Trie vs Hash Map for Prefix Matching in DSA Go - Build Both Approaches

Trie vs Hash Map for Prefix Matching

📖 Scenario: Imagine you are building a simple search feature for a phone contact list. You want to find all contacts whose names start with a certain prefix quickly.

🎯 Goal: Build two ways to find contacts by prefix: one using a map[string]bool to store contacts and check prefixes, and another using a Trie data structure for efficient prefix matching.

📋 What You'll Learn

Create a contacts map with exact names

Create a prefix string variable

Implement a function searchWithMap that finds contacts starting with prefix using the map

Implement a Trie with Insert and SearchPrefix methods

Use the Trie to find contacts starting with prefix

Print results from both methods

💡 Why This Matters

🌍 Real World

Searching contacts or words quickly by prefix is common in phone apps, autocomplete, and search engines.

💼 Career

Understanding Tries and hash maps helps in building efficient search features and improves problem-solving skills in coding interviews.

1

Create the contacts map

Create a contacts map of type map[string]bool with these exact entries: "Alice": true, "Bob": true, "Charlie": true, "David": true, "Daniel": true.

DSA Go

# Your code here

Hint

Use map[string]bool and add the exact names as keys with value true.

2

Create the prefix variable

Create a string variable called prefix and set it to "Da".

DSA Go

package main

func main() {
    contacts := map[string]bool{
        "Alice": true,
        "Bob": true,
        "Charlie": true,
        "David": true,
        "Daniel": true,
    }
    // Your code here
}

Hint

Use prefix := "Da" to create the variable.

3

Implement searchWithMap function

Write a function called searchWithMap that takes contacts map[string]bool and prefix string, and returns a []string of contact names starting with prefix. Use a for loop with name to iterate over contacts. Inside the loop, use strings.HasPrefix(name, prefix) to check the prefix and append matching names to a result slice.

DSA Go

package main

import "strings"

func main() {
    contacts := map[string]bool{
        "Alice": true,
        "Bob": true,
        "Charlie": true,
        "David": true,
        "Daniel": true,
    }
    prefix := "Da"

    // Write searchWithMap function below
}

Hint

Use for name := range contacts and strings.HasPrefix(name, prefix) to find matches.

4

Build Trie and print results

Define a TrieNode struct with a children map[rune]*TrieNode and isEnd bool. Implement Insert(word string) and SearchPrefix(prefix string) []string methods on TrieNode. In main, create a root TrieNode, insert all contacts, then call searchWithMap and root.SearchPrefix(prefix). Print the results with fmt.Println as shown: fmt.Println("Map search:", mapResults) and fmt.Println("Trie search:", trieResults).

DSA Go

package main

import "strings"

func searchWithMap(contacts map[string]bool, prefix string) []string {
    var result []string
    for name := range contacts {
        if strings.HasPrefix(name, prefix) {
            result = append(result, name)
        }
    }
    return result
}

func main() {
    contacts := map[string]bool{
        "Alice": true,
        "Bob": true,
        "Charlie": true,
        "David": true,
        "Daniel": true,
    }
    prefix := "Da"

    // Build Trie, insert contacts, search and print results
    // Your code here
}

Hint

Define TrieNode with Insert and SearchPrefix. Insert all contacts, then print results from both methods.

Practice

1. Which data structure is generally more efficient for checking if a prefix exists in a large set of strings?

easy

A. Linked List, because it allows sequential access to elements

B. Hash Map, because it stores keys and values with constant time lookup

C. Array, because it stores elements in order and allows binary search

D. Trie, because it stores prefixes explicitly and allows fast prefix search

Solution

1. Step 1: Understand prefix search requirements

   Prefix search needs to quickly find if any string starts with a given prefix.

2. Step 2: Compare Trie and Hash Map for prefix search

   Trie stores prefixes explicitly, allowing fast traversal to check prefixes. Hash Map is good for exact key lookup but not for prefix matching.

3. Final Answer:

   Trie, because it stores prefixes explicitly and allows fast prefix search -> Option D

4. Quick Check:

   Prefix search = Trie [OK]

Hint: Trie stores prefixes explicitly, ideal for prefix search [OK]

Common Mistakes:

• Thinking hash map supports prefix search efficiently
• Confusing prefix search with exact key lookup
• Assuming arrays or lists are efficient for prefix matching

2. Which of the following is the correct way to declare a Trie node struct in Go for prefix matching?

easy

A. type TrieNode struct { next map[string]int; end bool }

B. type TrieNode struct { children map[rune]*TrieNode; isEnd bool }

C. type TrieNode struct { children []int; isEnd string }

D. type TrieNode struct { children map[int]string; isEnd int }

Solution

1. Step 1: Identify correct field types for TrieNode

   Children should map characters (rune) to TrieNode pointers; isEnd is a boolean indicating word end.

2. Step 2: Check each option's correctness

   type TrieNode struct { children map[rune]*TrieNode; isEnd bool } uses map[rune]*TrieNode and bool for isEnd, which is correct. Others have wrong types or field names.

3. Final Answer:

   type TrieNode struct { children map[rune]*TrieNode; isEnd bool } -> Option B

4. Quick Check:

   TrieNode struct with map[rune]*TrieNode and bool isEnd = type TrieNode struct { children map[rune]*TrieNode; isEnd bool } [OK]

Hint: Trie children map keys are runes, isEnd is bool [OK]

Common Mistakes:

• Using incorrect types for children map keys or values
• Using string instead of bool for isEnd
• Confusing field names or types

3. Given the following Go code snippet using a Trie for prefix matching, what will be the output?

type TrieNode struct {
    children map[rune]*TrieNode
    isEnd    bool
}

func (t *TrieNode) Insert(word string) {
    node := t
    for _, ch := range word {
        if node.children[ch] == nil {
            node.children[ch] = &TrieNode{children: make(map[rune]*TrieNode)}
        }
        node = node.children[ch]
    }
    node.isEnd = true
}

func (t *TrieNode) StartsWith(prefix string) bool {
    node := t
    for _, ch := range prefix {
        if node.children[ch] == nil {
            return false
        }
        node = node.children[ch]
    }
    return true
}

func main() {
    root := &TrieNode{children: make(map[rune]*TrieNode)}
    root.Insert("cat")
    root.Insert("car")
    root.Insert("dog")
    fmt.Println(root.StartsWith("ca"))
    fmt.Println(root.StartsWith("do"))
    fmt.Println(root.StartsWith("bat"))
}

medium

A. true
true
false

B. false
true
false

C. true
false
false

D. false
false
true

Solution

1. Step 1: Insert words into Trie

   Words "cat", "car", and "dog" are inserted, building paths for 'c'->'a'->'t', 'c'->'a'->'r', and 'd'->'o'->'g'.

2. Step 2: Check StartsWith calls

   StartsWith("ca") returns true because 'c'->'a' path exists. StartsWith("do") returns true because 'd'->'o' path exists. StartsWith("bat") returns false because 'b' path does not exist.

3. Final Answer:

   true true false -> Option A

4. Quick Check:

   Prefix "ca" and "do" found, "bat" not found = true true false [OK]

Hint: Check if prefix path exists in Trie for true/false [OK]

Common Mistakes:

• Assuming StartsWith returns true only if full word exists
• Confusing Insert with StartsWith behavior
• Ignoring that 'bat' prefix is missing

4. The following Go code attempts to check if a prefix exists using a Hash Map of strings to booleans. What is the main issue?

func PrefixExists(words map[string]bool, prefix string) bool {
    for word := range words {
        if strings.HasPrefix(word, prefix) {
            return true
        }
    }
    return false
}

func main() {
    words := map[string]bool{"apple": true, "app": true, "banana": true}
    fmt.Println(PrefixExists(words, "ap"))
}

medium

A. Inefficient for large datasets due to linear scan over all keys

B. Syntax error: strings.HasPrefix is not a valid function

C. Incorrect return type: should return string instead of bool

D. Map keys cannot be iterated in Go

Solution

1. Step 1: Analyze PrefixExists function logic

   The function iterates over all keys in the map and checks prefix using strings.HasPrefix.

2. Step 2: Identify performance issue

   Iterating all keys causes linear time complexity, which is inefficient for large datasets compared to Trie prefix search.

3. Final Answer:

   Inefficient for large datasets due to linear scan over all keys -> Option A

4. Quick Check:

   Linear scan in map for prefix = inefficient [OK]

Hint: Hash map prefix check needs full scan, slow for many keys [OK]

Common Mistakes:

• Thinking strings.HasPrefix is invalid
• Assuming map iteration is not allowed
• Confusing return types

5. You need to design a system to quickly find all words starting with a given prefix from a dictionary of 1 million words. Which approach is best and why? A) Use a Hash Map storing all words as keys and scan keys for prefix matches. B) Use a Trie to store all words and traverse nodes to find prefix matches. C) Store words in a sorted array and use binary search to find prefix range. D) Use a Linked List to store words and iterate to find prefix matches.

hard

A. Sorted array with binary search finds prefix range efficiently

B. Hash Map with linear scan is fast due to constant lookup

C. Trie allows efficient prefix traversal without scanning all words

D. Linked List is best for sequential prefix matching

Solution

1. Step 1: Evaluate each data structure for prefix search on large data

   Hash Map requires scanning all keys for prefix, inefficient. Trie is efficient and allows prefix traversal without scanning all words. Sorted array allows binary search to find prefix range quickly but requires additional steps to find all matches.

2. Step 2: Compare Trie and sorted array for large datasets

   Trie uses more memory but provides direct prefix traversal and efficient prefix queries. Sorted array with binary search is efficient for prefix range queries but retrieving all matches requires scanning the range.

3. Final Answer:

   Trie allows efficient prefix traversal without scanning all words -> Option C

4. Quick Check:

   Trie prefix traversal = efficient prefix search [OK]

Hint: Trie allows direct prefix traversal for fast prefix search [OK]

Common Mistakes:

• Choosing sorted array without considering retrieval cost
• Assuming hash map supports fast prefix search
• Ignoring Trie advantages for prefix queries