规模/架构挑战

规模/架构挑战通常涉及处理大规模数据、高并发、高可用性等方面的问题。以下是一些解决这些问题的代码示例：

1. 多线程处理大规模数据：

import threading

def process_data(data):
    # 处理数据的逻辑

def handle_large_data(data):
    num_threads = 4  # 定义线程数
    chunk_size = len(data) // num_threads  # 将数据分成多个块

    # 创建线程
    threads = []
    for i in range(num_threads):
        start = i * chunk_size
        end = start + chunk_size if i < num_threads - 1 else len(data)
        thread = threading.Thread(target=process_data, args=(data[start:end],))
        thread.start()
        threads.append(thread)

    # 等待所有线程完成
    for thread in threads:
        thread.join()

# 使用示例
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
handle_large_data(data)

2. 使用负载均衡处理高并发请求：

from flask import Flask, request
import random

app = Flask(__name__)

@app.route('/process_request', methods=['POST'])
def process_request():
    # 处理请求的逻辑
    return 'Request processed successfully'

if __name__ == '__main__':
    app.run()

# 使用示例
import requests
import threading

def send_requests():
    url = 'http://localhost:5000/process_request'
    data = {'param': 'value'}
    response = requests.post(url, json=data)

num_requests = 100  # 定义请求数
threads = []
for i in range(num_requests):
    thread = threading.Thread(target=send_requests)
    thread.start()
    threads.append(thread)

# 等待所有线程完成
for thread in threads:
    thread.join()

3. 使用负载均衡和故障转移实现高可用性：

from flask import Flask, request
import random

app = Flask(__name__)

@app.route('/process_request', methods=['POST'])
def process_request():
    # 处理请求的逻辑
    return 'Request processed successfully'

if __name__ == '__main__':
    app.run()

# 使用示例
import requests

def send_request():
    url = 'http://localhost:5000/process_request'
    data = {'param': 'value'}
    try:
        response = requests.post(url, json=data)
        print('Request processed successfully')
    except requests.exceptions.RequestException as e:
        print('Request failed: ', str(e))

while True:
    send_request()
    # 添加故障转移逻辑，例如切换到备用服务器
    # 确保程序能够持续运行，即使某个服务器出现故障