Meta·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jul 2026

Summary

Meta system design screen for a software engineer role, focused entirely on a frequency tracking problem with two distinct workload scenarios. The question had a lot of depth to it and the conversation went pretty long.

Questions Asked (1)

Design a data structure that supports adding item occurrences and querying the top-K most frequent items. Propose different designs optimized for read-heavy vs write-heavy workloads, analyze time and space complexity for each, and explain the trade-offs.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This one looked straightforward at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: define the operations (add, query top-K), constraints (K size, data distribution, update frequency), and whether the data fits in memory. Then propose at least two designs: one optimized for read-heavy workloads (e.g., precomputed top-K with lazy updates) and one for write-heavy workloads (e.g., min-heap with frequency map). For each, analyze time and space complexity, and discuss trade-offs in terms of update latency, query latency, memory overhead, and scalability.

Pro tip: Mention that in real systems, you'd likely combine approaches or use a hybrid (e.g., count-min sketch for approximate frequencies with a heap for top-K) and that the choice depends on the read/write ratio and acceptable error margins. Also, discuss how to handle ties and dynamic K.

1. Clarify Requirements and Constraints

Ask about the expected read/write ratio, the maximum number of distinct items, the size of K, whether exact or approximate results are acceptable, and if the data fits in memory. This guides the design choices.

2. Propose a Read-Heavy Design

For read-heavy workloads, precompute and maintain the top-K list eagerly. Use a hash map for frequencies and a sorted structure (e.g., balanced BST or sorted array) for top-K, updating it on each write. Query is O(1) or O(K).

3. Propose a Write-Heavy Design

For write-heavy workloads, prioritize fast updates. Use a hash map for frequencies and a min-heap of size K for top-K, updating the heap only when necessary. Query is O(K log K) or O(K) if heap is maintained, but updates are O(log K) amortized.

4. Analyze Time and Space Complexity

For each design, detail the time complexity of add and query operations, and the space complexity. Consider worst-case and average-case scenarios, and the impact of K and number of distinct items.

5. Discuss Trade-offs and Extensions

Compare the designs: read-heavy design has fast queries but slower updates and higher memory; write-heavy design has fast updates but slower queries. Mention extensions like approximate algorithms (count-min sketch) for scalability, and how to handle dynamic K or distributed settings.

Key Points to Mention

Hash map for frequency counting: O(1) average update and lookup.
Min-heap of size K for top-K: O(log K) update, O(K) query if heap is maintained.
Precomputed sorted list for read-heavy: O(1) query, but O(N) update in worst case.
Trade-off between update latency and query latency; memory vs. speed.
Approximate algorithms (e.g., count-min sketch) for large-scale or streaming data.
Handling ties and dynamic K: use secondary criteria or maintain multiple structures.

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