Design a Top-K trending service
Quick Overview
This question evaluates skills in distributed system design, real-time stream processing, scalable aggregation, windowing semantics, and trade-offs around latency, accuracy, and fault tolerance when computing top-K frequencies.
eBay
Jan 15, 2026, 12:00 AM
Software Engineer
Technical Screen
System Design
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Scenario
You implemented a function to compute “top K” (e.g., most frequent items). Now design a backend service that provides similar functionality at scale.
Requirements
Design a service that continuously computes and serves the top K most frequent items (e.g., hashtags, products, search queries).
Functional requirements
-
Ingest a high-volume stream of events: each event includes at least
(item_id, timestamp). -
Support an API to query
GET /topk?k=K&window=...returning the top-K items by frequency. -
Support a time window, at minimum:
- Either all-time counts, or
- A sliding window (e.g., last 5 minutes / last 1 hour). State your choice/assumptions.
Non-functional requirements
- Low latency queries (e.g., p95 < 100 ms) at high QPS.
- High write throughput ingestion.
- Horizontal scalability (add machines to scale).
- Fault tolerance and reasonable correctness guarantees (clearly state consistency model).
Clarifications to address
- What are the expected event rate, number of distinct items, and query QPS?
- Do you need exact results or are approximate results acceptable?
Solution
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