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A data structure that maps terms to their document occurrences for fast retrieval.
DESCRIPTION
An inverted index stores a dictionary of terms pointing to postings lists of documents and positions. It enables efficient full-text search at scale. Construction involves tokenization, normalization, and indexing pipelines. Compression and skip lists optimize space and query speed. Ranking uses term statistics derived from postings. Inverted indexes underpin search engines and text-searching databases. They support phrase and proximity queries through positional information. Updates can be incremental or batched, depending on system design.
ALTERNATIVE DEFINITIONS
Wikipedia
In computer science, an inverted index (also known as a postings list) is an index data structure storing a mapping from content, such as words or numbers, to its locations in a database file, or in a document or a set of documents.
Highlights the mapping from content to locations across documents.
Stanford IR Book
The fundamental data structure for text indexing is the inverted index.
Emphasizes centrality of inverted indexes to text retrieval.
RELATED TERMS
Full-Text Search Postings List Dictionary Tokenization BM25 Term Frequency–Inverse Document Frequency Lucene Elasticsearch Index Compression Boolean Retrieval
COMPETING TERMS
Forward Index Signature File
RELATIONS
- Broader: Index
KNOWLEDGE AREAS
Data Storage & Operations Data Engineering
TARGET AUDIENCE
Data Engineer Software Developer Data Architect
EXTERNAL SOURCES
- Wikipedia - Inverted index
- Stanford IR Book - Authoritative IR textbook online
- Apache Lucene - Lucene search library documentation
- Elastic - Elasticsearch indexing internals guide
TOP QUESTIONS
What is an Inverted Index in search systems?
It maps terms to documents containing them, enabling fast retrieval. It’s the core structure behind full-text search engines.
Category: Overview
Why is an Inverted Index efficient?
It avoids scanning all documents by directly accessing postings lists for terms, drastically reducing query time.
Category: Benefits
What are postings lists in an Inverted Index?
Postings lists record document IDs and often term positions, supporting ranking and phrase or proximity queries.
Category: Components
How is an Inverted Index built?
Text is tokenized and normalized; terms are added to a dictionary, and document references populate postings lists, often with compression.
Category: Process
What optimizations are common in Inverted Indexes?
Compression, skip pointers, tiered indexing, and sharding improve storage efficiency and query performance.
Category: Practices
What is a competing structure to an Inverted Index?
Forward indexes and signature files are alternatives but generally less efficient for text retrieval tasks.
Category: Comparison
How does an Inverted Index support phrase queries?
Storing term positions allows verifying term adjacency and order to match exact phrases within documents.
Category: Mechanics
What risks exist with Inverted Index updates?
Frequent updates may fragment indexes, degrade performance, and require periodic merges or reindexing.
Category: Risk
Where can I learn Inverted Index fundamentals?
The Stanford IR Book covers concepts thoroughly; Lucene documentation shows practical implementation details.
Category: Learning