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
- 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()