How does an inverted index work in a database?

An inverted index in a database works by mapping each unique word to its locations in the database, enhancing search efficiency.

An inverted index, also known as a posting file, is a critical component in a database system, particularly in the context of search engines. It is a data structure that stores a mapping from words, or terms, to their locations in a set of documents. This is the reverse of the traditional approach where you would look up a document and then find the words within it. Hence, it's called an 'inverted' index.

The process begins with the database system scanning through every document and breaking them down into individual words or terms. Each unique term is then used as a key in the index. The value associated with each key is a list of locations, or pointers, where that term appears in the database. These locations could refer to entire documents, specific rows in a table, or even precise positions within a text field, depending on the granularity of the index.

The primary advantage of an inverted index is that it significantly speeds up search queries. When a user searches for a specific term, the system can quickly look up the term in the index and directly retrieve all the locations where the term appears. This is much faster than scanning through every document in the database.

However, building and maintaining an inverted index can be resource-intensive. The index can become quite large if the database contains a vast number of unique terms, and it needs to be updated every time a document is added, deleted, or modified. Therefore, database systems often use various optimisation techniques to manage inverted indexes efficiently. These may include compressing the index to reduce its size, or using tiered indexes where frequently searched terms are kept in a smaller, faster-to-search index.

In summary, an inverted index is a powerful tool for improving the speed of search queries in a database. It works by reversing the traditional indexing approach, mapping each unique term to its locations in the database, which allows for quick and efficient retrieval of data.