Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Google SWE coding round with a math-heavy chunk splitting problem. Pretty clean problem statement but the constraint going up to 1e18 is what makes it interesting.

Questions Asked (1)

Given a total data size and a maximum chunk size, split the data into the minimum number of chunks where each chunk is a positive integer and no chunk exceeds the max size. If there are multiple valid splits with the same number of chunks, return a near-uniform distribution where sizes differ by at most 1.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The example they give is 1800 with max 700 and the answer is three chunks of 600 each, not two chunks of 700 and one of 400.

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AI HintsAI Generated

Suggested Approach

First, compute the minimum number of chunks k as ceil(total / maxChunk). Then distribute the total as evenly as possible across k chunks: each chunk gets floor(total / k), and the first (total mod k) chunks get one extra. This guarantees all chunks are positive integers, none exceed maxChunk, and sizes differ by at most 1.

Pro tip: Mention that this is a classic greedy distribution problem and that the near-uniform split is optimal for minimizing the maximum chunk size, which is often a hidden goal in real systems like load balancing or data partitioning.

1. Understand the problem and constraints

Clarify that chunks must be positive integers, no chunk exceeds maxChunk, and we need the minimum number of chunks. Also note the tie-breaking rule: if multiple splits have the same minimum chunk count, return the most uniform one.

2. Compute the minimum number of chunks

Calculate k = ceil(total / maxChunk). This is the theoretical minimum because each chunk can hold at most maxChunk, so fewer chunks would be impossible.

3. Distribute data uniformly

Set base = total // k and remainder = total % k. Assign base to every chunk, then add 1 to the first remainder chunks. This ensures sizes differ by at most 1 and all are positive.

4. Verify constraints and edge cases

Check that base + 1 <= maxChunk (which holds because k = ceil(total/maxChunk)) and that all chunks are >= 1. Handle edge cases like total = 0 (return empty list) or maxChunk = 0 (invalid input).

5. Analyze complexity and trade-offs

The algorithm runs in O(k) time and O(k) space for the output. Discuss that this is optimal since we must output k chunks. Mention that the uniform distribution minimizes the maximum chunk size, which is beneficial for parallel processing.

Key Points to Mention

Minimum number of chunks is ceil(total / maxChunk).
Uniform distribution: base = total // k, remainder = total % k; first remainder chunks get base+1.
All chunks are positive integers and none exceed maxChunk.
Sizes differ by at most 1, satisfying the near-uniform requirement.
Time and space complexity are O(k), which is optimal for producing k chunks.
Edge cases: total = 0, maxChunk = 0, or total < maxChunk (k=1).

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