⚡ Bolt: Offload synchronous SQLite calls in FastAPI#70
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Wrapped synchronous `persistence` module calls (`create_transfer_record`, `set_agent_b`, `list_transfers`, and `get_transfer`) in `backend/main.py` with `asyncio.to_thread`. This prevents blocking the FastAPI event loop, improving overall application concurrency and responsiveness. Co-authored-by: Deepaksingh7238 <110552872+Deepaksingh7238@users.noreply.github.com>
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Pull request overview
This PR improves FastAPI endpoint concurrency by offloading the backend’s synchronous SQLite persistence operations onto a threadpool so they don’t block the event loop.
Changes:
- Wrapped
persistence.create_transfer_record(...)calls ininitiate_transferwithawait asyncio.to_thread(...). - Wrapped
persistence.set_agent_b(...),persistence.list_transfers(...), andpersistence.get_transfer(...)withawait asyncio.to_thread(...). - Added a short Bolt/Jules note documenting the rationale for offloading synchronous DB work in async endpoints.
Reviewed changes
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| File | Description |
|---|---|
| backend/main.py | Offloads synchronous SQLite persistence calls via asyncio.to_thread to prevent event-loop blocking in async endpoints. |
| .jules/bolt.md | Documents the “offload sync DB calls from async endpoints” guidance for this repo. |
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💡 What:
Wrapped all synchronous SQLite database operations (
create_transfer_record,set_agent_b,list_transfers, andget_transfer) insidebackend/main.pyusingasyncio.to_thread.🎯 Why:
FastAPI's asynchronous endpoints operate on a single thread event loop. Any blocking I/O calls (like executing SQLite disk queries, especially those that enforce thread locks under high volume) will block the loop, delaying all other pending concurrent network requests (e.g. standard endpoints like health checks or other active client connections). Offloading them directly improves the backend's capability to safely multiplex.
📊 Impact:
Significantly improves concurrent request handling capabilities and responsiveness. While exact metrics depend on disk I/O constraints under load, this prevents zero-concurrency bottlenecks when the database executes complex queries or awaits disk writes.
🔬 Measurement:
Verification can be handled by concurrently hitting endpoints (e.g., using
pytestasync concurrent mocks or standard benchmark tools likewrkorabagainst the/transfersendpoints) and verifying that parallel non-database endpoints (like/health) remain responsive.PR created automatically by Jules for task 17486751300406044669 started by @Deepaksingh7238