Text Retrieval System

The Text-based Search System Design interview assesses your ability to design a web-scale text search engine similar to those used by platforms like Google, Bing, and Elasticsearch. This mock interview evaluates your understanding of crawling, indexing, query processing, ranking algorithms, and scalable serving architectures, along with how machine learning models can enhance retrieval relevance and personalization.

You’ll learn to design systems that handle massive text corpora, manage distributed storage and retrieval, and support real-time query execution with high throughput and low latency. The interview emphasizes building efficient indexing pipelines, choosing appropriate ranking strategies (TF-IDF, BM25, or neural embeddings), and integrating ML-driven relevance models for modern semantic search.

Goals of the Interview:

1. Define the problem and requirements for a large-scale text search system.
2. Design efficient crawling and indexing pipelines.
3. Create ranking and retrieval architectures combining traditional IR and ML models.
4. Evaluate and optimize for relevance, latency, and scalability.
5. Deploy, monitor, and scale the search system in production.