数学之美系列二十一 - 布隆过滤器(Bloom Filter) | Acm之家


数学之美系列二十一 - 布隆过滤器(Bloom Filter) | Acm之家
由于哈希表的存储效率一般只有 50%.
布隆过滤器的数学工具,它只需要哈希表 1/8 到 1/4 的大小就能解决同样的问题。它实际上是一个很长的二进制向量和一系列随机映射函数。
假定我们存储一亿个电子邮件地址,我们先建立一个十六亿二进制(比特),即两亿字节的向量,然后将这十六亿个二进制全部设置为零。对于每一个电子邮件地址 X,我们用八个不同的随机数产生器(F1,F2, …,F8) 产生八个信息指纹(f1, f2, …, f8)。再用一个随机数产生器 G 把这八个信息指纹映射到 1 到十六亿中的八个自然数 g1, g2, …,g8。现在我们把这八个位置的二进制全部设置为一。当我们对这一亿个 email 地址都进行这样的处理后。一个针对这些 email 地址的布隆过滤器就建成了.
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