Search Engine Index Project
Build a high-performance search engine index using Tries and Hashing to search through thousands of documents instantly.
Build a high-performance search engine index using Tries and Hashing to search through thousands of documents instantly. This hands-on tutorial focuses on practical implementation of search engine index project concepts.
Capstone Project: Search Engine Index
In this project, you will apply your knowledge of Tries and Hashing to build the core indexing component of a search engine.
1. The Challenge
Searching through a massive list of documents linearly for a keyword is $O(N \times L)$. We want to find documents containing a prefix or a word in $O(L)$ time, where $L$ is the word length.
2. The Architecture
3. Implementation
4. Key Learnings
- Tries are perfect for auto-complete and prefix matching.
- Hash Maps (Inverted Index) are the standard way to map keywords to documents in modern search engines like Elasticsearch.
AI Mentor
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Quiz
Quiz
Question 1 of 1Which data structure is typically used for a search engine's 'autocomplete' feature?