Swift Algorithm Club: Hash Tables

The Swift Algorithm Club is an open source project to implement popular algorithms and data structures in Swift.

Every month, the SAC team will feature a cool data structure or algorithm from the club in a tutorial on this site. If your want to learn more about algorithms and data structures, follow along with us!

In this tutorial, you’ll learn how to implement a Swift hash table. This data structure was first implemented by Matthijs Hollemans, and is now refactored for tutorial format.

Getting Started

As a Swift developer, you’re familiar with the Dictionary structure. If you’re also a Kotlin developer, you would be familiar with the HashMap class. The underlying data structure that powers both of these constructs is the hash table data structure.

The Swift dictionary is a collection of key-value pairs. Storing an value in a dictionary requires you to pass in a value with a key:

var dictionary: [String: String] = [:]

// add key-value pair
dictionary["firstName"] = "Steve"

// retrieve value for a key
dictionary["firstName"] // outputs "Steve"

Underneath the hood, the dictionary will pass the key-value pair to a hash table for storage. In this tutorial, you’ll learn about a basic implementation of the hash table and learn about its performance.

Hash Tables

A hash table is nothing more than an array. Initially, this array is empty. When you put a value into the hash table under a certain key, it uses that key to calculate an index in the array. Here is an example:

In this example, the key "firstName" maps to array index 1.

Adding a value under a different key puts it at another array index:

The trick is how the hash table calculates those array indices. That is where the hashing comes in. When you write the following statement,

hashTable["firstName"] = "Steve"

the hash table takes the key “firstName” and asks it for its hashValue property. Hence, keys must conform to the Hashable protocol.

Hash Functions

When you write "firstName".hashValue, it returns a big integer: -8378883973431208045. Likewise, "hobbies".hashValue has the hash value 477845223140190530.

Hash values are calculated by a hash function, which takes some input and returns an integer:

To achieve a better understanding of how hash functions work, you’ll implement two simple hash functions of your own. Open up a playground and write the following in the playground page:

func naiveHash(_ string: String) -> Int {
  let unicodeScalars = string.unicodeScalars.map { Int($0.value) }
  return unicodeScalars.reduce(0, +)
}

This above hash function uses the unicode scalar representation of each character and sums it up. Write the following into the playground page:

naiveHash("abc") // outputs 294

While this hash function does map a string to an integer value, it’s not very good. See why by writing the following:

naiveHash("bca") // also outputs 294

Since naiveHash only sums up the unicode scalar representation of each character, any permutation of a specific string will yield the same result.

A single lock is being opened by many keys! This isn’t good. A significantly improved hash function can be obtained with just a bit more effort. Write the following in the playground page:

// sourced from https://gist.github.com/kharrison/2355182ac03b481921073c5cf6d77a73#file-country-swift-L31
func djb2Hash(_ string: String) -> Int {
  let unicodeScalars = string.unicodeScalars.map { $0.value }
  return unicodeScalars.reduce(5381) {
    ($0 << 5) &+ $0 &+ Int($1)
  }
}

djb2Hash("abc") // outputs 193485963
djb2Hash("bca") // outputs 193487083

This time, the two permutations have different hash values. The hash function implementation in the Swift Standard Library for String is far more complicated, and uses the SipHash algorithm. That's beyond the scope of this tutorial.

• If you're interested in how the strings compute the hash value, you can check out the source code here.
• Swift uses the SipHash hash function to handle many of the hash value calculations. The API to this implementation is private, but a public implementation has been done here.

Keeping Arrays Small

"firstName" has a hash value of -8378883973431208045. Not only is this number large, it's also negative. So how do you fit it into an array?

A common way to make these big numbers suitable is to first make the hash positive and then take the modulo with the array size.

In the example earlier, the array had a size of three. The index for the "firstName" key becomes abs(-8378883973431208045) % 3 = 1. You can calculate the array index for "hobbies" is 2.

Using hashes in this manner is what makes the dictionary efficient; To find an element in the hash table, you must hash the key to get an array index and then look up the element in the underlying array. All these operations take a constant amount of time, so inserting, retrieving, and removing are all O(1).

Avoiding Collisions

There is one problem. Taking the modulo of the hash value with a number can lead to a same value. Here's one such example:

djb2Hash("firstName") % 2 // outputs 1
djb2Hash("lastName") % 2 // outputs 1

This example is a little contrived, but serves the purpose of highlighting the possibility of a hash mapping to the same index. This is known as a collision. A common way to handle collisions is to use chaining. The array looks as follows:

With chaining, keys and their values are not stored directly in the array. Instead, each array element is a list of zero or more key-value pairs. The array elements are usually called buckets, and the lists are called the chains. Here we have 3 buckets, and the bucket at index 2 has a chain. The other buckets are empty.