scrapy基础知识之 CrawlSpiders爬取lagou招聘保存在mysql（分布式）：

items.py

 import scrapy
 class LagouItem(scrapy.Item):
     # define the fields for your item here like:
     # name = scrapy.Field()
     #id
     # obj_id=scrapy.Field()
     #职位名
     positon_name=scrapy.Field()
     #工作地点
     work_place=scrapy.Field()
     #发布日期
     publish_time=scrapy.Field()
     #工资
     salary=scrapy.Field()
     #工作经验
     work_experience=scrapy.Field()
     #学历
     education=scrapy.Field()
     #full_time
     full_time=scrapy.Field()
     #标签
     tags=scrapy.Field()
     #公司名字
     company_name=scrapy.Field()
     # #产业
     # industry=scrapy.Field()
     #职位诱惑
     job_temptation=scrapy.Field()
     #工作描述
     job_desc=scrapy.Field()
     #公司logo地址
     logo_image=scrapy.Field()
      #领域
     field=scrapy.Field()
     #发展阶段
     stage=scrapy.Field()
     #公司规模
     company_size=scrapy.Field()
     # 公司主页
     home = scrapy.Field()
     #职位发布者
     job_publisher=scrapy.Field()
     #投资机构
     financeOrg=scrapy.Field()
     #爬取时间
     crawl_time=scrapy.Field()

lagou.py

# -*- coding: utf-8 -*-
import scrapy
from scrapy.linkextractors import LinkExtractor
from scrapy.spiders import CrawlSpider, Rule
from LaGou.items import LagouItem
from LaGou.utils.MD5 import get_md5
from datetime import datetime

class LagouSpider(CrawlSpider):
    name = 'lagou'
    allowed_domains = ['lagou.com']
    start_urls = ['https://www.lagou.com/zhaopin/']
    content_links=LinkExtractor(allow=(r"https://www.lagou.com/jobs/\d+.html"))
    page_links=LinkExtractor(allow=(r"https://www.lagou.com/zhaopin/\d+"))
    rules = (
        Rule(content_links, callback="parse_item", follow=False),
        Rule(page_links,follow=True)
    )

    def parse_item(self, response):
        item=LagouItem()
        #获取到公司拉钩主页的url作为ID
        # item["obj_id"]=get_md5(response.url)
        #公司名称
        item["company_name"]=response.xpath('//dl[@class="job_company"]//a/img/@alt').extract()[0]
        # 职位
        item["positon_name"]=response.xpath('//div[@class="job-name"]//span[@class="name"]/text()').extract()[0]
        #工资
        item["salary"]=response.xpath('//dd[@class="job_request"]//span[1]/text()').extract()[0]
        # 工作地点
        work_place=response.xpath('//dd[@class="job_request"]//span[2]/text()').extract()[0]
        item["work_place"]=work_place.replace("/","")
        # 工作经验
        work_experience=response.xpath('//dd[@class="job_request"]//span[3]/text()').extract()[0]
        item["work_experience"]=work_experience.replace("/","")
        # 学历
        education=response.xpath('//dd[@class="job_request"]//span[4]/text()').extract()[0]
        item["education"]=education.replace("/","")
        # full_time
        item['full_time']=response.xpath('//dd[@class="job_request"]//span[5]/text()').extract()[0]
        #tags
        tags=response.xpath('//dd[@class="job_request"]//li[@class="labels"]/text()').extract()
        item["tags"]=",".join(tags)
        #publish_time
        item["publish_time"]=response.xpath('//dd[@class="job_request"]//p[@class="publish_time"]/text()').extract()[0]
        # 职位诱惑
        job_temptation=response.xpath('//dd[@class="job-advantage"]/p/text()').extract()
        item["job_temptation"]=",".join(job_temptation)
        # 工作描述
        job_desc=response.xpath('//dd[@class="job_bt"]/div//p/text()').extract()
        item["job_desc"]=",".join(job_desc).replace("\xa0","").strip()
        #job_publisher
        item["job_publisher"]=response.xpath('//div[@class="publisher_name"]//span[@class="name"]/text()').extract()[0]
        # 公司logo地址
        logo_image=response.xpath('//dl[@class="job_company"]//a/img/@src').extract()[0]
        item["logo_image"]=logo_image.replace("//","")
        # 领域
        field=response.xpath('//ul[@class="c_feature"]//li[1]/text()').extract()
        item["field"]="".join(field).strip()
        # 发展阶段
        stage=response.xpath('//ul[@class="c_feature"]//li[2]/text()').extract()
        item["stage"]="".join(stage).strip()
        # 投资机构
        financeOrg=response.xpath('//ul[@class="c_feature"]//li[3]/p/text()').extract()
        if financeOrg:
            item["financeOrg"]="".join(financeOrg)
        else:
            item["financeOrg"]=""
        #公司规模
        if financeOrg:
             company_size= response.xpath('//ul[@class="c_feature"]//li[4]/text()').extract()
             item["company_size"]="".join(company_size).strip()
        else:
            company_size = response.xpath('//ul[@class="c_feature"]//li[3]/text()').extract()
            item["company_size"] = "".join(company_size).strip()
        # 公司主页
        item["home"]=response.xpath('//ul[@class="c_feature"]//li/a/@href').extract()[0]
        # 爬取时间
        item["crawl_time"]=datetime.now()

        yield item

pipelines.py

# -*- coding: utf-8 -*-

# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html

import pymysql
class LagouPipeline(object):

    def process_item(self, item, spider):
        con = pymysql.connect(host="127.0.0.1", user="root", passwd="", db="lagou",charset="utf8")
        cur = con.cursor()
        sql = ("insert into lagouwang(company_name,positon_name,salary,work_place,work_experience,education,full_time,tags,publish_time,job_temptation,job_desc,job_publisher,logo_image,field,stage,financeOrg,company_size,home,crawl_time)"
               "VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)")
        lis=(item["company_name"],item["positon_name"],item["salary"],item["work_place"],item["work_experience"],item["education"],item['full_time'],item["tags"],item["publish_time"],item["job_temptation"],item["job_desc"],item["job_publisher"],item["logo_image"],item["field"],item["stage"],item["financeOrg"],item["company_size"],item["home"],item["crawl_time"])
        cur.execute(sql, lis)
        con.commit()
        cur.close()
        con.close()

        return item

middlewares.py (主要是User_Agent的随机切换 没有加ip代理）

import random
from LaGou.settings import USER_AGENTS

class RandomUserAgent(object):
    def process_request(self, request, spider):
        useragent = random.choice(USER_AGENTS)

        request.headers.setdefault("User-Agent", useragent)

settings.py

BOT_NAME = 'LaGou'

SPIDER_MODULES = ['LaGou.spiders']
NEWSPIDER_MODULE = 'LaGou.spiders'
ROBOTSTXT_OBEY = False
DOWNLOAD_DELAY = 5
COOKIES_ENABLED = False
USER_AGENTS = [
    "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 2.0.50727; Media Center PC 6.0)",
    "Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.0.3705; .NET CLR 1.1.4322)",
    "Mozilla/4.0 (compatible; MSIE 7.0b; Windows NT 5.2; .NET CLR 1.1.4322; .NET CLR 2.0.50727; InfoPath.2; .NET CLR 3.0.04506.30)",
    "Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN) AppleWebKit/523.15 (KHTML, like Gecko, Safari/419.3) Arora/0.3 (Change: 287 c9dfb30)",
    "Mozilla/5.0 (X11; U; Linux; en-US) AppleWebKit/527+ (KHTML, like Gecko, Safari/419.3) Arora/0.6",
    "Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.2pre) Gecko/20070215 K-Ninja/2.1.1",
    "Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN; rv:1.9) Gecko/20080705 Firefox/3.0 Kapiko/3.0",
    "Mozilla/5.0 (X11; Linux i686; U;) Gecko/20070322 Kazehakase/0.4.5"
   ]
DOWNLOADER_MIDDLEWARES = {
      'LaGou.middlewares.RandomUserAgent': 1,
#    'LaGou.middlewares.MyCustomDownloaderMiddleware': 543,
}
ITEM_PIPELINES = {
      #'scrapy_redis.pipelines.RedisPipeline':300,

    'LaGou.pipelines.LagouPipeline': 300,
}

main.py(用于启动调试)

 #coding=utf-8
 from scrapy.cmdline import execute
 execute(["scrapy","crawl","lagou"])

在settings.py配置加入如下代码会实现分布式数据保存在redis里面，怎么从redis取出数据参考前几章

DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"
SCHEDULER = "scrapy_redis.scheduler.Scheduler"
SCHEDULER_PERSIST = True
ITEM_PIPELINES = {
      'scrapy_redis.pipelines.RedisPipeline':300,

    #'LaGou.pipelines.LagouPipeline': 300,
}

主要用到知识点：CrawlSpider的(LinkExtractor,Rule)，内容的处理(xpath,extract),字符的处理（join,replace,strip,split），User_Agent随机切换等