Keyword search index
A keyword search index is a data structure that organizes searchable terms or representations and maps them to associated records or content for efficient retrieval.
MongoDB 8.0 in Action, Third Edition
A search index is an index structure that organizes searchable content for efficient full-text or relevance-based retrieval.
- How it is used
- Enables efficient full-text retrieval of MongoDB documents through Atlas Search queries.
- How deep it goes
- Explains term-to-document mappings, field configuration, creation, readiness checks, and resource trade-offs without implementing the index structure.
- Examples
- Uses an inspections collection for CLI and local index creation, then fuzzy search with filters and search-score sorting.
- What you can do after
- Operate/configure it with Atlas CLI or MongoDB methods, verify readiness, and query the named index with `$search`.
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Machine Learning System Design
A search index is a data structure that organizes searchable representations so that relevant items can be retrieved efficiently.
- How it is used
- Supports photo retrieval by organizing item vectors for filtered, low-latency candidate search and relevance ranking.
- How deep it goes
- Explains separate batch-update and real-time-query paths, scale targets, and model-triggered rebuilds without detailing index internals.
- Examples
- Uses a PhotoStock search API, daily photo indexing, model-triggered reindexing, Qdrant evaluation, and ranked photo results.
- What you can do after
- Operate/configure a search service with separate index maintenance, query handling, filtering, candidate retrieval, and ranking responsibilities.
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Natural Language Processing in Action, Second Edition
A search index is a data structure that organizes terms and their associated documents to support efficient retrieval.
- How it is used
- Supports efficient keyword retrieval and serves as an initial filter for information-retrieval systems.
- How deep it goes
- Explains vocabulary construction, stop-word removal, binary vectors, compression, and constant-time similarity, but not index internals.
- Examples
- Uses extracted vocabulary listings, an imaginary book corpus, and Elasticsearch, Solr, and PostgreSQL.
- What you can do after
- Apply binary bag-of-words vectors to filter documents for information retrieval and full-text search.
Read how Natural Language Processing in Action, Second Edition explains keyword search index →
Related concepts
- db.collection.getSearchIndexes()used by · db.collection.getSearchIndexes() is a MongoDB collection method that returns the Atlas Search index definitions associated with a collection.
- ivfflatincludes · IVFFlat is an approximate nearest-neighbor vector index that partitions vectors into centroid-based inverted lists and compares vectors within selected lists.
- hybrid searchused by · Hybrid search is an information-retrieval technique that combines multiple retrieval methods or relevance signals to find or rank relevant results.
- similarity searchuses · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
- lexical searchuses · Lexical search retrieves or ranks items by matching query terms or other surface-text features against indexed content, often using sparse representations and statistical weighting.
- mongotused by · mongot is the MongoDB process that maintains Atlas Search indexes, tracks database changes, and processes Atlas Search queries alongside mongod.
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