1. 使用run_in_threadpool或asyncio.to_thread 将同步代码放到线程池中执行,避免阻塞事件循环。 fromfastapiimportFastAPIfromfastapi.concurrencyimportrun_in_threadpoolimporttime app=FastAPI()defsync_function(): time.sleep(5)#模拟一个耗时的同步操作return"Done"@app.get("/") asyncdefroot(): result= awai...
然后 starlette 将在单独的线程中运行它。 使用fastapi.concurrency.run_in_threadpool,这也将在单独的线程中运行它。像这样: from fastapi.concurrency import run_in_threadpool async def task(data): otherdata = await db.fetch("some sql") newdata = await run_in_threadpool(lambda: somelongcomputation(...
@app.middleware("http")asyncdefsync_middleware(request:Request,call_next):request_json=awaitrequest.json()_data={"ip":request.client.host,"X-Sign":request.headers.get("X-Sign"),"body":request_json,}# 同步代码,做鉴权result=awaitrun_in_threadpool(sync_code,_data)ifresult!=200:returnRespons...
call(**values) else: return await run_in_threadpool(dependant.call, **values) starlette/concurrency.py async def run_in_threadpool( func: typing.Callable[P, T], *args: P.args, **kwargs: P.kwargs ) -> T: if kwargs: # pragma: no cover # run_sync doesn't accept 'kwargs', ...
举个简单的例子,我们可以使用我们著名的来自 starlette 的 run_in_threadpool。 from fastapi import FastAPI from fastapi.concurrency import run_in_threadpool from my_sync_library import SyncAPIClient app = FastAPI() @app.get("/") async def call_my_sync_library(): my_data = await service.get_...
使用fastapi.concurrency.run_in_threadpool,这也将在单独的线程中运行它。像这样: from fastapi.concurrency import run_in_threadpool async def task(data): otherdata = await db.fetch("some sql") newdata = await run_in_threadpool(lambda: somelongcomputation(data, otherdata)) ...
使用fastapi.concurrency.run_in_threadpool ,这也将在单独的线程中运行它。像这样: from fastapi.concurrency import run_in_threadpool async def task(data): otherdata = await db.fetch("some sql") newdata = await run_in_threadpool(lambda: somelongcomputation(data, otherdata)) await db.execute("...
使用fastapi.concurrency.run_in_threadpool,这也将在单独的线程中运行它。像这样: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from fastapi.concurrencyimportrun_in_threadpoolasyncdeftask(data):otherdata=awaitdb.fetch("some sql")newdata=awaitrun_in_threadpool(lambda:somelongcomputation(data,otherda...
对于计算密集型任务,可以使用 concurrent.futures.ProcessPoolExecutor 或ThreadPoolExecutor 来创建进程池或线程池,以便在多个核心上并行处理任务。 通过asyncio.get_event_loop().run_in_executor() 方法,可以在事件循环中提交任务到进程池或线程池执行。 性能优化: 为了进一步提高并发性能,可以使用缓存技术(如 Redis、...
await run_in_threadpool(func) repetitions += 1 except Exception as exc: logger.error(f'执行重复任务异常: {exc}') if raise_exceptions: raise exc await asyncio.sleep(seconds) ensure_future(loop()) return wrapped return decorator 我们基于异步功能来实现一个装饰器repeat_task。