如何在 Scrapy 上同时分页和解析多个页面

Question

我正在尝试从房地产网站上抓取多个网页。我已经成功地抓取了我的 URL 的第一页，但无法处理分页。我试图找到一个带有“红色”的类标签并识别下一个兄弟姐妹。我相信这会得到下一页的响应，并继续一遍又一遍。我读过一些例子，人们编写代码是为了能够同时解析多个页面。

是否可以进行并行/并发解析？我希望能够尽可能快地解析 90 页，但不知道如何实现。任何和所有的赞赏都非常感激。谢谢。

进度更新 1： 我弄清楚为什么我的 CSV 输出 UTF-8 并在我的 Pycharm IDE 中正确返回西里尔字符，但返回 ??使用 Excel 时的占位符。我已经能够通过 Excel Data>From Text/CSV 导入 CSV 文件来绕过这个问题。

进度更新 2： 我知道我可以在我的 start_request 函数和循环页面 (1,90) 甚至 (1,120) 中实现一个 for 循环，但这不是我想要的，这将让我的代码逐页解析，而不是同时解析。

HTML 片段：

<ul class="number-list">
  <li>
    <a href="/l-hdlh/l-hdlh-zarna/oron-suuts-zarna/ulan-bator/?cities=1" class="page-number js-page-filter red" data-page="1">1</a>
  </li>
 
  <li>
    <a href="/l-hdlh/l-hdlh-zarna/oron-suuts-zarna/ulan-bator/?cities=1&amp;page=2" class="page-number js-page-filter " data-page="2">2</a>
  </li>
  <li>
    <a href="/l-hdlh/l-hdlh-zarna/oron-suuts-zarna/ulan-bator/?cities=1&amp;page=3" class="page-number js-page-filter " data-page="3">3</a>
  </li>
  <li><span class="page-number">...</span></li>
  <li>
    <a href="/l-hdlh/l-hdlh-zarna/oron-suuts-zarna/ulan-bator/?cities=1&amp;page=89" class="page-number js-page-filter " data-page="89">89</a>
  </li>
  <li>
    <a href="/l-hdlh/l-hdlh-zarna/oron-suuts-zarna/ulan-bator/?cities=1&amp;page=90" class="page-number js-page-filter " data-page="90">90</a>
  </li>
  <div class="clear"></div>
</ul>

分页片段：

# handling pagination
next_page = response.xpath("//a[contains(@class,'red')]/parent::li/following-sibling::li/a/@href").extract_first()
if next_page:
    yield response.follow(next_page, callback=self.parse)

完整代码：

# -*- coding: utf-8 -*-
import scrapy
from scrapy import Request
import unicodecsv as csv
from datetime import datetime
from scrapy.crawler import CrawlerProcess

dt_today = datetime.now().strftime('%Y%m%d')
file_name = dt_today+' HPI Data'


# Create Spider class
class UneguiApartments(scrapy.Spider):
    name = "unegui_apts"
    allowed_domains = ["www.unegui.mn"]
    custom_settings = {"FEEDS": {f'{file_name}.csv': {'format': 'csv'}}
                       }

    def start_requests(self):
        urls = ['https://www.unegui.mn/l-hdlh/l-hdlh-zarna/oron-suuts-zarna/ulan-bator/']
        for url in urls:
            yield Request(url, self.parse)

    def parse(self, response, **kwargs):
        cards = response.xpath("//li[contains(@class,'announcement-container')]")

        # parse details
        for card in cards:
            name = card.xpath(".//a[@itemprop='name']/@content").extract_first()
            price = card.xpath(".//*[@itemprop='price']/@content").extract_first()
            rooms = card.xpath(".//div[contains(@class,'announcement-block__breadcrumbs')]/text()").extract_first().split('»')[0].strip()
            link = card.xpath(".//a[@itemprop='url']/@href").extract_first()
            date_block = card.xpath("normalize-space(.//div[contains(@class,'announcement-block__date')]/text())").extract_first().split(',')
            date = date_block[0].strip()
            city = date_block[1].strip()

            item = {'name': name,
                    'date': date,
                    'rooms': rooms,
                    'price': price,
                    'city': city,
                    }
            # follow absolute link to scrape deeper level
            yield response.follow(link, callback=self.parse_item, meta={'item': item})

    def parse_item(self, response):
        # retrieve previously scraped item between callbacks
        item = response.meta['item']

        # parse additional details
        list_span = response.xpath(".//span[contains(@class,'value-chars')]//text()").extract()
        list_a = response.xpath(".//a[contains(@class, 'value-chars')]//text()").extract()

        # get additional details from list of <span> tags, element by element
        floor_type = list_span[0].strip()
        num_balcony = list_span[1].strip()
        garage = list_span[2].strip()
        window_type = list_span[3].strip()
        door_type = list_span[4].strip()
        num_window = list_span[5].strip()

        # get additional details from list of <a> tags, element by element
        commission_year = list_a[0].strip()
        num_floors = list_a[1].strip()
        area_sqm = list_a[2].strip()
        floor = list_a[3].strip()
        leasing = list_a[4].strip()
        district = list_a[5].strip()
        address = list_a[6].strip()

        # update item with newly parsed data
        item.update({
            'district': district,
            'address': address,
            'area_sqm': area_sqm,
            'floor': floor,
            'commission_year': commission_year,
            'num_floors': num_floors,
            'num_windows': num_window,
            'num_balcony': num_balcony,
            'floor_type': floor_type,
            'window_type': window_type,
            'door_type': door_type,
            'garage': garage,
            'leasing': leasing
        })
        yield item

        # handling pagination
        next_page = response.xpath("//a[contains(@class,'red')]/parent::li/following-sibling::li/a/@href").extract_first()
        if next_page:
            yield response.follow(next_page, callback=self.parse)


# main driver
if __name__ == "__main__":
    process = CrawlerProcess()
    process.crawl(UneguiApartments)
    process.start()

Answer 1

如果我理解正确，您需要将“下一页”移至解析函数。我也只是获取“下一页”按钮的值并按照它。

# -*- coding: utf-8 -*-
import scrapy
from scrapy import Request
import unicodecsv as csv
from datetime import datetime
from scrapy.crawler import CrawlerProcess

dt_today = datetime.now().strftime('%Y%m%d')
file_name = dt_today+' HPI Data'


# Create Spider class
class UneguiApartments(scrapy.Spider):
    name = "unegui_apts"
    allowed_domains = ["www.unegui.mn"]
    custom_settings = {"FEEDS": {f'{file_name}.csv': {'format': 'csv'}}
                       }

    def start_requests(self):
        urls = ['https://www.unegui.mn/l-hdlh/l-hdlh-zarna/oron-suuts-zarna/ulan-bator/']
        for url in urls:
            yield Request(url, self.parse)

    def parse(self, response, **kwargs):
        cards = response.xpath("//li[contains(@class,'announcement-container')]")

        # parse details
        for card in cards:
            name = card.xpath(".//a[@itemprop='name']/@content").extract_first()
            price = card.xpath(".//*[@itemprop='price']/@content").extract_first()
            rooms = card.xpath(".//div[contains(@class,'announcement-block__breadcrumbs')]/text()").extract_first().split('»')[0].strip()
            link = card.xpath(".//a[@itemprop='url']/@href").extract_first()
            date_block = card.xpath("normalize-space(.//div[contains(@class,'announcement-block__date')]/text())").extract_first().split(',')
            date = date_block[0].strip()
            city = date_block[1].strip()

            item = {'name': name,
                    'date': date,
                    'rooms': rooms,
                    'price': price,
                    'city': city,
                    }
            # follow absolute link to scrape deeper level
            yield response.follow(link, callback=self.parse_item, meta={'item': item})

        # handling pagination
        next_page = response.xpath('//a[contains(@class, "number-list-next js-page-filter number-list-line")]/@href').get()
        if next_page:
            yield response.follow(next_page, callback=self.parse)

    def parse_item(self, response):
        # retrieve previously scraped item between callbacks
        item = response.meta['item']

        # parse additional details
        list_span = response.xpath(".//span[contains(@class,'value-chars')]//text()").extract()
        list_a = response.xpath(".//a[contains(@class, 'value-chars')]//text()").extract()

        # get additional details from list of <span> tags, element by element
        floor_type = list_span[0].strip()
        num_balcony = list_span[1].strip()
        garage = list_span[2].strip()
        window_type = list_span[3].strip()
        door_type = list_span[4].strip()
        num_window = list_span[5].strip()

        # get additional details from list of <a> tags, element by element
        commission_year = list_a[0].strip()
        num_floors = list_a[1].strip()
        area_sqm = list_a[2].strip()
        floor = list_a[3].strip()
        leasing = list_a[4].strip()
        district = list_a[5].strip()
        address = list_a[6].strip()

        # update item with newly parsed data
        item.update({
            'district': district,
            'address': address,
            'area_sqm': area_sqm,
            'floor': floor,
            'commission_year': commission_year,
            'num_floors': num_floors,
            'num_windows': num_window,
            'num_balcony': num_balcony,
            'floor_type': floor_type,
            'window_type': window_type,
            'door_type': door_type,
            'garage': garage,
            'leasing': leasing
        })
        yield item


# main driver
if __name__ == "__main__":
    process = CrawlerProcess()
    process.crawl(UneguiApartments)
    process.start()

这应该可行。