eBay·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026Remote

Summary

eBay coding round, pretty standard algorithmic stuff. They gave me a top-K frequency problem and wanted me to walk through the heap-based approach and talk complexity. Nothing too wild but the follow-up on bucket sort tripped me up a bit.

Questions Asked (1)

Given an array and an integer k, return the k most frequent elements. Walk through your approach and analyze the time complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went straight to the frequency map and then a min-heap of size k, which is the expected path.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., input size, whether k is always valid, and if the order of output matters). Then present a solution using a hash map to count frequencies and a min-heap of size k to efficiently find the top k elements, analyzing time and space complexity. Optionally, mention alternative approaches like bucket sort for O(n) time and discuss trade-offs.

Pro tip: Demonstrate awareness of real-world constraints: if the input is large and k is small, a heap is efficient; if k is close to the number of unique elements, a full sort might be simpler. Also, mention that eBay often deals with large-scale data, so discussing scalability and memory usage can set you apart.

1. Clarify requirements and constraints

Ask about input size, range of values, whether k is guaranteed valid, and if the output order matters. This shows attention to detail and helps tailor the solution.

2. Outline the approach

Propose using a hash map to count frequencies, then a min-heap of size k to keep the top k frequent elements. Explain why this is efficient: O(n log k) time.

3. Walk through an example

Pick a small array and k, and manually trace the algorithm to demonstrate correctness. This makes your explanation concrete and easy to follow.

4. Analyze time and space complexity

State that building the frequency map takes O(n), heap operations take O(n log k), and space is O(n) for the map and O(k) for the heap. Discuss how this scales.

5. Discuss alternatives and trade-offs

Mention bucket sort for O(n) time when frequencies are bounded, or sorting all unique elements for O(n log n). Compare based on constraints and mention when each is preferable.

Key Points to Mention

Hash map for frequency counting: O(n) time and space.
Min-heap of size k to efficiently extract top k frequent elements: O(n log k) time.
Time complexity: O(n log k) overall, which is optimal for comparison-based approaches when k << n.
Space complexity: O(n) for the frequency map and O(k) for the heap.
Alternative: bucket sort can achieve O(n) time when frequencies are bounded by n.
Edge cases: k equals number of unique elements, all elements same, empty array, k=0.

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