实战 | 如何利用 Scrapy 编写一个完整的爬虫!
大家好,我是安果!
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()
当然,除了 Scrapy 外,我们也可以考虑另外一款爬虫框架 Feapder
使用方法可以参考之前写的一篇文章
介绍一款能取代 Scrapy 的爬虫框架 - feapder
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