Contents

- 1 Why are prime numbers better for hashing?
- 2 Why prime number is used in double hashing?
- 3 What is consistent hashing and where is it used?
- 4 Is there a perfect hash function?
- 5 What makes a good hashing algorithm?
- 6 When to use primes in a hash function?
- 7 Why is the frequency distribution of hashes more uniform?

## Why are prime numbers better for hashing?

In the case of non-random data, a hash table of a prime number length will produce the most wide-spread distribution of integers to indices. Thus, choosing to set your hash table length to a large prime number will greatly reduce the occurrence of collisions.

### Why prime number is used in double hashing?

Double hashing requires that the size of the hash table is a prime number. Using a prime number as the array size makes it impossible for any number to divide it evenly, so the probe sequence will eventually check every cell.

**What is the modulus of a prime number?**

(Recall that a prime number is a whole number, greater than or equal to 2, whose only factors are 1 and itself. So 2,3,5,7,11 are prime numbers whilst, 6=2×3 and 35 = 5 × 7 aren’t.) au = 1 (mod n). a = bu (mod n).

**Why do hashing algorithms use the MOD function?**

The modulo function can take that very, very large number, and turn it into a number that identifies one of the storage cells in the hash table, and if the hash function evenly spreads its data across the (e.g. 128 bit) domain, then the modulo of a hash value will also be very well spread across the much smaller …

## What is consistent hashing and where is it used?

Consistent Hashing is a distributed hashing scheme that operates independently of the number of servers or objects in a distributed hash table by assigning them a position on an abstract circle, or hash ring. This allows servers and objects to scale without affecting the overall system.

### Is there a perfect hash function?

In computer science, a perfect hash function for a set S is a hash function that maps distinct elements in S to a set of integers, with no collisions. A perfect hash function has many of the same applications as other hash functions, but with the advantage that no collision resolution has to be implemented.

**How do you find modulus with prime numbers?**

Take the square root of the number, rounded up to the next integer. This is optional for small prime numbers, but speeds up the determination for larger numbers. Loop from 5 to the square root of the number (or the number), incrementing by 2. Divide the loop number by the prime numbers determined so far.

**What is the purpose of hashing?**

Hashing is mapping data of any length to a fixed-length output using an algorithm. Typically, the hashing algorithm most people know of is SHA-2 or SHA-256. That’s because it’s the current standard for SSL encryption. The purpose of hashing is authentication.

## What makes a good hashing algorithm?

Characteristics of a Good Hash Function. There are four main characteristics of a good hash function: 1) The hash value is fully determined by the data being hashed. 2) The hash function uses all the input data. 4) The hash function generates very different hash values for similar strings.

### When to use primes in a hash function?

By choosing m to be a number that has very few factors: a prime number. Whether a collision is less likely using primes depends on the distribution of your keys. If many of your keys have the form a + k ⋅ b and your hash function is H ( n) = n mod m, then these keys go to a small subset of the buckets iff b divides n.

**How does the modulus of a hash function work?**

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.

**What should be the range of a hash function?**

In a lot of the old (and current) literature on hashing, the advice is that the hash function should be taken modulo a prime number (e.g. hash tables should have a prime size). For a hash function to be as useful as possible, its range needs to be relatively uniform, even when its domain is not.

## Why is the frequency distribution of hashes more uniform?

If q is a prime number, then a lot of other numbers are relatively prime to it, and in particular, the sum (especially if p is also prime!). This makes the frequency distribution of the hash values more uniform, even though the hash function is relatively weak. It’s important to understand that we do this because the hash function is weak.