Top View of Binary Tree in DSA Rust - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new binary tree node with given value.

DSA Rust

fn new_node(val: i32) -> Box<TreeNode> {
    Box::new(TreeNode {
        val: [1],
        left: None,
        right: None,
    })
}

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A0

Bval

CNone

DSome(val)

Attempts:

3 left

💡 Hint

Common Mistakes

Using None instead of val for the node's value.

Assigning 0 instead of the given val.

The function should assign the passed value 'val' to the node's val field.

2fill in blank

medium

Complete the code to insert a node into the binary tree at the correct horizontal distance.

DSA Rust

fn insert_node(
    node: &mut Option<Box<TreeNode>>,
    val: i32,
    hd: i32,
    map: &mut std::collections::HashMap<i32, i32>
) {
    if let Some(n) = node {
        if !map.contains_key(&hd) {
            map.insert(hd, [1]);
        }
        insert_node(&mut n.left, val, hd - 1, map);
        insert_node(&mut n.right, val, hd + 1, map);
    } else {
        *node = Some(Box::new(TreeNode { val, left: None, right: None }));
    }
}

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Aval

Bhd

Cnode.val

Dmap[&hd]

Attempts:

3 left

💡 Hint

Common Mistakes

Inserting the horizontal distance instead of the node's value.

Trying to insert map values instead of the node's value.

We insert the node's value at the horizontal distance if it is not already present.

3fill in blank

hard

Fix the error in the code to correctly collect the top view nodes from the map.

DSA Rust

fn top_view(root: Option<Box<TreeNode>>) -> Vec<i32> {
    let mut map = std::collections::BTreeMap::new();
    fn traverse(node: &Option<Box<TreeNode>>, hd: i32, map: &mut std::collections::BTreeMap<i32, i32>) {
        if let Some(n) = node {
            map.entry(hd).[1];
            traverse(&n.left, hd - 1, map);
            traverse(&n.right, hd + 1, map);
        }
    }
    traverse(&root, 0, &mut map);
    map.values().cloned().collect::<Vec<i32>>()
}

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Aor_insert(n.val)

Binsert(hd, n.val)

Cor_insert_with(|| n.val)

Dor_default()

Attempts:

3 left

💡 Hint

Common Mistakes

Using or_insert directly with n.val causing borrow checker errors.

Using insert which overwrites existing entries.

or_insert_with with a closure ensures n.val is evaluated only if needed, avoiding borrowing issues.

4fill in blank

hard

Fill both blanks to correctly implement the top view traversal and map insertion.

DSA Rust

fn traverse(node: &Option<Box<TreeNode>>, hd: i32, map: &mut std::collections::BTreeMap<i32, i32>) {
    if let Some(n) = node {
        map.entry(hd).[1](|| n.val);
        traverse(&n.[2], hd - 1, map);
        traverse(&n.right, hd + 1, map);
    }
}

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

Aor_insert_with

Bor_insert

Cleft

Dright

Attempts:

3 left

💡 Hint

Common Mistakes

Using or_insert instead of or_insert_with causing borrow issues.

Using n.right instead of n.left for left subtree traversal.

Use or_insert_with to insert lazily, and traverse left child with n.left.

5fill in blank

hard

Fill all three blanks to complete the function that returns the top view of the binary tree.

DSA Rust

fn top_view(root: Option<Box<TreeNode>>) -> Vec<i32> {
    let mut map = std::collections::BTreeMap::new();
    fn traverse(node: &Option<Box<TreeNode>>, hd: i32, map: &mut std::collections::BTreeMap<i32, i32>) {
        if let Some(n) = node {
            map.entry(hd).[1](|| n.val);
            traverse(&n.[2], hd - 1, map);
            traverse(&n.[3], hd + 1, map);
        }
    }
    traverse(&root, 0, &mut map);
    map.values().cloned().collect()
}

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

Aor_insert_with

Bleft

Cright

Dor_insert

Attempts:

3 left

💡 Hint

Common Mistakes

Using or_insert instead of or_insert_with.

Mixing up left and right child traversal.

Use or_insert_with for lazy insertion, and traverse left and right children correctly.

Practice

1. What does the top view of a binary tree represent?

easy

A. Nodes visible when the tree is viewed from the top

B. Nodes visible when the tree is viewed from the left side

C. Nodes visible when the tree is viewed from the bottom

D. Nodes visible when the tree is viewed from the right side

Solution

1. Step 1: Understand the top view concept

   The top view of a binary tree shows all nodes that are visible when looking down vertically from above the tree.

2. Step 2: Compare with other views

   Left, right, and bottom views show nodes visible from those sides, not from the top.

3. Final Answer:

   Nodes visible when the tree is viewed from the top -> Option A

4. Quick Check:

   Top view = nodes seen from above [OK]

Hint: Top view shows nodes visible from above the tree [OK]

Common Mistakes:

• Confusing top view with left or right view
• Thinking bottom view is the same as top view

2. Which Rust data structure is best suited to store nodes with their horizontal distances for computing the top view of a binary tree?

easy

A. HashMap

B. Vec<Node>

C. LinkedList<Node>

D. BinaryHeap<Node>

Solution

1. Step 1: Identify the need for horizontal distance mapping

   Top view requires tracking nodes by their horizontal distance from the root, which can be negative or positive integers.

2. Step 2: Choose a data structure for key-value mapping

   A HashMap with key as horizontal distance (i32) and value as Node is ideal for quick lookups and updates.

3. Final Answer:

   HashMap -> Option A

4. Quick Check:

   Use HashMap for horizontal distance mapping [OK]

Hint: Use HashMap to map horizontal distance to nodes [OK]

Common Mistakes:

• Using Vec which doesn't map keys to values
• Using LinkedList which is inefficient for lookups
• Using BinaryHeap which is for priority queues

3. Given the following Rust code snippet for top view traversal, what will be printed?

struct Node {
    val: i32,
    left: Option,
    right: Option,
}

// Tree:
//       1
//      / \
//     2   3
//      \   \
//       4   5

fn main() {
    let root = Node {
        val: 1,
        left: Some(Box::new(Node {
            val: 2,
            left: None,
            right: Some(Box::new(Node { val: 4, left: None, right: None })),
        })),
        right: Some(Box::new(Node {
            val: 3,
            left: None,
            right: Some(Box::new(Node { val: 5, left: None, right: None })),
        })),
    };
    // Assume top_view function prints top view nodes in order from left to right
    top_view(&root);
}

medium

A. [2, 4, 1, 3, 5]

B. [2, 1, 3, 5]

C. [4, 2, 1, 3, 5]

D. [1, 2, 3]

Solution

1. Step 1: Assign horizontal distances to nodes

   Root (1) is at 0, left child (2) at -1, right child (3) at +1, node 4 at 0 (right child of 2), node 5 at +2 (right child of 3).

2. Step 2: Determine top view nodes by horizontal distance

   For each horizontal distance, the first node encountered from top is chosen: -1: 2, 0: 1, +1: 3, +2: 5. Node 4 at 0 is below 1, so ignored.

3. Final Answer:

   [2, 1, 3, 5] -> Option B

4. Quick Check:

   Top view nodes by horizontal distance = [2, 1, 3, 5] [OK]

Hint: Pick first node at each horizontal distance from top [OK]

Common Mistakes:

• Including nodes below first at same horizontal distance
• Ignoring horizontal distances for nodes
• Confusing order of output

4. The following Rust code attempts to compute the top view of a binary tree but has a bug. What is the error?

fn top_view(root: &Node) {
    let mut map = std::collections::HashMap::new();
    let mut queue = std::collections::VecDeque::new();
    queue.push_back((root, 0));

    while let Some((node, hd)) = queue.pop_front() {
        // Insert node if horizontal distance not present
        map.entry(hd).or_insert(node.val);

        if let Some(left) = &node.left {
            queue.push_back((left, hd - 1));
        }
        if let Some(right) = &node.right {
            queue.push_back((right, hd + 1));
        }
    }

    for key in map.keys() {
        println!("{}", map[key]);
    }
}

medium

A. The code inserts duplicate nodes for same horizontal distance

B. The queue is not initialized properly

C. The map keys are not sorted before printing, so output order is random

D. The code does not handle empty tree (root is None)

Solution

1. Step 1: Analyze map insertion and printing

   The code inserts nodes correctly using or_insert to keep first node per horizontal distance.

2. Step 2: Check output order

   HashMap keys are unordered, so iterating with map.keys() prints nodes in random order, not left to right.

3. Final Answer:

   The map keys are not sorted before printing, so output order is random -> Option C

4. Quick Check:

   Sort keys before printing top view [OK]

Hint: Sort horizontal distances before printing top view [OK]

Common Mistakes:

• Assuming HashMap keys are ordered
• Ignoring empty tree case (not asked here)
• Thinking queue initialization is wrong

5. You want to find the top view of a binary tree but only have the tree nodes and no horizontal distance stored. Which approach correctly computes the top view in Rust? A) Use a recursive function passing horizontal distance and level, store first node per horizontal distance with smallest level. B) Use a stack to traverse nodes and store last node per horizontal distance. C) Traverse the tree in preorder and print nodes as you visit. D) Use a queue but ignore horizontal distances and print nodes in insertion order.

hard

A. Preorder traversal printing nodes directly

B. Use stack and store last node per horizontal distance

C. Use queue ignoring horizontal distances

D. Use recursion with horizontal distance and level to track top nodes

Solution

1. Step 1: Understand top view requires horizontal distance and level

   Top view needs to know horizontal distance and the vertical level to pick the topmost node at each horizontal distance.

2. Step 2: Choose approach that tracks horizontal distance and level

   Recursive traversal passing horizontal distance and level, storing first node per horizontal distance with smallest level, correctly computes top view.

3. Final Answer:

   Use recursion with horizontal distance and level to track top nodes -> Option D

4. Quick Check:

   Track horizontal distance and level recursively [OK]

Hint: Track horizontal distance and level to find top nodes [OK]

Common Mistakes:

• Ignoring horizontal distance in traversal
• Using stack without tracking levels
• Printing nodes in preorder without filtering