asyncio oracle 异步,Python使用asyncio+aiohttp异步爬取猫眼电影专业版

asyncio oracle 异步,Python使用asyncio+aiohttp异步爬取猫眼电影专业版

asyncio是从pytohn3.4开始添加到标准库中的一个强大的异步并发库，可以很好地解决python中高并发的问题，入门学习可以参考官方文档

并发访问能极大的提高爬虫的性能，但是requests访问网页是阻塞的，无法并发，所以我们需要一个更牛逼的库 aiohttp ，它的用法与requests相似，可以看成是异步版的requests，下面通过实战爬取猫眼电影专业版来熟悉它们的使用：

1. 分析

分析网页源代码发现猫眼专业版是一个动态网页，其中的数据都是后台传送的，打开F12调试工具，再刷新网页选择XHR后发现第一条就是后台发来的电影数据，由此得到接口

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2. 异步爬取

创建20个任务来并发爬取20天的电影信息并写入csv文件，同时计算一下耗费的时间

import asyncio

from aiohttp import ClientSession

import aiohttp

import time

import csv

import ssl

ssl._create_default_https_context = ssl._create_unverified_context

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '

'AppleWebKit/537.36 (KHTML, like Gecko) '

'Chrome/67.0.3396.99 Safari/537.36'}

# 协程函数，完成一个无阻塞的任务

async def get_one_page(url):

try:

conn = aiohttp.TCPConnector(verify_ssl=False) # 防止ssl报错

async with aiohttp.ClientSession(connector=conn) as session: # 创建session

async (url, headers=headers) as r:

# 返回解析为字典的电影数据

return await r.json()

except Exception as e:

print('请求异常： ' + str(e))

return {}

# 解析函数，提取每一条内容并写入csv文件

def parse_one_page(movie_dict, writer):

try:

movie_list = movie_dict['data']['list']

for movie in movie_list:

movie_name = movie['movieName']

release = movie['releaseInfo']

sum_box = movie['sumBoxInfo']

box_info = movie['boxInfo']

box_rate = movie['boxRate']

show_info = movie['showInfo']

show_rate = movie['showRate']

avg_show_view = movie['avgShowView']

avg_seat_view = movie['avgSeatView']

writer.writerow([movie_name, release, sum_box, box_info, box_rate,

show_info, show_rate, avg_show_view, avg_seat_view])

return('写入成功')

except Exception as e:

return('解析异常： ' + str(e))

# 并发爬取

async def main():

# 待访问的20个URL链接

urls = ['.json?beginDate=201904{}{}'.format(i, j) for i in range(1, 3) for j in range(10)]

# 任务列表

tasks = [get_one_page(url) for url in urls]

# 并发执行并保存每一个任务的返回结果

results = await asyncio.gather(*tasks)

# 处理每一个结果

with open('pro_info.csv', 'w') as f:

writer = csv.writer(f)

for result in results:

print(parse_one_page(result, writer))

if __name__ == "__main__":

start = time.time()

# asyncio.run(main())

# python3.7之前的写法

loop = _event_loop()

loop.run_until_complete(main())

loop.close()

print(time.time()-start)

3. 对比同步爬取

import requests

import csv

import time

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) ' 'AppleWebKit/537.36 (KHTML, like Gecko) ' 'Chrome/67.0.3396.99 Safari/537.36'} def get_one_page(url): try: r = (url, headers=headers) r.raise_for_status() r.encoding = r.apparent_encoding return r.json() except Exception as e: print('请求异常： ' + e) return {} def parse_one_page(movie_dict, writer): try: movie_list = movie_dict['data']['list'] for movie in movie_list: movie_name = movie['movieName'] release = movie['releaseInfo'] sum_box = movie['sumBoxInfo'] box_info = movie['boxInfo'] box_rate = movie['boxRate'] show_info = movie['showInfo'] show_rate = movie['showRate'] avg_show_view = movie['avgShowView'] avg_seat_view = movie['avgSeatView'] writer.writerow([movie_name, release, sum_box, box_info, box_rate, show_info, show_rate, avg_show_view, avg_seat_view]) print('写入成功') except Exception as e: print('解析异常： ' + e) def main(): # 待访问的20个URL链接 urls = ['.json?beginDate=201903{}{}'.format(i, j) for i in range(1, 3) for j in range(10)] with open('out/pro_info.csv', 'w') as f: writer = csv.writer(f) for url in urls: # 逐一处理 movie_dict = get_one_page(url) parse_one_page(movie_dict, writer) if __name__ == '__main__': a = time.time() main() print(time.time() - a)

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可以看到使用asyncio+aiohttp的异步爬取方式要比简单的requests同步爬取快上不少，尤其是爬取大量网页的时候，这种差距会非常明显。