Trie vs Hash Map for Prefix Matching in DSA Java - Interactive Comparison Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to insert a word into the Trie.

DSA Java

public void insert(String word) {
    TrieNode node = root;
    for (char c : word.toCharArray()) {
        if (!node.children.containsKey(c)) {
            node.children.put(c, new TrieNode());
        }
        node = node.children[1];
    }
    node.isWord = true;
}

Drag options to blanks, or click blank then click option'

A.get(c)

C.children[c]

D.children.get()

Attempts:

3 left

💡 Hint

Common Mistakes

Using node.get(c) instead of node.children.get(c).

Trying to access children with square brackets like an array.

Calling get() without arguments.

We must move to the child node corresponding to character c by accessing node.children.get(c).

2fill in blank

medium

Complete the code to check if a prefix exists in the Trie.

DSA Java

public boolean startsWith(String prefix) {
    TrieNode node = root;
    for (char c : prefix.toCharArray()) {
        if (!node.children.containsKey(c)) {
            return false;
        }
        node = node.children[1];
    }
    return true;
}

Drag options to blanks, or click blank then click option'

A.get(c)

B[c]

C.contains(c)

D.get()

Attempts:

3 left

💡 Hint

Common Mistakes

Using square brackets to access children.

Using contains instead of get.

Calling get without arguments.

To move to the next node, we use node.children.get(c) to get the child node for character c.

3fill in blank

hard

Fix the error in the HashMap prefix search method to return true if any key starts with the prefix.

DSA Java

public boolean startsWithPrefix(HashMap<String, Integer> map, String prefix) {
    for (String key : map.keySet()) {
        if (key[1](prefix)) {
            return true;
        }
    }
    return false;
}

Drag options to blanks, or click blank then click option'

A.contains

B.startsWith

C.equals

D.endsWith

Attempts:

3 left

💡 Hint

Common Mistakes

Using contains which checks anywhere in the string.

Using equals which checks exact match.

Using endsWith which checks the end of the string.

The String method startsWith checks if the key begins with the given prefix.

4fill in blank

hard

Fill both blanks to create a TrieNode class with a children map and a boolean flag.

DSA Java

class TrieNode {
    Map<Character, TrieNode> children = new HashMap<>();
    boolean [1];

    public TrieNode() {
        this.[2] = false;
    }
}

Drag options to blanks, or click blank then click option'

AisWord

BisPrefix

ChasChild

DisEnd

Attempts:

3 left

💡 Hint

Common Mistakes

Using isPrefix which is less common and confusing.

Using hasChild which is not a boolean flag for word end.

Using isEnd which is valid but not used here.

The boolean flag is commonly named isWord to mark the end of a word in the Trie.

5fill in blank

hard

Fill the blanks to implement a method that counts words with a given prefix using a HashMap.

DSA Java

public int countPrefixWords(HashMap<String, Integer> map, String prefix) {
    int count = 0;
    for (String key : map.keySet()) {
        if (key[1](prefix)) {
            count [2] map.get(key);
        }
    }
    return count;
}

Drag options to blanks, or click blank then click option'

A.startsWith

B+=

C-=

D.contains

Attempts:

3 left

💡 Hint

Common Mistakes

Using contains instead of startsWith for prefix check.

Using -= instead of += for counting.

Using contains in the if condition which is incorrect here.

We check if the key starts with the prefix, then add the value from the map to count. The contains method is a distractor here.

Practice

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

easy

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

B. Trie, because it stores characters in a tree structure allowing fast prefix search

C. Hash Map, because it stores keys with direct access and no traversal

D. Linked List, because it allows sequential access to all elements

Solution

1. Step 1: Understand prefix matching requirement

   Prefix matching means finding all strings starting with a given prefix quickly.

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

   Trie stores characters in nodes linked by edges, allowing traversal by prefix. Hash Map requires checking all keys or storing prefixes separately, which is less efficient.

3. Final Answer:

   Trie, because it stores characters in a tree structure allowing fast prefix search -> Option B

4. Quick Check:

   Trie = Fast prefix search [OK]

Hint: Trie is designed for prefix search, hash map is not [OK]

Common Mistakes:

• Thinking hash map can do prefix search directly
• Confusing array or linked list with prefix search efficiency

2. Which of the following is the correct way to check if a prefix exists in a Trie in Java?

easy

A. Use a loop to traverse nodes for each character of the prefix and check if nodes exist

B. Use HashMap.containsKey(prefix) directly on the Trie root

C. Use binary search on the Trie nodes array

D. Use recursion to check if prefix is a substring of any word

Solution

1. Step 1: Understand Trie prefix search method

   To check prefix, we traverse the Trie nodes character by character along the prefix path.

2. Step 2: Evaluate options for prefix check

   Use a loop to traverse nodes for each character of the prefix and check if nodes exist correctly describes traversal. Use HashMap.containsKey(prefix) directly on the Trie root is incorrect because HashMap.containsKey checks full keys, not prefixes. Use binary search on the Trie nodes array and D do not apply to Trie structure.

3. Final Answer:

   Use a loop to traverse nodes for each character of the prefix and check if nodes exist -> Option A

4. Quick Check:

   Traverse nodes for prefix = Use a loop to traverse nodes for each character of the prefix and check if nodes exist [OK]

Hint: Traverse Trie nodes for each prefix character [OK]

Common Mistakes:

• Using HashMap.containsKey for prefix check
• Trying binary search on Trie nodes
• Confusing substring with prefix

3. Given the following Java Trie code snippet, what will be the output after inserting words "cat", "car", "dog" and checking prefix "ca"?

Trie trie = new Trie();
trie.insert("cat");
trie.insert("car");
trie.insert("dog");
boolean result = trie.startsWith("ca");
System.out.println(result);

medium

A. Compilation error

B. false

C. true

D. Runtime exception

Solution

1. Step 1: Insert words into Trie

   Words "cat", "car", and "dog" are inserted, creating nodes for 'c', 'a', 't', 'r', 'd', 'o', 'g'.

2. Step 2: Check prefix "ca"

   Prefix "ca" matches nodes 'c' and 'a' in the Trie, so startsWith returns true.

3. Final Answer:

   true -> Option C

4. Quick Check:

   Prefix "ca" exists = true [OK]

Hint: Inserted words with prefix "ca" means startsWith("ca") is true [OK]

Common Mistakes:

• Assuming startsWith returns false if prefix is not a full word
• Confusing startsWith with search for full word
• Expecting compilation or runtime errors

4. Consider this Java code snippet using a HashMap to store words and check prefixes:

HashMap map = new HashMap<>();
map.put("apple", true);
map.put("app", true);
boolean prefixExists = map.containsKey("ap");
System.out.println(prefixExists);

What is the output and why?

medium

A. false, because "ap" is not a key in the map

B. true, because "ap" is a prefix of "app" and "apple"

C. Compilation error due to wrong HashMap usage

D. Runtime exception due to null key

Solution

1. Step 1: Understand HashMap key storage

   HashMap stores exact keys "apple" and "app", but not "ap".

2. Step 2: Check containsKey("ap")

   containsKey checks for exact key match, so returns false for "ap".

3. Final Answer:

   false, because "ap" is not a key in the map -> Option A

4. Quick Check:

   HashMap exact key check = false for "ap" [OK]

Hint: HashMap keys must match exactly, no prefix matching [OK]

Common Mistakes:

• Assuming containsKey checks prefixes
• Expecting true because "ap" is prefix of stored keys
• Confusing HashMap with Trie behavior

5. You need to implement a system that supports fast prefix queries on millions of words and also allows quick insertion and deletion. Which data structure is best suited and why?

hard

A. ArrayList, because it allows fast insertion and deletion at the end

B. Hash Map, because it provides constant time insertion and deletion

C. Balanced Binary Search Tree, because it keeps data sorted for prefix search

D. Trie, because it supports efficient prefix queries and dynamic updates

Solution

1. Step 1: Analyze prefix query requirement

   Fast prefix queries require a data structure that can traverse prefixes efficiently.

2. Step 2: Compare data structures for insertion, deletion, and prefix search

   Trie supports prefix queries efficiently and allows insertion/deletion by node updates. Hash Map is fast for exact keys but not prefix queries. Balanced BST supports sorted data but prefix search is slower. ArrayList is inefficient for prefix queries and deletions.

3. Final Answer:

   Trie, because it supports efficient prefix queries and dynamic updates -> Option D

4. Quick Check:

   Trie = best for prefix queries + updates [OK]

Hint: Trie handles prefix queries and updates efficiently [OK]

Common Mistakes:

• Choosing Hash Map for prefix queries
• Assuming balanced BST is best for prefix search
• Ignoring deletion complexity in ArrayList