提到爬虫框架，这里不得不提 Scrapy，它是一款非常强大的分布式异步爬虫框架，更加适用于企业级的爬虫！

项目地址：

https://github.com/scrapy/scrapy

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()