Uber·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Uber SWE interview, one meaty design-and-implement question about a hierarchical revenue structure. Pretty heavy on data structure justification, not just code.

Questions Asked (1)

Design a data structure that stores revenue entries in a parent-child hierarchy. It needs two insert operations: one for top-level entries and one that links an entry to a specific parent by revenue value. At any point it should support returning the top K smallest revenues across the whole collection. Walk through your choice of underlying structures, how you handle ties, and the time complexity of each operation.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I went with a hash map for the parent relationships and a min-heap for the top-K queries, which felt right.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a hybrid data structure that combines a hash map for parent-child links and a balanced BST or heap for efficient top-K queries. Walk through each operation, discussing trade-offs and tie-breaking strategies, and analyze time complexities.

Pro tip: Mention that for top-K smallest revenues, a max-heap of size K is often optimal for streaming data, but if K varies, a balanced BST or skip list provides flexibility. Also, discuss how to handle ties by storing counts or using a stable ordering.

1. Clarify Requirements

Ask about expected data volume, frequency of operations, whether K is fixed or dynamic, and if parent lookup by revenue value implies uniqueness. This shows you consider practical constraints.

2. Propose Core Structures

Suggest using a hash map to store entries and parent-child relationships, and a balanced BST (e.g., red-black tree) or a heap for maintaining sorted revenues. Explain how they interact.

3. Detail Insert Operations

For top-level insert, add to hash map and insert revenue into BST/heap. For child insert, find parent by revenue (using BST or separate map), link child, and insert child's revenue into the sorted structure.

4. Handle Ties and Top-K Query

Discuss tie-breaking: store counts in BST nodes or use a heap with a secondary key. For top-K, if using a max-heap of size K, iterate through all revenues; if using BST, do in-order traversal to get K smallest.

5. Analyze Time Complexity

State complexities: insert O(log n) for BST, O(1) for hash map; top-K O(K log n) with BST or O(n log K) with heap. Compare trade-offs and justify choice based on use case.

Key Points to Mention

Use of a hash map for O(1) parent lookup by revenue value, assuming revenues are unique or using a multimap.
Balanced BST (e.g., red-black tree) for O(log n) insert and O(K) in-order traversal for top-K, with tie handling via node counts.
Alternative: max-heap of size K for top-K smallest, but note that it requires O(n log K) time per query if K is not fixed.
Tie-breaking strategies: store (revenue, id) pairs to ensure deterministic ordering, or maintain a count in BST nodes.
Time complexity analysis: insert O(log n), top-K O(K log n) with BST, or O(n log K) with heap; space O(n).
Consideration of concurrency and scalability if this is for a system like Uber, where high throughput might require distributed structures.

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