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## 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.
LanceDB Python SDK
A Python library for LanceDB.
Installation
pip install lancedb
Preview Releases
Stable releases are created about every 2 weeks. For the latest features and bug fixes, you can install the preview release. These releases receive the same level of testing as stable releases, but are not guaranteed to be available for more than 6 months after they are released. Once your application is stable, we recommend switching to stable releases.
pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
Usage
Basic Example
import lancedb
db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>')
table = db.open_table('my_table')
results = table.search([0.1, 0.3]).limit(20).to_list()
print(results)
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.