Bilal AtiqueandBilal Atique
dc771eb94f
Closes #3059. `io::object_store::test::test_e2e` has been `#[ignore]`d since Lance started writing local tables through `LocalWriter`. For a `file://` store, `ObjectStore::create()` returns a `LocalWriter` that writes straight to disk, so `MirroringObjectStoreWrapper` never sees the writes and the secondary stays empty. Lance's `file-object-store://` scheme uses the same local provider, but `create()` only takes the `LocalWriter` path for `file`, so every other scheme writes through the wrapped store. This connects the test to `file-object-store://<dir1>` and drops the `#[ignore]`. The table files still land in `dir1`, so the existing file-list comparison covers the mirroring wrapper on real files again. The two relative file lists are sorted before comparison so the assertion detects missing and extra files independently of `WalkDir` traversal order. The row and vector-query assertions remain unchanged. Validation: - Rust object-store module at `b11f7a9684d94100f40818d1249479c726b2b006`: 3 passed, with the focused `test_e2e` included. - Hosted Linux test merge: `test_e2e` passed and the library reported 2046 passed. - Complete applicable CI at the final head: 8 workflows and 39 successful checks passed; 2 conditional jobs were skipped. Co-authored-by: Bilal Atique <bilalbinatique@gmail.com>
The Multimodal Lakehouse for AI
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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.
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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.
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- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
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Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
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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 |
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