Scrapy-redis分布式爬虫的实战案例【细节总结】

Myth丶恋晨 2021-09-19 00:44 357阅读 0赞

### **Scrapy-redis的原理分析：** ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzM0MzE0NA_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzM0MzE0NA_size_16_color_FFFFFF_t_70 1][]

### **实战案例【仅是在普通爬虫的基础上修改了几个细节】：** ###

第一步：配置setting.py【这里非常重要，决定分布式爬虫的成败关键】

### **配置官方文档是最完善的：[https://pypi.org/project/scrapy-redis/][https_pypi.org_project_scrapy-redis]** ###

**我的爬虫项目【scrapy\_distributed】配置【一定要开启你的redis数据库-否则会链接失败】**

// setting.py文件
    
    # -*- coding: utf-8 -*-
    
    # Scrapy settings for scrapy_first project
    #
    # For simplicity, this file contains only settings considered important or
    # commonly used. You can find more settings consulting the documentation:
    #
    #     https://doc.scrapy.org/en/latest/topics/settings.html
    #     https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
    #     https://doc.scrapy.org/en/latest/topics/spider-middleware.html
    
    BOT_NAME = 'scrapy_distributed'
    
    SPIDER_MODULES = ['scrapy_distributed.spiders']
    NEWSPIDER_MODULE = 'scrapy_distributed.spiders'
    
    # Crawl responsibly by identifying yourself (and your website) on the user-agent
    # 你要爬虫那个网站，就伪装它支持的爬虫平台,如百度阅读支持百度的爬虫
    # https://yuedu.baidu.com/robots.txt第一个User-agent: Baiduspider
    USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36"
    # Obey robots.txt rules
    ROBOTSTXT_OBEY = False
    
    # Configure item pipelines
    # Store scraped item in redis for post-processing.
    # See https://doc.scrapy.org/en/latest/topics/item-pipeline.html
    ITEM_PIPELINES = {
        'scrapy_distributed.pipelines.BookPipeline': 100,
        'scrapy_redis.pipelines.RedisPipeline': 300
    }
    # Enables scheduling storing requests queue in redis.
    SCHEDULER = "scrapy_redis.scheduler.Scheduler"
    
    # Ensure all spiders share same duplicates filter through redis.
    DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"
    
    # Specify the host and port to use when connecting to Redis (optional).
    REDIS_HOST = 'localhost'
    REDIS_PORT = 6379
    # Specify the full Redis URL for connecting (optional).
    # If set, this takes precedence over the REDIS_HOST and REDIS_PORT settings.
    # REDIS_URL = 'redis://user:pass@hostname:9001'
    REDIS_URL = 'redis://localhost:6379'

//myspider.py爬虫文件
    
    from scrapy import Request
    from scrapy_distributed.items import BookItem
    from scrapy.linkextractors import LinkExtractor
    from scrapy_redis.spiders import RedisSpider
    
    
    class BookSpider(RedisSpider):
        # name属性就是spider_name,【scrapy crawl spider_name】执行的文件名(必须是唯一)
        name = 'redis'
        allowed_domains = ['yuedu.baidu.com']
        # 分布式爬虫最大的区别之一就是start_urls是手动推动redis数据库,让爬虫程序从redis数据库中读取！
        # 然后所有爬虫【多台电脑】爬出来的数据进行汇总存取redis数据库中！
        # start_urls = ['https://yuedu.baidu.com/rank/hotsale']
    
        def __init__(self, name=None, **kwargs):
            super().__init__(name=name, **kwargs)
            self.title = ""
            self.price = ""
            self.tags = ""
            self.author = ""
            self.copyright = ""
            self.score = ""
    
        # Scrapy爬虫第一步：获取所有的链接,并用yield关键字创建一个所有链接的生成器
        def parse(self, response):
    
            url = response.urljoin(response.xpath("//div[@class='pager-inner']/a/@href").get())
            if url:
                yield Request(url=url, callback=self.parse)
    
            extractor = LinkExtractor(restrict_css=".book .al.title-link")
            for link in extractor.extract_links(response):
                yield Request(url=link.url, callback=self.parse_book)
    
        # Scrapy爬虫第二步：根据不同的链接发送Request请求,设定不同的parse解析回调函数逐步处理！
        def parse_book(self, response):
            for book in response.css(".doc-info-bd.clearfix .content-block"):
                item = BookItem()
                item["title"] = book.xpath(".//h1[@class='book-title']/text()").get()
                item["price"] = book.xpath(".//span[@class='numeric']/text()").get()
                item["author"] = book.xpath(".//a[@class='doc-info-field-val doc-info-author-link']/text()").get()
                item["copyright"] = book.xpath(".//a[@class='doc-info-field-val']/text()").get()
                item["tags"] = [value.get() for value in book.xpath(".//a[@class='tag-item doc-info-field-val mb5']/text()")]
                item["score"] = book.xpath(".//span[@class='doc-info-read-count']/text()").get()
                yield item

//items.py文件
    
    from scrapy  import Item,Field
    
    
    class BookItem(Item):
        title = Field()
        price = Field()
        tags = Field()
        author = Field()
        copyright = Field()
        score = Field()

//pipelines.py文件
    
    from scrapy.exceptions import DropItem
    from scrapy.pipelines.files import FilesPipeline
    from scrapy.pipelines.images import ImagesPipeline
    from scrapy import Request
    import re
    
    
    class BookPipeline:
    
        # from_crawler返回一个实例对象,初始化配置文件读取的数据
        def __init__(self, count):
            self.count = count
    
        # 所有的数据都会经过这个方法，这个方法会自动重复调用(items列表组数)
        # 如果要过滤某些数据，在这个函数中过滤条件抛出DropItem异常即可！
        def process_item(self, item, spider):
            # 数据全部去空格和\n
            item["title"] = item.get("title").replace(r"\n", "").strip()
            return item
    
        @classmethod
        def from_crawler(cls, crawler):
            return cls(
                # 从./settings.py文件中读取字段的方法
                count=crawler.settings["BOOK_FILTER_COUNT"]
            )
    
        def open_spider(self, spider):
            self.file = open('items.jl', 'w')
    
        def close_spider(self, spider):
            self.file.close()

准备开始启动分布式爬虫,执行下面两条命令
    
    
    // 在多台电脑同时运行爬虫【必须连接同一个redis数据库】
    scrapy crawl redis
    
    // 给你的爬虫名【你爬虫类的name属性】的start_urls设置起始url
    redis-cli lpush redis:start_urls https://yuedu.baidu.com/rank/hotsale

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzM0MzE0NA_size_16_color_FFFFFF_t_70 2][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzM0MzE0NA_size_16_color_FFFFFF_t_70]: /images/20210812/0b425380714f4d088e1c0214c143a989.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzM0MzE0NA_size_16_color_FFFFFF_t_70 1]: /images/20210812/001bf2eecfb24a628858faba3f74848a.png
[https_pypi.org_project_scrapy-redis]: https://pypi.org/project/scrapy-redis/
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzM0MzE0NA_size_16_color_FFFFFF_t_70 2]: /images/20210812/b41270707f72492eb9f1899cba769170.png