Why should hash functions use a prime number modulus?

Question

A long time ago, I bought a data structures book off the bargain table for $1.25. In it, the explanation for a hashing function said that it should ultimately mod by a prime number because of "the nature of math".

What do you expect from a $1.25 book?

Anyway, I've had years to think about the nature of math, and still can't figure it out.

Is the distribution of numbers truly more even when there are a prime number of buckets?

Or is this an old programmer's tale that everyone accepts because everybody else accepts it?

Whimsical

Perfectly reasonable question: Why should there be a prime number of buckets? – Effortful

This question appears to be off-topic because it more than likely belongs on Computer Science. – Bernstein

cs.stackexchange.com/a/64191/64222 another well argued explanation. – Sancha

related: Why is it best to use a prime number as a mod in a hashing function? and Why does Java's hashCode() in String use 31 as a multiplier? and this answer – Jaquiss

Here's another great explanation to a somewhat related question with some startling evidentiary numbers - quora.com/… – Sheol

The book was $1.25 probably because its topic just didn't sell as well at that store, or it was damaged/used; not necessarily because it is bad in some sense. I've found some of the best books in the trash. There also could have been a lot of inflation since your story took place; you said it was a long time ago after all. – Dutiable

Explaining with just "the nature of math" is technically not false. All of the Answers her are pretty mathematical right? If that's truly all the book said, then just interpret that as its way of saying TL;DR – Dutiable

fyi - if anyone interested, i found this post which explains md5 hashing comparitech.com/blog/information-security/… – Marquess

Answer 1

Usually a simple hash function works by taking the "component parts" of the input (characters in the case of a string), and multiplying them by the powers of some constant, and adding them together in some integer type. So for example a typical (although not especially good) hash of a string might be:

(first char) + k * (second char) + k^2 * (third char) + ...

Then if a bunch of strings all having the same first char are fed in, then the results will all be the same modulo k, at least until the integer type overflows.

[As an example, Java's string hashCode is eerily similar to this - it does the characters reverse order, with k=31. So you get striking relationships modulo 31 between strings that end the same way, and striking relationships modulo 2^32 between strings that are the same except near the end. This doesn't seriously mess up hashtable behaviour.]

A hashtable works by taking the modulus of the hash over the number of buckets.

It's important in a hashtable not to produce collisions for likely cases, since collisions reduce the efficiency of the hashtable.

Now, suppose someone puts a whole bunch of values into a hashtable that have some relationship between the items, like all having the same first character. This is a fairly predictable usage pattern, I'd say, so we don't want it to produce too many collisions.

It turns out that "because of the nature of maths", if the constant used in the hash, and the number of buckets, are coprime, then collisions are minimised in some common cases. If they are not coprime, then there are some fairly simple relationships between inputs for which collisions are not minimised. All the hashes come out equal modulo the common factor, which means they'll all fall into the 1/n th of the buckets which have that value modulo the common factor. You get n times as many collisions, where n is the common factor. Since n is at least 2, I'd say it's unacceptable for a fairly simple use case to generate at least twice as many collisions as normal. If some user is going to break our distribution into buckets, we want it to be a freak accident, not some simple predictable usage.

Now, hashtable implementations obviously have no control over the items put into them. They can't prevent them being related. So the thing to do is to ensure that the constant and the bucket counts are coprime. That way you aren't relying on the "last" component alone to determine the modulus of the bucket with respect to some small common factor. As far as I know they don't have to be prime to achieve this, just coprime.

But if the hash function and the hashtable are written independently, then the hashtable doesn't know how the hash function works. It might be using a constant with small factors. If you're lucky it might work completely differently and be nonlinear. If the hash is good enough, then any bucket count is just fine. But a paranoid hashtable can't assume a good hash function, so should use a prime number of buckets. Similarly a paranoid hash function should use a largeish prime constant, to reduce the chance that someone uses a number of buckets which happens to have a common factor with the constant.

In practice, I think it's fairly normal to use a power of 2 as the number of buckets. This is convenient and saves having to search around or pre-select a prime number of the right magnitude. So you rely on the hash function not to use even multipliers, which is generally a safe assumption. But you can still get occasional bad hashing behaviours based on hash functions like the one above, and prime bucket count could help further.

Putting about the principle that "everything has to be prime" is as far as I know a sufficient but not a necessary condition for good distribution over hashtables. It allows everybody to interoperate without needing to assume that the others have followed the same rule.

[Edit: there's another, more specialized reason to use a prime number of buckets, which is if you handle collisions with linear probing. Then you calculate a stride from the hashcode, and if that stride comes out to be a factor of the bucket count then you can only do (bucket_count / stride) probes before you're back where you started. The case you most want to avoid is stride = 0, of course, which must be special-cased, but to avoid also special-casing bucket_count / stride equal to a small integer, you can just make the bucket_count prime and not care what the stride is provided it isn't 0.]

Rusticus

Just as a side note: a discussion for a sensible choice of the factor k for hashCodes is here: https://mcmap.net/q/30342/-what-is-a-sensible-prime-for-hashcode-calculation/21499 – Conclude

this is an awesome answer. can you please explain this further "So you get striking relationships modulo 31 between strings that end the same way, and striking relationships modulo 2^32 between strings that are the same except near the end. This doesn't seriously mess up hashtable behaviour." I especially dont understand the 2^32 part – Donavon

Additional note to make things more clear about this: "All the hashes come out equal modulo the common factor" -> This is because, if you consider the example hash function hash = 1st char + 2nd char*k + ... , and take strings with the same first character, hash%k will be the same for these strings. If M is the size of the hashtable and g is the gcd of M and k, then (hash%k)%g equals hash%g (since g divides k) and hence hash%g will also be the same for these strings. Now consider (hash%M)%g, this is equal to hash%g (since g divides M). So (hash%M)%g is equal for all these strings. – Foulk

This kind of raises the question "why are we using a hash function of that form in the first place?" – Cherokee

@DanielMcLaury: well, whoever wrote that function for Sun might have their own version of the story, but generally it's because it's easy to write, it runs fast, and it has adequate distribution for most practical purposes. Anyway the rule of thumb the questioner is asking about, "hashes should ultimately mod by a prime number", doesn't apply if your hash is designed to use some other mathematical means to ensure there are no nasty surprises modulo 2^N, N being the number of bits in the hash. – Rusticus

@DanielMcLaury Joshua Bloch explained why for Java - it was recommended in two popular books (K&R, Dragon book) and performed well with low collisions on the English dictionary. It is fast (uses Horner's method). Apparently even K&R don't remember where it came from. Similar function is Rabin fingerprint from Rabin-Karp algorithm (1981) but K&R (1978) predates that. – Jaquiss

@SteveJessop, please can you explain "striking relationships modulo 2^32 between strings that are the same except near the end."? Thanks. – Slate

Powers of 2 aren't used because they're "convenient and save having to search around or pre-select a prime number of the right magnitude" but rather because they allow the modulo to be done with a bitwise operator, which is much faster. E.g., when n is a power of 2, x % n = x & (n - 1). – Infestation

I didn't understand this "if a bunch of strings all having the same first char are fed in, then the results will all be the same modulo k". What are you trying to say here? – Clubfoot

@Nawaz: I'm trying to say that given my formula for the simple hash function I'm talking about, the value of the hash mod k depends only on the first character, not on the other characters. – Rusticus

Oops. yes. I read it again and now it makes perfect sense. Thanks for the comment. :-) – Clubfoot

Answer 2

The first thing you do when inserting/retreiving from hash table is to calculate the hashCode for the given key and then find the correct bucket by trimming the hashCode to the size of the hashTable by doing hashCode % table_length. Here are 2 'statements' that you most probably have read somewhere

1. If you use a power of 2 for table_length, finding (hashCode(key) % 2^n ) is as simple and quick as (hashCode(key) & (2^n -1)). But if your function to calculate hashCode for a given key isn't good, you will definitely suffer from clustering of many keys in a few hash buckets.
2. But if you use prime numbers for table_length, hashCodes calculated could map into the different hash buckets even if you have a slightly stupid hashCode function.

And here is the proof.

If suppose your hashCode function results in the following hashCodes among others {x , 2x, 3x, 4x, 5x, 6x...}, then all these are going to be clustered in just m number of buckets, where m = table_length/GreatestCommonFactor(table_length, x). (It is trivial to verify/derive this). Now you can do one of the following to avoid clustering

Make sure that you don't generate too many hashCodes that are multiples of another hashCode like in {x, 2x, 3x, 4x, 5x, 6x...}.But this may be kind of difficult if your hashTable is supposed to have millions of entries. Or simply make m equal to the table_length by making GreatestCommonFactor(table_length, x) equal to 1, i.e by making table_length coprime with x. And if x can be just about any number then make sure that table_length is a prime number.

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

Testamentary

Answer 3

Just to put down some thoughts gathered from the answers.

• 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

Jigger

I only understand your explanation very well. – Diaphragm

What a great explanation. – Cultch

Answer 4

http://computinglife.wordpress.com/2008/11/20/why-do-hash-functions-use-prime-numbers/

Pretty clear explanation, with pictures too.

Edit: As a summary, primes are used because you have the best chance of obtaining a unique value when multiplying values by the prime number chosen and adding them all up. For example given a string, multiplying each letter value with the prime number and then adding those all up will give you its hash value.

A better question would be, why exactly the number 31?

Lacrosse

Although, I think a summary would be helpful, in case that site is ever dead, some remnant of its content will be saved here on SO. – Arundel

The article does not explain why, but says "Researchers found that using a prime of 31 gives a better distribution to the keys, and lesser no of collisions. No one knows why..." Funny, asking the same question as me in effect. – Whimsical

> A better question would be, why exactly the number 31? If you mean why is the number 31 used, then the article you point tells you why, ie because it is quick to multiple by and cos tests show it is the best one to use. The other popular multiplier I have seen is 33 which lends weight to the theory that the speed issue was (at least initially) an important factor. If you mean, what is it about 31 that makes it better in the tests, then I'm afraid I don't know. – Popple

Exactly, so the only reason that it could have been used as a multiplier was because it was easy to multiply by. (When I say I have seen 33 used as a multiplier, I don't mean recently, this was probably decades ago, and possible before a lot of analysis was done on hashing). – Popple

They probably used it because the factors of 33 (besides itself and 1) are 3 and 11, both primes. – Lacrosse

Well obviously the less factors the better, but if you knew that, why pick something that has an extra factor. I assumed it was picked by someone who did not realise the importance of primes. Maybe someone thought addition would be quicker than subtraction and hence x33 would be faster than x31. – Popple

@SteveJessop The number 31 is easily optimized by the CPU as a (x*32)-1 operation, in which *32 is a simple bit shift, or even better an immediate address scale factor (e.g. lea eax,eax*8; leax, eax,eax*4 on x86/x64). So *31 is a good candidate for prime number multiplication. This was pretty much true some years ago - now latest CPUs architecture have an almost instant multiplication - division is always slower... – Malocclusion

@Lacrosse If that were so, they could as well have used 15 since its factors (besides itself and 1) are 3 and 5, both primes. Many other numbers: 10 (2 and 5),14 (2 and 7), 21 (3, 7) etc – Maccarthy

Answer 5

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.

Skewness

While the explanation seems now obvious, it got to me after reading a book by A.Shen "Programming: Theorems and problems" (in Russian), see discussion of Rabin algorithm. Not sure if an English translation exists. – Skewness

Answer 6

tl;dr

index[hash(input)%2] would result in a collision for half of all possible hashes and a range of values. index[hash(input)%prime] results in a collision of <2 of all possible hashes. Fixing the divisor to the table size also ensures that the number cannot be greater than the table.

Mangonel

2 is a prime number dude – Kernan

Even if we interpreted this answer as index[hash(input)%prime] results in a collision of <2 of all possible hashes, where prime > 2 it would still make no sense, as that condition would be true for any number >2. – Gosport

Answer 7

Just to provide an alternate viewpoint there's this site:

http://www.codexon.com/posts/hash-functions-the-modulo-prime-myth

Which contends that you should use the largest number of buckets possible as opposed to to rounding down to a prime number of buckets. It seems like a reasonable possibility. Intuitively, I can certainly see how a larger number of buckets would be better, but I'm unable to make a mathematical argument of this.

Placard

Larger number of buckets means less collisions: See the pigeonhole principle. – Estimative

@Unknown: I don't believe that's true. Please correct me if I'm wrong, but I believe applying the pigeonhole principle to hash tables only allows you to assert that there WILL be collisions if you have more elements than bins, not to draw any conclusions on the amount or density of collisions. I still believe that the larger number of bins is the correct route, however. – Placard

If you assume that the collisions are for all intents and purposes random, then by the birthday paradox a larger space (buckets) will reduce the probability of a collision occurring. – Estimative

@Estimative you have missed that collisions also depend on the hash function itself. So if the has function is really bad, then no matter how big you increase the size, there still might be significant amount of collisions – Monosyllable

The original article seems to be gone, but there are some insightful comments here, including a discussion with the original author. news.ycombinator.com/item?id=650487 – Harmonize

@AdrianMcCarthy: but watch out: someone says “if n is prime, then so's 2^n-1”, which is back to front. Some make the point that if you write a library allowing the user to provide a hash function, it may be favour certain residues modulo some N, which can be countered by a table size prime to N. And it sounds plausible that the cost of a slightly smaller table (~Δ#buckets) could be less than the benefit using all buckets (~#buckets/x). – Arteriosclerosis

Answer 8

Copying from my other answer https://mcmap.net/q/30354/-why-is-the-size-127-prime-better-than-128-for-a-hash-table. See it for more details and examples.

I believe that it just has to do with the fact that computers work with in base 2. Just think at how the same thing works for base 10:

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

It doesn't matter what the number is: as long as it ends with 8, its modulo 10 will be 8.

Picking a big enough, non-power-of-two number will make sure the hash function really is a function of all the input bits, rather than a subset of them.

Aimless

This is great, even if it may not be complete. I don't what other people are talking about. – Jigger

Answer 9

It depends on the choice of hash function.

Many hash functions combine the various elements in the data by multiplying them with some factors modulo the power of two corresponding to the word size of the machine (that modulus is free by just letting the calculation overflow).

You don't want any common factor between a multiplier for a data element and the size of the hash table, because then it could happen that varying the data element doesn't spread the data over the whole table. If you choose a prime for the size of the table such a common factor is highly unlikely.

On the other hand, those factors are usually made up from odd primes, so you should also be safe using powers of two for your hash table (e.g. Eclipse uses 31 when it generates the Java hashCode() method).

Iaverne

Answer 10

"The nature of math" regarding prime power moduli is that they are one building block of a finite field. The other two building blocks are an addition and a multiplication operation. The special property of prime moduli is that they form a finite field with the "regular" addition and multiplication operations, just taken to the modulus. This means every multiplication maps to a different integer modulo the prime, so does every addition.

Prime moduli are advantageous because:

• They give the most freedom when choosing the secondary multiplier in secondary hashing, all multipliers except 0 will end up visiting all elements exactly once
• If all hashes are less than the modulus there will be no collisions at all
• Random primes mix better than power of two moduli and compress the information of all the bits not just a subset

They however have a big downside, they require an integer division, which takes many (~ 15-40) cycles, even on a modern CPU. With around half the computation one can make sure the hash is mixed up very well. Two multiplications and xorshift operations will mix better than a prime moudulus. Then we can use whatever hash table size and hash reduction is fastest, giving 7 operations in total for power of 2 table sizes and around 9 operations for arbitrary sizes.

I recently looked at many of the fastest hash table implementations and most of them don't use prime moduli.

The distribution of the hash table indices are mainly dependent on the hash function in use. A prime modulus can't fix a bad hash function and a good hash function does not benefit from a prime modulus. There are cases where they can be advantageous however. It can mend a half-bad hash function for example.

Hiller

Why do you say a prime modulus require an integer division? I guess people were talking about that common implementation that requires addition and multiplication only: hash = modulus * hash + field1; hash = modulus * hash + field2; ... – Conclude

@Hans-PeterStörr The way a computer calculates the modulus is via an integer division: n%m = n - floor(n/d) . What you are referring to is not a prime modulus, but it seems to be linear probing (field1, field2 ...) with a multiplier paradoxically called modulus. But how do you make sure that your hash is a number smaller than the size of the table? You need to reduce it, and often this is done with modulo. – Hiller

Answer 11

Primes are unique numbers. They are unique in that, the product of a prime with any other number has the best chance of being unique (not as unique as the prime itself of-course) due to the fact that a prime is used to compose it. This property is used in hashing functions.

Given a string “Samuel”, you can generate a unique hash by multiply each of the constituent digits or letters with a prime number and adding them up. This is why primes are used.

However using primes is an old technique. The key here to understand that as long as you can generate a sufficiently unique key you can move to other hashing techniques too. Go here for more on this topic about http://www.azillionmonkeys.com/qed/hash.html

http://computinglife.wordpress.com/2008/11/20/why-do-hash-functions-use-prime-numbers/

Lentigo

hahahah.... actually doesn't the product of 2 primes have a better chance of being 'unique' than the product of a prime and any other number? – Phia

@Beska Here "uniqueness" is defined recursively, so I believe "non-uniqueness" should be defined in the same way :) – Skewness

Answer 12

Suppose your table-size (or the number for modulo) is T = (B*C). Now if hash for your input is like (N*A*B) where N can be any integer, then your output won't be well distributed. Because every time n becomes C, 2C, 3C etc., your output will start repeating. i.e. your output will be distributed only in C positions. Note that C here is (T / HCF(table-size, hash)).

This problem can be eliminated by making HCF 1. Prime numbers are very good for that.

Another interesting thing is when T is 2^N. These will give output exactly same as all the lower N bits of input-hash. As every number can be represented powers of 2, when we will take modulo of any number with T, we will subtract all powers of 2 form number, which are >= N, hence always giving off number of specific pattern, dependent on the input. This is also a bad choice.

Similarly, T as 10^N is bad as well because of similar reasons (pattern in decimal notation of numbers instead of binary).

So, prime numbers tend to give a better distributed results, hence are good choice for table size.

Carburize

Answer 13

I would say the first answer at this link is the clearest answer I found regarding this question.

Consider the set of keys K = {0,1,...,100} and a hash table where the number of buckets is m = 12. Since 3 is a factor of 12, the keys that are multiples of 3 will be hashed to buckets that are multiples of 3:

• Keys {0,12,24,36,...} will be hashed to bucket 0.
• Keys {3,15,27,39,...} will be hashed to bucket 3.
• Keys {6,18,30,42,...} will be hashed to bucket 6.
• Keys {9,21,33,45,...} will be hashed to bucket 9.

If K is uniformly distributed (i.e., every key in K is equally likely to occur), then the choice of m is not so critical. But, what happens if K is not uniformly distributed? Imagine that the keys that are most likely to occur are the multiples of 3. In this case, all of the buckets that are not multiples of 3 will be empty with high probability (which is really bad in terms of hash table performance).

This situation is more common that it may seem. Imagine, for instance, that you are keeping track of objects based on where they are stored in memory. If your computer's word size is four bytes, then you will be hashing keys that are multiples of 4. Needless to say that choosing m to be a multiple of 4 would be a terrible choice: you would have 3m/4 buckets completely empty, and all of your keys colliding in the remaining m/4 buckets.

In general:

Every key in K that shares a common factor with the number of buckets m will be hashed to a bucket that is a multiple of this factor.

Therefore, to minimize collisions, it is important to reduce the number of common factors between m and the elements of K. How can this be achieved? By choosing m to be a number that has very few factors: a prime number.

FROM THE ANSWER BY Mario.

Sharpe

Answer 14

I'd like to add something for Steve Jessop's answer(I can't comment on it since I don't have enough reputation). But I found some helpful material. His answer is very help but he made a mistake: the bucket size should not be a power of 2. I'll just quote from the book "Introduction to Algorithm" by Thomas Cormen, Charles Leisersen, et al on page263:

When using the division method, we usually avoid certain values of m. For example, m should not be a power of 2, since if m = 2^p, then h(k) is just the p lowest-order bits of k. Unless we know that all low-order p-bit patterns are equally likely, we are better off designing the hash function to depend on all the bits of the key. As Exercise 11.3-3 asks you to show, choosing m = 2^p-1 when k is a character string interpreted in radix 2^p may be a poor choice, because permuting the characters of k does not change its hash value.

Hope it helps.

Eduino

Answer 15

This question was merged with the more appropriate question, why hash tables should use prime sized arrays, and not power of 2. For hash functions itself there are plenty of good answers here, but for the related question, why some security-critical hash tables, like glibc, use prime-sized arrays, there's none yet.

Generally power of 2 tables are much faster. There the expensive h % n => h & bitmask, where the bitmask can be calculated via clz ("count leading zeros") of the size n. A modulo function needs to do integer division which is about 50x slower than a logical and. There are some tricks to avoid a modulo, like using Lemire's https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/, but generally fast hash tables use power of 2, and secure hash tables use primes.

Why so?

Security in this case is defined by attacks on the collision resolution strategy, which is with most hash tables just linear search in a linked list of collisions. Or with the faster open-addressing tables linear search in the table directly. So with power of 2 tables and some internal knowledge of the table, e.g. the size or the order of the list of keys provided by some JSON interface, you get the number of right bits used. The number of ones on the bitmask. This is typically lower than 10 bits. And for 5-10 bits it's trivial to brute force collisions even with the strongest and slowest hash functions. You don't get the full security of your 32bit or 64 bit hash functions anymore. And the point is to use fast small hash functions, not monsters such as murmur or even siphash.

So if you provide an external interface to your hash table, like a DNS resolver, a programming language, ... you want to care about abuse folks who like to DOS such services. It's normally easier for such folks to shut down your public service with much easier methods, but it did happen. So people did care.

So the best options to prevent from such collision attacks is either

1) to use prime tables, because then

• all 32 or 64 bits are relevant to find the bucket, not just a few.
• the hash table resize function is more natural than just double. The best growth function is the fibonacci sequence and primes come closer to that than doubling.

2) use better measures against the actual attack, together with fast power of 2 sizes.

• count the collisions and abort or sleep on detected attacks, which is collision numbers with a probability of <1%. Like 100 with 32bit hash tables. This is what e.g. djb's dns resolver does.
• convert the linked list of collisions to tree's with O(log n) search not O(n) when an collision attack is detected. This is what e.g. java does.

There's a wide-spread myth that more secure hash functions help to prevent such attacks, which is wrong as I explained. There's no security with low bits only. This would only work with prime-sized tables, but this would use a combination of the two slowest methods, slow hash plus slow prime modulo.

Hash functions for hash tables primarily need to be small (to be inlinable) and fast. Security can come only from preventing linear search in the collisions. And not to use trivially bad hash functions, like ones insensitive to some values (like \0 when using multiplication).

Using random seeds is also a good option, people started with that first, but with enough information of the table even a random seed does not help much, and dynamic languages typically make it trivial to get the seed via other methods, as it's stored in known memory locations.

Frescobaldi

Answer 16

For a hash function it's not only important to minimize colisions generally but to make it impossible to stay with the same hash while chaning a few bytes.

Say you have an equation: (x + y*z) % key = x with 0<x<key and 0<z<key. If key is a primenumber n*y=key is true for every n in N and false for every other number.

An example where key isn't a prime example: x=1, z=2 and key=8 Because key/z=4 is still a natural number, 4 becomes a solution for our equation and in this case (n/2)*y = key is true for every n in N. The amount of solutions for the equation have practially doubled because 8 isn't a prime.

If our attacker already knows that 8 is possible solution for the equation he can change the file from producing 8 to 4 and still gets the same hash.

Enneahedron

Answer 17

I've read the popular wordpress website linked in some of the above popular answers at the top. From what I've understood, I'd like to share a simple observation I made.

You can find all the details in the article here, but assume the following holds true:

• Using a prime number gives us the "best chance" of an unique value

A general hashmap implementation wants 2 things to be unique.

• Unique hash code for the key
• Unique index to store the actual value

How do we get the unique index? By making the initial size of the internal container a prime as well. So basically, prime is involved because it possesses this unique trait of producing unique numbers which we end up using to ID objects and finding indexes inside the internal container.

Example:

key = "key"

value = "value" uniqueId = "k" * 31 ^ 2 + "e" * 31 ^ 1` + "y"

maps to unique id

Now we want a unique location for our value - so we

uniqueId % internalContainerSize == uniqueLocationForValue , assuming internalContainerSize is also a prime.

I know this is simplified, but I'm hoping to get the general idea through.

Wolfsbane

I don't think it's the best chance of a "unique" value.. but rather that a prime number will spread out the numbers throughout the array in a more "random-like" way than what you'd get with numbers that share common factors. The uniqueness is going to depend on how much room you have in the array. If you only have 4 buckets for 5 items, a collision is inevitable. But 5 items in 100 buckets can be rare to the extreme. So load factor is what will determine "uniqueness". But a good hash algorithm (for example a prime) will give you a good spread. – Salad

If you don't want to be bound by prime numbers you can use hash by multiplication where you multiply times the decimals of some random irrational number like PI or the golden ratio. This has the same randomization as I explained above. – Salad