문자열 필드에 텍스트 인덱스 만들기
학습자는 단일 필드 텍스트 인덱스와 와일드카드 텍스트 인덱스를 만들고 MongoDB가 적용하는 토큰화 및 어간 추출을 이해합니다.
문자열 필드에 텍스트 인덱스 만들기은(는) CoddyKit의 무료 MongoDB Academy 강의입니다. 이것은 4개 중 1번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 MongoDB Academy 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. MongoDB Academy 강의에는 총 4개의 강의가 포함되어 있습니다.
이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.
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 resultsLanguage 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 documentsCombining 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.
자주 묻는 질문
“문자열 필드에 텍스트 인덱스 만들기” 강의는 무료인가요?
네 — “문자열 필드에 텍스트 인덱스 만들기” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 MongoDB Academy 강의 전체를 잠금 해제할 수 있습니다. MongoDB Academy 강의에는 총 4개의 강의가 포함되어 있습니다.
“문자열 필드에 텍스트 인덱스 만들기”에서 뭘 배우나요?
학습자는 단일 필드 텍스트 인덱스와 와일드카드 텍스트 인덱스를 만들고 MongoDB가 적용하는 토큰화 및 어간 추출을 이해합니다. 브라우저에서 직접 실행하는 실습 코드로 MongoDB Academy을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.
MongoDB Academy을(를) 시작하는 데 경험이 필요한가요?
사전 경험은 필요하지 않습니다. CoddyKit의 MongoDB Academy은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 1번째 강의입니다.
“문자열 필드에 텍스트 인덱스 만들기” 강의는 얼마나 걸리나요?
대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.
이 MongoDB Academy 강의에서 코드를 작성하고 실행할 수 있나요?
네. 모든 MongoDB Academy 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.