Group a tree by vertical columns

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Quick Overview

This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Group a tree by vertical columns states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Group a tree by vertical columns

Company: Meta

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

Given the root of a binary tree, produce a vertical listing of nodes: assign each node an x-coordinate (root at x=0; left child x−1; right child x+ 1). For each x from smallest to largest, list node values from top to bottom; when multiple nodes share the same (x, depth), order them by their left-to-right appearance in level order. Return a list of columns (each a list of values). Describe your traversal strategy, tie-breaking, and data structures. Analyze time and space complexity.

Overview: This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Group a tree by vertical columns states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Read the full Meta Software Engineer interview experience this question came from

Solution

# Solution Alignment The prompt asks for an implementation-level answer. The safest way to present it is to define the state, maintain clear invariants, then walk through complexity and tests. ## Problem Restatement Given the root of a binary tree, produce a vertical listing of nodes: assign each node an x-coordinate (root at x=0; left child x−1; right child x+ 1). For each x from smallest to largest, list node values from top to bottom; when multiple nodes share the same (x, depth), order them by their left-to-right appearance in level order. Return a list of columns (each a list of values). Describe your traversal strategy, tie-breaking, and data structures. Analyze time and space complexity. ## Recommended Approach Choose traversal based on the required output. DFS is natural for subtree computations, reconstruction, and range pruning; BFS is natural for level order or side views. Keep per-depth or per-position state when the output depends on columns, rows, or depths. ## Correctness The implementation should maintain an invariant after each loop or operation that directly matches the problem statement. At termination, that invariant implies the returned value has considered every valid candidate exactly once, or has preserved the required data-structure state after every API call. ## Complexity Most tree traversals are O(n) time and O(h) recursion stack for DFS or O(w) queue space for BFS, where h is height and w is maximum width. ## Edge Cases and Tests Empty tree, one node, skewed tree, duplicate values when reconstruction assumes uniqueness, deep recursion, and tie-breaking for same row/column nodes.
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Aug 7, 2025
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Group a tree by vertical columns

Given the root of a binary tree, produce a vertical listing of nodes: assign each node an x-coordinate (root at x=0; left child x−1; right child x+ 1). For each x from smallest to largest, list node values from top to bottom; when multiple nodes share the same (x, depth), order them by their left-to-right appearance in level order. Return a list of columns (each a list of values). Describe your traversal strategy, tie-breaking, and data structures. Analyze time and space complexity.

Clarifying Questions to Ask Guidance

  • Clarify input sizes, value ranges, mutability, return format, and tie-breaking.
  • State the target time and space complexity before coding.
  • Call out edge cases such as empty inputs, duplicates, invalid values, overflow, and boundary sizes.

What a Strong Answer Covers Guidance

  • A clear algorithm with the right data structures and enough pseudocode or code-level detail to implement it.
  • A correctness argument that explains why the algorithm covers all required cases.
  • Time and space complexity, plus at least one alternative approach when relevant.
  • Focused tests for normal cases, edge cases, and failure modes.

Follow-up Questions Guidance

  • How would the approach change if the input were streaming or too large for memory?
  • What invariants would you assert in production code?
  • Which tests would catch off-by-one, duplicate, or tie-breaking bugs?

Submit Your Answer to Earn 20XP

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