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MongoDB Academy · レッスン

テキストインデックスの制限とAtlas Searchを使う場面

ネイティブテキストインデックスの制約（コレクションごとに1つ、言語サポートなど）を把握し、Atlas Searchを選ぶべき場面を判断します。

「テキストインデックスの制限とAtlas Searchを使う場面」はCoddyKit上の無料MongoDB Academyレッスンです。 これはレッスン4/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはMongoDB Academy学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 MongoDB Academyコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

Text Index Limitations Overview

MongoDB's built-in text index is a great starting point for full-text search, but it has several important limitations that become significant as your search requirements grow. Understanding these constraints helps you decide early whether to invest in the native text index or migrate to a dedicated search engine like Atlas Search.

One Text Index Per Collection

The most impactful limitation is that each collection can have only one text index. This means you cannot have separate text indexes for different search scenarios on the same collection—everything must be combined into a single index definition. If your search requirements change and you need to add or remove fields from the index, you must drop and rebuild the entire text index, which can be time-consuming on large collections.

// Can't have two text indexes on the same collection
db.articles.createIndex({ title: 'text' });  // OK
// db.articles.createIndex({ body: 'text' });  // ERROR!

// Must combine everything into one:
db.articles.dropIndex('title_text');  // rebuild required
db.articles.createIndex({ title: 'text', body: 'text' });

No Autocomplete Support

Native text indexes have no autocomplete capability. They match whole words (after stemming) but cannot suggest completions for partial input. For example, searching for 'mongo' will NOT match documents containing 'mongodb' because 'mongo' and 'mongodb' stem to different tokens. Implementing autocomplete with native text indexes requires complex workarounds like storing n-grams, which is inefficient.

// Native text index: partial word does NOT match
db.articles.find({ $text: { $search: 'mongo' } });
// Will NOT return docs with 'mongodb' because
// 'mongo' stems to 'mongo', 'mongodb' stems to 'mongodb'
// These are different index tokens

// Atlas Search: autocomplete field supports partial matching
// { $search: { autocomplete: { query: 'mongo', path: 'title' } } }

No Fuzzy Matching

Native text indexes have no fuzzy (approximate) matching. A typo like 'mongodab' will not match 'mongodb'—the token must be close enough to stem to the same root. For user-facing search boxes where typos are common, this results in frustrating zero-result searches. Atlas Search provides a fuzzy option with configurable edit distance to handle typos gracefully.

// Native $text: typo returns NO results
db.articles.find({ $text: { $search: 'mongodab' } }); // 0 results

// Atlas Search fuzzy option handles typos
// db.articles.aggregate([{
//   $search: {
//     text: {
//       query: 'mongodab',
//       path: 'title',
//       fuzzy: { maxEdits: 2 }  // tolerates up to 2 character edits
//     }
//   }
// }]);

Limited Language Support

Native text indexes support around 15 languages for stemming and stop words. While this covers most Western European languages, it has no support for Chinese, Japanese, Korean, Arabic, and many other languages that require word-boundary detection before tokenisation. Atlas Search uses Lucene analyzers that support a much broader set of languages and script-specific tokenisation rules.

// Supported natively: english, french, german, spanish, portuguese,
// italian, dutch, danish, norwegian, swedish, finnish,
// romanian, turkish, russian (with caveats)

// NOT supported natively:
// Chinese (no word boundaries), Japanese, Korean, Arabic
// Must use Atlas Search or an external search engine for these
db.articles.createIndex({ body: 'text' }, { default_language: 'english' });

No Synonym Support

Native text indexes cannot expand search terms to include synonyms. If a user searches for 'automobile', documents containing only 'car' will not match. Atlas Search supports synonym mappings that you configure in the index definition, enabling rich synonym expansion without application-level query rewriting.

// Native text: no synonym expansion
// 'car' search does NOT match 'automobile' documents
db.articles.find({ $text: { $search: 'car' } });

// Workaround: client-side synonym expansion (brittle)
const synonyms = { car: ['automobile', 'vehicle', 'auto'] };
const expandedSearch = [search, ...synonyms[search]].join(' ');
db.articles.find({ $text: { $search: expandedSearch } });

// Atlas Search: configure synonyms in the index definition

Performance at Scale

Native text indexes store all tokens in a single WiredTiger B-tree within the same storage engine as your operational data. For large corpora (tens of millions of documents with long text fields), this can cause write amplification and cache pressure that degrades overall database performance. Dedicated search engines like Lucene (which powers Atlas Search) are architecturally optimised for large token stores using inverted indexes with compression.

// Monitor text index size vs other indexes
const stats = db.articles.stats();
console.log('Index sizes:', stats.indexSizes);
// If text index is 10x larger than next biggest index,
// consider Atlas Search to offload the storage overhead

What Is Atlas Search?

Atlas Search is a fully managed, Apache Lucene-based search engine built into MongoDB Atlas. It runs as a separate service within your Atlas cluster and replicates data from your collections automatically. Atlas Search uses the aggregation $search stage rather than $text, and it provides autocomplete, fuzzy matching, facets, synonyms, custom scoring, and deep language support—all with the same MongoDB connection string.

// Atlas Search uses $search aggregation stage
db.articles.aggregate([
  { $search: {
    text: {
      query: 'mongodb tutorial',
      path: ['title', 'body'],
      fuzzy: { maxEdits: 1 }
    }
  }},
  { $limit: 10 },
  { $project: { title: 1, score: { $meta: 'searchScore' } } }
]);

Decision Matrix: Text Index vs Atlas Search

Use the native text index when: your collection has fewer than 1 million documents; your users search in one Western European language; you need basic keyword search with no typos or autocomplete; you want zero additional infrastructure cost. Use Atlas Search when: you need autocomplete or fuzzy search; your user base searches in multiple or non-Latin-script languages; you need faceted navigation; or your text search is central to the product and must scale.

// Native text: simple, zero extra cost, one language
// Good for: admin search, internal tools, small product
db.products.createIndex({ name: 'text', description: 'text' });

// Atlas Search: richer, requires Atlas M10+ cluster
// Good for: customer-facing search, multilingual, autocomplete
// Create via Atlas UI or API, then use $search in aggregation

Migrating From Text Index to Atlas Search

Migrating from native text to Atlas Search does not require changing your data model—Atlas Search indexes the same collection. The migration steps are: 1) create an Atlas Search index on the collection via the Atlas UI or API; 2) rewrite your find({ $text: ... }) queries to use the $search aggregation stage; 3) test and validate results; 4) drop the old text index to free up storage. Your documents don't move; only the index and query syntax change.

// BEFORE (native text)
db.articles.find(
  { $text: { $search: 'mongodb' } },
  { score: { $meta: 'textScore' } }
).sort({ score: { $meta: 'textScore' } });

// AFTER (Atlas Search - same result set, richer options)
db.articles.aggregate([
  { $search: { text: { query: 'mongodb', path: ['title', 'body'] } } },
  { $addFields: { score: { $meta: 'searchScore' } } },
  { $sort: { score: -1 } }
]);

When Neither Is Enough

For very demanding search requirements—custom ML-based ranking, vector similarity search (semantic search), multi-tenancy at scale, or full observability into query plans—even Atlas Search may not suffice. In those cases, dedicated search platforms like Elasticsearch, Solr, or Typesense are used alongside MongoDB, with a synchronisation layer (change streams or ETL) keeping the search index in sync with the MongoDB source of truth.

// Architecture pattern: MongoDB + external search engine
// 1. Write data to MongoDB (source of truth)
// 2. Change stream replicates new/updated docs to Elasticsearch
// 3. Search queries go to Elasticsearch
// 4. Read queries for known IDs go directly to MongoDB

// MongoDB change stream listener (Node.js)
const changeStream = db.articles.watch();
for await (const change of changeStream) {
  await elasticsearchClient.index({ id: change.documentKey._id, ...change.fullDocument });
}

Quick Check

Test your understanding of text index limitations and Atlas Search.

Lesson Recap

In this lesson you learned: native text indexes have key limitations including one-per-collection, no autocomplete, no fuzzy matching, and limited language support, Atlas Search (Lucene-based) solves these with autocomplete, fuzzy, synonyms, and broad language coverage, and the right choice depends on scale and feature requirements. Next up we explore array operators and queries.

よくある質問

「テキストインデックスの制限とAtlas Searchを使う場面」レッスンは無料ですか？

はい。「テキストインデックスの制限とAtlas Searchを使う場面」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、MongoDB Academyコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 MongoDB Academyコースには全4レッスンが含まれています。

「テキストインデックスの制限とAtlas Searchを使う場面」で何を学びますか？

ネイティブテキストインデックスの制約（コレクションごとに1つ、言語サポートなど）を把握し、Atlas Searchを選ぶべき場面を判断します。 ブラウザで直接実行するハンズオンコードでMongoDB Academyを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

MongoDB Academyを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのMongoDB Academyは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン4/4です。

「テキストインデックスの制限とAtlas Searchを使う場面」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このMongoDB Academyレッスンでコードを書いて実行できますか？

はい。すべてのMongoDB Academyレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

  1. 文字列フィールドへのテキストインデックスの作成
  2. フレーズと否定を使った$textクエリの実行
  3. $metaによるテキストスコアでのソート
  4. テキストインデックスの制限とAtlas Searchを使う場面
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