Imagine!

Imagine a vast digital library containing 100 billion books, covering 100 knowledge categories—from religion to science, medicine to history, cities to tribes—each book spanning thousands of pages, sentences, and words. Now, picture this data being searchable in 1,000 languages with both keyword-based and AI-powered semantic search capabilities.

This isn’t just an ambitious idea; it’s a real challenge for anyone building a large-scale Q&A web and mobile app. In this article, we will explore how to efficiently store, search, and retrieve this massive dataset using cutting-edge technologies like Elasticsearch, FAISS, BM25 ranking, and NLP-based search.

Understanding the Scale of the Challenge

Each book in our system can have:

• 1 to 900 volumes, each with 500 to 5,000 pages
• 500 to 3,000 words per page (~100KB per page)
• Sentences marked as truth (green), lie (red), or doubtful (yellow)
• Multiple word meanings, each with 950 attributes
• Supporting materials (PDFs, audio, video, images) classified as green, red, or yellow
• Authorship references to 6,000 books
• Information derived from 7,000 books

Storing and querying 1 billion books per category requires an efficient data structure, indexing strategy, and retrieval mechanism.

Database Selection: Elasticsearch vs. Alternatives

Elasticsearch stands out as a powerful, distributed search engine, but are there alternatives? Let’s compare:

Alternative	Pros	Cons
Elasticsearch	Fast full-text + vector search, scalable	Expensive at large scale
Solr	Open-source, highly customizable	Higher operational complexity
Milvus	Optimized for vector search	Needs external storage for text data
Weaviate	GraphQL-powered hybrid search	Smaller community support
Annoy (Spotify)	Memory-efficient vector search	Slower than FAISS
HNSWlib	High recall, fast	High RAM usage

For hybrid search, Elasticsearch + FAISS is the best combination.

Indexing 100 Billion Books in Elasticsearch

Index Structure

We use separate indices for different data layers:

• index_books (metadata, categories, references)
• index_pages (full text, sentences, attributes)
• index_words (dictionary meanings, multilingual support)
• index_authors (book-page-sentence references instead of just names)
• index_materials (PDFs, audio, video, classified by trust level)

Sharding Strategy

• Each category gets its own shards to balance data load.
• Index hot books separately for faster lookups.
• Cross-cluster search (CCS) distributes queries efficiently.

Searching 100 Billion Books: BM25, FAISS, and NLP

1️⃣ Keyword Search with BM25

BM25 ranks documents based on:

• Term Frequency (TF): How often a word appears.
• Inverse Document Frequency (IDF): How unique the word is.
• Document Length Normalization: Adjusting scores for long vs. short documents.

Example Query:

“In Allah’s name, the Most Merciful, the Most Beneficent.”

• BM25 finds exact matches in Quran translations.
• Pros: Fast, scalable.
• Cons: Doesn’t understand word meanings.

2️⃣ Semantic Search with FAISS

FAISS stores vector embeddings of books, pages, and words, enabling nearest neighbor searches.

• Example: Finding verses with a similar meaning to “Allah’s mercy is boundless.”
• Pros: Finds meaning-based matches.
• Cons: Needs high RAM for large-scale indexing.

3️⃣ Hybrid Search: FAISS + Elasticsearch BM25

• Query FAISS for semantic similarity.
• Query Elasticsearch for keyword ranking.
• Combine results using ranking models (LTR, weighted scoring).

Handling 100 Million Queries Per Second (QPS)

Choosing the Right API Framework

API	Performance	Scalability	Cost
FastAPI (REST)	Very High	Excellent	Low
NestJS (REST)	High	Good	Medium
GraphQL	Medium	Complex	High

🔹 Verdict: FastAPI is the best for handling 100M QPS.

Optimizing Frequently Accessed Books (Hot Indexing)

Some books are referenced millions of times, requiring special treatment:

• Hot indices: Store frequently accessed books separately.
• Caching: Use Elasticsearch query caching for speed.
• Dedicated nodes: Assign powerful nodes to “hot” books.

Milvus + Elasticsearch for Advanced Vector Search

If FAISS becomes costly, Milvus is a solid alternative:

• Store text data in Elasticsearch.
• Store vector embeddings in Milvus.
• Query both and merge results dynamically.

Would you like step-by-step implementation details?

Conclusion: The Future of AI-Powered Book Search

Storing and searching 100 billion books isn’t just about big data; it’s about making knowledge accessible. Using Elasticsearch, FAISS, BM25, and NLP, we can build a blazing-fast, intelligent Q&A system for any web or mobile app.

Are you ready to revolutionize search?