Why Trie Exists and What Hash Map Cannot Do for Strings in DSA Go - See It Work

Why Trie Exists and What Hash Map Cannot Do for Strings

📖 Scenario: Imagine you are building a phone contact app. You want to store many names and quickly find all contacts starting with certain letters. Using a hash map can find exact names fast, but it cannot help find names by prefix efficiently.We will build a simple example to see why a Trie is useful and what a hash map cannot do well with strings.

🎯 Goal: Build a small program to store a list of words in a hash map and then try to find all words starting with a prefix. Then, see why this is hard with a hash map and how a Trie can help.

📋 What You'll Learn

Create a hash map with given words

Create a prefix string variable

Write code to find words starting with the prefix using the hash map

Print the found words

💡 Why This Matters

🌍 Real World

Phone contact apps, autocomplete features, and search engines need fast prefix search for strings.

💼 Career

Understanding why Tries are used helps in roles involving search optimization, text processing, and building efficient data retrieval systems.

1

Create a hash map with words

Create a variable called wordsMap of type map[string]bool and add these exact words as keys with value true: "apple", "app", "banana", "bat", "bath".

DSA Go

# Your code here

Hint

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

2

Create a prefix variable

Create a variable called prefix of type string and set it to the exact value "ba".

DSA Go

package main

func main() {
    wordsMap := map[string]bool{
        "apple": true,
        "app": true,
        "banana": true,
        "bat": true,
        "bath": true,
    }
    // Your code here
}

Hint

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

3

Find words starting with prefix using hash map

Use a for loop with variable word to iterate over wordsMap keys. Inside the loop, use strings.HasPrefix(word, prefix) to check if word starts with prefix. If yes, append word to a slice called foundWords of type []string. Initialize foundWords as an empty slice before the loop.

DSA Go

package main

import "strings"

func main() {
    wordsMap := map[string]bool{
        "apple": true,
        "app": true,
        "banana": true,
        "bat": true,
        "bath": true,
    }
    prefix := "ba"
    // Your code here
}

Hint

Initialize foundWords as an empty slice. Loop over wordsMap keys. Use strings.HasPrefix to check prefix and append matching words.

4

Print the found words

Use fmt.Println(foundWords) to print the slice foundWords. Import fmt package at the top.

DSA Go

package main

import "strings"

func main() {
    wordsMap := map[string]bool{
        "apple": true,
        "app": true,
        "banana": true,
        "bat": true,
        "bath": true,
    }
    prefix := "ba"
    foundWords := []string{}
    for word := range wordsMap {
        if strings.HasPrefix(word, prefix) {
            foundWords = append(foundWords, word)
        }
    }
    // Your code here
}

Hint

Use fmt.Println(foundWords) to print the list of words starting with the prefix.

Practice

1. Why do we use a Trie data structure instead of a Hash Map for storing many strings?

easy

A. Because Trie can efficiently find all strings with a common prefix

B. Because Hash Map uses less memory than Trie

C. Because Trie stores strings as keys directly without nodes

D. Because Hash Map automatically sorts strings alphabetically

Solution

1. Step 1: Understand Trie prefix search

   Trie stores strings in a tree form where each node represents a character, allowing quick prefix searches.

2. Step 2: Compare with Hash Map capabilities

   Hash Map stores full strings as keys but cannot efficiently find all strings sharing a prefix without checking all keys.

3. Final Answer:

   Because Trie can efficiently find all strings with a common prefix -> Option A

4. Quick Check:

   Trie = Efficient prefix search [OK]

Hint: Trie excels at prefix search, Hash Map does not [OK]

Common Mistakes:

• Thinking Hash Map sorts keys automatically
• Believing Trie uses less memory always
• Confusing Trie nodes with full string keys

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

easy

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

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

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

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

Solution

1. Step 1: Identify correct children type for TrieNode

   Children should map characters (rune) to child nodes (pointer to TrieNode).

2. Step 2: Check isEnd field type

   isEnd should be a boolean indicating if node marks end of a word.

3. Final Answer:

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

4. Quick Check:

   Children map[rune]*TrieNode and isEnd bool [OK]

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

Common Mistakes:

• Using wrong map key or value types
• Setting isEnd as string or int instead of bool
• Using slice instead of map for children

3. What will be the output of the following Go code snippet?

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

func main() {
  root := &TrieNode{children: make(map[rune]*TrieNode)}
  word := "cat"
  node := root
  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
  
  // Check if 'car' is in Trie
  node = root
  checkWord := "car"
  found := true
  for _, ch := range checkWord {
    if node.children[ch] == nil {
      found = false
      break
    }
    node = node.children[ch]
  }
  if found && node.isEnd {
    println("Found")
  } else {
    println("Not Found")
  }
}

medium

A. Found

B. Not Found

C. Compilation error

D. Runtime panic

Solution

1. Step 1: Insert 'cat' into Trie

   The code adds nodes for 'c', 'a', 't' and marks 't' node as end of word.

2. Step 2: Search for 'car' in Trie

   Traversal finds 'c' and 'a' nodes but no child for 'r', so found becomes false.

3. Final Answer:

   Not Found -> Option B

4. Quick Check:

   Only 'cat' inserted, 'car' missing last node [OK]

Hint: Check each character node; missing means Not Found [OK]

Common Mistakes:

• Assuming partial prefix means found
• Confusing isEnd flag usage
• Expecting runtime errors from nil map access

4. The following Go code tries to insert a word into a Trie but causes a runtime panic. What is the error?

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

func insert(root *TrieNode, word string) {
  node := root
  for _, ch := range word {
    if node.children[ch] == nil {
      node.children[ch] = &TrieNode{}
    }
    node = node.children[ch]
  }
  node.isEnd = true
}

func main() {
  root := &TrieNode{}
  insert(root, "dog")
}

medium

A. children map is not initialized before use

B. isEnd should be an int, not bool

C. Function insert missing return type

D. root pointer is nil

Solution

1. Step 1: Check initialization of children map

   Root node's children map is nil because it was not created with make(), causing panic on access.

2. Step 2: Understand panic cause

   Accessing or assigning to nil map causes runtime panic in Go.

3. Final Answer:

   children map is not initialized before use -> Option A

4. Quick Check:

   Nil map access causes panic [OK]

Hint: Always initialize maps with make() before use [OK]

Common Mistakes:

• Ignoring map initialization
• Thinking isEnd type causes panic
• Assuming nil pointer for root

5. You want to find all words in a large dictionary that start with the prefix "pre". Why is a Trie better than a Hash Map for this task, and how would you implement the search?

hard

A. Trie uses hashing internally to speed up prefix search

B. Hash Map sorts keys internally, so prefix search is a simple range query

C. Hash Map stores words as linked lists, making prefix search faster

D. Trie stores words by prefix nodes, allowing direct traversal to prefix end; then collect all descendant words

Solution

1. Step 1: Understand Trie prefix traversal

   In Trie, each character is a node; to find prefix "pre", traverse nodes 'p' -> 'r' -> 'e'.

2. Step 2: Collect all words under prefix node

   From the 'e' node, recursively collect all words by exploring child nodes marking word ends.

3. Final Answer:

   Trie stores words by prefix nodes, allowing direct traversal to prefix end; then collect all descendant words -> Option D

4. Quick Check:

   Trie = Direct prefix traversal + collect descendants [OK]

Hint: Traverse prefix nodes, then gather all child words [OK]

Common Mistakes:

• Assuming Hash Map sorts keys automatically
• Thinking Trie uses hashing internally
• Confusing linked list with Hash Map storage