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
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
"""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
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
"""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 scrapyclass 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