Design a Top-K trending items service

Q: Design a Top-K trending items service

This question evaluates competency in large-scale system design, real-time streaming ingestion, distributed aggregation, and trade-offs between exact and approximate Top-K approaches within the System Design domain.

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Question

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Design a system that returns the Top K items (e.g., videos/posts/products) by popularity.

Requirements

API to fetch: GET /topk?window=10m&k=100 returning the top 100 items in the last 10 minutes.
Support multiple windows (e.g., 1m, 10m, 1h, 24h).
Near real-time updates (freshness target e.g. < 5 seconds).
Read QPS is high (e.g., 10k–100k QPS); write events can be much higher (e.g., millions/minute).
Popularity is driven by events (views/clicks/likes). Event stream may include duplicates and out-of-order delivery.

Non-functional

Low latency for reads (e.g., p99 < 100 ms).
Fault tolerant, scalable, and cost-aware.
Discuss exact vs approximate Top-K and trade-offs.

Produce an end-to-end design: ingestion, storage/compute, APIs, data model, and operations/monitoring.

Design a Top-K trending items service

Quick Overview

Requirements

Non-functional

Solution

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