We are working on updating this book for the latest version. Some content might be out of date.
Databases that deal purely in structured data (such as dates, numbers, and string enums) have it easy: they just have to check whether a document (or a row, in a relational database) matches the query.
While Boolean yes/no matches are an essential part of full-text search, they are not enough by themselves. Instead, we also need to know how relevant each document is to the query. Full-text search engines have to not only find the matching documents, but also sort them by relevance.
Full-text relevance formulae, or similarity algorithms, combine several
factors to produce a single relevance
_score for each document. In this
chapter, we examine the various moving parts and discuss how they can be
Of course, relevance is not just about full-text queries; it may need to take structured data into account as well. Perhaps we are looking for a vacation home with particular features (air-conditioning, sea view, free WiFi). The more features that a property has, the more relevant it is. Or perhaps we want to factor in sliding scales like recency, price, popularity, or distance, while still taking the relevance of a full-text query into account.
All of this is possible thanks to the powerful scoring infrastructure available in Elasticsearch.
We will start by looking at the theoretical side of how Lucene calculates relevance, and then move on to practical examples of how you can control the process.