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

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May 2026

Summary

LinkedIn system design round, one question the whole time. The problem sounds deceptively straightforward but there's a lot of surface area once you get into the scaling constraints.

Questions Asked (1)

Design a system that tracks the top K most frequently searched words and returns them in real-time or near real-time, at high query volume and update frequency.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

My first instinct was a simple hash map with a min-heap and I said so out loud, which I think was a mistake because it anchored the conversation on the naive solution for too long.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: scale (queries per second, number of unique words), definition of 'top K', real-time vs near-real-time latency, and accuracy guarantees. Then propose a distributed architecture with sharded stream processing (e.g., Kafka + Flink) to count words in windows, and a serving layer that merges top-K from shards using a heap or merge algorithm. Discuss trade-offs between exact and approximate counting (e.g., Count-Min Sketch) and how to handle updates efficiently.

Pro tip: Emphasize that the top-K problem is often solved with a combination of streaming aggregation and a merge step; mention that using a Count-Min Sketch with a heap can provide approximate results with bounded error, which is often acceptable for real-time analytics and scales better than exact counting.

1. Clarify Requirements and Constraints

Ask about scale (QPS, unique words), latency (real-time vs near-real-time), accuracy (exact vs approximate), and update frequency. Also clarify if the top-K is over a sliding window or all-time.

2. High-Level Architecture

Propose a pipeline: ingest queries via a distributed message queue (e.g., Kafka), process streams in parallel using a stream processor (e.g., Flink, Spark Streaming), and maintain counts in a distributed store. Then have a serving layer that aggregates top-K from shards.

3. Counting and Aggregation Strategy

Decide between exact counting (e.g., hash map per shard) and approximate counting (e.g., Count-Min Sketch) to handle high cardinality. For exact, shard by word to parallelize; for approximate, use sketches and merge. Use a min-heap of size K per shard to track local top-K.

4. Merging and Serving Top-K

Periodically (e.g., every second) collect local top-K from each shard and merge them using a global heap or merge algorithm to produce the global top-K. Cache the result for fast reads and update it incrementally.

5. Trade-offs and Optimizations

Discuss trade-offs: exact vs approximate (memory vs accuracy), windowing (sliding vs tumbling), and consistency (eventual vs strong). Optimize by using efficient data structures, compression, and backpressure handling.

Key Points to Mention

Sharding by word to distribute counting load and enable parallel processing.
Use of Count-Min Sketch for approximate counting with bounded error and low memory.
Min-heap of size K per shard to efficiently maintain local top-K.
Periodic merge of local top-K using a global heap or merge sort.
Windowing strategies (sliding, tumbling) to handle time-based top-K.
Trade-offs between exact and approximate results, and between latency and accuracy.

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