scrapyredis使用以及剖析[Python基础]
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提供了下面四种组件(components):(四种组件意味着这四个模块都要做相应的修改)
- Scheduler
- Duplication Filter
- Item Pipeline
- Base Spider
scrapy-redis组件
scrapy-redis架构
URL去重
python">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"
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
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
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
1 # DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter" 2 #
3 #
4 # from scrapy_redis.scheduler import Scheduler
5 # from scrapy_redis.queue import PriorityQueue
6 # SCHEDULER = "scrapy_redis.scheduler.Scheduler"
7 # 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)
8 # SCHEDULER_QUEUE_KEY = "%(spider)s:requests" # 调度器中请求存放在redis中的key
9 # SCHEDULER_SERIALIZER = "scrapy_redis.picklecompat" # 对保存到redis中的数据进行序列化,默认使用pickle
10 # 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
11 # 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
12 # 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)。
13 # SCHEDULER_DUPEFILTER_KEY = "%(spider)s:dupefilter" # 去重规则,在redis中保存时对应的key
14 # SCHEDULER_DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"# 去重规则对应处理的类
15 #
16 #
17 #
18 # REDIS_HOST = "10.211.55.13" # 主机名
19 # REDIS_PORT = 6379 # 端口
20 # # REDIS_URL = "redis://user:pass@hostname:9001" # 连接URL(优先于以上配置)
21 # # 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,})
22 # # REDIS_PARAMS["redis_cls"] = "myproject.RedisClient" # 指定连接Redis的Python模块 默认:redis.StrictRedis
23 # REDIS_ENCODING = "utf-8" # redis编码类型 默认:"utf-8"
24
25 配置文件
配置文件
1 import scrapy 2
3
4 class ChoutiSpider(scrapy.Spider):
5 name = "chouti"
6 allowed_domains = ["chouti.com"]
7 start_urls = (
8 "http://www.chouti.com/",
9 )
10
11 def parse(self, response):
12 for i in range(0,10):
13 yield
爬虫文件
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