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DSA Course: Interview Patterns and Problem Solving
Module 6: Trees
Best Time to Buy and Sell Stock: Greedy Pattern
Maximum Subarray: Kadane Pattern
Move Zeroes: Two pointers Pattern
Contains Duplicate: Set Pattern
Valid Anagram: Frequency map Pattern
Longest Substring Without Repeating Characters: Sliding window Pattern
Valid Palindrome: Two pointers Pattern
Longest Palindromic Substring: Expand around center Pattern
Group Anagrams: Hash key Pattern
Binary Search: Classic search Pattern
Search Insert Position: Lower bound Pattern
First Bad Version: Predicate search Pattern
Search in Rotated Sorted Array: Rotated search Pattern
Find Minimum in Rotated Sorted Array: Rotated minimum Pattern
Valid Parentheses: Stack matching Pattern
Min Stack: Auxiliary stack Pattern
Daily Temperatures: Monotonic stack Pattern
Next Greater Element I: Monotonic stack Pattern
Evaluate Reverse Polish Notation: Stack evaluation Pattern
Reverse Linked List: Pointer reversal Pattern
Merge Two Sorted Lists: Dummy node Pattern
Linked List Cycle: Fast and slow pointers Pattern
Middle of the Linked List: Fast and slow pointers Pattern
Remove Nth Node From End: Two pointers Pattern
Binary Tree Traversals: DFS recursion Pattern
Maximum Depth of Binary Tree: Height recursion Pattern
Binary Tree Level Order Traversal: BFS queue Pattern
Validate Binary Search Tree: Range bounds Pattern
Lowest Common Ancestor: Recursive split Pattern
Connected Components: Adjacency DFS Pattern
Number of Islands: Grid DFS Pattern
Flood Fill: Boundary DFS Pattern
Clone Graph: Hash Map DFS Pattern
Course Schedule: Topological Sort Pattern
Union Find Components: Disjoint Set Pattern
Shortest Path in Unweighted Graph: BFS Distance Pattern
Climbing Stairs: Fibonacci DP Pattern
House Robber: Pick or Skip DP Pattern
Coin Change: Minimum Coins DP Pattern
Longest Increasing Subsequence: Binary Search DP Pattern
Longest Common Subsequence: 2D DP Pattern
0/1 Knapsack: Capacity DP Pattern
Longest Consecutive Sequence: Hash Set Pattern
Subarray Sum Equals K: Prefix Sum Hashmap Pattern
First Unique Character: Frequency Map Pattern
Find Duplicates: Frequency Map Pattern
Ransom Note: Character Availability Pattern
Sort Colors: Dutch National Flag Pattern
Next Permutation: Pivot and Suffix Reversal Pattern
Merge Intervals: Sort and Sweep Pattern
Find First and Last Position: Boundary Binary Search Pattern
Search a 2D Matrix: Flattened Binary Search Pattern
Subsets: Pick or Skip Recursion Pattern
Generate Parentheses: Valid State Backtracking Pattern
Combination Sum: Reuse Choice Backtracking Pattern
N-Queens: Constraint Backtracking Pattern
Word Search: Grid Backtracking Pattern
Kth Largest Element: Size-K Min-Heap Pattern
Top K Frequent Elements: Frequency Heap Pattern
Merge K Sorted Lists: Min-Heap Multiway Merge Pattern
Median Finder: Two Heaps Pattern
Task Scheduler: Greedy Max-Heap Pattern
Jump Game: Farthest Reach Greedy Pattern
Gas Station: Greedy Reset Pattern
Non-overlapping Intervals: Earliest End Greedy Pattern
Minimum Arrows to Burst Balloons: Interval End Greedy Pattern
Partition Labels: Last Occurrence Greedy Pattern
Single Number: XOR Cancellation Pattern
Power of Two: n and n-1 Pattern
Number of 1 Bits: Brian Kernighan Pattern
Single Number III: Rightmost Set Bit Pattern
XOR From 1 to N: Modulo Cycle Pattern
Prime Check: Square Root Trial Division Pattern
Sieve of Eratosthenes: Prime Marking Pattern
GCD: Euclidean Remainder Pattern
Binary Exponentiation: Fast Power Pattern
Modular Inverse: Extended Euclid Pattern
Implement Trie: Prefix Tree Pattern
Longest Common Prefix: Single Branch Trie Pattern
LRU Cache: Hash Map Plus Recency List Pattern
Segment Tree: Range Sum Query Pattern
Fenwick Tree: Binary Indexed Prefix Sum Pattern
CONTENTS

Maximum Depth of Binary Tree: Height recursion Pattern

Compute the longest root-to-leaf depth using recursive height logic.

DSA Course: Interview Patterns and Problem Solving
Module 6: Trees
dsa
trees
+1
May 29, 2026
20
A

Learning Outcome

After this lesson, you should be able to define tree height recursively and return the maximum depth from the root.

Problem Statement

Given the root of a binary tree, return its maximum depth. The maximum depth is the number of nodes on the longest path from the root to a leaf.

InputOutputWhy
[3,9,20,null,null,15,7]3The longest root-to-leaf path has 3 nodes.

Brute Force Approach

Store every root-to-leaf path, then return the length of the longest path.

This works but stores unnecessary path lists. The depth can be computed directly.

Optimized Approach

For any node, the maximum depth is 1 + max(depth(left), depth(right)). A null node contributes depth 0.

Exact Pseudocode

maxDepth(node):
  if node is null:
    return 0
  leftDepth = maxDepth(node.left)
  rightDepth = maxDepth(node.right)
  return 1 + max(leftDepth, rightDepth)

Reference Code

class Solution:
    def maxDepth(self, root):
        if not root:
            return 0
        return 1 + max(self.maxDepth(root.left), self.maxDepth(root.right))

Sample Dry Run

NodeleftDepthrightDepthReturn
9001
15001
7001
20112
3123

Complexity

MeasureValueReason
TimeO(n)Each node contributes once.
SpaceO(h)The recursion stack depends on height.

Edge Cases

  • Empty tree returns 0.
  • Single-node tree returns 1.
  • Skewed tree has height n.

Interview Checklist

  • Use 0 for null depth.
  • Add 1 for the current node.
  • Take the maximum of left and right depth.

FAQs

Why is null depth zero?

A null child contributes no nodes to a root-to-leaf path.

Is depth counted in nodes or edges?

This common version counts nodes. Always confirm wording if the interviewer says edges.

What is the core pattern?

Recursive height calculation.

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Maximum Depth of Binary Tree - Height recursion Pattern Practice Quiz
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Binary Tree Traversals: DFS recursion Pattern
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