Search Systems

Search is a core feature in most applications, from e-commerce product catalogs to document repositories and social platforms. Building effective search goes far beyond simple database queries -- it involves tokenization, indexing, ranking, and delivering results with low latency even across massive datasets. Search systems must balance relevance, speed, and freshness of results.

At scale, search introduces its own set of distributed systems challenges: sharding indexes across nodes, replicating for availability, handling schema changes without downtime, and tuning relevance algorithms to surface the right results. Understanding these tradeoffs is essential for designing systems where users can quickly find what they need.

What You'll Learn

• Full-Text Search - How inverted indexes, tokenizers, analyzers, and stemming work together to enable efficient text matching, and how engines like Elasticsearch and Solr implement these concepts.
• Search Ranking & Relevance - Scoring and ranking algorithms including TF-IDF and BM25, boosting, filtering versus scoring, and techniques for tuning result quality.
• Autocomplete & Typeahead - Designing low-latency prefix search and suggestion systems using tries, completion suggesters, and precomputed result sets.
• Distributed Search - Scaling search across multiple nodes through index sharding, replication strategies, scatter-gather query execution, and handling cluster coordination.

Prerequisites

A working knowledge of data structures (hash maps, trees, tries), database indexing concepts, and distributed systems fundamentals will help you follow along. Familiarity with HTTP APIs and basic text processing is also beneficial.