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Fora Travel·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jul 2026Remote

Summary

Interviewed for a software engineering role at Fora Travel and got a two-part algorithms question centered on merging sorted arrays. Pretty classic stuff but the K-array generalization is where things get interesting and where I probably could've done better.

Questions Asked (2)

Q1

Given two sorted arrays in nondecreasing order, implement a function that merges them into a single sorted array while preserving duplicates. The function should not mutate the inputs.

Algorithms & Data Structures
Author's notes

Went with the classic two-pointer approach, which felt right.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a two-pointer approach that iterates through both arrays simultaneously, comparing elements and appending the smaller one to the result. Emphasize that the inputs are not mutated by creating a new array for the output.

Pro tip: Mention that you can optimize space by pre-allocating the result array to the combined length, and discuss how the two-pointer technique generalizes to merging k sorted arrays using a heap.

1. Clarify requirements and edge cases

Ask about input sizes, data types, and whether duplicates should be preserved. Confirm that inputs must not be mutated and discuss edge cases like empty arrays or one array being much larger.

2. Outline the two-pointer approach

Explain that you will use two pointers, one for each array, starting at index 0. At each step, compare the elements at the pointers and append the smaller one to the result, then advance that pointer.

3. Handle remaining elements

Once one pointer reaches the end of its array, append all remaining elements from the other array to the result. This ensures all elements are included.

4. Analyze complexity and optimize

State that the time complexity is O(n + m) and space complexity is O(n + m) for the output. Mention that pre-allocating the result array can improve performance by avoiding dynamic resizing.

5. Test with examples

Walk through a concrete example, such as merging [1,3,5] and [2,4,6], to demonstrate correctness. Also test edge cases like empty arrays or arrays with all duplicates.

Key Points to Mention

  • Two-pointer technique for linear time complexity
  • Preserving duplicates by using <= comparison to maintain stability
  • Not mutating inputs by creating a new result array
  • Time and space complexity analysis
  • Edge cases: empty arrays, one array exhausted, duplicates
  • Potential optimization: pre-allocating result array to combined length

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

Q2

Generalize your merge solution to handle K sorted arrays instead of two, returning one combined sorted array. Discuss the time and space complexity of your approach.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This is where I stumbled a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., K and total elements N) and then propose a min-heap of size K to repeatedly extract the smallest element. Compare this with alternative approaches like divide-and-conquer merging, and analyze time and space complexity for each.

Pro tip: Mention that the heap approach is optimal when K is much smaller than N, but if K is large, a divide-and-conquer approach may be more cache-friendly and have lower constant factors. Also, discuss how to handle edge cases like empty arrays.

1. Clarify constraints and assumptions

Ask about the size of K, total number of elements, memory limits, and whether arrays can be empty. This shows you consider practical scenarios.

2. Propose heap-based solution

Describe initializing a min-heap with the first element of each array, then repeatedly extract the minimum and insert the next element from the same array. This yields a merged sorted array.

3. Analyze time and space complexity

Time: O(N log K) where N is total elements and K is number of arrays. Space: O(K) for the heap plus O(N) for the output. Compare with naive concatenation and sort (O(N log N)).

4. Discuss alternative approaches

Mention divide-and-conquer (pairwise merge) with O(N log K) time and O(N) space, and note trade-offs like constant factors and cache performance.

5. Handle edge cases and optimize

Address empty arrays, K=0 or K=1, and potential optimizations like using a priority queue with indices or early termination if one array is exhausted.

Key Points to Mention

  • Min-heap of size K to efficiently find the next smallest element
  • Time complexity O(N log K) and why it's better than O(N log N) when K << N
  • Space complexity O(K) for heap and O(N) for output
  • Divide-and-conquer approach as an alternative with similar complexity
  • Handling edge cases: empty arrays, K=0, K=1
  • Stability and whether the merge should be stable (if elements are equal)

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