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# Thread Pool
Starlette uses a thread pool in several scenarios to avoid blocking the event loop:
- When you create a synchronous endpoint using `def` instead of `async def`
- When serving files with [`FileResponse`](responses.md#fileresponse)
- When handling file uploads with [`UploadFile`](requests.md#request-files)
- When running synchronous background tasks with [`BackgroundTask`](background.md)
- And some other scenarios that may not be documented...
Starlette will run your code in a thread pool to avoid blocking the event loop.
This applies for endpoint functions and background tasks you create, but also for internal Starlette code.
To be more precise, Starlette uses `anyio.to_thread.run_sync` to run the synchronous code.
## Concurrency Limitations
The default thread pool size is only 40 _tokens_. This means that only 40 threads can run at the same time.
This limit is shared with other libraries: for example FastAPI also uses `anyio` to run sync dependencies, which also uses up thread capacity.
If you need to run more threads, you can increase the number of _tokens_:
```py
import anyio.to_thread
limiter = anyio.to_thread.current_default_thread_limiter()
limiter.total_tokens = 100
```
The above code will increase the number of _tokens_ to 100.
Increasing the number of threads may have a performance and memory impact, so be careful when doing so.
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