Scrapy基础

Scrapy 使用了 Twisted异步网络库来处理网络通讯。整体架构大致如下

Scrapy主要包括了以下组件：

  • 引擎(Scrapy)
    用来处理整个系统的数据流处理, 触发事务(框架核心)
  • 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（抓取网页的网址或者说是链接）的优先队列, 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(Downloader)
    用于下载网页内容, 并将网页内容返回给蜘蛛(Scrapy下载器是建立在twisted这个高效的异步模型上的)
  • 爬虫(Spiders)
    爬虫是主要干活的, 用于从特定的网页中提取自己需要的信息, 即所谓的实体(Item)。用户也可以从中提取出链接,让Scrapy继续抓取下一个页面
  • 项目管道(Pipeline)
    负责处理爬虫从网页中抽取的实体，主要的功能是持久化实体、验证实体的有效性、清除不需要的信息。当页面被爬虫解析后，将被发送到项目管道，并经过几个特定的次序处理数据。
  • 下载器中间件(Downloader Middlewares)
    位于Scrapy引擎和下载器之间的框架，主要是处理Scrapy引擎与下载器之间的请求及响应。
  • 爬虫中间件(Spider Middlewares)
    介于Scrapy引擎和爬虫之间的框架，主要工作是处理蜘蛛的响应输入和请求输出。
  • 调度中间件(Scheduler Middewares)
    介于Scrapy引擎和调度之间的中间件，从Scrapy引擎发送到调度的请求和响应。

Scrapy运行流程大概如下：

  1. 引擎从调度器中取出一个链接(URL)用于接下来的抓取
  2. 引擎把URL封装成一个请求(Request)传给下载器
  3. 下载器把资源下载下来，并封装成应答包(Response)
  4. 爬虫解析Response
  5. 解析出实体（Item）,则交给实体管道进行进一步的处理
  6. 解析出的是链接（URL）,则把URL交给调度器等待抓取

一、基本使用

1. 基本命令

1. scrapy startproject 项目名称
   - 在当前目录中创建中创建一个项目文件（类似于Django）
 
2. scrapy genspider [-t template] <name> <domain>
   - 创建爬虫应用
   如：
      scrapy gensipider -t basic oldboy oldboy.com
      scrapy gensipider -t xmlfeed autohome autohome.com.cn
   PS:
      查看所有命令：scrapy gensipider -l
      查看模板命令：scrapy gensipider -d 模板名称
 
3. scrapy list
   - 展示爬虫应用列表
 
4. scrapy crawl 爬虫应用名称
   - 运行单独爬虫应用

2.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。（真正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
  • spiders      爬虫目录，如：创建文件，编写爬虫规则
# -*- coding: utf-8 -*-
import scrapy
from copy import deepcopy
import re


class SnSpider(scrapy.Spider):
    name = 'sn'
    allowed_domains = ['suning.com']
    start_urls = ['https://book.suning.com/']

    def parse(self, response):
        # 第一大类分组
        fir_div_list = response.xpath("//div[@class='menu-list']/div[@class='menu-item']")  # 文学艺术 少儿 社科
        # 第二大类分组
        sec_div_list = response.xpath("//div[@class='menu-sub']")

        nums = -1
        for fir_div in fir_div_list:
            item = dict()
            item["fir_category"] = fir_div.xpath("./dl/dt/h3/a/text()").extract_first()  # 文学艺术

            nums += 1
            sec_div = sec_div_list[nums]
            p_list = sec_div.xpath("./div[@class='submenu-left']/p")  # 小说 青春文学 艺术 动漫/幽默
            for p in p_list:
                item["sec_category"] = p.xpath("./a/text()").extract_first()
                item["href"] = p.xpath("./a/@href").extract_first()
                if item["href"] is not None:
                    yield scrapy.Request(
                        item["href"],
                        callback=self.parse_url,
                        meta={"item":deepcopy(item)}
                    )
            nums += 1
            # print(item)

    # 到达新的页面
    def parse_url(self, response):
        item = response.meta["item"]
        li_list = response.xpath("//ul[@class='clearfix']/li")
        for li in li_list:
            # 商品详情链接
            if li.xpath(".//a[1]/@href") is not None:
                product_href = "https:" + li.xpath(".//a[1]/@href").extract_first()
                # item["product_href"] = product_href
                # print(product_href)
                yield scrapy.Request(
                    product_href,
                    callback=self.get_deatil_content,
                    meta={"item": deepcopy(item)}
                )
        # 翻页
        cur_page = int(response.xpath("//a[@class='cur']/text()").extract_first())
        # 最大页码数
        page_nums_str = response.xpath("//span[@class='page-more']/text()").extract_first()
        # print(page_nums_str, "*"*20)
        if page_nums_str is not None:
            # print(page_nums_str, "*"*20)
            page_nums = int(re.findall(r"d+", page_nums_str)[0])
            print(page_nums, "*"*10)
            while cur_page <= page_nums:
                cur_url = response.xpath("//a[@class='cur']/@href").extract_first()
                # print(type(cur_url), "*"*50)
                # next_url = cur_url.replace(cur_url[11], str(cur_page), 1)
                next_url = cur_url[:10] + str(cur_page) + cur_url[11:]
                cur_page += 1
                print("下一页", next_url)
                # print(next_url)
                if next_url is not None:
                    next_url = "https://list.suning.com" + next_url
                    print("翻页")
                    yield scrapy.Request(
                        next_url,
                        callback=self.parse_url,
                        meta={"item": deepcopy(item)}
                    )

    def get_deatil_content(self, responce):
        item = responce.meta["item"]
        item["title"] = responce.xpath("//h1[@id='itemDisplayName']/text()").extract_first()
        item["author"] = responce.xpath("//ul[@class='bk-publish clearfix']/li[1]/text()").extract_first()
        if responce.xpath("//a[@id='bigImg']/img/@src") is not None:
            item["img"] = "https:" + responce.xpath("//a[@id='bigImg']/img/@src").extract_first()
        item["book_publish"] = responce.xpath("//ul[@class='bk-publish clearfix']/li[2]/text()").extract_first()
        item["publish_date"] = responce.xpath("//ul[@class='bk-publish clearfix']/li[3]/span[2]/text()").extract_first()
        yield item
        # print(item)
示例

3、选择器

from scrapy.selector import Selector, HtmlXPathSelector
from scrapy.http import HtmlResponse
html = """<!DOCTYPE html>
<html>
    <head lang="en">
        <meta charset="UTF-8">
        <title></title>
    </head>
    <body>
        <ul>
            <li class="item-"><a id='i1' href="link.html">first item</a></li>
            <li class="item-0"><a id='i2' href="llink.html">first item</a></li>
            <li class="item-1"><a href="llink2.html">second item<span>vv</span></a></li>
        </ul>
        <div><a href="llink2.html">second item</a></div>
    </body>
</html>
"""
response = HtmlResponse(url='http://example.com', body=html,encoding='utf-8')
# hxs = HtmlXPathSelector(response)
# print(hxs)
# hxs = Selector(response=response).xpath('//a')
# print(hxs)
# hxs = Selector(response=response).xpath('//a[2]')
# print(hxs)
# hxs = Selector(response=response).xpath('//a[@id]')
# print(hxs)
# hxs = Selector(response=response).xpath('//a[@id="i1"]')
# print(hxs)
# hxs = Selector(response=response).xpath('//a[@href="link.html"][@id="i1"]')
# print(hxs)
# hxs = Selector(response=response).xpath('//a[contains(@href, "link")]')
# print(hxs)
# hxs = Selector(response=response).xpath('//a[starts-with(@href, "link")]')
# print(hxs)
# hxs = Selector(response=response).xpath('//a[re:test(@id, "id+")]')
# print(hxs)
# hxs = Selector(response=response).xpath('//a[re:test(@id, "id+")]/text()').extract()
# print(hxs)
# hxs = Selector(response=response).xpath('//a[re:test(@id, "id+")]/@href').extract()
# print(hxs)
# hxs = Selector(response=response).xpath('/html/body/ul/li/a/@href').extract()
# print(hxs)
# hxs = Selector(response=response).xpath('//body/ul/li/a/@href').extract_first()
# print(hxs)
 
# ul_list = Selector(response=response).xpath('//body/ul/li')
# for item in ul_list:
#     v = item.xpath('./a/span')
#     # 或
#     # v = item.xpath('a/span')
#     # 或
#     # v = item.xpath('*/a/span')
#     print(v)

4、settings.py配置文件介绍

# -*- coding: utf-8 -*-

#  爬虫名称
BOT_NAME = 'step8_king'

#  爬虫应用路径
SPIDER_MODULES = ['step8_king.spiders']
NEWSPIDER_MODULE = 'step8_king.spiders'

# Crawl responsibly by identifying yourself (and your website) on the user-agent
# 客户端 user-agent请求头
# USER_AGENT = 'step8_king (+http://www.yourdomain.com)'

# Obey robots.txt rules
# 是否遵循机器人协议
# ROBOTSTXT_OBEY = False

# Configure maximum concurrent requests performed by Scrapy (default: 16)
# 并发请求数
# CONCURRENT_REQUESTS = 4

# Configure a delay for requests for the same website (default: 0)
# See http://scrapy.readthedocs.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
#  延迟下载秒数
# DOWNLOAD_DELAY = 2


# The download delay setting will honor only one of:
# 单域名访问并发数，并且延迟下次秒数也应用在每个域名
# CONCURRENT_REQUESTS_PER_DOMAIN = 2
# 单IP访问并发数，如果有值则忽略：CONCURRENT_REQUESTS_PER_DOMAIN，并且延迟下次秒数也应用在每个IP
# CONCURRENT_REQUESTS_PER_IP = 3

# Disable cookies (enabled by default)
#  是否支持cookie，cookiejar进行操作cookie
# COOKIES_ENABLED = True
# COOKIES_DEBUG = True

# Disable Telnet Console (enabled by default)
# Telnet用于查看当前爬虫的信息，操作爬虫等...
#    使用telnet ip port ，然后通过命令操作
# TELNETCONSOLE_ENABLED = True
# TELNETCONSOLE_HOST = '127.0.0.1'
# TELNETCONSOLE_PORT = [6023,]


# 默认请求头
# 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',
# }

# 爬虫允许的最大深度，可以通过meta查看当前深度；0表示无深度
# DEPTH_LIMIT = 3

# 爬取时，0表示深度优先Lifo(默认)；1表示广度优先FiFo
# 后进先出，深度优先
# DEPTH_PRIORITY = 0
# SCHEDULER_DISK_QUEUE = 'scrapy.squeue.PickleLifoDiskQueue'
# SCHEDULER_MEMORY_QUEUE = 'scrapy.squeue.LifoMemoryQueue'

# 先进先出，广度优先
# DEPTH_PRIORITY = 1
# SCHEDULER_DISK_QUEUE = 'scrapy.squeue.PickleFifoDiskQueue'
# SCHEDULER_MEMORY_QUEUE = 'scrapy.squeue.FifoMemoryQueue'

# 调度器队列
# SCHEDULER = 'scrapy.core.scheduler.Scheduler'
# from scrapy.core.scheduler import Scheduler


# 自动限速算法
    from scrapy.contrib.throttle import AutoThrottle
    自动限速设置
    1. 获取最小延迟 DOWNLOAD_DELAY
    2. 获取最大延迟 AUTOTHROTTLE_MAX_DELAY
    3. 设置初始下载延迟 AUTOTHROTTLE_START_DELAY
    4. 当请求下载完成后，获取其"连接"时间 latency，即：请求连接到接受到响应头之间的时间
    5. 用于计算的... AUTOTHROTTLE_TARGET_CONCURRENCY
    target_delay = latency / self.target_concurrency
    new_delay = (slot.delay + target_delay) / 2.0 # 表示上一次的延迟时间
    new_delay = max(target_delay, new_delay)
    new_delay = min(max(self.mindelay, new_delay), self.maxdelay)
    slot.delay = new_delay
"""

# 开始自动限速
# AUTOTHROTTLE_ENABLED = True
# The initial download delay
# 初始下载延迟
# AUTOTHROTTLE_START_DELAY = 5
# The maximum download delay to be set in case of high latencies
# 最大下载延迟
# AUTOTHROTTLE_MAX_DELAY = 10
# The average number of requests Scrapy should be sending in parallel to each remote server
# 平均每秒并发数
# AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0

# Enable showing throttling stats for every response received:
# 是否显示
# AUTOTHROTTLE_DEBUG = True

# Enable and configure HTTP caching (disabled by default)
# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings


"""
   启用缓存
   目的用于将已经发送的请求或相应缓存下来，以便以后使用
    
    from scrapy.downloadermiddlewares.httpcache import HttpCacheMiddleware
    from scrapy.extensions.httpcache import DummyPolicy
    from scrapy.extensions.httpcache import FilesystemCacheStorage
"""
# 是否启用缓存策略
# HTTPCACHE_ENABLED = True

# 缓存策略：所有请求均缓存，下次在请求直接访问原来的缓存即可
# HTTPCACHE_POLICY = "scrapy.extensions.httpcache.DummyPolicy"
# 缓存策略：根据Http响应头：Cache-Control、Last-Modified 等进行缓存的策略
# HTTPCACHE_POLICY = "scrapy.extensions.httpcache.RFC2616Policy"

# 缓存超时时间
# HTTPCACHE_EXPIRATION_SECS = 0

# 缓存保存路径
# HTTPCACHE_DIR = 'httpcache'

# 缓存忽略的Http状态码
# HTTPCACHE_IGNORE_HTTP_CODES = []

# 缓存存储的插件
# HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'
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原文地址：https://www.cnblogs.com/linyuhong/p/10171227.html