Meta·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Meta SWE coding round, one question the whole time, pretty focused on array manipulation and then they pushed into a generalization discussion at the end. Felt manageable but the heap follow-up is where things got interesting.

Questions Asked (1)

Given three sorted arrays, merge them into a single sorted array in O(n_a + n_b + n_c) time using three pointers. Then generalize your approach to merging k sorted arrays.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Three pointers part was fine, I'd done something similar before.

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AI HintsAI Generated

Suggested Approach

Start by explaining the three-pointer merge for three sorted arrays, emphasizing linear time and space. Then generalize to k sorted arrays by discussing two main approaches: a min-heap of size k (O(N log k)) and iterative pairwise merging (O(N log k)), and compare their trade-offs. Conclude with when to use each based on constraints and memory.

Pro tip: Mention that for small k (like 3), the pointer approach is simpler and faster due to lower constant factors, but for large k, a heap is more scalable. Also, note that if the arrays are very different in size, a divide-and-conquer merge can be more efficient.

1. Clarify the problem and constraints

Confirm that the arrays are sorted, the desired output is a single sorted array, and discuss time/space constraints. Ask if k is known or variable, and if extra space is allowed.

2. Explain the three-pointer merge

Describe initializing three pointers at the start of each array, repeatedly selecting the smallest current element, and appending it to the result. Continue until all elements are processed, achieving O(n_a + n_b + n_c) time.

3. Generalize to k sorted arrays with a min-heap

Explain using a min-heap of size k to store the current smallest element from each array. Pop the minimum, add it to the result, and push the next element from the same array. This yields O(N log k) time and O(k) space.

4. Discuss alternative: iterative pairwise merging

Describe merging arrays in pairs iteratively (like merge sort) until one array remains. This also achieves O(N log k) time but may use more space depending on implementation.

5. Compare trade-offs and conclude

Compare the heap and pairwise merge approaches in terms of time, space, and implementation complexity. Recommend the heap for large k and the pointer method for small k, and mention edge cases like empty arrays.

Key Points to Mention

Time complexity: O(n_a + n_b + n_c) for three arrays, O(N log k) for k arrays with heap or pairwise merge.
Space complexity: O(1) extra space for three-pointer merge (excluding output), O(k) for heap, O(N) for naive pairwise merge if not careful.
Min-heap implementation details: store (value, array_index, element_index) tuples.
Stability: if equal elements, order may matter; discuss if stability is required.
Edge cases: empty arrays, one array empty, all arrays empty, very large k.
Trade-offs: heap has log k factor but better for large k; pairwise merge may have higher constant factors but simpler for small k.

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