13.scrapy框架的日志等级和请求传参

今日概要

• 日志等级
• 请求传参
• 如何提升scrapy的爬取效率

今日详情

一.Scrapy的日志等级

　　- 在使用scrapy crawl spiderFileName运行程序时，在终端里打印输出的就是scrapy的日志信息。

　　- 日志信息的种类：

　　　　　　　　ERROR ： 通常错误

　　　　　　　　WARNING : 警告

　　　　　　　　INFO : 通常的信息

　　　　　　　　DEBUG ： 调试信息

　　　　　　　

　　- 设置日志信息指定输出：

　　　　在settings.py配置文件中，加入

                    LOG_LEVEL = ‘指定日志信息种类’便可。

                    LOG_FILE = 'log.txt'则表示将日志信息写入到指定文件中进行存储。

二.请求传参

　　- 在某些状况下，咱们爬取的数据不在同一个页面中，例如，咱们爬取一个电影网站，电影的名称，评分在一级页面，而要爬取的其余电影详情在其二级子页面中。这时咱们就须要用到请求传参。

　　- 案例展现：爬取www.id97.com电影网，将一级页面中的电影名称，类型，评分一级二级页面中的上映时间，导演，片长进行爬取。

　　爬虫文件：

# -*- coding: utf-8 -*- import scrapy from moviePro.items import MovieproItem class MovieSpider(scrapy.Spider): name = 'movie' allowed_domains = ['www.id97.com'] start_urls = ['http://www.id97.com/'] def parse(self, response): div_list = response.xpath('//div[@class="col-xs-1-5 movie-item"]') for div in div_list: item = MovieproItem() item['name'] = div.xpath('.//h1/a/text()').extract_first() item['score'] = div.xpath('.//h1/em/text()').extract_first() #xpath(string(.))表示提取当前节点下全部子节点中的数据值（.）表示当前节点 item['kind'] = div.xpath('.//div[@class="otherinfo"]').xpath('string(.)').extract_first() item['detail_url'] = div.xpath('./div/a/@href').extract_first() #请求二级详情页面，解析二级页面中的相应内容,经过meta参数进行Request的数据传递 yield scrapy.Request(url=item['detail_url'],callback=self.parse_detail,meta={'item':item}) def parse_detail(self,response): #经过response获取item item = response.meta['item'] item['actor'] = response.xpath('//div[@class="row"]//table/tr[1]/a/text()').extract_first() item['time'] = response.xpath('//div[@class="row"]//table/tr[7]/td[2]/text()').extract_first() item['long'] = response.xpath('//div[@class="row"]//table/tr[8]/td[2]/text()').extract_first() #提交item到管道 yield item

　　items文件：

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://doc.scrapy.org/en/latest/topics/items.html import scrapy class MovieproItem(scrapy.Item): # define the fields for your item here like: name = scrapy.Field() score = scrapy.Field() time = scrapy.Field() long = scrapy.Field() actor = scrapy.Field() kind = scrapy.Field() detail_url = scrapy.Field()

    管道文件：

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html import json class MovieproPipeline(object): def __init__(self): self.fp = open('data.txt','w') def process_item(self, item, spider): dic = dict(item) print(dic) json.dump(dic,self.fp,ensure_ascii=False) return item def close_spider(self,spider): self.fp.close()

三.如何提升scrapy的爬取效率

增长并发：
    默认scrapy开启的并发线程为32个，能够适当进行增长。在settings配置文件中修改CONCURRENT_REQUESTS = 100值为100,并发设置成了为100。

下降日志级别：
    在运行scrapy时，会有大量日志信息的输出，为了减小CPU的使用率。能够设置log输出信息为INFO或者ERROR便可。在配置文件中编写：LOG_LEVEL = ‘INFO’

禁止cookie：
    若是不是真的须要cookie，则在scrapy爬取数据时能够进制cookie从而减小CPU的使用率，提高爬取效率。在配置文件中编写：COOKIES_ENABLED = False

禁止重试：
    对失败的HTTP进行从新请求（重试）会减慢爬取速度，所以能够禁止重试。在配置文件中编写：RETRY_ENABLED = False

减小下载超时：
    若是对一个很是慢的连接进行爬取，减小下载超时能够能让卡住的连接快速被放弃，从而提高效率。在配置文件中进行编写：DOWNLOAD_TIMEOUT = 10 超时时间为10s

测试案例：爬取校花网校花图片 www.521609.com

# -*- coding: utf-8 -*- import scrapy from xiaohua.items import XiaohuaItem class XiahuaSpider(scrapy.Spider): name = 'xiaohua' allowed_domains = ['www.521609.com'] start_urls = ['http://www.521609.com/daxuemeinv/'] pageNum = 1 url = 'http://www.521609.com/daxuemeinv/list8%d.html' def parse(self, response): li_list = response.xpath('//div[@class="index_img list_center"]/ul/li') for li in li_list: school = li.xpath('./a/img/@alt').extract_first() img_url = li.xpath('./a/img/@src').extract_first() item = XiaohuaItem() item['school'] = school item['img_url'] = 'http://www.521609.com' + img_url yield item if self.pageNum < 10: self.pageNum += 1 url = format(self.url % self.pageNum) #print(url) yield scrapy.Request(url=url,callback=self.parse) 

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://doc.scrapy.org/en/latest/topics/items.html import scrapy class XiaohuaItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() school=scrapy.Field() img_url=scrapy.Field() 

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html import json import os import urllib.request class XiaohuaPipeline(object): def __init__(self): self.fp = None def open_spider(self,spider): print('开始爬虫') self.fp = open('./xiaohua.txt','w') def download_img(self,item): url = item['img_url'] fileName = item['school']+'.jpg' if not os.path.exists('./xiaohualib'): os.mkdir('./xiaohualib') filepath = os.path.join('./xiaohualib',fileName) urllib.request.urlretrieve(url,filepath) print(fileName+"下载成功") def process_item(self, item, spider): obj = dict(item) json_str = json.dumps(obj,ensure_ascii=False) self.fp.write(json_str+'\n') #下载图片 self.download_img(item) return item def close_spider(self,spider): print('结束爬虫') self.fp.close() 

配置文件：

USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36' # Obey robots.txt rules ROBOTSTXT_OBEY = False # Configure maximum concurrent requests performed by Scrapy (default: 16) CONCURRENT_REQUESTS = 100 COOKIES_ENABLED = False LOG_LEVEL = 'ERROR' RETRY_ENABLED = False DOWNLOAD_TIMEOUT = 3 # Configure a delay for requests for the same website (default: 0) # See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 DOWNLOAD_DELAY = 3