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Author SHA1 Message Date
Will Jones f66285dd3c feat: expand IndexConfig with rich per-index metadata
IndexConfig (returned by Table::list_indices) previously exposed only
name, index_type, and columns. Lance's describe_indices provides richer
per-index info cheaply, so this surfaces it as Option<T> fields populated
in NativeTable::list_indices from the IndexDescription:

- index_uuid, type_url, created_at, num_indexed_rows, num_unindexed_rows,
  size_bytes, num_segments, index_version, index_details

The field set mirrors the lance-namespace IndexContent contract so client
and server agree on the same shape. RemoteTable leaves the new fields None
until a follow-up wires them through the server response.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-11 11:27:43 -07:00
lancedb automation 7ef49beaa8 chore: update lance dependency to v8.0.0-beta.11 2026-06-10 22:52:01 +00:00
154 changed files with 2470 additions and 17243 deletions
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@@ -1,137 +0,0 @@
---
name: lancedb-branch-ops
description: Branch management for LanceDB tables via the REST API. Use this skill whenever someone wants to create, delete, list, or switch branches on a LanceDB table — or needs to make sure a write (metadata update, index build, etc.) lands on a specific branch instead of main. Invoke it even without the word "branch" if context makes clear they want an experimental copy of a table, want to isolate changes, or want to confirm a mutation didn't touch main. Covers: branches/list, branches/create, branches/delete, and passing "branch" in describe/update_field_metadata/create_index to target a non-main version.
---
## Goal
Manage branches on a LanceDB table: list what exists, create new ones, delete stale ones, and direct read/write operations at a specific branch without touching main.
## Step 0: Establish the connection
Use the `lancedb-connect` skill to resolve the base URL and auth headers (`x-api-key`, `x-lancedb-database`). Skip this only if the connection is already known from the current conversation.
All examples below use `{base_url}` — substitute the resolved endpoint and include the auth headers on every request.
## The branch model (important)
LanceDB branches are named snapshots that diverge from the table's current state at creation time. There is **no checkout command** — you never switch the whole table to a branch. Instead, you **pass `"branch": "<name>"` in the request body** of any operation to target that branch. Omitting the key (or sending an empty body) always targets main.
`branches/list` returns only non-main branches. Main always exists and is not listed.
## List branches
```http
POST {base_url}/v1/table/{table_id}/branches/list
Content-Type: application/json
{}
```
Response:
```json
{
"branches": {
"experiment-reindex": {"parentVersion": 1, "createAt": 1782506085, "manifestSize": 1029}
}
}
```
If `branches` is `{}`, the table has no branches besides main.
## Create a branch
```http
POST {base_url}/v1/table/{table_id}/branches/create
Content-Type: application/json
{"name": "experiment-reindex"}
```
HTTP 200 with `{}` body = success. The branch is created off the table's current state on main.
Verify by calling `branches/list` and confirming the new name appears.
## Delete a branch
```http
POST {base_url}/v1/table/{table_id}/branches/delete
Content-Type: application/json
{"name": "stale-2024"}
```
HTTP 200 with `{}` body = success. Only the branch pointer is removed — main and all row data remain intact.
Verify by calling `branches/list` (name gone) and `describe` with no branch param (main still responds).
## Operate on a specific branch
Pass `"branch": "<name>"` in the body of any operation to scope it to that branch:
**Read schema on a branch:**
```http
POST {base_url}/v1/table/{table_id}/describe
Content-Type: application/json
{"branch": "wip-branch"}
```
**Write metadata to a branch (not main):**
```http
POST {base_url}/v1/table/{table_id}/update_field_metadata
Content-Type: application/json
{
"branch": "wip-branch",
"updates": [
{
"path": "category",
"metadata": {"lancedb:description": "Product category label."},
"replace": false
}
]
}
```
**Build an index on a branch:**
```http
POST {base_url}/v1/table/{table_id}/create_index
Content-Type: application/json
{
"branch": "wip-branch",
"column": "category",
"index_type": "BTREE"
}
```
## Verifying isolation
After writing to a branch, always confirm the change did NOT land on main:
```bash
# Should show the new metadata
curl -s -X POST {base_url}/v1/table/{table_id}/describe \
-H "x-api-key: <key>" -H "x-lancedb-database: <db>" \
-H "content-type: application/json" \
-d '{"branch": "wip-branch"}'
# Should NOT show the new metadata
curl -s -X POST {base_url}/v1/table/{table_id}/describe \
-H "x-api-key: <key>" -H "x-lancedb-database: <db>" \
-H "content-type: application/json" \
-d '{}'
```
## Quick reference
| Goal | Endpoint | Body |
|------|----------|------|
| List all branches | `branches/list` | `{}` |
| Create a branch | `branches/create` | `{"name": "..."}` |
| Delete a branch | `branches/delete` | `{"name": "..."}` |
| Read schema on branch | `describe` | `{"branch": "..."}` |
| Write metadata on branch | `update_field_metadata` | `{"branch": "...", "updates": [...]}` |
| Build index on branch | `create_index` | `{"branch": "...", "column": ..., "index_type": ...}` |
| Target main (default) | any endpoint | omit `"branch"` key |
@@ -1,178 +0,0 @@
---
name: lancedb-column-metadata
description: Column metadata authoring for LanceDB tables via the REST API. This skill is required for tasks like writing field descriptions, setting tags on columns (field_type, model, project_id, version), classifying columns as embeddings vs labels vs eval metrics, or grouping versioned columns into logical families — because it has the API integration needed to read the schema and persist metadata back. Invoke whenever someone wants to document, annotate, tag, or classify what their table columns ARE. Trigger even without an explicit "LanceDB" mention, as long as the context is column-level documentation or tagging for an ML or vector database table.
metadata:
short-description: Write column descriptions, tags, and logical groupings to a LanceDB table
---
## Overview
This skill authors column-level metadata for a LanceDB table. It connects to a LanceDB deployment over its REST API, inspects the table schema, generates appropriate metadata, and writes it back.
## Step 0: Establish the connection
Use the `lancedb-connect` skill (invoke it via the Skill tool) to resolve the base URL and auth headers (`x-api-key`, `x-lancedb-database`) for whichever deployment the user is working against — enterprise/self-hosted or a local dev server. Skip it only if the connection details are already established in the conversation.
All examples below use `{base_url}` — substitute the resolved endpoint and include the resolved headers on every request.
## Metadata keys
All metadata uses namespaced keys:
| Key | Purpose | Example value |
|-----|---------|---------------|
| `lancedb:description` | Human-readable explanation of what the column contains | `"CLIP ViT-L/14 image embedding, L2-normalized (768-dim)"` |
| `lancedb:tag:<name>` | Flexible key-value tag; the suffix names the tag category | `lancedb:tag:field_type: "embedding"`, `lancedb:tag:model: "clip"`, `lancedb:tag:project_id: "foo"` |
| `lancedb:logical-column` | Logical group/family this column belongs to | `"clip_features"` |
Tags are open-ended — use whatever key suffix and value make sense given the user's intent. The tag suffix should describe *what is being classified* (e.g., `field_type`, `model`, `project_id`) and the value describes *how*.
## Step 1: Resolve the table identifier
You need:
- **Table name** (required) — e.g., `my_table` or `my_namespace.my_table`
- **Database name** — ask if not provided and not inferable from context; it goes in the `x-lancedb-database` header, never in the URL path
The table identifier in the URL path is typically `table_name` for a top-level table, or `namespace$table_name` if the table lives in a namespace. The API accepts a `delimiter` query parameter to parse compound identifiers (default `$`).
## Step 2: Describe the table
```http
POST {base_url}/v1/table/{table_id}/describe
Content-Type: application/json
{}
```
The response contains `schema.fields` — an array of field objects:
```json
{
"schema": {
"fields": [
{
"name": "clip_embedding_v3",
"type": { "type": "FixedSizeList", "fields": [...], "listSize": 768 },
"nullable": true,
"metadata": { "lancedb:description": "..." }
}
]
}
}
```
Each field has:
- `name` — field name
- `type` — Arrow data type (check `type.type` for the type string)
- `nullable` — boolean
- `metadata` — existing key-value metadata (read this before writing to avoid redundant updates)
For struct/nested fields, recurse into `type.fields` and represent them as dot-notation paths (e.g., `parent.child`).
If the user hasn't specified which columns to update, work with all columns.
## Step 3: Generate metadata
Decide what to generate based on the user's request.
### Writing descriptions (`lancedb:description`)
Base descriptions on:
- The column name and Arrow type (e.g., `FixedSizeList` of floats → likely an embedding)
- User-supplied context (upstream pipeline, sample values, domain knowledge)
- Name patterns: `_embedding`/`_vec`/`_embed` → vector; `_label`/`_class` → label; `_score`/`_eval`/`_metric` → evaluation metric
Be specific and concise. Good: `"Sentence-BERT embedding of the query text (768-dim)."` Not: `"An embedding column."`
### Tagging columns (`lancedb:tag:<name>`)
Choose tag key names that match what the user asked to annotate. Common patterns:
- Semantic field type → `lancedb:tag:field_type: "embedding"` / `"text"` / `"image"` / `"label"` / `"eval"` / `"id"` / `"metadata"`
- Model or source → `lancedb:tag:model: "clip"` / `"bert"` / `"vit"`
- Project affiliation → `lancedb:tag:project_id: "<name>"`
- Version → `lancedb:tag:version: "v3"` (and `lancedb:tag:latest: "true"` for the newest)
Use Arrow type as a hint: `FixedSizeList` + float → embedding; `Utf8`/`LargeUtf8` → text; `Binary` → image or blob.
Multiple tags on the same column are fine — each is a separate key.
### Grouping into logical columns (`lancedb:logical-column`)
Look for naming patterns across columns:
- `clip_v1`, `clip_v2`, `clip_v3` → logical column `"clip"`, latest is `v3`
- `text_embed_20240101`, `text_embed_20240601` → logical column `"text_embed"`, latest is the most recent date suffix
Write `lancedb:logical-column` on all members of a group. Mark the newest with `lancedb:tag:latest: "true"` (in addition to its version tag).
## Step 4: Write the metadata
```http
POST {base_url}/v1/table/{table_id}/update_field_metadata
Content-Type: application/json
{
"updates": [
{
"path": "clip_v3",
"metadata": {
"lancedb:description": "CLIP ViT-L/14 image embedding, L2-normalized (1024-dim).",
"lancedb:tag:field_type": "embedding",
"lancedb:tag:model": "clip",
"lancedb:tag:version": "v3",
"lancedb:tag:latest": "true",
"lancedb:logical-column": "clip"
},
"replace": false
},
{
"path": "clip_v2",
"metadata": {
"lancedb:description": "CLIP ViT-B/32 image embedding (768-dim), superseded by v3.",
"lancedb:tag:field_type": "embedding",
"lancedb:tag:model": "clip",
"lancedb:tag:version": "v2",
"lancedb:logical-column": "clip"
},
"replace": false
}
]
}
```
Rules:
- **Use `"replace": false`** (merge) by default — this preserves existing metadata the user didn't ask to change
- Use `"replace": true` only if the user explicitly asks to overwrite all existing metadata on a column
- Set a value to `null` to delete a specific key
- Batch all updates in a single request when possible
The response includes `version` (new table version) and `fields` (the updated metadata per field).
## Step 5: Confirm
Report back:
- Which columns were updated and what was written
- The new table version number
- Any columns skipped (e.g., already had up-to-date metadata)
---
## Quick examples
**"Write descriptions for all columns in the `product_embeddings` table"**
1. POST `/v1/table/product_embeddings/describe` → get all fields
2. Generate a `lancedb:description` for each column based on name + type
3. POST `update_field_metadata` with descriptions
4. Report
**"Tag the columns in `model_outputs` with their field type and model"**
1. Describe `model_outputs`
2. For each field, classify by name + Arrow type → set `lancedb:tag:field_type` and `lancedb:tag:model` where applicable
3. POST `update_field_metadata`
4. Report
**"Group the feature columns in `training_features` into logical families and mark the latest version"**
1. Describe the table
2. Find version patterns → assign `lancedb:logical-column` and `lancedb:tag:version`; mark newest with `lancedb:tag:latest: "true"`
3. POST `update_field_metadata`
4. Show the grouping
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@@ -1,42 +0,0 @@
---
name: lancedb-connect
description: Resolve how to connect to a LanceDB deployment over the REST API — figure out the base URL, API key, and database header. Use this before making any REST requests to a LanceDB table, whenever the endpoint or auth setup is not already known. Also useful on its own when someone asks how to connect, authenticate, or curl their LanceDB instance.
metadata:
short-description: Resolve the base URL and auth headers for a LanceDB deployment
---
## Goal
Produce two things every REST request needs:
1. **Base URL** — the endpoint
2. **Headers**`x-api-key`, and usually `x-lancedb-database`
## Resolution steps
1. If the user already gave a URL and API key (or said which environment they're working against), use that.
2. Otherwise, look for credentials already available in the environment:
- Env vars like `LANCEDB_URI` / `LANCEDB_HOST` / `LANCEDB_API_KEY`
- A LanceDB endpoint already running or port-forwarded locally (the REST default port is 2333, i.e. `http://localhost:2333`)
3. If you didn't find both pieces, ask the user directly: **"What's your LanceDB endpoint's URL, and what's your API key?"** Also ask which database to use if it isn't obvious. Don't guess or probe further — the user knows their deployment.
## Validating the connection
Make a cheap authenticated request and check the status:
```bash
curl -s -w "\n%{http_code}" "{base_url}/v1/table/?limit=1" \
-H "x-api-key: <key>" \
-H "x-lancedb-database: <database>"
```
- `200` — connection, key, and database header all good
- `401` — API key missing or wrong
- `400` mentioning a database header — this deployment expects `x-lancedb-database`
## Non-REST equivalents
If the caller would rather use the SDK or CLI than raw REST, the same credentials work:
- Python SDK: `lancedb.connect("db://<database>", api_key="<key>", host_override="<base_url>")`
- `lancedb` CLI: a `[profiles.<name>]` entry in `~/.lancedb/config.toml` with `http_server_url`, `api_key`, `database`
+1 -3
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@@ -1,5 +1,5 @@
[tool.bumpversion]
current_version = "0.31.0-beta.6"
current_version = "0.30.1-beta.2"
parse = """(?x)
(?P<major>0|[1-9]\\d*)\\.
(?P<minor>0|[1-9]\\d*)\\.
@@ -23,8 +23,6 @@ allow_dirty = true
commit = true
message = "Bump version: {current_version} → {new_version}"
commit_args = ""
# bump-my-version >=1.4.0 rejects pre_commit_hooks containing shell syntax unless opted in.
allow_shell_hooks = true
# Java maven files
pre_commit_hooks = [
-21
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@@ -1,21 +0,0 @@
# CODEOWNERS
#
# These owners will be the default owners for everything in the repo.
# They will be requested for review when someone opens a pull request.
#
# See https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners
# Default owners for everything
* @jackye1995 @wjones127
# Release and publish workflows — changes here can affect supply chain security
/.github/workflows/ @jackye1995 @wjones127 @Xuanwo
# Remote client and auth — sensitive networking and auth code
/rust/lancedb/src/remote/ @jackye1995 @wjones127
# Python FFI boundary
/python/src/ @jackye1995 @wjones127 @AyushExel
# NodeJS FFI boundary
/nodejs/src/ @jackye1995 @wjones127
+9 -11
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@@ -34,16 +34,15 @@ runs:
maturin-version: "1.12.4"
command: build
working-directory: python
docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc"
docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/'"
target: x86_64-unknown-linux-gnu
manylinux: ${{ inputs.manylinux }}
args: ${{ inputs.args }}
before-script-linux: |
set -e
curl -fsSL https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-x86_64.zip -o /tmp/protoc.zip
unzip /tmp/protoc.zip -d /usr/local
rm /tmp/protoc.zip
/usr/local/bin/protoc --version
curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-$(uname -m).zip > /tmp/protoc.zip \
&& unzip /tmp/protoc.zip -d /usr/local \
&& rm /tmp/protoc.zip
- name: Build Arm Manylinux Wheel
if: ${{ inputs.arm-build == 'true' }}
uses: PyO3/maturin-action@v1
@@ -51,14 +50,13 @@ runs:
maturin-version: "1.12.4"
command: build
working-directory: python
docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc"
docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/'"
target: aarch64-unknown-linux-gnu
manylinux: ${{ inputs.manylinux }}
args: ${{ inputs.args }}
before-script-linux: |
set -e
yum install -y clang
curl -fsSL https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-aarch_64.zip -o /tmp/protoc.zip
unzip /tmp/protoc.zip -d /usr/local
rm /tmp/protoc.zip
/usr/local/bin/protoc --version
yum install -y clang \
&& curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-aarch_64.zip > /tmp/protoc.zip \
&& unzip /tmp/protoc.zip -d /usr/local \
&& rm /tmp/protoc.zip
+2 -2
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@@ -25,7 +25,7 @@ jobs:
# Only runs on tags that matches the make-release action
if: startsWith(github.ref, 'refs/tags/v')
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- uses: Swatinem/rust-cache@v2
with:
workspaces: rust
@@ -47,7 +47,7 @@ jobs:
contents: read
issues: write
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- uses: ./.github/actions/create-failure-issue
with:
job-results: ${{ toJSON(needs) }}
+3 -3
View File
@@ -36,14 +36,14 @@ jobs:
echo "guidelines = ${{ inputs.guidelines }}"
- name: Checkout Repo
uses: actions/checkout@v6
uses: actions/checkout@v4
with:
ref: ${{ inputs.branch }}
fetch-depth: 0
persist-credentials: true
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
# pnpm 11 (used by the nodejs install step below) requires
# Node >= 22.13; use 24 since 22 hits EOL in October.
@@ -82,7 +82,7 @@ jobs:
cache: maven
- name: Setup pnpm
uses: pnpm/action-setup@v6
uses: pnpm/action-setup@v4
with:
version: 11.1.1
- name: Install Node.js dependencies for TypeScript bindings
@@ -30,13 +30,13 @@ jobs:
echo "tag = ${{ inputs.tag || 'latest' }}"
- name: Checkout Repo LanceDB
uses: actions/checkout@v6
uses: actions/checkout@v4
with:
fetch-depth: 0
persist-credentials: true
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: 20
+1 -1
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@@ -27,7 +27,7 @@ jobs:
name: Verify PR title / description conforms to semantic-release
runs-on: ubuntu-latest
steps:
- uses: actions/setup-node@v6
- uses: actions/setup-node@v4
with:
node-version: "18"
# These rules are disabled because Github will always ensure there
+2 -2
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@@ -35,7 +35,7 @@ jobs:
runs-on: ubuntu-24.04
steps:
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v4
- name: Install dependencies needed for ubuntu
run: |
sudo apt install -y protobuf-compiler libssl-dev
@@ -53,7 +53,7 @@ jobs:
python -m pip install --extra-index-url https://pypi.fury.io/lance-format/ --extra-index-url https://pypi.fury.io/lancedb/ -e .
python -m pip install --extra-index-url https://pypi.fury.io/lance-format/ --extra-index-url https://pypi.fury.io/lancedb/ -r ../docs/requirements.txt
- name: Set up node
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: 20
cache: 'npm'
+2 -2
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@@ -32,7 +32,7 @@ jobs:
working-directory: ./java
steps:
- name: Checkout repository
uses: actions/checkout@v6
uses: actions/checkout@v4
- name: Set up Java 8
uses: actions/setup-java@v4
with:
@@ -73,7 +73,7 @@ jobs:
contents: read
issues: write
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- uses: ./.github/actions/create-failure-issue
with:
job-results: ${{ toJSON(needs) }}
+1 -1
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@@ -36,7 +36,7 @@ jobs:
working-directory: ./java
steps:
- name: Checkout repository
uses: actions/checkout@v6
uses: actions/checkout@v4
- name: Set up Java 17
uses: actions/setup-java@v4
with:
+1 -1
View File
@@ -19,7 +19,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v6
uses: actions/checkout@v4
- name: Install license-header-checker
working-directory: /tmp
run: |
+1 -1
View File
@@ -49,7 +49,7 @@ jobs:
steps:
- name: Output Inputs
run: echo "${{ toJSON(github.event.inputs) }}"
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
+10 -10
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@@ -38,14 +38,14 @@ jobs:
CC: gcc-12
CXX: g++-12
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
- uses: pnpm/action-setup@v6
- uses: pnpm/action-setup@v4
with:
version: 11.1.1
- uses: actions/setup-node@v6
- uses: actions/setup-node@v4
with:
# pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
# in October. The library itself still supports Node >= 18
@@ -86,14 +86,14 @@ jobs:
shell: bash
working-directory: nodejs
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
- uses: pnpm/action-setup@v6
- uses: pnpm/action-setup@v4
with:
version: 11.1.1
- uses: actions/setup-node@v6
- uses: actions/setup-node@v4
name: Setup Node.js 24 for build
with:
# pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
@@ -130,7 +130,7 @@ jobs:
echo "Run 'pnpm run docs', fix any warnings, and commit the changes."
exit 1
fi
- uses: actions/setup-node@v6
- uses: actions/setup-node@v4
name: Setup Node.js ${{ matrix.node-version }} for test
with:
node-version: ${{ matrix.node-version }}
@@ -166,14 +166,14 @@ jobs:
shell: bash
working-directory: nodejs
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
- uses: pnpm/action-setup@v6
- uses: pnpm/action-setup@v4
with:
version: 11.1.1
- uses: actions/setup-node@v6
- uses: actions/setup-node@v4
with:
# pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
# in October.
+19 -19
View File
@@ -32,7 +32,7 @@ jobs:
permissions:
contents: write
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -170,13 +170,13 @@ jobs:
run:
working-directory: nodejs
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Setup pnpm
uses: pnpm/action-setup@v6
uses: pnpm/action-setup@v4
with:
version: 11.1.1
- name: Setup node
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
# pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
# in October.
@@ -190,7 +190,7 @@ jobs:
toolchain: stable
targets: ${{ matrix.settings.target }}
- name: Cache cargo
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: |
~/.cargo/registry/index/
@@ -244,7 +244,7 @@ jobs:
if: ${{ !matrix.settings.docker }}
shell: bash
- name: Upload artifact
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v4
with:
name: lancedb-${{ matrix.settings.target }}
path: nodejs/dist/*.node
@@ -256,7 +256,7 @@ jobs:
run: pnpm tsc
- name: Upload Generic Artifacts
if: ${{ matrix.settings.target == 'aarch64-apple-darwin' }}
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v4
with:
name: nodejs-dist
path: |
@@ -287,13 +287,13 @@ jobs:
shell: bash
working-directory: nodejs
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Setup pnpm
uses: pnpm/action-setup@v6
uses: pnpm/action-setup@v4
with:
version: 11.1.1
- name: Setup Node.js 24 for install
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
# pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
# in October.
@@ -303,18 +303,18 @@ jobs:
- name: Install dependencies
run: pnpm install --frozen-lockfile
- name: Setup Node.js ${{ matrix.node }} for test
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: ${{ matrix.node }}
- name: Download artifacts
uses: actions/download-artifact@v8
uses: actions/download-artifact@v4
with:
name: lancedb-${{ matrix.settings.target }}
path: nodejs/dist/
# For testing purposes:
# run-id: 13982782871
# github-token: ${{ secrets.GITHUB_TOKEN }} # token with actions:read permissions on target repo
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: nodejs-dist
path: nodejs/dist
@@ -339,13 +339,13 @@ jobs:
needs:
- test-lancedb
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Setup pnpm
uses: pnpm/action-setup@v6
uses: pnpm/action-setup@v4
with:
version: 11.1.1
- name: Setup node
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: 24
cache: pnpm
@@ -353,14 +353,14 @@ jobs:
registry-url: "https://registry.npmjs.org"
- name: Install dependencies
run: pnpm install --frozen-lockfile
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
with:
name: nodejs-dist
path: nodejs/dist
# For testing purposes:
# run-id: 13982782871
# github-token: ${{ secrets.GITHUB_TOKEN }} # token with actions:read permissions on target repo
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v4
name: Download arch-specific binaries
with:
pattern: lancedb-*
@@ -398,7 +398,7 @@ jobs:
contents: read
issues: write
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- uses: ./.github/actions/create-failure-issue
with:
job-results: ${{ toJSON(needs) }}
+7 -7
View File
@@ -41,7 +41,7 @@ jobs:
shell: bash
working-directory: python
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -66,7 +66,7 @@ jobs:
shell: bash
working-directory: python
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -95,7 +95,7 @@ jobs:
shell: bash
working-directory: python
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -126,7 +126,7 @@ jobs:
shell: bash
working-directory: python
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -160,7 +160,7 @@ jobs:
shell: bash
working-directory: python
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -189,7 +189,7 @@ jobs:
shell: bash
working-directory: python
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -212,7 +212,7 @@ jobs:
shell: bash
working-directory: python
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
+7 -7
View File
@@ -40,7 +40,7 @@ jobs:
CC: clang-18
CXX: clang++-18
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -65,7 +65,7 @@ jobs:
timeout-minutes: 10
runs-on: ubuntu-24.04
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- uses: EmbarkStudios/cargo-deny-action@v2
with:
command: check advisories bans licenses sources
@@ -78,7 +78,7 @@ jobs:
CC: clang
CXX: clang++
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
# Building without a lock file often requires the latest Rust version since downstream
# dependencies may have updated their minimum Rust version.
- uses: actions-rust-lang/setup-rust-toolchain@v1
@@ -113,7 +113,7 @@ jobs:
CXX: clang++-18
GH_TOKEN: ${{ secrets.SOPHON_READ_TOKEN }}
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -152,7 +152,7 @@ jobs:
shell: bash
working-directory: rust
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
lfs: true
@@ -181,7 +181,7 @@ jobs:
run:
working-directory: rust/lancedb
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
- name: Set target
run: rustup target add ${{ matrix.target }}
- uses: Swatinem/rust-cache@v2
@@ -210,7 +210,7 @@ jobs:
CC: clang-18
CXX: clang++-18
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
submodules: true
- name: Install dependencies
@@ -11,7 +11,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v4
with:
ref: main
persist-credentials: false
@@ -11,7 +11,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
uses: actions/checkout@v4
with:
ref: main
persist-credentials: false
Generated
+131 -373
View File
File diff suppressed because it is too large Load Diff
+14 -17
View File
@@ -13,25 +13,24 @@ categories = ["database-implementations"]
rust-version = "1.91.0"
[workspace.dependencies]
lance = { "version" = "=9.0.0-beta.19", default-features = false, "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-core = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-datagen = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-file = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-io = { "version" = "=9.0.0-beta.19", default-features = false, "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-index = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-linalg = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-namespace = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-namespace-impls = { "version" = "=9.0.0-beta.19", default-features = false, "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-table = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-testing = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-datafusion = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-encoding = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance-arrow = { "version" = "=9.0.0-beta.19", "tag" = "v9.0.0-beta.19", "git" = "https://github.com/lance-format/lance.git" }
lance = { "version" = "=8.0.0-beta.11", default-features = false, "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-core = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-datagen = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-file = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-io = { "version" = "=8.0.0-beta.11", default-features = false, "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-index = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-linalg = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-namespace = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-namespace-impls = { "version" = "=8.0.0-beta.11", default-features = false, "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-table = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-testing = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-datafusion = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-encoding = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
lance-arrow = { "version" = "=8.0.0-beta.11", "tag" = "v8.0.0-beta.11", "git" = "https://github.com/lance-format/lance.git" }
ahash = "0.8"
# Note that this one does not include pyarrow
arrow = { version = "58.0.0", optional = false }
arrow-array = "58.0.0"
arrow-buffer = "58.0.0"
arrow-data = "58.0.0"
arrow-ipc = "58.0.0"
arrow-ord = "58.0.0"
@@ -54,8 +53,6 @@ half = { "version" = "2.7.1", default-features = false, features = [
] }
futures = "0"
log = "0.4"
metrics = "0.24"
metrics-util = "0.19"
moka = { version = "0.12", features = ["future"] }
object_store = "0.13.2"
pin-project = "1.0.7"
+29 -17
View File
@@ -51,6 +51,18 @@ ignore = [
# https://rustsec.org/advisories/RUSTSEC-2024-0436
{ id = "RUSTSEC-2024-0436", reason = "transitive via datafusion; awaiting ecosystem migration" },
# encoding: unmaintained. Reached through lindera-dictionary, which is
# required by the native Lindera tokenizer path. Lindera has not migrated
# off this crate yet.
# https://rustsec.org/advisories/RUSTSEC-2021-0153
{ id = "RUSTSEC-2021-0153", reason = "transitive via lindera-dictionary for native Lindera tokenizer" },
# fast-float: unsound and unmaintained. Reached only through polars-arrow
# from the optional Polars integration; replacement requires a Polars
# dependency upgrade.
# https://rustsec.org/advisories/RUSTSEC-2024-0379
{ id = "RUSTSEC-2024-0379", reason = "transitive via polars-arrow; waiting on Polars migration" },
# tantivy: segfault on malformed input due to missing bounds check.
# Pulled in via lance for full-text search. We only feed tantivy
# documents we construct ourselves, not attacker-controlled bytes.
@@ -68,6 +80,18 @@ ignore = [
# https://rustsec.org/advisories/RUSTSEC-2025-0119
{ id = "RUSTSEC-2025-0119", reason = "transitive via hf-hub/indicatif; cosmetic formatting crate" },
# bincode: unmaintained. Reached through lindera and lindera-dictionary,
# which are required by the native Lindera tokenizer path. Lindera has not
# migrated to another serialization format yet.
# https://rustsec.org/advisories/RUSTSEC-2025-0141
{ id = "RUSTSEC-2025-0141", reason = "transitive via lindera/lindera-dictionary for native Lindera tokenizer" },
# lru: soundness issue in IterMut. Reached only through aws-sdk-s3 in
# LanceDB's dev-dependency graph; LanceDB does not use that iterator
# directly. Clearing this requires the AWS SDK chain to update lru.
# https://rustsec.org/advisories/RUSTSEC-2026-0002
{ id = "RUSTSEC-2026-0002", reason = "transitive via aws-sdk-s3 dev-dependency; waiting on AWS SDK lru upgrade" },
# rustls-webpki 0.101.7 (old major line): name-constraint checks for
# URI / wildcard names. Pulled in only via the legacy rustls 0.21 chain
# from aws-smithy-http-client. The 0.103 line we actively use is patched.
@@ -84,23 +108,11 @@ ignore = [
# https://rustsec.org/advisories/RUSTSEC-2026-0104
{ id = "RUSTSEC-2026-0104", reason = "only affects rustls-webpki 0.101 from legacy aws-smithy/rustls 0.21 chain" },
# pyo3 advisories in the Python bindings; tracked pending a patched pyo3 release.
# https://rustsec.org/advisories/RUSTSEC-2026-0176
# https://rustsec.org/advisories/RUSTSEC-2026-0177
{ id = "RUSTSEC-2026-0176", reason = "pyo3 in Python bindings; awaiting patched pyo3 release" },
{ id = "RUSTSEC-2026-0177", reason = "pyo3 in Python bindings; awaiting patched pyo3 release" },
# quick-xml < 0.41.0: quadratic runtime on duplicate attribute names (DoS).
# quick-xml < 0.41.0: unbounded namespace-declaration allocation in NsReader (DoS).
# Pulled in transitively by inferno (dev-only flame-graph dep), lance-namespace-impls
# (git dep from lance), and opendal/reqsign (cloud storage XML parsing). The XML
# parsed by opendal/reqsign comes from trusted cloud-storage endpoints (S3, GCS,
# Azure), not attacker-controlled input. Clearing requires upstream crates to migrate
# to quick-xml >= 0.41.0.
# https://rustsec.org/advisories/RUSTSEC-2026-0194
# https://rustsec.org/advisories/RUSTSEC-2026-0195
{ id = "RUSTSEC-2026-0194", reason = "transitive via inferno/lance/opendal; XML from trusted cloud endpoints, not attacker-controlled" },
{ id = "RUSTSEC-2026-0195", reason = "transitive via inferno/lance/opendal; XML from trusted cloud endpoints, not attacker-controlled" },
# rand 0.8.5: soundness issue only when ThreadRng reseeds inside a custom
# logger. Reached through several transitive chains. LanceDB does not use
# rand from a custom logger; upgrade once all pinned chains accept 0.8.6+.
# https://rustsec.org/advisories/RUSTSEC-2026-0097
{ id = "RUSTSEC-2026-0097", reason = "transitive rand 0.8.5; LanceDB does not call ThreadRng from custom logging" },
]
# ---------------------------------------------------------------------------
+1 -1
View File
@@ -14,7 +14,7 @@ Add the following dependency to your `pom.xml`:
<dependency>
<groupId>com.lancedb</groupId>
<artifactId>lancedb-core</artifactId>
<version>0.31.0-beta.6</version>
<version>0.30.1-beta.2</version>
</dependency>
```
-3
View File
@@ -518,9 +518,6 @@ x > 5 OR y = 'test'
Filtering performance can often be improved by creating a scalar index
on the filter column(s).
Calling this multiple times combines the filters with a logical AND rather
than replacing the previous filter.
```
#### Inherited from
-37
View File
@@ -295,23 +295,6 @@ await table.createIndex("my_float_col");
***
### currentBranch()
```ts
abstract currentBranch(): null | string
```
The branch this table handle is scoped to, or `null` for the main branch.
A handle returned by [Branches.create](Branches.md#create) or [Branches.checkout](Branches.md#checkout)
reports the branch it targets; a handle opened normally reports `null`.
#### Returns
`null` \| `string`
***
### delete()
```ts
@@ -398,26 +381,6 @@ Drop an index from the table.
***
### getLsmWriteSpec()
```ts
abstract getLsmWriteSpec(): Promise<undefined | LsmWriteSpec>
```
Read the [LsmWriteSpec](../interfaces/LsmWriteSpec.md) currently installed on this table.
Resolves to `undefined` when the MemWAL LSM write path is not enabled (no
spec has been set, or it was removed with [Table#unsetLsmWriteSpec](Table.md#unsetlsmwritespec)).
The returned spec — including its `maintainedIndexes` and
`writerConfigDefaults` — mirrors what was passed to
[Table#setLsmWriteSpec](Table.md#setlsmwritespec).
#### Returns
`Promise`&lt;`undefined` \| [`LsmWriteSpec`](../interfaces/LsmWriteSpec.md)&gt;
***
### indexStats()
```ts
-3
View File
@@ -767,9 +767,6 @@ x > 5 OR y = 'test'
Filtering performance can often be improved by creating a scalar index
on the filter column(s).
Calling this multiple times combines the filters with a logical AND rather
than replacing the previous filter.
```
#### Inherited from
-29
View File
@@ -1,29 +0,0 @@
[**@lancedb/lancedb**](../README.md) • **Docs**
***
[@lancedb/lancedb](../globals.md) / OAuthFlowType
# Enumeration: OAuthFlowType
OAuth authentication flow types.
## Enumeration Members
### AzureManagedIdentity
```ts
AzureManagedIdentity: "azure_managed_identity";
```
Azure Managed Identity via IMDS.
***
### ClientCredentials
```ts
ClientCredentials: "client_credentials";
```
Client Credentials grant (service-to-service / M2M).
@@ -1,42 +0,0 @@
[**@lancedb/lancedb**](../README.md) • **Docs**
***
[@lancedb/lancedb](../globals.md) / instrumentLanceDbMetrics
# Function: instrumentLanceDbMetrics()
```ts
function instrumentLanceDbMetrics(meterProvider?): boolean
```
Register LanceDB metrics as OpenTelemetry observable instruments.
Installs a process-global metrics recorder and creates one observable
instrument per LanceDB metric (currently object store request counts, bytes,
latency, errors, and throttles) on the given (or global) `MeterProvider`. The
configured `MetricReader` then collects them on its own schedule.
Counters and gauges map directly to observable counters/gauges. Because
OpenTelemetry has no asynchronous histogram instrument, each histogram is
exported Prometheus-style as cumulative `le` bucket counts (`<name>_bucket`,
with an `le` attribute) plus `<name>_count` and `<name>_sum`.
Requires `@opentelemetry/api` (a dependency) and, to actually export, an
OpenTelemetry SDK such as `@opentelemetry/sdk-metrics`.
## Parameters
* **meterProvider?**: `MeterProvider`
The provider to register instruments on. Defaults to the
global provider from `@opentelemetry/api`.
## Returns
`boolean`
`true` if the recorder is installed and instruments are registered.
`false` if a different `metrics` recorder is already installed in this
process (only one global recorder is permitted), in which case a warning is
emitted and no instruments are created. Calling this more than once is safe;
instruments are created only on the first successful call.
-4
View File
@@ -12,7 +12,6 @@
## Enumerations
- [FullTextQueryType](enumerations/FullTextQueryType.md)
- [OAuthFlowType](enumerations/OAuthFlowType.md)
- [Occur](enumerations/Occur.md)
- [Operator](enumerations/Operator.md)
@@ -86,8 +85,6 @@
- [ListNamespacesResponse](interfaces/ListNamespacesResponse.md)
- [LsmWriteSpec](interfaces/LsmWriteSpec.md)
- [MergeResult](interfaces/MergeResult.md)
- [NativeOAuthConfig](interfaces/NativeOAuthConfig.md)
- [OAuthConfig](interfaces/OAuthConfig.md)
- [OpenTableOptions](interfaces/OpenTableOptions.md)
- [OptimizeOptions](interfaces/OptimizeOptions.md)
- [OptimizeStats](interfaces/OptimizeStats.md)
@@ -131,7 +128,6 @@
- [RecordBatchIterator](functions/RecordBatchIterator.md)
- [connect](functions/connect.md)
- [connectNamespace](functions/connectNamespace.md)
- [instrumentLanceDbMetrics](functions/instrumentLanceDbMetrics.md)
- [makeArrowTable](functions/makeArrowTable.md)
- [packBits](functions/packBits.md)
- [permutationBuilder](functions/permutationBuilder.md)
@@ -64,19 +64,6 @@ client used by manifest-enabled native connections.
***
### oauthConfig?
```ts
optional oauthConfig: NativeOAuthConfig;
```
(For LanceDB cloud only): OAuth configuration for IdP-based
authentication (e.g., Azure Entra ID). When set, token acquisition
and refresh are handled entirely in Rust. TypeScript users should pass
the public `OAuthConfig` type exported from `@lancedb/lancedb`.
***
### readConsistencyInterval?
```ts
-109
View File
@@ -23,31 +23,6 @@ be more columns to represent composite indices.
***
### createdAt?
```ts
optional createdAt: Date;
```
When the index was created.
`undefined` for remote tables or indices created before timestamps were tracked.
***
### indexDetails?
```ts
optional indexDetails: any;
```
Index-type-specific details parsed as a JavaScript object.
Falls back to a raw string if JSON parsing fails. `undefined` for
remote tables or when details are unavailable.
***
### indexType
```ts
@@ -58,30 +33,6 @@ The type of the index
***
### indexUuid?
```ts
optional indexUuid: string;
```
The UUID of the first segment of the index.
`undefined` for remote tables, which do not yet surface this.
***
### indexVersion?
```ts
optional indexVersion: number;
```
The on-disk index format version.
`undefined` for remote tables.
***
### name
```ts
@@ -89,63 +40,3 @@ name: string;
```
The name of the index
***
### numIndexedRows?
```ts
optional numIndexedRows: number;
```
The number of rows indexed, across all segments.
`undefined` for remote tables.
***
### numSegments?
```ts
optional numSegments: number;
```
The number of segments that make up the index.
`undefined` for remote tables.
***
### numUnindexedRows?
```ts
optional numUnindexedRows: number;
```
The number of rows not yet covered by this index.
`undefined` for remote tables.
***
### sizeBytes?
```ts
optional sizeBytes: number;
```
The total size in bytes of all index files across all segments.
`undefined` for remote tables or indices without size tracking.
***
### typeUrl?
```ts
optional typeUrl: string;
```
The protobuf type URL, a precise type identifier for the index.
`undefined` for remote tables.
@@ -1,88 +0,0 @@
[**@lancedb/lancedb**](../README.md) • **Docs**
***
[@lancedb/lancedb](../globals.md) / NativeOAuthConfig
# Interface: NativeOAuthConfig
OAuth configuration for LanceDB authentication.
This is the generated napi-rs binding shape. TypeScript users should prefer
the public `OAuthConfig` type exported from `@lancedb/lancedb`.
All token acquisition and refresh is handled in the Rust layer.
## Properties
### clientId
```ts
clientId: string;
```
Application / Client ID.
***
### clientSecret?
```ts
optional clientSecret: string;
```
Client secret (required for client_credentials).
***
### flow?
```ts
optional flow: string;
```
Authentication flow: "client_credentials" or "azure_managed_identity"
***
### issuerUrl
```ts
issuerUrl: string;
```
OIDC issuer URL or OAuth authority URL.
For Azure: `https://login.microsoftonline.com/{tenant_id}/v2.0`
***
### managedIdentityClientId?
```ts
optional managedIdentityClientId: string;
```
Client ID for user-assigned managed identity (azure_managed_identity).
***
### refreshBufferSecs?
```ts
optional refreshBufferSecs: number;
```
Seconds before expiry to trigger proactive refresh (default: 300).
Keep this well below the token TTL; if it is greater than or equal to
the TTL, each request refreshes the token.
***
### scopes
```ts
scopes: string[];
```
OAuth scopes to request. For Azure managed identity, exactly one scope
or resource is required. For example: `["api://{app_id}/.default"]`
-111
View File
@@ -1,111 +0,0 @@
[**@lancedb/lancedb**](../README.md) • **Docs**
***
[@lancedb/lancedb](../globals.md) / OAuthConfig
# Interface: OAuthConfig
OAuth configuration for LanceDB authentication.
This is the public TypeScript OAuth configuration type. The generated
`NativeOAuthConfig` type has the same runtime shape but is an implementation
detail of the napi-rs binding.
All token acquisition and refresh is handled in the Rust layer.
This config is passed through to Rust via napi-rs.
## Examples
```typescript
const config: OAuthConfig = {
issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
clientId: "app-id",
clientSecret: "secret",
scopes: ["api://lancedb-api/.default"],
};
```
```typescript
const config: OAuthConfig = {
issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
clientId: "app-id",
scopes: ["api://lancedb-api/.default"],
flow: OAuthFlowType.AzureManagedIdentity,
};
```
## Properties
### clientId
```ts
clientId: string;
```
Application / Client ID.
***
### clientSecret?
```ts
optional clientSecret: string;
```
Client secret (required for ClientCredentials).
***
### flow?
```ts
optional flow: OAuthFlowType;
```
Authentication flow (default: ClientCredentials).
***
### issuerUrl
```ts
issuerUrl: string;
```
OIDC issuer URL or OAuth authority URL.
For Azure: `https://login.microsoftonline.com/{tenant_id}/v2.0`
***
### managedIdentityClientId?
```ts
optional managedIdentityClientId: string;
```
Client ID for user-assigned managed identity (AzureManagedIdentity).
***
### refreshBufferSecs?
```ts
optional refreshBufferSecs: number;
```
Seconds before expiry to trigger proactive refresh (default: 300).
Keep this well below the token TTL; if it is greater than or equal to
the TTL, each request refreshes the token.
***
### scopes
```ts
scopes: string[];
```
OAuth scopes to request.
For Azure managed identity, exactly one scope or resource is required.
For example: `["api://{app_id}/.default"]`
@@ -8,14 +8,6 @@
## Properties
### clumpSize?
```ts
optional clumpSize: number;
```
***
### counts?
```ts
+1 -1
View File
@@ -8,7 +8,7 @@
<parent>
<groupId>com.lancedb</groupId>
<artifactId>lancedb-parent</artifactId>
<version>0.31.0-beta.6</version>
<version>0.30.1-beta.2</version>
<relativePath>../pom.xml</relativePath>
</parent>
+2 -2
View File
@@ -6,7 +6,7 @@
<groupId>com.lancedb</groupId>
<artifactId>lancedb-parent</artifactId>
<version>0.31.0-beta.6</version>
<version>0.30.1-beta.2</version>
<packaging>pom</packaging>
<name>${project.artifactId}</name>
<description>LanceDB Java SDK Parent POM</description>
@@ -28,7 +28,7 @@
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<arrow.version>15.0.0</arrow.version>
<lance-core.version>9.0.0-beta.19</lance-core.version>
<lance-core.version>8.0.0-beta.11</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>
+3 -7
View File
@@ -1,7 +1,7 @@
[package]
name = "lancedb-nodejs"
edition.workspace = true
version = "0.31.0-beta.6"
version = "0.30.1-beta.2"
publish = false
license.workspace = true
description.workspace = true
@@ -25,12 +25,8 @@ lancedb = { path = "../rust/lancedb", default-features = false }
lance-namespace.workspace = true
napi = { version = "3.8.3", default-features = false, features = [
"napi9",
"async",
"chrono_date",
"serde-json",
"async"
] }
chrono = { version = "0.4", default-features = false, features = ["clock"] }
serde_json = "1"
napi-derive = "3.5.2"
# Prevent dynamic linking of lzma, which comes from datafusion
lzma-sys = { version = "0.1", features = ["static"] }
@@ -44,6 +40,6 @@ aws-lc-rs = "=1.16.3"
napi-build = "2.3.1"
[features]
default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/goosefs", "lancedb/metrics-otel"]
default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
fp16kernels = ["lancedb/fp16kernels"]
remote = ["lancedb/remote"]
-114
View File
@@ -1,114 +0,0 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
import {
MeterProvider,
type MetricData,
MetricReader,
} from "@opentelemetry/sdk-metrics";
import * as tmp from "tmp";
import { connect, instrumentLanceDbMetrics } from "../lancedb";
// snapshotLancedbMetrics is internal plumbing (not part of the public API), so
// it is imported from the native module rather than the package entry point.
import { snapshotLancedbMetrics } from "../lancedb/native";
// The metrics recorder is process-global and installed once, so the whole
// bridge is exercised in a single test to avoid cross-test global-state coupling.
// A minimal pull-based reader whose `collect()` we drive directly, invoking the
// observable-instrument callbacks. `@opentelemetry/sdk-metrics` ships no
// in-memory reader, so we subclass the abstract base.
class TestMetricReader extends MetricReader {
protected async onForceFlush(): Promise<void> {
// no-op: collection is driven directly via collect()
}
protected async onShutdown(): Promise<void> {
// no-op: nothing to release
}
}
async function metricsByName(
reader: TestMetricReader,
): Promise<Map<string, MetricData>> {
const collected = await reader.collect();
const result = new Map<string, MetricData>();
for (const scope of collected.resourceMetrics.scopeMetrics) {
for (const metric of scope.metrics) {
result.set(metric.descriptor.name, metric);
}
}
return result;
}
describe("OpenTelemetry metrics bridge", () => {
let tmpDir: tmp.DirResult;
beforeEach(() => {
tmpDir = tmp.dirSync({ unsafeCleanup: true });
});
afterEach(() => tmpDir.removeCallback());
it("snapshot is safe to call regardless of install state", () => {
expect(Array.isArray(snapshotLancedbMetrics())).toBe(true);
});
it("exports object store metrics via observable instruments", async () => {
const reader = new TestMetricReader();
const provider = new MeterProvider({ readers: [reader] });
expect(instrumentLanceDbMetrics(provider)).toBe(true);
// Generate object store activity on the local filesystem (scheme "file").
const db = await connect(tmpDir.name);
const data = Array.from({ length: 256 }, (_, i) => ({ id: i }));
const table = await db.createTable("t", data);
expect(await table.countRows()).toBe(256);
const metrics = await metricsByName(reader);
const requests = metrics.get("lance_object_store_requests_total");
expect(requests).toBeDefined();
// biome-ignore lint/suspicious/noExplicitAny: SDK point shape
const requestPoints = (requests!.dataPoints as any[]) ?? [];
expect(requestPoints.length).toBeGreaterThan(0);
for (const p of requestPoints) {
// Labelled by `operation` and `base` (the store scheme by default).
expect(p.attributes).toHaveProperty("base");
expect(p.attributes).toHaveProperty("operation");
}
const totalRequests = requestPoints.reduce((acc, p) => acc + p.value, 0);
expect(totalRequests).toBeGreaterThan(0);
// Histograms are decomposed into bucket / count / sum observable counters.
const bucket = metrics.get(
"lance_object_store_request_duration_seconds_bucket",
);
expect(bucket).toBeDefined();
// biome-ignore lint/suspicious/noExplicitAny: SDK point shape
const bucketPoints = (bucket!.dataPoints as any[]) ?? [];
expect(bucketPoints.length).toBeGreaterThan(0);
expect(bucketPoints.every((p) => "le" in p.attributes)).toBe(true);
// The implicit +Inf bucket must be present.
expect(bucketPoints.some((p) => p.attributes.le === "+Inf")).toBe(true);
const count = metrics.get(
"lance_object_store_request_duration_seconds_count",
);
expect(count).toBeDefined();
// biome-ignore lint/suspicious/noExplicitAny: SDK point shape
const countPoints = (count!.dataPoints as any[]) ?? [];
expect(countPoints.reduce((acc, p) => acc + p.value, 0)).toBeGreaterThan(0);
const sum = metrics.get("lance_object_store_request_duration_seconds_sum");
expect(sum).toBeDefined();
// biome-ignore lint/suspicious/noExplicitAny: SDK point shape
const sumPoints = (sum!.dataPoints as any[]) ?? [];
expect(sumPoints.reduce((acc, p) => acc + p.value, 0)).toBeGreaterThan(0);
// Unit handling: only `_sum` keeps the histogram's unit (seconds); `_bucket`
// and `_count` observe cumulative counts and are unitless.
expect(sum!.descriptor.unit).toBe("s");
expect(bucket!.descriptor.unit).toBe("");
expect(count!.descriptor.unit).toBe("");
await provider.shutdown();
});
});
-14
View File
@@ -215,20 +215,6 @@ describe("Query orderBy", () => {
expect(results[2].score).toBeCloseTo(4.1, 0.001);
});
it("should combine repeated where clauses with AND", async () => {
const results = await table
.query()
.where("score > 1.0")
.where("score < 3.0")
.orderBy({ columnName: "score" })
.toArray();
// Only rows matching both predicates should be returned, rather than the
// second where() silently replacing the first.
expect(results.length).toBe(2);
expect(results[0].score).toBeCloseTo(1.2, 0.001);
expect(results[1].score).toBeCloseTo(2.8, 0.001);
});
it("should support method chaining with limit", async () => {
const results = await table
.query()
+19 -25
View File
@@ -191,36 +191,30 @@ describe("remote connection", () => {
);
});
it("supports version time-travel and branches on remote", async () => {
it("allows version on remote but rejects a non-main branch", async () => {
await withMockDatabase(
(req, res) => {
const body = req.url?.includes("/branches/list")
? JSON.stringify({
branches: {
exp: { parentVersion: 1, createAt: 1, manifestSize: 1 },
},
})
: JSON.stringify({ name: "t", version: 2, schema: { fields: [] } });
(_req, res) => {
// describe (table open + version validation) always succeeds
const body = JSON.stringify({
name: "t",
version: 2,
schema: { fields: [] },
});
res.writeHead(200, { "Content-Type": "application/json" }).end(body);
},
async (db) => {
// version-only (and "main" + version) time-travel the main chain
const v2 = await db.openTable("t", undefined, { version: 2 });
expect(v2.currentBranch()).toBeNull();
const mainV2 = await db.openTable("t", undefined, {
branch: "main",
version: 2,
});
expect(mainV2.currentBranch()).toBeNull();
// version-only (and "main" + version) is allowed: remote supports
// version time-travel even though it has no branches
await db.openTable("t", undefined, { version: 2 });
await db.openTable("t", undefined, { branch: "main", version: 2 });
// a non-main branch opens a handle scoped to that branch
const exp = await db.openTable("t", undefined, { branch: "exp" });
expect(exp.currentBranch()).toBe("exp");
const expV2 = await db.openTable("t", undefined, {
branch: "exp",
version: 2,
});
expect(expV2.currentBranch()).toBe("exp");
// a non-main branch is rejected, with or without a version
await expect(
db.openTable("t", undefined, { branch: "exp" }),
).rejects.toThrow(/branching/);
await expect(
db.openTable("t", undefined, { branch: "exp", version: 2 }),
).rejects.toThrow(/branching/);
},
);
});
+32 -119
View File
@@ -89,11 +89,8 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
await table.add([{ id: 1 }]);
expect(await table.countRows()).toBe(1);
expect(table.currentBranch()).toBeNull();
// fork an isolated, writable branch from main
const branch = await (await table.branches()).create("exp");
expect(branch.currentBranch()).toBe("exp");
expect(await branch.countRows()).toBe(1);
await branch.add([{ id: 2 }]);
expect(await branch.countRows()).toBe(2);
@@ -112,7 +109,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
// checkout returns a handle scoped to the branch's latest
const checkedOut = await (await table.branches()).checkout("exp");
expect(checkedOut.currentBranch()).toBe("exp");
expect(await checkedOut.countRows()).toBe(2);
// delete removes it
@@ -849,13 +845,11 @@ describe("When creating an index", () => {
expect(fs.readdirSync(indexDir)).toHaveLength(1);
const indices = await tbl.listIndices();
expect(indices.length).toBe(1);
expect(indices[0]).toEqual(
expect.objectContaining({
name: "vec_idx",
indexType: "IvfPq",
columns: ["vec"],
}),
);
expect(indices[0]).toEqual({
name: "vec_idx",
indexType: "IvfPq",
columns: ["vec"],
});
const stats = await tbl.indexStats("vec_idx");
expect(stats).toBeDefined();
@@ -1017,51 +1011,51 @@ describe("When creating an index", () => {
const indices = await nestedTable.listIndices();
expect(indices).toEqual(
expect.arrayContaining([
expect.objectContaining({
{
name: "row_id_idx",
indexType: "BTree",
columns: ["rowId"],
}),
expect.objectContaining({
},
{
name: "row_dash_id_idx",
indexType: "BTree",
columns: ["`row-id`"],
}),
expect.objectContaining({
},
{
name: "top_user_id_idx",
indexType: "BTree",
columns: ["userId"],
}),
expect.objectContaining({
},
{
name: "nested_user_id_idx",
indexType: "BTree",
columns: ["metadata.user_id"],
}),
expect.objectContaining({
},
{
name: "mixed_case_metadata_user_id_idx",
indexType: "BTree",
columns: ["MetaData.userId"],
}),
expect.objectContaining({
},
{
name: "escaped_names_idx",
indexType: "BTree",
columns: ["`meta-data`.`user-id`"],
}),
expect.objectContaining({
},
{
name: "literal_dot_idx",
indexType: "BTree",
columns: ["literal.`a.b`"],
}),
expect.objectContaining({
},
{
name: "image_embedding_idx",
indexType: "IvfPq",
columns: ["image.embedding"],
}),
expect.objectContaining({
},
{
name: "payload_text_idx",
indexType: "FTS",
columns: ["payload.text"],
}),
},
]),
);
@@ -1115,16 +1109,16 @@ describe("When creating an index", () => {
const indicesAfterOptimize = await nestedTable.listIndices();
expect(indicesAfterOptimize).toEqual(
expect.arrayContaining([
expect.objectContaining({
{
name: "mixed_case_metadata_user_id_idx",
indexType: "BTree",
columns: ["MetaData.userId"],
}),
expect.objectContaining({
},
{
name: "image_embedding_idx",
indexType: "IvfPq",
columns: ["image.embedding"],
}),
},
]),
);
});
@@ -1260,13 +1254,11 @@ describe("When creating an index", () => {
expect(fs.readdirSync(indexDir)).toHaveLength(1);
const indices = await tbl.listIndices();
expect(indices.length).toBe(1);
expect(indices[0]).toEqual(
expect.objectContaining({
name: "vec_idx",
indexType: "IvfHnswSq",
columns: ["vec"],
}),
);
expect(indices[0]).toEqual({
name: "vec_idx",
indexType: "IvfHnswSq",
columns: ["vec"],
});
// Search without specifying the column
let rst = await tbl
@@ -1612,35 +1604,6 @@ describe("When creating an index", () => {
expect(rst64Query.toString()).toEqual(rst64Search.toString());
expect(rst64Query.numRows).toBe(2);
});
it("should expose rich metadata fields on IndexConfig", async () => {
await tbl.createIndex("id", { config: Index.btree() });
await tbl.createIndex("vec");
const indicesByName = Object.fromEntries(
(await tbl.listIndices()).map((idx) => [idx.name, idx]),
);
const scalarIdx = indicesByName["id_idx"];
expect(scalarIdx).toBeDefined();
expect(typeof scalarIdx.indexUuid).toBe("string");
expect(scalarIdx.numIndexedRows).toBe(300);
expect(scalarIdx.numUnindexedRows).toBe(0);
expect(scalarIdx.numSegments).toBeGreaterThanOrEqual(1);
expect(scalarIdx.sizeBytes).toBeGreaterThan(0);
// Use toString check to avoid cross-realm instanceof failures with native Date objects
expect(Object.prototype.toString.call(scalarIdx.createdAt)).toBe(
"[object Date]",
);
expect((scalarIdx.createdAt as Date).getTime()).toBeGreaterThan(0);
expect(typeof scalarIdx.indexDetails).toBe("object");
const vectorIdx = indicesByName["vec_idx"];
expect(vectorIdx).toBeDefined();
expect(typeof vectorIdx.indexUuid).toBe("string");
expect(vectorIdx.numIndexedRows).toBe(300);
expect(typeof vectorIdx.indexDetails).toBe("object");
});
});
describe("When querying a table", () => {
@@ -2992,56 +2955,6 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
}),
).rejects.toThrow();
});
it("reads back the installed spec via getLsmWriteSpec", async () => {
const conn = await connect(tmpDir.name);
const table = await makeTable(conn);
await table.setUnenforcedPrimaryKey("id");
// Nothing installed yet.
expect(await table.getLsmWriteSpec()).toBeUndefined();
// A real scalar index is needed to name it as a maintained index.
await table.add([{ id: 1 }, { id: 2 }, { id: 3 }]);
await table.createIndex("id");
const indexName = (await table.listIndices())[0].name;
// Bucket spec round-trips, including maintained indexes and writer config
// defaults. Lance writer-config keys are canonically snake_case.
// biome-ignore lint/style/useNamingConvention: Lance writer-config keys are snake_case
const writerConfigDefaults = { durable_write: "false" };
await table.setLsmWriteSpec({
specType: "bucket",
column: "id",
numBuckets: 4,
maintainedIndexes: [indexName],
writerConfigDefaults,
});
const spec = await table.getLsmWriteSpec();
expect(spec).toBeDefined();
expect(spec?.specType).toBe("bucket");
expect(spec?.column).toBe("id");
expect(spec?.numBuckets).toBe(4);
expect(spec?.maintainedIndexes).toEqual([indexName]);
expect(spec?.writerConfigDefaults).toEqual(writerConfigDefaults);
// After unset, undefined again.
await table.unsetLsmWriteSpec();
expect(await table.getLsmWriteSpec()).toBeUndefined();
// Identity round-trips (column recovered from the schema).
await table.setLsmWriteSpec({ specType: "identity", column: "id" });
const identity = await table.getLsmWriteSpec();
expect(identity?.specType).toBe("identity");
expect(identity?.column).toBe("id");
await table.unsetLsmWriteSpec();
// Unsharded round-trips (no routing column).
await table.setLsmWriteSpec({ specType: "unsharded" });
const unsharded = await table.getLsmWriteSpec();
expect(unsharded?.specType).toBe("unsharded");
expect(unsharded?.column).toBeFalsy();
});
});
describe("LSM merge insert", () => {
-8
View File
@@ -20,11 +20,6 @@ import { HeaderProvider } from "./header";
// Re-export native header provider for use with connectWithHeaderProvider
export { JsHeaderProvider as NativeJsHeaderProvider } from "./native.js";
// OpenTelemetry metrics bridge. Only the high-level entry point is public; the
// underlying recorder/catalog/snapshot functions remain internal plumbing that
// `otel.ts` consumes from the native module.
export { instrumentLanceDbMetrics } from "./otel";
export {
AddColumnsSql,
ConnectionOptions,
@@ -57,7 +52,6 @@ export {
SplitHashOptions,
SplitSequentialOptions,
ShuffleOptions,
OAuthConfig as NativeOAuthConfig,
} from "./native.js";
export {
@@ -136,8 +130,6 @@ export {
TokenResponse,
} from "./header";
export { OAuthConfig, OAuthFlowType } from "./oauth";
export { MergeInsertBuilder, WriteExecutionOptions } from "./merge";
export * as embedding from "./embedding";
-76
View File
@@ -1,76 +0,0 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
/**
* OAuth authentication flow types.
*/
export enum OAuthFlowType {
/** Client Credentials grant (service-to-service / M2M). */
ClientCredentials = "client_credentials",
/** Azure Managed Identity via IMDS. */
AzureManagedIdentity = "azure_managed_identity",
}
/**
* OAuth configuration for LanceDB authentication.
*
* This is the public TypeScript OAuth configuration type. The generated
* `NativeOAuthConfig` type has the same runtime shape but is an implementation
* detail of the napi-rs binding.
*
* All token acquisition and refresh is handled in the Rust layer.
* This config is passed through to Rust via napi-rs.
*
* @example Client Credentials (service-to-service):
* ```typescript
* const config: OAuthConfig = {
* issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
* clientId: "app-id",
* clientSecret: "secret",
* scopes: ["api://lancedb-api/.default"],
* };
* ```
*
* @example Azure Managed Identity:
* ```typescript
* const config: OAuthConfig = {
* issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
* clientId: "app-id",
* scopes: ["api://lancedb-api/.default"],
* flow: OAuthFlowType.AzureManagedIdentity,
* };
* ```
*/
export interface OAuthConfig {
/**
* OIDC issuer URL or OAuth authority URL.
* For Azure: `https://login.microsoftonline.com/{tenant_id}/v2.0`
*/
issuerUrl: string;
/** Application / Client ID. */
clientId: string;
/**
* OAuth scopes to request.
* For Azure managed identity, exactly one scope or resource is required.
* For example: `["api://{app_id}/.default"]`
*/
scopes: string[];
/** Authentication flow (default: ClientCredentials). */
flow?: OAuthFlowType;
/** Client secret (required for ClientCredentials). */
clientSecret?: string;
/** Client ID for user-assigned managed identity (AzureManagedIdentity). */
managedIdentityClientId?: string;
/**
* Seconds before expiry to trigger proactive refresh (default: 300).
* Keep this well below the token TTL; if it is greater than or equal to
* the TTL, each request refreshes the token.
*/
refreshBufferSecs?: number;
}
-137
View File
@@ -1,137 +0,0 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
import {
type Attributes,
type MeterProvider,
type ObservableResult,
metrics,
} from "@opentelemetry/api";
import {
lancedbMetricsCatalog,
registerLancedbMetricsRecorder,
snapshotLancedbMetrics,
} from "./native";
let instrumented = false;
/**
* Register LanceDB metrics as OpenTelemetry observable instruments.
*
* Installs a process-global metrics recorder and creates one observable
* instrument per LanceDB metric (currently object store request counts, bytes,
* latency, errors, and throttles) on the given (or global) `MeterProvider`. The
* configured `MetricReader` then collects them on its own schedule.
*
* Counters and gauges map directly to observable counters/gauges. Because
* OpenTelemetry has no asynchronous histogram instrument, each histogram is
* exported Prometheus-style as cumulative `le` bucket counts (`<name>_bucket`,
* with an `le` attribute) plus `<name>_count` and `<name>_sum`.
*
* Requires `@opentelemetry/api` (a dependency) and, to actually export, an
* OpenTelemetry SDK such as `@opentelemetry/sdk-metrics`.
*
* @param meterProvider The provider to register instruments on. Defaults to the
* global provider from `@opentelemetry/api`.
* @returns `true` if the recorder is installed and instruments are registered.
* `false` if a different `metrics` recorder is already installed in this
* process (only one global recorder is permitted), in which case a warning is
* emitted and no instruments are created. Calling this more than once is safe;
* instruments are created only on the first successful call.
*/
export function instrumentLanceDbMetrics(
meterProvider?: MeterProvider,
): boolean {
if (!registerLancedbMetricsRecorder()) {
console.warn(
"Could not install the LanceDB metrics recorder: another `metrics` " +
"recorder is already installed in this process. LanceDB metrics will " +
"not be exported via OpenTelemetry.",
);
return false;
}
if (instrumented) {
return true;
}
const provider = meterProvider ?? metrics.getMeterProvider();
const meter = provider.getMeter("lancedb");
const scalarCallback = (metricName: string) => (result: ObservableResult) => {
for (const point of snapshotLancedbMetrics()) {
if (point.name === metricName && point.value != null) {
result.observe(point.value, point.attributes);
}
}
};
const bucketCallback = (metricName: string) => (result: ObservableResult) => {
for (const point of snapshotLancedbMetrics()) {
if (point.name !== metricName || point.buckets == null) {
continue;
}
for (const bucket of point.buckets) {
const attributes: Attributes = {
...point.attributes,
le: bucket.le,
};
result.observe(bucket.cumulativeCount, attributes);
}
}
};
const fieldCallback =
(metricName: string, field: "count" | "sum") =>
(result: ObservableResult) => {
for (const point of snapshotLancedbMetrics()) {
if (point.name !== metricName) {
continue;
}
const value = point[field];
if (value != null) {
result.observe(value, point.attributes);
}
}
};
for (const desc of lancedbMetricsCatalog()) {
const unit = desc.unit ?? "";
if (desc.kind === "counter") {
const counter = meter.createObservableCounter(desc.name, {
unit,
description: desc.description,
});
counter.addCallback(scalarCallback(desc.name));
} else if (desc.kind === "gauge") {
const gauge = meter.createObservableGauge(desc.name, {
unit,
description: desc.description,
});
gauge.addCallback(scalarCallback(desc.name));
} else if (desc.kind === "histogram") {
// `_bucket` and `_count` observe cumulative sample counts, not the
// histogram's measured quantity, so they are unitless; only `_sum`
// carries the histogram's unit.
const bucket = meter.createObservableCounter(`${desc.name}_bucket`, {
description: `${desc.description} (cumulative buckets)`,
});
bucket.addCallback(bucketCallback(desc.name));
const count = meter.createObservableCounter(`${desc.name}_count`, {
description: `${desc.description} (count)`,
});
count.addCallback(fieldCallback(desc.name, "count"));
const sum = meter.createObservableCounter(`${desc.name}_sum`, {
unit,
description: `${desc.description} (sum)`,
});
sum.addCallback(fieldCallback(desc.name, "sum"));
}
}
instrumented = true;
return true;
}
-3
View File
@@ -362,9 +362,6 @@ export class StandardQueryBase<
*
* Filtering performance can often be improved by creating a scalar index
* on the filter column(s).
*
* Calling this multiple times combines the filters with a logical AND rather
* than replacing the previous filter.
*/
where(predicate: string): this {
this.doCall((inner: NativeQueryType) => inner.onlyIf(predicate));
-32
View File
@@ -585,17 +585,6 @@ export abstract class Table {
* @returns {Promise<void>}
*/
abstract unsetLsmWriteSpec(): Promise<void>;
/**
* Read the {@link LsmWriteSpec} currently installed on this table.
*
* Resolves to `undefined` when the MemWAL LSM write path is not enabled (no
* spec has been set, or it was removed with {@link Table#unsetLsmWriteSpec}).
* The returned spec including its `maintainedIndexes` and
* `writerConfigDefaults` mirrors what was passed to
* {@link Table#setLsmWriteSpec}.
* @returns {Promise<LsmWriteSpec | undefined>}
*/
abstract getLsmWriteSpec(): Promise<LsmWriteSpec | undefined>;
/**
* Drain and close any cached MemWAL shard writers held for this table.
*
@@ -674,14 +663,6 @@ export abstract class Table {
*/
abstract branches(): Promise<Branches>;
/**
* The branch this table handle is scoped to, or `null` for the main branch.
*
* A handle returned by {@link Branches.create} or {@link Branches.checkout}
* reports the branch it targets; a handle opened normally reports `null`.
*/
abstract currentBranch(): string | null;
/**
* Restore the table to the currently checked out version
*
@@ -1102,15 +1083,6 @@ export class LocalTable extends Table {
return await this.inner.unsetLsmWriteSpec();
}
async getLsmWriteSpec(): Promise<LsmWriteSpec | undefined> {
// The native binding types `specType` as a plain `string`; narrow it back
// to the public union. The Rust `From` impl only ever emits one of the
// three valid values, so the cast is safe.
return ((await this.inner.getLsmWriteSpec()) ?? undefined) as
| LsmWriteSpec
| undefined;
}
async closeLsmWriters(): Promise<void> {
return await this.inner.closeLsmWriters();
}
@@ -1150,10 +1122,6 @@ export class LocalTable extends Table {
return new Branches(await this.inner.branches());
}
currentBranch(): string | null {
return this.inner.currentBranch() ?? null;
}
async optimize(options?: Partial<OptimizeOptions>): Promise<OptimizeStats> {
let cleanupOlderThanMs;
if (
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "@lancedb/lancedb-darwin-arm64",
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"os": ["darwin"],
"cpu": ["arm64"],
"main": "lancedb.darwin-arm64.node",
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "@lancedb/lancedb-linux-arm64-gnu",
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"os": ["linux"],
"cpu": ["arm64"],
"main": "lancedb.linux-arm64-gnu.node",
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "@lancedb/lancedb-linux-arm64-musl",
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"os": ["linux"],
"cpu": ["arm64"],
"main": "lancedb.linux-arm64-musl.node",
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "@lancedb/lancedb-linux-x64-gnu",
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"os": ["linux"],
"cpu": ["x64"],
"main": "lancedb.linux-x64-gnu.node",
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "@lancedb/lancedb-linux-x64-musl",
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"os": ["linux"],
"cpu": ["x64"],
"main": "lancedb.linux-x64-musl.node",
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "@lancedb/lancedb-win32-arm64-msvc",
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"os": [
"win32"
],
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "@lancedb/lancedb-win32-x64-msvc",
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"os": ["win32"],
"cpu": ["x64"],
"main": "lancedb.win32-x64-msvc.node",
+2 -2
View File
@@ -1,12 +1,12 @@
{
"name": "@lancedb/lancedb",
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "@lancedb/lancedb",
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"cpu": [
"x64",
"arm64"
+1 -3
View File
@@ -11,7 +11,7 @@
"ann"
],
"private": false,
"version": "0.31.0-beta.6",
"version": "0.30.1-beta.2",
"main": "dist/index.js",
"exports": {
".": "./dist/index.js",
@@ -44,7 +44,6 @@
"@biomejs/biome": "^1.7.3",
"@jest/globals": "^29.7.0",
"@napi-rs/cli": "3.7.0",
"@opentelemetry/sdk-metrics": "^1.30.0",
"@types/axios": "^0.14.0",
"@types/jest": "^29.1.2",
"@types/node": "22.7.4",
@@ -93,7 +92,6 @@
"version": "napi version"
},
"dependencies": {
"@opentelemetry/api": "^1.9.0",
"reflect-metadata": "^0.2.2"
},
"optionalDependencies": {
-53
View File
@@ -8,9 +8,6 @@ importers:
.:
dependencies:
'@opentelemetry/api':
specifier: ^1.9.0
version: 1.9.1
apache-arrow:
specifier: '>=15.0.0 <=18.1.0'
version: 18.1.0
@@ -36,9 +33,6 @@ importers:
'@napi-rs/cli':
specifier: 3.7.0
version: 3.7.0(@emnapi/core@1.10.0)(@emnapi/runtime@1.10.0)(@types/node@22.7.4)
'@opentelemetry/sdk-metrics':
specifier: ^1.30.0
version: 1.30.1(@opentelemetry/api@1.9.1)
'@types/axios':
specifier: ^0.14.0
version: 0.14.4
@@ -1313,32 +1307,6 @@ packages:
'@octokit/types@16.0.0':
resolution: {integrity: sha512-sKq+9r1Mm4efXW1FCk7hFSeJo4QKreL/tTbR0rz/qx/r1Oa2VV83LTA/H/MuCOX7uCIJmQVRKBcbmWoySjAnSg==}
'@opentelemetry/api@1.9.1':
resolution: {integrity: sha512-gLyJlPHPZYdAk1JENA9LeHejZe1Ti77/pTeFm/nMXmQH/HFZlcS/O2XJB+L8fkbrNSqhdtlvjBVjxwUYanNH5Q==}
engines: {node: '>=8.0.0'}
'@opentelemetry/core@1.30.1':
resolution: {integrity: sha512-OOCM2C/QIURhJMuKaekP3TRBxBKxG/TWWA0TL2J6nXUtDnuCtccy49LUJF8xPFXMX+0LMcxFpCo8M9cGY1W6rQ==}
engines: {node: '>=14'}
peerDependencies:
'@opentelemetry/api': '>=1.0.0 <1.10.0'
'@opentelemetry/resources@1.30.1':
resolution: {integrity: sha512-5UxZqiAgLYGFjS4s9qm5mBVo433u+dSPUFWVWXmLAD4wB65oMCoXaJP1KJa9DIYYMeHu3z4BZcStG3LC593cWA==}
engines: {node: '>=14'}
peerDependencies:
'@opentelemetry/api': '>=1.0.0 <1.10.0'
'@opentelemetry/sdk-metrics@1.30.1':
resolution: {integrity: sha512-q9zcZ0Okl8jRgmy7eNW3Ku1XSgg3sDLa5evHZpCwjspw7E8Is4K/haRPDJrBcX3YSn/Y7gUvFnByNYEKQNbNog==}
engines: {node: '>=14'}
peerDependencies:
'@opentelemetry/api': '>=1.3.0 <1.10.0'
'@opentelemetry/semantic-conventions@1.28.0':
resolution: {integrity: sha512-lp4qAiMTD4sNWW4DbKLBkfiMZ4jbAboJIGOQr5DvciMRI494OapieI9qiODpOt0XBr1LjIDy1xAGAnVs5supTA==}
engines: {node: '>=14'}
'@protobufjs/aspromise@1.1.2':
resolution: {integrity: sha512-j+gKExEuLmKwvz3OgROXtrJ2UG2x8Ch2YZUxahh+s1F2HZ+wAceUNLkvy6zKCPVRkU++ZWQrdxsUeQXmcg4uoQ==}
@@ -4957,27 +4925,6 @@ snapshots:
dependencies:
'@octokit/openapi-types': 27.0.0
'@opentelemetry/api@1.9.1': {}
'@opentelemetry/core@1.30.1(@opentelemetry/api@1.9.1)':
dependencies:
'@opentelemetry/api': 1.9.1
'@opentelemetry/semantic-conventions': 1.28.0
'@opentelemetry/resources@1.30.1(@opentelemetry/api@1.9.1)':
dependencies:
'@opentelemetry/api': 1.9.1
'@opentelemetry/core': 1.30.1(@opentelemetry/api@1.9.1)
'@opentelemetry/semantic-conventions': 1.28.0
'@opentelemetry/sdk-metrics@1.30.1(@opentelemetry/api@1.9.1)':
dependencies:
'@opentelemetry/api': 1.9.1
'@opentelemetry/core': 1.30.1(@opentelemetry/api@1.9.1)
'@opentelemetry/resources': 1.30.1(@opentelemetry/api@1.9.1)
'@opentelemetry/semantic-conventions@1.28.0': {}
'@protobufjs/aspromise@1.1.2':
optional: true
-6
View File
@@ -112,12 +112,6 @@ impl Connection {
builder = builder.client_config(rust_config);
if let Some(oauth_config) = options.oauth_config {
let config: lancedb::remote::oauth::OAuthConfig =
oauth_config.try_into().default_error()?;
builder = builder.oauth_config(config);
}
if let Some(api_key) = options.api_key {
builder = builder.api_key(&api_key);
}
-6
View File
@@ -12,7 +12,6 @@ mod header;
mod index;
mod iterator;
pub mod merge;
pub mod otel;
pub mod permutation;
mod query;
pub mod remote;
@@ -66,11 +65,6 @@ pub struct ConnectionOptions {
/// (For LanceDB cloud only): the host to use for LanceDB cloud. Used
/// for testing purposes.
pub host_override: Option<String>,
/// (For LanceDB cloud only): OAuth configuration for IdP-based
/// authentication (e.g., Azure Entra ID). When set, token acquisition
/// and refresh are handled entirely in Rust. TypeScript users should pass
/// the public `OAuthConfig` type exported from `@lancedb/lancedb`.
pub oauth_config: Option<remote::OAuthConfig>,
}
#[napi(object)]
+6 -4
View File
@@ -3,7 +3,7 @@
use std::time::Duration;
use lancedb::{ipc::ipc_file_to_batches, table::merge::MergeInsertBuilder};
use lancedb::{arrow::IntoArrow, ipc::ipc_file_to_batches, table::merge::MergeInsertBuilder};
use napi::bindgen_prelude::*;
use napi_derive::napi;
@@ -66,9 +66,11 @@ impl NativeMergeInsertBuilder {
#[napi(catch_unwind)]
pub async fn execute(&self, buf: Buffer) -> napi::Result<MergeResult> {
let data = ipc_file_to_batches(buf.to_vec()).map_err(|e| {
napi::Error::from_reason(format!("Failed to read IPC file: {}", convert_error(&e)))
})?;
let data = ipc_file_to_batches(buf.to_vec())
.and_then(IntoArrow::into_arrow)
.map_err(|e| {
napi::Error::from_reason(format!("Failed to read IPC file: {}", convert_error(&e)))
})?;
let this = self.clone();
-119
View File
@@ -1,119 +0,0 @@
// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The LanceDB Authors
//! Node.js bindings over [`lancedb::metrics_otel`].
//!
//! The aggregation, catalog, and histogram bucketing all live in the LanceDB
//! core crate; this module only converts the core snapshot types into napi
//! objects and exposes the three entry points to JavaScript, where
//! `lancedb/otel.ts` bridges them into the user's OpenTelemetry `MeterProvider`.
use std::collections::HashMap;
use lancedb::metrics_otel::{MetricPoint as CoreMetricPoint, MetricValue};
use napi_derive::napi;
/// One cumulative histogram bucket: all samples with value `<= le`.
#[napi(object)]
pub struct MetricBucket {
/// The inclusive upper bound of the bucket, or `"+Inf"` for the final bucket.
pub le: String,
/// Cumulative number of samples less than or equal to `le`.
pub cumulative_count: f64,
}
/// One aggregated metric data point. For counters and gauges only `value` is
/// set; for histograms `buckets` (cumulative `le` counts), `count`, and `sum`
/// are set.
#[napi(object)]
pub struct MetricPoint {
pub name: String,
pub kind: String,
pub attributes: HashMap<String, String>,
pub value: Option<f64>,
pub buckets: Option<Vec<MetricBucket>>,
pub count: Option<f64>,
pub sum: Option<f64>,
}
impl From<CoreMetricPoint> for MetricPoint {
fn from(point: CoreMetricPoint) -> Self {
let kind = point.kind.as_str().to_string();
let (value, buckets, count, sum) = match point.value {
MetricValue::Scalar(v) => (Some(v), None, None, None),
MetricValue::Histogram {
buckets,
count,
sum,
} => (
None,
Some(
buckets
.into_iter()
// Counts stay well within the f64-exact integer range
// (2^53), so this cast is lossless in practice and keeps
// the values plain JS numbers for OpenTelemetry.
.map(|(le, cumulative_count)| MetricBucket {
le,
cumulative_count: cumulative_count as f64,
})
.collect(),
),
Some(count as f64),
Some(sum),
),
};
Self {
name: point.name,
kind,
attributes: point.attributes,
value,
buckets,
count,
sum,
}
}
}
/// A described metric, used by the JavaScript layer to create instruments up front.
#[napi(object)]
pub struct MetricDescription {
pub name: String,
pub kind: String,
pub unit: Option<String>,
pub description: String,
}
/// Install the LanceDB metrics recorder as the process-global `metrics` recorder.
///
/// Returns `true` if the recorder is installed (now or previously). Returns
/// `false` if a *different* recorder is already installed — `metrics` allows
/// only one global recorder per process, so LanceDB cannot coexist with another.
#[napi]
pub fn register_lancedb_metrics_recorder() -> bool {
lancedb::metrics_otel::register_metrics_recorder()
}
/// The catalog of described LanceDB metrics. Empty until the recorder is installed.
#[napi]
pub fn lancedb_metrics_catalog() -> Vec<MetricDescription> {
lancedb::metrics_otel::metrics_catalog()
.into_iter()
.map(|desc| MetricDescription {
name: desc.name,
kind: desc.kind.as_str().to_string(),
unit: desc.unit,
description: desc.description,
})
.collect()
}
/// A point-in-time snapshot of every recorded metric. Empty until the recorder
/// is installed.
#[napi]
pub fn snapshot_lancedb_metrics() -> Vec<MetricPoint> {
lancedb::metrics_otel::snapshot_metrics()
.into_iter()
.map(MetricPoint::from)
.collect()
}
+1 -7
View File
@@ -16,7 +16,6 @@ pub struct SplitRandomOptions {
pub counts: Option<Vec<i64>>,
pub fixed: Option<i64>,
pub seed: Option<i64>,
pub clump_size: Option<i64>,
pub split_names: Option<Vec<String>>,
}
@@ -126,15 +125,10 @@ impl PermutationBuilder {
};
let seed = options.seed.map(|s| s as u64);
let clump_size = options.clump_size.map(|c| c as u64);
self.modify(|builder| {
builder.with_split_strategy(
SplitStrategy::Random {
seed,
sizes,
clump_size,
},
SplitStrategy::Random { seed, sizes },
options.split_names.clone(),
)
})
-121
View File
@@ -3,7 +3,6 @@
use std::collections::HashMap;
use lancedb::error::Error;
use napi_derive::*;
/// Timeout configuration for remote HTTP client.
@@ -141,84 +140,6 @@ impl From<TlsConfig> for lancedb::remote::TlsConfig {
}
}
/// OAuth configuration for LanceDB authentication.
///
/// This is the generated napi-rs binding shape. TypeScript users should prefer
/// the public `OAuthConfig` type exported from `@lancedb/lancedb`.
///
/// All token acquisition and refresh is handled in the Rust layer.
#[napi(object)]
#[derive(Clone)]
pub struct OAuthConfig {
/// OIDC issuer URL or OAuth authority URL.
/// For Azure: `https://login.microsoftonline.com/{tenant_id}/v2.0`
pub issuer_url: String,
/// Application / Client ID.
pub client_id: String,
/// OAuth scopes to request. For Azure managed identity, exactly one scope
/// or resource is required. For example: `["api://{app_id}/.default"]`
pub scopes: Vec<String>,
/// Authentication flow: "client_credentials" or "azure_managed_identity"
pub flow: Option<String>,
/// Client secret (required for client_credentials).
pub client_secret: Option<String>,
/// Client ID for user-assigned managed identity (azure_managed_identity).
pub managed_identity_client_id: Option<String>,
/// Seconds before expiry to trigger proactive refresh (default: 300).
/// Keep this well below the token TTL; if it is greater than or equal to
/// the TTL, each request refreshes the token.
pub refresh_buffer_secs: Option<u32>,
}
impl std::fmt::Debug for OAuthConfig {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("OAuthConfig")
.field("issuer_url", &self.issuer_url)
.field("client_id", &self.client_id)
.field("scopes", &self.scopes)
.field("flow", &self.flow)
.field(
"client_secret",
&self.client_secret.as_deref().map(|_| "<redacted>"),
)
.field(
"managed_identity_client_id",
&self.managed_identity_client_id,
)
.field("refresh_buffer_secs", &self.refresh_buffer_secs)
.finish()
}
}
impl TryFrom<OAuthConfig> for lancedb::remote::oauth::OAuthConfig {
type Error = Error;
fn try_from(config: OAuthConfig) -> Result<Self, Self::Error> {
use lancedb::remote::oauth::OAuthFlow;
let flow = match config.flow.as_deref().unwrap_or("client_credentials") {
"client_credentials" => OAuthFlow::ClientCredentials,
"azure_managed_identity" => OAuthFlow::AzureManagedIdentity {
client_id: config.managed_identity_client_id,
},
other => {
return Err(Error::InvalidInput {
message: format!("Unknown OAuth flow type: {other}"),
});
}
};
Ok(Self {
issuer_url: config.issuer_url,
client_id: config.client_id,
client_secret: config.client_secret,
scopes: config.scopes,
flow,
refresh_buffer_secs: config.refresh_buffer_secs.map(|v| v as u64),
})
}
}
impl From<ClientConfig> for lancedb::remote::ClientConfig {
fn from(config: ClientConfig) -> Self {
Self {
@@ -235,45 +156,3 @@ impl From<ClientConfig> for lancedb::remote::ClientConfig {
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_unknown_oauth_flow_returns_invalid_input() {
let config = OAuthConfig {
issuer_url: "https://issuer.example.com".to_string(),
client_id: "client-id".to_string(),
scopes: vec!["scope".to_string()],
flow: Some("typo".to_string()),
client_secret: None,
managed_identity_client_id: None,
refresh_buffer_secs: None,
};
let err = lancedb::remote::oauth::OAuthConfig::try_from(config).unwrap_err();
assert!(matches!(
err,
Error::InvalidInput { message }
if message == "Unknown OAuth flow type: typo"
));
}
#[test]
fn test_oauth_config_debug_redacts_client_secret() {
let config = OAuthConfig {
issuer_url: "https://issuer.example.com".to_string(),
client_id: "client-id".to_string(),
scopes: vec!["scope".to_string()],
flow: Some("client_credentials".to_string()),
client_secret: Some("super-secret".to_string()),
managed_identity_client_id: None,
refresh_buffer_secs: None,
};
let debug = format!("{config:?}");
assert!(!debug.contains("super-secret"));
assert!(debug.contains("client_secret: Some(\"<redacted>\")"));
}
}
-107
View File
@@ -3,8 +3,6 @@
use std::collections::HashMap;
use chrono::{DateTime, Utc};
use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};
use lancedb::table::{
AddDataMode, ColumnAlteration as LanceColumnAlteration, Duration,
@@ -411,16 +409,6 @@ impl Table {
.default_error()
}
#[napi(catch_unwind)]
pub async fn get_lsm_write_spec(&self) -> napi::Result<Option<LsmWriteSpec>> {
let spec = self
.inner_ref()?
.get_lsm_write_spec()
.await
.default_error()?;
Ok(spec.map(LsmWriteSpec::from))
}
#[napi(catch_unwind)]
pub async fn close_lsm_writers(&self) -> napi::Result<()> {
self.inner_ref()?.close_lsm_writers().await.default_error()
@@ -497,12 +485,6 @@ impl Table {
})
}
/// The branch this handle is scoped to, or `null` for the main branch.
#[napi]
pub fn current_branch(&self) -> napi::Result<Option<String>> {
Ok(self.inner_ref()?.current_branch())
}
#[napi(catch_unwind)]
pub async fn optimize(
&self,
@@ -620,43 +602,6 @@ pub struct IndexConfig {
/// Currently this is always an array of size 1. In the future there may
/// be more columns to represent composite indices.
pub columns: Vec<String>,
/// The UUID of the first segment of the index.
///
/// `undefined` for remote tables, which do not yet surface this.
pub index_uuid: Option<String>,
/// The protobuf type URL, a precise type identifier for the index.
///
/// `undefined` for remote tables.
pub type_url: Option<String>,
/// When the index was created.
///
/// `undefined` for remote tables or indices created before timestamps were tracked.
pub created_at: Option<DateTime<Utc>>,
/// The number of rows indexed, across all segments.
///
/// `undefined` for remote tables.
pub num_indexed_rows: Option<i64>,
/// The number of rows not yet covered by this index.
///
/// `undefined` for remote tables.
pub num_unindexed_rows: Option<i64>,
/// The total size in bytes of all index files across all segments.
///
/// `undefined` for remote tables or indices without size tracking.
pub size_bytes: Option<i64>,
/// The number of segments that make up the index.
///
/// `undefined` for remote tables.
pub num_segments: Option<i32>,
/// The on-disk index format version.
///
/// `undefined` for remote tables.
pub index_version: Option<i32>,
/// Index-type-specific details parsed as a JavaScript object.
///
/// Falls back to a raw string if JSON parsing fails. `undefined` for
/// remote tables or when details are unavailable.
pub index_details: Option<serde_json::Value>,
}
impl From<lancedb::index::IndexConfig> for IndexConfig {
@@ -666,17 +611,6 @@ impl From<lancedb::index::IndexConfig> for IndexConfig {
index_type,
columns: value.columns,
name: value.name,
index_uuid: value.index_uuid,
type_url: value.type_url,
created_at: value.created_at,
num_indexed_rows: value.num_indexed_rows.map(|n| n as i64),
num_unindexed_rows: value.num_unindexed_rows.map(|n| n as i64),
size_bytes: value.size_bytes.map(|n| n as i64),
num_segments: value.num_segments.map(|n| n as i32),
index_version: value.index_version,
index_details: value
.index_details
.and_then(|s| serde_json::from_str(&s).ok()),
}
}
}
@@ -738,47 +672,6 @@ impl TryFrom<LsmWriteSpec> for lancedb::table::LsmWriteSpec {
}
}
impl From<lancedb::table::LsmWriteSpec> for LsmWriteSpec {
fn from(spec: lancedb::table::LsmWriteSpec) -> Self {
use lancedb::table::LsmWriteSpec as Native;
match spec {
Native::Bucket {
column,
num_buckets,
maintained_indexes,
writer_config_defaults,
} => Self {
spec_type: "bucket".to_string(),
column: Some(column),
num_buckets: Some(num_buckets),
maintained_indexes: Some(maintained_indexes),
writer_config_defaults: Some(writer_config_defaults),
},
Native::Identity {
column,
maintained_indexes,
writer_config_defaults,
} => Self {
spec_type: "identity".to_string(),
column: Some(column),
num_buckets: None,
maintained_indexes: Some(maintained_indexes),
writer_config_defaults: Some(writer_config_defaults),
},
Native::Unsharded {
maintained_indexes,
writer_config_defaults,
} => Self {
spec_type: "unsharded".to_string(),
column: None,
num_buckets: None,
maintained_indexes: Some(maintained_indexes),
writer_config_defaults: Some(writer_config_defaults),
},
}
}
}
/// Statistics about a compaction operation.
#[napi(object)]
#[derive(Clone, Debug)]
+1 -3
View File
@@ -1,5 +1,5 @@
[tool.bumpversion]
current_version = "0.35.0-beta.0"
current_version = "0.33.1-beta.2"
parse = """(?x)
(?P<major>0|[1-9]\\d*)\\.
(?P<minor>0|[1-9]\\d*)\\.
@@ -23,8 +23,6 @@ allow_dirty = true
commit = true
message = "Bump version: {current_version} → {new_version}"
commit_args = ""
# bump-my-version >=1.4.0 rejects pre_commit_hooks containing shell syntax unless opted in.
allow_shell_hooks = true
# Update Cargo.lock after version bump
pre_commit_hooks = [
+3 -4
View File
@@ -1,6 +1,6 @@
[package]
name = "lancedb-python"
version = "0.35.0-beta.0"
version = "0.33.1-beta.2"
publish = false
edition.workspace = true
description = "Python bindings for LanceDB"
@@ -26,8 +26,7 @@ lance-namespace-impls.workspace = true
lance-io.workspace = true
env_logger.workspace = true
log.workspace = true
pyo3 = { version = "0.28", features = ["extension-module", "abi3-py39", "chrono"] }
chrono = { version = "0.4", default-features = false, features = ["clock"] }
pyo3 = { version = "0.28", features = ["extension-module", "abi3-py39"] }
pyo3-async-runtimes = { version = "0.28", features = [
"attributes",
"tokio-runtime",
@@ -47,6 +46,6 @@ pyo3-build-config = { version = "0.28", features = [
] }
[features]
default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/cos", "lancedb/goosefs", "lancedb/metrics-otel"]
default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
fp16kernels = ["lancedb/fp16kernels"]
remote = ["lancedb/remote"]
@@ -1,135 +0,0 @@
#!/usr/bin/env python3
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright The LanceDB Authors
"""Benchmark for StreamingDataset throughput.
Sweeps read_batch_size from 1 to 16384 to show how amortising the per-request
overhead scales. Each row at each chunk size is timed via the real
StreamingDataset so the numbers reflect production code.
Run with:
cd python
uv run --extra tests benchmarks/bench_streaming_dataloader.py
Optional env vars:
BENCH_NUM_ROWS total rows in the table (default 49152 = 24 × 2048)
BENCH_NUM_SPLITS number of splits (default 24)
BENCH_STEPS round-robin cycles to time per chunk size (default 100)
BENCH_ROW_BYTES approximate bytes per row padded with a binary column
(default 4096, mimics a small embedding/image patch)
"""
import os
import time
import tempfile
import pyarrow as pa
import lancedb
from lancedb.streaming import StreamingDataset
# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
NUM_SPLITS = int(os.environ.get("BENCH_NUM_SPLITS", 24))
# Default: 2048 rows per split so every chunk size up to 16Ki has ≥1 full
# chunk (except 16Ki itself which gets a single full-split fetch — still valid).
NUM_ROWS = int(os.environ.get("BENCH_NUM_ROWS", NUM_SPLITS * 2048))
STEPS = int(os.environ.get("BENCH_STEPS", 100))
ROW_BYTES = int(os.environ.get("BENCH_ROW_BYTES", 4096))
assert NUM_ROWS % NUM_SPLITS == 0, "NUM_ROWS must be divisible by NUM_SPLITS"
CHUNK_SIZES = [1, 4, 16, 64, 256, 1024, 4096, 16384]
# ---------------------------------------------------------------------------
# Table helpers
# ---------------------------------------------------------------------------
def make_table(db_path: str) -> lancedb.table.Table:
db = lancedb.connect(db_path)
payload = b"x" * ROW_BYTES
data = pa.table(
{
"id": pa.array(range(NUM_ROWS), type=pa.int32()),
"payload": pa.array([payload] * NUM_ROWS, type=pa.large_binary()),
}
)
return db.create_table("bench", data, mode="overwrite")
# ---------------------------------------------------------------------------
# Timing
# ---------------------------------------------------------------------------
def bench_chunk(table, chunk_size: int, steps: int) -> tuple[int, float]:
"""Return (rows_drained, elapsed_seconds) for one timed run."""
total_rows = steps * NUM_SPLITS
ds = StreamingDataset(
table, num_splits=NUM_SPLITS, shuffle_seed=42, read_batch_size=chunk_size
)
count = 0
t0 = time.perf_counter()
for _ in ds:
count += 1
if count >= total_rows:
break
return count, time.perf_counter() - t0
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main() -> None:
rows_per_split = NUM_ROWS // NUM_SPLITS
print("Benchmark config:")
print(
f" NUM_ROWS={NUM_ROWS} NUM_SPLITS={NUM_SPLITS} "
f"rows/split={rows_per_split} STEPS={STEPS} ROW_BYTES={ROW_BYTES}"
)
print(f" ~{NUM_ROWS * ROW_BYTES / 1024 / 1024:.1f} MB total table size")
print()
with tempfile.TemporaryDirectory() as tmp:
print("Creating table...", flush=True)
table = make_table(tmp)
cols = (
f"{'chunk':>6} {'rows':>6} {'elapsed':>8} {'rows/s':>10} {'ms/step':>9}"
)
print(f"\n{cols}")
print("-" * 52)
for chunk in CHUNK_SIZES:
# Warm-up pass (one step's worth of rows)
warmup_ds = StreamingDataset(
table, num_splits=NUM_SPLITS, shuffle_seed=42, read_batch_size=chunk
)
warmup_count = 0
for _ in warmup_ds:
warmup_count += 1
if warmup_count >= NUM_SPLITS:
break
drained, elapsed = bench_chunk(table, chunk, STEPS)
rows_per_sec = drained / elapsed if elapsed > 0 else float("inf")
ms_per_step = elapsed / STEPS * 1000
print(
f"{chunk:>6} {drained:>6} {elapsed:>7.3f}s "
f"{rows_per_sec:>10.0f} {ms_per_step:>8.1f}ms"
)
print()
print("Done.")
if __name__ == "__main__":
main()
-5
View File
@@ -47,10 +47,6 @@ repository = "https://github.com/lancedb/lancedb"
pylance = [
"pylance>=5.0.0b5",
]
# A library only needs the OpenTelemetry API; the application supplies and
# configures the SDK (the actual exporter/reader). See
# https://opentelemetry.io/docs/languages/python/instrumentation/
otel = ["opentelemetry-api"]
tests = [
"aiohttp>=3.9.0",
"boto3>=1.28.57",
@@ -65,7 +61,6 @@ tests = [
"pylance>=5.0.0b5",
"requests>=2.31.0",
"datafusion>=52,<53",
"opentelemetry-sdk>=1.30.0",
]
dev = [
"ruff>=0.3.0",
+2 -16
View File
@@ -89,8 +89,6 @@ def connect(
If presented, connect to LanceDB cloud.
Otherwise, connect to a database on file system or cloud storage.
Can be set via environment variable `LANCEDB_API_KEY`.
OAuth configuration is currently supported only by ``connect_async``;
synchronous LanceDB Cloud connections require an API key.
region: str, default "us-east-1"
The region to use for LanceDB Cloud.
host_override: str, optional
@@ -149,14 +147,8 @@ def connect(
For object storage, use a URI prefix:
>>> db = lancedb.connect( # doctest: +SKIP
... "s3://my-bucket/lancedb",
... storage_options={
... "aws_access_key_id": "***",
... "aws_secret_access_key": "***",
... "aws_region": "us-east-1",
... },
... )
>>> db = lancedb.connect("s3://my-bucket/lancedb",
... storage_options={"aws_access_key_id": "***"})
For tests and temporary data, use an in-memory database:
@@ -348,7 +340,6 @@ async def connect_async(
session: Optional[Session] = None,
manifest_enabled: bool = False,
namespace_client_properties: Optional[Dict[str, str]] = None,
oauth_config=None,
) -> AsyncConnection:
"""Connect to a LanceDB database.
@@ -398,10 +389,6 @@ async def connect_async(
namespace_client_properties : dict, optional
Additional directory namespace client properties to use with
``manifest_enabled=True``.
oauth_config : OAuthConfig, optional
OAuth configuration for LanceDB Cloud/Enterprise. This is supported by
``connect_async`` only; synchronous ``connect`` uses API key
authentication for ``db://`` URIs.
Examples
--------
@@ -448,7 +435,6 @@ async def connect_async(
session,
manifest_enabled,
namespace_client_properties,
oauth_config,
)
)
+3 -53
View File
@@ -1,5 +1,4 @@
from datetime import date, datetime, timedelta
from decimal import Decimal
from datetime import timedelta
from typing import Dict, List, Optional, Tuple, Any, TypedDict, Union, Literal
import pyarrow as pa
@@ -30,25 +29,6 @@ IvfHnswPq: type[HnswPq] = HnswPq
IvfHnswSq: type[HnswSq] = HnswSq
IvfHnswFlat: type[HnswFlat] = HnswFlat
class MetricPoint:
name: str
kind: str
attributes: Dict[str, str]
value: Optional[float]
buckets: Optional[List[Tuple[str, int]]]
count: Optional[int]
sum: Optional[float]
class MetricDescription:
name: str
kind: str
unit: Optional[str]
description: str
def register_lancedb_metrics_recorder() -> bool: ...
def lancedb_metrics_catalog() -> List[MetricDescription]: ...
def snapshot_lancedb_metrics() -> List[MetricPoint]: ...
class PyExpr:
"""A type-safe DataFusion expression node (Rust-side handle)."""
@@ -73,9 +53,7 @@ class PyExpr:
def to_sql(self) -> str: ...
def expr_col(name: str) -> PyExpr: ...
def expr_lit(
value: Union[bool, int, float, str, bytes, date, datetime, Decimal],
) -> PyExpr: ...
def expr_lit(value: Union[bool, int, float, str, bytes]) -> PyExpr: ...
def expr_func(name: str, args: List[PyExpr]) -> PyExpr: ...
class Session:
@@ -227,7 +205,7 @@ class Table:
async def prewarm_index(self, index_name: str) -> None: ...
async def prewarm_data(self, columns: Optional[List[str]] = None) -> None: ...
async def list_indices(self) -> list[IndexConfig]: ...
async def delete(self, filter: Union[str, PyExpr]) -> DeleteResult: ...
async def delete(self, filter: str) -> DeleteResult: ...
async def add_columns(self, columns: list[tuple[str, str]]) -> AddColumnsResult: ...
async def add_columns_with_schema(self, schema: pa.Schema) -> AddColumnsResult: ...
async def alter_columns(
@@ -248,7 +226,6 @@ class Table:
async def set_unenforced_primary_key(self, columns: List[str]) -> None: ...
async def set_lsm_write_spec(self, spec: LsmWriteSpec) -> None: ...
async def unset_lsm_write_spec(self) -> None: ...
async def get_lsm_write_spec(self) -> Optional[LsmWriteSpec]: ...
async def close_lsm_writers(self) -> None: ...
@property
def tags(self) -> Tags: ...
@@ -282,15 +259,6 @@ class IndexConfig:
name: str
index_type: str
columns: List[str]
index_uuid: Optional[str]
type_url: Optional[str]
created_at: Optional[datetime]
num_indexed_rows: Optional[int]
num_unindexed_rows: Optional[int]
size_bytes: Optional[int]
num_segments: Optional[int]
index_version: Optional[int]
index_details: Optional[Any]
async def connect(
uri: str,
@@ -303,24 +271,6 @@ async def connect(
session: Optional[Session],
manifest_enabled: bool = False,
namespace_client_properties: Optional[Dict[str, str]] = None,
oauth_config: Optional[Any] = None,
) -> Connection: ...
def connect_namespace(
namespace_client_impl: str,
namespace_client_properties: Dict[str, str],
read_consistency_interval: Optional[float] = None,
storage_options: Optional[Dict[str, str]] = None,
session: Optional[Session] = None,
namespace_client_pushdown_operations: Optional[List[str]] = None,
) -> Connection: ...
def connect_namespace_client(
namespace_client: Any,
read_consistency_interval: Optional[float] = None,
storage_options: Optional[Dict[str, str]] = None,
session: Optional[Session] = None,
namespace_client_pushdown_operations: Optional[List[str]] = None,
namespace_client_impl: Optional[str] = None,
namespace_client_properties: Optional[Dict[str, str]] = None,
) -> Connection: ...
class RecordBatchStream:
@@ -81,7 +81,6 @@ class ColPaliEmbeddings(EmbeddingFunction):
warnings.warn(
"use_token_pooling is deprecated, use pooling_strategy=None instead",
DeprecationWarning,
stacklevel=2,
)
self.pooling_strategy = None
+1 -11
View File
@@ -2,7 +2,7 @@
# SPDX-FileCopyrightText: Copyright The LanceDB Authors
from functools import cached_property
from typing import TYPE_CHECKING, Any, List, Optional, Sequence, Union
from typing import TYPE_CHECKING, List, Optional, Sequence, Union
import numpy as np
@@ -56,16 +56,6 @@ class OllamaEmbeddings(TextEmbeddingFunction):
embeddings = self._compute_embedding(texts)
return list(embeddings)
def __getstate__(self) -> dict[str, Any]:
state = super().__getstate__()
state["__dict__"] = {
k: v for k, v in state["__dict__"].items() if k != "_ollama_client"
}
return state
def __setstate__(self, state: dict[str, Any]) -> None:
super().__setstate__(state)
@cached_property
def _ollama_client(self) -> "ollama.Client":
ollama = attempt_import_or_raise("ollama")
+3 -17
View File
@@ -19,8 +19,6 @@ operators::
from __future__ import annotations
from datetime import date, datetime
from decimal import Decimal
from typing import Iterable, Union
import pyarrow as pa
@@ -65,7 +63,7 @@ def _coerce(value: "ExprLike") -> "Expr":
# Type alias used in annotations.
ExprLike = Union["Expr", bool, int, float, str, bytes, date, datetime, Decimal]
ExprLike = Union["Expr", bool, int, float, str, bytes]
class Expr:
@@ -120,18 +118,10 @@ class Expr:
"""Logical AND (``expr_a & expr_b``)."""
return Expr(self._inner.and_(_coerce(other)._inner))
def __rand__(self, other: ExprLike) -> "Expr":
"""Right-hand logical AND (``True & expr``)."""
return Expr(_coerce(other)._inner.and_(self._inner))
def __or__(self, other: "Expr") -> "Expr":
"""Logical OR (``expr_a | expr_b``)."""
return Expr(self._inner.or_(_coerce(other)._inner))
def __ror__(self, other: ExprLike) -> "Expr":
"""Right-hand logical OR (``False | expr``)."""
return Expr(_coerce(other)._inner.or_(self._inner))
def __invert__(self) -> "Expr":
"""Logical NOT (``~expr``)."""
return Expr(self._inner.not_())
@@ -276,14 +266,13 @@ def col(name: str) -> Expr:
return Expr(expr_col(name))
def lit(value: Union[bool, int, float, str, bytes, date, datetime, Decimal]) -> Expr:
def lit(value: Union[bool, int, float, str, bytes]) -> Expr:
"""Create a literal (constant) value expression.
Parameters
----------
value:
A Python ``bool``, ``int``, ``float``, ``str``, ``bytes``, ``date``,
``datetime``, or ``Decimal``.
A Python ``bool``, ``int``, ``float``, ``str``, or ``bytes``.
Examples
--------
@@ -291,9 +280,6 @@ def lit(value: Union[bool, int, float, str, bytes, date, datetime, Decimal]) ->
>>> col("price") * lit(1.1)
Expr((price * 1.1))
"""
if not isinstance(value, (bool, int, float, str, bytes, date, datetime, Decimal)):
raise TypeError(f"Unsupported literal type: {type(value).__name__}")
return Expr(expr_lit(value))
+6 -21
View File
@@ -5,9 +5,7 @@
from __future__ import annotations
from datetime import timedelta
from typing import TYPE_CHECKING, List, Optional, Union
from .expr import Expr
from typing import TYPE_CHECKING, List, Optional
if TYPE_CHECKING:
from .common import DATA
@@ -34,7 +32,6 @@ class LanceMergeInsertBuilder(object):
self._when_not_matched_insert_all = False
self._when_not_matched_by_source_delete = False
self._when_not_matched_by_source_condition = None
self._when_not_matched_by_source_condition_expr = None
self._timeout = None
self._use_index = True
self._use_lsm_write = None
@@ -51,15 +48,6 @@ class LanceMergeInsertBuilder(object):
If there are multiple matches then the behavior is undefined.
Currently this causes multiple copies of the row to be created
but that behavior is subject to change.
Parameters
----------
where: Optional[str], default None
An optional filter to limit which rows are updated. Column
references in this expression must be prefixed with "target."
to refer to the existing table data. For example, to only
update rows where the existing color is red, use:
``where="target.color = 'red'"``
"""
self._when_matched_update_all = True
self._when_matched_update_all_condition = where
@@ -74,7 +62,7 @@ class LanceMergeInsertBuilder(object):
return self
def when_not_matched_by_source_delete(
self, condition: Union[str, Expr, None] = None
self, condition: Optional[str] = None
) -> LanceMergeInsertBuilder:
"""
Rows that exist only in the target table (old data) will be
@@ -83,16 +71,13 @@ class LanceMergeInsertBuilder(object):
Parameters
----------
condition: str or :class:`~lancedb.expr.Expr` or None, default None
condition: Optional[str], default None
If None then all such rows will be deleted. Otherwise the
condition will be used as a filter to limit what rows are deleted.
Can be a SQL string or a type-safe :class:`~lancedb.expr.Expr`
built with :func:`~lancedb.expr.col` and :func:`~lancedb.expr.lit`.
condition will be used as an SQL filter to limit what rows
are deleted.
"""
self._when_not_matched_by_source_delete = True
if isinstance(condition, Expr):
self._when_not_matched_by_source_condition_expr = condition._inner
elif condition is not None:
if condition is not None:
self._when_not_matched_by_source_condition = condition
return self
+160 -264
View File
@@ -38,13 +38,15 @@ from lance_namespace_urllib3_client.models.query_table_request_vector import (
QueryTableRequestVector,
)
from lance_namespace_urllib3_client.models.string_fts_query import StringFtsQuery
from lance_namespace.errors import NamespaceNotEmptyError, TableNotFoundError
from lancedb._lancedb import (
connect_namespace as _connect_namespace,
connect_namespace_client as _connect_namespace_client,
)
from lance_namespace.errors import TableNotFoundError
from lancedb._lancedb import connect_namespace_client as _connect_namespace_client
from lancedb.background_loop import LOOP
from lancedb.db import AsyncConnection, DBConnection
from lancedb.namespace_utils import (
_normalize_create_namespace_mode,
_normalize_drop_namespace_mode,
_normalize_drop_namespace_behavior,
)
from lance_namespace import (
LanceNamespace,
connect as namespace_connect,
@@ -53,6 +55,12 @@ from lance_namespace import (
DropNamespaceResponse,
ListNamespacesResponse,
ListTablesResponse,
ListTablesRequest,
DescribeNamespaceRequest,
DropTableRequest,
ListNamespacesRequest,
CreateNamespaceRequest,
DropNamespaceRequest,
)
from lancedb.table import AsyncTable, LanceTable, Table
from lancedb.util import validate_table_name
@@ -62,9 +70,6 @@ from lancedb.embeddings import EmbeddingFunctionConfig
from ._lancedb import Session
_MAX_QUERY_K = 2**31 - 1
def _query_to_namespace_request(
table_id: List[str],
query: "Query",
@@ -142,8 +147,7 @@ def _query_to_namespace_request(
if query.limit is not None:
k = query.limit
elif query.vector is None and query.full_text_query is None:
# limit k to max i32 value to avoid client overflows
k = _MAX_QUERY_K
k = sys.maxsize
else:
k = 10
@@ -364,23 +368,6 @@ def _convert_pyarrow_schema_to_json(schema: pa.Schema) -> JsonArrowSchema:
return JsonArrowSchema(fields=fields, metadata=meta)
def _builds_namespace_natively(
namespace_client_impl: Optional[str],
namespace_client_properties: Optional[Dict[str, str]],
) -> bool:
"""Whether ``connect_namespace_client`` builds the namespace client natively
in Rust (installing the read-freshness context provider) rather than wrapping
the pre-built Python client.
Must mirror Rust ``build_namespace_natively`` in ``python/src/connection.rs``.
"""
return namespace_client_impl == "rest" and bool(namespace_client_properties)
def _supports_native_namespace(namespace_client_impl: str) -> bool:
return namespace_client_impl in {"dir", "rest"}
class LanceNamespaceDBConnection(DBConnection):
"""
A LanceDB connection that uses a namespace for table management.
@@ -391,7 +378,7 @@ class LanceNamespaceDBConnection(DBConnection):
def __init__(
self,
namespace_client: Optional[LanceNamespace] = None,
namespace_client: LanceNamespace,
*,
read_consistency_interval: Optional[timedelta] = None,
storage_options: Optional[Dict[str, str]] = None,
@@ -399,7 +386,6 @@ class LanceNamespaceDBConnection(DBConnection):
namespace_client_pushdown_operations: Optional[List[str]] = None,
namespace_client_impl: Optional[str] = None,
namespace_client_properties: Optional[Dict[str, str]] = None,
_inner: Optional[AsyncConnection] = None,
):
"""
Initialize a namespace-based LanceDB connection.
@@ -441,36 +427,23 @@ class LanceNamespaceDBConnection(DBConnection):
)
self._namespace_client_impl = namespace_client_impl
self._namespace_client_properties = namespace_client_properties
# When the namespace connection or client is built natively in Rust, the
# underlying Rust table performs QueryTable pushdown through the
# read-freshness context provider, which the pure-Python ``query_table``
# path bypasses.
self._route_pushdown_to_rust = _inner is not None or _builds_namespace_natively(
namespace_client_impl, namespace_client_properties
)
if _inner is not None:
self._inner = _inner
else:
if namespace_client is None:
raise ValueError("namespace_client is required without a native _inner")
self._inner = AsyncConnection(
_connect_namespace_client(
namespace_client,
read_consistency_interval=(
read_consistency_interval.total_seconds()
if read_consistency_interval is not None
else None
),
storage_options=self.storage_options or None,
session=session,
namespace_client_pushdown_operations=(
list(self._namespace_client_pushdown_operations)
),
namespace_client_impl=namespace_client_impl,
namespace_client_properties=namespace_client_properties,
)
self._inner = AsyncConnection(
_connect_namespace_client(
namespace_client,
read_consistency_interval=(
read_consistency_interval.total_seconds()
if read_consistency_interval is not None
else None
),
storage_options=self.storage_options or None,
session=session,
namespace_client_pushdown_operations=(
list(self._namespace_client_pushdown_operations)
),
namespace_client_impl=namespace_client_impl,
namespace_client_properties=namespace_client_properties,
)
self._uri = self._inner.uri
)
@override
def serialize(self) -> str:
@@ -516,11 +489,11 @@ class LanceNamespaceDBConnection(DBConnection):
)
if namespace_path is None:
namespace_path = []
return LOOP.run(
self._inner.table_names(
namespace_path=namespace_path, start_after=page_token, limit=limit
)
request = ListTablesRequest(
id=namespace_path, page_token=page_token, limit=limit
)
response = self._namespace_client.list_tables(request)
return response.tables if response.tables else []
@override
def create_table(
@@ -565,7 +538,6 @@ class LanceNamespaceDBConnection(DBConnection):
namespace_path=namespace_path,
namespace_client=self._namespace_client,
pushdown_operations=self._namespace_client_pushdown_operations,
route_pushdown_to_rust=self._route_pushdown_to_rust,
_async=async_table,
)
@@ -591,8 +563,8 @@ class LanceNamespaceDBConnection(DBConnection):
index_cache_size=index_cache_size,
)
)
except (RuntimeError, ValueError) as e:
if "Table not found" in str(e) or "was not found" in str(e):
except RuntimeError as e:
if "Table not found" in str(e):
table_id = namespace_path + [name]
raise TableNotFoundError(f"Table not found: {'$'.join(table_id)}")
raise
@@ -603,7 +575,6 @@ class LanceNamespaceDBConnection(DBConnection):
namespace_path=namespace_path,
namespace_client=self._namespace_client,
pushdown_operations=self._namespace_client_pushdown_operations,
route_pushdown_to_rust=self._route_pushdown_to_rust,
_async=async_table,
)
if branch is not None:
@@ -614,9 +585,12 @@ class LanceNamespaceDBConnection(DBConnection):
@override
def drop_table(self, name: str, namespace_path: Optional[List[str]] = None):
# Use namespace drop_table directly
if namespace_path is None:
namespace_path = []
LOOP.run(self._inner.drop_table(name, namespace_path=namespace_path))
table_id = namespace_path + [name]
request = DropTableRequest(id=table_id)
self._namespace_client.drop_table(request)
@override
def rename_table(
@@ -630,19 +604,9 @@ class LanceNamespaceDBConnection(DBConnection):
cur_namespace_path = []
if new_namespace_path is None:
new_namespace_path = []
try:
LOOP.run(
self._inner.rename_table(
cur_name,
new_name,
cur_namespace_path=cur_namespace_path,
new_namespace_path=new_namespace_path,
)
)
except RuntimeError as e:
if "rename_table not implemented" in str(e):
raise NotImplementedError("rename_table not implemented") from e
raise
raise NotImplementedError(
"rename_table is not supported for namespace connections"
)
@override
def drop_database(self):
@@ -654,7 +618,8 @@ class LanceNamespaceDBConnection(DBConnection):
def drop_all_tables(self, namespace_path: Optional[List[str]] = None):
if namespace_path is None:
namespace_path = []
LOOP.run(self._inner.drop_all_tables(namespace_path=namespace_path))
for table_name in self.table_names(namespace_path=namespace_path):
self.drop_table(table_name, namespace_path=namespace_path)
@override
def list_namespaces(
@@ -684,10 +649,13 @@ class LanceNamespaceDBConnection(DBConnection):
"""
if namespace_path is None:
namespace_path = []
return LOOP.run(
self._inner.list_namespaces(
namespace_path=namespace_path, page_token=page_token, limit=limit
)
request = ListNamespacesRequest(
id=namespace_path, page_token=page_token, limit=limit
)
response = self._namespace_client.list_namespaces(request)
return ListNamespacesResponse(
namespaces=response.namespaces if response.namespaces else [],
page_token=response.page_token,
)
@override
@@ -715,12 +683,14 @@ class LanceNamespaceDBConnection(DBConnection):
CreateNamespaceResponse
Response containing the properties of the created namespace.
"""
return LOOP.run(
self._inner.create_namespace(
namespace_path=namespace_path,
mode=mode,
properties=properties,
)
request = CreateNamespaceRequest(
id=namespace_path,
mode=_normalize_create_namespace_mode(mode),
properties=properties,
)
response = self._namespace_client.create_namespace(request)
return CreateNamespaceResponse(
properties=response.properties if hasattr(response, "properties") else None
)
@override
@@ -748,18 +718,20 @@ class LanceNamespaceDBConnection(DBConnection):
DropNamespaceResponse
Response containing properties and transaction_id if applicable.
"""
try:
return LOOP.run(
self._inner.drop_namespace(
namespace_path=namespace_path,
mode=mode,
behavior=behavior,
)
)
except RuntimeError as e:
if "Namespace not empty" in str(e):
raise NamespaceNotEmptyError(str(e)) from e
raise
request = DropNamespaceRequest(
id=namespace_path,
mode=_normalize_drop_namespace_mode(mode),
behavior=_normalize_drop_namespace_behavior(behavior),
)
response = self._namespace_client.drop_namespace(request)
return DropNamespaceResponse(
properties=(
response.properties if hasattr(response, "properties") else None
),
transaction_id=(
response.transaction_id if hasattr(response, "transaction_id") else None
),
)
@override
def describe_namespace(
@@ -778,7 +750,11 @@ class LanceNamespaceDBConnection(DBConnection):
DescribeNamespaceResponse
Response containing the namespace properties.
"""
return LOOP.run(self._inner.describe_namespace(namespace_path))
request = DescribeNamespaceRequest(id=namespace_path)
response = self._namespace_client.describe_namespace(request)
return DescribeNamespaceResponse(
properties=response.properties if hasattr(response, "properties") else None
)
@override
def list_tables(
@@ -808,10 +784,13 @@ class LanceNamespaceDBConnection(DBConnection):
"""
if namespace_path is None:
namespace_path = []
return LOOP.run(
self._inner.list_tables(
namespace_path=namespace_path, page_token=page_token, limit=limit
)
request = ListTablesRequest(
id=namespace_path, page_token=page_token, limit=limit
)
response = self._namespace_client.list_tables(request)
return ListTablesResponse(
tables=response.tables if response.tables else [],
page_token=response.page_token,
)
def _lance_table_from_uri(
@@ -867,18 +846,6 @@ class LanceNamespaceDBConnection(DBConnection):
LanceNamespace
The namespace client for this connection.
"""
if self._namespace_client is None:
if (
self._namespace_client_impl is None
or self._namespace_client_properties is None
):
raise ValueError(
"Cannot construct a Python namespace client without "
"namespace implementation properties"
)
self._namespace_client = namespace_connect(
self._namespace_client_impl, self._namespace_client_properties
)
return self._namespace_client
@@ -892,15 +859,12 @@ class AsyncLanceNamespaceDBConnection:
def __init__(
self,
namespace_client: Optional[LanceNamespace] = None,
namespace_client: LanceNamespace,
*,
read_consistency_interval: Optional[timedelta] = None,
storage_options: Optional[Dict[str, str]] = None,
session: Optional[Session] = None,
namespace_client_pushdown_operations: Optional[List[str]] = None,
namespace_client_impl: Optional[str] = None,
namespace_client_properties: Optional[Dict[str, str]] = None,
_inner: Optional[AsyncConnection] = None,
):
"""
Initialize an async namespace-based LanceDB connection.
@@ -926,12 +890,6 @@ class AsyncLanceNamespaceDBConnection:
namespace.create_table() instead of using declare_table + local write.
Default is None (no pushdown, all operations run locally).
namespace_client_impl : Optional[str]
The namespace implementation name used to create this connection.
Required (with ``namespace_client_properties``) for the Rust client to
be built natively and install the read-freshness provider.
namespace_client_properties : Optional[Dict[str, str]]
The namespace properties used to create this connection.
"""
self._namespace_client = namespace_client
self.read_consistency_interval = read_consistency_interval
@@ -940,37 +898,23 @@ class AsyncLanceNamespaceDBConnection:
self._namespace_client_pushdown_operations = set(
namespace_client_pushdown_operations or []
)
self._namespace_client_impl = namespace_client_impl
self._namespace_client_properties = namespace_client_properties
# See LanceNamespaceDBConnection: when Rust owns the namespace
# connection/client, its table performs QueryTable pushdown through the
# read-freshness provider, so defer to it rather than the urllib3 client
# path (which omits x-lancedb-min-timestamp).
self._route_pushdown_to_rust = _inner is not None or _builds_namespace_natively(
namespace_client_impl, namespace_client_properties
)
if _inner is not None:
self._inner = _inner
else:
if namespace_client is None:
raise ValueError("namespace_client is required without a native _inner")
self._inner = AsyncConnection(
_connect_namespace_client(
namespace_client,
read_consistency_interval=(
read_consistency_interval.total_seconds()
if read_consistency_interval is not None
else None
),
storage_options=self.storage_options or None,
session=session,
namespace_client_pushdown_operations=(
list(self._namespace_client_pushdown_operations)
),
namespace_client_impl=namespace_client_impl,
namespace_client_properties=namespace_client_properties,
)
self._inner = AsyncConnection(
_connect_namespace_client(
namespace_client,
read_consistency_interval=(
read_consistency_interval.total_seconds()
if read_consistency_interval is not None
else None
),
storage_options=self.storage_options or None,
session=session,
namespace_client_pushdown_operations=(
list(self._namespace_client_pushdown_operations)
),
namespace_client_impl=None,
namespace_client_properties=None,
)
)
async def table_names(
self,
@@ -994,9 +938,11 @@ class AsyncLanceNamespaceDBConnection:
)
if namespace_path is None:
namespace_path = []
return await self._inner.table_names(
namespace_path=namespace_path, start_after=page_token, limit=limit
request = ListTablesRequest(
id=namespace_path, page_token=page_token, limit=limit
)
response = self._namespace_client.list_tables(request)
return response.tables if response.tables else []
async def create_table(
self,
@@ -1036,7 +982,6 @@ class AsyncLanceNamespaceDBConnection:
namespace_path=namespace_path,
namespace_client=self._namespace_client,
pushdown_operations=self._namespace_client_pushdown_operations,
route_pushdown_to_rust=self._route_pushdown_to_rust,
)
async def open_table(
@@ -1059,8 +1004,8 @@ class AsyncLanceNamespaceDBConnection:
storage_options=storage_options,
index_cache_size=index_cache_size,
)
except (RuntimeError, ValueError) as e:
if "Table not found" in str(e) or "was not found" in str(e):
except RuntimeError as e:
if "Table not found" in str(e):
table_id = namespace_path + [name]
raise TableNotFoundError(f"Table not found: {'$'.join(table_id)}")
raise
@@ -1074,14 +1019,15 @@ class AsyncLanceNamespaceDBConnection:
namespace_path=namespace_path,
namespace_client=self._namespace_client,
pushdown_operations=self._namespace_client_pushdown_operations,
route_pushdown_to_rust=self._route_pushdown_to_rust,
)
async def drop_table(self, name: str, namespace_path: Optional[List[str]] = None):
"""Drop a table from the namespace."""
if namespace_path is None:
namespace_path = []
await self._inner.drop_table(name, namespace_path=namespace_path)
table_id = namespace_path + [name]
request = DropTableRequest(id=table_id)
self._namespace_client.drop_table(request)
async def rename_table(
self,
@@ -1090,22 +1036,14 @@ class AsyncLanceNamespaceDBConnection:
cur_namespace_path: Optional[List[str]] = None,
new_namespace_path: Optional[List[str]] = None,
):
"""Rename a table in the namespace."""
"""Rename is not supported for namespace connections."""
if cur_namespace_path is None:
cur_namespace_path = []
if new_namespace_path is None:
new_namespace_path = []
try:
await self._inner.rename_table(
cur_name,
new_name,
cur_namespace_path=cur_namespace_path,
new_namespace_path=new_namespace_path,
)
except RuntimeError as e:
if "rename_table not implemented" in str(e):
raise NotImplementedError("rename_table not implemented") from e
raise
raise NotImplementedError(
"rename_table is not supported for namespace connections"
)
async def drop_database(self):
"""Deprecated method."""
@@ -1117,7 +1055,9 @@ class AsyncLanceNamespaceDBConnection:
"""Drop all tables in the namespace."""
if namespace_path is None:
namespace_path = []
await self._inner.drop_all_tables(namespace_path=namespace_path)
table_names = await self.table_names(namespace_path=namespace_path)
for table_name in table_names:
await self.drop_table(table_name, namespace_path=namespace_path)
async def list_namespaces(
self,
@@ -1146,8 +1086,13 @@ class AsyncLanceNamespaceDBConnection:
"""
if namespace_path is None:
namespace_path = []
return await self._inner.list_namespaces(
namespace_path=namespace_path, page_token=page_token, limit=limit
request = ListNamespacesRequest(
id=namespace_path, page_token=page_token, limit=limit
)
response = self._namespace_client.list_namespaces(request)
return ListNamespacesResponse(
namespaces=response.namespaces if response.namespaces else [],
page_token=response.page_token,
)
async def create_namespace(
@@ -1174,11 +1119,15 @@ class AsyncLanceNamespaceDBConnection:
CreateNamespaceResponse
Response containing the properties of the created namespace.
"""
return await self._inner.create_namespace(
namespace_path=namespace_path,
mode=mode,
request = CreateNamespaceRequest(
id=namespace_path,
mode=_normalize_create_namespace_mode(mode),
properties=properties,
)
response = self._namespace_client.create_namespace(request)
return CreateNamespaceResponse(
properties=response.properties if hasattr(response, "properties") else None
)
async def drop_namespace(
self,
@@ -1204,16 +1153,20 @@ class AsyncLanceNamespaceDBConnection:
DropNamespaceResponse
Response containing properties and transaction_id if applicable.
"""
try:
return await self._inner.drop_namespace(
namespace_path=namespace_path,
mode=mode,
behavior=behavior,
)
except RuntimeError as e:
if "Namespace not empty" in str(e):
raise NamespaceNotEmptyError(str(e)) from e
raise
request = DropNamespaceRequest(
id=namespace_path,
mode=_normalize_drop_namespace_mode(mode),
behavior=_normalize_drop_namespace_behavior(behavior),
)
response = self._namespace_client.drop_namespace(request)
return DropNamespaceResponse(
properties=(
response.properties if hasattr(response, "properties") else None
),
transaction_id=(
response.transaction_id if hasattr(response, "transaction_id") else None
),
)
async def describe_namespace(
self, namespace_path: List[str]
@@ -1231,7 +1184,11 @@ class AsyncLanceNamespaceDBConnection:
DescribeNamespaceResponse
Response containing the namespace properties.
"""
return await self._inner.describe_namespace(namespace_path)
request = DescribeNamespaceRequest(id=namespace_path)
response = self._namespace_client.describe_namespace(request)
return DescribeNamespaceResponse(
properties=response.properties if hasattr(response, "properties") else None
)
async def list_tables(
self,
@@ -1260,8 +1217,13 @@ class AsyncLanceNamespaceDBConnection:
"""
if namespace_path is None:
namespace_path = []
return await self._inner.list_tables(
namespace_path=namespace_path, page_token=page_token, limit=limit
request = ListTablesRequest(
id=namespace_path, page_token=page_token, limit=limit
)
response = self._namespace_client.list_tables(request)
return ListTablesResponse(
tables=response.tables if response.tables else [],
page_token=response.page_token,
)
async def namespace_client(self) -> LanceNamespace:
@@ -1275,18 +1237,6 @@ class AsyncLanceNamespaceDBConnection:
LanceNamespace
The namespace client for this connection.
"""
if self._namespace_client is None:
if (
self._namespace_client_impl is None
or self._namespace_client_properties is None
):
raise ValueError(
"Cannot construct a Python namespace client without "
"namespace implementation properties"
)
self._namespace_client = namespace_connect(
self._namespace_client_impl, self._namespace_client_properties
)
return self._namespace_client
@@ -1337,32 +1287,6 @@ def connect_namespace(
LanceNamespaceDBConnection
A namespace-based connection to LanceDB
"""
if _supports_native_namespace(namespace_client_impl):
inner = AsyncConnection(
_connect_namespace(
namespace_client_impl,
namespace_client_properties,
read_consistency_interval=(
read_consistency_interval.total_seconds()
if read_consistency_interval is not None
else None
),
storage_options=storage_options,
session=session,
namespace_client_pushdown_operations=namespace_client_pushdown_operations,
)
)
return LanceNamespaceDBConnection(
namespace_client=None,
read_consistency_interval=read_consistency_interval,
storage_options=storage_options,
session=session,
namespace_client_pushdown_operations=namespace_client_pushdown_operations,
namespace_client_impl=namespace_client_impl,
namespace_client_properties=namespace_client_properties,
_inner=inner,
)
namespace_client = namespace_connect(
namespace_client_impl, namespace_client_properties
)
@@ -1438,32 +1362,6 @@ def connect_namespace_async(
... tables = await db.table_names()
... table = await db.create_table("my_table", schema=schema)
"""
if _supports_native_namespace(namespace_client_impl):
inner = AsyncConnection(
_connect_namespace(
namespace_client_impl,
namespace_client_properties,
read_consistency_interval=(
read_consistency_interval.total_seconds()
if read_consistency_interval is not None
else None
),
storage_options=storage_options,
session=session,
namespace_client_pushdown_operations=namespace_client_pushdown_operations,
)
)
return AsyncLanceNamespaceDBConnection(
namespace_client=None,
read_consistency_interval=read_consistency_interval,
storage_options=storage_options,
session=session,
namespace_client_pushdown_operations=namespace_client_pushdown_operations,
namespace_client_impl=namespace_client_impl,
namespace_client_properties=namespace_client_properties,
_inner=inner,
)
namespace_client = namespace_connect(
namespace_client_impl, namespace_client_properties
)
@@ -1474,6 +1372,4 @@ def connect_namespace_async(
storage_options=storage_options,
session=session,
namespace_client_pushdown_operations=namespace_client_pushdown_operations,
namespace_client_impl=namespace_client_impl,
namespace_client_properties=namespace_client_properties,
)
-170
View File
@@ -1,170 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright The LanceDB Authors
"""Bridge LanceDB's internal metrics into OpenTelemetry.
LanceDB (through Lance core) publishes metrics (currently object store request
counts, bytes, latency, errors, and throttles) through the Rust ``metrics``
facade. This module installs a process-global recorder that aggregates them and
registers OpenTelemetry observable instruments that report the aggregated values
into the user's ``MeterProvider``.
The bridge is generic: every metric LanceDB describes is surfaced automatically,
with no per-metric Python code. Histograms have no asynchronous OpenTelemetry
instrument, so each is exported Prometheus-style as cumulative ``le`` buckets
plus ``_count`` and ``_sum`` observable counters.
"""
from __future__ import annotations
import warnings
from typing import TYPE_CHECKING, Optional
from ._lancedb import (
lancedb_metrics_catalog,
register_lancedb_metrics_recorder,
snapshot_lancedb_metrics,
)
if TYPE_CHECKING:
from opentelemetry.metrics import MeterProvider
_INSTRUMENTED = False
def instrument_lancedb_metrics(
meter_provider: Optional["MeterProvider"] = None,
) -> bool:
"""Register LanceDB metrics as OpenTelemetry observable instruments.
Installs a process-global metrics recorder and creates one observable
instrument per LanceDB metric on the given (or global) ``MeterProvider``. The
user's configured ``MetricReader`` then collects them on its own schedule.
Counters and gauges map directly to observable counters/gauges. Each
histogram is exported as cumulative ``le`` bucket counts (``<name>_bucket``,
with an ``le`` attribute) plus ``<name>_count`` and ``<name>_sum``.
Parameters
----------
meter_provider : opentelemetry.metrics.MeterProvider, optional
The provider to register instruments on. Defaults to the global provider
from ``opentelemetry.metrics.get_meter_provider()``.
Returns
-------
bool
``True`` if the recorder is installed and instruments are registered.
``False`` if a different ``metrics`` recorder is already installed in
this process (``metrics`` permits only one global recorder), in which
case a warning is emitted and no instruments are created.
Notes
-----
Requires the OpenTelemetry API (``pip install lancedb[otel]``) and, to
actually export, an OpenTelemetry SDK (``pip install opentelemetry-sdk``)
configured by the application. Calling this more than once is safe;
instruments are created only on the first successful call.
"""
global _INSTRUMENTED
try:
from opentelemetry.metrics import Observation, get_meter_provider
except ImportError as exc:
raise ImportError(
"instrument_lancedb_metrics requires the OpenTelemetry API/SDK. "
"Install it with `pip install lancedb[otel]` or "
"`pip install opentelemetry-sdk`."
) from exc
if not register_lancedb_metrics_recorder():
warnings.warn(
"Could not install the LanceDB metrics recorder: another `metrics` "
"recorder is already installed in this process. LanceDB metrics will "
"not be exported via OpenTelemetry.",
stacklevel=2,
)
return False
if _INSTRUMENTED:
return True
provider = meter_provider or get_meter_provider()
meter = provider.get_meter("lancedb")
def scalar_callback(metric_name: str):
def callback(_options):
return [
Observation(point.value, point.attributes)
for point in snapshot_lancedb_metrics()
if point.name == metric_name and point.value is not None
]
return callback
def bucket_callback(metric_name: str):
def callback(_options):
observations = []
for point in snapshot_lancedb_metrics():
if point.name != metric_name or point.buckets is None:
continue
for le, cumulative in point.buckets:
attributes = dict(point.attributes)
attributes["le"] = le
observations.append(Observation(cumulative, attributes))
return observations
return callback
def field_callback(metric_name: str, field: str):
def callback(_options):
observations = []
for point in snapshot_lancedb_metrics():
if point.name != metric_name:
continue
value = getattr(point, field)
if value is not None:
observations.append(Observation(value, point.attributes))
return observations
return callback
for desc in lancedb_metrics_catalog():
unit = desc.unit or ""
if desc.kind == "counter":
meter.create_observable_counter(
desc.name,
callbacks=[scalar_callback(desc.name)],
unit=unit,
description=desc.description,
)
elif desc.kind == "gauge":
meter.create_observable_gauge(
desc.name,
callbacks=[scalar_callback(desc.name)],
unit=unit,
description=desc.description,
)
elif desc.kind == "histogram":
# `_bucket` and `_count` observe cumulative sample counts, not the
# histogram's measured quantity, so they are unitless; only `_sum`
# carries the histogram's unit.
meter.create_observable_counter(
f"{desc.name}_bucket",
callbacks=[bucket_callback(desc.name)],
description=f"{desc.description} (cumulative buckets)",
)
meter.create_observable_counter(
f"{desc.name}_count",
callbacks=[field_callback(desc.name, "count")],
description=f"{desc.description} (count)",
)
meter.create_observable_counter(
f"{desc.name}_sum",
callbacks=[field_callback(desc.name, "sum")],
unit=unit,
description=f"{desc.description} (sum)",
)
_INSTRUMENTED = True
return True
+4 -24
View File
@@ -11,7 +11,7 @@ import pyarrow as pa
from ._lancedb import async_permutation_builder, PermutationReader
from .table import LanceTable, Table
from .background_loop import LOOP
from .util import batch_to_tensor, batch_to_tensor_dict, batch_to_tensor_rows
from .util import batch_to_tensor, batch_to_tensor_rows
from typing import Any, Callable, Iterator, Literal, Optional, TYPE_CHECKING, Union
if TYPE_CHECKING:
@@ -48,14 +48,6 @@ class PermutationBuilder:
By default, the permutation builder will create a single split that contains all
rows in the same order as the base table.
"""
if not hasattr(table, "_inner"):
raise TypeError(
f"PermutationBuilder requires a local LanceTable, "
f"got {type(table).__name__}. "
"The permutation API is not supported on remote tables. "
"Remote tables connect to LanceDB Cloud or Enterprise and do not have "
"direct access to the underlying Lance dataset needed for permutations."
)
self._async = async_permutation_builder(table)
def split_random(
@@ -65,7 +57,6 @@ class PermutationBuilder:
counts: Optional[list[int]] = None,
fixed: Optional[int] = None,
seed: Optional[int] = None,
clump_size: Optional[int] = None,
split_names: Optional[list[str]] = None,
) -> "PermutationBuilder":
"""
@@ -88,9 +79,6 @@ class PermutationBuilder:
Rows will be randomly assigned to splits. The optional seed can be provided to
make the assignment deterministic.
If clump_size is provided, rows are shuffled as contiguous groups of that size,
preserving I/O locality while still randomising the split assignment.
The optional split_names can be provided to name the splits. If not provided,
the splits can only be referenced by their index.
"""
@@ -99,7 +87,6 @@ class PermutationBuilder:
counts=counts,
fixed=fixed,
seed=seed,
clump_size=clump_size,
split_names=split_names,
)
return self
@@ -946,7 +933,6 @@ class Permutation:
"pandas",
"arrow",
"torch",
"torch_row",
"torch_col",
"polars",
],
@@ -962,19 +948,15 @@ class Permutation:
- "python_col" - the batch will be a dict of lists (one entry 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 per-row dicts mapping column
name to a 0-D torch tensor. Works with the default
``torch.utils.data.DataLoader`` collate, which stacks the per-row
dicts back into a dict of batched tensors.
- "torch_row" - the batch will be a list of tensors, one per row
- "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)
- "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.
Conversion to torch and torch_col will be zero-copy but will only support a
subset of data types (numeric types).
Conversion to torch_col 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
types. Conversion of strings, lists, and structs will require creating python
@@ -995,8 +977,6 @@ class Permutation:
elif format == "arrow":
return self.with_transform(Transforms.arrow2arrow)
elif format == "torch":
return self.with_transform(batch_to_tensor_dict)
elif format == "torch_row":
return self.with_transform(batch_to_tensor_rows)
elif format == "torch_col":
return self.with_transform(batch_to_tensor)
+1 -12
View File
@@ -275,18 +275,7 @@ def _py_type_to_arrow_type(py_type: Type[Any], field: FieldInfo) -> pa.DataType:
tz = get_extras(field, "tz")
return pa.timestamp("us", tz=tz)
elif getattr(py_type, "__origin__", None) in (list, tuple):
# A bare, unparameterised ``typing.List`` / ``typing.Tuple`` matches this
# branch (its ``__origin__`` is ``list`` / ``tuple``) but has no
# ``__args__``, so we cannot infer the element type. Raise a clear
# ``TypeError`` instead of crashing with an opaque ``AttributeError``.
args = getattr(py_type, "__args__", None)
if not args:
raise TypeError(
"Converting Pydantic type to Arrow Type: unsupported type "
f"{py_type}. Specify the element type, e.g. List[int] instead "
"of a bare List."
)
child = args[0]
child = py_type.__args__[0]
return _pydantic_list_child_to_arrow(child, field)
raise TypeError(
f"Converting Pydantic type to Arrow Type: unsupported type {py_type}."
+2 -36
View File
@@ -119,27 +119,6 @@ def _filter_to_sql(filter: Optional[Union[str, Expr]]) -> Optional[str]:
return filter
def _combine_where(
existing: Optional[Union[str, Expr]], new: Union[str, Expr]
) -> Union[str, Expr]:
"""Combine a new filter with an existing one using a logical AND.
Calling ``where`` more than once composes the filters with AND instead of
replacing the previous filter. Two :class:`~lancedb.expr.Expr` filters are
combined as an expression; otherwise both filters are lowered to SQL strings
and combined as SQL.
"""
if existing is None:
return new
existing_is_expr = isinstance(existing, Expr)
new_is_expr = isinstance(new, Expr)
if existing_is_expr and new_is_expr:
return existing & new
existing_sql = existing.to_sql() if existing_is_expr else existing
new_sql = new.to_sql() if new_is_expr else new
return f"({existing_sql}) AND ({new_sql})"
def _projection_to_scanner_kwargs(
columns: Optional[
Union[
@@ -1169,13 +1148,8 @@ class LanceQueryBuilder(ABC):
-------
LanceQueryBuilder
The LanceQueryBuilder object.
Notes
-----
Calling this multiple times combines the filters with a logical AND
rather than replacing the previous filter.
"""
self._where = _combine_where(self._where, where)
self._where = where
self._postfilter = not prefilter
return self
@@ -1719,13 +1693,8 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
-------
LanceQueryBuilder
The LanceQueryBuilder object.
Notes
-----
Calling this multiple times combines the filters with a logical AND
rather than replacing the previous filter.
"""
self._where = _combine_where(self._where, where)
self._where = where
if prefilter is not None:
self._postfilter = not prefilter
return self
@@ -2925,9 +2894,6 @@ class AsyncStandardQuery(AsyncQueryBase):
Filtering performance can often be improved by creating a scalar index
on the filter column(s).
Calling this multiple times combines the filters with a logical AND
rather than replacing the previous filter.
"""
if isinstance(predicate, Expr):
self._inner.where_expr(predicate._inner)
-3
View File
@@ -9,7 +9,6 @@ from typing import List, Optional
from lancedb import __version__
from .header import HeaderProvider
from .oauth import OAuthConfig, OAuthFlowType
__all__ = [
"TimeoutConfig",
@@ -17,8 +16,6 @@ __all__ = [
"TlsConfig",
"ClientConfig",
"HeaderProvider",
"OAuthConfig",
"OAuthFlowType",
]
+11 -11
View File
@@ -124,7 +124,6 @@ class RemoteDBConnection(DBConnection):
"request_thread_pool is no longer used and will be removed in "
"a future release.",
DeprecationWarning,
stacklevel=2,
)
if connection_timeout is not None:
@@ -133,7 +132,6 @@ class RemoteDBConnection(DBConnection):
"release. Please use client_config.timeout_config.connect_timeout "
"instead.",
DeprecationWarning,
stacklevel=2,
)
client_config.timeout_config.connect_timeout = timedelta(
seconds=connection_timeout
@@ -144,7 +142,6 @@ class RemoteDBConnection(DBConnection):
"read_timeout is deprecated and will be removed in a future release. "
"Please use client_config.timeout_config.read_timeout instead.",
DeprecationWarning,
stacklevel=2,
)
client_config.timeout_config.read_timeout = timedelta(seconds=read_timeout)
@@ -399,13 +396,13 @@ class RemoteDBConnection(DBConnection):
The namespace to open the table from.
None or empty list represents root namespace.
branch: str, optional
If provided, open a handle scoped to this branch instead of the
default branch. Reads and writes operate in the branch's context.
Branching is not yet supported on remote tables, so only the
default branch is accepted (``None`` or ``"main"``); any other
value raises ``NotImplementedError``.
version: int, optional
If provided, open the table pinned to this version, producing a
read-only handle. Composes with ``branch``: when both are given,
opens that branch at the version; otherwise opens ``main`` at the
version. Call ``checkout_latest`` to return to a writable state.
read-only handle. Call ``checkout_latest`` to return to a writable
state.
Returns
-------
@@ -413,6 +410,11 @@ class RemoteDBConnection(DBConnection):
"""
from .table import RemoteTable
# Remote supports version time-travel but not branches: reject a non-main
# branch, but allow a version-only open (or "main").
if branch is not None and branch != "main":
raise NotImplementedError("branching is not yet supported on remote tables")
if namespace_path is None:
namespace_path = []
if index_cache_size is not None:
@@ -428,9 +430,7 @@ class RemoteDBConnection(DBConnection):
connection_state=self.serialize,
namespace_path=namespace_path,
)
if branch is not None:
tbl = tbl.branches.checkout(branch, version)
elif version is not None:
if version is not None:
tbl.checkout(version)
return tbl
-75
View File
@@ -1,75 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright The LanceDB Authors
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional
class OAuthFlowType(str, Enum):
"""OAuth authentication flow types."""
CLIENT_CREDENTIALS = "client_credentials"
"""Client Credentials grant (service-to-service / M2M)."""
AZURE_MANAGED_IDENTITY = "azure_managed_identity"
"""Azure Managed Identity via IMDS."""
@dataclass
class OAuthConfig:
"""OAuth configuration for LanceDB authentication.
All token acquisition and refresh is handled in the Rust layer.
This config is passed through to Rust via PyO3.
Parameters
----------
issuer_url : str
OIDC issuer URL or OAuth authority URL.
For Azure: ``https://login.microsoftonline.com/{tenant_id}/v2.0``
client_id : str
Application / Client ID.
scopes : List[str]
OAuth scopes to request.
For Azure managed identity, exactly one scope or resource is required.
For example: ``["api://{app_id}/.default"]``
flow : OAuthFlowType
Authentication flow to use. Default: CLIENT_CREDENTIALS.
client_secret : Optional[str]
Client secret (required for CLIENT_CREDENTIALS).
managed_identity_client_id : Optional[str]
Client ID for user-assigned managed identity (AZURE_MANAGED_IDENTITY).
refresh_buffer_secs : Optional[int]
Seconds before expiry to trigger proactive refresh (default: 300).
Keep this well below the token TTL; if it is greater than or equal to
the TTL, each request refreshes the token.
Examples
--------
Client Credentials (service-to-service):
>>> config = OAuthConfig(
... issuer_url="https://login.microsoftonline.com/{tenant}/v2.0",
... client_id="app-id",
... client_secret="secret",
... scopes=["api://lancedb-api/.default"],
... )
Azure Managed Identity:
>>> config = OAuthConfig(
... issuer_url="https://login.microsoftonline.com/{tenant}/v2.0",
... client_id="app-id",
... scopes=["api://lancedb-api/.default"],
... flow=OAuthFlowType.AZURE_MANAGED_IDENTITY,
... )
"""
issuer_url: str
client_id: str
scopes: List[str]
flow: OAuthFlowType = OAuthFlowType.CLIENT_CREDENTIALS
client_secret: Optional[str] = field(default=None, repr=False)
managed_identity_client_id: Optional[str] = None
refresh_buffer_secs: Optional[int] = None
+7 -52
View File
@@ -56,7 +56,7 @@ from lancedb.embeddings import EmbeddingFunctionRegistry
from lancedb.table import _normalize_progress
from ..query import LanceVectorQueryBuilder, LanceQueryBuilder, LanceTakeQueryBuilder
from ..table import AsyncTable, BlobMode, Branches, IndexStatistics, Query, Table, Tags
from ..table import AsyncTable, BlobMode, IndexStatistics, Query, Table, Tags
from ..types import BaseTokenizerType
@@ -75,9 +75,6 @@ class RemoteTable(Table):
self._connection_state = connection_state
self._namespace_path = list(namespace_path or [])
self._checkout_version: Optional[int] = None
# The branch this handle is scoped to (None == main). Persisted so a
# fork/pickle reopen restores the branch instead of reverting to main.
self._branch: Optional[str] = None
self._pid = os.getpid()
def _serialized_connection_state(self) -> str:
@@ -112,14 +109,9 @@ class RemoteTable(Table):
from lancedb import deserialize_conn
db = deserialize_conn(self._serialized_connection_state(), for_worker=True)
# Reopen on the same branch and pinned version (branch=None / version=None
# reproduce the plain main-latest open).
table = db.open_table(
self._name,
namespace_path=self._namespace_path,
branch=self._branch,
version=self._checkout_version,
)
table = db.open_table(self._name, namespace_path=self._namespace_path)
if self._checkout_version is not None:
table.checkout(self._checkout_version)
self._table_handle = table._table
self.db_name = table.db_name
@@ -132,7 +124,6 @@ class RemoteTable(Table):
"name": self.name,
"namespace_path": self._namespace_path,
"checkout_version": self._checkout_version,
"branch": self._branch,
}
def __setstate__(self, state: dict) -> None:
@@ -142,7 +133,6 @@ class RemoteTable(Table):
self._connection_state = state["connection_state"]
self._namespace_path = state["namespace_path"]
self._checkout_version = state["checkout_version"]
self._branch = state.get("branch")
self._pid = None
@property
@@ -170,34 +160,6 @@ class RemoteTable(Table):
def tags(self) -> Tags:
return Tags(self._table)
@property
def branches(self) -> Branches:
"""Branch management for the table.
``create``/``checkout`` return a new table handle scoped to the branch;
writes on it do not affect ``main``.
"""
return Branches(self)
def current_branch(self) -> Optional[str]:
"""The branch this table handle is scoped to, or ``None`` for ``main``."""
return self._table.current_branch()
def _wrap_branch_handle(
self, async_table: AsyncTable, version: Optional[int] = None
) -> "RemoteTable":
# A branch handle stays a RemoteTable with the same connection context.
# Record the branch and version pin so a fork/pickle reopen restores both.
handle = RemoteTable(
async_table,
self.db_name,
connection_state=self._connection_state,
namespace_path=self._namespace_path,
)
handle._branch = async_table.current_branch()
handle._checkout_version = version
return handle
@cached_property
def embedding_functions(self) -> Dict[str, EmbeddingFunctionConfig]:
"""
@@ -845,8 +807,7 @@ class RemoteTable(Table):
"""
warnings.warn(
"cleanup_old_versions() is a no-op on LanceDB Cloud. "
"Tables are automatically cleaned up and optimized.",
stacklevel=2,
"Tables are automatically cleaned up and optimized."
)
pass
@@ -858,8 +819,7 @@ class RemoteTable(Table):
"""
warnings.warn(
"compact_files() is a no-op on LanceDB Cloud. "
"Tables are automatically compacted and optimized.",
stacklevel=2,
"Tables are automatically compacted and optimized."
)
pass
@@ -876,8 +836,7 @@ class RemoteTable(Table):
"""
warnings.warn(
"optimize() is a no-op on LanceDB Cloud. "
"Indices are optimized automatically.",
stacklevel=2,
"Indices are optimized automatically."
)
pass
@@ -912,10 +871,6 @@ class RemoteTable(Table):
"""Not supported on LanceDB Cloud."""
return LOOP.run(self._table.unset_lsm_write_spec())
def get_lsm_write_spec(self) -> Optional["LsmWriteSpec"]:
"""Read the installed LsmWriteSpec, or ``None``."""
return LOOP.run(self._table.get_lsm_write_spec())
def close_lsm_writers(self) -> None:
"""No-op on LanceDB Cloud (no local shard writers)."""
return LOOP.run(self._table.close_lsm_writers())
+1 -8
View File
@@ -156,16 +156,9 @@ class MRRReranker(Reranker):
reciprocal_rank = 1.0 / rank
mrr_score_map[result_id].append(reciprocal_rank)
# MRR averages the reciprocal rank across *all* ranking systems, treating
# a system in which a document does not appear as a reciprocal rank of 0.
# We therefore divide by the total number of systems, not by the number of
# systems the document happens to appear in -- otherwise a document found
# by a single ranking would outrank one ranked highly by every system,
# defeating the purpose of fusing the rankings.
num_systems = len(vector_results)
final_mrr_scores = {}
for result_id, reciprocal_ranks in mrr_score_map.items():
mean_rr = float(np.sum(reciprocal_ranks)) / num_systems
mean_rr = np.mean(reciprocal_ranks)
final_mrr_scores[result_id] = mean_rr
combined = pa.concat_tables(vector_results, **self._concat_tables_args)
+1 -4
View File
@@ -86,10 +86,7 @@ def _from_list(data: list) -> Scannable:
@to_scannable.register(dict)
def _from_dict(data: dict) -> Scannable:
raise ValueError(
"Cannot create or add rows from a single dictionary. "
"Use a list of dictionaries instead."
)
raise ValueError("Cannot add a single dictionary to a table. Use a list.")
@to_scannable.register(LanceModel)
-607
View File
@@ -1,607 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright The LanceDB Authors
"""Elastic streaming dataloader for PyTorch.
Provides StreamingDataset, a PyTorch IterableDataset that guarantees:
- **Elastic determinism**: for a fixed (num_splits, shuffle_seed, epoch) the set
of samples that forms each global training step is identical regardless of
world_size or num_workers.
- **Resumability**: state_dict / load_state_dict capture per-split consumption
counts so training can resume from an exact mid-epoch position even when the
distributed topology changes between runs.
"""
import ctypes
import logging
import os
import random
import threading
import time
from collections import deque
from concurrent.futures import ThreadPoolExecutor
from multiprocessing import RawArray
from typing import Any, Callable, Iterator, Optional
from torch.utils.data import IterableDataset, get_worker_info
from .permutation import (
Permutation,
Transforms,
permutation_builder,
_table_from_pickle_state,
_table_to_pickle_state,
)
logger = logging.getLogger(__name__)
# Multiplier used to combine shuffle_seed and epoch into a single permutation
# seed. Chosen to be a large prime so different (seed, epoch) pairs produce
# distinct seeds for any practically encountered epoch count.
_EPOCH_PRIME = 100003
DEFAULT_READ_BATCH_SIZE = 64
DEFAULT_PREFETCH_BATCHES = 4
class StreamingDataset(IterableDataset):
"""An elastic, resumable PyTorch IterableDataset backed by a LanceDB table.
The table is partitioned into ``num_splits`` fixed splits using a
deterministic random shuffle controlled by ``shuffle_seed`` and ``epoch``.
Each rank is assigned a contiguous block of splits, and within a rank each
DataLoader worker is assigned a contiguous sub-block. Samples are yielded
by round-robining over the assigned splits, one sample per split per cycle.
Internally ``__iter__`` runs a two-stage pipeline:
- **Stage 1 (I/O)**: one thread pool with ``num_splits * prefetch_batches``
workers fetches raw ``RecordBatch`` objects from LanceDB in parallel
across all splits and places them in a per-split raw-batch queue.
- **Stage 2 (transform)**: a second thread pool with ``os.cpu_count()``
workers picks up raw batches, applies the transform, and places the
results in a per-split cooked-row queue.
The main thread round-robins over the cooked queues, yielding one row per
split per cycle.
Parameters
----------
table:
LanceDB table to stream from.
num_splits:
Number of fixed splits to partition the table into. Must be divisible
by ``world_size``. When used with DataLoader workers it must also be
divisible by ``world_size * num_workers``. Defaults to ``world_size``.
If the row count (after any ``filter``) is not evenly divisible by
``num_splits``, the surplus rows at most ``num_splits - 1`` per epoch
are silently dropped to keep all splits the same length.
shuffle:
Whether to randomly assign rows to splits. When ``True`` (the
default) rows are shuffled using ``shuffle_seed`` and ``epoch``.
When ``False`` rows are divided into splits sequentially in storage
order, which can be useful for deterministic debugging or evaluation.
shuffle_seed:
Base seed for the random permutation. Combined with ``epoch`` so
each epoch produces a different ordering. Pass ``None`` to generate
a random seed at construction time.
epoch:
Current training epoch. Combined with ``shuffle_seed`` so that each
epoch produces a different sample ordering.
rank:
This process's rank in the distributed training group.
world_size:
Total number of processes in the distributed training group.
read_batch_size:
Number of rows fetched from each split in a single ``take_offsets``
call. Larger values amortise per-request overhead (critical on object
storage) at the cost of higher memory usage per split buffer. Defaults
to ``DEFAULT_READ_BATCH_SIZE`` (64).
prefetch_batches:
Number of I/O batches to keep in flight per split. Higher values
overlap storage latency with transform and training compute at the cost
of more memory and threads. Defaults to ``DEFAULT_PREFETCH_BATCHES``
(4).
columns:
Optional list of column names to read. When set, only those columns
are fetched from storage; all others are omitted. ``None`` (the
default) reads every column.
shuffle_clump_size:
When set, rows are shuffled in contiguous groups of this size rather
than individually. Larger clumps improve I/O locality (important on
object storage) at the cost of reduced randomness. ``None`` (the
default) shuffles rows individually.
filter:
Optional SQL filter expression (e.g. ``"label = 'dog'"``). Only rows
that satisfy the predicate are included in the permutation. The filter
is applied during permutation construction so split sizes reflect the
filtered row count.
transform:
Optional callable applied to each ``pyarrow.RecordBatch`` before rows
are yielded. Receives one batch at a time and must return an iterable
whose length equals the number of rows in the batch. When ``None``
(the default) rows are returned as plain Python dicts.
worker_info_override:
If set, used in place of ``torch.utils.data.get_worker_info()`` to
determine the DataLoader worker assignment. Intended for unit tests
that need to simulate multiple workers without spawning real processes.
If both this and the real worker info are non-None a warning is logged
and the override takes precedence.
"""
def __init__(
self,
table,
*,
num_splits: Optional[int] = None,
shuffle: bool = True,
shuffle_seed: Optional[int] = 0,
epoch: int = 0,
rank: int = 0,
world_size: int = 1,
read_batch_size: int = DEFAULT_READ_BATCH_SIZE,
prefetch_batches: int = DEFAULT_PREFETCH_BATCHES,
columns: Optional[list[str]] = None,
shuffle_clump_size: Optional[int] = None,
filter: Optional[str] = None,
transform: Optional[Callable] = None,
connection_factory: Optional[Callable[[str], Any]] = None,
worker_info_override=None,
):
super().__init__()
if num_splits is None:
num_splits = world_size
if shuffle_seed is None:
shuffle_seed = random.randrange(2**32)
if num_splits % world_size != 0:
raise ValueError(
f"num_splits ({num_splits}) must be divisible by "
f"world_size ({world_size})"
)
self._table = table
self._num_splits = num_splits
self._shuffle = shuffle
self._shuffle_seed = shuffle_seed
self._epoch = epoch
self._rank = rank
self._world_size = world_size
self._read_batch_size = read_batch_size
self._prefetch_batches = prefetch_batches
self._columns = columns
self._shuffle_clump_size = shuffle_clump_size
self._filter = filter
self._transform = transform
self._connection_factory = connection_factory
self._worker_info_override = worker_info_override
# Live references to pipeline state, set only while __iter__ is running
# in the same process. Used by the observability properties when the
# DataLoader runs with num_workers=0.
self._raw_batches_ref: Optional[list[deque]] = None
self._cooked_ref: Optional[list[deque]] = None
self._fetch_head_ref: Optional[list[int]] = None
self._split_sizes_ref: Optional[list[int]] = None
self._local_consumed_ref: Optional[list[int]] = None
# Shared-memory counters written by __iter__ (which may run in a
# DataLoader worker process) and read by the observability properties
# in the main process. RawArray is picklable via the forkserver
# reduction protocol so it survives the dataset pickle round-trip.
# Layout: [unscanned_rows, raw_rows, cooked_rows, consumed_rows,
# bytes_loaded, fetch_time_us, transform_time_us]
self._worker_stats: RawArray = RawArray(ctypes.c_int64, 7)
# Cumulative bytes of Arrow buffer data fetched across all iterations.
self._bytes_loaded: int = 0
# Cumulative seconds spent in LanceDB I/O and in transform functions.
self._fetch_time: float = 0.0
self._transform_time: float = 0.0
# Number of samples each split has already been consumed. At global
# step boundaries all splits have consumed this many samples, so a
# single scalar captures the topology-independent checkpoint state.
self._resume_offset: int = 0
# Build the permutation table once, deterministically.
builder = permutation_builder(table)
if filter is not None:
builder = builder.filter(filter)
if shuffle:
perm_seed = shuffle_seed + epoch * _EPOCH_PRIME
self._perm_table = builder.split_random(
fixed=num_splits, seed=perm_seed, clump_size=shuffle_clump_size
).execute()
else:
self._perm_table = builder.split_sequential(fixed=num_splits).execute()
# Contiguous block of global split indices assigned to this rank.
splits_per_rank = num_splits // world_size
rank_start = rank * splits_per_rank
self._rank_splits: list[int] = list(
range(rank_start, rank_start + splits_per_rank)
)
def _resolve_my_splits(self) -> list[int]:
"""Return the split indices this instance should read in __iter__."""
torch_worker_info = get_worker_info()
if self._worker_info_override is not None:
if torch_worker_info is not None:
logger.warning(
"worker_info_override is set but get_worker_info() also returned a "
"non-None value; ignoring the real torch worker info and using the "
"override instead. This may lead to duplicated or incorrect data "
"from the dataset."
)
worker_info = self._worker_info_override
else:
worker_info = torch_worker_info
if worker_info is None:
return self._rank_splits
num_workers: int = worker_info.num_workers
worker_id: int = worker_info.id
n_rank_splits = len(self._rank_splits)
if n_rank_splits % num_workers != 0:
raise ValueError(
f"Number of rank splits ({n_rank_splits}) must be divisible by "
f"num_workers ({num_workers})"
)
splits_per_worker = n_rank_splits // num_workers
start = worker_id * splits_per_worker
return self._rank_splits[start : start + splits_per_worker]
def __iter__(self) -> Iterator[dict[str, Any]]:
if self._raw_batches_ref is not None:
raise RuntimeError(
"StreamingDataset does not support concurrent iteration. "
"Only one active iterator per dataset instance is allowed."
)
my_splits = self._resolve_my_splits()
if not my_splits:
return
# Set identity transform on each Permutation so __getitems__ returns
# the raw RecordBatch. Stage 2 applies the real transform.
permutations: list[Permutation] = []
for split_idx in my_splits:
perm = Permutation.from_tables(
self._table, self._perm_table, split=split_idx
)
if self._columns is not None:
perm = perm.select_columns(self._columns)
perm = perm.with_transform(lambda batch: batch)
if self._resume_offset > 0:
perm = perm.with_skip(self._resume_offset)
permutations.append(perm)
n = len(permutations)
split_sizes = [perm.num_rows for perm in permutations]
initial_offset = self._resume_offset
local_consumed = [0] * n
batch_size = self._read_batch_size
max_prefetch = self._prefetch_batches
cpu_workers = os.cpu_count() or 1
final_transform = (
self._transform if self._transform is not None else Transforms.arrow2python
)
# Per-split pipeline state.
fetch_head = [0] * n
io_pending = [deque() for _ in range(n)] # Future[RecordBatch]
raw_batches = [deque() for _ in range(n)] # RecordBatch — fetched, awaiting tx
tx_pending = [deque() for _ in range(n)] # Future[list[Any]]
cooked = [deque() for _ in range(n)] # rows ready to yield
# Limit simultaneous transforms to cpu_workers across all splits.
tx_semaphore = threading.Semaphore(cpu_workers)
# ── Stage 1 helpers ───────────────────────────────────────────────────
def _io_call(perm, indices):
t0 = time.perf_counter()
batch = perm.__getitems__(indices)
self._bytes_loaded += batch.nbytes
self._fetch_time += time.perf_counter() - t0
return batch
def _submit_io(i: int) -> None:
remaining = split_sizes[i] - fetch_head[i]
if remaining <= 0:
return
fetch = min(batch_size, remaining)
start = fetch_head[i]
fetch_head[i] += fetch
perm_i = permutations[i]
indices = list(range(start, start + fetch))
io_pending[i].append(io_pool.submit(_io_call, perm_i, indices))
def _fill_io(i: int) -> None:
while len(io_pending[i]) < max_prefetch and fetch_head[i] < split_sizes[i]:
_submit_io(i)
def _drain_io(i: int) -> None:
"""Move completed I/O futures into raw_batches non-blockingly."""
while io_pending[i] and io_pending[i][0].done():
raw_batches[i].append(io_pending[i].popleft().result())
# ── Stage 2 helpers ───────────────────────────────────────────────────
def _tx_call_guarded(batch):
try:
t0 = time.perf_counter()
result = final_transform(batch)
self._transform_time += time.perf_counter() - t0
return result
finally:
tx_semaphore.release()
def _try_submit_tx(i: int) -> None:
"""Submit transforms for raw_batches[i] up to available capacity."""
while raw_batches[i] and tx_semaphore.acquire(blocking=False):
batch = raw_batches[i].popleft()
tx_pending[i].append(tx_pool.submit(_tx_call_guarded, batch))
def _drain_tx(i: int) -> None:
"""Move completed transform futures into cooked non-blockingly."""
while tx_pending[i] and tx_pending[i][0].done():
cooked[i].extend(tx_pending[i].popleft().result())
# ── Combined advance ──────────────────────────────────────────────────
def _advance(i: int) -> None:
"""Non-blocking pipeline pump for split i."""
_drain_io(i)
_drain_tx(i)
_try_submit_tx(i)
_fill_io(i)
def _ensure_cooked(i: int) -> None:
"""Ensure cooked[i] has at least one row, blocking if necessary."""
_advance(i)
while not cooked[i]:
if tx_pending[i]:
# Wait for the oldest in-flight transform.
cooked[i].extend(tx_pending[i].popleft().result())
_advance(i)
elif raw_batches[i]:
# Acquire a transform slot (may block briefly if all
# cpu_workers are busy with other splits).
tx_semaphore.acquire()
batch = raw_batches[i].popleft()
tx_pending[i].append(tx_pool.submit(_tx_call_guarded, batch))
elif io_pending[i]:
# Block on the oldest in-flight I/O fetch.
raw_batches[i].append(io_pending[i].popleft().result())
_advance(i)
else:
break # split exhausted
# ── Main loop ─────────────────────────────────────────────────────────
with ThreadPoolExecutor(max_workers=n * max_prefetch) as io_pool:
with ThreadPoolExecutor(max_workers=cpu_workers) as tx_pool:
self._raw_batches_ref = raw_batches
self._cooked_ref = cooked
self._fetch_head_ref = fetch_head
self._split_sizes_ref = split_sizes
self._local_consumed_ref = local_consumed
try:
for i in range(n):
_fill_io(i)
while True:
# Stop when any split is exhausted (all exhaust
# simultaneously: equal split sizes + round-robin).
if any(local_consumed[i] >= split_sizes[i] for i in range(n)):
break
for i in range(n):
_ensure_cooked(i)
row = cooked[i].popleft()
local_consumed[i] += 1
_advance(i)
# After the last split in each cycle: update the
# global offset and refresh the shared-memory stats
# so the main process can observe pipeline depth
# even when __iter__ runs in a worker process.
if i == n - 1:
self._resume_offset = initial_offset + local_consumed[i]
ws = self._worker_stats
ws[0] = sum(
split_sizes[j] - fetch_head[j] for j in range(n)
)
ws[1] = sum(
batch.num_rows for q in raw_batches for batch in q
)
ws[2] = sum(len(q) for q in cooked)
ws[3] = sum(local_consumed)
ws[4] = self._bytes_loaded
ws[5] = int(self._fetch_time * 1_000_000)
ws[6] = int(self._transform_time * 1_000_000)
yield row
finally:
self._raw_batches_ref = None
self._cooked_ref = None
self._fetch_head_ref = None
self._split_sizes_ref = None
self._local_consumed_ref = None
@property
def bytes_loaded(self) -> int:
"""Cumulative bytes of raw Arrow buffer data fetched from storage.
Measured on the ``RecordBatch`` before any transform is applied, so
the value reflects actual I/O rather than the size of transformed
output. Accumulates across multiple iterations of the same dataset
instance and is never reset automatically.
"""
if self._raw_batches_ref is not None:
return self._bytes_loaded
return int(self._worker_stats[4])
@property
def fetch_time(self) -> float:
"""Cumulative seconds spent waiting for data from LanceDB.
Measured per batch in the Stage 1 I/O threads as the total elapsed
time of the ``take_offsets`` call. Accumulates across all splits and
all iterations.
"""
if self._raw_batches_ref is not None:
return self._fetch_time
return self._worker_stats[5] / 1_000_000
@property
def transform_time(self) -> float:
"""Cumulative seconds spent applying the transform.
Measured per batch in the Stage 2 transform threads as the elapsed
time inside the transform callable (or the default ``arrow2python``
conversion when no transform is set). Accumulates across all splits
and all iterations.
"""
if self._raw_batches_ref is not None:
return self._transform_time
return self._worker_stats[6] / 1_000_000
@property
def raw_queue_depth(self) -> int:
"""Number of raw rows waiting for a transform thread across all splits.
A persistently non-zero value means Stage 2 (transform) is the
bottleneck: I/O is completing faster than transforms can consume
batches. Returns 0 when not iterating.
"""
if self._raw_batches_ref is not None:
return sum(batch.num_rows for q in self._raw_batches_ref for batch in q)
return int(self._worker_stats[1])
@property
def prefetch_queue_depth(self) -> int:
"""Number of rows transformed and ready to yield across all splits.
Counts rows whose transform has completed and are sitting in memory
waiting for the main thread rows that can be handed off with no
I/O or CPU wait. Returns 0 when not iterating.
"""
if self._cooked_ref is not None:
return sum(len(q) for q in self._cooked_ref)
return int(self._worker_stats[2])
@property
def unscanned_rows(self) -> int:
"""Number of rows not yet submitted to the I/O stage across all splits.
Decreases as the I/O stage submits fetch requests. When this reaches
zero all data has been requested from storage (though it may not have
arrived yet). Returns 0 when not iterating.
"""
if self._fetch_head_ref is not None:
return sum(
size - head
for size, head in zip(self._split_sizes_ref, self._fetch_head_ref)
)
return int(self._worker_stats[0])
@property
def consumed_rows(self) -> int:
"""Number of rows already yielded to the caller across all splits.
Monotonically increases throughout iteration. Returns 0 when not
iterating.
"""
if self._local_consumed_ref is not None:
return sum(self._local_consumed_ref)
return int(self._worker_stats[3])
def __getstate__(self):
"""Support pickling for multi-worker DataLoader (forkserver / spawn).
The live LanceDB table object contains non-picklable connection state
(sockets, Rust-backed PyO3 objects). If a ``connection_factory`` was
supplied only the table name is serialised; the factory is called in
the worker to reopen the connection without embedding any credentials.
Without a factory the table's own picklable reopen state is captured
via ``_table_to_pickle_state`` (mirrors the ``Permutation`` approach).
"""
state = self.__dict__.copy()
# _table: replace with reconnect info (credentials must not be embedded).
state["_table_name"] = self._table.name
if self._connection_factory is not None:
state["_table"] = None
else:
state["_table"] = _table_to_pickle_state(self._table)
# _perm_table: always in-memory; serialise as Arrow data (mirrors
# how Permutation.__getstate__ handles its permutation_table).
state["_perm_table"] = (
self._perm_table.name,
self._perm_table.to_arrow(),
)
for key in (
"_raw_batches_ref",
"_cooked_ref",
"_fetch_head_ref",
"_split_sizes_ref",
"_local_consumed_ref",
):
state[key] = None
return state
def __setstate__(self, state):
"""Reconnect to LanceDB after unpickling in a worker process."""
from . import connect as _connect
table_name = state.pop("_table_name")
table_state = state.pop("_table")
perm_name, perm_data = state.pop("_perm_table")
self.__dict__.update(state)
if self._connection_factory is not None:
self._table = self._connection_factory(table_name)
else:
self._table = _table_from_pickle_state(table_state)
self._perm_table = _connect("memory://").create_table(perm_name, perm_data)
def state_dict(self) -> dict:
"""Snapshot the dataset's consumption state.
The returned dict is topology-independent: at global step boundaries
every split has been consumed the same number of times (by the
round-robin design), so the per-split count is a single uniform value
that is identical across all ranks and DataLoader workers.
"""
return {
"shuffle_seed": self._shuffle_seed,
"num_splits": self._num_splits,
"epoch": self._epoch,
"samples_consumed_per_split": [self._resume_offset] * self._num_splits,
}
def load_state_dict(self, state: dict) -> None:
"""Resume from a previously snapshotted state.
Raises ``ValueError`` if ``num_splits`` or ``shuffle_seed`` differ
from the checkpoint, since a different split structure or shuffle order
makes mid-epoch resumption meaningless.
"""
if state["num_splits"] != self._num_splits:
raise ValueError(
f"num_splits mismatch: checkpoint has {state['num_splits']}, "
f"current dataset has {self._num_splits}"
)
if state["shuffle_seed"] != self._shuffle_seed:
raise ValueError(
f"shuffle_seed mismatch: checkpoint has {state['shuffle_seed']}, "
f"current dataset has {self._shuffle_seed}"
)
consumed = state["samples_consumed_per_split"]
# All entries are equal at step boundaries; use the first.
if isinstance(consumed, list):
self._resume_offset = consumed[0] if consumed else 0
else:
self._resume_offset = int(consumed)
+41 -134
View File
@@ -61,7 +61,6 @@ from .index import (
HnswFlat,
FTS,
)
from .expr import Expr
from .merge import LanceMergeInsertBuilder
from .pydantic import LanceModel, model_to_dict
from .query import (
@@ -243,10 +242,7 @@ def _into_pyarrow_reader(
raise ValueError("Cannot add a single LanceModel to a table. Use a list.")
if isinstance(data, dict):
raise ValueError(
"Cannot create or add rows from a single dictionary. "
"Use a list of dictionaries instead."
)
raise ValueError("Cannot add a single dictionary to a table. Use a list.")
if isinstance(data, list):
# Handle empty list case
@@ -651,16 +647,6 @@ def _append_vector_columns(
col_data = func.compute_source_embeddings_with_retry(
batch[conf.source_column]
)
# Replace vectors with wrong length (including empty lists
# returned for inputs like empty strings) with None so that
# _handle_bad_vectors can process them according to the
# on_bad_vectors policy instead of crashing when PyArrow
# tries to cast them into a fixed-size list array.
expected_ndims = conf.function.ndims()
col_data = [
v if v is not None and len(v) == expected_ndims else None
for v in col_data
]
if no_vector_column:
batch = batch.append_column(
schema.field(vector_column),
@@ -809,10 +795,6 @@ class Table(ABC):
"""
raise NotImplementedError
def current_branch(self) -> Optional[str]:
"""The branch this table handle is scoped to, or ``None`` for ``main``."""
raise NotImplementedError
def __len__(self) -> int:
"""The number of rows in this Table"""
return self.count_rows(None)
@@ -1551,7 +1533,7 @@ class Table(ABC):
) -> MergeResult: ...
@abstractmethod
def delete(self, where: Union[str, Expr]) -> DeleteResult:
def delete(self, where: str) -> DeleteResult:
"""Delete rows from the table.
This can be used to delete a single row, many rows, all rows, or
@@ -1559,10 +1541,10 @@ class Table(ABC):
Parameters
----------
where: str or :class:`~lancedb.expr.Expr`
The filter condition. Can be a SQL string or a type-safe
:class:`~lancedb.expr.Expr` built with :func:`~lancedb.expr.col`
and :func:`~lancedb.expr.lit`.
where: str
The SQL where clause to use when deleting rows.
- For example, 'x = 2' or 'x IN (1, 2, 3)'.
The filter must not be empty, or it will error.
@@ -2032,7 +2014,6 @@ class LanceTable(Table):
namespace_client: Optional[Any] = None,
managed_versioning: Optional[bool] = None,
pushdown_operations: Optional[set] = None,
route_pushdown_to_rust: bool = False,
_async: AsyncTable = None,
):
if namespace_path is None:
@@ -2042,14 +2023,6 @@ class LanceTable(Table):
self._location = location # Store location for use in _dataset_path
self._namespace_client = namespace_client
self._pushdown_operations = pushdown_operations or set()
# When the connection built the namespace client natively (e.g. an
# enterprise "rest" connection), the underlying Rust table already
# executes QueryTable pushdown itself -- and, unlike this Python urllib3
# path, it routes through the read-freshness context provider that emits
# the ``x-lancedb-min-timestamp`` header. So we must defer pushdown to
# Rust instead of calling the Python ``namespace_client.query_table``
# directly, or reads silently bypass read-freshness (stale results).
self._route_pushdown_to_rust = route_pushdown_to_rust
if _async is not None:
self._table = _async
else:
@@ -2152,19 +2125,12 @@ class LanceTable(Table):
branch = self.current_branch()
version = None if branch is not None else self.version
namespace_client = self._namespace_client
if namespace_client is None:
conn_uri = getattr(self._conn, "uri", "")
if get_uri_scheme(conn_uri) == "namespace":
namespace_client = self._conn.namespace_client()
self._namespace_client = namespace_client
if namespace_client is not None:
if self._namespace_client is not None:
table_id = self._namespace_path + [self.name]
ds = lance.dataset(
version=version,
storage_options=self._conn.storage_options,
namespace_client=namespace_client,
namespace_client=self._namespace_client,
table_id=table_id,
**kwargs,
)
@@ -2256,22 +2222,6 @@ class LanceTable(Table):
"""The branch this table handle is scoped to, or ``None`` for ``main``."""
return self._table.current_branch()
def _wrap_branch_handle(
self, async_table: "AsyncTable", version: Optional[int] = None
) -> "LanceTable":
# version is unused locally: the pin already lives on async_table and a
# local handle is not reopened via a serialized connection.
return LanceTable(
self._conn,
async_table.name,
namespace_path=self._namespace_path,
namespace_client=self._namespace_client,
pushdown_operations=self._pushdown_operations,
route_pushdown_to_rust=self._route_pushdown_to_rust,
location=self._location,
_async=async_table,
)
def checkout(self, version: Union[int, str]):
"""Checkout a version of the table. This is an in-place operation.
@@ -2418,11 +2368,8 @@ class LanceTable(Table):
Returns
-------
pa.Table"""
if (
_should_push_down_query_table(
self._namespace_client, self._pushdown_operations
)
and not self._route_pushdown_to_rust
if _should_push_down_query_table(
self._namespace_client, self._pushdown_operations
):
return self._execute_query(Query()).read_all()
@@ -3374,7 +3321,6 @@ class LanceTable(Table):
location: Optional[str] = None,
namespace_client: Optional[Any] = None,
pushdown_operations: Optional[set] = None,
route_pushdown_to_rust: bool = False,
):
"""
Create a new table.
@@ -3437,24 +3383,21 @@ class LanceTable(Table):
self._location = location
self._namespace_client = namespace_client
self._pushdown_operations = pushdown_operations or set()
self._route_pushdown_to_rust = route_pushdown_to_rust
if data_storage_version is not None:
warnings.warn(
"setting data_storage_version directly on create_table is deprecated. "
"setting data_storage_version directly on create_table is deprecated. ",
"Use database_options instead.",
DeprecationWarning,
stacklevel=2,
)
if storage_options is None:
storage_options = {}
storage_options["new_table_data_storage_version"] = data_storage_version
if enable_v2_manifest_paths is not None:
warnings.warn(
"setting enable_v2_manifest_paths directly on create_table is "
"setting enable_v2_manifest_paths directly on create_table is ",
"deprecated. Use database_options instead.",
DeprecationWarning,
stacklevel=2,
)
if storage_options is None:
storage_options = {}
@@ -3480,9 +3423,8 @@ class LanceTable(Table):
)
return self
def delete(self, where: Union[str, Expr]) -> DeleteResult:
predicate = where._inner if isinstance(where, Expr) else where
return LOOP.run(self._table.delete(predicate))
def delete(self, where: str) -> DeleteResult:
return LOOP.run(self._table.delete(where))
def update(
self,
@@ -3551,7 +3493,6 @@ class LanceTable(Table):
_should_push_down_query_table(
self._namespace_client, self._pushdown_operations
)
and not self._route_pushdown_to_rust
and self.current_branch() is None
):
from lancedb.namespace import _execute_server_side_query
@@ -3759,11 +3700,6 @@ class LanceTable(Table):
[`AsyncTable.unset_lsm_write_spec`][lancedb.AsyncTable.unset_lsm_write_spec]."""
return LOOP.run(self._table.unset_lsm_write_spec())
def get_lsm_write_spec(self) -> Optional["LsmWriteSpec"]:
"""Read the installed LsmWriteSpec, or ``None``. See
[`AsyncTable.get_lsm_write_spec`][lancedb.AsyncTable.get_lsm_write_spec]."""
return LOOP.run(self._table.get_lsm_write_spec())
def close_lsm_writers(self) -> None:
"""Close cached MemWAL shard writers. See
[`AsyncTable.close_lsm_writers`][lancedb.AsyncTable.close_lsm_writers]."""
@@ -4035,16 +3971,7 @@ def _handle_bad_vector_column(
dim = _infer_vector_dim(vec_arr)
if dim is None:
return data
is_null = pc.is_null(vec_arr)
# pc.list_value_length returns null for null list entries, so
# pc.not_equal(null, dim) also returns null. Use or_kleene so that
# True OR null = True (Kleene three-valued logic), ensuring null vectors
# are counted as wrong-dim.
has_wrong_dim = pc.or_kleene(
is_null,
pc.not_equal(pc.list_value_length(vec_arr), dim),
)
has_wrong_dim = pc.not_equal(pc.list_value_length(vec_arr), dim)
has_bad_vectors = pc.any(has_nan).as_py() or pc.any(has_wrong_dim).as_py()
@@ -4307,7 +4234,6 @@ class AsyncTable:
namespace_path: Optional[List[str]] = None,
namespace_client: Optional[Any] = None,
pushdown_operations: Optional[set] = None,
route_pushdown_to_rust: bool = False,
):
"""Create a new AsyncTable object.
@@ -4320,9 +4246,6 @@ class AsyncTable:
self._namespace_path = namespace_path or []
self._namespace_client = namespace_client
self._pushdown_operations = pushdown_operations or set()
# See LanceTable.__init__: defer QueryTable pushdown to Rust (which emits
# the read-freshness header) for natively-built namespace clients.
self._route_pushdown_to_rust = route_pushdown_to_rust
def _set_namespace_context(
self,
@@ -4330,12 +4253,10 @@ class AsyncTable:
namespace_path: Optional[List[str]] = None,
namespace_client: Optional[Any] = None,
pushdown_operations: Optional[set] = None,
route_pushdown_to_rust: bool = False,
) -> "AsyncTable":
self._namespace_path = namespace_path or []
self._namespace_client = namespace_client
self._pushdown_operations = pushdown_operations or set()
self._route_pushdown_to_rust = route_pushdown_to_rust
return self
def __repr__(self):
@@ -4422,17 +4343,6 @@ class AsyncTable:
"""
await self._inner.unset_lsm_write_spec()
async def get_lsm_write_spec(self) -> Optional["LsmWriteSpec"]:
"""Read the LsmWriteSpec currently installed on this table.
Returns ``None`` when the MemWAL LSM write path is not enabled (no
spec has been set, or it was removed with `unset_lsm_write_spec`).
The returned spec including its ``maintained_indexes`` and
``writer_config_defaults`` mirrors what was passed to
`set_lsm_write_spec`.
"""
return await self._inner.get_lsm_write_spec()
async def close_lsm_writers(self) -> None:
"""Drain and close any cached MemWAL shard writers for this table.
@@ -4556,11 +4466,8 @@ class AsyncTable:
-------
pa.Table
"""
if (
_should_push_down_query_table(
self._namespace_client, self._pushdown_operations
)
and not self._route_pushdown_to_rust
if _should_push_down_query_table(
self._namespace_client, self._pushdown_operations
):
return (await self._execute_query(Query())).read_all()
@@ -5244,11 +5151,8 @@ class AsyncTable:
batch_size: Optional[int] = None,
timeout: Optional[timedelta] = None,
) -> pa.RecordBatchReader:
if (
_should_push_down_query_table(
self._namespace_client, self._pushdown_operations
)
and not self._route_pushdown_to_rust
if _should_push_down_query_table(
self._namespace_client, self._pushdown_operations
):
from lancedb.namespace import _execute_server_side_query
@@ -5310,7 +5214,6 @@ class AsyncTable:
when_not_matched_insert_all=merge._when_not_matched_insert_all,
when_not_matched_by_source_delete=merge._when_not_matched_by_source_delete,
when_not_matched_by_source_condition=merge._when_not_matched_by_source_condition,
when_not_matched_by_source_condition_expr=merge._when_not_matched_by_source_condition_expr,
timeout=merge._timeout,
use_index=merge._use_index,
use_lsm_write=merge._use_lsm_write,
@@ -5318,7 +5221,7 @@ class AsyncTable:
),
)
async def delete(self, where: Union[str, Expr]) -> DeleteResult:
async def delete(self, where: str) -> DeleteResult:
"""Delete rows from the table.
This can be used to delete a single row, many rows, all rows, or
@@ -5326,10 +5229,10 @@ class AsyncTable:
Parameters
----------
where: str or :class:`~lancedb.expr.Expr`
The filter condition. Can be a SQL string or a type-safe
:class:`~lancedb.expr.Expr` built with :func:`~lancedb.expr.col`
and :func:`~lancedb.expr.lit`.
where: str
The SQL where clause to use when deleting rows.
- For example, 'x = 2' or 'x IN (1, 2, 3)'.
The filter must not be empty, or it will error.
@@ -5368,8 +5271,7 @@ class AsyncTable:
x vector
0 3 [5.0, 6.0]
"""
predicate = where._inner if isinstance(where, Expr) else where
return await self._inner.delete(predicate)
return await self._inner.delete(where)
async def update(
self,
@@ -5734,7 +5636,6 @@ class AsyncTable:
"The 'retrain' parameter is deprecated and will be removed in a "
"future version.",
DeprecationWarning,
stacklevel=2,
)
return await self._inner.optimize(
@@ -6029,7 +5930,7 @@ class Branches:
name: str,
from_ref: Optional[str] = None,
from_version: Optional[int] = None,
) -> "Table":
) -> "LanceTable":
"""Create a branch and return a handle scoped to it.
Parameters
@@ -6046,7 +5947,7 @@ class Branches:
)
return self._wrap(async_table)
def checkout(self, name: str, version: Optional[int] = None) -> "Table":
def checkout(self, name: str, version: Optional[int] = None) -> "LanceTable":
"""Check out an existing branch and return a handle scoped to it.
Parameters
@@ -6059,19 +5960,25 @@ class Branches:
the branch's latest and stays writable.
"""
async_table = LOOP.run(self._table.branches.checkout(name, version))
return self._wrap(async_table, version)
return self._wrap(async_table)
def delete(self, name: str) -> None:
"""Delete a branch."""
LOOP.run(self._table.branches.delete(name))
def _wrap(
self, async_table: "AsyncTable", version: Optional[int] = None
) -> "Table":
# Delegate to the parent so the branch handle keeps its concrete type
# (LanceTable / RemoteTable) and connection context; `version` is the
# explicit pin so a remote handle can restore branch+version on reopen.
return self._parent._wrap_branch_handle(async_table, version)
def _wrap(self, async_table: "AsyncTable") -> "LanceTable":
# Reuse the parent's connection + namespace context; from_inner would drop
# it and break identity/query routing for namespace-backed tables.
parent = self._parent
return LanceTable(
parent._conn,
async_table.name,
namespace_path=parent._namespace_path,
namespace_client=parent._namespace_client,
pushdown_operations=parent._pushdown_operations,
location=parent._location,
_async=async_table,
)
class AsyncTags:
+2 -46
View File
@@ -177,10 +177,7 @@ def flatten_columns(tbl: pa.Table, flatten: Optional[Union[int, bool]] = None):
continue
else:
break
# `bool` is a subclass of `int`, so guard against it explicitly: `flatten=False`
# (and `None`) must mean "do not flatten" rather than falling into the integer
# branch and raising on the `flatten <= 0` check.
elif isinstance(flatten, int) and not isinstance(flatten, bool):
elif isinstance(flatten, int):
if flatten <= 0:
raise ValueError(
"Please specify a positive integer for flatten or the boolean "
@@ -376,15 +373,9 @@ def _(value: list):
@value_to_sql.register(dict)
def _(value: dict):
# https://datafusion.apache.org/user-guide/sql/scalar_functions.html#named-struct
# Render the field name through value_to_sql(str(...)) as well so that keys
# containing characters meaningful in SQL (e.g. a single quote) are escaped
# the same way string values are. A bare f"'{k}'" would emit invalid SQL for
# a key like "it's".
return (
"named_struct("
+ ", ".join(
f"{value_to_sql(str(k))}, {value_to_sql(v)}" for k, v in value.items()
)
+ ", ".join(f"'{k}', {value_to_sql(v)}" for k, v in value.items())
+ ")"
)
@@ -518,38 +509,3 @@ def batch_to_tensor_rows(batch: pa.RecordBatch):
stacked = torch.tensor(numpy.column_stack(columns))
rows = list(stacked.unbind(dim=0))
return rows
def batch_to_tensor_dict(batch: pa.RecordBatch):
"""
Convert a PyArrow RecordBatch to a list of per-row dicts of PyTorch Tensors.
Each column is first converted to a 1-D tensor (zero-copy via DLPack), then
sliced per row. The result is a list whose length is ``batch.num_rows`` and
whose items are dicts keyed by column name. This shape composes directly
with PyTorch's default ``DataLoader`` collate, which stacks the per-row
dicts back into a dict of batched tensors.
Fails if torch is not installed.
Fails if a column's data type is not supported by PyTorch.
Parameters
----------
batch : pa.RecordBatch
The record batch to convert.
Returns
-------
list[dict[str, torch.Tensor]]
One per-row dict per row in the batch. Each dict maps column name to a
0-D tensor view into the column.
"""
torch = attempt_import_or_raise("torch", "torch")
tensors = {
name: torch.from_dlpack(col)
for name, col in zip(batch.schema.names, batch.columns)
}
return [
{name: tensor[i] for name, tensor in tensors.items()}
for i in range(batch.num_rows)
]
File diff suppressed because it is too large Load Diff
-60
View File
@@ -2,7 +2,6 @@
# SPDX-FileCopyrightText: Copyright The LanceDB Authors
import os
import pickle
from typing import List, Optional, Union
from unittest.mock import MagicMock, patch
@@ -243,49 +242,6 @@ def test_embedding_with_bad_results(tmp_path):
assert tbl["vector"].null_count == 1
def test_embedding_with_empty_output_vectors(tmp_path):
"""Regression test for issue #1672.
When an embedding function returns an empty list (e.g. for empty-string
inputs), _append_vector_columns used to crash because PyArrow cannot cast
[] into a fixed-size list element. The fix replaces wrong-length vectors
with None before building the Arrow array so that _handle_bad_vectors can
process them normally.
"""
@register("empty-vec-embedding")
class EmptyVecEmbeddingFunction(TextEmbeddingFunction):
def ndims(self):
return 128
def generate_embeddings(self, texts: Union[List[str], np.ndarray]) -> list:
# Simulate a model that returns an empty list for blank inputs
return [
[] if text.strip() == "" else np.random.randn(self.ndims()).tolist()
for text in texts
]
db = lancedb.connect(tmp_path)
registry = EmbeddingFunctionRegistry.get_instance()
model = registry.get("empty-vec-embedding").create()
class Schema(LanceModel):
text: str = model.SourceField()
vector: Vector(model.ndims()) = model.VectorField()
table = db.create_table("test_empty_vec", schema=Schema, mode="overwrite")
# Should not crash; the row with the empty string should be dropped
table.add(
[{"text": "hello world"}, {"text": ""}, {"text": "foo"}],
on_bad_vectors="drop",
)
assert len(table) == 2
texts = table.to_arrow()["text"].to_pylist()
assert "" not in texts
def test_with_existing_vectors(tmp_path):
@register("mock-embedding")
class MockEmbeddingFunction(TextEmbeddingFunction):
@@ -592,22 +548,6 @@ def test_openai_no_retry_on_401(mock_sleep):
assert mock_sleep.call_count == 0
def test_ollama_embeddings_pickle():
"""OllamaEmbeddings must pickle even after the cached client is created."""
registry = get_registry()
model = registry.get("ollama").create(name="nomic-embed-text")
# Simulate accessing the cached client, which stores it on the instance.
model.__dict__["_ollama_client"] = MagicMock()
pickled = pickle.dumps(model)
restored = pickle.loads(pickled)
assert restored.name == "nomic-embed-text"
assert restored.host == "http://localhost:11434"
assert "_ollama_client" not in restored.__dict__
def test_url_retrieve_downloads_image():
"""
Embedding functions like open-clip, siglip, and jinaai use url_retrieve()
+5 -36
View File
@@ -91,9 +91,7 @@ async def test_create_scalar_index(some_table: AsyncTable):
# Can recreate if replace=True
await some_table.create_index("id", replace=True)
indices = await some_table.list_indices()
assert str(indices).startswith(
'[IndexConfig(name="id_idx", index_type="BTree", columns=["id"]'
)
assert str(indices) == '[Index(BTree, columns=["id"], name="id_idx")]'
assert len(indices) == 1
assert indices[0].index_type == "BTree"
assert indices[0].columns == ["id"]
@@ -108,27 +106,6 @@ async def test_create_scalar_index(some_table: AsyncTable):
assert len(indices) == 0
@pytest.mark.asyncio
async def test_index_config_repr(db_async):
# Use >= 1000 rows so the thousands separator in the repr is exercised.
nrows = 1500
table = await db_async.create_table(
"repr_table", pa.Table.from_pydict({"id": list(range(nrows))})
)
await table.create_index("id", config=BTree())
indices = await table.list_indices()
assert len(indices) == 1
r = repr(indices[0])
assert r.startswith('IndexConfig(name="id_idx", index_type="BTree", columns=["id"]')
# Integer counts use `_` thousands separators (valid Python int syntax).
assert "num_indexed_rows=1_500" in r
assert "num_unindexed_rows=0" in r
# created_at renders as a datetime so the value round-trips.
assert "created_at=datetime.datetime(" in r
assert r.endswith(")")
@pytest.mark.asyncio
async def test_create_nested_scalar_index_lists_canonical_paths(db_async):
metadata_type = pa.struct(
@@ -221,9 +198,7 @@ async def test_create_nested_scalar_index_lists_canonical_paths(db_async):
async def test_create_fixed_size_binary_index(some_table: AsyncTable):
await some_table.create_index("fsb", config=BTree())
indices = await some_table.list_indices()
assert str(indices).startswith(
'[IndexConfig(name="fsb_idx", index_type="BTree", columns=["fsb"]'
)
assert str(indices) == '[Index(BTree, columns=["fsb"], name="fsb_idx")]'
assert len(indices) == 1
assert indices[0].index_type == "BTree"
assert indices[0].columns == ["fsb"]
@@ -272,9 +247,7 @@ async def test_create_bitmap_index(some_table: AsyncTable):
async def test_create_label_list_index(some_table: AsyncTable):
await some_table.create_index("tags", config=LabelList())
indices = await some_table.list_indices()
assert str(indices).startswith(
'[IndexConfig(name="tags_idx", index_type="LabelList", columns=["tags"]'
)
assert str(indices) == '[Index(LabelList, columns=["tags"], name="tags_idx")]'
plan = await some_table.query().where("array_has(tags, 'tag0')").explain_plan()
assert "ScalarIndexQuery" in plan
@@ -289,9 +262,7 @@ async def test_create_large_list_label_list_index(db_async):
await table.create_index("tags", config=LabelList())
indices = await table.list_indices()
assert str(indices).startswith(
'[IndexConfig(name="tags_idx", index_type="LabelList", columns=["tags"]'
)
assert str(indices) == '[Index(LabelList, columns=["tags"], name="tags_idx")]'
plan = await table.query().where("array_has(tags, 'shared')").explain_plan()
assert "ScalarIndexQuery" in plan
@@ -328,9 +299,7 @@ async def test_create_label_list_index_rejects_list_struct(db_async):
async def test_full_text_search_index(some_table: AsyncTable):
await some_table.create_index("tags", config=FTS(with_position=False))
indices = await some_table.list_indices()
assert str(indices).startswith(
'[IndexConfig(name="tags_idx", index_type="FTS", columns=["tags"]'
)
assert str(indices) == '[Index(FTS, columns=["tags"], name="tags_idx")]'
await some_table.prewarm_index("tags_idx")
@@ -11,7 +11,6 @@ import lancedb
import pyarrow as pa
import pytest
from lancedb._lancedb import LsmWriteSpec
from lancedb.index import BTree
SCHEMA = pa.schema(
[
@@ -137,76 +136,3 @@ def test_lsm_write_spec_identity_and_writer_config_defaults():
s = s.with_writer_config_defaults({"durable_write": "false"})
assert s.writer_config_defaults == {"durable_write": "false"}
assert "durable_write" in repr(s)
def test_get_lsm_write_spec(tmp_path):
_db, table = _make_table(tmp_path)
table.set_unenforced_primary_key("id")
# None when nothing is installed.
assert table.get_lsm_write_spec() is None
# A real scalar index is needed to name it as a maintained index.
table.create_index("id", config=BTree())
idx_name = table.list_indices()[0].name
# Bucket spec round-trips, including maintained indexes and writer config
# defaults.
table.set_lsm_write_spec(
LsmWriteSpec.bucket("id", 4)
.with_maintained_indexes([idx_name])
.with_writer_config_defaults({"durable_write": "false"})
)
spec = table.get_lsm_write_spec()
assert spec is not None
assert spec.spec_type == "bucket"
assert spec.column == "id"
assert spec.num_buckets == 4
assert spec.maintained_indexes == [idx_name]
assert spec.writer_config_defaults == {"durable_write": "false"}
# After unset, None again.
table.unset_lsm_write_spec()
assert table.get_lsm_write_spec() is None
# Identity round-trips (column recovered from the schema).
table.set_lsm_write_spec(LsmWriteSpec.identity("id"))
spec = table.get_lsm_write_spec()
assert spec.spec_type == "identity"
assert spec.column == "id"
table.unset_lsm_write_spec()
# Unsharded round-trips (no routing column).
table.set_lsm_write_spec(LsmWriteSpec.unsharded())
spec = table.get_lsm_write_spec()
assert spec.spec_type == "unsharded"
assert spec.column is None
@pytest.mark.asyncio
async def test_async_get_lsm_write_spec(tmp_path):
db = await lancedb.connect_async(
tmp_path, read_consistency_interval=timedelta(seconds=0)
)
table = await db.create_table(
"t",
pa.RecordBatchReader.from_batches(SCHEMA, [_batch(["seed"], [0])]),
)
assert await table.get_lsm_write_spec() is None
# A real scalar index is needed to name it as a maintained index.
await table.create_index("id", config=BTree())
idx_name = (await table.list_indices())[0].name
await table.set_lsm_write_spec(
LsmWriteSpec.bucket("id", 8).with_maintained_indexes([idx_name])
)
spec = await table.get_lsm_write_spec()
assert spec is not None
assert spec.spec_type == "bucket"
assert spec.column == "id"
assert spec.num_buckets == 8
assert spec.maintained_indexes == [idx_name]
await table.unset_lsm_write_spec()
assert await table.get_lsm_write_spec() is None
+10 -154
View File
@@ -5,12 +5,11 @@
import tempfile
import shutil
import importlib
import sys
import pytest
import pyarrow as pa
import lancedb
from lance_namespace.errors import NamespaceNotEmptyError, TableNotFoundError
from lancedb.namespace import _MAX_QUERY_K
from lancedb.table import AsyncTable, LanceTable
@@ -66,9 +65,6 @@ def _namespace_lance_table(namespace_client: _NamespaceClient) -> LanceTable:
table._namespace_path = ["geneva"]
table._namespace_client = namespace_client
table._pushdown_operations = {"QueryTable"}
# This test exercises the Python-side pushdown path (non-native client), so
# pushdown is not routed to Rust.
table._route_pushdown_to_rust = False
return table
@@ -104,40 +100,6 @@ class TestNamespaceConnection:
assert isinstance(db, lancedb.LanceNamespaceDBConnection)
assert len(list(db.table_names())) == 0
def test_sync_builtin_namespace_uses_rust_without_python_client(self, monkeypatch):
"""Built-in sync namespace connections should not construct or call the
Python namespace client for normal namespace/table management."""
namespace_module = importlib.import_module("lancedb.namespace")
def fail_namespace_connect(*args, **kwargs):
raise AssertionError("Python namespace client should not be constructed")
monkeypatch.setattr(
namespace_module, "namespace_connect", fail_namespace_connect
)
db = lancedb.connect_namespace("dir", {"root": self.temp_dir})
assert isinstance(db, lancedb.LanceNamespaceDBConnection)
assert db._namespace_client is None
assert db._route_pushdown_to_rust is True
db.create_namespace(["test_ns"])
assert "test_ns" in db.list_namespaces().namespaces
schema = pa.schema([pa.field("id", pa.int64())])
table = db.create_table("test_table", schema=schema, namespace_path=["test_ns"])
assert table.namespace == ["test_ns"]
assert "test_table" in db.table_names(namespace_path=["test_ns"])
assert "test_table" in db.list_tables(namespace_path=["test_ns"]).tables
opened = db.open_table("test_table", namespace_path=["test_ns"])
assert opened.namespace == ["test_ns"]
db.drop_table("test_table", namespace_path=["test_ns"])
assert db.list_tables(namespace_path=["test_ns"]).tables == []
db.drop_namespace(["test_ns"])
assert "test_ns" not in db.list_namespaces().namespaces
def test_create_table_through_namespace(self):
"""Test creating a table through namespace."""
db = lancedb.connect_namespace("dir", {"root": self.temp_dir})
@@ -295,15 +257,8 @@ class TestNamespaceConnection:
assert table_schema.field("id").type == pa.int64()
assert table_schema.field("text").type == pa.string()
def test_rename_table(self):
"""Test that rename_table renames a table in the namespace.
The `dir` namespace implementation in lance-namespace-impls does not
implement `rename_table` yet (only the `rest` backend does), so it
currently falls back to the default trait method which raises
NotSupported. This is expected to start passing once the `dir`
backend gains rename_table support upstream.
"""
def test_rename_table_not_supported(self):
"""Test that rename_table raises NotImplementedError."""
db = lancedb.connect_namespace("dir", {"root": self.temp_dir})
# Create a child namespace first
@@ -318,14 +273,9 @@ class TestNamespaceConnection:
)
db.create_table("old_name", schema=schema, namespace_path=["test_ns"])
# Rename the table within the same namespace
with pytest.raises(NotImplementedError, match="rename_table not implemented"):
db.rename_table(
"old_name",
"new_name",
cur_namespace_path=["test_ns"],
new_namespace_path=["test_ns"],
)
# Rename should raise NotImplementedError
with pytest.raises(NotImplementedError, match="rename_table is not supported"):
db.rename_table("old_name", "new_name")
def test_drop_all_tables(self):
"""Test dropping all tables through namespace."""
@@ -599,61 +549,6 @@ class TestAsyncNamespaceConnection:
table_names = await db.table_names()
assert len(list(table_names)) == 0
async def test_async_builtin_namespace_uses_rust_without_python_client(
self, monkeypatch
):
"""Built-in async namespace connections should not construct or call the
Python namespace client for normal namespace/table management."""
namespace_module = importlib.import_module("lancedb.namespace")
def fail_namespace_connect(*args, **kwargs):
raise AssertionError("Python namespace client should not be constructed")
monkeypatch.setattr(
namespace_module, "namespace_connect", fail_namespace_connect
)
db = lancedb.connect_namespace_async("dir", {"root": self.temp_dir})
assert isinstance(db, lancedb.AsyncLanceNamespaceDBConnection)
assert db._namespace_client is None
assert db._route_pushdown_to_rust is True
await db.create_namespace(["test_ns"])
assert "test_ns" in (await db.list_namespaces()).namespaces
schema = pa.schema([pa.field("id", pa.int64())])
table = await db.create_table(
"test_table", schema=schema, namespace_path=["test_ns"]
)
assert table._namespace_path == ["test_ns"]
assert table._namespace_client is None
assert table._route_pushdown_to_rust is True
assert "test_table" in await db.table_names(namespace_path=["test_ns"])
assert "test_table" in (await db.list_tables(namespace_path=["test_ns"])).tables
opened = await db.open_table("test_table", namespace_path=["test_ns"])
assert opened._namespace_path == ["test_ns"]
await db.drop_table("test_table", namespace_path=["test_ns"])
assert (await db.list_tables(namespace_path=["test_ns"])).tables == []
await db.drop_namespace(["test_ns"])
assert "test_ns" not in (await db.list_namespaces()).namespaces
async def test_async_namespace_client_is_lazy(self):
"""namespace_client() should still return the backing client on demand."""
pytest.importorskip("lance")
from lance.namespace import DirectoryNamespace
db = lancedb.connect_namespace_async("dir", {"root": self.temp_dir})
assert db._namespace_client is None
ns_client = await db.namespace_client()
assert isinstance(ns_client, DirectoryNamespace)
namespace_id = ns_client.namespace_id().replace("\\\\", "\\")
assert str(self.temp_dir) in namespace_id
assert db._namespace_client is ns_client
# Async connect via namespace helper is not enabled yet.
async def test_create_table_async(self):
@@ -898,39 +793,6 @@ class TestPushdownOperations:
db = lancedb.connect_namespace("dir", {"root": self.temp_dir})
assert len(db._namespace_client_pushdown_operations) == 0
def test_route_pushdown_to_rust_for_native_rest(self):
"""A natively-built rest connection must defer QueryTable pushdown to
Rust so reads carry the x-lancedb-min-timestamp read-freshness header."""
db = lancedb.connect_namespace(
"rest",
{"uri": "http://localhost:12345"},
namespace_client_pushdown_operations=["QueryTable"],
)
assert db._route_pushdown_to_rust is True
def test_route_pushdown_to_rust_for_native_dir(self):
"""The sync dir connection is natively built and defers QueryTable
pushdown to Rust."""
db = lancedb.connect_namespace("dir", {"root": self.temp_dir})
assert db._route_pushdown_to_rust is True
def test_async_route_pushdown_to_rust_for_native_rest(self):
"""The async connection must not silently bypass the read-freshness fix:
a natively-built rest connection defers pushdown to Rust (regression test
for the async path omitting the freshness header)."""
db = lancedb.connect_namespace_async(
"rest",
{"uri": "http://localhost:12345"},
namespace_client_pushdown_operations=["QueryTable"],
)
assert db._route_pushdown_to_rust is True
def test_async_route_pushdown_to_rust_for_native_dir(self):
"""The async dir connection is natively built and defers QueryTable
pushdown to Rust."""
db = lancedb.connect_namespace_async("dir", {"root": self.temp_dir})
assert db._route_pushdown_to_rust is True
def test_lance_table_to_arrow_uses_query_pushdown(self):
namespace_client = _NamespaceClient()
table = _namespace_lance_table(namespace_client)
@@ -942,13 +804,10 @@ class TestPushdownOperations:
["geneva", "hist"],
["geneva", "hist"],
]
# Unlimited reads cap k at i32::MAX (the namespace query_table `k`
# field is i32); sys.maxsize would overflow the Rust binding.
assert [request.k for request in namespace_client.requests] == [
_MAX_QUERY_K,
_MAX_QUERY_K,
sys.maxsize,
sys.maxsize,
]
assert all(r.k <= 2**31 - 1 for r in namespace_client.requests)
@pytest.mark.asyncio
@@ -1003,13 +862,10 @@ class TestAsyncPushdownOperations:
["geneva", "hist"],
["geneva", "hist"],
]
# Unlimited reads cap k at i32::MAX (the namespace query_table `k`
# field is i32); sys.maxsize would overflow the Rust binding.
assert [request.k for request in namespace_client.requests] == [
_MAX_QUERY_K,
_MAX_QUERY_K,
sys.maxsize,
sys.maxsize,
]
assert all(r.k <= 2**31 - 1 for r in namespace_client.requests)
def test_local_table_to_arrow_and_to_pandas_are_unchanged(tmp_path):
-686
View File
@@ -1,686 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright The LanceDB Authors
"""Regression matrix for nested field support across LanceDB Python APIs.
Covers the lifecycle described in lancedb/lancedb#3406:
- Nested scalar, vector, and FTS index creation with full dotted paths
- list_indices / index_stats return canonical full paths (not leaf names)
- search, filter, append, optimize behaviour
- Field-name edge cases: mixed case, literal-dot field names, same-name leaves
- Both sync and async Python table APIs
The matrix uses the following field-name variants from the acceptance criteria:
- rowId (camelCase top-level)
- `row-id` (hyphenated top-level, escaped)
- parent.`leaf.name` (struct leaf whose name contains a literal dot)
- MetaData.userId (mixed-case nested path)
- `meta-data`.`user-id` (hyphenated struct with hyphenated leaf)
Note: Lance forbids top-level field names that contain a '.', so the literal-dot
edge case is exercised via a struct leaf field (parent.`leaf.name`) instead.
"""
from datetime import timedelta
import pyarrow as pa
import pytest
import pytest_asyncio
import lancedb
from lancedb.db import AsyncConnection, DBConnection
from lancedb.index import BTree, FTS, IvfPq
# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
DIM = 8
# IvfPq requires at least num_partitions * 256 rows by default; keeping rows
# small means we must drop num_sub_vectors and num_partitions very low.
NROWS = 256
def _vec(row: int) -> list:
return [float((row * DIM + i) % 256) for i in range(DIM)]
# ---------------------------------------------------------------------------
# Fixtures
# ---------------------------------------------------------------------------
@pytest.fixture
def sync_db(tmp_path) -> DBConnection:
return lancedb.connect(tmp_path)
@pytest_asyncio.fixture
async def async_db(tmp_path) -> AsyncConnection:
return await lancedb.connect_async(
tmp_path, read_consistency_interval=timedelta(seconds=0)
)
# ---------------------------------------------------------------------------
# Schema / data builders
# ---------------------------------------------------------------------------
def _nested_scalar_schema() -> pa.Schema:
"""Schema with nested scalar fields covering the acceptance-criteria names.
Top-level columns:
- rowId int32 (camelCase top-level)
- row-id int32 (hyphenated top-level name)
- MetaData struct{userId int32} (mixed-case nested path)
- meta-data struct{user-id int32} (hyphenated struct + hyphenated leaf)
Lance disallows top-level field names that contain '.' (e.g. a field
literally named 'a.b'), so that edge case is tested separately using
_literal_dot_schema() below.
"""
return pa.schema(
[
pa.field("rowId", pa.int32()),
pa.field("row-id", pa.int32()),
pa.field(
"MetaData",
pa.struct([pa.field("userId", pa.int32())]),
),
pa.field(
"meta-data",
pa.struct([pa.field("user-id", pa.int32())]),
),
]
)
def _nested_scalar_data(nrows: int = NROWS) -> pa.Table:
schema = _nested_scalar_schema()
return pa.table(
{
"rowId": pa.array(list(range(nrows)), pa.int32()),
"row-id": pa.array(list(range(nrows)), pa.int32()),
"MetaData": pa.array(
[{"userId": i} for i in range(nrows)],
type=pa.struct([pa.field("userId", pa.int32())]),
),
"meta-data": pa.array(
[{"user-id": i} for i in range(nrows)],
type=pa.struct([pa.field("user-id", pa.int32())]),
),
},
schema=schema,
)
def _literal_dot_schema() -> pa.Schema:
"""Schema where a struct *leaf* field is named with a literal dot.
The path used in the index API is ``parent.`leaf.name` ``.
"""
return pa.schema(
[
pa.field("id", pa.int32()),
pa.field(
"parent",
pa.struct([pa.field("leaf.name", pa.int32())]),
),
]
)
def _literal_dot_data(nrows: int = NROWS) -> pa.Table:
parent_type = pa.struct([pa.field("leaf.name", pa.int32())])
return pa.table(
{
"id": pa.array(list(range(nrows)), pa.int32()),
"parent": pa.array(
[{"leaf.name": i} for i in range(nrows)],
type=parent_type,
),
},
schema=_literal_dot_schema(),
)
def _same_leaf_schema() -> pa.Schema:
return pa.schema(
[
pa.field("StructA", pa.struct([pa.field("userId", pa.int32())])),
pa.field("StructB", pa.struct([pa.field("userId", pa.int32())])),
]
)
def _same_leaf_data(nrows: int = NROWS) -> pa.Table:
t = pa.struct([pa.field("userId", pa.int32())])
return pa.table(
{
"StructA": pa.array([{"userId": i} for i in range(nrows)], type=t),
"StructB": pa.array([{"userId": i * 10} for i in range(nrows)], type=t),
},
schema=_same_leaf_schema(),
)
def _nested_vector_schema() -> pa.Schema:
return pa.schema(
[
pa.field("id", pa.int32()),
pa.field(
"image",
pa.struct([pa.field("embedding", pa.list_(pa.float32(), DIM))]),
),
pa.field(
"MetaData",
pa.struct([pa.field("userId", pa.int32())]),
),
]
)
def _nested_vector_data(nrows: int = NROWS) -> pa.Table:
embedding_type = pa.list_(pa.float32(), DIM)
image_type = pa.struct([pa.field("embedding", embedding_type)])
meta_type = pa.struct([pa.field("userId", pa.int32())])
return pa.table(
{
"id": pa.array(list(range(nrows)), pa.int32()),
"image": pa.array(
[{"embedding": _vec(i)} for i in range(nrows)],
type=image_type,
),
"MetaData": pa.array(
[{"userId": i} for i in range(nrows)],
type=meta_type,
),
},
schema=_nested_vector_schema(),
)
def _nested_fts_schema() -> pa.Schema:
return pa.schema(
[
pa.field("id", pa.int32()),
pa.field(
"payload",
pa.struct([pa.field("text", pa.utf8())]),
),
pa.field(
"MetaData",
pa.struct([pa.field("userId", pa.int32())]),
),
]
)
def _nested_fts_data(nrows: int = NROWS) -> pa.Table:
words = ["alpha", "bravo", "charlie", "delta", "echo"]
payload_type = pa.struct([pa.field("text", pa.utf8())])
meta_type = pa.struct([pa.field("userId", pa.int32())])
return pa.table(
{
"id": pa.array(list(range(nrows)), pa.int32()),
"payload": pa.array(
[{"text": words[i % len(words)]} for i in range(nrows)],
type=payload_type,
),
"MetaData": pa.array(
[{"userId": i} for i in range(nrows)],
type=meta_type,
),
},
schema=_nested_fts_schema(),
)
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _columns_by_name_sync(tbl) -> dict:
return {idx.name: idx.columns for idx in tbl.list_indices()}
async def _columns_by_name_async(tbl) -> dict:
return {idx.name: idx.columns for idx in await tbl.list_indices()}
# ===========================================================================
# SYNC TESTS
# ===========================================================================
#
# The sync LanceTable API uses:
# - create_scalar_index(column, ...) for scalar (BTree/Bitmap/LabelList) indices
# - create_fts_index(column, ...) for full-text-search indices
# - create_index(...) for vector indices (older positional API)
# ===========================================================================
class TestNestedScalarIndexSync:
"""Sync regression matrix for nested scalar (BTree) indices."""
def test_top_level_camelcase_field(self, sync_db):
"""list_indices must return the full camelCase field name."""
tbl = sync_db.create_table("t", _nested_scalar_data())
tbl.create_scalar_index("rowId", index_type="BTREE", name="rowid_idx")
col_map = _columns_by_name_sync(tbl)
assert col_map["rowid_idx"] == ["rowId"], (
"list_indices must return 'rowId', not a truncated leaf name"
)
def test_top_level_hyphenated_field_escaped(self, sync_db):
"""Top-level field 'row-id' (hyphenated) accessed via escaped path."""
tbl = sync_db.create_table("t", _nested_scalar_data())
tbl.create_scalar_index("`row-id`", index_type="BTREE", name="rowid_hyph_idx")
col_map = _columns_by_name_sync(tbl)
assert col_map["rowid_hyph_idx"] == ["`row-id`"], (
"list_indices must return escaped path '`row-id`'"
)
def test_struct_leaf_literal_dot_field_escaped(self, sync_db):
"""Struct leaf with a literal-dot name: parent.`leaf.name`.
The index listing must use the full escaped path, not just the leaf.
"""
tbl = sync_db.create_table("t", _literal_dot_data())
tbl.create_scalar_index(
"parent.`leaf.name`", index_type="BTREE", name="leaf_dot_idx"
)
col_map = _columns_by_name_sync(tbl)
assert col_map["leaf_dot_idx"] == ["parent.`leaf.name`"], (
"list_indices must return 'parent.`leaf.name`', not just '`leaf.name`'"
)
def test_nested_mixed_case_path(self, sync_db):
"""Nested path MetaData.userId (mixed case) must appear as full path."""
tbl = sync_db.create_table("t", _nested_scalar_data())
tbl.create_scalar_index(
"MetaData.userId", index_type="BTREE", name="metadata_userid_idx"
)
col_map = _columns_by_name_sync(tbl)
assert col_map["metadata_userid_idx"] == ["MetaData.userId"], (
"list_indices must return 'MetaData.userId', not leaf 'userId'"
)
def test_nested_hyphenated_path_escaped(self, sync_db):
"""`meta-data`.`user-id` path with both parts escaped."""
tbl = sync_db.create_table("t", _nested_scalar_data())
tbl.create_scalar_index(
"`meta-data`.`user-id`", index_type="BTREE", name="metauid_idx"
)
col_map = _columns_by_name_sync(tbl)
assert col_map["metauid_idx"] == ["`meta-data`.`user-id`"], (
"list_indices must return '`meta-data`.`user-id`', not 'user-id'"
)
def test_filter_on_nested_mixed_case(self, sync_db):
"""WHERE filter on a nested dotted path works after index creation."""
tbl = sync_db.create_table("t", _nested_scalar_data())
tbl.create_scalar_index(
"MetaData.userId", index_type="BTREE", name="metadata_userid_idx"
)
rows = tbl.search().where("MetaData.userId = 5").to_list()
assert len(rows) == 1
assert rows[0]["MetaData"]["userId"] == 5
def test_append_and_list_indices_stable(self, sync_db):
"""After appending rows the index listing must remain unchanged."""
tbl = sync_db.create_table("t", _nested_scalar_data())
tbl.create_scalar_index(
"MetaData.userId", index_type="BTREE", name="meta_uid_idx"
)
tbl.add(_nested_scalar_data(nrows=4))
col_map = _columns_by_name_sync(tbl)
assert col_map["meta_uid_idx"] == ["MetaData.userId"]
def test_optimize_and_list_indices_stable(self, tmp_path):
"""After optimize the index listing must still show full paths."""
db = lancedb.connect(tmp_path / "opt_db")
tbl = db.create_table("t", _nested_scalar_data())
tbl.create_scalar_index(
"MetaData.userId", index_type="BTREE", name="meta_uid_idx"
)
tbl.add(_nested_scalar_data(nrows=4))
tbl.optimize()
col_map = _columns_by_name_sync(tbl)
assert col_map["meta_uid_idx"] == ["MetaData.userId"]
def test_same_name_leaves_are_distinct(self, sync_db):
"""Two structs sharing a leaf name must produce distinct index paths."""
tbl = sync_db.create_table("same_leaf", _same_leaf_data())
tbl.create_scalar_index(
"StructA.userId", index_type="BTREE", name="a_userid_idx"
)
tbl.create_scalar_index(
"StructB.userId", index_type="BTREE", name="b_userid_idx"
)
col_map = _columns_by_name_sync(tbl)
assert col_map["a_userid_idx"] == ["StructA.userId"]
assert col_map["b_userid_idx"] == ["StructB.userId"]
def test_index_stats_canonical_path(self, sync_db):
"""index_stats round-trip: create on nested field, verify row count."""
tbl = sync_db.create_table("t", _nested_scalar_data())
tbl.create_scalar_index(
"MetaData.userId", index_type="BTREE", name="meta_uid_idx"
)
stats = tbl.index_stats("meta_uid_idx")
assert stats is not None
assert stats.index_type == "BTREE"
assert stats.num_indexed_rows == NROWS
class TestNestedVectorIndexSync:
"""Sync regression matrix for nested vector (IvfPq) indices."""
def test_nested_vector_index_full_path(self, sync_db):
"""Listing after vector index creation must use the full dotted path."""
tbl = sync_db.create_table("vt", _nested_vector_data())
tbl.create_index(
num_partitions=2,
num_sub_vectors=2,
vector_column_name="image.embedding",
name="image_emb_idx",
)
col_map = _columns_by_name_sync(tbl)
assert col_map["image_emb_idx"] == ["image.embedding"], (
"list_indices must return 'image.embedding', not leaf 'embedding'"
)
def test_nested_vector_search(self, sync_db):
"""Vector search on nested embedding field must return results."""
tbl = sync_db.create_table("vt", _nested_vector_data())
tbl.create_index(
num_partitions=2,
num_sub_vectors=2,
vector_column_name="image.embedding",
name="image_emb_idx",
)
results = (
tbl.search(_vec(0), vector_column_name="image.embedding").limit(5).to_list()
)
assert len(results) > 0
def test_nested_vector_index_stats(self, sync_db):
"""index_stats for a nested vector index must reflect correct row count."""
tbl = sync_db.create_table("vt", _nested_vector_data())
tbl.create_index(
num_partitions=2,
num_sub_vectors=2,
vector_column_name="image.embedding",
name="image_emb_idx",
)
stats = tbl.index_stats("image_emb_idx")
assert stats is not None
assert stats.num_indexed_rows == NROWS
def test_nested_vector_append_optimize(self, tmp_path):
"""After append and optimize the vector index listing must be stable."""
db = lancedb.connect(tmp_path / "vec_opt_db")
tbl = db.create_table("vt", _nested_vector_data())
tbl.create_index(
num_partitions=2,
num_sub_vectors=2,
vector_column_name="image.embedding",
name="image_emb_idx",
)
tbl.add(_nested_vector_data(nrows=4))
tbl.optimize()
col_map = _columns_by_name_sync(tbl)
assert col_map["image_emb_idx"] == ["image.embedding"]
class TestNestedFTSIndexSync:
"""Sync regression matrix for nested FTS indices."""
def test_nested_fts_index_full_path(self, sync_db):
"""FTS index on payload.text must be listed with the full path."""
tbl = sync_db.create_table("ft", _nested_fts_data())
tbl.create_fts_index("payload.text", name="payload_text_idx")
col_map = _columns_by_name_sync(tbl)
assert col_map["payload_text_idx"] == ["payload.text"], (
"list_indices must return 'payload.text', not leaf 'text'"
)
def test_nested_fts_search(self, sync_db):
"""FTS search on a nested text field must return correct results."""
tbl = sync_db.create_table("ft", _nested_fts_data())
tbl.create_fts_index("payload.text", name="payload_text_idx")
results = (
tbl.search("alpha", query_type="fts", fts_columns="payload.text")
.limit(10)
.to_list()
)
assert len(results) > 0
assert all(row["payload"]["text"] == "alpha" for row in results)
def test_nested_fts_append_optimize(self, tmp_path):
"""After append and optimize the FTS index listing must be stable."""
db = lancedb.connect(tmp_path / "fts_opt_db")
tbl = db.create_table("ft", _nested_fts_data())
tbl.create_fts_index("payload.text", name="payload_text_idx")
tbl.add(_nested_fts_data(nrows=4))
tbl.optimize()
col_map = _columns_by_name_sync(tbl)
assert col_map["payload_text_idx"] == ["payload.text"]
# ===========================================================================
# ASYNC TESTS
# ===========================================================================
#
# The async AsyncTable API uses create_index(column, config=...) uniformly
# for scalar, vector, and FTS indices.
# ===========================================================================
class TestNestedScalarIndexAsync:
"""Async regression matrix for nested scalar (BTree) indices."""
@pytest.mark.asyncio
async def test_top_level_camelcase_field(self, async_db):
"""list_indices must return the full camelCase field name."""
tbl = await async_db.create_table("t", _nested_scalar_data())
await tbl.create_index("rowId", config=BTree(), name="rowid_idx")
col_map = await _columns_by_name_async(tbl)
assert col_map["rowid_idx"] == ["rowId"]
@pytest.mark.asyncio
async def test_top_level_hyphenated_field_escaped(self, async_db):
"""Hyphenated top-level field accessed via escaped path."""
tbl = await async_db.create_table("t", _nested_scalar_data())
await tbl.create_index("`row-id`", config=BTree(), name="rowid_hyph_idx")
col_map = await _columns_by_name_async(tbl)
assert col_map["rowid_hyph_idx"] == ["`row-id`"]
@pytest.mark.asyncio
async def test_struct_leaf_literal_dot_field_escaped(self, async_db):
"""Struct leaf with a literal-dot name: parent.`leaf.name`."""
tbl = await async_db.create_table("t", _literal_dot_data())
await tbl.create_index(
"parent.`leaf.name`", config=BTree(), name="leaf_dot_idx"
)
col_map = await _columns_by_name_async(tbl)
assert col_map["leaf_dot_idx"] == ["parent.`leaf.name`"]
@pytest.mark.asyncio
async def test_nested_mixed_case_path(self, async_db):
"""Mixed-case nested path MetaData.userId must appear as full path."""
tbl = await async_db.create_table("t", _nested_scalar_data())
await tbl.create_index(
"MetaData.userId", config=BTree(), name="metadata_userid_idx"
)
col_map = await _columns_by_name_async(tbl)
assert col_map["metadata_userid_idx"] == ["MetaData.userId"]
@pytest.mark.asyncio
async def test_nested_hyphenated_path_escaped(self, async_db):
"""`meta-data`.`user-id` path with both parts escaped."""
tbl = await async_db.create_table("t", _nested_scalar_data())
await tbl.create_index(
"`meta-data`.`user-id`", config=BTree(), name="metauid_idx"
)
col_map = await _columns_by_name_async(tbl)
assert col_map["metauid_idx"] == ["`meta-data`.`user-id`"]
@pytest.mark.asyncio
async def test_filter_on_nested_mixed_case(self, async_db):
"""WHERE filter on a nested dotted path works after index creation."""
tbl = await async_db.create_table("t", _nested_scalar_data())
await tbl.create_index(
"MetaData.userId", config=BTree(), name="metadata_userid_idx"
)
rows = await tbl.query().where("MetaData.userId = 5").to_list()
assert len(rows) == 1
assert rows[0]["MetaData"]["userId"] == 5
@pytest.mark.asyncio
async def test_index_stats_canonical_path(self, async_db):
"""index_stats round-trip: create on nested field, verify stats."""
tbl = await async_db.create_table("t", _nested_scalar_data())
await tbl.create_index("MetaData.userId", config=BTree(), name="meta_uid_idx")
stats = await tbl.index_stats("meta_uid_idx")
assert stats is not None
assert stats.index_type == "BTREE"
assert stats.num_indexed_rows == NROWS
@pytest.mark.asyncio
async def test_append_and_list_indices_stable(self, async_db):
"""After appending rows the index listing must remain unchanged."""
tbl = await async_db.create_table("t", _nested_scalar_data())
await tbl.create_index("MetaData.userId", config=BTree(), name="meta_uid_idx")
await tbl.add(_nested_scalar_data(nrows=4))
col_map = await _columns_by_name_async(tbl)
assert col_map["meta_uid_idx"] == ["MetaData.userId"]
@pytest.mark.asyncio
async def test_optimize_and_list_indices_stable(self, tmp_path):
"""After optimize the index listing must still show full paths."""
db = await lancedb.connect_async(
tmp_path / "opt_db", read_consistency_interval=timedelta(seconds=0)
)
tbl = await db.create_table("t", _nested_scalar_data())
await tbl.create_index("MetaData.userId", config=BTree(), name="meta_uid_idx")
await tbl.add(_nested_scalar_data(nrows=4))
await tbl.optimize()
col_map = await _columns_by_name_async(tbl)
assert col_map["meta_uid_idx"] == ["MetaData.userId"]
@pytest.mark.asyncio
async def test_same_name_leaves_are_distinct(self, async_db):
"""Two structs sharing a leaf name must produce distinct index paths."""
tbl = await async_db.create_table("same_leaf", _same_leaf_data())
await tbl.create_index("StructA.userId", config=BTree(), name="a_userid_idx")
await tbl.create_index("StructB.userId", config=BTree(), name="b_userid_idx")
col_map = await _columns_by_name_async(tbl)
assert col_map["a_userid_idx"] == ["StructA.userId"]
assert col_map["b_userid_idx"] == ["StructB.userId"]
class TestNestedVectorIndexAsync:
"""Async regression matrix for nested vector (IvfPq) indices."""
@pytest.mark.asyncio
async def test_nested_vector_index_full_path(self, async_db):
"""Listing after vector index creation must use the full dotted path."""
tbl = await async_db.create_table("vt", _nested_vector_data())
await tbl.create_index(
"image.embedding",
config=IvfPq(num_partitions=2, num_sub_vectors=2),
name="image_emb_idx",
)
col_map = await _columns_by_name_async(tbl)
assert col_map["image_emb_idx"] == ["image.embedding"]
@pytest.mark.asyncio
async def test_nested_vector_search(self, async_db):
"""Vector search on nested embedding field must return results."""
tbl = await async_db.create_table("vt", _nested_vector_data())
await tbl.create_index(
"image.embedding",
config=IvfPq(num_partitions=2, num_sub_vectors=2),
name="image_emb_idx",
)
results = (
await tbl.query()
.nearest_to(_vec(0))
.column("image.embedding")
.limit(5)
.to_list()
)
assert len(results) > 0
@pytest.mark.asyncio
async def test_nested_vector_index_stats(self, async_db):
"""index_stats for a nested vector index must reflect correct row count."""
tbl = await async_db.create_table("vt", _nested_vector_data())
await tbl.create_index(
"image.embedding",
config=IvfPq(num_partitions=2, num_sub_vectors=2),
name="image_emb_idx",
)
stats = await tbl.index_stats("image_emb_idx")
assert stats is not None
assert stats.num_indexed_rows == NROWS
@pytest.mark.asyncio
async def test_nested_vector_append_optimize(self, tmp_path):
"""After append and optimize the vector index listing must be stable."""
db = await lancedb.connect_async(
tmp_path / "vec_opt_db", read_consistency_interval=timedelta(seconds=0)
)
tbl = await db.create_table("vt", _nested_vector_data())
await tbl.create_index(
"image.embedding",
config=IvfPq(num_partitions=2, num_sub_vectors=2),
name="image_emb_idx",
)
await tbl.add(_nested_vector_data(nrows=4))
await tbl.optimize()
col_map = await _columns_by_name_async(tbl)
assert col_map["image_emb_idx"] == ["image.embedding"]
class TestNestedFTSIndexAsync:
"""Async regression matrix for nested FTS indices."""
@pytest.mark.asyncio
async def test_nested_fts_index_full_path(self, async_db):
"""FTS index on payload.text must be listed with the full path."""
tbl = await async_db.create_table("ft", _nested_fts_data())
await tbl.create_index("payload.text", config=FTS(), name="payload_text_idx")
col_map = await _columns_by_name_async(tbl)
assert col_map["payload_text_idx"] == ["payload.text"]
@pytest.mark.asyncio
async def test_nested_fts_search(self, async_db):
"""FTS search on a nested text field must return correct results."""
tbl = await async_db.create_table("ft", _nested_fts_data())
await tbl.create_index("payload.text", config=FTS(), name="payload_text_idx")
results = (
await tbl.query()
.nearest_to_text("alpha", columns="payload.text")
.limit(10)
.to_list()
)
assert len(results) > 0
assert all(row["payload"]["text"] == "alpha" for row in results)
@pytest.mark.asyncio
async def test_nested_fts_append_optimize(self, tmp_path):
"""After append and optimize the FTS index listing must be stable."""
db = await lancedb.connect_async(
tmp_path / "fts_opt_db", read_consistency_interval=timedelta(seconds=0)
)
tbl = await db.create_table("ft", _nested_fts_data())
await tbl.create_index("payload.text", config=FTS(), name="payload_text_idx")
await tbl.add(_nested_fts_data(nrows=4))
await tbl.optimize()
col_map = await _columns_by_name_async(tbl)
assert col_map["payload_text_idx"] == ["payload.text"]
+7 -34
View File
@@ -935,41 +935,14 @@ def test_transform_fn(mem_db):
try:
import torch
# "torch" returns a list of per-row dicts. Default DataLoader collate
# stacks the per-row dicts back into a dict of batched tensors.
torch_perm = permutation.with_format("torch")
torch_batch = list(torch_perm.iter(10, skip_last_batch=False))[0]
assert isinstance(torch_batch, list)
assert len(torch_batch) == 10
assert isinstance(torch_batch[0], dict)
assert set(torch_batch[0].keys()) == {"id", "value"}
assert isinstance(torch_batch[0]["id"], torch.Tensor)
assert torch_batch[0]["id"].dtype == torch.int64
rows = torch_perm.__getitems__([0, 1, 2])
assert isinstance(rows, list)
assert len(rows) == 3
assert isinstance(rows[0], dict)
assert set(rows[0].keys()) == {"id", "value"}
assert isinstance(rows[0]["id"], torch.Tensor)
# "torch_row" returns a list of tensors, one per row.
torch_rows = list(
permutation.with_format("torch_row").iter(10, skip_last_batch=False)
torch_result = list(
permutation.with_format("torch").iter(10, skip_last_batch=False)
)[0]
assert isinstance(torch_rows, list)
assert len(torch_rows) == 10
assert isinstance(torch_rows[0], torch.Tensor)
assert torch_rows[0].shape == (2,)
assert torch_rows[0].dtype == torch.int64
# "torch_col" stacks columns into a single 2D tensor.
torch_col = list(
permutation.with_format("torch_col").iter(10, skip_last_batch=False)
)[0]
assert isinstance(torch_col, torch.Tensor)
assert torch_col.shape == (2, 10)
assert torch_col.dtype == torch.int64
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
except ImportError:
# Skip check if torch is not installed
pass
-12
View File
@@ -188,18 +188,6 @@ def test_nested_struct_list():
assert schema == expect_schema
def test_bare_generic_raises_type_error():
# A bare, unparameterised List/Tuple has no element type to map to Arrow.
# It should raise a clear TypeError, not crash with AttributeError: __args__.
for bare in (List, Tuple):
class TestModel(pydantic.BaseModel):
items: bare
with pytest.raises(TypeError, match="unsupported type"):
pydantic_to_schema(TestModel)
def test_nested_struct_list_optional():
class SplitInfo(pydantic.BaseModel):
start_frame: int
-55
View File
@@ -502,61 +502,6 @@ def test_with_row_id(table: lancedb.table.Table):
assert rs["_rowid"].to_pylist() == [0, 1]
def test_where_repeated_combines_with_and(table: lancedb.table.Table):
# Calling where() more than once should AND the filters together instead of
# silently replacing the previous one (regression test for #2649).
builder = table.search().where("id >= 1").where("id < 2")
assert builder._where == "(id >= 1) AND (id < 2)"
ids = [row["id"] for row in builder.limit(10).to_list()]
assert ids == [1]
def test_where_repeated_combines_expr(table: lancedb.table.Table):
from lancedb.expr import col, lit
builder = table.search().where(col("id") >= lit(1)).where(col("id") < lit(2))
ids = [row["id"] for row in builder.limit(10).to_list()]
assert ids == [1]
def test_where_mixed_filter_kinds_combines(table: lancedb.table.Table):
# Mixing a SQL string filter with an expression filter lowers the
# expression to SQL and combines them as SQL strings.
from lancedb.expr import col, lit
builder = table.search().where("id >= 1").where(col("id") < lit(2))
ids = [row["id"] for row in builder.limit(10).to_list()]
assert ids == [1]
@pytest.mark.asyncio
async def test_where_repeated_combines_with_and_async(table_async: AsyncTable):
ids = [
row["id"]
for row in (
await table_async.query().where("id >= 1").where("id < 2").to_list()
)
]
assert ids == [1]
@pytest.mark.asyncio
async def test_where_mixed_filter_kinds_combines_async(table_async: AsyncTable):
from lancedb.expr import col, lit
ids = [
row["id"]
for row in (
await table_async.query()
.where("id >= 1")
.where(col("id") < lit(2))
.to_list()
)
]
assert ids == [1]
def test_distance_range(table: lancedb.table.Table):
q = [0, 0]
rs = table.search(q).to_arrow()
+33 -101
View File
@@ -154,116 +154,50 @@ async def test_async_checkout():
assert await table.count_rows() == 300
def _branch_open_handler(request):
if "/branches/list" in request.path:
body = json.dumps(
{
"branches": {
"exp": {
"parentBranch": None,
"parentVersion": 1,
"createAt": 1,
"manifestSize": 1,
}
}
}
).encode()
else:
# describe (table open + version/branch validation)
body = json.dumps({"version": 2, "schema": {"fields": []}}).encode()
request.send_response(200)
request.send_header("Content-Type", "application/json")
request.end_headers()
request.wfile.write(body)
def test_remote_open_table_branch_and_version():
with mock_lancedb_connection(_branch_open_handler) as db:
# version-only (and "main" + version) time-travels the main chain
assert db.open_table("test", version=2) is not None
assert db.open_table("test", branch="main", version=2).current_branch() is None
# a non-main branch opens a handle scoped to that branch, with or
# without a version
assert db.open_table("test", branch="exp").current_branch() == "exp"
assert db.open_table("test", branch="exp", version=2).current_branch() == "exp"
def test_remote_table_branches_sync():
# Branch CRUD + current_branch on the sync RemoteTable. The handle returned
# by create/checkout must stay a RemoteTable scoped to the branch.
from lancedb.remote.table import RemoteTable
def handler(request):
if "/branches/list" in request.path:
body = json.dumps(
{
"branches": {
"exp": {
"parentBranch": None,
"parentVersion": 1,
"createAt": 1,
"manifestSize": 1,
}
}
}
).encode()
elif "/branches/create" in request.path or "/branches/delete" in request.path:
body = b"{}"
else:
# describe (table open + checkout validation)
body = json.dumps({"version": 1, "schema": {"fields": []}}).encode()
# describe (table open + version validation) always succeeds
request.send_response(200)
request.send_header("Content-Type", "application/json")
request.end_headers()
request.wfile.write(body)
request.wfile.write(
json.dumps({"version": 2, "schema": {"fields": []}}).encode()
)
with mock_lancedb_connection(handler) as db:
table = db.open_table("test")
assert isinstance(table, RemoteTable)
assert table.current_branch() is None
# version-only (and "main" + version) is allowed: remote supports
# version time-travel even though it has no branches
assert db.open_table("test", version=2) is not None
assert db.open_table("test", branch="main", version=2) is not None
branch = table.branches.create("exp")
assert isinstance(branch, RemoteTable)
assert branch.current_branch() == "exp"
# list + checkout round trip; checkout also yields a branch-scoped handle
assert "exp" in table.branches.list()
checked = table.branches.checkout("exp")
assert isinstance(checked, RemoteTable)
assert checked.current_branch() == "exp"
table.branches.delete("exp")
# a non-main branch is rejected, with or without a version
with pytest.raises(NotImplementedError, match="branching"):
db.open_table("test", branch="exp")
with pytest.raises(NotImplementedError, match="branching"):
db.open_table("test", branch="exp", version=2)
@pytest.mark.asyncio
async def test_async_remote_open_table_branch_and_version():
async with mock_lancedb_connection_async(_branch_open_handler) as db:
# version-only (and "main" + version) time-travels the main chain
def handler(request):
request.send_response(200)
request.send_header("Content-Type", "application/json")
request.end_headers()
request.wfile.write(
json.dumps({"version": 2, "schema": {"fields": []}}).encode()
)
async with mock_lancedb_connection_async(handler) as db:
# version-only (and "main" + version) is allowed: "main" is the default
# branch, so it must not hit the unsupported remote branch path
assert await db.open_table("test", version=2) is not None
main_v2 = await db.open_table("test", branch="main", version=2)
assert main_v2.current_branch() is None
assert await db.open_table("test", branch="main", version=2) is not None
# a non-main branch opens a handle scoped to that branch
exp = await db.open_table("test", branch="exp")
assert exp.current_branch() == "exp"
exp_v2 = await db.open_table("test", branch="exp", version=2)
assert exp_v2.current_branch() == "exp"
def test_remote_table_branch_survives_pickle():
# Regression: a branch-scoped handle must keep its branch across a
# pickle/fork round-trip (it used to reopen on main).
with mock_lancedb_connection(_branch_open_handler) as db:
branch = db.open_table("test", branch="exp")
assert branch.current_branch() == "exp"
restored = pickle.loads(pickle.dumps(branch))
assert restored.current_branch() == "exp"
# the pinned version is carried through as well
branch_v2 = db.open_table("test", branch="exp", version=2)
restored_v2 = pickle.loads(pickle.dumps(branch_v2))
assert restored_v2.current_branch() == "exp"
# a non-main branch is rejected, with or without a version
with pytest.raises(NotImplementedError, match="branching"):
await db.open_table("test", branch="exp")
with pytest.raises(NotImplementedError, match="branching"):
await db.open_table("test", branch="exp", version=2)
def test_table_len_sync():
@@ -412,12 +346,10 @@ def test_remote_permutation_is_picklable():
content_len = int(request.headers.get("Content-Length"))
body = json.loads(request.rfile.read(content_len))
if "filter" in body:
match = re.search(
r"_rowoffset\s+in\s+\((.*?)\)", body["filter"], re.IGNORECASE
)
offsets = [int(o.strip()) for o in match.group(1).split(",")]
match = re.search(r"_rowoffset in \((.*?)\)", body["filter"])
offsets = [int(offset.strip()) for offset in match.group(1).split(",")]
else:
offsets = list(range(len(rows)))
offsets = rows
table = pa.table({"a": [rows[offset] for offset in offsets]})
request.send_response(200)
-32
View File
@@ -350,38 +350,6 @@ def test_mrr_reranker_empty_input():
reranker.rerank_multivector([])
def test_mrr_multivector_rewards_consensus():
# Reciprocal ranks must be averaged across *all* ranking systems, treating a
# missing system as 0. A document ranked first by every system must outrank a
# document ranked first by only one of them.
reranker = MRRReranker()
def ranking(row_ids):
return pa.table({"_rowid": pa.array(row_ids, type=pa.int64())})
# Doc 1 is rank 1 in only the first system; doc 2 is rank 1 in two systems
# and rank 2 in the third (strong cross-system consensus).
rs1 = ranking([1, 2, 3])
rs2 = ranking([2, 3, 4])
rs3 = ranking([2, 5, 6])
result = reranker.rerank_multivector([rs1, rs2, rs3])
scores = {
row_id: score
for row_id, score in zip(
result["_rowid"].to_pylist(),
result["_relevance_score"].to_pylist(),
)
}
# sum of reciprocal ranks / number of systems
assert scores[1] == pytest.approx(1.0 / 3)
assert scores[2] == pytest.approx((0.5 + 1.0 + 1.0) / 3)
assert scores[2] > scores[1]
# The consensus document ranks first overall.
assert result["_rowid"].to_pylist()[0] == 2
def test_rrf_reranker_distance():
data = pa.table(
{

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