10个python线程池|进程池|小案例

作者：YXN-python 阅读量：66 发布日期：2024-05-21

线程池

1、执行并发任务

import concurrent.futures

def task(name):
    print(f"Task {name} started")
    # 执行任务的代码
    print(f"Task {name} completed")

with concurrent.futures.ThreadPoolExecutor() as executor:
    executor.map(task, range(10))

2、进行并发计算

from concurrent.futures import ThreadPoolExecutor, as_completed

# 要在线程中运行的函数
def worker(n):
    return n * n

# 主程序
def main():
    # 创建一个线程池，最大线程数为4
    with ThreadPoolExecutor(max_workers=4) as executor:
        # 提交任务到线程池中
        future_to_result = {executor.submit(worker, n): n for n in range(10)}

        # 获取结果
        for future in as_completed(future_to_result):
            n = future_to_result[future]
            try:
                data = future.result()
                print(f'{n} 平方: {data}')
            except Exception as e:
                print(f'{n} 异常: {e}')

if __name__ == '__main__':
    main()

3、下载多个文件

import concurrent.futures
import requests

def download_file(url):
    response = requests.get(url)
    # 将文件保存到本地
    filename = url.split("/")[-1]
    with open(filename, "wb") as file:
        file.write(response.content)
    print(f"Downloaded {url}")

urls = [
    "https://example.com/file1.txt",
    "https://example.com/file2.txt",
    "https://example.com/file3.txt",
]

with concurrent.futures.ThreadPoolExecutor() as executor:
    executor.map(download_file, urls)

4、惰性读取大文件

import concurrent.futures

def process_line(line):
    # 处理每行数据的代码
    pass

with open("large_file.txt", "r") as file:
    with concurrent.futures.ThreadPoolExecutor() as executor:
        executor.map(process_line, file)

5、惰性筛选质数

import concurrent.futures

def is_prime(number):
    # 判断一个数是否为质数的代码
    pass

def generate_numbers():
    number = 2
    while True:
        yield number
        number += 1

with concurrent.futures.ThreadPoolExecutor() as executor:
    numbers = generate_numbers()
    primes = filter(is_prime, numbers)

    for prime in primes:
        print(prime)

进程池

1、执行并发任务

import concurrent.futures

def task(name):
    print(f"Task {name} started")
    # 执行任务的代码
    print(f"Task {name} completed")

with concurrent.futures.ProcessPoolExecutor() as executor:
    executor.map(task, range(10))

2、压缩多个文件

import concurrent.futures
import shutil

def compress_file(filename):
    compressed_filename = f"{filename}.zip"
    shutil.make_archive(compressed_filename, "zip", filename)
    print(f"Compressed {filename}")

files = [
    "file1.txt",
    "file2.txt",
    "file3.txt",
]

with concurrent.futures.ProcessPoolExecutor() as executor:
    executor.map(compress_file, files)

3、处理大规模数据

import concurrent.futures

def process_data(data):
    # 处理数据的代码
    return processed_data

data = [...]  # 大规模数据
with concurrent.futures.ProcessPoolExecutor() as executor:
    results = executor.map(process_data, data)

for result in results:
    # 处理处理后的数据
    pass

4、计算无限序列的平方数

import concurrent.futures

def calculate_square(number):
    return number ** 2

with concurrent.futures.ProcessPoolExecutor() as executor:
    numbers = range(1, 1000000)
    results = executor.map(calculate_square, numbers)

for result in results:
    print(result)

5、进行惰性计算斐波那契数列的第n项

import concurrent.futures

def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)

with concurrent.futures.ProcessPoolExecutor() as executor:
    n = 10
    result = executor.submit(fibonacci, n)
    print(result.result())

 

YXN-python

2024-05-21