1.多线程的方法使用
在python3中,主线程主进程结束,子线程,子进程不会结束
为了能够让主线程回收子线程,可以把子线程设置为守护线程,即该线程不重要,主线程结束,子线程结束.
t1 = threading.Thread(targe=func,args=(,))
t1.setDaemon(True)
t1.start() #此时线程才会启动
2.队列模块的使用
from queue import Queue
q = Queue(maxsize=100)
item = {}
q.put_nowait(item) #不等待直接放,队列满的时候会报错
q.put(item) #放入数据,队列满的时候回等待
q.get_nowait() #不等待直接取,队列空的时候会报错
q.get() #取出数据,队列为空的时候会等待
q.qsize() #获取队列中现存数据的个数
q.join() #队列中维持了一个计数,计数不为0时候让主线程阻塞等待,队列计数为0的时候才会继续往后执行
q.task_done()
# put的时候计数+1,get不会-1,get需要和task_done 一起使用才会-1
3.多线程实现思路剖析
- 把爬虫中的每个步骤封装成函数,分别用线程去执行
- 不同的函数通过队列相互通信,函数间解耦
代码如下:
# coding=utf-8
import requests
from lxml import etree
from queue import Queue
import threading
import time
class QiuBai:
def __init__(self):
self.temp_url = "http://www.qiushibaike.com/8hr/page/{}"
self.headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36"}
self.url_queue = Queue()
self.html_queue = Queue()
self.content_list_queue = Queue()
def get_url_list(self):
# return [self.temp_url.format(i) for i in range(1,14)]
for i in range(1,14):
self.url_queue.put(self.temp_url.format(i))
def parse_url(self):
while True:
url = self.url_queue.get()
response = requests.get(url,headers=self.headers)
print(response)
if response.status_code != 200:
self.url_queue.put(url)
else:
self.html_queue.put(response.content.decode())
self.url_queue.task_done() #让队列的计数-1
def get_content_list(self):#提取数据
while True:
html_str = self.html_queue.get()
html = etree.HTML(html_str)
div_list = html.xpath("//div[@id='content-left']/div")
content_list = []
for div in div_list:
item = {}
item["user_name"] = div.xpath(".//h2/text()")[0].strip()
item["content"] = [i.strip() for i in div.xpath(".//div[@class='content']/span/text()")]
content_list.append(item)
self.content_list_queue.put(content_list)
self.html_queue.task_done()
def save_content_list(self): #保存
while True:
content_list = self.content_list_queue.get()
for content in content_list:
# print(content)
pass
self.content_list_queue.task_done()
def run(self):#实现做主要逻辑
thread_list = []
#1. 准备url列表
t_url = threading.Thread(target=self.get_url_list)
thread_list.append(t_url)
#2. 遍历发送请求,获取响应
for i in range(3):
t_parse = threading.Thread(target=self.parse_url)
thread_list.append(t_parse)
#3. 提取数据
t_content = threading.Thread(target=self.get_content_list)
thread_list.append(t_content)
#4. 保存
t_save = threading.Thread(target=self.save_content_list)
thread_list.append(t_save)
for t in thread_list:
t.setDaemon(True) #把子线程设置为守护线程
t.start()
for q in [self.url_queue,self.html_queue,self.content_list_queue]:
q.join() #让主线程阻塞,等待队列计数为0
if __name__ == '__main__':
t1 = time.time()
qiubai = QiuBai()
qiubai.run()
print("total cost:",time.time()-t1)
多进程程的方法使用
from multiprocessing import Process
t1 = Process(targe=func,args=(,))
t1.daemon = True #设置为守护进程
t1.start() #此时线程才会启动
多进程中队列的使用
多进程中使用普通的队列模块会发生阻塞,对应的需要使用multiprocessing
提供的JoinableQueue
模块,其使用过程和在线程中使用的queue方法相同.
代码如下:
# coding=utf-8
import requests
from lxml import etree
# from queue import Queue
# import threading
from multiprocessing import Process
from multiprocessing import JoinableQueue as Queue
import time
class QiuBai:
def __init__(self):
self.temp_url = "http://www.qiushibaike.com/8hr/page/{}"
self.headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36"}
self.url_queue = Queue()
self.html_queue = Queue()
self.content_list_queue = Queue()
self.proxies = {"http":"http://58.247.179.94:8060"}
def get_url_list(self):
# return [self.temp_url.format(i) for i in range(1,14)]
for i in range(1,14):
self.url_queue.put(self.temp_url.format(i))
def parse_url(self):
while True:
url = self.url_queue.get()
response = requests.get(url,headers=self.headers,proxies=self.proxies)
print(response)
if response.status_code != 200:
self.url_queue.put(url)
else:
self.html_queue.put(response.content.decode())
self.url_queue.task_done() #让队列的计数-1
def get_content_list(self):#提取数据
while True:
html_str = self.html_queue.get()
html = etree.HTML(html_str)
div_list = html.xpath("//div[@id='content-left']/div")
content_list = []
for div in div_list:
item = {}
item["user_name"] = div.xpath(".//h2/text()")[0].strip()
item["content"] = [i.strip() for i in div.xpath(".//div[@class='content']/span/text()")]
content_list.append(item)
self.content_list_queue.put(content_list)
self.html_queue.task_done()
def save_content_list(self): #保存
while True:
content_list = self.content_list_queue.get()
for content in content_list:
# print(content)
pass
self.content_list_queue.task_done()
def run(self):#实现做主要逻辑
thread_list = []
#1. 准备url列表
t_url = Process(target=self.get_url_list)
thread_list.append(t_url)
#2. 遍历发送请求,获取响应
for i in range(13):
t_parse = Process(target=self.parse_url)
thread_list.append(t_parse)
#3. 提取数据
t_content = Process(target=self.get_content_list)
thread_list.append(t_content)
#4. 保存
t_save = Process(target=self.save_content_list)
thread_list.append(t_save)
for process in thread_list:
process.daemon = True #把子线程设置为守护线程
process.start()
for q in [self.url_queue,self.html_queue,self.content_list_queue]:
q.join() #让主线程阻塞,等待队列计数为0
if __name__ == '__main__':
t1 = time.time()
qiubai = QiuBai()
qiubai.run()
print("total cost:",time.time()-t1)
通过线程池实现更快的爬虫
1. 线程池使用方法介绍
1.实例化线程池对象
from multiprocessing.dummy import Pool
pool = Pool(process=5) #默认大小是cup的个数
2. 把从发送请求,提取数据,到保存合并成一个函数,交给线程池异步执行
使用方法pool.apply_async(func)
def exetute_requests_item_save(self):
url = self.queue.get()
html_str = self.parse_url(url)
content_list = self.get_content_list(html_str)
self.save_content_list(content_list)
self.total_response_num +=1
pool.apply_async(self.exetute_requests_item_save)
3.添加回调函数
通过apply_async
的方法能够让函数异步执行,但是只能够执行一次
为了让其能够被反复执行,通过添加回调函数的方式能够让_callback 递归的调用自己
同时需要指定递归退出的条件.
def _callback(self,temp):
if self.is_running:
pool.apply_async(self.exetute_requests_item_save,callback=self._callback)
pool.apply_async(self.exetute_requests_item_save,callback=self._callback)
4.确定程序结束的条件 程序在获取的响应和url数量相同的时候可以结束
while True: #防止主线程结束
time.sleep(0.0001) #避免cpu空转,浪费资源
if self.total_response_num>=self.total_requests_num:
self.is_running= False
break
self.pool.close() #关闭线程池,防止新的线程开启
# self.pool.join() #等待所有的子线程结束
2. 使用线程池实现爬虫的具体实现
# coding=utf-8
import requests
from lxml import etree
import time
from queue import Queue
from multiprocessing.dummy import Pool
class QiuBai:
def __init__(self):
self.temp_url = "http://www.qiushibaike.com/8hr/page/{}"
self.headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36"}
self.queue = Queue()
self.pool= Pool(5)
self.is_running = True
self.total_request_num = 0
self.total_response_num =0
self.proxies = {"http":"http://58.247.179.94:8060"}
def get_url_list(self):
for i in range(1,14):
self.queue.put(self.temp_url.format(i))
self.total_request_num += 1
def parse_url(self,url):
# response = requests.get(url,headers=self.headers,proxies=self.proxies)
response = requests.get(url,headers=self.headers)
print(response)
return response.content.decode()
def get_content_list(self,html_str):#提取数据
html = etree.HTML(html_str)
div_list = html.xpath("//div[@id='content-left']/div")
content_list = []
for div in div_list:
item = {}
item["user_name"] = div.xpath(".//h2/text()")[0].strip()
item["content"] = [i.strip() for i in div.xpath(".//div[@class='content']/span/text()")]
content_list.append(item)
return content_list
def save_content_list(self,content_list): #保存
for content in content_list:
# print(content)
pass
def _execete_request_content_save(self): #进行一次url地址的请求,提取,保存
url = self.queue.get()
html_str = self.parse_url(url)
#3. 提取数据
content_list = self.get_content_list(html_str)
#4. 保存
self.save_content_list(content_list)
self.total_response_num +=1
def _callback(self,temp):
if self.is_running:
self.pool.apply_async(self._execete_request_content_save,callback=self._callback)
def run(self):#实现做主要逻辑
#1. 准备url列表
self.get_url_list()
for i in range(3): #设置并发数为3
self.pool.apply_async(self._execete_request_content_save,callback=self._callback)
while True:
time.sleep(0.0001)
if self.total_response_num>= self.total_request_num:
self.is_running = False
break
if __name__ == '__main__':
t1 = time.time()
qiubai = QiuBai()
qiubai.run()
print("total cost:",time.time()-t1)
3. 使用协程池实现爬虫的具体实现
# coding=utf-8
import gevent.monkey
gevent.monkey.patch_all()
from gevent.pool import Pool
import requests
from lxml import etree
import time
from queue import Queue
# from multiprocessing.dummy import Pool
class QiuBai:
def __init__(self):
self.temp_url = "http://www.qiushibaike.com/8hr/page/{}"
self.headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36"}
self.queue = Queue()
self.pool= Pool(5)
self.is_running = True
self.total_request_num = 0
self.total_response_num =0
self.proxies = {"http":"http://58.247.179.94:8060"}
def get_url_list(self):
for i in range(1,14):
self.queue.put(self.temp_url.format(i))
self.total_request_num += 1
def parse_url(self,url):
# response = requests.get(url,headers=self.headers,proxies=self.proxies)
response = requests.get(url,headers=self.headers)
print(response)
return response.content.decode()
def get_content_list(self,html_str):#提取数据
html = etree.HTML(html_str)
div_list = html.xpath("//div[@id='content-left']/div")
content_list = []
for div in div_list:
item = {}
item["user_name"] = div.xpath(".//h2/text()")[0].strip()
item["content"] = [i.strip() for i in div.xpath(".//div[@class='content']/span/text()")]
content_list.append(item)
return content_list
def save_content_list(self,content_list): #保存
for content in content_list:
# print(content)
pass
def _execete_request_content_save(self): #进行一次url地址的请求,提取,保存
url = self.queue.get()
html_str = self.parse_url(url)
#3. 提取数据
content_list = self.get_content_list(html_str)
#4. 保存
self.save_content_list(content_list)
self.total_response_num +=1
def _callback(self,temp):
if self.is_running:
self.pool.apply_async(self._execete_request_content_save,callback=self._callback)
def run(self):#实现做主要逻辑
#1. 准备url列表
self.get_url_list()
for i in range(3): #设置并发数为3
self.pool.apply_async(self._execete_request_content_save,callback=self._callback)
while True:
time.sleep(0.0001)
if self.total_response_num>= self.total_request_num:
self.is_running = False
break
if __name__ == '__main__':
t1 = time.time()
qiubai = QiuBai()
qiubai.run()
print("total cost:",time.time()-t1)