Welcome to our focused exploration of Go's maps and their valuable applications in solving algorithmic challenges. Building upon the foundation laid in the first unit, this lesson will delve into how these efficient data structures can be leveraged to address and solve various types of problems commonly encountered in technical interviews.

Picture this: you're given a vast list of words, and you must identify the final word that stands proudly solitary — the last word that is not repeated. Imagine sorting through a database of repeated identifiers and finding one identifier towards the end of the list that is unlike any other.

The straightforward approach would be to examine each word in reverse, comparing it to every other word for uniqueness. This brute-force method would result in poor time complexity, $O(n^2)$, which is less than ideal for large datasets.

Here is the naive approach in Go:

Go
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1func FindLastUniqueWordNaive(words []string) string {
2    // Traverse the list from the end
3    for i := len(words) - 1; i >= 0; i-- {
4        isUnique := true
5        // Compare the current word to all other words
6        for j := 0; j < len(words); j++ {
7            // If a duplicate is found, mark as not unique
8            if i != j && words[i] == words[j] {
9                isUnique = false
10                break
11            }
12        }
13
14        // If the word is unique, return it
15        if isUnique {
16            return words[i]
17        }
18    }
19
20    // If no unique word is found, return an empty string
21    return ""
22}

We can utilize two maps: wordsMap to maintain the count of each word and duplicatesMap to keep track of duplicate words. By the end, we can remove all duplicated words to achieve our goal. Here's how to solve the problem using Go's map:

Go
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1func FindLastUniqueWordEfficient(words []string) string {
2    // Initialize a map to store the count of each word
3    wordsMap := make(map[string]int)
4
5    // Traverse the list to populate word counts
6    for _, word := range words {
7        wordsMap[word]++
8    }
9
10    // Iterate from the end to find the last unique word
11    for i := len(words) - 1; i >= 0; i-- {
12        if wordsMap[words[i]] == 1 {
13            return words[i] // Return the last unique word
14        }
15    }
16    return "" // If no unique word is found
17}

Explanation:

• We first initialize a map called wordsMap to keep track of the frequency of each word within the list.
• We then iterate over the words list using a loop to populate wordsMap, incrementing the count for each occurrence of a word.
• To find the last unique word, we again loop through the list, but from the end this time. We check wordsMap to see if the word appears exactly once (i.e., has a count of 1). If it is unique, we return it immediately.

The time complexity of the FindLastUniqueWordEfficient function is $O(n)$, where n is the number of words in the input slice.

Now, imagine a different scenario in which you have two arrays of strings, and your task is to find all the unique words from the first array that have an anagram in the second array. An anagram is a word or phrase formed by rearranging the letters of another word or phrase, such as forming "listen" from "silent."

A basic approach would involve checking every word in the first array against every word in the second array by generating and comparing their sorted character strings. The time complexity is $O(n \cdot m \cdot k \log k)$, where n is the number of words in array1, m is the number of words in array2, and k is the average length of the words.

Here is the naive approach in Go:

Go
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1import "sort"
2
3func SortCharacters(word string) string {
4    chars := []rune(word)
5    sort.Slice(chars, func(i, j int) bool {
6        return chars[i] < chars[j]
7    })
8    return string(chars)
9}
10
11func FindAnagramsNaive(array1, array2 []string) []string {
12    result := []string{}
13    // Compare every word in array1 with every word in array2
14    for _, word1 := range array1 {
15        for _, word2 := range array2 {
16            // If sorted characters match, they are anagrams
17            if SortCharacters(word1) == SortCharacters(word2) {
18                // Avoid duplicates in the result
19                if !contains(result, word1) {
20                    result = append(result, word1)
21                }
22                break
23            }
24        }
25    }
26    return result
27}
28
29func contains(s []string, e string) bool {
30    // Check for the presence of a word in the result slice
31    for _, a := range s {
32        if a == e {
33            return true
34        }
35    }
36    return false
37}

We'll create a unique signature for each word by sorting its characters and then compare these signatures for matches. We'll use a map to store signatures for efficient access.

Here's the implementation for efficiently finding anagrams:

Go
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1import "sort"
2
3// Function to sort characters of a word
4func SortCharacters(word string) string {
5    chars := []rune(word)
6    sort.Slice(chars, func(i, j int) bool {
7        return chars[i] < chars[j]
8    })
9    return string(chars)
10}
11
12func FindAnagramsEfficient(array1, array2 []string) []string {
13    // Create a map to store sorted signatures of words in array2
14    sortedWordsInArray2 := make(map[string]bool)
15    for _, word := range array2 {
16        sortedWordsInArray2[SortCharacters(word)] = true
17    }
18
19    result := []string{}
20    // Track matched anagrams to avoid duplicates
21    anagramsMatched := make(map[string]bool)
22    for _, word := range array1 {
23        sortedWord := SortCharacters(word)
24        // Check for anagram match and ensure no duplicate in result
25        if sortedWordsInArray2[sortedWord] && !anagramsMatched[word] {
26            result = append(result, word)
27            anagramsMatched[word] = true
28        }
29    }
30
31    return result
32}

Explanation:

• We define a helper function SortCharacters that returns the sorted character representation of a word, which acts as a signature for an anagram.
• We create a map sortedWordsInArray2 where each key represents the sorted signature of words from array2. This helps quickly identify if a word from array1 has an anagram in array2.
• As we iterate over array1, we find the sorted signature for each word and check if it exists in sortedWordsInArray2. We use another map anagramsMatched to avoid adding duplicate words to the result list.

By utilizing Go's maps in this manner, we achieve efficient anagram checking with reduced complexity. The time complexity is $O(m \cdot k \log k + n \cdot k \log k)$, where m is the number of words in array2, n is the number of words in array1, and k is the average length of the words.

Notice how the time complexity now doesn't multiply n and m, but rather adds them. This shows that we have significantly reduced the time needed to get to a solution compared to the naive approach.

In this lesson, we have utilized Go's maps to improve the efficiency of solving the "Unique Echo" and "Anagram Matcher" problems. These strategies help us manage complexity by leveraging the constant-time performance of map operations and efficiently managing unique collections. This steers us away from less efficient methods and aligns us with the standards expected in technical interviews. As we progress, you'll encounter hands-on practice problems, which will test your ability to apply these concepts. Through nuanced algorithmic practice with maps, you'll refine your skills and deepen your understanding of their computational benefits.