Need to search for words or phrases across thousands of documents? Basic string matching with regex is slow and doesn't understand language. You need MongoDB Text Search! In this essential 2025 guide, we’ll show you how to build a powerful keyword-based search engine directly inside your database.

This tutorial covers the two main ways to perform text searches: the standard $text operator for self-managed databases and a brief look at the advanced Atlas Search for cloud users.

What you will learn in this video:

Setting Up the Text Index: You can only have ONE text index per collection, but it can cover multiple fields. We’ll show you the syntax: db.collection.createIndex({ title: "text", body: "text" }).

The $text Operator: How to use the $search key to find documents.

OR Search: Finding documents that contain any of the words (e.g., "coffee cake").

Exact Phrase Search: How to use escaped quotes \" \" to find a specific phrase.

Excluding Words: Using the minus sign - to filter out results (e.g., "coffee -tea").

Sorting by Relevance (Search Score): By default, results aren't sorted by how well they match. We'll show you how to use the Metadata Score:

How to project the score using { score: { $meta: "textScore" } }.

How to sort results so the best matches appear at the top!

Text Search in Aggregation: Using the $match stage with $text as the first step in your pipeline.

Language Support: How to specify languages (like Spanish or French) for better stemming and stop-word filtering.

Why Text Search Beats Regex: Regular expressions (Regex) perform a "full scan" of your data, which is incredibly slow. Text indexes tokenize your words and use an "inverted index" to find matches instantly, even in massive collections.

By the end of this tutorial, you’ll be able to implement a professional search bar experience for your apps using nothing but MongoDB commands!

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