textsearch

Textsearch refers to the process and systems used to locate relevant textual information within a collection of documents or streams. It combines indexing, querying, and ranking to retrieve documents that match a user’s query, often under latency constraints. Text search is a core component of search engines, digital libraries, enterprise content management, and log analysis.

Key techniques include preprocessing steps such as normalization, tokenization, stop-word removal, and stemming or lemmatization. The central data structure is the inverted index, which maps terms to postings lists containing document identifiers and, often, term positions. Query processing supports keyword, boolean, phrase, proximity, wildcard, and fuzzy searches. Relevance ranking typically uses probabilistic or vector-space methods such as TF-IDF or BM25, sometimes augmented by dense vector representations and machine-learned ranking models.

Indexing and architecture often employ distributed design to scale across machines, with inverted indices and document stores partitioned into shards and replicated for fault tolerance. At query time, results are merged, scored, and returned, with caching and incremental indexing to handle updates efficiently.

Applications span web and enterprise search, e-commerce product search, code and document repositories, and log analytics. Popular implementations include open-source engines and libraries such as Lucene, Solr, Elasticsearch, as well as database-supported approaches like PostgreSQL full-text search and SQLite FTS. Challenges include multilingual support, synonym handling, semantic or contextual search, handling typos, and balancing relevance with performance and privacy considerations.