lancedb-gatefixer[bot]
369b10a377
## Summary - add regression coverage for repeated table opens through one database connection - assert that each open reuses the connection object-store client without another registry miss - exercise the table after every open so the test covers the complete dataset-loading path ## Root cause At the commit reported in #1600, opening a table constructed a separate object-store client rather than reusing the client that had already connected to the database. On S3 this repeated credential discovery, which could fail intermittently in AWS Lambda and surface as TableNotFound. The connection-owned Session reuse added later fixed the runtime path, but no focused test protected the open-table invariant. ## Fix Add a regression test backed by ObjectStoreRegistry statistics. Three successive opens must add cache hits while leaving the miss count unchanged, proving that open_table uses the connection Session and its authenticated object-store client. ## Validation - cargo fmt --all - cargo test --quiet --features remote -p lancedb database::listing::tests::test_open_table_reuses_connection_object_store - cargo check --quiet --features remote --tests --examples - cargo clippy --quiet --features remote --tests --examples - cargo test --quiet --features remote --tests Fixes #1600 <!-- lance-gatekeeper-fix:v1 agent=974491978c3e42840f32dbc35492d856 generation=1 --> Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
The Multimodal AI Lakehouse
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
Demo: Multimodal Search by Keyword, Vector or with SQL
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Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
- Comprehensive Search: Support for vector similarity search, full-text search and SQL.
- Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
- Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/rest |
Join Us and Contribute
We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.
Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
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