Word Search in Trie in DSA Go - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Trie data structure?

A Trie is a tree-like data structure used to store a dynamic set of strings where keys are usually strings. Each node represents a character, and paths from root to nodes represent prefixes of words.

beginner

How does a Trie help in word search?

A Trie allows fast prefix-based search by traversing nodes corresponding to characters of the word. If traversal reaches the end of the word and the node marks a word end, the word exists in the Trie.

beginner

What does the 'isEnd' flag in a Trie node represent?

The 'isEnd' flag indicates that the path from the root to this node forms a complete word stored in the Trie.

intermediate

In Go, how do you represent children nodes in a Trie?

Children nodes are typically represented as a map from rune (character) to *TrieNode, allowing dynamic branching for each character.

intermediate

What is the time complexity of searching a word in a Trie?

The time complexity is O(m), where m is the length of the word, because we traverse one node per character.

What does each node in a Trie represent?

A An integer value

B A character of a word

C A whole word

D A number of words

Each node in a Trie represents a single character of a word.

What does the 'isEnd' flag in a Trie node indicate?

A The node is a leaf

B The node is the root

C The node has no children

D The node marks the end of a word

The 'isEnd' flag marks that the path to this node forms a complete word.

What is the best data type to store children nodes in a Go Trie implementation?

A Map from rune to *TrieNode

B Boolean array

C Array of integers

D Slice of strings

A map from rune to *TrieNode allows flexible branching for each character.

What is the time complexity to search a word of length m in a Trie?

A O(m)

B O(1)

C O(log m)

D O(m^2)

Searching takes O(m) time, one step per character.

If a word is not in the Trie, what happens during search?

A Search returns true anyway

B Search loops infinitely

C Search fails when a character node is missing

D Search returns the closest word

Search fails when a character node is missing, meaning the word is not stored.

Explain how to search for a word in a Trie step-by-step.

Think about walking down the tree one letter at a time.

Describe the structure of a Trie node in Go for word search.

Focus on how children and word end are represented.

Practice

1. What is the main purpose of using a Trie data structure in word search problems?

easy

A. To reverse words quickly

B. To sort words alphabetically

C. To store words efficiently for quick prefix-based searches

D. To count the frequency of words

Solution

1. Step 1: Understand Trie structure

   A Trie stores characters of words in a tree form, sharing prefixes.

2. Step 2: Identify Trie use in word search

   This structure allows fast prefix checks and word existence queries.

3. Final Answer:

   To store words efficiently for quick prefix-based searches -> Option C

4. Quick Check:

   Trie = prefix search efficiency [OK]

Hint: Trie is best for prefix-based word searches [OK]

Common Mistakes:

• Confusing Trie with sorting structures
• Thinking Trie counts word frequency
• Assuming Trie reverses words

2. Which of the following Go code snippets correctly initializes an empty Trie node struct with a children map and end-of-word flag?

easy

A. type TrieNode struct { children map[string]TrieNode; isEnd bool }
root := TrieNode{children: map[string]TrieNode{}, isEnd: false}

B. type TrieNode struct { children []TrieNode; isEnd int }
root := TrieNode{children: []TrieNode{}, isEnd: 0}

C. type TrieNode struct { children map[rune]TrieNode; isEnd bool }
root := &TrieNode{children: make(map[rune]TrieNode), isEnd: false}

D. type TrieNode struct { children map[rune]*TrieNode; isEnd bool }
root := &TrieNode{children: make(map[rune]*TrieNode), isEnd: false}

Solution

1. Step 1: Check struct field types

   Children should be a map from rune to pointer to TrieNode for dynamic nodes.

2. Step 2: Verify initialization syntax

   Use make(map[rune]*TrieNode) and isEnd as bool with false initial value.

3. Final Answer:

   type TrieNode struct { children map[rune]*TrieNode; isEnd bool } root := &TrieNode{children: make(map[rune]*TrieNode), isEnd: false} -> Option D

4. Quick Check:

   Correct map and pointer usage = type TrieNode struct { children map[rune]*TrieNode; isEnd bool } root := &TrieNode{children: make(map[rune]*TrieNode), isEnd: false} [OK]

Hint: Use map[rune]*TrieNode and make() to initialize children [OK]

Common Mistakes:

• Using slice instead of map for children
• Using non-pointer TrieNode in map values
• Incorrect map key type (string instead of rune)

3. Given the following Go code snippet for searching a word in a Trie, what will be the output if the word "cat" is searched after inserting "car" and "cat"?

func (t *TrieNode) Search(word string) bool {
    node := t
    for _, ch := range word {
        if _, ok := node.children[ch]; !ok {
            return false
        }
        node = node.children[ch]
    }
    return node.isEnd
}

medium

A. true

B. false

C. Compilation error

D. Runtime panic

Solution

1. Step 1: Understand insertion of "car" and "cat"

   Both words share prefix 'ca', nodes created for 'c', 'a', then separate nodes for 'r' and 't' with isEnd true.

2. Step 2: Trace search for "cat"

   Traverse nodes for 'c', 'a', 't' successfully, last node has isEnd true, so returns true.

3. Final Answer:

   true -> Option A

4. Quick Check:

   Word inserted and found = true [OK]

Hint: Search returns true if last node's isEnd is true [OK]

Common Mistakes:

• Assuming search returns true for prefixes only
• Confusing isEnd flag usage
• Thinking search returns true if partial path exists

4. Identify the bug in the following Go code snippet for searching a word in a Trie:

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

medium

A. Returns true even if the word is only a prefix, not a full word

B. Panics if children map is nil

C. Does not check for nil children map before access

D. Incorrect loop variable type causes compilation error

Solution

1. Step 1: Analyze search return condition

   Function returns true after traversing all characters without checking isEnd flag.

2. Step 2: Understand consequence of missing isEnd check

   Words that are prefixes but not complete words will incorrectly return true.

3. Final Answer:

   Returns true even if the word is only a prefix, not a full word -> Option A

4. Quick Check:

   Missing isEnd check causes false positives [OK]

Hint: Always check isEnd after traversal to confirm full word [OK]

Common Mistakes:

• Forgetting to check isEnd flag
• Assuming traversal success means word exists
• Ignoring nil map checks (not in this code)

5. You have a list of words: ["apple", "app", "application", "bat", "bath", "baton"]. You want to build a Trie and then search for the word "bat". Which of the following statements is true about the search result and the Trie structure after insertion?

hard

A. Searching "bat" returns false because "bat" is a prefix of "bath" and "baton"

B. Searching "bat" returns true; nodes for 'b', 'a', 't' share prefixes with other words

C. Trie stores each word as a separate branch without shared nodes

D. Searching "bat" returns true only if "bat" is inserted last

Solution

1. Step 1: Insert all words into Trie

   Words share prefixes: "app" and "apple" share 'app', "bat", "bath", "baton" share 'bat'.

2. Step 2: Search for "bat"

   Since "bat" is inserted, its node's isEnd is true, so search returns true.

3. Final Answer:

   Searching "bat" returns true; nodes for 'b', 'a', 't' share prefixes with other words -> Option B

4. Quick Check:

   Shared prefix nodes and isEnd true = Searching "bat" returns true; nodes for 'b', 'a', 't' share prefixes with other words [OK]

Hint: Shared prefixes save space; isEnd marks full words [OK]

Common Mistakes:

• Thinking prefix words are not found
• Assuming separate branches for each word
• Believing insertion order affects search result