Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Google SWE coding round, pretty standard algorithmic problem but the trick is knowing the right data structure pattern. One question, clean setup, no behavioral stuff from what I remember.

Questions Asked (1)

You're given an array of zeros and a list of range update operations, each adding a value to every element between two indices inclusive. Apply all updates and return the final array as efficiently as possible.

Algorithms & Data Structures

Author's notes

I knew there was a smarter way than looping through every range but my brain went to brute force first, which would've been O(n*k) for k updates.

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AI HintsAI Generated

Suggested Approach

Use a difference array to apply each range update in O(1) time, then compute the prefix sum to get the final array in O(n) time. This avoids the naive O(n*m) approach and demonstrates efficient algorithm design.

Pro tip: Mention that the difference array technique is a standard pattern for range update problems and can be extended to 2D or multiple updates. Also, clarify that the updates are inclusive of both endpoints, and handle edge cases like empty array or no updates.

1. Clarify the problem

Confirm the input format: array of zeros of size n, and a list of operations where each operation is (start, end, value). Ensure updates are inclusive and that the array is 0-indexed.

2. Choose the optimal data structure

Explain that a difference array allows O(1) range updates by adding value at start and subtracting at end+1, then prefix sum yields the final array.

3. Apply updates to difference array

Initialize a difference array of size n+1 with zeros. For each operation (l, r, val), do diff[l] += val and diff[r+1] -= val (if r+1 < n).

4. Compute final array via prefix sum

Iterate through the difference array, maintaining a running sum, and assign each element to the result array. This gives the final values after all updates.

5. Analyze complexity and edge cases

State time complexity O(n + m) and space O(n). Discuss handling of empty array, no operations, and large values (potential overflow).

Key Points to Mention

Difference array technique for O(1) range updates
Prefix sum to reconstruct the final array
Time complexity O(n + m) vs naive O(n*m)
Space complexity O(n) for the difference array
Inclusive range updates and index bounds
Handling edge cases: empty array, no updates, large values

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