mirror of
https://github.com/lancedb/lancedb.git
synced 2026-07-08 05:20:41 +00:00
3868965413
## Summary `AsyncTable.search()` computes the query embedding with `loop.run_in_executor(None, ...)`, which uses asyncio's **default** `ThreadPoolExecutor`. That pool is shared with all other `run_in_executor(None, ...)` work, so a slow embedding call — a heavy local model or an HTTP request to an embeddings API — ties up those threads and starves unrelated async I/O under concurrent load. This moves the (potentially blocking) embedding call onto a **dedicated executor**, isolating it from the default pool. Closes #3310. ## Problem `python/lancedb/table.py`, `AsyncTable.search()`: ```python return ( await loop.run_in_executor( None, # asyncio's default executor, shared with other blocking I/O embedding.function.compute_query_embeddings_with_retry, query, ) )[0] ``` Under load, concurrent searches whose embeddings block (or any other code using the default executor) contend for the same small thread pool. ## Change - Add a dedicated `ThreadPoolExecutor(thread_name_prefix="lancedb-embedding")` in `background_loop.py`, exposed via `embedding_executor()`. - Use it in `AsyncTable.search()`'s `make_embedding` instead of the default executor. - Reset the executor in the existing `_reset_after_fork` hook — its worker threads don't survive `fork()`, same as the background event loop. It's recreated lazily, so this is cheap. ## Design notes The issue asked whether maintainers preferred a configurable executor, a dedicated internal one, or another approach (no response in the thread). I went with a **dedicated internal executor**: it fixes the starvation with no public API change and stays consistent with the existing `LOOP` singleton. Making the pool size configurable would be an easy follow-up if preferred. Scope is limited to `search()`. The broader "embedding functions need real async support" (including `add()`) is tracked separately in #3268. ## Testing - Added `test_async_search_runs_embedding_on_dedicated_executor`: patches the embedding function to record the executing thread during an async search and asserts it runs on a `lancedb-embedding` thread. Verified it **fails** against the previous `run_in_executor(None, ...)` and passes with the fix. - `ruff format`, `ruff check`, and `pyright` pass on the changed files.
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
Star LanceDB to get updates!
⭐ 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.
Contributors
Stay in Touch With Us