scrapy自动抓取蛋壳公寓最新房源信息并存入sql数据库

Posted 2020-11-14 温良Miner

利用scrapy抓取蛋壳公寓上的房源信息，以北京市为例，目标url：https://www.dankegongyu.com/room/bj

思路分析

每次更新最新消息，都是在第一页上显示，因此考虑隔一段时间自动抓取第一页上的房源信息，实现抓取最新消息。

利用redis的set数据结构的特征，将每次抓取后的url存到redis中；

每次请求，将请求url与redis中的url对比，若redis中已存在该url，代表没有更新，忽略该次请求；若redis中不存在该url，代表该信息是新信息，抓取并将url存入到redis中。

分析页面源码，发现该网页属于静态网页；首先获取最新页面每条数据的url，请求该url，得到详细页面情况，所有数据均从详情页面获取。

代码实现

明确抓取字段

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

# Define here the models for your scraped items
#
# See documentation in:
# http://doc.scrapy.org/en/latest/topics/items.html

import scrapy

class DankeItem(scrapy.Item):
    """
    编辑带爬取信息字段
    """
    # 数据来源
    source = scrapy.Field()
    # 抓取时间
    utc_time = scrapy.Field()

    # 房间名称
    room_name = scrapy.Field()
    # 房间租金
    room_money = scrapy.Field()
    # 房间面积
    room_area = scrapy.Field()
    # 房间编号
    room_numb = scrapy.Field()
    # 房间户型
    room_type = scrapy.Field()
    # 租房方式
    rent_type = scrapy.Field()
    # 房间楼层
    room_floor = scrapy.Field()
    # 所在区域
    room_loca = scrapy.Field()
    # 所在楼盘
    estate_name = scrapy.Field()

编写爬虫逻辑

# -*- coding: utf-8 -*-
import scrapy
from scrapy.linkextractors import LinkExtractor
from scrapy.spiders import CrawlSpider, Rule
from danke.items import DankeItem


class DankeSpider(CrawlSpider):

    # 爬虫名
    name = ‘dkgy3‘

    # 允许抓取的url
    allowed_domains = [‘dankegongyu.com‘]

    custom_settings = {‘DOWNLOAD_DELAY‘: 0.2}

    # 请求开始的url
    start_urls = [‘https://www.dankegongyu.com/room/sz‘]


    # rules属性
    rules = (

        #编写匹配详情页的规则，抓取到详情页的链接后不用跟进
        Rule(LinkExtractor(allow=r‘https://www.dankegongyu.com/room/d+‘), callback=‘parse_detail‘, follow=False),
    )

    def parse_detail(self, response):
        """
        解析详情页数据
        :param response:
        :return:
        """
        node_list = response.xpath(‘//div[@class="room-detail-right"]‘)
        for node in node_list:
            item = DankeItem()

            # 房间名称
            room_name = node.xpath(‘./div/h1/text()‘)
            item[‘room_name‘] = room_name.extract_first()

            # 房间租金
            room_money = node.xpath(‘./div[@class="room-price"]/div/span‘).xpath(‘string(.)‘).extract_first()

            # 有的房子有首月租金，和普通租金不同，因此匹配方式也不同
            if room_money:
                item[‘room_money‘] = room_money
            else:
                room_money = node.xpath(‘./div[@class="room-price hot"]/div/div[@class="room-price-num"]/text()‘).extract_first()
                item[‘room_money‘] = room_money
                print(room_money)

            # 房间面积
            room_area = node.xpath(‘./*/div[@class="room-detail-box"]/div[1]/label/text()‘).extract_first().split(‘：‘)[-1]
            item[‘room_area‘] = room_area

            # 房间编号
            room_numb = node.xpath(‘./*/div[@class="room-detail-box"]/div[2]/label/text()‘).extract_first().split(‘：‘)[-1]
            item[‘room_numb‘] = room_numb

            # 房间户型
            room_type = node.xpath(‘./*/div[@class="room-detail-box"]/div[3]/label/text()‘).extract_first().split(‘：‘)[-1]
            item[‘room_type‘] = room_type

            # 租房方式
            rent_type = node.xpath(‘./*/div[@class="room-detail-box"]/div[3]/label/b/text()‘).extract_first().split(‘：‘)[
                -1]
            item[‘rent_type‘] = rent_type

            # 所在楼层
            room_floor = node.xpath(‘./div[@class="room-list-box"]/div[2]/div[2]‘).xpath(‘string(.)‘).extract_first().split(‘：‘)[-1]
            item[‘room_floor‘] = room_floor

            # 所在区域
            room_loca = node.xpath(‘./div[@class="room-list-box"]/div[2]/div[3]/label/div/a[1]/text()‘).extract_first()
            item[‘room_loca‘] = room_loca

            # 所在楼盘
            estate_name = node.xpath(‘./div[@class="room-list-box"]/div[2]/div[3]/label/div/a[3]/text()‘).extract_first()
            item[‘estate_name‘] = estate_name

            yield item

编写下载中间件

下载中间件中实现两个逻辑：添加随机请求头和url存入redis中

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

# Define here the models for your spider middleware
#
# See documentation in:
# http://doc.scrapy.org/en/latest/topics/spider-middleware.html
import time
import random
import hashlib
import redis
from scrapy.exceptions import IgnoreRequest
from danke.settings import USER_AGENTS as ua

class DankeSpiderMiddleware(object):
    def process_request(self, request, spider):
        """
        给每一个请求随机分配一个代理
        :param request:
        :param spider:
        :return:
        """
        user_agent = random.choice(ua)
        request.headers[‘User-Agent‘] = user_agent

class DankeRedisMiddleware(object):
    """
    将第一个页面上的每一个url放入redis的set类型中，防止重复爬取
    """
    # 连接redis
    def __init__(self):
        self.redis = redis.StrictRedis(host=‘39.106.116.21‘, port=6379, db=3)

    def process_request(self, request, spider):

        # 将来自详情页的链接存到redis中
        if request.url.endswith(".html"):
            # MD5加密详情页链接
            url_md5 = hashlib.md5(request.url.encode()).hexdigest()

            # 添加到redis，添加成功返回True,否则返回False
            result = self.redis.sadd(‘dk_url‘, url_md5)

            # 添加失败，说明链接已爬取，忽略该请求
            if not result:
                raise IgnoreRequest

数据存储

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

from datetime import datetime
import pymysql

class DankeSourcePipeline(object):
    def process_item(self, item, spider):
        item[‘source‘] = spider.name
        item[‘utc_time‘] = str(datetime.utcnow())
        return item

class DankePipeline(object):

    def __init__(self):

        self.conn = pymysql.connect(
            host=‘39.106.116.21‘,
            port=3306,
            database=‘***‘,
            user=‘***‘,
            password=‘****‘,
            charset=‘utf8‘
        )
        # 实例一个游标
        self.cursor = self.conn.cursor()

    def process_item(self, item, spider):

        sql = ("insert into result_latest(标题, 租金, 面积, "
               "编号, 户型, 出租方式, 楼层, "
               "区域, 楼盘, 抓取时间, 数据来源)"
               "values (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)")

        item = dict(item)

        data = [
                item[‘room_name‘],
                item[‘room_money‘],
                item[‘room_area‘],
                item[‘room_numb‘],
                item[‘room_type‘],
                item[‘rent_type‘],
                item[‘room_floor‘],
                item[‘room_loca‘],
                item[‘estate_name‘],
                item[‘utc_time‘],
                item[‘source‘],
                ]
        self.cursor.execute(sql, data)
        # 提交数据
        self.conn.commit()

        return item

    def close_spider(self, spider):
        self.cursor.close()
        self.conn.close()

实现自动爬取

import os
import time

while True:
    """
    每隔20*60*60 自动爬取一次，实现自动更新
    """
    os.system("scrapy crawl dkgy3")
    time.sleep(20*60*60)


# from scrapy import cmdline
# cmdline.execute("scrapy crawl dkgy3".split())

完整代码

参见：https://github.com/zInPython/danke