Search is at the core of user experience in any modern platform. A fast, relevant, and scalable search system not only drives engagement but becomes a foundational capability for future features like autocomplete, personalization, and intelligent ranking.
This post outlines the technical considerations and implementation plan for building robust search functionality, intended for engineers and technical stakeholders.
Performs intersection-based filtering to find candidates matching all tokens.
Supports fuzzy fallback for unmatched tokens (e.g., via .contains() or edit distance).
Applies prefix match boosting for higher relevance of initial query tokens.
Result Scoring & Filtering
Assign scores based on:
Number of matching n-grams
Prefix matches on first token
Position or frequency of matches
Results are sorted by final score before display.
Future extensibility for:
Faceted filters (by category, type)
Personalized rankings
Synonym expansion
Highlighting
Matched substrings are dynamically highlighted in the results to guide users visually.
Tokens are matched in a case-insensitive manner and wrapped (e.g., with ** or <mark>).
Performance & Scalability
Complexity Analysis
Complexity Analysis
┌─────────────────────────────────────────────────────────┐│ PERFORMANCE CHARACTERISTICS ││││ Index Building: O(N × L) ││─────────────── N = documents, L = avg length ││ One-time cost at ingestion ││││ Query Processing: O(Q × T) ││──────────────── Q = query tokens, T = token length ││ Fast: typically O(1) per trigram ││││ Search Lookup: O(I × C) ││───────────── I = trigrams per token ││ C = set intersection cost ││││┌─────────────────────────────────────────────────┐│││ MEMORY vs SPEED TRADEOFF ││││││││ More trigrams ──▶ More memory, faster search ││││ Fewer trigrams ──▶ Less memory, slower search ││││││││ Sweet spot: 3-grams for most use cases │││└─────────────────────────────────────────────────┘│└─────────────────────────────────────────────────────────┘
The implementation must be optimized for both speed and memory:
Component
Time Complexity
Space Complexity
Notes
Index Building
O(N × L)
O(N × G)
N = # entries, L = avg string len, G = # of n-grams
Include unit and integration tests covering edge cases like:
Empty queries
Partial matches
Multi-token inputs
Document the index schema and scoring rules for future team members.
Plan for future support of features like:
Synonyms
Stop word handling
Autocomplete
Developer Takeaway
The implementation of search is more than just querying text — it’s about designing an engine that understands intent, delivers relevant results quickly, and remains flexible for growth. This blueprint ensures our engineering approach is systematic, scalable, and built for users.
The foundation starts here. Let’s build it right.
Want to dive into the Java code for our current implementation or run sample benchmarks? Stay tuned for part 2!
Let me know if you’d like a version tailored for external audiences (like users or clients), or if you want diagrams added!