The simple patterns used in this post can be expanded to all different search scenarios as described in this introduction to full-text search. Really, this is a deep-dive topic all of its own. Analyzing text across multiple languages.Identifying where in a document particular text exists.The system compares that text to the indexes and returns a list of documents with matches.įull-text search is straightforward, but there’s an infinite set of options and questions to consider, like: The querying stage (a.k.a., the search) sends a piece of text to the server for it to hunt for. You can also just tell the system to index every text field in the document, though for large datasets this may not be efficient in production. The indexing stage is similar to creating secondary indexes for relational/tabular data where you describe the fields or elements to be indexed and the system keeps track of them for you. There are two steps to using a Search system: (1) indexing/analyzing the text in each document and (2) requesting a list of documents that contain text-based matches. Likewise, wildcards, prefixes, and fuzzy matching are possible with robust search systems. In a full-text search scenario you hunt for text with more sophistication.įor example, search systems understand root-words using a concept known as stemming, so you don’t have to look for many permutations of a term manually. In the database domain we just call it “search” for shorthand, and it’s focused on finding text within JSON documents.Īpplication developers use search-related tools to find matches without having to write SQL queries which usually require you to know how/where to find the data of interest. What Is Full-Text Search?įull-text search (FTS) is a strange name, but it’s a well-developed concept in academic areas focused on analyzing large pieces of text content. You’ll learn how to find JSON documents that contain the text you’re after by adding functionality to your app that uses the Couchbase Search API. The previous post in this series used Express to build a basic API for creating N1QL queries. In this tutorial, you’ll add the full-text search capabilities of Couchbase to the basic REST API built with Express that we’ve been building throughout this Node.js series. It’s unavoidable: If you’re working with a document database, you’re eventually going to need to search for (and through) your JSON documents.
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