Google·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Google SWE coding round, one algorithmic problem that looked simple until you had to actually defend the complexity tradeoffs out loud. Came away feeling okay about it but not great.

Questions Asked (1)

Given an integer array and an integer k, return the k most frequent elements. You must do better than O(n log n) time. Be ready to discuss multiple approaches and their tradeoffs, including how ties in frequency are handled.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went straight for the heap approach, size k, O(n log k), which felt safe.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and tie-breaking rules, then propose a solution using a hash map for frequency counting and bucket sort for O(n) time. Be prepared to discuss alternative approaches like quickselect or heap-based methods, and analyze their time/space tradeoffs.

Pro tip: Demonstrate awareness of real-world constraints by mentioning that bucket sort is optimal when frequencies are bounded by n, but for streaming data or when k is small, a heap might be more practical. Also, explicitly state how ties are handled (e.g., any order is acceptable unless specified).

1. Clarify requirements and constraints

Ask about input size, range of values, whether k is always valid, and how ties in frequency should be handled (e.g., any order, or by value).

2. Outline multiple approaches

Briefly describe heap-based (O(n log k)), bucket sort (O(n)), and quickselect (average O(n)) solutions, highlighting their time and space complexities.

3. Detail the optimal approach

Explain the bucket sort method: count frequencies with a hash map, then create buckets indexed by frequency and collect the top k elements.

4. Discuss tradeoffs and edge cases

Compare approaches: heap is simpler but O(n log k); bucket sort is O(n) but uses extra space; quickselect has good average performance but worst-case O(n^2). Mention edge cases like all elements same frequency.

5. Conclude with tie-breaking and implementation notes

State that ties are typically resolved arbitrarily unless specified, and mention potential optimizations like early termination when k is small.

Key Points to Mention

Hash map for frequency counting: O(n) time and space.
Bucket sort: array of lists indexed by frequency, O(n) time and space.
Heap approach: min-heap of size k, O(n log k) time, O(n) space for frequency map.
Quickselect: average O(n) time, worst-case O(n^2), in-place but modifies input.
Tie-breaking: usually any order is acceptable; if not, specify sorting by value or other criteria.
Space-time tradeoff: bucket sort uses O(n) extra space, while heap uses O(n) for map but O(k) for heap.

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