Design trending queries ranking system
Company: Perplexity
Role: Software Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
You are designing a system to power the **Top Trending Queries** section on the home page of an AI search/Q&A platform (similar to Perplexity).
The product shows a list of trending user queries (search prompts) to visitors. Initially this list can be global, but over time you may want to **personalize** it (e.g., by country, language, or user interests).
**Requirements and goals**:
- Continuously identify queries that are "trending" (rising in popularity) in near real time.
- Rank top K trending queries for the **home page**.
- Support:
- Global trending queries.
- Trending queries per region or segment (e.g., country, language, topic).
- Eventually allow **personalization**: show trending queries that are more relevant to a specific user or user segment.
- Handle very high query volume (e.g., tens of thousands of QPS).
- Keep results fresh (updated within a few seconds to a few minutes).
**Design task**:
Describe how you would design this trending queries system, including:
1. How you collect and process raw query events.
2. How you define and compute "trending" (not just absolute popularity but spikes/velocity).
3. The data architecture for real-time or near-real-time aggregation (streaming vs batch).
4. How you store and serve the top K trending queries globally and by segment.
5. How you might add personalization over time (per user or per user group).
6. Key metrics, scaling considerations, and how to handle spam/abuse.
You do not need to provide code, but you should describe main components, data flows, and algorithms or approximations (e.g., for top-K) that you would use.
Quick Answer: This question evaluates the ability to design scalable, low-latency ML-driven systems for detecting and ranking trending queries, covering competencies in real-time event ingestion, streaming aggregation, ranking and scoring methods, personalization, and abuse mitigation.