A text index within a database functions by mapping words or phrases to their corresponding locations, thereby accelerating search queries.

To elaborate, a text index is a data structure designed to enhance the efficiency of data retrieval operations in a database. It operates similarly to the index found at the end of a book, which offers a rapid means to locate information without the need to peruse every page. In the realm of databases, an index enables the database management system (DBMS) to quickly find and retrieve specific rows, significantly faster than it could without the presence of an index.

The creation of a text index involves scanning the text to compile a list of words, commonly referred to as tokens, along with their respective locations within the text. This process is known as tokenization. The tokens are then organized within a data structure, such as a B-tree or a hash table, which facilitates efficient searching.

When a search query is executed, the DBMS utilizes the index to identify the locations of the words or phrases included in the query, rather than scanning the entire database. This method can dramatically expedite search operations, particularly in large databases. However, it is important to note that while indexes enhance data retrieval speed, they can hinder data insertion, deletion, and updating since the index must be modified each time these operations occur.

There are various types of text indexes, including full-text indexes and inverted indexes. A full-text index encompasses every word within the text along with its location, whereas an inverted index lists each word and the documents or rows in which it appears. Inverted indexes are especially beneficial for search engines and information retrieval systems, as they enable rapid and efficient searching through extensive volumes of text.

In summary, a text index in a database efficiently maps words or phrases to their locations, providing a quick and effective means for executing search queries.