python如何提升爬虫效率

单线程+多任务异步协程

协程

在函数(特殊函数)定义的时候,使用async修饰,函数调用后,内部语句不会立即执行,而是会返回一个协程对象

任务对象

任务对象=高级的协程对象(进一步封装)=特殊的函数任务对象必须要注册到时间循环对象中给任务对象绑定回调:爬虫的数据解析中

事件循环

当做是一个装载任务对象的容器当启动事件循环对象的时候,存储在内的任务对象会异步执行

特殊函数内部不能写不支持异步请求的模块,如time,requests...否则虽然不报错但实现不了异步

time.sleep -- asyncio.sleeprequests -- aiohttp

import asyncioimport timestart_time = time.time()async def get_request(url): await asyncio.sleep(2) print(url,’下载完成!’)urls = [ ’www.1.com’, ’www.2.com’,]task_lst = [] # 任务对象列表for url in urls: c = get_request(url) # 协程对象 task = asyncio.ensure_future(c) # 任务对象 # task.add_done_callback(...) # 绑定回调 task_lst.append(task)loop = asyncio.get_event_loop() # 事件循环对象loop.run_until_complete(asyncio.wait(task_lst)) # 注册,手动挂起

线程池+requests模块

# 线程池import timefrom multiprocessing.dummy import Poolstart_time = time.time()url_list = [ ’www.1.com’, ’www.2.com’, ’www.3.com’,]def get_request(url): print(’正在下载...’,url) time.sleep(2) print(’下载完成!’,url)pool = Pool(3)pool.map(get_request,url_list)print(’总耗时:’,time.time()-start_time)

两个方法提升爬虫效率

起一个flask服务端

from flask import Flaskimport timeapp = Flask(__name__)@app.route(’/bobo’)def index_bobo(): time.sleep(2) return ’hello bobo!’@app.route(’/jay’)def index_jay(): time.sleep(2) return ’hello jay!’@app.route(’/tom’)def index_tom(): time.sleep(2) return ’hello tom!’if __name__ == ’__main__’: app.run(threaded=True)

aiohttp模块+单线程多任务异步协程

import asyncioimport aiohttpimport requestsimport timestart = time.time()async def get_page(url): # page_text = requests.get(url=url).text # print(page_text) # return page_text async with aiohttp.ClientSession() as s: #生成一个session对象 async with await s.get(url=url) as response: page_text = await response.text() print(page_text) return page_texturls = [ ’http://127.0.0.1:5000/bobo’, ’http://127.0.0.1:5000/jay’, ’http://127.0.0.1:5000/tom’,]tasks = []for url in urls: c = get_page(url) task = asyncio.ensure_future(c) tasks.append(task)loop = asyncio.get_event_loop()loop.run_until_complete(asyncio.wait(tasks))end = time.time()print(end-start)# 异步执行!# hello tom!# hello bobo!# hello jay!# 2.0311079025268555

’’’aiohttp模块实现单线程+多任务异步协程并用xpath解析数据’’’import aiohttpimport asynciofrom lxml import etreeimport timestart = time.time()# 特殊函数:请求的发送和数据的捕获# 注意async with await关键字async def get_request(url): async with aiohttp.ClientSession() as s: async with await s.get(url=url) as response: page_text = await response.text() return page_text # 返回页面源码# 回调函数,解析数据def parse(task): page_text = task.result() tree = etree.HTML(page_text) msg = tree.xpath(’/html/body/ul//text()’) print(msg)urls = [ ’http://127.0.0.1:5000/bobo’, ’http://127.0.0.1:5000/jay’, ’http://127.0.0.1:5000/tom’,]tasks = []for url in urls: c = get_request(url) task = asyncio.ensure_future(c) task.add_done_callback(parse) #绑定回调函数! tasks.append(task)loop = asyncio.get_event_loop()loop.run_until_complete(asyncio.wait(tasks))end = time.time()print(end-start)

requests模块+线程池

import timeimport requestsfrom multiprocessing.dummy import Poolstart = time.time()urls = [ ’http://127.0.0.1:5000/bobo’, ’http://127.0.0.1:5000/jay’, ’http://127.0.0.1:5000/tom’,]def get_request(url): page_text = requests.get(url=url).text print(page_text) return page_textpool = Pool(3)pool.map(get_request, urls)end = time.time()print(’总耗时:’, end-start)# 实现异步请求# hello jay!# hello bobo!# hello tom!# 总耗时: 2.0467123985290527

小结

爬虫的加速目前掌握了两种方法:

aiohttp模块+单线程多任务异步协程requests模块+线程池

爬虫接触的模块有三个:

requestsurllibaiohttp

接触了一下flask开启服务器

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