How to implement distributed processing and scheduling of requests in FastAPI

How to implement distributed processing and scheduling of requests in FastAPI

Introduction: With the rapid development of the Internet, distributed systems have been widely used in all walks of life, and for high-concurrency request processing and scheduling, distributed systems play an important role. FastAPI is a modern, fast (high-performance) web framework developed based on Python, providing us with a powerful tool for building high-performance APIs. This article will introduce how to implement distributed processing and scheduling of requests in FastAPI to improve system performance and reliability.

1. Introduction to distributed systems

A distributed system is a system composed of a group of independent computer nodes connected through a network, and these nodes work together to complete a task. The key characteristics of a distributed system are: nodes are independent of each other, and each node coordinates its work through message passing and shared storage.

The benefit of a distributed system is that it can effectively utilize the resources of multiple computers and provide higher performance and reliability. At the same time, distributed systems also bring some challenges, such as distributed transactions, inter-node communication and concurrency control. These challenges need to be considered when implementing distributed processing and scheduling.

2. Introduction to FastAPI

FastAPI is a web framework based on Starlette and Pydantic. It provides many powerful functions and tools, allowing us to quickly develop high-performance APIs. FastAPI supports asynchronous and concurrent processing, and its performance is better than other frameworks.

3. Implementation of distributed processing and scheduling

To implement distributed processing and scheduling of requests in FastAPI, you first need to configure a distributed task queue and start multiple worker nodes for processing Task.

Step 1: Install task queue

In FastAPI, we can use Redis as the task queue. First, we need to install Redis. Install Redis through the following command:

$ pip install redis

Step 2: Create a task queue

Create a task_queue.py module in the project and add the following code:

import redis

# 创建Redis连接
redis_conn = redis.Redis(host='localhost', port=6379)

def enqueue_task(task_name, data):
    # 将任务数据序列化为JSON格式
    data_json = json.dumps(data)
    # 将任务推入队列
    redis_conn.rpush(task_name, data_json)

Step 3: Create a worker node

Create a worker.py module in the project and add the following code:

import redis

# 创建Redis连接
redis_conn = redis.Redis(host='localhost', port=6379)

def process_task(task_name, callback):
    while True:
        # 从队列中获取任务
        task = redis_conn.blpop(task_name)
        task_data = json.loads(task[1])
        # 调用回调函数处理任务
        callback(task_data)

Step 4: Use it in FastAPI Distributed processing

In FastAPI, we can use the background_tasks module to implement background tasks. In the routing processing function, push the task into the queue and call the worker node to process the task through the background_tasks module.

The following is an example:

from fastapi import BackgroundTasks

@app.post("/process_task")
async def process_task(data: dict, background_tasks: BackgroundTasks):
    # 将任务推入队列
    enqueue_task('task_queue', data)
    # 调用worker节点处理任务
    background_tasks.add_task(process_task, 'task_queue', callback)
    return {"message": "任务已开始处理，请稍后查询结果"}

Step 5: Get task processing results

In FastAPI, we can use the Task model to process tasks status and results.

First, create a models.py file in the project and add the following code:

from pydantic import BaseModel

class Task(BaseModel):
    id: int
    status: str
    result: str

Then, in the routing processing function, create a task instance and Returns the status and results of this instance.

The following is an example:

@app.get("/task/{task_id}")
async def get_task(task_id: int):
    # 查询任务状态和结果
    status = get_task_status(task_id)
    result = get_task_result(task_id)
    # 创建任务实例
    task = Task(id=task_id, status=status, result=result)
    return task

Conclusion

This article introduces the method of implementing distributed processing and scheduling of requests in FastAPI and provides corresponding code examples. By using distributed systems and task queues, we can achieve high-performance, reliable request processing and scheduling in FastAPI. I hope these contents will be helpful to your distributed implementation of FastAPI.

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