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Weston Pace
a17c241e86
## Summary PyTorch's `DataLoader` uses fork-based multiprocessing by default on Linux, but threads do not survive `fork()`. LanceDB's Python bindings drive async work through two threaded layers, both of which become inert in a forked child: - `BackgroundEventLoop` runs an asyncio loop on a Python `threading.Thread`. - `pyo3-async-runtimes::tokio` holds a global multi-threaded tokio runtime whose worker threads also die on fork — and its runtime lives in a `OnceLock` that cannot be replaced after first use. As a result, any `Permutation` (or other async API) used inside a fork-based `DataLoader` worker hangs indefinitely. This PR makes both layers fork-safe so `Permutation` works as a `torch.utils.data.Dataset` with `num_workers > 0`. ## Approach ### Rust — new `python/src/runtime.rs` Mirrors the pattern used in [Lance's Python bindings](https://github.com/lance-format/lance/blob/456198cd6f42be07f99617a6d7e39d6209cdf3cc/python/src/lib.rs#L139), adapted for the async-bridge use case. - `LanceRuntime` implements `pyo3_async_runtimes::generic::Runtime + ContextExt`, backed by an `AtomicPtr<tokio::runtime::Runtime>` we own (sidestepping `pyo3-async-runtimes`'s frozen `OnceLock` global). - A `pthread_atfork(after_in_child)` handler nulls the pointer; the next `spawn` rebuilds the runtime in the child. The previous runtime is intentionally **leaked** — calling `Drop` would try to join now-dead worker threads and hang. - `runtime::future_into_py` is a drop-in for `pyo3_async_runtimes::tokio::future_into_py`. All ~80 call sites in `arrow.rs` / `connection.rs` / `permutation.rs` / `query.rs` / `table.rs` are updated to route through it. - `python/Cargo.toml` adds `libc = "0.2"` and the tokio `rt-multi-thread` feature. ### Python — `lancedb/background_loop.py` - Refactors `BackgroundEventLoop.__init__` to a reusable `_start()` method. - An `os.register_at_fork(after_in_child=…)` hook calls `LOOP._start()` to give the singleton a fresh asyncio loop and thread **in place**. This matters because the rest of the codebase imports `LOOP` via `from .background_loop import LOOP` — rebinding the module attribute would leave those references holding the dead loop. ### Python — `lancedb/__init__.py` Removes the `__warn_on_fork` pre-fork warning (and the now-unused `import warnings`). Fork is supported. ## Test plan - [x] New `test_permutation_dataloader_fork_workers` in `python/tests/test_torch.py`: runs a `Permutation` through `torch.utils.data.DataLoader(num_workers=2, multiprocessing_context="fork")` inside a spawn-isolated child with a 30s hang detector. **Pre-fix**: timed out at 36s. **Post-fix**: passes in ~3.6s. - [x] New `test_remote_connection_after_fork` in `python/tests/test_remote_db.py`: forks a child that creates a fresh `lancedb.connect(...)` against a mock HTTP server and calls `table_names()`; passes in <1s, validates the runtime reset is sufficient for fresh remote clients. - [x] All 62 tests in `test_torch.py` + `test_permutation.py` pass. - [x] All 35 tests in `test_remote_db.py` pass. - [x] `test_table.py` (87) + `test_db.py` + `test_query.py` (157, minus one unrelated `sentence_transformers` import skip) — 244 passing. - [x] `cargo clippy -p lancedb-python --tests` clean. - [x] `cargo fmt`, `ruff check`, `ruff format` all clean. ## Known limitation (follow-up) This PR makes a **freshly-built** `lancedb.connect(...)` work in a forked child. An **inherited** `Connection` from the parent still carries an inherited `reqwest::Client` whose hyper connection pool references socket FDs and TCP/TLS state shared with the parent — using it from the child after fork is unsafe (especially with HTTP/1.1 keep-alive). The recommended pattern for fork-based `DataLoader` workers that hit a remote DB is to construct a new connection inside the worker. Auto-clearing inherited HTTP client pools on fork would require tracking live `Connection` instances in `lancedb` core and is left for a follow-up PR. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.7 (1M context) <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 |
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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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