基于Scrapy 编写爬虫的完整流程，实战案例

本篇文章将借助一个简单实例来聊聊使用 Scrapy 编写爬虫的完整流程

1. 实战
   目标对象：

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我们需要爬取目标网站下帖子的基本信息

2-1 安装依赖

# 安装依赖
pip3 install Scrapy

# Mysql
pip3 install mysqlclient

2-2 创建项目及爬虫

分析目前地址，获取网站 HOST 及爬取地址，在某个文件夹下利用命令创建一个爬虫项目及一个爬虫

# 创建一个爬虫项目
scrapy startproject cqmmgo

# 打开文件夹
cd cqmmgo

# 创建一个爬虫
scrapy genspider talk 网站HOST

2-3 定义 Item 实体对象

在 items.py 文件中，将需要爬取的数据定义为 Item

比如，这里就需要爬取帖子标题、作者、阅读数、评论数、贴子 URL、发布时间

# items.py

import scrapy

# 杂谈
class CqTalkItem(scrapy.Item):
    # 标题
    title = scrapy.Field()

    # 作者
    author = scrapy.Field()

    # 查看次数
    watch_num = scrapy.Field()

    # 评论次数
    comment_num = scrapy.Field()

    # 地址
    address_url = scrapy.Field()

    # 发布时间
    create_time = scrapy.Field()

2-4 编写爬虫

在 spiders 文件夹下的爬虫文件中编写具体的爬虫逻辑

通过分析发现，帖子数据是通过模板直接渲染，非动态加载，因此我们直接对 response 进行数据解析

PS：解析方式这里推荐使用 Xpath

解析完成的数据组成上面定义的 Item 实体添加到生成器中

# spiders/talk.py

import scrapy
from cqmmgo.items import CqTalkItem
from cqmmgo.settings import talk_hour_before
from cqmmgo.utils import calc_interval_hour

class TalkSpider(scrapy.Spider):
    name = 'talk'
    allowed_domains = ['HOST']

    # 第1-5页数据
    start_urls = ['https://HOST/forum-233-{}.html'.format(i + 1) for i in range(5)]

    def parse(self, response):
        # 直接Xpath解析
        elements = response.xpath('//div[contains(@class,"list-data-item")]')

        for element in elements:
            item = CqTalkItem()
            # title = element.xpath('.//span[@class="has-businessTag"]/text()').extract_first()
            # title = element.xpath('.//span[@class="has-businessTag"]/text()').extract()
            # title = element.xpath('.//*[@class="subject"]/a').extract_first()
            title = element.xpath('.//*[@class="subject"]/a/@title').extract_first()
            author = element.xpath(".//span[@itemprop='帖子作者']/text()").extract_first()
            watch_num = element.xpath(".//span[@class='num-read']/text()").extract_first()
            comment_num = element.xpath(".//span[@itemprop='回复数']/text()").extract_first()
            address_url = "https:" + element.xpath('.//*[@class="subject"]/a/@href').extract_first()
            create_time = element.xpath('.//span[@class="author-time"]/text()').extract_first().strip()

            # 过滤超过设定小时之前的数据
            if calc_interval_hour(create_time) > talk_hour_before:
                continue

            print(
                f"标题:{title},作者:{author},观看：{watch_num},评论：{comment_num},地址：{address_url},发布时间:{create_time}")

            item['title'] = title
            item['author'] = author
            item['watch_num'] = watch_num
            item['comment_num'] = comment_num
            item['address_url'] = address_url
            item['create_time'] = create_time

            yield item

2-5 自定义随机 UA 下载中间件

在 middlewares.py 文件中自定义随机 User Agent 下载中间件

# middlewares.py
import random  # 导入随机模块

class RandomUADownloaderMiddleware(object):
    def process_request(self, request, spider):
        # UA列表
        USER_AGENT_LIST = [
            'Opera/9.20 (Macintosh; Intel Mac OS X; U; en)',
            'Opera/9.0 (Macintosh; PPC Mac OS X; U; en)',
            'iTunes/9.0.3 (Macintosh; U; Intel Mac OS X 10_6_2; en-ca)',
            'Mozilla/4.76 [en_jp] (X11; U; SunOS 5.8 sun4u)',
            'iTunes/4.2 (Macintosh; U; PPC Mac OS X 10.2)',
            'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.8; rv:16.0) Gecko/20120813 Firefox/16.0',
            'Mozilla/4.77 [en] (X11; I; IRIX;64 6.5 IP30)',
            'Mozilla/4.8 [en] (X11; U; SunOS; 5.7 sun4u)'
        ]

        # 随机生成一个UA
        agent = random.choice(USER_AGENT_LIST)

        # 设置到请求头中
        request.headers['User_Agent'] = agent

2-6 自定义下载管道 Pipline

在 piplines.py 文件中，自定义两个下载管道，分别将数据写入到本地 CSV 文件和 Mysql 数据中

PS：为了演示方便，这里仅展示同步写入 Mysql 数据库的方式

# piplines.py

from scrapy.exporters import CsvItemExporter
from cqmmgo.items import CqTalkItem
import MySQLdb  # 导入数据库模块

class TalkPipeline(object):
    """杂谈"""

    def __init__(self):
        self.file = open("./result/talk.csv", 'wb')
        self.exporter = CsvItemExporter(self.file, fields_to_export=[
            'title', 'author', 'watch_num', 'comment_num', 'create_time', 'address_url'
        ])
        self.exporter.start_exporting()

    def process_item(self, item, spider):
        if isinstance(item, CqTalkItem):
            self.exporter.export_item(item)
        return item

    # 关闭资源
    def close_spider(self, spider):
        self.exporter.finish_exporting()
        self.file.close()

# 数据存入到数据库（同步）
class MysqlPipeline(object):
    def __init__(self):
        # 链接mysql数据库
        self.conn = MySQLdb.connect("host", "root", "pwd", "cq", charset="utf8", use_unicode=True)
        self.cursor = self.conn.cursor()

    def process_item(self, item, spider):
        table_name = 'talk'

        # sql语句
        insert_sql = """
            insert into  {}(title,author,watch_num,comment_num,address_url,create_time,insert_time) values(%s,%s,%s,%s,%s,%s,%s)  
        """.format(table_name)

        # 从item获得数据，保存为元祖，插入数据库
        params = list()
        params.append(item.get("title", ""))
        params.append(item.get("author", ""))
        params.append(item.get("watch_num", 0))
        params.append(item.get("comment_num", 0))
        params.append(item.get("address_url", ""))
        params.append(item.get("create_time", ""))
        params.append(current_date())

        # 执行插入数据到数据库操作
        self.cursor.execute(insert_sql, tuple(params))

        # 提交，保存到数据库
        self.conn.commit()

        return item

    def close_spider(self, spider):
        """释放数据库资源"""
        self.cursor.close()
        self.conn.close()

当然，这里也可以定义一个数据去重的数据管道，通过帖子标题，对重复的数据不进行处理即可

# piplines.py

from scrapy.exceptions import DropItem

class DuplicatesPipeline(object):
    """
    Pipline去重
    """

    def __init__(self):
        self.talk_set = set()

    def process_item(self, item, spider):
        name = item['title']
        if name in self.talk_set:
            raise DropItem("重复数据，抛弃:%s" % item)

        self.talk_set.add(name)
        return item

2-7 配置爬虫配置文件

打开 settings.py 文件，对下载延迟时间、默认请求头、下载中间件、数据管道进行编辑

# settings.py

# Obey robots.txt rules
ROBOTSTXT_OBEY = False

DOWNLOAD_DELAY = 3

# Override the default request headers:
DEFAULT_REQUEST_HEADERS = {
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',
    'Accept-Language': 'zh-CN,zh;q=0.9',
    'Host': 'HOST',
    'Referer': 'https://HOST/forum-233-1.html',
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36',
}

DOWNLOADER_MIDDLEWARES = {
    'cqmmgo.middlewares.RandomUADownloaderMiddleware': 543,
    'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware': None,
}

ITEM_PIPELINES = {
    'cqmmgo.pipelines.TalkPipeline': 1,
    'cqmmgo.pipelines.MysqlPipeline': 6,
    'cqmmgo.pipelines.DuplicatesPipeline': 200,
    'cqmmgo.pipelines.CqmmgoPipeline': 300,
}

# 爬取时间限制
talk_hour_before = 24

2-8 爬虫主入口

在爬虫项目根目录下创建一个文件，通过下面的方式运行单个爬虫

# main.py

from scrapy.cmdline import execute
import sys, os

def start():
    sys.path.append(os.path.dirname(__file__))
    # 运行单个爬虫
    execute(["scrapy", "crawl", "talk"])

if __name__ == '__main__':
    start()

2. 最后
   如果 Scrapy 项目中包含多个爬虫，我们可以利用 CrawlerProcess 类并发执行多个爬虫

# main.py

from scrapy.utils.project import get_project_settings
from scrapy.crawler import CrawlerProcess

# 同时运行项目下的多个爬虫
def start():
    setting = get_project_settings()
    process = CrawlerProcess(setting)

    # 不运行的爬虫
    spider_besides = ['other']

    # 所有爬虫
    for spider_name in process.spiders.list():
        if spider_name in spider_besides:
            continue
        print("现在执行爬虫：%s" % (spider_name))
        process.crawl(spider_name)
    process.start()

if __name__ == '__main__':
    start()