N-ary Tree Level Order Traversal

N-ary Tree Level Order Traversal

What is this problem about?

The N-ary Tree Level Order Traversal problem asks you to traverse an N-ary tree (where each node can have multiple children) level by level, returning a list of lists where each inner list contains all node values at that depth. This N-ary Tree Level Order Traversal coding problem extends standard binary tree BFS to trees with arbitrary branching factors.

Why is this asked in interviews?

Microsoft, Amazon, and Google ask this as a standard BFS problem on N-ary trees. It tests whether candidates can generalize from binary trees (2 children) to N-ary trees (any number of children) cleanly. The BFS and tree interview pattern is the core, and clean level separation using queue size counting is the key implementation detail.

Algorithmic pattern used

BFS with level separation. Use a queue. Initialize with the root. For each level: record the current queue size levelSize. Process exactly levelSize nodes, collecting their values into a list, and enqueue all their children. Append the level's list to the result. Repeat until the queue is empty.

Example explanation

Tree: root=1, children=[3,2,4]. Node 3's children=[5,6].

• Level 0: [1]. Queue after: [3,2,4].
• Level 1: [3,2,4]. Queue after: [5,6].
• Level 2: [5,6]. Result: [[1],[3,2,4],[5,6]].

Common mistakes candidates make

• Not recording levelSize before processing the level (queue grows as children are added).
• Confusing N-ary tree's children list with binary tree's left/right.
• Using DFS instead of BFS (DFS gives correct values but wrong level grouping without extra tracking).
• Empty tree edge case — return [] not [[]].

Interview preparation tip

Level order traversal is fundamental to BFS mastery. The key technique: record the queue size at the start of each level loop to know exactly how many nodes belong to the current depth. This technique works identically for binary trees, N-ary trees, and graphs. Practice implementing it until you can write it fluently from memory — it's a prerequisite for dozens of harder BFS tree/graph problems at Microsoft, Amazon, and Google.