深入理解Python Scrapy一【初识Scrapy】

文章目录

为什么选择Scrapy

在开始讲解Scrapy之前，先来和大家谈谈为什么选择Scrapy。在一年的爬虫开发过程中，我遇到过大大小小的问题，例如：

网页/接口不稳定从而导致超时
开发的爬虫性能低下，通常需要启动多个进程来提升性能
不同工程师开发的爬虫程序结构和风格参差不齐难以管理和维护
程序耦合度过高难以拓展
随机UA，随机代理，日志记录等额外工具需自己实现增大工作量
…

针对以上种种问题，我们选择使用Scrapy。

Scrapy是什么

An open source and collaborative framework for extracting the data you need from websites.
In a fast, simple, yet extensible way.

Scrapy是适用于Python的一个快速、简单、功能强大的web爬虫框架，通常用于抓取web站点并从页面中提取结构化的数据，也可以用来做监控与自动化测试。架构图如下所示：
在这里插入图片描述

Scrapy如何工作

理解工作原理更有益于后面的学习（也可先看后面的快速上手后再返回来看这里），Scrapy运行流程图如下所示：
在这里插入图片描述

运行过程如下：

程序启动后将会创建一个/多个Spiders（爬虫），Spiders会将Requests（请求）经过SpiderMiddlewares（爬虫中间件）加工，再交给Engine（引擎）。
Engine将Spiders传递过来多个请求转交给Scheduler（调度器），由调度器来安排请求。
Scheduler将需要马上执行的请求交回给Engine。
Engine将请求经过DownloaderMiddlewares（下载器中间件）加工，再发送给Downloader（下载器）。
Downloader使用Requests完成页面/接口的下载，并生成Responses（响应）, 将Responses经过DownloaderMiddlewares再转交给Engine。
Engine将Responses经过SpiderMiddlewares交回给爬虫处理Responses。
Spiders处理Responses后产生的结果返回给Engine。（Spiders处理 Responses）
步骤7 中Spiders处理结果返回的Requests对象将回到步骤2;返回的Items（数据结构化对象）或者dict（字典对象）将交给ItemPipelines（数据管道）处理。
通过定制ItemPipelines来控制数据如何持久化及处理。

开始使用Scrapy

1.安装Scrapy

通过如下命令安装Scrapy：

pip install scrapy

Scrapy安装完成会提供一个scrapy工具, 通过命令scrapy --help显示如下则表示安装成功：

> scrapy --help
Scrapy 2.6.2 - no active project

Usage:
  scrapy <command> [options] [args]

Available commands:
  bench         Run quick benchmark test
  commands
  fetch         Fetch a URL using the Scrapy downloader
  genspider     Generate new spider using pre-defined templates
  runspider     Run a self-contained spider (without creating a project)
  settings      Get settings values
  shell         Interactive scraping console
  startproject  Create new project
  version       Print Scrapy version
  view          Open URL in browser, as seen by Scrapy

  [ more ]      More commands available when run from project directory

Use "scrapy <command> -h" to see more info about a command

2.创建scrapy项目

通过命令scrapy startproject xxx创建一个Scrapy项目：

scrapy startproject MySpider

命令执行之后在当前目录下会生成一个MySpider的目录，目录结构如下所示：

MySpider/
├─scrapy.cfg
└─MySpider/
  ├─items.py
  ├─middlewares.py
  ├─pipelines.py
  ├─settings.py
  ├─__init__.py
  └─spiders/
    └─__init__.py

items.py文件存放自定义的Items
middlewares.py 文件存放SpiderMiddlewares和DownloaderMiddlewares
pipelines.py 文件存放自定义的ItemPipelines
settings.py 文件存放全局的配置信息
spiders/ 目录存放所有Spiders

之后以第一个MySpider/目录作为项目根目录

3.创建Scrapy爬虫

创建Scrapy爬虫命令scrapy genspider [spidername] [allow_domain] 以慢慢买历史价格接口为例，创建慢慢买爬虫：

scrapy genspider manmanbuy manmanbuy.com

慢慢买历史价格爬取流程：

访问 http://tool.manmanbuy.com/HistoryLowest.aspx 页面获取隐藏的 <input id=“ticket” …> 标签的 value 值。

通过步骤1 获取的 value 值，加工生成请求头的 Authorization 参数

生成请求参数 token 的值

调用 http://tool.manmanbuy.com/api.ashx 接口获取商品历史价格 (该接口依赖有效cookie, 如何获取有效cookie不是本文重点暂不说明)

此时在spiders/目录下就能找到生成的manmanbuy.py文件,文件内容如下:

import scrapy


class ManmanbuySpider(scrapy.Spider):
    name = 'manmanbuy'
    allowed_domains = ['manmanbuy.com']
    start_urls = ['http://manmanbuy.com/']

    def parse(self, response):
        pass

其中name为爬虫名称, allowed_domains 为允许访问的域名, start_urls 为启动爬取的地址。

Spider 支持两种启动爬取方式, 一种为便捷的配置start_urls方式, 启动后将直接爬取配置的url, 另一种为重写start_requests方法，返回自定义初始化的Request

4.Scrapy爬虫开发

4.1. 编写Spiders（MySpider/spiders/manmanbuy.py）：

import scrapy
from urllib.parse import quote
import hashlib
import time
import copy


class ManmanbuySpider(scrapy.Spider):
    name = 'manmanbuy'
    allowed_domains = ['manmanbuy.com']

    def start_requests(self):
        # 以京东单个商品查询历史价格为例, 商品ID: 100011493273
        item_urls = ['https://item.jd.com/100011493273.html']
        # 定义请求头
        headers = {
    
    
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36',
        }
        # 第一步先从h5页面获取ticket参数
        for item_url in item_urls:
            yield scrapy.Request(url='http://tool.manmanbuy.com/HistoryLowest.aspx?url=' + item_url,
                                 headers=headers,
                                 # 透传参数
                                 meta={
    
    'key': item_url})

    def parse(self, response: scrapy.http.Response):
        # 从页面中获取ticket值
        ticket = response.css('#ticket')[0].attrib['value']
        # 获取下一段接口请求参数
        req = parse_req({
    
    'key': response.meta['key'], 'method': 'getHistoryTrend'})
        # 请求头
        headers = {
    
    
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36',
            # 计算auth
            'Authorization': parse_basic_auth(ticket),
        }
        return scrapy.FormRequest(url='http://tool.manmanbuy.com/api.ashx',
                                  method='POST',
                                  formdata=req,
                                  headers=headers,
                                  cookies=self.get_cookies(),
                                  # 自定义回调地址
                                  callback=self.parse_history_price)

    def get_cookies(self):
        # 省略获取cookie逻辑
        cks = '_ga=GA1.2.604426644.1596510819; ASP.NET_SessionId=bbyuxdftfkcf5mrijdgkmnc5; Hm_lvt_01a310dc95b71311522403c3237671ae=1658906329; Hm_lvt_85f48cee3e51cd48eaba80781b243db3=1658748396,1658906330; _gid=GA1.2.472137414.1658906330; _gat_gtag_UA_145348783_1=1; 60014_mmbuser=VQYJA1IFBTBSVwNdClFVUgdRUQcLUgdXDg1RBgNTAVVUVAZRBQFeAw%3d%3d; Hm_lpvt_85f48cee3e51cd48eaba80781b243db3=1658906625; Hm_lpvt_01a310dc95b71311522403c3237671ae=1658906625'
        cookies = {
    
    }
        for one in cks.split(';'):
            k, v = one.strip().split("=")
            cookies[k] = v
        return cookies

    def parse_history_price(self, response: scrapy.http.Response):
        # 输出相应
        self.logger.info(response.text)


def parse_basic_auth(ticket):
    """
    这是解析ticket的值啊，就是上面说的那逻辑
    """
    return 'BasicAuth ' + ticket[:160][-4:] + ticket[:160 - 4]


def parse_req(d):
    """
    这是解析请求,增加t和token参数
    """
    d['t'] = str(int(time.time() * 1000))
    n = copy.deepcopy(d)
    ks = list(n.keys())
    ks.sort()
    ask = 'c5c3f201a8e8fc634d37a766a0299218'
    mask = ask
    for k in ks:
        mask += f'{
      
      k}{
      
      quote(str(n[k])).replace("/", "%2F")}'
    mask += ask
    mask = mask.upper()
    md5 = hashlib.md5()
    md5.update(mask.encode('utf-8'))
    d['token'] = md5.hexdigest().upper()
    return d

4.2. 修改Settings（MySpider/settings.py）：

# robots.txt 文件检查, 默认为: true, 需要改为Flase
ROBOTSTXT_OBEY = False

运行scrapy crawl manmanbuy命令启动爬虫, 观察日志能够正常获取数据：

{
    
    "msg":"","code":0,"data":{
    
    "haveTrend":1,"changPriceRemark":"降幅5%","runtime":41,"zouShi_test":2,"changePriceCount":14,"spbh":"1|100011493273","spUrl":"https://item.jd.com/100011493273.html","spPic":"http://img13.360buyimg.com/n7/jfs/t1/201578/31/15673/77560/619479ceEd1bde507/c0dab826b71e0b84.jpg","currentPrice":1049.00,"spName":"荣耀Play5T 22.5W超级快充 5000mAh大电池 6.5英寸护眼屏 全网通8GB+128GB极光蓝","lowerDate":"2022-03-08T00:00:00+08:00","lowerPrice":899.00,"bjid":551120462,"zouShi":2,"siteId":1,"siteName":"京东商城","datePrice":"[1621353600000,1199.00,\"\"],[1621440000000,1199.00,\"\"],[1621526400000,1199.00,\"\"],[1621612800000,1199.00,\"\"],[1621699200000,1199.00,\"\"],[1621785600000,1199.00,\"\"],[1621872000000,1199.00,\"\"],[1621958400000,1199.00,\"\"],[1622044800000,1199.00,\"\"],[1622131200000,1199.00,\"\"],[1622217600000,1199.00,\"\"],[1622304000000,1199.00,\"\"],[1622390400000,1199.00,\"\"],[1622476800000,1199.00,\"\"],[1622563200000,1199.00,\"\"],[1622649600000,1199.00,\"\"],[1622736000000,1199.00,\"\"],[1622822400000,1199.00,\"\"],[1622908800000,1199.00,\"\"],[1622995200000,1199.00,\"\"],[1623081600000,1199.00,\"\"],[1623168000000,1199.00,\"\"],[1623254400000,1199.00,\"\"],[1623340800000,1199.00,\"1199元\"],[1623427200000,1199.00,\"\"],[1623513600000,1199.00,\"\"],[1623600000000,1199.00,\"\"],[1623686400000,1199.00,\"\"],[1623772800000,1199.00,\"\"],[1623859200000,1199.00,\"\"],[1623945600000,1099.00,\"购买1件,当前价:1199.00,满减：每满1180减100\"],[1624032000000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624118400000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624204800000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624291200000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624377600000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624464000000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624550400000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624636800000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624723200000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624809600000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624896000000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1624982400000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1625068800000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1625155200000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1625241600000,1139.00,\"购买1件,当前价:1199.00,可叠加优惠券2：满750减60\"],[1625328000000,1199.00,\"\"],[1625414400000,1199.00,\"\"],[1625500800000,1189.0,\"京东秒杀价:1189\"],[1625587200000,1199.00,\"\"],[1625673600000,1189.0,\"\"],[1625760000000,1199.0,\"\"],[1625846400000,1189.0,\"\"],[1625932800000,1199.0000,\"\"],[1626019200000,1199.0000,\"\"],[1626105600000,1189.0,\"\"],[1626192000000,1199.0,\"\"],[1626278400000,1189.0,\"\"],[1626364800000,1199.0000,\"\"],[1626451200000,1199.0000,\"\"],[1626537600000,1199.0000,\"\"],[1626624000000,1199.0000,\"\"],[1626710400000,1199.0000,\"\"],[1626796800000,1199.0000,\"\"],[1626883200000,1189.00,\"\"],[1626969600000,1199.0000,\"\"],[1627056000000,1199.0000,\"\"],[1627142400000,1199.0000,\"\"],[1627228800000,1199.0000,\"\"],[1627315200000,1189.00,\"\"],[1627401600000,1199.0000,\"\"],[1627488000000,1189.00,\"1189元\"],[1627574400000,1189.00,\"\"],[1627660800000,1199.00,\"\"],[1627747200000,1179.00,\"1179元\"],[1627833600000,1189.0000,\"\"],[1627920000000,1199.00,\"\"],[1628006400000,1199.00,\"\"],[1628092800000,1189.00,\"\"],[1628179200000,1189.0000,\"\"],[1628265600000,1189.0000,\"\"],[1628352000000,1189.0000,\"\"],[1628438400000,1199.00,\"\"],[1628524800000,1189.00,\"\"],[1628611200000,1199.0,\"\"],[1628697600000,1189.0000,\"\"],[1628784000000,1189.0000,\"\"],[1628870400000,1189.0000,\"\"],[1628956800000,1199.00,\"1199元\"],[1629043200000,1189.0000,\"\"],[1629129600000,1199.00,\"\"],[1629216000000,1189.00,\"\"],[1629302400000,1199.0000,\"\"],[1629388800000,1169.0,\"京东秒杀价:1169\"],[1629475200000,1199.00,\"\"],[1629561600000,1199.00,\"\"],[1629648000000,1199.00,\"\"],[1629734400000,1169.00,\"\"],[1629820800000,1199.0,\"\"],[1629907200000,1189.0,\"京东秒杀价:1189\"],[1629993600000,1199.00,\"\"],[1630080000000,1199.00,\"\"],[1630166400000,1199.00,\"\"],[1630252800000,1199.00,\"\"],[1630339200000,1189.00,\"1189元包邮\"],[1630425600000,1189.00,\"\"],[1630512000000,1175.00,\"购买1件,plus价格1175\"],[1630598400000,1189.00,\"\"],[1630684800000,1199.0,\"\"],[1630771200000,1189.0000,\"\"],[1630857600000,1199.0,\"\"],[1630944000000,1189.0000,\"\"],[1631030400000,1199.0,\"\"],[1631116800000,1099.00,\"购买1件,当前价:1199.00,满减：每满1180减100\"],[1631203200000,1189.0000,\"\"],[1631289600000,1189.00,\"\"],[1631376000000,1199.00,\"\"],[1631462400000,1189.0000,\"\"],[1631548800000,1189.0,\"\"],[1631635200000,1199.0,\"\"],[1631721600000,1189.00,\"\"],[1631808000000,1199.00,\"\"],[1631894400000,1189.0,\"\"],[1631980800000,1199.0,\"\"],[1632067200000,1169.00,\"\"],[1632153600000,1169.00,\"\"],[1632240000000,1169.00,\"\"],[1632326400000,1189.0,\"\"],[1632412800000,1169.0,\"\"],[1632499200000,1199.0,\"\"],[1632585600000,1169.00,\"\"],[1632672000000,1199.00,\"\"],[1632758400000,1169.0,\"\"],[1632844800000,1199.0000,\"\"],[1632931200000,1169.0,\"\"],[1633017600000,1169.0,\"\"],[1633104000000,1169.0,\"\"],[1633190400000,1169.00,\"\"],[1633276800000,1169.0000,\"\"],[1633363200000,1199.00,\"\"],[1633449600000,1199.00,\"\"],[1633536000000,1169.0,\"\"],[1633622400000,1169.0,\"\"],[1633708800000,1169.0,\"京东秒杀价:1169\"],[1633795200000,1169.0,\"\"]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5.Scrapy数据持久化开发

5.1. 编写Items（NySpider/items.py）：

import scrapy

class HistoryPriceItem(scrapy.Item):
    """
    自定义历史价格存储Item
    """
    # 商品URL
    itemUrl = scrapy.Field()
    # 图片URL
    picUrl = scrapy.Field()
    # 历史价格信息
    detailPrice = scrapy.Field()

5.2. 编写ItemPipelines（MySpider/pipelines.py）, 以文件存储为例：

import scrapy.crawler
from itemadapter import ItemAdapter
from scrapy import signals

class FilePipeline:

    def __init__(self, filename='store.txt'):
        self.filename = filename

    def process_item(self, item, spider):
        # 使用适配器包装item, 防止直接对item进行修改/删除影响后续Pipeline
        adapter = ItemAdapter(item)
        # 写入文件
        self.fp.write(adapter.get('itemUrl') + "    " + adapter.get('picUrl') + "    " + adapter.get('detailPrice') + '\n')
        return item

    @classmethod
    def from_crawler(cls, crawler:scrapy.crawler.Crawler):
        s = cls()
        # 通过信号绑定行为
        # 爬虫启动时创建文件fp
        crawler.signals.connect(s.opened, signal=signals.spider_opened)
        # 爬虫停止时关闭文件fp
        crawler.signals.connect(s.closed, signal=signals.spider_closed)
        return s

    def closed(self, spider):
        self.fp.close()

    def opened(self, spider):
        self.fp = open(self.filename, 'w', encoding='utf-8')
        self.fp.write('商品URL    主图URL    历史价格信息\n')

5.3. 修改Spiders（MySpider/spiders/manmanbuy.py）：

import scrapy
import json
from MySpider.items import HistoryPriceItem


class ManmanbuySpider(scrapy.Spider):
     # 省略未修改内容
     
     custom_settings = {
    
    
         # 配置使用的Item管道
        'ITEM_PIPELINES': {
    
    
            'MySpider.pipelines.FilePipeline': 300,
        }
    }
     
    def parse_history_price(self, response: scrapy.http.Response):
        # 解析价格响应
        self.logger.info(response.text)
        data = json.loads(response.text)
        # 返回Item
        return HistoryPriceItem(itemUrl=data['data']['spUrl'], picUrl=data['data']['spPic'], detailPrice=data['data']['datePrice'])

这里说明一下scrapy有5种添加配置方式，常用的有3种，高优先级配置会覆盖低优先级相同的Key的配置，不同的Key的配置则组合起来，按优先级从高到底分别是：

命令行配置
爬虫配置
项目全局配置

Spiders中的custom_settings参数就是爬虫配置

5.4. 运行scrapy crawl manmanbuy命令启动爬虫，观察当前目录发现生成一个store.txt文件，文件内容如下：

商品URL    主图URL    历史价格信息
https://item.jd.com/100011493273.html    http://img13.360buyimg.com/n7/jfs/t1/201578/31/15673/77560/619479ceEd1bde507/c0dab826b71e0b84.jpg    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则说明程序执行正常

PS: 若想要将数据持久化至Mysql/MongoDB/Elasticsearch，只需编写对应的ItemPipelines实现, 修改爬虫导入的ITEM_PIPELINES配置即可，实现数据持久化与爬虫逻辑的解耦。

结语

本文为大家简要说明了使用Scrapy的理由，以及通过一个按理为大家演示了如何开发一个Scrapy爬虫项目。后续将持续为大家带来Scrapy更多详细讲解。