DoorDash·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

DoorDash coding round with one meaty tree problem that took up the whole session. The question had enough layers to it that I kept second-guessing my approach mid-explanation.

Questions Asked (1)

Given two rooted trees where each node has a unique string key among its siblings and a value, compute the total number of differing nodes. Nodes only in one tree count as additions or deletions. If two nodes share the same key path but have different values, treat the whole subtree as deleted and recreated, counting every node in both subtrees. Child order doesn't matter. Design an efficient algorithm, analyze complexity, and handle edge cases like empty trees or heavily skewed structures.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with a naive recursive diff and pretty quickly realized the 'delete + recreate' rule for value mismatches was the tricky part.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then propose a recursive tree diff algorithm that matches children by key and handles value mismatches by counting entire subtrees. Analyze time and space complexity, and discuss trade-offs between recursion and iteration for skewed trees.

Pro tip: Mention that you would use an iterative approach or increase recursion limit to avoid stack overflow on skewed trees, and that you would hash subtrees to quickly detect identical subtrees for optimization.

1. Clarify requirements and edge cases

Ask questions to confirm understanding: empty trees, nodes with same key but different values, child order irrelevance, and whether keys are unique among siblings. Discuss how to handle skewed trees (e.g., recursion depth).

2. Design recursive diff algorithm

Propose a function that takes two nodes and returns the number of differing nodes. If both null, return 0; if one null, return size of the other subtree; if keys differ, treat as delete+add (count both subtrees); if keys match but values differ, count both subtrees entirely; if keys and values match, recurse on children matched by key.

3. Analyze complexity and optimize

Time complexity: O(n+m) if we can match children in O(1) per key (e.g., using hash maps). Space: O(h1+h2) for recursion stack. Mention optimization: precompute subtree hashes to skip identical subtrees, reducing time for large identical parts.

4. Handle edge cases and trade-offs

Discuss handling empty trees, skewed trees (iterative approach or tail recursion), and memory constraints. Mention trade-off between recursion simplicity and iterative robustness.

5. Summarize and test

Walk through a small example to validate the algorithm. Summarize key points: matching by key, counting subtrees on mismatch, complexity, and edge cases.

Key Points to Mention

Matching children by key using hash maps for O(1) lookup
Counting entire subtrees when values differ at a node
Time complexity O(n+m) and space complexity O(h1+h2)
Handling empty trees and skewed trees (recursion depth, iterative alternative)
Optimization using subtree hashing to skip identical subtrees
Trade-offs between recursive and iterative implementations

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.