Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Meta SWE interview that leaned hard into algorithm depth. One question, two approaches, plus a stream variant tacked on at the end. More to unpack than it looks.

Questions Asked (1)

Given an unsorted array of n integers and a value k, return the k-th largest element without sorting the whole array. Walk through two approaches: a randomized linear-time selection algorithm and a heap-based method. For each, cover the algorithm itself, time and space complexity, and how you'd extend it to handle a data stream instead of a static array.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This felt like three questions stitched together.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., duplicates, memory limits, stream characteristics) and then present two distinct approaches: randomized Quickselect for static arrays and a min-heap for streaming data. For each, explain the algorithm step-by-step, derive time and space complexities, and discuss how to adapt it to a data stream by maintaining a heap of size k.

Pro tip: Emphasize the trade-offs: Quickselect is faster on average (O(n)) but has poor worst-case and requires random access, while the heap approach is O(n log k) but works seamlessly for streams and is more predictable. Mention that for streams, a min-heap of size k is the standard solution, and you can optimize with a balanced BST or a more advanced structure if k is large.

1. Clarify requirements and constraints

Ask about input size, memory limits, whether the array can be modified, and if the data is static or streaming. Confirm that k is 1-indexed and within bounds.

2. Present randomized Quickselect

Explain the partition-based selection: randomly choose a pivot, partition the array, and recurse on the side containing the k-th largest. Derive average O(n) time, worst-case O(n^2), and O(1) extra space (if in-place).

3. Present heap-based method

Describe maintaining a min-heap of size k: iterate through the array, push elements, and if size exceeds k, pop the smallest. The heap top is the k-th largest. Time O(n log k), space O(k).

4. Extend to data stream

For Quickselect, note it's not suitable for streams due to random access and multiple passes. For heap, it naturally extends: maintain the min-heap as elements arrive, ensuring O(log k) per element and O(k) space.

5. Compare and conclude

Summarize trade-offs: Quickselect is optimal for static arrays with average O(n) but risky worst-case; heap is robust for streams and large n with small k. Mention potential optimizations like using a balanced BST for stream if k is large.

Key Points to Mention

Randomized pivot selection and its impact on expected time complexity
In-place partitioning and space complexity of Quickselect
Min-heap of size k for k-th largest (not max-heap)
Time complexity O(n log k) and space O(k) for heap approach
Streaming adaptation: heap maintains top k elements online
Trade-offs: Quickselect faster on average but not stream-friendly; heap predictable and stream-compatible

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