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:

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:

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:

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:

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.