lancedb-gatefixer[bot]
2c06a48bd8
## Summary - add deterministic regression coverage that `Table.add()` releases backing Arrow buffers without cyclic garbage collection - track the foreign buffer owner rather than RSS, separating live input retention from allocator high-water behavior - preserve the bounded-lifetime behavior of the Scannable writer that superseded the historical preprocessing path ## Root cause The historical Python preprocessing/write path produced a high allocator RSS while ingesting very wide IPC batches. The current Scannable writer releases each input buffer when `Table.add()` completes; remaining RSS is allocator high-water rather than a live Arrow reference. The resolved behavior had no regression coverage, so a future native lifetime regression could silently reintroduce the original failure mode. ## Validation - `uv run --extra tests --extra dev maturin develop` - `uv run --project python --extra tests pytest python/python/tests/test_table.py::test_add python/python/tests/test_table.py::test_add_releases_arrow_buffers_without_gc -q` - `uv run --project python --extra dev ruff format --check python/python/tests/test_table.py` - `uv run --project python --extra dev ruff check .` Fixes #2512 <!-- lance-gatekeeper-fix:v1 agent=29226408a8d07da592daf341d5384e37 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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