并发编程学习笔记 基于Python

并发编程学习 基于Python

学习笔记

P1 python对并发编程的支持P2选择并发执行的方式P3 Python 全局解释器锁GILP4 python多线程爬虫P5 生产者消费者模式爬虫P6 线程安全问题P7 线程池P8 在Web服务中使用线程池加速P9多进程P10 在Flask服务中使用进程池加速P11 asyncioP12 在异步IO中使用信号量使用信号量控制爬虫并发度

学习笔记

链接: bilibili

P1 python对并发编程的支持

那些程序提速方法：

多线程threading ，利用CPU和IO可以同时执行的原理，让CPU不会干巴巴的等待IO完成；多进程multiprocessing 利用多核CPU的能力，真正的并行执行任务；异步IO asyncio 协程Coroutine在单线程利用CPU和IO同时执行的原理，实现函数异步执行；

实现方法：

Lock使用Lock对资源加锁，防止访问冲突 ；Queue使用Queue实现不同线程/进程之间的数据通信，实现生产者-消费者模式；Pool使用线程/进程池Pool，简化进程线程的任务提交、等待结束、获取结果；subprocess使用subprocess启动外部程序的进程，并进行输入输出交互； P2选择并发执行的方式

什么是CPU密集型计算、IO密集型计算

CPU密集型(CPU-bound):
cpu密集型也叫计算密集型，是指IO在很短的时间就可完成，而cpu需要大量的计算和处理，特点是cpu占用率高。如，zip、unzip、正则表达式搜索IO密集型(IO-bound):
IO密集型指的是系统运作大部分状况是CPU等待IO的r/w操作，CPU的占用了比较低。如，文件处理、网络爬虫、读写数据库

多进程、多线程、多协程的对比
怎样根据任务选择对应的技术

P3 Python 全局解释器锁GIL

Python速度慢的两大原因
python相较于C/C++，java确实慢，在一些特俗场景下，速度相差100~200倍。
 - 原因1，Python是动态类型语言，边解释边执行；
 - 原因2，GIL导致无法利用多喝CPU并发执行；

GIL是什么
全局解释器锁(Global Interpreter Lock,GIL)，是计算机程序设计语言解释器用于线程同步的一种机制，它使任何时刻仅有一个线程在执行。即使在多核处理器上，GIL的计时器也只允许同一时间执行一个线程。


为何引入GIL


怎样规避GIL带来的限制

P4 python多线程爬虫

Python创建多线程的方法

# 1.主备一个函数
def my_func(a, b):
	do_craw(a, b)
# 2.创建一个线程
import threading
t = threading.Thread(target = my_func, args = (100, 200))
# 3.启动线程
t.srart()
# 4.等待结束
t.join()

改写爬虫程序，单线程到多线程

# blog_spider.py
import requests
from bs4 import BeautifulSoup

# 博客园url列表
urls = [
    f"https://www.cnblogs.com/#p{page}"
    for page in range(1, 50 + 1)
]

# 获取网页信息，输出url+内容长度，返回网页内容
def craw(url):
    r = requests.get(url)
    print(url, len(r.text))
    return r.text

# 下文生产者-消费者模式要用到的爬取信息处理函数
# def parse(html):
#     soup = BeautifulSoup(html, "html.parser")
#     links = soup.find_all("a", class_="post-item-title")
#     return [(link["href"], link.get_text()) for link in links]

if __name__ == "__main__":
    for result in parse(craw(urls[2])):
        print(result)

# 01.multi_thread_craw.py
import blog_spider
import threading
import time

# 单线程爬虫
def single_thread():
    print("single_thread begin")
    for url in blog_spider.urls:
        blog_spider.craw(url)
    print("single_thread end")

# 多线程爬虫
def multi_thread():
    print("multi_thread begin")
    threads = []
    for url in blog_spider.urls:
        threads.append(
            threading.Thread(target=blog_spider.craw, args=(url,))
            # 不加括号是不调用
        )

    for thread in threads:
        thread.start()

    for thread in threads:
        thread.join()

    print("multi_thread end")


if __name__ == "__main__":
    strat = time.time()
    single_thread()
    end = time.time()
    print("single thread cost:", end - strat, "sec")

    strat = time.time()
    multi_thread()
    end = time.time()
    print("multi thread cost:", end - strat, "sec")

速度对比：单线程爬虫V多线程爬虫

single_thread begin
single_thread end
single thread cost: 7.926880836486816 sec
multi_thread begin
multi_thread end
multi thread cost: 0.5494334697723389 sec

P5 生产者消费者模式爬虫

多组件的Pipeline技术架构

生产者消费者爬虫的架构

多线程数据通信的queue.Queue

代码编写实现生产者消费者爬虫

import queue
import blog_spider
import time
import random
import threading

# 生产者函数
def do_craw(url_queue: queue.Queue, html_queue: queue.Queue):
    while True:
        url = url_queue.get()
        html = blog_spider.craw(url)
        html_queue.put(html)

        print(threading.current_thread().name, f"craw{url}",
              "url_queue.size=", url_queue.qsize())
        time.sleep(random.randint(1, 2))

# 消费者函数
def do_parse(html_queue: queue.Queue, fout):
    while True:
        html = html_queue.get()
        results = blog_spider.parse(html)
        for result in results:
            fout.write(str(result) + "n")
        print(threading.current_thread().name, f"results.size",
              len(results), "html_queue.size=", html_queue.qsize())
        time.sleep(random.randint(1, 2))


if __name__ == "__main__":
    url_queue = queue.Queue()
    html_queue = queue.Queue()
    for url in blog_spider.urls:
        url_queue.put(url)

    for idx in range(3):
        t = threading.Thread(target=do_craw, args=(url_queue, html_queue),
                             name=f"craw{idx}")
        t.start()

    fout = open("02.data.txt", "w", encoding='utf-8')

    for idx in range(2):
        t = threading.Thread(target=do_parse, args=(html_queue, fout),
                             name=f"parse{idx}")
        t.start()

P6 线程安全问题

线程安全概念介绍

Lock用于解决线程安全问题

实例代码演示问题以及解决方案

# Lock_concurrent.py
import threading
import time

lock = threading.Lock()


class Account:  # 创建Circle类
    def __init__(self, balance):  # 约定成俗这里应该使用r，它与self.r中的r同名
        self.balance = balance


def draw(account, amount):
    with lock:
        if account.balance >= amount:
            time.sleep(0.1)
            print(threading.current_thread().name, "successful")
            account.balance -= amount
            print(threading.current_thread().name, "balance", account.balance)
        else:
            print(threading.current_thread().name, "failed")


if __name__ == "__main__":
    account = Account(1000)
    ta = threading.Thread(name="ta", target=draw, args=(account, 800))
    tb = threading.Thread(name="tb", target=draw, args=(account, 800))

    ta.start()
    tb.start()

P7 线程池

线程池的原理

使用线程池的好处

ThreadPoolExecutor的使用方法

使用线程池改造爬虫程序

# thread_pool.py
import concurrent.futures
import blog_spider

# craw
with concurrent.futures.ThreadPoolExecutor() as pool:
    htmls = pool.map(blog_spider.craw, blog_spider.urls)
    htmls = list(zip(blog_spider.urls, htmls))
    for url, html in htmls:
        print(url, len(html))

    # futures = [pool.submit(blog_spider.craw, url) for url in blog_spider.urls]
    # for future in futures:
    #     print(future.result())
    # for future in as_completed(futures):
    #     print(future.result())

print("craw over")

# parse
with concurrent.futures.ThreadPoolExecutor() as pool:
    futures = {}
    for url, html in htmls:
        future = pool.submit(blog_spider.parse, html)
        futures[future] = url

    for future, url in futures.items():
        print(url, future.result())

    # for future in concurrent.futures.as_completed(futures):
    #     url = futures[future]
    #     print(url, future.result())

P8 在Web服务中使用线程池加速

web服务的架构以及特点

使用线程池Thread PoolExecutor加速

代码用Flask实现Web服务并实现加速

#flask_thread_pool.py
import json
import flask
import time
from concurrent.futures import ThreadPoolExecutor

pool = ThreadPoolExecutor()
app = flask.Flask(__name__)

def read_file():
    time.sleep(0.1)
    print('file')
    return "file result"

def read_db():
    time.sleep(0.2)
    print('db')
    return "db result"

def read_api():
    time.sleep(0.3)
    print('api')
    return "api result"

@app.route("/")
def index():
    start = time.time()
    result_file = pool.submit(read_file)
    result_db = pool.submit(read_db)
    result_api = pool.submit(read_api)

    # result_file = read_file()
    # result_db = read_db()
    # result_api = read_api()

    end = time.time()
    return json.dumps({
        "result_file": result_file.result(),
        "result_db": result_db.result(),
        "result_api": result_api.result(),

        # "result_file": result_file,
        # "result_db": result_db,
        # "result_api": result_api,
        "time": end - start,
    })

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

P9多进程

Why multiprocessing

multiprocessing梳理

代码实战：单线程、多线程、多进程对CPU密集型计算速度的影响

single_thread, cost: 36.88734722137451 sec
multi_thread, cost: 37.69435501098633 sec
multi_process, cost: 6.157953977584839 sec

#Thread_process_cpu_borad.py
import math
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import time

# 导入100个大素数
PRIMRS = [112272535095293] * 100

# 函数用来判断是否为素数
def is_prime(n):
    if n < 2:
        return False
    if n == 2:
        return True
    if n % 2 == 0:
        return False
    sqrt_n = int(math.floor(math.sqrt(n)))
    for i in range(3, sqrt_n + 1, 2):
        if n % i == 0:
            return False
    return True

def single_thread():
    for number in PRIMRS:
        is_prime(number)

def multi_thread():
    with ThreadPoolExecutor() as pool:
        pool.map(is_prime, PRIMRS)

def multi_process():
    with ProcessPoolExecutor() as pool:
        pool.map(is_prime, PRIMRS)

if __name__ == "__main__":
    start = time.time()
    single_thread()
    end = time.time()
    print("single_thread, cost:", end - start, "sec")

    start = time.time()
    multi_thread()
    end = time.time()
    print("multi_thread, cost:", end - start, "sec")

    start = time.time()
    multi_process()
    end = time.time()
    print("multi_process, cost:", end - start, "sec")

P10 在Flask服务中使用进程池加速

import flask
import math
import json
from concurrent.futures import ProcessPoolExecutor

process_pool = ProcessPoolExecutor()
app = flask.Flask(__name__)

# PRIMRS = [112272535095293] * 100

def is_prime(n):
    if n < 2:
        return False
    if n == 2:
        return True
    if n % 2 == 0:
        return False
    sqrt_n = int(math.floor(math.sqrt(n)))
    for i in range(3, sqrt_n + 1, 2):
        if n % i == 0:
            return False
    return True

# 接口
@app.route("/is_prime/")

def api_is_prime(numbers):
    number_list = [int(x) for x in numbers.split(",")]
    results = process_pool.map(is_prime, number_list)
    return json.dumps(dict(zip(number_list, results)))

if __name__ == "__main__":
    process_pool = ProcessPoolExecutor()
    app.run()

P11 asyncio

import asyncio
import aiohttp
import blog_spider
import time


async def async_craw(url):
    print("craw url:", url)
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as resp:
            result = await resp.text()
            print(f"craw url:{url}, {len(result)}")


loop = asyncio.get_event_loop()
tasks = [loop.create_task(async_craw(url))
         for url in blog_spider.urls]

start = time.time()
loop.run_until_complete(asyncio.wait(tasks))
end = time.time()
print("spend time:", end - start, "sec")

P12 在异步IO中使用信号量使用信号量控制爬虫并发度

import asyncio
import aiohttp
import blog_spider
import time

semaphore = asyncio.Semaphore(1)

async def async_craw(url):
    async with semaphore:
        print("craw url:", url)
        async with aiohttp.ClientSession() as session:
            async with session.get(url) as resp:
                result = await resp.text()
                print(f"craw url:{url}, {len(result)}")


loop = asyncio.get_event_loop()
tasks = [loop.create_task(async_craw(url))
         for url in blog_spider.urls]

start = time.time()
loop.run_until_complete(asyncio.wait(tasks))
end = time.time()
print("spend time:", end - start, "sec")