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MongoDB Academy · Lesson

Creating a Text Index on String Fields

Learners will create single-field and wildcard text indexes and understand the tokenisation and stemming MongoDB applies.

Creating a Text Index on String Fields is a free MongoDB Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MongoDB Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

What Is a Text Index?

A text index is a special MongoDB index type that tokenises and stems the words in string fields so you can run full-text keyword searches. Unlike a regular index that stores exact field values, a text index breaks each string into individual words, removes stop words (like 'the', 'is'), and stores the resulting tokens in a B-tree. This makes it possible to search for 'mongodb tutorial' and match documents containing 'mongodb tutorials'.

Creating a Single-Field Text Index

Pass the string 'text' as the index direction to tell MongoDB to create a text index on that field. You can only have one text index per collection, but it can span multiple fields. The index builds in the background and maintains the token store automatically as documents are inserted or updated.

// Text index on the 'description' field
db.products.createIndex({ description: 'text' });

// Now you can run full-text searches
db.products.find({ $text: { $search: 'wireless headphones' } });

Multi-Field Text Indexes

A single text index can cover multiple string fields, allowing $text queries to search across all of them simultaneously. Each field can be assigned a different weight to influence relevance scoring—a match in a high-weight field like title counts more than a match in a lower-weight field like body.

// Multi-field text index with weights
db.articles.createIndex(
  { title: 'text', body: 'text', tags: 'text' },
  {
    weights: {
      title: 10,  // matches in title score 10x
      tags: 5,
      body: 1
    },
    name: 'idx_articles_text'
  }
);

Wildcard Text Indexes

If you want to search across every string field in a document without listing them all, you can use a wildcard text index with the special '$**' key. MongoDB will automatically tokenise all string-valued fields at any nesting level. This is convenient but creates a larger index than a targeted multi-field text index, so use it thoughtfully.

// Index ALL string fields in every document
db.articles.createIndex({ '$**': 'text' });

// This now searches title, body, author, tags, comments.text, etc.
db.articles.find({ $text: { $search: 'nosql' } });

Tokenisation and Stemming

When MongoDB indexes a string like 'Learning MongoDB databases', it tokenises it into individual words (learning, mongodb, databases), removes stop words, and then stems each token to its root form (e.g., databases → databas). Stemming means a search for 'database' matches documents containing 'databases', 'database', or 'databasing' without needing wildcards.

// The text 'Learning MongoDB databases' is indexed as:
// tokens (after stop-word removal and stemming):
// 'learn', 'mongodb', 'databas'

// All of these queries match the document:
db.articles.find({ $text: { $search: 'learning' } });
db.articles.find({ $text: { $search: 'database' } });
db.articles.find({ $text: { $search: 'databases' } });

Stop Words Are Ignored

Stop words are common words like 'the', 'is', 'at', 'which', and 'on' that carry little meaning and are excluded from the text index to keep it lean. If your search term consists entirely of stop words, the $text query returns no results. Stop word lists are language-specific and controlled by the default_language option on the index.

// Create text index with explicit language
db.articles.createIndex(
  { body: 'text' },
  { default_language: 'english' }  // english stop words (default)
);

// Stop words for English include: the, is, are, at, on, in, a, an...
// Searching for 'the' alone returns nothing
db.articles.find({ $text: { $search: 'the' } }); // 0 results

Language Support

MongoDB's text index supports many languages including english, french, german, spanish, portuguese, italian, dutch, and more. Each language has its own stop word list and stemming rules. You can also set the language to 'none' to disable stop word removal and stemming, treating every token as a literal string.

// Spanish text index
db.articulos.createIndex(
  { contenido: 'text' },
  { default_language: 'spanish' }
);

// Per-document language override (store language in a field)
db.posts.createIndex(
  { body: 'text' },
  { language_override: 'lang' }  // read language from doc.lang field
);
db.posts.insertOne({ body: 'Bonjour le monde', lang: 'french' });

One Text Index Per Collection Rule

MongoDB enforces a hard limit of one text index per collection. This means you must plan all the string fields you want searchable and include them in a single multi-field text index definition. Trying to create a second text index on the same collection will throw an error. If you need to add a field to an existing text index, you must drop and recreate the index.

// First text index on 'title'
db.articles.createIndex({ title: 'text' });

// Trying to add a second text index FAILS:
// db.articles.createIndex({ body: 'text' });
// Error: only one text index per collection allowed

// Correct approach: drop old, recreate with both fields
db.articles.dropIndex('title_text');
db.articles.createIndex({ title: 'text', body: 'text' });

Text Index Storage Overhead

Text indexes can be significantly larger than regular indexes because they store one entry per unique token per document rather than one entry per document. A document with a 500-word description might add hundreds of index entries. Monitor text index size with db.collection.stats().indexSizes and consider whether a dedicated search service (Atlas Search, Elasticsearch) would be more efficient for very large corpora.

// Check text index size
const stats = db.articles.stats();
console.log('Index sizes:', stats.indexSizes);
// idx_articles_text might be 10x larger than a regular index
// on the same number of documents

Combining Text Index With Other Indexes

A text index can be combined with a regular field in a compound index. For example, you can index { category: 1, description: 'text' } to allow filtering by category alongside the text search. In this case the category equality filter dramatically reduces the number of token entries the planner has to examine, making the text query much faster.

// Compound text index with category prefix
db.products.createIndex({ category: 1, description: 'text' });

// This query can use the compound text index efficiently:
// MongoDB filters by category first, then does text search
db.products.find({
  category: 'electronics',
  $text: { $search: 'wireless' }
});

Verifying the Text Index

After creating a text index, use db.collection.getIndexes() to confirm it was created with the correct fields and weights, and run a simple $text query with .explain() to verify the query planner uses a TEXT stage. A TEXT stage in the plan means the text index is actively being used for keyword matching.

// Inspect the text index definition
db.articles.getIndexes().filter(idx => idx.textIndexVersion !== undefined);

// Verify TEXT stage in explain output
db.articles
  .find({ $text: { $search: 'mongodb' } })
  .explain();
// Look for: { stage: 'TEXT', ... }

Quick Check

Test your understanding of MongoDB text indexes from this lesson.

Lesson Recap

In this lesson you learned: text indexes tokenise and stem string fields to enable full-text keyword searches, only one text index is allowed per collection but it can span multiple fields with custom weights, and language determines stop words and stemming rules. Next up we learn to run $text queries with phrases and negation.

Frequently asked questions

Is the “Creating a Text Index on String Fields” lesson free?

Yes — the full text of “Creating a Text Index on String Fields” is free to read here on the web, and the MongoDB Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MongoDB Academy course, upgrade to CoddyKit PRO.

What will I learn in “Creating a Text Index on String Fields”?

Learners will create single-field and wildcard text indexes and understand the tokenisation and stemming MongoDB applies. You practise MongoDB Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start MongoDB Academy?

No prior experience is required. MongoDB Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Creating a Text Index on String Fields” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this MongoDB Academy lesson?

Yes. Every MongoDB Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Creating a Text Index on String Fields
  2. Running $text Queries With Phrases and Negation
  3. Sorting by Text Score With $meta
  4. Text Index Limitations and When to Use Atlas Search
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