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

Intermediate

Jun 2026

Summary

Google SWE coding round, one problem the whole time. The question looked approachable at first glance but the constraint about not brute-forcing all pairs is where things get interesting.

Questions Asked (1)

Given two sorted integer arrays and an integer k, return the k pairs (one element from each array) with the smallest sums. A brute-force approach that generates all possible pairs is considered too slow.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The naive solution came to me immediately, generate everything, sort by sum, take k.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose an efficient solution using a min-heap to merge the sorted arrays, similar to finding the k smallest sums. Explain the algorithm step-by-step, analyze its time and space complexity, and discuss potential optimizations or trade-offs.

Pro tip: Demonstrate awareness of the heap's initialization and duplicate handling by using indices and a visited set or by leveraging the sorted property to avoid duplicates. Also, mention that the solution can be adapted for streaming data or when k is large.

1. Clarify the problem

Ask about input constraints (e.g., array sizes, k value, duplicates, negative numbers) and expected output format. Confirm that the arrays are sorted in ascending order.

2. Outline the brute-force and its inefficiency

Briefly mention that generating all pairs is O(m*n) and too slow, then transition to the need for a more efficient approach.

3. Propose the heap-based approach

Explain using a min-heap to store pairs (sum, i, j), starting with (A[0]+B[0], 0, 0). Pop the smallest sum, add the pair to results, and push (i+1, j) and (i, j+1) if within bounds, avoiding duplicates.

4. Analyze complexity and trade-offs

State that time complexity is O(k log k) and space O(k). Discuss alternatives like binary search on sum or using a priority queue with a visited set, and their trade-offs.

5. Handle edge cases and conclude

Mention handling empty arrays, k larger than m*n, and negative numbers. Summarize why the heap approach is optimal for this problem.

Key Points to Mention

Min-heap (priority queue) to efficiently retrieve the smallest sums.
Initialization with the smallest pair (0,0) and expansion to neighbors.
Duplicate avoidance using a visited set or index-based traversal.
Time complexity O(k log k) and space O(k).
Edge cases: empty arrays, k > m*n, negative numbers.
Comparison with brute-force and other approaches (e.g., binary search on sum).

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