Fulltextindexering

Fulltextindexering is the process of constructing and maintaining an index of the full textual content of documents to enable efficient searching. It is central to search engines, document management systems, and many database applications. By indexing the actual words found in documents, users can locate relevant material even when the search terms do not appear in titles or metadata.

The typical workflow includes text extraction, normalization, tokenization, stop-word removal, stemming or lemmatization, and handling of synonyms or language-specific rules. Non-textual content such as metadata, code, or formatting may be ignored or processed separately. The goal is to produce a stable, searchable representation of document text suitable for fast lookup.

The core data structure is the inverted index, which maps terms to the documents (and often to specific positions) where they occur. Additional fields may store term frequency, document frequency, and field-level statistics to support ranking and advanced queries. Indexes are commonly stored on disk and, in large deployments, distributed across multiple machines.

Query processing starts with parsing the user input, retrieving candidate documents via the inverted index, and ranking results with scoring models such as TF-IDF, BM25, or language models. The system may support phrase queries, proximity queries, wildcards, and relevance feedback to improve results.

Common implementations include fulltext indexing libraries and platforms such as Apache Lucene, Elasticsearch, Solr, Xapian, Sphinx, and database-backed solutions in PostgreSQL or MySQL.

Challenges include language diversity and stemming accuracy, handling stop words, scalability, real-time indexing of new content, updates and deletions in the index, and balancing precision and recall for user queries.