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

IntermediatePrefer not to say

Jul 2026

Summary

Meta SWE coding round, one problem the whole session: kth largest element with a follow-up on handling data that doesn't fit in memory. Pretty standard algorithmic interview but the follow-up is where things got interesting.

Questions Asked (1)

Given an integer array that may contain duplicates, find the k-th largest element by value. Implement an average O(n) solution, then discuss or implement an alternative approach for when the dataset is too large to fit in memory.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

The quickselect part I had down.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: define 'k-th largest by value' with duplicates (e.g., sorted descending, k-th element). Then implement Quickselect with random pivot for average O(n) time, handling duplicates via three-way partitioning. Finally, discuss external memory approaches like a min-heap of size k or distributed selection for large datasets.

Pro tip: Explicitly state the time and space complexity of each approach and when to choose one over the other. For the external case, mention that a min-heap of size k is O(n log k) time and O(k) memory, which is often more practical than complex distributed algorithms unless k is huge.

1. Clarify the problem

Confirm the definition of k-th largest with duplicates (e.g., sorted descending, k-th element). Discuss edge cases: k out of bounds, empty array, all duplicates.

2. Present in-memory solution

Explain Quickselect with random pivot and three-way partitioning for duplicates. Analyze average O(n) time, O(1) extra space (or O(log n) recursion).

3. Discuss alternative in-memory approaches

Mention min-heap of size k (O(n log k)) and sorting (O(n log n)) as alternatives, and when they might be preferable (e.g., k small, or need sorted order).

4. Address external memory scenario

Propose approaches: min-heap of size k streaming through data (O(n log k) time, O(k) memory), or distributed selection if data is sharded. Discuss trade-offs.

5. Summarize and recommend

Conclude with a recommendation based on constraints (k size, memory, data distribution) and mention potential optimizations like sampling or MapReduce.

Key Points to Mention

Quickselect algorithm with random pivot and three-way partitioning to handle duplicates efficiently.
Time complexity: average O(n) for Quickselect, worst-case O(n^2) but can be mitigated with random pivot or median-of-medians.
Min-heap of size k for streaming data: O(n log k) time, O(k) memory, suitable for large datasets.
External sorting or distributed selection (e.g., MapReduce) for data too large to fit in memory.
Edge cases: k <= 0, k > n, all elements equal, negative numbers.
Trade-offs between time, space, and implementation complexity for each approach.

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