Scrapy一个开源和协作的框架，其最初是为了页面抓取 (更确切来说, 网络抓取 )所设计的，使用它可以以快速、简单、可扩展的方式从网站中提取所需的数据。但目前Scrapy的用途十分广泛，可用于如数据挖掘、监测和自动化测试等领域，也可以应用在获取API所返回的数据(例如 Amazon Associates Web Services ) 或者通用的网络爬虫。
Scrapy 是基于twisted框架开发而来，twisted是一个流行的事件驱动的python网络框架。因此Scrapy使用了一种非阻塞（又名异步）的代码来实现并发。整体架构大致如下
The data flow in Scrapy is controlled by the execution engine, and goes like this:
- The Engine gets the initial Requests to crawl from the Spider.
- The Engine schedules the Requests in the Scheduler and asks for the next Requests to crawl.
- The Scheduler returns the next Requests to the Engine.
- The Engine sends the Requests to the Downloader, passing through the Downloader Middlewares (see
process_request()). - Once the page finishes downloading the Downloader generates a Response (with that page) and sends it to the Engine, passing through the Downloader Middlewares (see
process_response()). - The Engine receives the Response from the Downloader and sends it to the Spider for processing, passing through the Spider Middleware (see
process_spider_input()). - The Spider processes the Response and returns scraped items and new Requests (to follow) to the Engine, passing through the Spider Middleware (see
process_spider_output()). - The Engine sends processed items to Item Pipelines, then send processed Requests to the Scheduler and asks for possible next Requests to crawl.
- The process repeats (from step 1) until there are no more requests from the Scheduler.
Components：
- 引擎(EGINE)
引擎负责控制系统所有组件之间的数据流，并在某些动作发生时触发事件。有关详细信息，请参见上面的数据流部分。
- 调度器(SCHEDULER)
用来接受引擎发过来的请求, 压入队列中, 并在引擎再次请求的时候返回. 可以想像成一个URL的优先级队列, 由它来决定下一个要抓取的网址是什么, 同时去除重复的网址 - 下载器(DOWLOADER)
用于下载网页内容, 并将网页内容返回给EGINE，下载器是建立在twisted这个高效的异步模型上的 - 爬虫(SPIDERS)
SPIDERS是开发人员自定义的类，用来解析responses，并且提取items，或者发送新的请求 - 项目管道(ITEM PIPLINES)
在items被提取后负责处理它们，主要包括清理、验证、持久化（比如存到数据库）等操作 - 下载器中间件(Downloader Middlewares)
位于Scrapy引擎和下载器之间，主要用来处理从EGINE传到DOWLOADER的请求request，已经从DOWNLOADER传到EGINE的响应response，你可用该中间件做以下几件事- process a request just before it is sent to the Downloader (i.e. right before Scrapy sends the request to the website);
- change received response before passing it to a spider;
- send a new Request instead of passing received response to a spider;
- pass response to a spider without fetching a web page;
- silently drop some requests.
- 爬虫中间件(Spider Middlewares)
位于EGINE和SPIDERS之间，主要工作是处理SPIDERS的输入（即responses）和输出（即requests）
官网链接：https://docs.scrapy.org/en/latest/topics/architecture.html
二 安装
#Windows平台
1、pip3 install wheel #安装后，便支持通过wheel文件安装软件，wheel文件官网：https://www.lfd.uci.edu/~gohlke/pythonlibs
3、pip3 install lxml
4、pip3 install pyopenssl
5、下载并安装pywin32：https://sourceforge.net/projects/pywin32/files/pywin32/
6、下载twisted的wheel文件：http://www.lfd.uci.edu/~gohlke/pythonlibs/#twisted
7、执行pip3 install 下载目录\Twisted-17.9.0-cp36-cp36m-win_amd64.whl
8、pip3 install scrapy
#Linux平台
1、pip3 install scrapy
三 命令行工具
#1 查看帮助
scrapy -h
scrapy <command> -h
#2 有两种命令：其中Project-only必须切到项目文件夹下才能执行，而Global的命令则不需要
Global commands:
startproject #创建项目
genspider #创建爬虫程序
settings #如果是在项目目录下，则得到的是该项目的配置
runspider #运行一个独立的python文件，不必创建项目
shell #scrapy shell url地址 在交互式调试，如选择器规则正确与否
fetch #独立于程单纯地爬取一个页面，可以拿到请求头
view #下载完毕后直接弹出浏览器，以此可以分辨出哪些数据是ajax请求
version #scrapy version 查看scrapy的版本，scrapy version -v查看scrapy依赖库的版本
Project-only commands:
crawl #运行爬虫，必须创建项目才行，确保配置文件中ROBOTSTXT_OBEY = False
check #检测项目中有无语法错误
list #列出项目中所包含的爬虫名
edit #编辑器，一般不用
parse #scrapy parse url地址 --callback 回调函数 #以此可以验证我们的回调函数是否正确
bench #scrapy bentch压力测试
#3 官网链接
https://docs.scrapy.org/en/latest/topics/commands.html
#1、执行全局命令：请确保不在某个项目的目录下，排除受该项目配置的影响
scrapy startproject MyProject
cd MyProject
scrapy genspider baidu www.baidu.com
scrapy settings --get XXX #如果切换到项目目录下，看到的则是该项目的配置
scrapy runspider baidu.py
scrapy shell https://www.baidu.com
response
response.status
response.body
view(response)
scrapy view https://www.taobao.com #如果页面显示内容不全，不全的内容则是ajax请求实现的，以此快速定位问题
scrapy fetch --nolog --headers https://www.taobao.com
scrapy version #scrapy的版本
scrapy version -v #依赖库的版本
#2、执行项目命令：切到项目目录下
scrapy crawl baidu
scrapy check
scrapy list
scrapy parse http://quotes.toscrape.com/ --callback parse
scrapy bench
示范用法
四 项目结构以及爬虫应用简介
project_name/
scrapy.cfg
project_name/
__init__.py
items.py
pipelines.py
settings.py
spiders/
__init__.py
爬虫1.py
爬虫2.py
爬虫3.py
文件说明：
- scrapy.cfg 项目的主配置信息，用来部署scrapy时使用，爬虫相关的配置信息在settings.py文件中。
- items.py 设置数据存储模板，用于结构化数据，如：Django的Model
- pipelines 数据处理行为，如：一般结构化的数据持久化
- settings.py 配置文件，如：递归的层数、并发数，延迟下载等。强调:配置文件的选项必须大写否则视为无效，正确写法USER_AGENT=‘xxxx‘
- spiders 爬虫目录，如：创建文件，编写爬虫规则
注意：一般创建爬虫文件时，以网站域名命名
import scrapy
class XiaoHuarSpider(scrapy.spiders.Spider):
name = "xiaohuar" # 爬虫名称 *****
allowed_domains = ["xiaohuar.com"] # 允许的域名
start_urls = [
"http://www.xiaohuar.com/hua/", # 其实URL
]
def parse(self, response):
# 访问起始URL并获取结果后的回调函数
爬虫1.py
import sys,os sys.stdout=io.TextIOWrapper(sys.stdout.buffer,encoding=‘gb18030‘)
关于windows编码
五 Spiders
#在项目目录下新建：entrypoint.py from scrapy.cmdline import execute execute([‘scrapy‘, ‘crawl‘, ‘xiaohua‘])
默认只能在cmd中执行爬虫，如果想在pycharm中执行需要做
强调：配置文件的选项必须是大写，如X=‘1‘
# -*- coding: utf-8 -*-
import scrapy
from scrapy.linkextractors import LinkExtractor
from scrapy.spiders import CrawlSpider, Rule
class BaiduSpider(CrawlSpider):
name = ‘xiaohua‘
allowed_domains = [‘www.xiaohuar.com‘]
start_urls = [‘http://www.xiaohuar.com/v/‘]
# download_delay = 1
rules = (
Rule(LinkExtractor(allow=r‘p\-\d\-\d+\.html$‘), callback=‘parse_item‘,follow=True,),
)
def parse_item(self, response):
if url:
print(‘======下载视频==============================‘, url)
yield scrapy.Request(url,callback=self.save)
def save(self,response):
print(‘======保存视频==============================‘,response.url,len(response.body))
import time
import hashlib
m=hashlib.md5()
m.update(str(time.time()).encode(‘utf-8‘))
m.update(response.url.encode(‘utf-8‘))
filename=r‘E:\\mv\\%s.mp4‘ %m.hexdigest()
with open(filename,‘wb‘) as f:
f.write(response.body)
模版：CrawlSpider
https://docs.scrapy.org/en/latest/topics/spiders.html
六 Selectors
#1 //与/ #2 text #3、extract与extract_first:从selector对象中解出内容 #4、属性：xpath的属性加前缀@ #4、嵌套查找 #5、设置默认值 #4、按照属性查找 #5、按照属性模糊查找 #6、正则表达式 #7、xpath相对路径 #8、带变量的xpath
response.selector.css() response.selector.xpath() 可简写为 response.css() response.xpath() #1 //与/ response.xpath(‘//body/a/‘)# response.css(‘div a::text‘) >>> response.xpath(‘//body/a‘) #开头的//代表从整篇文档中寻找,body之后的/代表body的儿子 [] >>> response.xpath(‘//body//a‘) #开头的//代表从整篇文档中寻找,body之后的//代表body的子子孙孙 [<Selector xpath=‘//body//a‘ data=‘<a href="image1.html">Name: My image 1 <‘>, <Selector xpath=‘//body//a‘ data=‘<a href="image2.html">Name: My image 2 <‘>, <Selector xpath=‘//body//a‘ data=‘<a href=" image3.html">Name: My image 3 <‘>, <Selector xpath=‘//body//a‘ data=‘<a href="image4.html">Name: My image 4 <‘>, <Selector xpath=‘//body//a‘ data=‘<a href="image5.html">Name: My image 5 <‘>] #2 text >>> response.xpath(‘//body//a/text()‘) >>> response.css(‘body a::text‘) #3、extract与extract_first:从selector对象中解出内容 >>> response.xpath(‘//div/a/text()‘).extract() [‘Name: My image 1 ‘, ‘Name: My image 2 ‘, ‘Name: My image 3 ‘, ‘Name: My image 4 ‘, ‘Name: My image 5 ‘] >>> response.css(‘div a::text‘).extract() [‘Name: My image 1 ‘, ‘Name: My image 2 ‘, ‘Name: My image 3 ‘, ‘Name: My image 4 ‘, ‘Name: My image 5 ‘] >>> response.xpath(‘//div/a/text()‘).extract_first() ‘Name: My image 1 ‘ >>> response.css(‘div a::text‘).extract_first() ‘Name: My image 1 ‘ #4、属性：xpath的属性加前缀@ >>> response.xpath(‘//div/a/@href‘).extract_first() ‘image1.html‘ >>> response.css(‘div a::attr(href)‘).extract_first() ‘image1.html‘ #4、嵌套查找 >>> response.xpath(‘//div‘).css(‘a‘).xpath(‘@href‘).extract_first() ‘image1.html‘ #5、设置默认值 >>> response.xpath(‘//div[@id="xxx"]‘).extract_first(default="not found") ‘not found‘ #4、按照属性查找 response.xpath(‘//div[@id="images"]/a[@href="image3.html"]/text()‘).extract() response.css(‘#images a[@href="image3.html"]/text()‘).extract() #5、按照属性模糊查找 response.xpath(‘//a[contains(@href,"image")]/@href‘).extract() response.css(‘a[href*="image"]::attr(href)‘).extract() response.xpath(‘//a[contains(@href,"image")]/img/@src‘).extract() response.css(‘a[href*="imag"] img::attr(src)‘).extract() response.xpath(‘//*[@href="image1.html"]‘) response.css(‘*[href="image1.html"]‘) #6、正则表达式 response.xpath(‘//a/text()‘).re(r‘Name: (.*)‘) response.xpath(‘//a/text()‘).re_first(r‘Name: (.*)‘) #7、xpath相对路径 >>> res=response.xpath(‘//a[contains(@href,"3")]‘)[0] >>> res.xpath(‘img‘) [<Selector xpath=‘img‘ data=‘<img src="image3_thumb.jpg">‘>] >>> res.xpath(‘./img‘) [<Selector xpath=‘./img‘ data=‘<img src="image3_thumb.jpg">‘>] >>> res.xpath(‘.//img‘) [<Selector xpath=‘.//img‘ data=‘<img src="image3_thumb.jpg">‘>] >>> res.xpath(‘//img‘) #这就是从头开始扫描 [<Selector xpath=‘//img‘ data=‘<img src="image1_thumb.jpg">‘>, <Selector xpath=‘//img‘ data=‘<img src="image2_thumb.jpg">‘>, <Selector xpath=‘//img‘ data=‘<img src="image3_thumb.jpg">‘>, <Selector xpa th=‘//img‘ data=‘<img src="image4_thumb.jpg">‘>, <Selector xpath=‘//img‘ data=‘<img src="image5_thumb.jpg">‘>] #8、带变量的xpath >>> response.xpath(‘//div[@id=$xxx]/a/text()‘,xxx=‘images‘).extract_first() ‘Name: My image 1 ‘ >>> response.xpath(‘//div[count(a)=$yyy]/@id‘,yyy=5).extract_first() #求有5个a标签的div的id ‘images‘
https://docs.scrapy.org/en/latest/topics/selectors.html
七 Items
https://docs.scrapy.org/en/latest/topics/items.html
八 Item Pipeline
https://docs.scrapy.org/en/latest/topics/item-pipeline.html
九 Dowloader Middeware
https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
十 Spider Middleware
https://docs.scrapy.org/en/latest/topics/spider-middleware.html
十一 爬取亚马逊商品信息
1、
scrapy startproject Amazon
cd Amazon
scrapy genspider spider_goods www.amazon.cn
2、settings.py
ROBOTSTXT_OBEY = False
#请求头
DEFAULT_REQUEST_HEADERS = {
‘Referer‘:‘https://www.amazon.cn/‘,
‘User-Agent‘:‘Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.75 Safari/537.36‘
}
#打开注释
HTTPCACHE_ENABLED = True
HTTPCACHE_EXPIRATION_SECS = 0
HTTPCACHE_DIR = ‘httpcache‘
HTTPCACHE_IGNORE_HTTP_CODES = []
HTTPCACHE_STORAGE = ‘scrapy.extensions.httpcache.FilesystemCacheStorage‘
3、items.py
class GoodsItem(scrapy.Item):
# define the fields for your item here like:
# name = scrapy.Field()
#商品名字
goods_name = scrapy.Field()
#价钱
goods_price = scrapy.Field()
#配送方式
delivery_method=scrapy.Field()
4、spider_goods.py
# -*- coding: utf-8 -*-
import scrapy
from Amazon.items import GoodsItem
from scrapy.http import Request
from urllib.parse import urlencode
class SpiderGoodsSpider(scrapy.Spider):
name = ‘spider_goods‘
allowed_domains = [‘www.amazon.cn‘]
# start_urls = [‘http://www.amazon.cn/‘]
def __int__(self,keyword=None,*args,**kwargs):
super(SpiderGoodsSpider).__init__(*args,**kwargs)
self.keyword=keyword
def start_requests(self):
url=‘https://www.amazon.cn/s/ref=nb_sb_noss_1?‘
paramas={
‘__mk_zh_CN‘: ‘亚马逊网站‘,
‘url‘: ‘search - alias = aps‘,
‘field-keywords‘: self.keyword
}
url=url+urlencode(paramas,encoding=‘utf-8‘)
yield Request(url,callback=self.parse_index)
def parse_index(self, response):
print(‘解析索引页:%s‘ %response.url)
urls=response.xpath(‘//*[contains(@id,"result_")]/div/div[3]/div[1]/a/@href‘).extract()
for url in urls:
yield Request(url,callback=self.parse_detail)
next_url=response.urljoin(response.xpath(‘//*[@id="pagnNextLink"]/@href‘).extract_first())
print(‘下一页的url‘,next_url)
yield Request(next_url,callback=self.parse_index)
def parse_detail(self,response):
print(‘解析详情页:%s‘ %(response.url))
item=GoodsItem()
# 商品名字
item[‘goods_name‘] = response.xpath(‘//*[@id="productTitle"]/text()‘).extract_first().strip()
# 价钱
item[‘goods_price‘] = response.xpath(‘//*[@id="priceblock_ourprice"]/text()‘).extract_first().strip()
# 配送方式
item[‘delivery_method‘] = ‘‘.join(response.xpath(‘//*[@id="ddmMerchantMessage"]//text()‘).extract())
return item
5、自定义pipelines
#sql.py
import pymysql
import settings
MYSQL_HOST=settings.MYSQL_HOST
MYSQL_PORT=settings.MYSQL_PORT
MYSQL_USER=settings.MYSQL_USER
MYSQL_PWD=settings.MYSQL_PWD
MYSQL_DB=settings.MYSQL_DB
conn=pymysql.connect(
host=MYSQL_HOST,
port=int(MYSQL_PORT),
user=MYSQL_USER,
password=MYSQL_PWD,
db=MYSQL_DB,
charset=‘utf8‘
)
cursor=conn.cursor()
class Mysql(object):
@staticmethod
def insert_tables_goods(goods_name,goods_price,deliver_mode):
sql=‘insert into goods(goods_name,goods_price,delivery_method) values(%s,%s,%s)‘
cursor.execute(sql,args=(goods_name,goods_price,deliver_mode))
conn.commit()
@staticmethod
def is_repeat(goods_name):
sql=‘select count(1) from goods where goods_name=%s‘
cursor.execute(sql,args=(goods_name,))
if cursor.fetchone()[0] >= 1:
return True
if __name__ == ‘__main__‘:
cursor.execute(‘select * from goods;‘)
print(cursor.fetchall())
#pipelines.py
from Amazon.mysqlpipelines.sql import Mysql
class AmazonPipeline(object):
def process_item(self, item, spider):
goods_name=item[‘goods_name‘]
goods_price=item[‘goods_price‘]
delivery_mode=item[‘delivery_method‘]
if not Mysql.is_repeat(goods_name):
Mysql.insert_table_goods(goods_name,goods_price,delivery_mode)
6、创建数据库表
create database amazon charset utf8;
create table goods(
id int primary key auto_increment,
goods_name char(30),
goods_price char(20),
delivery_method varchar(50)
);
7、settings.py
MYSQL_HOST=‘localhost‘
MYSQL_PORT=‘3306‘
MYSQL_USER=‘root‘
MYSQL_PWD=‘123‘
MYSQL_DB=‘amazon‘
#数字代表优先级程度（1-1000随意设置，数值越低，组件的优先级越高）
ITEM_PIPELINES = {
‘Amazon.mysqlpipelines.pipelines.mazonPipeline‘: 1,
}
#8、在项目目录下新建：entrypoint.py
from scrapy.cmdline import execute
execute([‘scrapy‘, ‘crawl‘, ‘spider_goods‘,‘-a‘,‘keyword=iphone8‘])
https://pan.baidu.com/s/1boCEBT1
时间： 2024-10-25 04:21:22