Using Text Search

MongoDB provides text search capabilities to perform full-text search on string fields within documents. With the help of text indexes, MongoDB can efficiently find documents containing specific words or phrases in text fields. Text search is ideal for situations where you need to perform searches on large amounts of unstructured text, such as descriptions, comments, articles, or other documents containing freeform text.

1. Creating a Text Index

A text index is an index on string fields that allows for full-text search functionality. You can create a text index on one or more fields, and MongoDB will automatically tokenize the text and provide features like stemming, case insensitivity, and support for wildcard searches.

Syntax:

To create a text index on one or more fields, you use the "text" option when calling createIndex().

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db.collection.createIndex({ fieldName: "text" })

Example:

Let’s say you have a posts collection, and you want to perform text searches on the title and content fields. You can create a text index on both fields:

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db.posts.createIndex({ title: "text", content: "text" })

This will allow MongoDB to perform full-text searches on both title and content fields.

2. Basic Text Search

Once a text index is created, you can use the $text operator in the find() method to search for specific words or phrases in text-indexed fields.

Syntax:

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db.collection.find({ $text: { $search: "searchTerm" } })

Example:

Let’s assume you want to search for documents containing the word “MongoDB” in the title or content fields of the posts collection:

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db.posts.find({ $text: { $search: "MongoDB" } })

This query will return all posts where either the title or content contains the word “MongoDB”.

3. Text Search with Phrases

You can search for exact phrases by enclosing the phrase in double quotes. MongoDB will search for documents containing that exact phrase.

Syntax:

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db.collection.find({ $text: { $search: "\"exact phrase\"" } })

Example:

Search for posts that contain the exact phrase “MongoDB tutorial”:

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db.posts.find({ $text: { $search: "\"MongoDB tutorial\"" } })

This will only return documents that contain the exact phrase “MongoDB tutorial” in either the title or content fields.

4. Text Search with AND and OR Operators

MongoDB supports basic boolean logic in text searches, including the AND and OR operators.

• AND: The space between words acts as an implicit AND. For example, searching for "MongoDB tutorial" will return documents that contain both “MongoDB” and “tutorial”.
• OR: You can use the "$search" operator to include multiple words or phrases and find documents that match any of them. Use a space between terms to indicate OR logic.

Example:

To search for documents that contain both “MongoDB” and “tutorial”:

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db.posts.find({ $text: { $search: "MongoDB tutorial" } })

This will return all documents containing both “MongoDB” and “tutorial”.

Example of OR (Multiple Words Search):

You can also search for documents that contain either of two words:

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db.posts.find({ $text: { $search: "MongoDB OR tutorial" } })

This will return documents that contain either “MongoDB” or “tutorial”, or both.

5. Text Search with Exclusion (-)

MongoDB allows excluding words from the search results using a minus sign (-). If you place a minus sign before a word, MongoDB will exclude documents that contain that word.

Syntax:

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db.collection.find({ $text: { $search: "term1 -term2" } })

Example:

To search for documents that contain the word “MongoDB” but not “tutorial”:

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db.posts.find({ $text: { $search: "MongoDB -tutorial" } })

This will return all documents that contain the word “MongoDB” but exclude those that also contain the word “tutorial”.

6. Text Search with Score

MongoDB assigns a relevance score to each document based on how well it matches the search terms. You can retrieve these scores to rank search results.

To include the relevance score in the query results, you can use the score in the projection.

Syntax:

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db.collection.find( { $text: { $search: "searchTerm" } }, { score: { $meta: "textScore" } } )

Example:

To search for “MongoDB” and include the relevance score in the result:

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db.posts.find( { $text: { $search: "MongoDB" } }, { score: { $meta: "textScore" } } ).sort({ score: { $meta: "textScore" } })

This query returns documents containing “MongoDB” and sorts them by relevance, with documents that match the search term more closely receiving a higher score.

7. Text Search with Language-Specific Features

MongoDB’s text search is language-aware, meaning that it uses language-specific rules for stemming (reducing words to their base form) and stop words (common words like “the”, “and”, etc., that are ignored during the search).

You can specify the language for a text index, or let MongoDB default to a language (usually english).

Syntax for Specifying Language:

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db.collection.createIndex({ fieldName: "text" }, { default_language: "english" })

Example:

Creating a text index for a collection with a specific language:

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db.posts.createIndex({ title: "text", content: "text" }, { default_language: "english" })

This index will apply English-specific rules for stemming and stop word removal.

8. Text Search Limitations

• Language Limitations: By default, MongoDB uses stemming and stop words specific to a default language (e.g., English). If you’re working with other languages, you may need to adjust the language settings when creating the text index.
• Field Type Limitations: Only string fields can be indexed as text fields. Text search cannot be performed on numeric, date, or binary fields.
• Search Performance: While text search is fast for smaller datasets, it can become slower for very large collections. Proper indexing and using projections to limit the number of fields returned can help.
• No Wildcards: MongoDB’s text search does not support wildcard characters (e.g., *, ?) or regular expressions for matching text in the way you might find in some search engines.

9. Text Search with Aggregation

You can also use text search in MongoDB’s aggregation framework to perform more advanced queries, like filtering, sorting, and transforming data based on text search results.

Example:

To perform a text search within an aggregation pipeline and include the score, use the $match and $project stages:

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db.posts.aggregate([ { $match: { $text: { $search: "MongoDB" } } }, { $project: { title: 1, score: { $meta: "textScore" } } }, { $sort: { score: { $meta: "textScore" } } } ])

This aggregation pipeline:

1. Filters documents to include only those that match the text search for “MongoDB”.
2. Projects the title field and includes the score.
3. Sorts the results by the relevance score in descending order.

10. Using Atlas Full-Text Search

For more advanced full-text search features, such as phrase matching, wildcard queries, and more complex analyzers (including support for multiple languages and custom analyzers), MongoDB Atlas offers a powerful Atlas Full-Text Search feature built on Lucene.

• Atlas Full-Text Search supports more advanced search features than the standard MongoDB text indexes, such as:
  • Phrase queries
  • Fuzzy matching
  • Autocompletion
  • Search suggestions
  • Complex filters

You can create and manage Atlas Search Indexes directly from the MongoDB Atlas UI.