這里我們通過請求網頁例子來一步步理解爬蟲性能

當我們有一個列表存放了一些url需要我們獲取相關數據，我們首先想到的是循環

簡單的循環串行

這一種方法相對來說是最慢的，因為一個一個循環，耗時是最長的，是所有的時間總和代碼如下：

import requestsurl_list = [ ’http://www.baidu.com’, ’http://www.pythonsite.com’, ’http://www.cnblogs.com/’]for url in url_list: result = requests.get(url) print(result.text)

通過線程池

通過線程池的方式訪問，這樣整體的耗時是所有連接里耗時最久的那個，相對循環來說快了很多

import requestsfrom concurrent.futures import ThreadPoolExecutordef fetch_request(url): result = requests.get(url) print(result.text)url_list = [ ’http://www.baidu.com’, ’http://www.bing.com’, ’http://www.cnblogs.com/’]pool = ThreadPoolExecutor(10)for url in url_list: #去線程池中獲取一個線程，線程去執行fetch_request方法 pool.submit(fetch_request,url)pool.shutdown(True)

線程池+回調函數

這里定義了一個回調函數callback

from concurrent.futures import ThreadPoolExecutorimport requestsdef fetch_async(url): response = requests.get(url) return responsedef callback(future): print(future.result().text)url_list = [ ’http://www.baidu.com’, ’http://www.bing.com’, ’http://www.cnblogs.com/’]pool = ThreadPoolExecutor(5)for url in url_list: v = pool.submit(fetch_async,url) #這里調用回調函數 v.add_done_callback(callback)pool.shutdown()

通過進程池

通過進程池的方式訪問，同樣的也是取決于耗時最長的，但是相對于線程來說，進程需要耗費更多的資源，同時這里是訪問url時IO操作，所以這里線程池比進程池更好

import requestsfrom concurrent.futures import ProcessPoolExecutordef fetch_request(url): result = requests.get(url) print(result.text)url_list = [ ’http://www.baidu.com’, ’http://www.bing.com’, ’http://www.cnblogs.com/’]pool = ProcessPoolExecutor(10)for url in url_list: #去進程池中獲取一個線程，子進程程去執行fetch_request方法 pool.submit(fetch_request,url)pool.shutdown(True)

進程池+回調函數

這種方式和線程+回調函數的效果是一樣的，相對來說開進程比開線程浪費資源

from concurrent.futures import ProcessPoolExecutorimport requestsdef fetch_async(url): response = requests.get(url) return responsedef callback(future): print(future.result().text)url_list = [ ’http://www.baidu.com’, ’http://www.bing.com’, ’http://www.cnblogs.com/’]pool = ProcessPoolExecutor(5)for url in url_list: v = pool.submit(fetch_async, url) # 這里調用回調函數 v.add_done_callback(callback)pool.shutdown()

主流的單線程實現并發的幾種方式

下面分別是這四種代碼的實現例子：

asyncio例子1：

import [email protected] #通過這個裝飾器裝飾def func1(): print(’before...func1......’) # 這里必須用yield from，并且這里必須是asyncio.sleep不能是time.sleep yield from asyncio.sleep(2) print(’end...func1......’)tasks = [func1(), func1()]loop = asyncio.get_event_loop()loop.run_until_complete(asyncio.gather(*tasks))loop.close()

上述的效果是同時會打印兩個before的內容，然后等待2秒打印end內容這里asyncio并沒有提供我們發送http請求的方法，但是我們可以在yield from這里構造http請求的方法。

asyncio例子2：

import [email protected] fetch_async(host, url=’/’): print('----',host, url) reader, writer = yield from asyncio.open_connection(host, 80) #構造請求頭內容 request_header_content = '''GET %s HTTP/1.0rnHost: %srnrn''' % (url, host,) request_header_content = bytes(request_header_content, encoding=’utf-8’) #發送請求 writer.write(request_header_content) yield from writer.drain() text = yield from reader.read() print(host, url, text) writer.close()tasks = [ fetch_async(’www.cnblogs.com’, ’/zhaof/’), fetch_async(’dig.chouti.com’, ’/pic/show?nid=4073644713430508&lid=10273091’)]loop = asyncio.get_event_loop()results = loop.run_until_complete(asyncio.gather(*tasks))loop.close()

asyncio + aiohttp 代碼例子：

import aiohttpimport [email protected] fetch_async(url): print(url) response = yield from aiohttp.request(’GET’, url) print(url, response) response.close()tasks = [fetch_async(’http://baidu.com/’), fetch_async(’http://www.chouti.com/’)]event_loop = asyncio.get_event_loop()results = event_loop.run_until_complete(asyncio.gather(*tasks))event_loop.close()

asyncio+requests代碼例子

import asyncioimport [email protected] fetch_async(func, *args): loop = asyncio.get_event_loop() future = loop.run_in_executor(None, func, *args) response = yield from future print(response.url, response.content)tasks = [ fetch_async(requests.get, ’http://www.cnblogs.com/wupeiqi/’), fetch_async(requests.get, ’http://dig.chouti.com/pic/show?nid=4073644713430508&lid=10273091’)]loop = asyncio.get_event_loop()results = loop.run_until_complete(asyncio.gather(*tasks))loop.close()

gevent+requests代碼例子

import geventimport requestsfrom gevent import monkeymonkey.patch_all()def fetch_async(method, url, req_kwargs): print(method, url, req_kwargs) response = requests.request(method=method, url=url, **req_kwargs) print(response.url, response.content)# ##### 發送請求 #####gevent.joinall([ gevent.spawn(fetch_async, method=’get’, url=’https://www.python.org/’, req_kwargs={}), gevent.spawn(fetch_async, method=’get’, url=’https://www.yahoo.com/’, req_kwargs={}), gevent.spawn(fetch_async, method=’get’, url=’https://github.com/’, req_kwargs={}),])# ##### 發送請求（協程池控制最大協程數量） ###### from gevent.pool import Pool# pool = Pool(None)# gevent.joinall([# pool.spawn(fetch_async, method=’get’, url=’https://www.python.org/’, req_kwargs={}),# pool.spawn(fetch_async, method=’get’, url=’https://www.yahoo.com/’, req_kwargs={}),# pool.spawn(fetch_async, method=’get’, url=’https://www.github.com/’, req_kwargs={}),# ])

grequests代碼例子這個是講requests+gevent進行了封裝

import grequestsrequest_list = [ grequests.get(’http://httpbin.org/delay/1’, timeout=0.001), grequests.get(’http://fakedomain/’), grequests.get(’http://httpbin.org/status/500’)]# ##### 執行并獲取響應列表 ###### response_list = grequests.map(request_list)# print(response_list)# ##### 執行并獲取響應列表（處理異常） ###### def exception_handler(request, exception):# print(request,exception)# print('Request failed')# response_list = grequests.map(request_list, exception_handler=exception_handler)# print(response_list)

twisted代碼例子

#getPage相當于requets模塊，defer特殊的返回值，rector是做事件循環from twisted.web.client import getPage, deferfrom twisted.internet import reactordef all_done(arg): reactor.stop()def callback(contents): print(contents)deferred_list = []url_list = [’http://www.bing.com’, ’http://www.baidu.com’, ]for url in url_list: deferred = getPage(bytes(url, encoding=’utf8’)) deferred.addCallback(callback) deferred_list.append(deferred)#這里就是進就行一種檢測，判斷所有的請求知否執行完畢dlist = defer.DeferredList(deferred_list)dlist.addBoth(all_done)reactor.run()

tornado代碼例子

from tornado.httpclient import AsyncHTTPClientfrom tornado.httpclient import HTTPRequestfrom tornado import ioloopdef handle_response(response): ''' 處理返回值內容（需要維護計數器，來停止IO循環），調用 ioloop.IOLoop.current().stop() :param response: :return: ''' if response.error: print('Error:', response.error) else: print(response.body)def func(): url_list = [ ’http://www.baidu.com’, ’http://www.bing.com’, ] for url in url_list: print(url) http_client = AsyncHTTPClient() http_client.fetch(HTTPRequest(url), handle_response)ioloop.IOLoop.current().add_callback(func)ioloop.IOLoop.current().start()

以上就是Python 爬蟲性能相關總結的詳細內容，更多關于Python 爬蟲性能的資料請關注好吧啦網其它相關文章！