爬虫_url去重策略

时间:2023-03-08 19:44:53
爬虫_url去重策略

如何对url去重?

  1. 将访问url保存到数据库中,效率低,最简单
  2. 将url保存到set中,查询速度快,但当url达到1亿多条时候,占用太多内存空间
  3. 将url经过md5等方法哈希后保存到set中
  4. 用bitmap,讲访问过的url通过hash函数映射到某一位,很容易出现冲突,更能压缩
  5. bloomfilter方法对bitmap进行改进,多重hash函数,避免冲突

那,如何实现bloomfilter方式url去重?

#!/usr/bin/python3

__author__ = 'beimenchuixue'
__blog__ = 'http://www.cnblogs.com/2bjiujiu/' import mmh3
import redis
import math
import time class PyBloomFilter():
#内置100个随机种子
SEEDS = [543, 460, 171, 876, 796, 607, 650, 81, 837, 545, 591, 946, 846, 521, 913, 636, 878, 735, 414, 372,
344, 324, 223, 180, 327, 891, 798, 933, 493, 293, 836, 10, 6, 544, 924, 849, 438, 41, 862, 648, 338,
465, 562, 693, 979, 52, 763, 103, 387, 374, 349, 94, 384, 680, 574, 480, 307, 580, 71, 535, 300, 53,
481, 519, 644, 219, 686, 236, 424, 326, 244, 212, 909, 202, 951, 56, 812, 901, 926, 250, 507, 739, 371,
63, 584, 154, 7, 284, 617, 332, 472, 140, 605, 262, 355, 526, 647, 923, 199, 518] #capacity是预先估计要去重的数量
#error_rate表示错误率
#conn表示redis的连接客户端
#key表示在redis中的键的名字前缀
def __init__(self, capacity=1000000000, error_rate=0.00000001, conn=None, key='BloomFilter'):
self.m = math.ceil(capacity*math.log2(math.e)*math.log2(1/error_rate)) #需要的总bit位数
self.k = math.ceil(math.log1p(2)*self.m/capacity) #需要最少的hash次数
self.mem = math.ceil(self.m/8/1024/1024) #需要的多少M内存
self.blocknum = math.ceil(self.mem/512) #需要多少个512M的内存块,value的第一个字符必须是ascii码,所有最多有256个内存块
self.seeds = self.SEEDS[0:self.k]
self.key = key
self.N = 2**31-1
self.redis = conn
print(self.mem)
print(self.k) def add(self, value):
name = self.key + "_" + str(ord(value[0]) % self.blocknum)
hashs = self.get_hashs(value)
for hash in hashs:
self.redis.setbit(name, hash, 1) def is_exist(self, value):
name = self.key + "_" + str(ord(value[0]) % self.blocknum)
hashs = self.get_hashs(value)
exist = True
for hash in hashs:
exist = exist & self.redis.getbit(name, hash)
return exist def get_hashs(self, value):
hashs = list()
for seed in self.seeds:
hash = mmh3.hash(value, seed)
if hash >= 0:
hashs.append(hash)
else:
hashs.append(self.N - hash)
return hashs pool = redis.ConnectionPool(host='127.0.0.1', port=6379, db=0)
conn = redis.StrictRedis(connection_pool=pool) if __name__ == '__main__':
start = time.time()
bf = PyBloomFilter(conn=conn)
bf.add('www.jobbole.com')
bf.add('www.zhihu.com')
print(bf.is_exist('www.zhihu.com'))
print(bf.is_exist('www.lagou.com'))