MongoDB Academy · 课时

为字符串字段创建文本索引

您将创建单字段文本索引和通配符文本索引，并了解 MongoDB 如何进行分词和词干提取。

第 1 / 4 课13 个步骤

为字符串字段创建文本索引 是 CoddyKit 上的免费 MongoDB Academy 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 MongoDB Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MongoDB Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

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.

在交互式课程中回答

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.

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常见问题解答

「为字符串字段创建文本索引」课时是免费的吗？

是的 — 「为字符串字段创建文本索引」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MongoDB Academy 课程的其余内容，请升级到 CoddyKit PRO。 MongoDB Academy 课程共包含 4 节课。

「为字符串字段创建文本索引」这节课中我会学到什么？

您将创建单字段文本索引和通配符文本索引，并了解 MongoDB 如何进行分词和词干提取。你通过在浏览器中直接运行的动手代码来练习 MongoDB Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 MongoDB Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 MongoDB Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「为字符串字段创建文本索引」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 MongoDB Academy 课中编写并运行代码吗？

能。每节 MongoDB Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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