InsightsFebruary 17, 2024

Writing a Generic Fuzzy Search Algorithm in Swift

Fuzzy searching finds matches even if there are spelling mistakes or slight variations in the search terms. It helps improve search accuracy by understanding what the user is likely looking for.
Tom Ludwig user imageTom Ludwig
Search Field

What is fuzzy search?

A fuzzy search algorithm is designed to find approximate matches for a given search query, rather than requiring an exact match. It takes into account the similarity between the search query and the data being searched, assigning scores to each potential match based on their resemblance. This allows the algorithm to return relevant results even when there are minor discrepancies or typos in the search input.

Let's say you have a list of products, and a user is looking for a "Cozy Sweater." With a fuzzy search algorithm: If they type "Czy Swetr," the fuzzy search considers the similarity and still returns the "Cozy Sweater."

Different Algorithms

There are many algorithms out there providing fuzzy search capabilities, some of which you may have encountered. These include the well-known Levenshtein Distance, the Jaro-Winkler Distance, N-Gram and the Hamming Distance Algorithm. Additionally, for those in pursuit of speed, two notable options are the BK Tree and Bitmap Algorithm. It's worth noting that the most efficient and fastest fuzzy search algorithms often include keeping an index of the data, most likely in the form of a trie.

In our example, we use an algorithm that emphasises matching prefixes and will provide a similarity score. The algorithm is fairly fast and easy to understand.

Putting search algorithms into practice

Let's start by setting everything up.

Go ahead and create a file called Models. As the name implies, this is where we define our data models. The first one represents a search result:

struct FuzzySearchMatchResult { let weight: Int let matchedParts: [NSRange] }

The weight is something like a score - the higher, the better. And the matchedParts are the ranges of the string that match the search query. Note that we are using an array of ranges not just one range, because there can be a few characters in between the matched characters. Here is an illustration:

Search results: 'conviw' query returns ContentView with the highest relevance.

Secondly we need to make sure that the data we searching within is case- and accent-insensitive. This is commonly referred to as 'normalising the string'. To achieve this, we need to add two structs:

struct FuzzySearchCharacter { let content: String let normalisedContent: String }


struct FuzzySearchString { var characters: [FuzzySearchCharacter] }

The first one represents one character, the content field holds the original character, while normalisedContent steps in as its case- and accent-insensitive counterpart.

And the FuzzySearchString represents a whole word, essentially an array of characters. Here is a representation of the frensh word summer: "été":

Representation of the word 'été' with normalized characters

Normalise Words

In order to normalise words, we can create an extension for Strings. Create a new file called: String+Normalise.swift. This naming convention enables others to quickly understand the purpose of the file.

Within the file, you'll need to create an extension for String to use the functionality later.

extension String { func normalise() -> [FuzzySearchCharacter] { return self.lowercased().map { char in guard let data = String(char).data(using: .ascii, allowLossyConversion: true), let normalisedCharacter = String(data: data, encoding: .ascii) else { return FuzzySearchCharacter(content: String(char), normalisedContent: String(char)) } return FuzzySearchCharacter(content: String(char), normalisedContent: normalisedCharacter) } } }

In this function, the String it's called on, is first converted to lowercase. Then, it will convert it to data using the ascii encoding and allow lossy conversion, which means that we lose data. Through losing data, we make the String accent-insensitive. The function then returns an array of FuzzySearchCharacter objects, representing the original and normalised content for each character.

Matching Prefix

Now we need to find out if the search query and the string we're comparing it against match. To achieve this, we can write another extension: hasPrefix. For better understanding, let's look at the illustration:

Depicts iterations checking prefixes for a match.

The function simply checks for matching characters from a specified starting index. This functionality will prove useful in later.

extension String { func hasPrefix(prefix: FuzzySearchCharacter, startingAt index: Int) -> Int? { guard let stringIndex = self.index(self.startIndex, offsetBy: index, limitedBy: self.endIndex) else { return nil } let searchString = self.sufflix(from: stringIndex) for prefix in [prefix.content, prefix.normalisedContent] where searchString.hasPrefix(prefix) { return prefix.count } return nil } }

Let's go through the code step by step:

  1. We accept a prefix that is of type FuzzySearchCharacter, this is the data against which we test our search query. And we need a starting index.
  2. Verify if the provided index is valid for the search query.
  3. Clip the search query (self) to start from the provided starting index.
  4. Iterate through the provided data and check if the prefixes are matching.
  5. If no match is found, return nil.

If this function doesn't seem clear at the moment, it will become clearer as we use it.

Fuzzy Searchable Protocol

In order to make the Fuzzy Search generic, i.e. make it applicable to every data type, we have to write a protocol. Other data types can adopt this protocol, enabling them to become fuzzy searchable.

protocol FuzzySearchable { var searchableString: String { get } func fuzzyMatch(query: String, characters: FuzzySearchString) -> FuzzySearchMatchResult }

If a data type wants to conform to FuzzySearchable, it only needs to add three simple lines. For instance, consider this Model:

struct Books: FuzzySearchable { var title: String var releaseDate: Date var searchableString: String { title } }

This approach enables us to use whatever is provided as the searchable string for comparison against the search query. At this point, you might be wondering why the fuzzyMatch function is not implemented in the example Books struct. However, we can address this by writing an extension on FuzzySearchable that offers a default fuzzyMatch function for all data types that adapt to it. This is relatively straightforward compared to other implementations:

extension FuzzySearchable { func fuzzyMatch(query: String, characters: FuzzySearchString) -> FuzzySearchMatchResult { [...] } }

This function takes the query, i.e., the search term, and the characters of a word as arguments. The characters represent the string that we compare the search query against. We use FuzzySearchString instead of a simple String, because we want a case- and accent-insensitive search.

The function then returns a FuzzySearchMatchResult, which just holds information about the search result, to be precise it includes let weight: Int and let matchedParts: [NSRange].

Now let's implement the function:

extension FuzzySearchable { func fuzzyMatch(query: String, characters: FuzzySearchString) -> FuzzySearchMatchResult { let compareString = characters.characters // the string we compare against let searchString = query.lowercased() // make the query case-insensitive var totalScore = 0 // represents the weight of the match var matchedParts = [NSRange]() // Ranges that match compareString and searchString // This is always the data for one match var patternIndex = 0 var currentScore = 0 var currentMatchedPart = NSRange(location: 0, length: 0) for (index, character) in compareString.enumerated() { if let prefixLength = searchString.hasPrefix(prefix: character, startingAt: patternIndex) { // A match was found, so we increment the score and the range patternIndex += prefixLength currentScore += 1 currentMatchedPart.length += 1 } else { // No match was found currentScore = 0 if currentMatchedPart.length != 0 { matchedParts.append(currentMatchedPart) } currentMatchedPart = NSRange(location: index + 1, length: 0) } totalScore += currentScore } if currentMatchedPart != 0 { matchedParts.append(currentMatchedPart) } if searchString.count == matchedParts.reduce(0, { partialResults, range in range.length + partialResult }) { return FuzzySearchMatchResult(weight: totalScore, matchedParts: matchedParts) } else { return FuzzySearchMatchResult(weight: 0, matchedParts: []) } } }

Here is where the magic happens.

First of course we extract the compare string from the provided FuzzySearchString, then we use a lowercased version of the search query.

With the necessary preparations in place, we set up our variables to track the matching process. The totalScore variable holds the cumulative weight of matches found within an object, while the matchedParts variable is an array of ranges representing the specific regions within the object that match the search query.

The next variables are used for each sub-match in the compareString.

patternIndex: This is the starting index of the current sub-match. It is incremented each time a matching character is found.

currentScore: This is the score for all sub-matches within the compareString. It is incremented each time a sub-match is found.

currentMatchedPart: This is a range that represents one sub-match. It stores the starting and ending indices of the sub-match.

We now iterate through the compare string using a for loop with .enumerated() to keep track of the current index. If we find a matching prefix, we can go ahead an increment all values for the current match. The parameters passed to the extension are the characters being compared and the patternIndex. It's important to use patternIndex instead of the index of the for loop to avoid double-counting. When a matching prefix is found, we update the patternIndex and shift it to the right, i.e. increase it, preventing the same letters from being matched twice. Additionally, we increment the score and adjust the currentMatchedPart to mark the correct range.

Then we add the currentScore to the totalScore.

However, when the prefixes don't align, we reset the currentScore to 0. In the scenario where there was a preceding match, indicated by a non-zero length of currentMatchedPart, we add the matched range to the matchedParts. Subsequently, we reset the currentMatchedParts length to 0, while simultaneously shifting the location to the right.

Upon completion of the for loop, we check again if the last iteration was still a match and if yes, we append the currentmatchPart to the matchedParts.

Finally, we make a comparison between the length of the searchString and the total length of the found ranges. If these lengths match, we return a FuzzySearchMatchResult containing the calculated totalScore and the matchedParts. However, if the lengths differ, we return a weight of 0 along with an empty array

You might be wondering whether you need to manually call this function and provide the FuzzySearchString. Fortunately, that's not necessary. We can just create two concise functions that make the process a bit simpler.

func normaliseString() -> FuzzySearchString { return FuzzySearchString(characters: searchableString.normalise()) }

This function returns the normalised version of the searchableString that is provided by the object that it is called from.

func fuzzyMatch(query: String) -> FuzzySearchMatchResult { let characters = normaliseString() return fuzzyMatch(query: query, characters: characters) }

Subsequently, we simply transform the characters into their normalised counterparts and invoke the fuzzyMatch function. This, in turn, provides us with the FuzzySearchMatchResult.

Fuzzy Search Extension

Now, let's make arrays fuzzy-searchable by writing an extension for them. This way, they can easily be searched using our fuzzy match functionality.

extension Collection where Iterator.Element: FuzzySearchable { func fuzzySearch(query: String) -> [(result: FuzzySearchmatchResult), item: Iterator.Element)] { return map { (result: $0.fuzzyMatch(query: query), item: $0) }.filter { $0.result.weight > 0 }.sorted { $0.result.weight > $1.result.weight } } }

To enable fuzzy searching, we extend Swift's Collection with a handy fuzzySearch method. However, to ensure compatibility, we limit this extension to collections whose elements conform to the FuzzySearchable protocol we defined earlier.

When using this method, you simply provide a search query. The function then iterates over the array, invoking the fuzzyMatch method on each element. The fuzzyMatch method, as you recall, provides a weight for every element. We leverage these weights to filter out elements with a score less than or equal to 0. Finally, we sort the array, placing the element with the highest score at the top.

Additionally, for a slight performance boost, consider utilising a concurrentMap from the CollectionConcurrencyKit package.


In CodeEdit we for one use the algorithm to provide a quick open overlay that lets user find files quickly. To apply fuzzy search to an array of URLs, you can easily extend the data type URL to conform to the FuzzySearchable protocol.

extension URL: FuzzySearchable { var searchableString: String { return self.lastPathComponent } }

If you've created your custom data type, making it compatible with fuzzy search is a breeze—simply ensure it conforms to the FuzzySearchable protocol, as shown here:

struct Users: Identifiable, Codable, FuzzySearchable { var id: Int var name: String var description: String var searchableString: String { return name } }

And now we can just use the fuzzy search function:

@State private var searchText: String = "" var users: [Users] = getUsers() var sortedUsers: [Users] { if searchText.isEmpty { return users } else { return users.fuzzySearch(query: searchText) map { $0.item } } }


Implementing a fuzzy search algorithm is relatively straightforward, yet it significantly enhances the overall user experience.

If you prefer to grab the entire code at once, you can find it conveniently shared in this gist. I also have a repository with a few implementations of the fuzzy search algorithm. It includes an option for utilizing fuzzy search with cached data, providing a slight performance boost. Check out the Demo Repository for a closer look. You can also check out the implementation in CodeEdit.

If you still have questions, feel free to contact me.

Happy coding!

About the Author

Tom Ludwig user image

Tom Ludwig


Coding in Swift, Dart, Go, and C++. SwiftUI first, with Flutter when the situation calls for it.