Скачать или смотреть Maximum Depth Of Binary Tree using BFS - Leetcode 104 Python

Recursion for tree depth? Groundbreaking. 🙄 Let's talk about the other way.

You see LeetCode 104: Maximum Depth of Binary Tree.

Your brain screams: "RECURSION!" return 1 + max(dfs(root.left), dfs(root.right))

...and you move on.

We've all done it. It's the "vanilla ice cream" of tree solutions. It works, it's fine, but is it fun? More importantly, does it show you understand all the tools in your box?

What if we went... wider?

What if, instead of instantly diving to the darkest depths of the tree (and praying we don't hit a stack overflow on some monstrosity), we did the polite thing?

We visit level by level. Like a party. We check out all the nodes on Level 1. Then all the nodes on Level 2. Then Level 3...

This, my friends, is Breadth-First Search (BFS). And using it for depth is surprisingly elegant.

"But wait," you say, "BFS is for finding the shortest path!" You're right! And what is the maximum depth?

It's just the "path" from the root to the farthest leaf node.

Instead of tracking depth with a recursive call stack, we just count how many levels we had to process before we ran out of nodes.

Process Level 1 (root) -- depth = 1

Process Level 2 (root's children) -- depth = 2

...

Process the last level of nodes -- depth = whatever_we_counted

No magic. No "trust me, the recursion works." Just a simple queue and a counter.

This iterative approach is not only a fantastic way to really master BFS and level-order traversal, but it's also a crucial pattern for many other tree problems (like Zigzag Traversal, Average of Levels, etc.).

So, why not add another weapon to your arsenal?

In my latest video, I'm ditching the "obvious" recursive path and building the BFS solution from scratch, line-by-line. We'll manage the queue, count the levels, and see why this works so intuitively.

What's your preferred way to find tree depth? Are you Team DFS for life, or can I tempt you over to the BFS side?

Let me know in the comments! 👇

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