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Wyatt Alt
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A declared view holds no rows; refresh computes them. It pins one source version and brings the view to exactly the definition's result at that version, recording the version as a watermark in the view's schema metadata. The refresh is incremental when the source changed by nothing but appends and compactions since the watermark: a transaction-log walk separates the two, so only genuinely appended fragments are computed -- compaction outputs are already-materialized rows rearranged and cost nothing, which is what keeps routine background compaction from rebuilding the view. One subtlety shapes the walk: transaction files record an Append's fragments with placeholder ids (real ids are assigned at commit), while a Rewrite's ids are reserved beforehand and real, so appends are derived as new-at-head minus rewrite outputs rather than read from the log. A fragment-signature check scoped to the columns the view reads is the fallback for deltas the walk cannot classify, and passes changes to unrelated columns. Anything else rebuilds: deletes, updates, a vacuumed watermark version, or an append a later compaction swallowed (its rows cannot be told apart from already-materialized ones without a provenance scan). A rebuild of an indexed view stages the new fragments uncommitted and commits one Update swapping out every old fragment, so index definitions are never absent and readers never observe an empty view; unindexed rebuilds overwrite, with the watermark riding the same commit. Refresh also accepts a pinned source version, and a row limit counts already-held rows. Two bounds, stated in the module docs: on the append path the watermark moves in a follow-up commit, so a crash between the two re-appends those rows on the next refresh; and concurrent refreshes of one view can double-append, since lance appends do not conflict -- run one at a time.
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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⭐ Click here ⭐ to see how fast we're growing!
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.
Contributors
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