Mastering Go: Implementing an Autocomplete Function for LinkedIn Searches

Problem description:

• On LinkedIn, users often search for other professionals, companies, job titles, and skills. As they type in the search bar, the platform needs to suggest relevant completions and searches in real time.
• The search space is large and includes diverse entities with potentially overlapping prefixes (like "Software Engineer" and "Software Developer").
• The system must be efficient in terms of speed and memory usage, given the large dataset and high user traffic.

Trie: Your Efficient String Assistant

Named for its purpose, the trie (pronounced "try") is a tree-like data structure rooted in efficient string retrieval.?Imagine a tree where each branch represents a letter, guiding you towards words like signposts in a forest. Here's how it works:

• Words share branches:?Words with common prefixes follow the same path until they diverge. This saves space and speeds up searches.
• Fast lookups:?Finding a word is like following a path in a tree—there is no need to compare the entire word at each step.
• Prefix power:?Not only can you find specific words, but you can also find words that start with a specific prefix – perfect for autocomplete and spell-checking!

Here are some key benefits of using tries:

• Efficient search:?Finding words is fast, especially for prefixes.
• Memory-friendly:?Saves space by sharing common prefixes.
• Flexible:?Can handle any alphabet and varying word lengths.

Implementation:

1. Building the Trie: Construct a trie where each node represents a character. Each path from the root to a leaf node represents a different name, title, or skill.

// Node represents a node in the trie
type Node[T any] struct {
	Children map[rune]*Node[T]
	IsEnd    bool
	Value    T
}

// Trie represents the trie and has a root Trie Node
type Trie[T any] struct {
	Root *Node[T]
}        

2. Inserting Data: Populate the trie with relevant data from LinkedIn's database - user names, company names, job titles, skills, etc.

// Insert adds a word to the trie
func (t *Trie[T]) Insert(word string, value T) {
	node := t.Root
	for _, ch := range word {
		if node.Children[ch] == nil {
			node.Children[ch] = NewTrieNode[T]()
		}
		node = node.Children[ch]
	}
	node.IsEnd = true
	node.Value = value
}        

3. Autocomplete Querying:As a user types a query, traverse the trie following the path of characters entered. Suggest completions by listing the end nodes reachable from the current node.

func Autocomplete[T any](trie *Trie[T], prefix string) []string {
    // ... (initialization)

    // Traverse the trie to the end of the prefix
    for _, ch := range prefix {
       if node.Children[ch] == nil {
          return results 
       }
       node = node.Children[ch]
    }

    // Use a stack for iterative DFS
    stack := []NodeAndWord[T]{{node, prefix}}
    for len(stack) > 0 {
       // ... (pop from stack, process node, push children)
    }

    return results  // Return matching suggestions
}        

Challenge:

• Frequency-Based Suggestions: The trie can be extended to store the frequency of searches or entities. This way, the most popular or trending profiles, skills, or jobs can be suggested first.
• Personalization: Integrating user data (like past searches, connections, and interests) to tailor the autocomplete suggestions for individual users.

Share your solution and discuss the choices you made. Checkout the repo for your reference - go-data-structure repo