Primes are used because you have good chances of obtaining a unique value for a typical hash-function which uses polynomials modulo P. Say, you use such hash-function for strings of length <= N, and you have a collision. That means that 2 different polynomials produce the same value modulo P. The difference of those polynomials is again a polynomial of the same degree N (or less). It has no more than N roots (this is here the nature of math shows itself, since this claim is only true for a polynomial over a field => prime number). So if N is much less than P, you are likely not to have a collision. After that, experiment can probably show that 37 is big enough to avoid collisions for a hash-table of strings which have length 5-10, and is small enough to use for calculations.

对于一个哈希函数来说，重要的不仅仅是尽量减少冲突，而且是不可能在改变几个字节的同时保持相同的哈希。

假设你有一个方程: (x + y*z) % key = x且0<x<key且0<z<key。 如果key是一个质数n*y=key对于n中的每一个n为真，对于其他所有数为假。

一个key不是主要示例的例子: X =1, z=2, key=8 因为key/z=4仍然是一个自然数，4成为我们方程的一个解，在这种情况下(n/2)*y = key对于n中的每一个n都成立。这个方程的解的数量实际上翻了一番，因为8不是质数。

如果我们的攻击者已经知道8是方程的可能解，他可以将文件从产生8改为产生4，并且仍然得到相同的哈希值。

插入/从哈希表中检索时要做的第一件事是计算给定键的hashCode，然后通过执行hashCode % table_length将hashCode修剪为哈希表的大小来找到正确的bucket。这里有两个“陈述”，你很可能在某处读到过

如果对table_length使用2的幂，那么查找(hashCode(key) % 2^n)就像查找(hashCode(key) & (2^n -1))一样简单快捷。但是如果你为一个给定的键计算hashCode的函数不是很好，你肯定会在几个散列桶中聚集许多键。 但是，如果table_length使用质数，即使使用稍微愚蠢的hashCode函数，计算出来的hashCode也可以映射到不同的散列桶中。

这就是证明。

如果假设你的hashCode函数的结果是以下hashCode {x, 2x, 3x, 4x, 5x, 6x…}，那么所有这些都将聚集在m个桶中，其中m = table_length/GreatestCommonFactor(table_length, x)。(验证/推导这个很简单)。现在可以执行以下操作之一来避免集群

确保你不会生成太多的hashCode，这些hashCode是另一个hashCode的倍数，比如{x, 2x, 3x, 4x, 5x, 6x…}。但如果你的hashTable应该有数百万个条目，这可能有点困难。 或者通过使GreatestCommonFactor(table_length, x)等于1使m等于table_length，即使table_length与x为coprime。如果x可以是任何数字，则确保table_length是质数。

来自- http://srinvis.blogspot.com/2006/07/hash-table-lengths-and-prime-numbers.html

抄袭我的其他答案https://stackoverflow.com/a/43126969/917428。有关更多细节和示例，请参阅它。

我相信这和电脑在2进制下工作有关。想想以10为基数的情况:

8%10 = 8 18%10 = 8 87865378%10 = 8

不管这个数是多少只要它以8结尾，它对10的模就是8。

选择一个足够大的、非2的幂的数字将确保哈希函数实际上是所有输入位的函数，而不是它们的子集。

Primes are used because you have good chances of obtaining a unique value for a typical hash-function which uses polynomials modulo P. Say, you use such hash-function for strings of length <= N, and you have a collision. That means that 2 different polynomials produce the same value modulo P. The difference of those polynomials is again a polynomial of the same degree N (or less). It has no more than N roots (this is here the nature of math shows itself, since this claim is only true for a polynomial over a field => prime number). So if N is much less than P, you are likely not to have a collision. After that, experiment can probably show that 37 is big enough to avoid collisions for a hash-table of strings which have length 5-10, and is small enough to use for calculations.

只是把从答案中得到的一些想法写下来。

Hashing uses modulus so any value can fit into a given range We want to randomize collisions Randomize collision meaning there are no patterns as how collisions would happen, or, changing a small part in input would result a completely different hash value To randomize collision, avoid using the base (10 in decimal, 16 in hex) as modulus, because 11 % 10 -> 1, 21 % 10 -> 1, 31 % 10 -> 1, it shows a clear pattern of hash value distribution: value with same last digits will collide Avoid using powers of base (10^2, 10^3, 10^n) as modulus because it also creates a pattern: value with same last n digits matters will collide Actually, avoid using any thing that has factors other than itself and 1, because it creates a pattern: multiples of a factor will be hashed into selected values For example, 9 has 3 as factor, thus 3, 6, 9, ...999213 will always be hashed into 0, 3, 6 12 has 3 and 2 as factor, thus 2n will always be hashed into 0, 2, 4, 6, 8, 10, and 3n will always be hashed into 0, 3, 6, 9 This will be a problem if input is not evenly distributed, e.g. if many values are of 3n, then we only get 1/3 of all possible hash values and collision is high So by using a prime as a modulus, the only pattern is that multiple of the modulus will always hash into 0, otherwise hash values distributions are evenly spread