Salesforce·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Salesforce SWE coding round, pretty standard algorithmic stuff. The main problem was a classic heap question and they wanted you to talk through complexity tradeoffs which I wasn't fully prepared for.

Questions Asked (1)

Given an array of 2D points and an integer k, return the k points nearest to the origin. Walk through your approach and its time complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew to use squared distance to skip the sqrt, that part was fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem (e.g., distance metric, tie-breaking, input size) and then present a solution using a max-heap of size k to efficiently track the k nearest points. Compare this with sorting all points by distance, highlighting the trade-offs in time and space complexity.

Pro tip: Mention that for very large datasets, a distributed approach like MapReduce or using a k-d tree for spatial indexing can be more efficient, showing awareness of scalability beyond the basic algorithm.

1. Clarify the problem

Ask about distance metric (Euclidean vs. squared), tie-breaking rules, and constraints (e.g., k <= n, memory limits). Confirm whether the output order matters.

2. Discuss brute-force and sorting approaches

Explain that computing distances for all points and sorting takes O(n log n) time, which is simple but may be inefficient for large n.

3. Present heap-based optimal solution

Describe using a max-heap of size k: iterate through points, compute squared distance, and maintain the k smallest distances. This yields O(n log k) time and O(k) space.

4. Analyze time and space complexity

Compare O(n log n) sorting vs. O(n log k) heap, noting that heap is better when k << n. Mention that quickselect can achieve average O(n) but has worst-case O(n^2).

5. Discuss trade-offs and edge cases

Address when to use each approach, handle edge cases (k=0, k=n, duplicate points), and mention alternative data structures like k-d trees for repeated queries.

Key Points to Mention

Use squared Euclidean distance to avoid expensive square root operations.
Max-heap of size k efficiently maintains the k nearest points in O(n log k) time.
Sorting all points by distance is O(n log n) and simpler but less efficient for large n and small k.
Quickselect can find the k-th smallest distance in average O(n) time, but worst-case O(n^2).
Space complexity: heap uses O(k) extra space, while sorting may require O(n) space depending on implementation.
For very large datasets, consider distributed computing or spatial indexing structures like k-d trees.

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