Senior LevelSystem Design & Architecture
How do you design an Enterprise Search Engine Indexer (like Elasticsearch / Lucene)?
❓ Interview Prompt: Explain Search Engine Indexing, Inverted Index data structure, posting lists, Term Dictionary, Finite State Transducers (FST), and segment merging.
Designing an Enterprise Search Engine Indexer (Elasticsearch/Lucene):
Core Architecture:
1
Inverted Index: Maps words to document IDs containing them ('"database" -> [Doc 1, Doc 4, Doc 10]').
2
Term Dictionary Compression: Uses Finite State Transducers (FST) to compress millions of search terms in RAM.
3
Posting Lists: Compresses document ID arrays using Frame-Of-Reference (FOR) integer compression for fast bitwise 'AND'/'OR' intersections.
Code Implementation Example
markdown
Inverted Index Structure:
Term Dictionary (FST in RAM) Posting Lists (Disk)
"database" ─────────────────────> [Doc 101, Doc 405, Doc 902]
"security" ─────────────────────> [Doc 204, Doc 405]
Intersection Query ("database AND security") = [Doc 405]💡Inverted Index structure mapping search terms to document posting lists.
🎯 Interview Key Takeaway
Design search indexers using Inverted Indexes with FST dictionary compression for high-speed text search.
#System Design#Search Engine#Inverted Index#Lucene#Senior
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