文章出处 https://www.cnblogs.com/wupeiqi/articles/6912807.html

scrapy-redis是一个基于redis的scrapy组件,通过它可以快速实现简单分布式爬虫程序,该组件本质上提供了三大功能:

  • scheduler - 调度器
  • dupefilter - URL去重规则(被调度器使用)
  • pipeline - 数据持久化

scrapy-redis组件

1. URL去重

?1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 定义去重规则(被调度器调用并应用)     `a. 内部会使用以下配置进行连接Redis         # REDIS_HOST = 'localhost'                            # 主机名        # REDIS_PORT = 6379                                   # 端口        # REDIS_URL = 'redis://user:pass@hostname:9001'       # 连接URL(优先于以上配置)        # REDIS_PARAMS  = {}                                  # Redis连接参数             默认:REDIS_PARAMS = {'socket_timeout': 30,'socket_connect_timeout': 30,'retry_on_timeout': True,'encoding': REDIS_ENCODING,})        # REDIS_PARAMS['redis_cls'] = 'myproject.RedisClient' # 指定连接Redis的Python模块  默认:redis.StrictRedis        # REDIS_ENCODING = "utf-8"                            # redis编码类型             默认:'utf-8'         b. 去重规则通过redis的集合完成,集合的Key为:             key = defaults.DUPEFILTER_KEY %` `{'timestamp': int(time.time())}        默认配置:            DUPEFILTER_KEY = 'dupefilter:%(timestamp)s'             ` `    c. 去重规则中将url转换成唯一标示,然后在redis中检查是否已经在集合中存在    ` `        from scrapy.utils import` `request        from` `scrapy.http import Request        ` `        req =` `Request(url='http://www.cnblogs.com/wupeiqi.html')        result =` `request.request_fingerprint(req)        print(result) # 8ea4fd67887449313ccc12e5b6b92510cc53675c                           PS:             -` `URL参数位置不同时,计算结果一致;            -` `默认请求头不在计算范围,include_headers可以设置指定请求头            示例:                from` `scrapy.utils import request                from scrapy.http import` `Request                                 req = Request(url='http://www.baidu.com?name=8&id=1',callback=lambda` `x:print(x),cookies={'k1':'vvvvv'})                result = request.request_fingerprint(req,include_headers=['cookies',])                ` `                print(result)                                 req = Request(url='http://www.baidu.com?id=1&name=8',callback=lambda` `x:print(x),cookies={'k1':666})                                 result = request.request_fingerprint(req,include_headers=['cookies',])                ` `                print(result)         """# Ensure all spiders share same duplicates filter through redis.# DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"`


2. 调度器

?1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 """`调度器,调度器使用PriorityQueue(有序集合)、FifoQueue(列表)、LifoQueue(列表)进行保存请求,并且使用RFPDupeFilter对URL去重    ` `    a. 调度器        SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.PriorityQueue'          # 默认使用优先级队列(默认),其他:PriorityQueue(有序集合),FifoQueue(列表)、LifoQueue(列表)        SCHEDULER_QUEUE_KEY = '%(spider)s:requests'                         # 调度器中请求存放在redis中的key        SCHEDULER_SERIALIZER = "scrapy_redis.picklecompat"                  # 对保存到redis中的数据进行序列化,默认使用pickle        SCHEDULER_PERSIST = True                                            # 是否在关闭时候保留原来的调度器和去重记录,True=保留,False=清空        SCHEDULER_FLUSH_ON_START = True                                     # 是否在开始之前清空 调度器和去重记录,True=清空,False=不清空        SCHEDULER_IDLE_BEFORE_CLOSE = 10                                    # 去调度器中获取数据时,如果为空,最多等待时间(最后没数据,未获取到)。        SCHEDULER_DUPEFILTER_KEY = '%(spider)s:dupefilter'                  # 去重规则,在redis中保存时对应的key        SCHEDULER_DUPEFILTER_CLASS = 'scrapy_redis.dupefilter.RFPDupeFilter'# 去重规则对应处理的类 """# Enables scheduling storing requests queue in redis.SCHEDULER =` `"scrapy_redis.scheduler.Scheduler"` `# Default requests serializer is pickle, but it can be changed to any module# with loads and dumps functions. Note that pickle is not compatible between# python versions.# Caveat: In python 3.x, the serializer must return strings keys and support# bytes as values. Because of this reason the json or msgpack module will not# work by default. In python 2.x there is no such issue and you can use# 'json' or 'msgpack' as serializers.# SCHEDULER_SERIALIZER = "scrapy_redis.picklecompat" # Don't cleanup redis queues, allows to pause/resume crawls.# SCHEDULER_PERSIST = True` `# Schedule requests using a priority queue. (default)# SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.PriorityQueue' # Alternative queues.# SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.FifoQueue'# SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.LifoQueue' # Max idle time to prevent the spider from being closed when distributed crawling.# This only works if queue class is SpiderQueue or SpiderStack,# and may also block the same time when your spider start at the first time (because the queue is empty).`# SCHEDULER_IDLE_BEFORE_CLOSE = 10  

3. 数据持久化

?1 2 3 4 5 6 7 8 2. 定义持久化,爬虫`yield Item对象时执行RedisPipeline    ` `    a. 将item持久化到redis时,指定key和序列化函数    ` `        REDIS_ITEMS_KEY =` `'%(spider)s:items'        REDIS_ITEMS_SERIALIZER = 'json.dumps'    ` `    b. 使用列表保存item数据`

4. 起始URL相关

?1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 """`起始URL相关     a. 获取起始URL时,去集合中获取还是去列表中获取?True,集合;False,列表        REDIS_START_URLS_AS_SET = False    # 获取起始URL时,如果为True,则使用self.server.spop;如果为False,则使用self.server.lpop    b. 编写爬虫时,起始URL从redis的Key中获取        REDIS_START_URLS_KEY = '%(name)s:start_urls'         """# If True, it uses redis' spop operation. This could be useful if you# want to avoid duplicates in your start urls list. In this cases, urls must# be added via sadd command or you will get a type error from redis.# REDIS_START_URLS_AS_SET = False # Default start urls key for RedisSpider and RedisCrawlSpider.`# REDIS_START_URLS_KEY = '%(name)s:start_urls'

scrapy-redis示例

scrapy-redis组件教程scrapy-redis组件教程scrapy-redis组件教程

# DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"
#
#
# from scrapy_redis.scheduler import Scheduler
# from scrapy_redis.queue import PriorityQueue
# SCHEDULER = "scrapy_redis.scheduler.Scheduler"
# SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.PriorityQueue'          # 默认使用优先级队列(默认),其他:PriorityQueue(有序集合),FifoQueue(列表)、LifoQueue(列表)
# SCHEDULER_QUEUE_KEY = '%(spider)s:requests'                         # 调度器中请求存放在redis中的key
# SCHEDULER_SERIALIZER = "scrapy_redis.picklecompat"                  # 对保存到redis中的数据进行序列化,默认使用pickle
# SCHEDULER_PERSIST = True                                            # 是否在关闭时候保留原来的调度器和去重记录,True=保留,False=清空
# SCHEDULER_FLUSH_ON_START = False                                    # 是否在开始之前清空 调度器和去重记录,True=清空,False=不清空
# SCHEDULER_IDLE_BEFORE_CLOSE = 10                                    # 去调度器中获取数据时,如果为空,最多等待时间(最后没数据,未获取到)。
# SCHEDULER_DUPEFILTER_KEY = '%(spider)s:dupefilter'                  # 去重规则,在redis中保存时对应的key
# SCHEDULER_DUPEFILTER_CLASS = 'scrapy_redis.dupefilter.RFPDupeFilter'# 去重规则对应处理的类
#
#
#
# REDIS_HOST = '10.211.55.13'                           # 主机名
# REDIS_PORT = 6379                                     # 端口
# # REDIS_URL = 'redis://user:pass@hostname:9001'       # 连接URL(优先于以上配置)
# # REDIS_PARAMS  = {}                                  # Redis连接参数             默认:REDIS_PARAMS = {'socket_timeout': 30,'socket_connect_timeout': 30,'retry_on_timeout': True,'encoding': REDIS_ENCODING,})
# # REDIS_PARAMS['redis_cls'] = 'myproject.RedisClient' # 指定连接Redis的Python模块  默认:redis.StrictRedis
# REDIS_ENCODING = "utf-8"                              # redis编码类型             默认:'utf-8'

scrapy-redis组件教程配置文件 scrapy-redis组件教程scrapy-redis组件教程scrapy-redis组件教程

import scrapy


class ChoutiSpider(scrapy.Spider):
    name = "chouti"
    allowed_domains = ["chouti.com"]
    start_urls = (
        'http://www.chouti.com/',
    )

    def parse(self, response):
        for i in range(0,10):
            yield

scrapy-redis组件教程爬虫文件

标签: URL, scrapy, redis, True, 组件, REDIS, SCHEDULER

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