scrapy-redis怎么使用
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参考技术A master的爬虫运行时会把提取到的url封装成request放到redis中的数据库:“dmoz:requests”,并且从该数据库中提取request后下载网页,再把网页的内容存放到redis的另一个数据库中“dmoz:items”slave从master的redis中取出待抓取的request,下载完网页之后就把网页的内容发送回master的redis!
scrapy-redis使用以及剖析
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
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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
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""" 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
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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
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""" 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
# 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\'
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
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
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