scrapy爬取新闻内容

转载 发布者:搬运代码打工人 发表于:2021-12-22

Scrapy的简介与安装

Scrapy 是一种快速的高级 web crawling 和 web scraping 框架,用于对网站内容进行爬取,并从其页面提取结构化数据

spider

spider是定义一个特定站点(或一组站点)如何被抓取的类,包括如何执行抓取(即跟踪链接)以及如何从页面中提取结构化数据(即抓取项)。换言之,spider是为特定站点(或者在某些情况下,一组站点)定义爬行和解析页面的自定义行为的地方。

Xpath

XPath 是一门在 XML 文档中查找信息的语言,XPath 可用来在 XML 文档中对元素和属性进行遍历

scrapy爬取新闻内容实战

在介绍这个项目之前先说一下这个项目的基本逻辑。

环境准备:

  1. 首先Ubuntu系统里面需要安装好MongoDB数据库,这个可以参考开源项目MongoDB基础
  2. python环境中安装好了scrapy, pymongo包

项目逻辑:

  1. 每天定时从新浪新闻网站上爬取新闻数据存储到mongodb数据库中,并且需要监控每天爬取新闻的状态(比如某天爬取的数据特别少可能是哪里出了问题,需要进行排查)
  2. 每天爬取新闻的时候只爬取当天日期的新闻,主要是为了防止相同的新闻重复爬取(当然这个也不能完全避免爬取重复的新闻,爬取新闻之后需要有一些单独的去重的逻辑)
  3. 爬虫项目中实现三个核心文件,分别是sina.py(spider),items.py(抽取数据的规范化及字段的定义),pipelines.py(数据写入数据库)

因为新闻爬取项目和新闻推荐系统是放在一起的,为了方便提前学习,下面直接给出项目的目录结构以及重要文件中的代码实现,最终的项目将会和新闻推荐系统一起开源出来

  1. 创建一个scrapy项目:
scrapy startproject sinanews
  1. 实现item.py逻辑
# Define here the models for your scraped items
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/items.html

import scrapy
from scrapy import Item, Field

# 定义新闻数据的字段
class SinanewsItem(scrapy.Item):
 """数据格式化,数据不同字段的定义 """
 title = Field() # 新闻标题
 ctime = Field() # 新闻发布时间
 url = Field() # 新闻原始url
 raw_key_words = Field() # 新闻关键词(爬取的关键词)
 content = Field() # 新闻的具体内容
 cate = Field() # 新闻类别
  1. 实现sina.py (spider)逻辑

    这里需要注意的一点,这里在爬取新闻的时候选择的是一个比较简洁的展示网站进行爬取的,相比直接去最新的新浪新闻观光爬取新闻简单很多,简洁的网站大概的链接:https://news.sina.com.cn/roll/#pageid=153&lid=2509&k=&num=50&page=1

# -*- coding: utf-8 -*-
import re
import json
import random
import scrapy
from scrapy import Request
from ..items import SinanewsItem
from datetime import datetime


class SinaSpider(scrapy.Spider):
 # spider的名字
 name = 'sina_spider'

 def __init__(self, pages=None):
  super(SinaSpider).__init__()

  self.total_pages = int(pages)
  # base_url 对应的是新浪新闻的简洁版页面,方便爬虫,并且不同类别的新闻也很好区分
  self.base_url = 'https://feed.mix.sina.com.cn/api/roll/get?pageid=153&lid={}&k=&num=50&page={}&r={}'
  # lid和分类映射字典
  self.cate_dict = {

"2510":  "国内",
"2511":  "国际",
"2669":  "社会",
"2512":  "体育",
"2513":  "娱乐",
"2514":  "军事",
"2515":  "科技",
"2516":  "财经",
"2517":  "股市",
"2518":  "美股"
  }

 def start_requests(self):
  """返回一个Request迭代器 """
  # 遍历所有类型的论文
  for cate_id in self.cate_dict.keys():
for page in range(1, self.total_pages + 1):
 lid = cate_id
 # 这里就是一个随机数,具体含义不是很清楚
 r = random.random()
 # cb_kwargs 是用来往解析函数parse中传递参数的
 yield Request(self.base_url.format(lid, page, r), callback=self.parse, cb_kwargs={
"cate_id": lid})
 
 def parse(self, response, cate_id):
  """解析网页内容,并提取网页中需要的内容 """
  json_result = json.loads(response.text) # 将请求回来的页面解析成json
  # 提取json中我们想要的字段
  # json使用get方法比直接通过字典的形式获取数据更方便,因为不需要处理异常
  data_list = json_result.get('result').get('data')
  for data in data_list:
item = SinanewsItem()

item['cate'] = self.cate_dict[cate_id]
item['title'] = data.get('title')
item['url'] = data.get('url')
item['raw_key_words'] = data.get('keywords')

# ctime = datetime.fromtimestamp(int(data.get('ctime')))
# ctime = datetime.strftime(ctime, '%Y-%m-%d %H:%M')

# 保留的是一个时间戳
item['ctime'] = data.get('ctime')

# meta参数传入的是一个字典,在下一层可以将当前层的item进行复制
yield Request(url=item['url'], callback=self.parse_content, meta={
'item': item})
 
 def parse_content(self, response):
  """解析文章内容 """
  item = response.meta['item']
  content = ''.join(response.xpath('//*[@id="artibody" or @id="article"]//p/text()').extract())
  content = re.sub(r'\u3000', '', content)
  content = re.sub(r'[ \xa0?]+', ' ', content)
  content = re.sub(r'\s*\n\s*', '\n', content)
  content = re.sub(r'\s*(\s)', r'\1', content)
  content = ''.join([x.strip() for x in content])
  item['content'] = content
  yield item 
  1. 数据持久化实现,piplines.py

    这里需要注意的就是实现SinanewsPipeline类的时候,里面很多方法都是固定的,不是随便写的,不同的方法又不同的功能,这个可以参考scrapy官方文档。

# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html
# useful for handling different item types with a single interface
import time
import datetime
import pymongo
from pymongo.errors import DuplicateKeyError
from sinanews.items import SinanewsItem
from itemadapter import ItemAdapter


# 新闻item持久化
class SinanewsPipeline:
 """数据持久化:将数据存放到mongodb中 """
 def __init__(self, host, port, db_name, collection_name):
  self.host = host
  self.port = port
  self.db_name = db_name
  self.collection_name = collection_name

 @classmethod 
 def from_crawler(cls, crawler):
  """自带的方法,这个方法可以重新返回一个新的pipline对象,并且可以调用配置文件中的参数 """
  return cls(
host = crawler.settings.get("MONGO_HOST"),
port = crawler.settings.get("MONGO_PORT"),
db_name = crawler.settings.get("DB_NAME"),
# mongodb中数据的集合按照日期存储
collection_name = crawler.settings.get("COLLECTION_NAME") + \
 "_" + time.strftime("%Y%m%d", time.localtime())
  )

 def open_spider(self, spider):
  """开始爬虫的操作,主要就是链接数据库及对应的集合 """
  self.client = pymongo.MongoClient(self.host, self.port)
  self.db = self.client[self.db_name]
  self.collection = self.db[self.collection_name]
  
 def close_spider(self, spider):
  """关闭爬虫操作的时候,需要将数据库断开 """
  self.client.close()

 def process_item(self, item, spider):
  """处理每一条数据,注意这里需要将item返回 注意:判断新闻是否是今天的,每天只保存当天产出的新闻,这样可以增量的添加新的新闻数据源 """
  if isinstance(item, SinanewsItem):
try:
 # TODO 物料去重逻辑,根据title进行去重,先读取物料池中的所有物料的title然后进行去重

 cur_time = int(item['ctime'])
 str_today = str(datetime.date.today())
 min_time = int(time.mktime(time.strptime(str_today + " 00:00:00", '%Y-%m-%d %H:%M:%S')))
 max_time = int(time.mktime(time.strptime(str_today + " 23:59:59", '%Y-%m-%d %H:%M:%S')))
 if cur_time > min_time and cur_time <= max_time:
  self.collection.insert(dict(item))
except DuplicateKeyError:
 """ 说明有重复 """
 pass
  return item
  1. 配置文件,settings.py
# Scrapy settings for sinanews project
#
# For simplicity, this file contains only settings considered important or
# commonly used. You can find more settings consulting the documentation:
#
# https://docs.scrapy.org/en/latest/topics/settings.html
# https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
# https://docs.scrapy.org/en/latest/topics/spider-middleware.html

from typing import Collection

BOT_NAME = 'sinanews'

SPIDER_MODULES = ['sinanews.spiders']
NEWSPIDER_MODULE = 'sinanews.spiders'


# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = 'sinanews (+http://www.yourdomain.com)'

# Obey robots.txt rules
ROBOTSTXT_OBEY = True

# Configure maximum concurrent requests performed by Scrapy (default: 16)
#CONCURRENT_REQUESTS = 32

# Configure a delay for requests for the same website (default: 0)
# See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
# DOWNLOAD_DELAY = 3
# The download delay setting will honor only one of:
#CONCURRENT_REQUESTS_PER_DOMAIN = 16
#CONCURRENT_REQUESTS_PER_IP = 16

# Disable cookies (enabled by default)
#COOKIES_ENABLED = False

# Disable Telnet Console (enabled by default)
#TELNETCONSOLE_ENABLED = False

# Override the default request headers:
#DEFAULT_REQUEST_HEADERS = {

# 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
# 'Accept-Language': 'en',
#}

# Enable or disable spider middlewares
# See https://docs.scrapy.org/en/latest/topics/spider-middleware.html
#SPIDER_MIDDLEWARES = {

# 'sinanews.middlewares.SinanewsSpiderMiddleware': 543,
#}

# Enable or disable downloader middlewares
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
#DOWNLOADER_MIDDLEWARES = {

# 'sinanews.middlewares.SinanewsDownloaderMiddleware': 543,
#}

# Enable or disable extensions
# See https://docs.scrapy.org/en/latest/topics/extensions.html
#EXTENSIONS = {

# 'scrapy.extensions.telnet.TelnetConsole': None,
#}

# Configure item pipelines
# See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
# 如果需要使用itempipline来存储item的话需要将这段注释打开
ITEM_PIPELINES = {

'sinanews.pipelines.SinanewsPipeline': 300,
}

# Enable and configure the AutoThrottle extension (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/autothrottle.html
#AUTOTHROTTLE_ENABLED = True
# The initial download delay
#AUTOTHROTTLE_START_DELAY = 5
# The maximum download delay to be set in case of high latencies
#AUTOTHROTTLE_MAX_DELAY = 60
# The average number of requests Scrapy should be sending in parallel to
# each remote server
#AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
# Enable showing throttling stats for every response received:
#AUTOTHROTTLE_DEBUG = False

# Enable and configure HTTP caching (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
#HTTPCACHE_ENABLED = True
#HTTPCACHE_EXPIRATION_SECS = 0
#HTTPCACHE_DIR = 'httpcache'
#HTTPCACHE_IGNORE_HTTP_CODES = []
#HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

MONGO_HOST = "127.0.0.1"
MONGO_PORT = 27017
DB_NAME = "SinaNews"
COLLECTION_NAME = "news"
  1. 监控脚本,monitor_news.py
# -*- coding: utf-8 -*-
import sys, time
import pymongo
import scrapy 
from sinanews.settings import MONGO_HOST, MONGO_PORT, DB_NAME, COLLECTION_NAME

if __name__ == "__main__":
 news_num = int(sys.argv[1])
 time_str = time.strftime("%Y%m%d", time.localtime())

 # 实际的collection_name
 collection_name = COLLECTION_NAME + "_" + time_str
 
 # 链接数据库
 client = pymongo.MongoClient(MONGO_HOST, MONGO_PORT)
 db = client[DB_NAME]
 collection = db[collection_name]

 # 查找当前集合中所有文档的数量
 cur_news_num = collection.count()

 print(cur_news_num)
 if (cur_news_num < news_num):
  print("the news nums of {}_{} collection is less then {}".\
format(COLLECTION_NAME, time_str, news_num))
  1. 运行脚本,run_scrapy_sina.sh
# -*- coding: utf-8 -*-
""" 新闻爬取及监控脚本 """

# 设置python环境
python="/home/recsys/miniconda3/envs/news_rec_py3/bin/python"

# 新浪新闻网站爬取的页面数量
page="1"
min_news_num="1000" # 每天爬取的新闻数量少于500认为是异常

# 爬取数据
scrapy crawl sina_spider -a pages=${
page}  
if [ $? -eq 0 ]; then
 echo "scrapy crawl sina_spider --pages ${page} success."
else
 echo "scrapy crawl sina_spider --pages ${page} fail."
fi

# 检查今天爬取的数据是否少于min_news_num篇文章,这里也可以配置邮件报警
python monitor_news.py ${
min_news_num}
if [ $? -eq 0 ]; then
 echo "run python monitor_news.py success."
else
 echo "run python monitor_news.py fail."
fi
  1. 运行项目命令
sh run_scrapy_sina.sh

最终查看数据库中的数据:

参考资料

  1. MongoDB基础

  2. Scrapy框架新手入门教程

  3. scrapy中文文档

  4. Xpath教程

  5. https://github.com/Ingram7/NewsinaSpider

  6. https://www.cnblogs.com/zlslch/p/6931838.html

相关标签:pythonmongodbscrapy♡♡♡♡♡♡建模通用♡♡♡♡♡♡
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