In FastAPI, what’s the difference between run_in_executor
and run_in_threadpool
? As far as I can tell, both are used to execute a task in another thread. When should I use one over the other?
Both run_in_executor
and run_in_threadpool
are used to run synchronous code (also called blocking code) in an asynchronous application without blocking the event loop. run_in_executor
is a relatively low-level operation, originating in Python’s asyncio
library, whereas run_in_threadpool
comes from Starlette, the ASGI framework underlying FastAPI.
As a result, run_in_threadpool
is simpler to use at the cost of flexibility, and run_in_executor
is more flexible at the cost of increased complexity. Consider the following two code examples:
# Using run_in_executor import asyncio from concurrent.futures import ThreadPoolExecutor def sync_function(argument1, argument2): # logic for the sync function here pass async def main(): loop = asyncio.get_running_loop() with ThreadPoolExecutor() as pool: result = await loop.run_in_executor(pool, sync_function, 'argument1', 'argument2')
# Using run_in_threadpool from fastapi import FastAPI from starlette.concurrency import run_in_threadpool app = FastAPI() def sync_function(argument1, argument2): # logic for the sync function here pass async def example_route(): result = await run_in_threadpool(sync_function, 'argument1', 'argument2')
As we can see, the run_in_threadpool
code is simpler – unlike the run_in_executor
code, it does not require us to specify an executor, as it will always use the default executor. Most of the time, this is what we want – run_in_threadpool
is integrated with FastAPI’s design and is the recommended approach for most use cases.
However, if we have a complicated application that needs to integrate a variety of disparate synchronous systems, we may want the additional control that run_in_executor
provides. For example, some applications may benefit from using ProcessPoolExecutor
instead of ThreadPoolExecutor
.
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