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ChilePiquinandClaude Sonnet 5
a53b6df689
The previous commit's shutdown() freed the shared Tokio runtime as soon as it looked unused (an AtomicPtr swap + Box::from_raw), but callers only ever held a bare reference into it, with no way to tell shutdown they were still using it. A concurrent caller could dereference memory shutdown had already freed. Switching to an ArcSwapOption<Runtime> so every caller holds an owned Arc clone for as long as it's using the runtime fixes the use-after-free, but isn't sufficient on its own, for two reasons: - future_into_py spawns a task that, once running, spawns a second one to do the real work and awaits its JoinHandle. If both nested calls independently re-resolved "the current runtime" via get_runtime(), a reclaim landing between them could bind the outer and inner task to two different runtime instances, permanently stalling the outer task if its own runtime was the one torn down while it awaited the inner one. - Runtime::shutdown_timeout gives spawned (non-blocking) tasks no grace period at all: a task keeps running until it next yields, then is dropped. An outermost caller that only held its Arc for the instant it submitted a task let the runtime's reference count fall back to baseline immediately, long before the task itself finished, so shutdown could reclaim the runtime while a top-level task was still genuinely in flight, abandoning it silently. spawn/spawn_blocking now use Handle::try_current() to pin any nested call to whatever runtime is already executing it, and the outermost call moves its Arc clone into the spawned task itself so the reference count stays elevated for the task's whole lifetime, not just its submission. Verified with two new targeted unit tests plus the existing concurrent stress test, and empirically against a concurrent reproducer built from this PR's own review feedback: 100+ clean runs with no hangs or crashes, versus a consistent ~35-55% failure rate beforehand. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.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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⭐ 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.
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