searchindex

A searchindex, or search index, is a data structure and associated algorithms that enable fast retrieval of documents based on their textual content. It is a core component of search systems, allowing queries to identify relevant documents with minimal scanning of the entire collection.

The primary data structure is the inverted index, which maps terms to postings lists containing the documents in which the term appears. Each posting can include metadata such as term frequency, positions for phrase queries, and optional payloads. A forward index, which lists the terms contained in each document, is often maintained as well. Indices may be partitioned by fields (for example title, body, or metadata) and are typically compressed to save space and improve I/O performance. Statistics such as document frequency and term frequency support ranking calculations.

Indexing involves several steps. Content is gathered, normalized, tokenized, and often subjected to stop-word removal and stemming or lemmatization. Tokens are inserted into the inverted index, with postings updated across index segments. Indexing can be performed in batch or in real time, and the chosen approach affects freshness and latency.

Query processing and ranking use the index to retrieve candidate documents efficiently. Terms are looked up in the postings, and a scoring function (such as BM25 or TF-IDF with field boosts) ranks the results. Relevance can be enhanced with features like document recency, term proximity, and synonym handling.

Searchindices are used in search engines, content management systems, and databases to support fast, scalable text search, while facing challenges around scaling, updates, multilingual support, and latency.