Walmart Labs·Mobile Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Did a coding round for a mobile engineer role at Walmart Labs. Pretty straightforward problem on the surface but the aggregation part tripped me up a bit.

Questions Asked (1)

Given a list of structs each containing a type, an id, and a num field, return a mapping from type to the sum of all num values and a list of (id, num) pairs sorted by num.

Algorithms & Data StructuresData Modeling

Author's notes

Took me a minute to even figure out what data structure to return.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and output requirements, then propose a single-pass solution using a hash map for aggregation and a list for sorting. Discuss time and space complexity, and consider edge cases such as empty input or duplicate ids.

Pro tip: Mention that in mobile environments, memory and CPU are constrained, so using a single pass and avoiding unnecessary data copies is crucial. Also, note that sorting can be done in-place to save memory.

1. Clarify requirements

Ask about input size, data types, whether the list can be empty, and if the output should be sorted ascending or descending. Confirm the expected output format.

2. Design aggregation

Use a hash map (dictionary) to accumulate the sum of num for each type. Iterate through the list once, updating the sum for each type.

3. Collect and sort pairs

While iterating, also collect (id, num) pairs into a list. After the loop, sort the list by num using an efficient sorting algorithm (e.g., quicksort or built-in sort).

4. Analyze complexity

State that the time complexity is O(n + m log m) where n is the number of structs and m is the number of pairs (m ≤ n). Space complexity is O(n) for the map and list.

5. Handle edge cases

Discuss handling empty input, duplicate ids, and negative numbers. Ensure the solution works for large datasets typical in mobile apps.

Key Points to Mention

Use a hash map for O(1) average-time aggregation of sums by type.
Sort the (id, num) pairs using a stable sort if order of equal elements matters, or specify the sorting algorithm.
Time complexity: O(n + m log m) and space complexity: O(n).
Consider mobile constraints: minimize memory allocations and avoid unnecessary data duplication.
Edge cases: empty list, single element, all same type, negative numbers.
Output format: map from type to sum, and list of pairs sorted by num.

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