Build a real-time Search Pipeline
Last updated: July 14, 2025
Quick Overview
Design a real-time search system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Elastic
System Design
Software Engineer
Elastic
July 14, 2025Software Engineer
Onsite
System Design
Easy
106
6
4,304 solved
Design a real-time search system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from Elastic's Onsite tests your ability to think about ML systems at scale. The interviewer expects discussion of data quality, feature stores, model serving infrastructure, and A/B testing strategy.
What the Interviewer Expects
- Map the business problem to a concrete ML objective
- Propose reasonable features and a baseline model
- Discuss basic model evaluation metrics
- Outline a simple serving architecture
Key Topics to Cover
Training pipeline and infrastructure
Data collection and labeling strategy
Feature engineering and feature stores
Model serving and latency optimization
Model selection and architecture
ML objective formulation and metric selection
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you run A/B tests on different model versions?
- What is your model retraining strategy?
- How would you ensure fairness and reduce bias in the model?
- How would you handle the cold start problem?
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Requirements
- Functional Requirements:
- Handle real-time search queries from users with low latency (under 200ms).
- Support text-based, image-based, and structured data searches.
- Provide autocomplet...
Capacity Estimation
Assuming Elastic handles about 1 million search requests per minute:
- Requests per second (RPS): 1,000,000 / 60 = approximately 16,667 RPS.
- Data Size: If each search query returns an averag...
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