文章出處 https://www.cnblogs.com/wupeiqi/articles/6912807.htmlhtml
scrapy-redis是一個基於redis的scrapy組件，經過它能夠快速實現簡單分佈式爬蟲程序，該組件本質上提供了三大功能：python
- scheduler - 調度器
- dupefilter - URL去重規則（被調度器使用）
- pipeline - 數據持久化
scrapy-redis組件
1. URL去重redis
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定義去重規則（被調度器調用並應用）
a. 內部會使用如下配置進行鏈接Redis
# REDIS_HOST = 'localhost' # 主機名
# REDIS_PORT = 6379 # 端口
# REDIS_URL = 'redis://user:pass@hostname:9001' # 鏈接URL（優先於以上配置）
# REDIS_PARAMS = {} # Redis鏈接參數 默認：REDIS_PARAMS = {'socket_timeout': 30,'socket_connect_timeout': 30,'retry_on_timeout': True,'encoding': REDIS_ENCODING,}）
# REDIS_PARAMS['redis_cls'] = 'myproject.RedisClient' # 指定鏈接Redis的Python模塊 默認：redis.StrictRedis
# REDIS_ENCODING = "utf-8" # redis編碼類型 默認：'utf-8'
b. 去重規則經過redis的集合完成，集合的Key爲：
key
=
defaults.DUPEFILTER_KEY
%
{
'timestamp'
:
int
(time.time())}
默認配置：
DUPEFILTER_KEY
=
'dupefilter:%(timestamp)s'
c. 去重規則中將url轉換成惟一標示，而後在redis中檢查是否已經在集合中存在
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參數位置不一樣時，計算結果一致；
-
默認請求頭不在計算範圍，include_headers能夠設置指定請求頭
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"
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2. 調度器json
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"""
調度器，調度器使用PriorityQueue（有序集合）、FifoQueue（列表）、LifoQueue（列表）進行保存請求，而且使用RFPDupeFilter對URL去重
a. 調度器
SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.PriorityQueue' # 默認使用優先級隊列（默認），其餘：PriorityQueue（有序集合），FifoQueue（列表）、LifoQueue（列表）
SCHEDULER_QUEUE_KEY = '%(spider)s:requests' # 調度器中請求存放在redis中的key
SCHEDULER_SERIALIZER = "scrapy_redis.picklecompat" # 對保存到redis中的數據進行序列化，默認使用pickle
SCHEDULER_PERSIST = True # 是否在關閉時候保留原來的調度器和去重記錄，True=保留，False=清空
SCHEDULER_FLUSH_ON_START = True # 是否在開始以前清空 調度器和去重記錄，True=清空，False=不清空
SCHEDULER_IDLE_BEFORE_CLOSE = 10 # 去調度器中獲取數據時，若是爲空，最多等待時間（最後沒數據，未獲取到）。
SCHEDULER_DUPEFILTER_KEY = '%(spider)s:dupefilter' # 去重規則，在redis中保存時對應的key
SCHEDULER_DUPEFILTER_CLASS = 'scrapy_redis.dupefilter.RFPDupeFilter'# 去重規則對應處理的類
"""
# 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
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3. 數據持久化cookie
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2.
定義持久化，爬蟲
yield
Item對象時執行RedisPipeline
a. 將item持久化到redis時，指定key和序列化函數
REDIS_ITEMS_KEY
=
'%(spider)s:items'
REDIS_ITEMS_SERIALIZER
=
'json.dumps'
b. 使用列表保存item數據
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4. 起始URL相關dom
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"""
起始URL相關
a. 獲取起始URL時，去集合中獲取仍是去列表中獲取？True，集合；False，列表
REDIS_START_URLS_AS_SET = False # 獲取起始URL時，若是爲True，則使用self.server.spop；若是爲False，則使用self.server.lpop
b. 編寫爬蟲時，起始URL從redis的Key中獲取
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'
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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' # 默認使用優先級隊列（默認），其餘：PriorityQueue（有序集合），FifoQueue（列表）、LifoQueue（列表） # SCHEDULER_QUEUE_KEY = '%(spider)s:requests' # 調度器中請求存放在redis中的key # SCHEDULER_SERIALIZER = "scrapy_redis.picklecompat" # 對保存到redis中的數據進行序列化，默認使用pickle # SCHEDULER_PERSIST = True # 是否在關閉時候保留原來的調度器和去重記錄，True=保留，False=清空 # SCHEDULER_FLUSH_ON_START = False # 是否在開始以前清空 調度器和去重記錄，True=清空，False=不清空 # SCHEDULER_IDLE_BEFORE_CLOSE = 10 # 去調度器中獲取數據時，若是爲空，最多等待時間（最後沒數據，未獲取到）。 # SCHEDULER_DUPEFILTER_KEY = '%(spider)s:dupefilter' # 去重規則，在redis中保存時對應的key # SCHEDULER_DUPEFILTER_CLASS = 'scrapy_redis.dupefilter.RFPDupeFilter'# 去重規則對應處理的類 # # # # REDIS_HOST = '10.211.55.13' # 主機名 # REDIS_PORT = 6379 # 端口 # # REDIS_URL = 'redis://user:pass@hostname:9001' # 鏈接URL（優先於以上配置） # # REDIS_PARAMS = {} # Redis鏈接參數 默認：REDIS_PARAMS = {'socket_timeout': 30,'socket_connect_timeout': 30,'retry_on_timeout': True,'encoding': REDIS_ENCODING,}） # # REDIS_PARAMS['redis_cls'] = 'myproject.RedisClient' # 指定鏈接Redis的Python模塊 默認：redis.StrictRedis # REDIS_ENCODING = "utf-8" # redis編碼類型 默認：'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