How to implement parallel processing and asynchronous calls of requests in FastAPI

How to implement parallel processing and asynchronous calls of requests in FastAPI

FastAPI is a high-performance Python web framework that supports parallel processing and asynchronous calls, which can help us process requests more efficiently. This article will introduce how to implement parallel processing and asynchronous calls of requests in FastAPI, and provide relevant code examples.

1. Parallel processing of requests

To implement parallel processing of requests in FastAPI, we can use Python's concurrent.futures module to achieve it. First, introduce the module into the project:

from concurrent.futures import ThreadPoolExecutor

Then, in the request processing function that needs to be processed in parallel, create a thread pool and use the executor.submit() method to transfer the task Submit to the thread pool. An example is as follows:

@app.get("/process")
async def process_request():
    with ThreadPoolExecutor() as executor:
        result1 = executor.submit(process_task1)
        result2 = executor.submit(process_task2)

    # 等待任务完成
    result1 = result1.result()
    result2 = result2.result()

    # 返回任务结果
    return {"result1": result1, "result2": result2}

In the above code, process_task1 and process_task2 are the task functions we need to process in parallel. The executor.submit() method submits the task to the thread pool and returns a Future object. The result() method can be used to obtain the execution result of the task.

2. Asynchronous call

To implement asynchronous call in FastAPI, we can use Python's asyncio module to achieve it. First, introduce this module into the project:

import asyncio

Then, in the request processing function that needs to be called asynchronously, encapsulate the task that needs to be executed asynchronously into a coroutine function, and use asyncio.create_task( ) method adds the task to the event loop. An example is as follows:

@app.get("/process")
async def process_request():
    loop = asyncio.get_event_loop()

    task1 = loop.create_task(process_task1())
    task2 = loop.create_task(process_task2())

    await asyncio.wait([task1, task2])

    # 返回任务结果
    return {"result1": task1.result(), "result2": task2.result()}

In the above code, process_task1 and process_task2 are coroutine functions that we need to call asynchronously. create_task()The method wraps the coroutine function into a task and adds it to the event loop. Use the await asyncio.wait() method to wait for all tasks to complete.

It should be noted that in order for FastAPI to support asynchronous calls, we need to use UVicorn as the web server. The example command is as follows:

uvicorn main:app --host 0.0.0.0 --port 8000 --workers 4 --proxy-headers

Among them, main is the entry file The name, app is the FastAPI application object, and the --workers parameter specifies the number of worker processes on the server.

Through the above steps, we can implement parallel processing and asynchronous calls of requests in FastAPI, improving request processing performance and concurrency capabilities. When there are a large number of requests to be processed, parallel processing and asynchronous calls can improve the response speed and throughput of the system, allowing us to handle requests under high concurrency situations more effectively.

To sum up, this article introduces how to implement parallel processing and asynchronous calling of requests in FastAPI, and provides corresponding code examples. By applying these techniques, we can better utilize the performance advantages of FastAPI and improve the performance and concurrent processing capabilities of web applications.

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