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Jack Ye
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A permutation stores `_rowid`s, which are row addresses unless stable row ids are enabled. Nothing in the data loader pinned a table version, so a compaction between building a permutation and reading it can resolve those ids to different rows. The exposure differs by backend but exists on both: - Remote never pins. `prepare_query_bodies` stamps `"version": current_version()` on every request, but `current_version()` is `None` unless `checkout` was called, so every request means "latest". - Native pins implicitly by holding an `Arc<Dataset>` under `ConsistencyMode::Lazy`, but `StreamingDataset.__setstate__` reopens the table in each DataLoader worker, so each worker pins to whatever is latest at fork time. ## Changes `Table::at_version` returns an independent handle pinned to a version without mutating the receiver. `checkout` cannot serve this: on remote the version cell is an `Arc<RwLock<Option<u64>>>` shared across clones, so pinning through it would silently pin the caller's table too. `PermutationBuilder::build` pins for the whole build and records the version in the permutation table's schema metadata, alongside the existing split names. `PermutationReader` pins the base table to that version before any take. Because the reader pins on construction, the Python worker fork is covered without touching the pickle format — `Permutation.__setstate__` drops the reader and `_ensure_open` rebuilds it, which re-pins. ## Behaviour change A permutation is now bound to the version it was built against, so rows appended to the base table afterwards are not visible through an existing permutation. That is the intended semantics — the permutation only addresses rows that existed when it was built — but it is a change worth flagging. Permutations written before this carry no version key and read exactly as they did before.
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 |
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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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