 @@ mod tests {
.unwrap();
let table = conn
.create_table("test_alter_nullable", batch)
.create_table(
"test_alter_nullable",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -313,9 +332,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("num", Int32, [1, 2, 3])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_cast_type", batch)
.create_table(
"test_cast_type",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -356,9 +379,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("num", Int32, [1, 2, 3])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_invalid_cast", batch)
.create_table(
"test_invalid_cast",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -380,9 +407,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("a", Int32, [1, 2, 3]), ("b", Int32, [4, 5, 6])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_alter_multi", batch)
.create_table(
"test_alter_multi",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -410,9 +441,13 @@ mod tests {
let batch =
record_batch!(("keep", Int32, [1, 2, 3]), ("remove", Int32, [4, 5, 6])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_drop_single", batch)
.create_table(
"test_drop_single",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -443,9 +478,13 @@ mod tests {
("d", Int32, [7, 8])
)
.unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_drop_multi", batch)
.create_table(
"test_drop_multi",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -472,9 +511,13 @@ mod tests {
("extra", Int32, [10, 20, 30])
)
.unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_drop_preserves", batch)
.create_table(
"test_drop_preserves",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -524,9 +567,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("existing", Int32, [1, 2, 3])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_drop_nonexistent", batch)
.create_table(
"test_drop_nonexistent",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -546,9 +593,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("existing", Int32, [1, 2, 3])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_alter_nonexistent", batch)
.create_table(
"test_alter_nonexistent",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();
@@ -572,8 +623,13 @@ mod tests {
let conn = connect("memory://").execute().await.unwrap();
let batch = record_batch!(("a", Int32, [1, 2, 3]), ("b", Int32, [4, 5, 6])).unwrap();
let schema = batch.schema();
let table = conn
.create_table("test_version_increment", batch)
.create_table(
"test_version_increment",
RecordBatchIterator::new(vec![Ok(batch)], schema),
)
.execute()
.await
.unwrap();

View File

@@ -117,8 +117,9 @@ mod tests {
use crate::query::{ExecutableQuery, Select};
use arrow_array::{
record_batch, Array, BooleanArray, Date32Array, FixedSizeListArray, Float32Array,
Float64Array, Int32Array, Int64Array, LargeStringArray, RecordBatch, StringArray,
TimestampMillisecondArray, TimestampNanosecondArray, UInt32Array,
Float64Array, Int32Array, Int64Array, LargeStringArray, RecordBatch, RecordBatchIterator,
RecordBatchReader, StringArray, TimestampMillisecondArray, TimestampNanosecondArray,
UInt32Array,
};
use arrow_data::ArrayDataBuilder;
use arrow_schema::{ArrowError, DataType, Field, Schema, TimeUnit};
@@ -166,46 +167,51 @@ mod tests {
),
]));
let batch = RecordBatch::try_new(
let record_batch_iter = RecordBatchIterator::new(
vec![RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Int32Array::from_iter_values(0..10)),
Arc::new(Int64Array::from_iter_values(0..10)),
Arc::new(UInt32Array::from_iter_values(0..10)),
Arc::new(StringArray::from_iter_values(vec![
"a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
])),
Arc::new(LargeStringArray::from_iter_values(vec![
"a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
])),
Arc::new(Float32Array::from_iter_values((0..10).map(|i| i as f32))),
Arc::new(Float64Array::from_iter_values((0..10).map(|i| i as f64))),
Arc::new(Into::<BooleanArray>::into(vec![
true, false, true, false, true, false, true, false, true, false,
])),
Arc::new(Date32Array::from_iter_values(0..10)),
Arc::new(TimestampNanosecondArray::from_iter_values(0..10)),
Arc::new(TimestampMillisecondArray::from_iter_values(0..10)),
Arc::new(
create_fixed_size_list(
Float32Array::from_iter_values((0..20).map(|i| i as f32)),
2,
)
.unwrap(),
),
Arc::new(
create_fixed_size_list(
Float64Array::from_iter_values((0..20).map(|i| i as f64)),
2,
)
.unwrap(),
),
],
)
.unwrap()]
.into_iter()
.map(Ok),
schema.clone(),
vec![
Arc::new(Int32Array::from_iter_values(0..10)),
Arc::new(Int64Array::from_iter_values(0..10)),
Arc::new(UInt32Array::from_iter_values(0..10)),
Arc::new(StringArray::from_iter_values(vec![
"a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
])),
Arc::new(LargeStringArray::from_iter_values(vec![
"a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
])),
Arc::new(Float32Array::from_iter_values((0..10).map(|i| i as f32))),
Arc::new(Float64Array::from_iter_values((0..10).map(|i| i as f64))),
Arc::new(Into::<BooleanArray>::into(vec![
true, false, true, false, true, false, true, false, true, false,
])),
Arc::new(Date32Array::from_iter_values(0..10)),
Arc::new(TimestampNanosecondArray::from_iter_values(0..10)),
Arc::new(TimestampMillisecondArray::from_iter_values(0..10)),
Arc::new(
create_fixed_size_list(
Float32Array::from_iter_values((0..20).map(|i| i as f32)),
2,
)
.unwrap(),
),
Arc::new(
create_fixed_size_list(
Float64Array::from_iter_values((0..20).map(|i| i as f64)),
2,
)
.unwrap(),
),
],
)
.unwrap();
);
let table = conn
.create_table("my_table", batch)
.create_table("my_table", record_batch_iter)
.execute()
.await
.unwrap();
@@ -332,13 +338,15 @@ mod tests {
Ok(FixedSizeListArray::from(data))
}
fn make_test_batch() -> RecordBatch {
fn make_test_batches() -> impl RecordBatchReader + Send + Sync + 'static {
let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..10))],
RecordBatchIterator::new(
vec![RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_iter_values(0..10))],
)],
schema,
)
.unwrap()
}
#[tokio::test]
@@ -359,8 +367,12 @@ mod tests {
)
.unwrap();
let schema = batch.schema();
// need the iterator for create table
let record_batch_iter = RecordBatchIterator::new(vec![Ok(batch)], schema);
let table = conn
.create_table("my_table", batch)
.create_table("my_table", record_batch_iter)
.execute()
.await
.unwrap();
@@ -418,7 +430,7 @@ mod tests {
.await
.unwrap();
let tbl = conn
.create_table("my_table", make_test_batch())
.create_table("my_table", make_test_batches())
.execute()
.await
.unwrap();

View File

@@ -3,4 +3,3 @@
pub mod connection;
pub mod datagen;
pub mod embeddings;

View File

@@ -34,7 +34,10 @@ impl LanceDbDatagenExt for BatchGeneratorBuilder {
schema,
));
let db = connect("memory:///").execute().await.unwrap();
db.create_table(table_name, stream).execute().await.unwrap()
db.create_table_streaming(table_name, stream)
.execute()
.await
.unwrap()
}
}
@@ -45,5 +48,8 @@ pub async fn virtual_table(name: &str, values: &RecordBatch) -> Table {
schema,
));
let db = connect("memory:///").execute().await.unwrap();
db.create_table(name, stream).execute().await.unwrap()
db.create_table_streaming(name, stream)
.execute()
.await
.unwrap()
}

View File

@@ -1,59 +0,0 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
use std::{borrow::Cow, sync::Arc};
use arrow_array::{Array, FixedSizeListArray, Float32Array};
use arrow_schema::{DataType, Field};
use crate::embeddings::EmbeddingFunction;
use crate::Result;
#[derive(Debug, Clone)]
pub struct MockEmbed {
name: String,
dim: usize,
}
impl MockEmbed {
pub fn new(name: impl Into<String>, dim: usize) -> Self {
Self {
name: name.into(),
dim,
}
}
}
impl EmbeddingFunction for MockEmbed {
fn name(&self) -> &str {
&self.name
}
fn source_type(&self) -> Result<Cow<'_, DataType>> {
Ok(Cow::Borrowed(&DataType::Utf8))
}
fn dest_type(&self) -> Result<Cow<'_, DataType>> {
Ok(Cow::Owned(DataType::new_fixed_size_list(
DataType::Float32,
self.dim as _,
true,
)))
}
fn compute_source_embeddings(&self, source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
// We can't use the FixedSizeListBuilder here because it always adds a null bitmap
// and we want to explicitly work with non-nullable arrays.
let len = source.len();
let inner = Arc::new(Float32Array::from(vec![Some(1.0); len * self.dim]));
let field = Field::new("item", inner.data_type().clone(), false);
let arr = FixedSizeListArray::new(Arc::new(field), self.dim as _, inner, None);
Ok(Arc::new(arr))
}
#[allow(unused_variables)]
fn compute_query_embeddings(&self, input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
todo!()
}
}

View File

@@ -15,6 +15,7 @@ use arrow_array::{
use arrow_schema::{DataType, Field, Schema};
use futures::StreamExt;
use lancedb::{
arrow::IntoArrow,
connect,
embeddings::{EmbeddingDefinition, EmbeddingFunction, EmbeddingRegistry},
query::ExecutableQuery,
@@ -252,7 +253,7 @@ async fn test_no_func_in_registry_on_add() -> Result<()> {
Ok(())
}
fn create_some_records() -> Result<Box<dyn arrow_array::RecordBatchReader + Send>> {
fn create_some_records() -> Result<impl IntoArrow> {
const TOTAL: usize = 2;
let schema = Arc::new(Schema::new(vec![

View File

@@ -4,7 +4,7 @@
#![cfg(feature = "s3-test")]
use std::sync::Arc;
use arrow_array::{Int32Array, RecordBatch, StringArray};
use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator, StringArray};
use arrow_schema::{DataType, Field, Schema};
use aws_config::{BehaviorVersion, ConfigLoader, Region, SdkConfig};
@@ -111,6 +111,7 @@ async fn test_minio_lifecycle() -> Result<()> {
.await?;
let data = test_data();
let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
let table = db.create_table("test_table", data).execute().await?;
@@ -126,6 +127,7 @@ async fn test_minio_lifecycle() -> Result<()> {
assert_eq!(row_count, 3);
let data = test_data();
let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
table.add(data).execute().await?;
db.drop_table("test_table", &[]).await?;
@@ -245,6 +247,7 @@ async fn test_encryption() -> Result<()> {
// Create a table with encryption
let data = test_data();
let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
let mut builder = db.create_table("test_table", data);
for (key, value) in CONFIG {
@@ -271,6 +274,7 @@ async fn test_encryption() -> Result<()> {
let table = db.open_table("test_table").execute().await?;
let data = test_data();
let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
table.add(data).execute().await?;
validate_objects_encrypted(&bucket.0, "test_table", &key.0).await;
@@ -296,6 +300,7 @@ async fn test_table_storage_options_override() -> Result<()> {
// Create table overriding with key2 encryption
let data = test_data();
let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
let _table = db
.create_table("test_override", data)
.storage_option("aws_sse_kms_key_id", &key2.0)
@@ -307,6 +312,7 @@ async fn test_table_storage_options_override() -> Result<()> {
// Also test that a table created without override uses connection settings
let data = test_data();
let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
let _table2 = db.create_table("test_inherit", data).execute().await?;
// Verify this table uses key1 from connection
@@ -413,6 +419,7 @@ async fn test_concurrent_dynamodb_commit() {
.unwrap();
let data = test_data();
let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
let table = db.create_table("test_table", data).execute().await.unwrap();
@@ -423,6 +430,7 @@ async fn test_concurrent_dynamodb_commit() {
let table = db.open_table("test_table").execute().await.unwrap();
let data = data.clone();
tasks.push(tokio::spawn(async move {
let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
table.add(data).execute().await.unwrap();
}));
}