What is Inverted Index

How It Works

An inverted index reverses the relationship between documents and terms. Instead of listing which terms appear in each document, it lists which documents contain each term. For a given search term, the index provides immediate access to all matching documents with their term positions and frequencies. Boolean operations (AND, OR, NOT) combine posting lists across terms for multi-term queries.

Technical Details

An inverted index consists of a dictionary (sorted list of all unique terms) and posting lists (sorted lists of document IDs containing each term). Posting lists may include term frequency, positions, and payloads. Compression techniques (variable-byte encoding, PFOR) reduce index size. Skip lists and block-max techniques accelerate query processing. Elasticsearch, Lucene, and Tantivy are widely used inverted index implementations.

Best Practices

• Apply consistent text analysis (tokenization, stemming, lowercasing) at both index and query time
• Use field-specific analyzers for different content types (titles, descriptions, technical terms)
• Maintain inverted indices alongside vector indices for hybrid search capabilities
• Optimize index refresh intervals to balance search freshness with indexing throughput

Common Pitfalls

• Using different text analyzers at index time and query time, causing missed matches
• Not updating the index when documents are modified or deleted, returning stale results
• Over-indexing fields that are never searched, wasting storage and slowing indexing
• Ignoring language-specific analysis needs for multilingual content

Advanced Tips

• Build inverted indices on text extracted from multimodal content (captions, transcripts, OCR) for keyword search
• Use inverted indices for sparse learned representations (SPLADE) as well as traditional term matching
• Implement phrase search and proximity queries using positional posting lists
• Combine inverted index scores with vector similarity scores in a weighted hybrid retrieval function