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Bruno RamirezandClaude Sonnet 5
3fca33fcb5
Short-lived Python processes using this client can occasionally crash with SIGABRT during interpreter shutdown, even after every operation they ran completed successfully. The cause is the shared Tokio runtime backing every async call: it's never told to shut down at normal process exit, only reset (and deliberately leaked) on `fork()`. Its worker threads keep running, uncoordinated with the interpreter, until the process actually ends, and if one is mid-task exactly as `Py_Finalize` starts tearing down interpreter state, it can panic on state that's already gone. That panic happens on a background thread with no PyO3-wrapped call frame to catch it, so Rust aborts the whole process instead of just failing that one call. This PR gives the runtime a coordinated, bounded shutdown by registering a Python `atexit` callback that runs while the interpreter is still fully valid. Getting the exit lifecycle right took a few rounds of review. Earlier versions freed the runtime as soon as `Arc::strong_count` looked low, but that's the wrong signal — it reflects who currently holds a reference, not who's logically still in flight. That mistake showed up three ways: a caller could dereference memory already freed out from under it; an install already in progress could finish invisibly after `shutdown()` had already decided there was nothing to do; and a spawned task could end up as the final owner of the `Runtime`, so completing it dropped the runtime from inside one of its own worker threads, which Tokio itself forbids and panics on (this reproduced unprompted in this branch's own test suite). Fixing all three meant replacing reference-count-based tracking with an explicit counter of in-flight top-level calls that `shutdown()` waits on directly. This was accomplished with the following changes: - The runtime lives in an `ArcSwapOption<Tagged>`, where `Tagged` pairs the `Runtime` with the fork generation it was built in. - An `OUTSTANDING` counter, incremented before a top-level `spawn`/`spawn_blocking`/`block_on` call does anything else and decremented only once it has truly finished (via an `OutstandingGuard` token that carries no reference to the runtime), is what `shutdown()` waits on — not `Arc::strong_count` or whether the slot looks empty. This closes the install-race and makes it impossible for a task's own completion to be the runtime's final drop. - Once `shutdown()`'s bound elapses, it stops waiting and attempts retirement anyway, rather than returning with the runtime and its workers left fully alive. - `spawn`/`spawn_blocking` use `Handle::try_current()` to pin any nested spawn (`future_into_py` spawns a task that itself spawns a second one for the real work) to whichever runtime is already executing it, so a reclaim landing between the two calls can't split one logical operation across two different runtime instances. - The fork-child handler now only bumps a bare `GENERATION` counter — no `ArcSwapOption` call of any kind from that context, since `swap`/`compare_and_swap` do real reader-reconciliation work (thread-local state, potentially an allocation) that isn't safe in a forked child. `get_runtime()` compares its installed runtime's generation against the live counter from ordinary context and rebuilds on a mismatch. - Registered `shutdown_runtime` as a Python `atexit` callback in the `_lancedb` module init, running with the GIL released (`Python::detach`) since the bounded wait could otherwise deadlock against any in-flight task that itself needs the GIL. ### Testing - Unit tests in `runtime.rs` cover: shutdown with no runtime created, shutdown after use and lazy rebuild afterward, calling shutdown twice in a row, a concurrent stress test racing many threads against shutdown, a nested-spawn test reproducing `future_into_py`'s own spawn-within-a-spawn shape under concurrent shutdown, a test confirming a top-level task in flight survives a concurrent shutdown reclaim, and a test forcing the install-vs-shutdown race directly. - Built the wheel and ran a concurrent reproducer (many threads hammering the client while `atexit` fires) over 100 times with no hangs or crashes, plus a 30-second-join variant and repeated runs of a short-lived process confirming clean exits with no added latency. --------- 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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