多线程爬虫
爬取糗事百科
# 使用了线程库
import threading
# 队列
from Queue import Queue
# 解析库
from lxml import etree
# 请求处理
import requests
# json处理
import json
import time
class ThreadCrawl(threading.Thread):
def __init__(self, threadName, pageQueue, dataQueue):
#threading.Thread.__init__(self)
# 调用父类初始化方法
super(ThreadCrawl, self).__init__()
# 线程名
self.threadName = threadName
# 页码队列
self.pageQueue = pageQueue
# 数据队列
self.dataQueue = dataQueue
# 请求报头
self.headers = {\'User-Agent\':\'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.101 Safari/537.36\'}
def run(self):
print ("启动 " + self.threadName)
while not CRAWL_EXIT:
try:
# 取出一个数字，先进先出
# 可选参数block，默认值为True
#1. 如果对列为空，block为True的话，不会结束，会进入阻塞状态，直到队列有新的数据
#2. 如果队列为空，block为False的话，就弹出一个Queue.empty()异常，
page = self.pageQueue.get(False)
url = "http://www.qiushibaike.com/8hr/page/" + str(page) +"/"
#print( url)
content = requests.get(url, headers = self.headers).text
time.sleep(1)
self.dataQueue.put(content)
#print (len(content))
except:
pass
print ("结束 " + self.threadName)
class ThreadParse(threading.Thread):
def __init__(self, threadName, dataQueue, filename, lock):
super(ThreadParse, self).__init__()
# 线程名
self.threadName = threadName
# 数据队列
self.dataQueue = dataQueue
# 保存解析后数据的文件名
self.filename = filename
# 锁
self.lock = lock
def run(self):
print ("启动" + self.threadName)
while not PARSE_EXIT:
try:
html = self.dataQueue.get(False)
self.parse(html)
except:
pass
print ("退出" + self.threadName)
def parse(self, html):
# 解析为HTML DOM
html = etree.HTML(html)
node_list = html.xpath(\'//div[contains(@id, "qiushi_tag")]\')
for node in node_list:
# xpath返回的列表，这个列表就这一个参数，用索引方式取出来，用户名
username = node.xpath(\'./div/a/@title\')[0]
# 图片连接
image = node.xpath(\'.//div[@class="thumb"]//@src\')#[0]
# 取出标签下的内容,段子内容
content = node.xpath(\'.//div[@class="content"]/span\')[0].text
# 取出标签里包含的内容，点赞
zan = node.xpath(\'.//i\')[0].text
# 评论
comments = node.xpath(\'.//i\')[1].text
items = {
"username" : username,
"image" : image,
"content" : content,
"zan" : zan,
"comments" : comments
}
# with 后面有两个必须执行的操作：__enter__ 和 _exit__
# 不管里面的操作结果如何，都会执行打开、关闭
# 打开锁、处理内容、释放锁
with self.lock:
# 写入存储的解析后的数据
self.filename.write(json.dumps(items, ensure_ascii = False).encode("utf-8") + "\n")
CRAWL_EXIT = False
PARSE_EXIT = False
def main():
# 页码的队列，表示20个页面
pageQueue = Queue(20)
# 放入1~10的数字，先进先出
for i in range(1, 21):
pageQueue.put(i)
# 采集结果(每页的HTML源码)的数据队列，参数为空表示不限制
dataQueue = Queue()
filename = open("duanzi.json", "a")
# 创建锁
lock = threading.Lock()
# 三个采集线程的名字
crawlList = ["采集线程1号", "采集线程2号", "采集线程3号"]
# 存储三个采集线程的列表集合
threadcrawl = []
for threadName in crawlList:
thread = ThreadCrawl(threadName, pageQueue, dataQueue)
thread.start()
threadcrawl.append(thread)
# 三个解析线程的名字
parseList = ["解析线程1号","解析线程2号","解析线程3号"]
# 存储三个解析线程
threadparse = []
for threadName in parseList:
thread = ThreadParse(threadName, dataQueue, filename, lock)
thread.start()
threadparse.append(thread)
# 等待pageQueue队列为空，也就是等待之前的操作执行完毕
while not pageQueue.empty():
pass
# 如果pageQueue为空，采集线程退出循环
global CRAWL_EXIT
CRAWL_EXIT = True
print( "pageQueue为空")
for thread in threadcrawl:
thread.join()
print ("1")
while not dataQueue.empty():
pass
global PARSE_EXIT
PARSE_EXIT = True
for thread in threadparse:
thread.join()
print ("2")
with lock:
# 关闭文件
filename.close()
print ("谢谢使用！")
if __name__ == "__main__":
main()
使用多进程并关注返回结果 - multiprocessing
import multiprocessing
import time
def func(msg):
print(\'hello :\', msg, time.ctime())
time.sleep(2)
print(\'end\', time.ctime())
return \'done\' + msg
if __name__ == \'__main__\':
pool = multiprocessing.Pool(2)
result = []
for i in range(3):
msg = \'hello %s\' % i
result.append(pool.apply_async(func=func, args=(msg,)))
pool.close()
pool.join()
for res in result:
print(\'***:\', res.get()) # get()函数得出每个返回结果的值
print(\'All end--\')
多进程执行多个函数 - multiprocessing.Pool
使用apply_async()或者apply()方法，可以实现多进程执行多个方法
import multiprocessing
import time
import os
def Lee():
print(\'\nRun task Lee--%s******ppid:%s\' % (os.getpid(), os.getppid()), \'~~~~\', time.ctime())
start = time.time()
time.sleep(5)
end = time.time()
print(\'Task Lee,runs %0.2f seconds.\' % (end - start), \'~~~~\', time.ctime())
def Marlon():
print("\nRun task Marlon-%s******ppid:%s" % (os.getpid(), os.getppid()), \'~~~~\', time.ctime())
start = time.time()
time.sleep(10)
end = time.time()
print(\'Task Marlon runs %0.2f seconds.\' % (end - start), \'~~~~\', time.ctime())
def Allen():
print("\nRun task Allen-%s******ppid:%s" % (os.getpid(), os.getppid()), \'~~~~\', time.ctime())
start = time.time()
time.sleep(15)
end = time.time()
print(\'Task Allen runs %0.2f seconds.\' % (end - start), \'~~~~\', time.ctime())
def Frank():
print("\nRun task Frank-%s******ppid:%s" % (os.getpid(), os.getppid()), \'~~~~\', time.ctime())
start = time.time()
time.sleep(20)
end = time.time()
print(\'Task Frank runs %0.2f seconds.\' % (end - start), \'~~~~\', time.ctime())
if __name__ == \'__main__\':
func_list = [Lee, Marlon, Allen, Frank]
print(\'parent process id %s\' % os.getpid())
pool = multiprocessing.Pool(4)
for func in func_list:
pool.apply_async(func)
print(\'Waiting for all subprocesses done...\')
pool.close()
pool.join()
print(\'All subprocesses done.\')
参考链接：