scrapy框架学习
- 一 什么是scrapy?
- Scrapy是一个为了爬取网站数据，提取结构性数据而编写的应用框架，非常出名，非常强悍。所谓的框架就是一个已经被集成了各种功能（高性能异步下载，队列，分布式，解析，持久化等）的具有很强通用性的项目模板。
- 安装
- linux: pip3 install scrapy
- windows:
- 1) pip3 install wheel
- 2) 下载Twisted (http://www.lfd.uci.edu/~gohlke/pythonlibs/#twisted)
- 3) 进入下载目录, 执行 pip3 install Twisted‑17.1.0‑cp35‑cp35m‑win_amd64.whl
- 4) pip3 install pywin32
- 5) pip3 install scrapy
- 二 使用
- 1) 创建项目:scrapy startproject 项目名称
- 2) 创建应用程序:
- 先进入项目目录: cd 项目名
- scrapy genspider 应用名 爬取网页的起始url:
- 生成的初始文件
# -*- coding: utf-8 -*- import scrapy class SpidermanSpider(scrapy.Spider): name = 'spiderman' #应用名称 #允许爬取的域名（如果遇到非该域名的url则爬取不到数据） allowed_domains = ['www.xxx.com'] #起始爬取的url start_urls = ['http://www.xxx.com/'] #访问起始URL并获取结果后的回调函数，该函数的response参数就是向起始的url发送请求后，获取的响应对象.该函数返回值必须为可迭代对象或者NUll def parse(self, response): pass
- settings.py基础配置文件设置：
# USER_AGENT = "" 伪装请求载体身份 # ROBOTSTXT_OBEY = False 可以忽略或者不遵守robots协议
- 简单爬取示例
# -*- coding: utf-8 -*- import scrapy class SpidermanSpider(scrapy.Spider): name = 'spiderman' # allowed_domains = ['www.xxx.com'] start_urls = ['https://www.qiushibaike.com/text/'] def parse(self, response): #xpath为response中的方法，可以将xpath表达式直接作用于该函数中 div_list = response.xpath('//div[@id="content-left"]/div') for div in div_list: #xpath函数返回的为列表，列表中存放的数据为Selector类型的数据。我们解析到的内容被封装在了Selector对象中，需要调用extract()函数将解析的内容从Selecor中取出。 author = div.xpath('./div[1]/a[2]/h2/text()').extract_first() content = div.xpath('./a[1]/div/span//text()').extract() content = "".join(content) print(author,content)
- 执行代码命令 : scrapy crawl 上面类中的name (--nolog) 是否显示日志
- 三 scrapy 框架持久化存储
- 1 )基于终端指令的持久化存储
# -*- coding: utf-8 -*- import scrapy class SpidermanSpider(scrapy.Spider): name = 'spiderman' # allowed_domains = ['www.xxx.com'] start_urls = ['https://www.qiushibaike.com/text/'] def parse(self, response): div_list = response.xpath('//div[@id="content-left"]/div') all_data = [] for div in div_list: author = div.xpath('./div[1]/a[2]/h2/text()').extract_first() content = div.xpath('./a[1]/div/span//text()').extract() content = "".join(content) dic = { "author":author, "content":content } all_data.append(dic) return all_data
- 执行输出指定格式进行存储：将爬取到的数据写入不同格式的文件中进行存储
- 执行命令 :
scrapy crawl 爬虫名称 -o xxxx.csv
.xml
.json
- 2 )基于管道的持久化存储
- scrapy框架中已经为我们专门集成好了高效、便捷的持久化操作功能，我们直接使用即可。
items.py：数据结构模板文件。定义数据属性。 pipelines.py：管道文件。接收数据（items），进行持久化操作。 持久化流程： 1.爬虫文件爬取到数据后，需要将数据封装到items对象中。 2.使用yield关键字将items对象提交给pipelines管道进行持久化操作。 3.在管道文件中的process_item方法中接收爬虫文件提交过来的item对象，然后编写持久化存储的代码将item对象中存储的数据进行持久化存储 4.settings.py配置文件中开启管道
- 爬取Boss网
# -*- coding: utf-8 -*- import scrapy from test222.items import Test222Item class SpidermanSpider(scrapy.Spider): name = 'spiderman' # allowed_domains = ['www.xxx.com'] start_urls = ['https://www.zhipin.com/c101010100/?query=python爬虫&page=1&ka=page-2'] # 解析+管道持久化存储 def parse(self, response): li_list = response.xpath('//div[@class="job-list"]/ul/li') for li in li_list: job_name = li.xpath('.//div[@class="info-primary"]/h3/a/div/text()').extract_first() salary = li.xpath('.//div[@class="info-primary"]/h3/a/span/text()').extract_first() company = li.xpath('.//div[@class="company-text"]/h3/a/text()').extract_first() # 实例化一个item对象 item = Test222Item() # 将解析到的数据全部封装到item对象中 item["job_name"] = job_name item['salary'] = salary item['company'] = company yield item
import scrapy class Test222Item(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() job_name = scrapy.Field() salary = scrapy.Field() company = scrapy.Field()
class Test222Pipeline(object): fp = None def open_spider(self,spider): print("start....") self.fp = open("./boss.txt","w",encoding="utf-8") def close_spider(self,spider): print("close....") self.fp.close() # 爬虫文件每向管道提交一次item,则该方法就会被调用一次. # 参数:item 就是管道接收到的item类型对象 def process_item(self, item, spider): print(item) self.fp.write(item["job_name"] + ":" + item["salary"] + ":" + item["company"] + "\n") return item
# settings.py ITEM_PIPELINES = { 'test222.pipelines.Test222Pipeline': 300, }
- 执行 scrapy crawl spiderman 生成 boss.txt文件
- 3) 基于mysql的管道存储
- 上述案例在管道文件将item对象中的数据值存储到了磁盘中,如果将item数据写入mysql数据库的话,只需要将上述案例中的管道文件修改即可:
import pymysql class Test222Pipeline(object): fp = None def open_spider(self,spider): print("start....") self.fp = open("./boss.txt","w",encoding="utf-8") def close_spider(self,spider): print("close....") self.fp.close() # 爬虫文件每向管道提交一次item,则该方法就会被调用一次. # 参数:item 就是管道接收到的item类型对象 def process_item(self, item, spider): print(item) self.fp.write(item["job_name"] + ":" + item["salary"] + ":" + item["company"] + "\n") return item class mysqlPipeline(object): conn = None cursor = None def open_spider(self,spider): self.conn = pymysql.connect( host="127.0.0.1", port=3306, user="root", password="", db="scrapy", charset="utf8") print(self.conn) def process_item(self, item, spider): self.cursor = self.conn.cursor() try: self.cursor.execute('insert into boss values ("%s","%s","%s")' % (item['job_name'], item['salary'], item['company'])) self.conn.commit() except Exception as e: print(e) self.conn.rollback() def close_spider(self,spider): self.conn.close() self.cursor.close()
# settings.py ITEM_PIPELINES = { # 'test222.pipelines.Test222Pipeline': 300, 'test222.pipelines.mysqlPipeline': 301, }
- 4) 基于redis的管道存储
- 同样redis只需要修改如下代码：
class redisPipeline(object): conn = None def open_spider(self,spider): self.conn = Redis(host="127.0.0.1",port=6379) print(self.conn) def process_item(self, item, spider): dic = { 'name':item["job_name"], 'salary':item["salary"], "company":item["company"] } self.conn.lpush("boss",dic)
ITEM_PIPELINES = { # 'test222.pipelines.Test222Pipeline': 300, # 'test222.pipelines.mysqlPipeline': 301, 'test222.pipelines.redisPipeline': 302, }
- 5）分页 scrapy.Request
# -*- coding: utf-8 -*- import scrapy from test222.items import Test222Item class SpidermanSpider(scrapy.Spider): name = 'spiderman' # allowed_domains = ['www.xxx.com'] start_urls = ['https://www.zhipin.com/job_detail/?query=python%E7%88%AC%E8%99%AB&scity=101010100&industry=&position='] url = "https://www.zhipin.com/c101010100/?query=python爬虫&page=%d&ka=page-2" page = 1 # 解析+管道持久化存储 def parse(self, response): li_list = response.xpath('//div[@class="job-list"]/ul/li') for li in li_list: job_name = li.xpath('.//div[@class="info-primary"]/h3/a/div/text()').extract_first() salary = li.xpath('.//div[@class="info-primary"]/h3/a/span/text()').extract_first() company = li.xpath('.//div[@class="company-text"]/h3/a/text()').extract_first() # 实例化一个item对象 item = Test222Item() # 将解析到的数据全部封装到item对象中 item["job_name"] = job_name item['salary'] = salary item['company'] = company yield item if self.page <=3: self.page +=1 new_url = format(self.url%self.page) print(new_url) # 手动请求发送 yield scrapy.Request(url=new_url,callback=self.parse)
- 四 post请求
# -*- coding: utf-8 -*-
import scrapy
class PostSpider(scrapy.Spider):
name = 'post'
allowed_domains = ['www.xxx.con']
start_urls = ['https://fanyi.baidu.com/sug'] # post请求的url
def start_requests(self): # 当start_urls是Post请求url时 必须重写start_requests
data = {
"kw":"dog"
}
for url in self.start_urls:
yield scrapy.FormRequest(url=url,formdata=data,callback=self.parse)
def parse(self, response):
print(response.text)
- 五 请求传参和日志等级
- 1) 请求传参
- 在某些情况下，我们爬取的数据不在同一个页面中，例如，我们爬取一个电影网站，电影的名称，评分在一级页面，而要爬取的其他电影详情在其二级子页面中。这时我们就需要用到请求传参。
# -*- coding: utf-8 -*- import scrapy from test222.items import Test222Item class MovieSpider(scrapy.Spider): name = 'movie' # allowed_domains = ['www.xxx.com'] start_urls = ['https://www.4567tv.tv/frim/index1.html'] def parse_detail(self, response): # response.meta返回接收到的meta字典 item = response.meta["item"] actor = response.xpath('/html/body/div[1]/div/div/div/div[2]/p[3]/a/text()').extract_first() item["actor"] = actor yield item def parse(self, response): li_list = response.xpath('//li[@class="col-md-6 col-sm-4 col-xs-3"]') print("111111") for li in li_list: item = Test222Item() name = li.xpath('./div/a/@title').extract_first() detail_url = 'https://www.4567tv.tv' + li.xpath('./div/a/@href').extract_first() item["name"] = name # meta参数:请求传参.meta字典就会传递给回调函数的response参数 yield scrapy.Request(url=detail_url,callback=self.parse_detail,meta={"item":item})
import scrapy class Test222Item(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() name = scrapy.Field() actor = scrapy.Field()
#settings: ITEM_PIPELINES = { 'test222.pipelines.Test222Pipeline': 300, }
- 最后在管道处查看item即可
- 2) 日志等级 （settings文件）
- 终端执行后 如果有错则会输出错误信息,如果没有,正常执行
LOG_LEVEL = "ERROR"
- 文件存储 (存储到指定文件)
LOG_FILE = "./log.txt"
- 六 五大核心组件工作流程
- 1 引擎(Scrapy)
- 用来处理整个系统的数据流处理, 触发事务(框架核心)
- 2 调度器(Scheduler)
- 用来接受引擎发过来的请求, 压入队列中, 并在引擎再次请求的时候返回. 可以想像成一个URL（抓取网页的网址或者说是链接）的优先队列, 由它来决定下一个要抓取的网址是什么, 同时去除重复的网址
- 3 下载器(Downloader)
- 用于下载网页内容, 并将网页内容返回给spider(Scrapy下载器是建立在twisted这个高效的异步模型上的)
- 4 爬虫(Spider)
- 爬虫是主要干活的, 用于从特定的网页中提取自己需要的信息, 即所谓的实体(Item)。用户也可以从中提取出链接,让Scrapy继续抓取下一个页面
- 5 项目管道(Pipeline)
- 负责处理爬虫从网页中抽取的实体，主要的功能是持久化实体、验证实体的有效性、清除不需要的信息。当页面被爬虫解析后，将被发送到项目管道，并经过几个特定的次序处理数据。
# -*- coding: utf-8 -*- # Define here the models for your spider middleware # # See documentation in: # https://doc.scrapy.org/en/latest/topics/spider-middleware.html from scrapy import signals import random class MiddleproDownloaderMiddleware(object): user_agent_list = [ "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 " "(KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1", "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 " "(KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 " "(KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 " "(KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6", "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 " "(KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1", "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 " "(KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5", "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 " "(KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 " "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 " "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 " "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 " "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 " "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 " "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 " "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 " "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3", "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 " "(KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3", "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.24 " "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24", "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 " "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24" ] # 可被选用的代理IP PROXY_http = [ '153.180.102.104:80', '195.208.131.189:56055', ] PROXY_https = [ '120.83.49.90:9000', '95.189.112.214:35508', ] #拦截所有未发生异常的请求 def process_request(self, request, spider): # Called for each request that goes through the downloader # middleware. # Must either: # - return None: continue processing this request # - or return a Response object # - or return a Request object # - or raise IgnoreRequest: process_exception() methods of # installed downloader middleware will be called #使用UA池进行请求的UA伪装 print('this is process_request') request.headers['User-Agent'] = random.choice(self.user_agent_list) print(request.headers['User-Agent']) # #使用代理池进行请求代理ip的设置 # if request.url.split(':')[0] == 'http': # request.meta['proxy'] = random.choice(self.PROXY_http) # else: # request.meta['proxy'] = random.choice(self.PROXY_https) return None #拦截所有的响应 def process_response(self, request, response, spider): # Called with the response returned from the downloader. # Must either; # - return a Response object # - return a Request object # - or raise IgnoreRequest return response #拦截到产生异常的请求 def process_exception(self, request, exception, spider): # Called when a download handler or a process_request() # (from other downloader middleware) raises an exception. # Must either: # - return None: continue processing this exception # - return a Response object: stops process_exception() chain # - return a Request object: stops process_exception() chain # 使用代理池进行请求代理ip的设置 print('this is process_exception!') if request.url.split(':')[0] == 'http': request.meta['proxy'] = random.choice(self.PROXY_http) else: request.meta['proxy'] = random.choice(self.PROXY_https)
- 七 scrapy中应用selenium
在scrapy中使用selenium的编码流程:
1.在spider的构造方法中创建一个浏览器对象(作为当前spider的一个属性)
2.重写spider的一个方法closed(self,spider),在该方法中执行浏览器关闭的操作
3.在下载中间件的process_response方法中,通过spider参数获取浏览器对象
4.在中间件的process_response中定制基于浏览器自动化的操作代码(获取动态加载出来的页面源码数据)
5.实例化一个响应对象,且将page_source返回的页面源码封装到该对象中
6.返回该新的响应对象
from scrapy import signals from scrapy.http import HtmlResponse from time import sleep class Test333DownloaderMiddleware(object): # Not all methods need to be defined. If a method is not defined, # scrapy acts as if the downloader middleware does not modify the # passed objects. def process_request(self, request, spider): # Called for each request that goes through the downloader # middleware. # Must either: # - return None: continue processing this request # - or return a Response object # - or return a Request object # - or raise IgnoreRequest: process_exception() methods of # installed downloader middleware will be called return None def process_response(self, request, response, spider): # Called with the response returned from the downloader. # Must either; # - return a Response object # - return a Request object # - or raise IgnoreRequest # 获取动态加载出来的数据 bro = spider.bro bro.get(url=request.url) sleep(3) bro.execute_script('window.scrollTo(0,document.body.scrollHeight)') # 包含了动态加载出来的新闻数据 page_text = bro.page_source sleep(3) return HtmlResponse(url=spider.bro.current_url,body=page_text,encoding="utf-8",request=request) def process_exception(self, request, exception, spider): # Called when a download handler or a process_request() # (from other downloader middleware) raises an exception. # Must either: # - return None: continue processing this exception # - return a Response object: stops process_exception() chain # - return a Request object: stops process_exception() chain pass
# -*- coding: utf-8 -*- import scrapy from selenium import webdriver class WangyiSpider(scrapy.Spider): name = 'wangyi' # allowed_domains = ['www.xxx.com'] start_urls = ['http://war.163.com/'] def __init__(self): self.bro = webdriver.Chrome(executable_path=r'D:\pathon 课程\123爬虫\s15day137\s15day137爬虫\day_03_爬虫\chromedriver.exe') def parse(self, response): div_list = response.xpath('//div[@class="data_row news_article clearfix "]') for div in div_list: title = div.xpath('.//div[@class="news_title"]/h3/a/text()').extract_first() print(title) def close(self, spider): print("关闭") self.bro.quit()
# settings: DOWNLOADER_MIDDLEWARES = { 'test333.middlewares.Test333DownloaderMiddleware': 543, }
- 八 如何提升scrapy爬取数据的i效率:
增加并发：
默认scrapy开启的并发线程为32个，可以适当进行增加。在settings配置文件中修改CONCURRENT_REQUESTS = 100值为100,并发设置成了为100。
降低日志级别：
在运行scrapy时，会有大量日志信息的输出，为了减少CPU的使用率。可以设置log输出信息为INFO或者ERROR即可。在配置文件中编写：LOG_LEVEL = ‘INFO’
禁止cookie：
如果不是真的需要cookie，则在scrapy爬取数据时可以禁止cookie从而减少CPU的使用率，提升爬取效率。在配置文件中编写：COOKIES_ENABLED = False
禁止重试：
对失败的HTTP进行重新请求（重试）会减慢爬取速度，因此可以禁止重试。在配置文件中编写：RETRY_ENABLED = False
减少下载超时：
如果对一个非常慢的链接进行爬取，减少下载超时可以能让卡住的链接快速被放弃，从而提升效率。在配置文件中进行
