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MongoDB Search Query Performance

Improve your MongoDB Search query performance by understanding how query complexity, operators, and aggregation pipeline stages affect cluster performance. Use the recommendations below to structure queries efficiently for your deployment type.

Consider Query Operators and Query Complexity

The complexity level of MongoDB Search queries and the type of operators used can affect database performance for the node that mongot runs on.

Highly complex queries, such as queries with multiple clauses that use the compound operator, or queries which use the regex (regular expression) or the wildcard operator, are resource-intensive.

Compound Queries

If your query includes multiple nested compound statements, ensure that these are not redundant. If the clauses are added programmatically, consider implementing the logic in the application to avoid inclusion of redundant clauses in the queries. Every score calculation per field that mongot performs, such as for the must and should clauses, increases execution time. To optimize performance, place non-scoring operators such as equals, range, and in in the filter clause to avoid unnecessary scoring operations.

Faceted Search

You can use the MongoDB Search facet (MongoDB Search Operator) collector to extract metadata and avoid running multiple queries for search results and metadata. You can use $search.facet to retrieve both documents and metadata in a single query. If you only need metadata, use $searchMeta.facet to avoid document lookup. For an example, see the Metadata and Search Results example.

Use $limit Before $facet

Using a $limit aggregation pipeline stage after a $facet aggregation pipeline stage might negatively impact query performance. To avoid performance bottlenecks, use $limit before $facet.

Example

{
  {
    "$search": {...}
  },
  { "$limit": 20 },
  {
    "$facet": {
      "results": [],
      "totalCount": $$SEARCH_META
    }
  }
}

For a demonstration, see the following examples:

Minimize Additional MQL Aggregation Stages

Try to encapsulate the entire search logic within the $search stage itself and minimize using additional blocking stages, such as $group, $count, $match, or $sort. This optimizes the MongoDB Search index usage, and reduces the need for additional database operations in mongod.

Use compound.filter Instead of $match

For queries that require multiple filtering operations, use the compound Operator operator with filter clauses. If you must use the $match stage in your aggregation pipeline, consider using the storedSource option to store only the fields that your $match condition needs. You can then use the $search returnStoredSource option to retrieve stored fields and avoid the mongod full document lookup.

Tip

Use facet Instead of $group

If you use $group to get basic counts for field aggregations, you can use facet (MongoDB Search Operator) inside the $search stage. If you need only metadata results, you can use facet (MongoDB Search Operator) inside inside the $searchMeta stage instead.

Use count Instead of $count

If you use $count to get a count of the number of documents, we recommend that you use count inside the $search or $searchMeta stage instead.

Use sort, near, or returnStoredSource Instead of $sort

Avoid Use of $skip After $search

Using $skip and $limit to retrieve results non-sequentially might be slow if the results for your query are large. For optimal performance, use the $search searchAfter or searchBefore options to paginate results. To learn more, see Paginate the Results.

To return non-sequential results, such as jumping from page 2 to page 5, you can use the following pipeline stages:

  • $search searchAfter the last result on Page 2

  • $skip documents on Pages 3 and 4

  • $limit results for Page 5

Here, your query is optimized to skip only 2 pages of results, instead of skipping 4 pages if you didn't use searchAfter. For a demonstration of this, see Paginate the Results.

Use $search Instead of $text or $regex

For applications that rely heavily on MongoDB $text and $regex queries, use the following recommendations to determine whether to refactor your applications or migrate your applications to MongoDB Search $search. The $search aggregation pipeline stage provides features that are either not available through the MongoDB operators or are available through the MongoDB operators but not as performant as MongoDB Search $search.

The following table shows how MongoDB $regex, $text, and MongoDB Search $search address your application's requirements.

If your application requires...
Use...
Because...

Datastore to respect write concerns

For transactions with heavy reads after writes, we recommend $regex. For $search, reads after writes should be rare.

Cluster optimized for write performance

MongoDB Search indexes don't degrade cluster write performance.

Searching through large data sets

MongoDB Search uses an inverted index, which enables fast document retrieval at very large scales.

Language awareness

MongoDB Search supports many language analyzers that can tokenize (create searchable terms) languages, remove stopwords, and interpret diacritics for improved search relevance.

Case-insensitive text search

$search offers more capabilities than $regex.

Highlighting result text

MongoDB Search highlighting allows you to contextualize the documents in the results, which is essential for natural language queries.

Geospatial-aware search queries

MongoDB $regex and MongoDB Search $search treat geospatial parameters differently. In MongoDB, lines between coordinates are spherical, which is well-suited for coordinates for long distance such as air flight. MongoDB Search uses Lucene, which draws a straight line between coordinates and is well-suited for short distance.

Autocompletion of search queries

For autocomplete of characters (nGrams), MongoDB Search includes edgeGrams for left-to-right autocomplete, nGrams for autocomplete of languages that don't have whitespace, and rightEdgeGram for autocomplete of languages that you write and read right-to-left.

For autocomplete of words (wordGrams), MongoDB Search includes shingle token filter, which supports word-based autocomplete by concatenating adjacent words to create a single token.

Fuzzy matching on text input

MongoDB Search text and autocomplete operators support fuzzy matching to filter on input text and address misspelled words (typos).

Filtering based on multiple strings

MongoDB Search compound supports filtering based on multiple strings.

Relevance score sorted search

MongoDB Search uses the BM25 algorithm for determining the search relevance score of documents. It supports advanced configuration through boost expressions like multiply and gaussian decay, as well as analyzers, search operators, and synonyms. To learn more, see Modify the Score of the Documents in the Results.

Partial indexes

MongoDB Search supports partial indexing by using a View with a $match expression. To learn more, see Example: Filter Documents.

Patial match

MongoDB Search wildcard and autocomplete operators support partial match queries.

Single compound index on arrays

MongoDB Search term indexes are intersected in a single MongoDB Search index and eliminate the need for compound indexes for filtering on arrays.

Synonyms search

MongoDB Search supports synonyms defined in a separate collection, which you can reference in your search index for use. To learn more, see the How to Use Synonyms with MongoDB Search tutorial.

Faceting for counts

MongoDB Search provides fast counts of documents based on text criteria, and also supports faceted search for numbers and dates. To learn more, see How to Use Facets with MongoDB Search.

Extract metadata

MongoDB Search facet (MongoDB Search Operator) collector returns metadata and doesn't require you to run multiple queries for retrieving metadata. To learn more, see the How to Use Facets with MongoDB Search tutorial.

Custom analyzers

MongoDB Search supports custom analyzers to suit your specific indexing requirements. For example, you can index and search email addresses and HTTP or HTTPS URLs using custom analyzers.

Searching phrases or multiple words

MongoDB Search phrase operator supports searching for a sequence of terms.

Searching with regular expression

MongoDB Search provides improved performance when you use the MongoDB Search autocomplete operator instead.

Tip

Learn More

Monitor Performance

You can monitor your Atlas cluster and view charts with performance statistics on the Atlas Metrics tab. These metrics can help you see how MongoDB Search queries and index building affect your cluster's performance. To learn more, see Review MongoDB Search Metrics.

Atlas might trigger some Atlas alerts when:

  • MongoDB Search queries your clusters, which can impact Atlas performance metrics, such as the query targeting metrics.

The change streams cursors that the MongoDB Search process (mongot) uses to keep MongoDB Search indexes updated can contribute to the query targeting ratio and trigger query targeting alerts if the ratio is high.

  • MongoDB Search replicates data from MongoDB, which contributes to the metrics measured in Atlas, such as the number of getmore operations.

Note

If your cluster's resources are stretched or near the limits of acceptable performance, consider upgrading to a larger cluster tier before implementing MongoDB Search functionality.

Tip

Continue Learning

Follow along with this video to learn how to understand, iterate, and improve your MongoDB Search results using explain and $search score details.

Duration: 5 Minutes

Consider Query Operators and Query Complexity

The complexity level of MongoDB Search queries and the type of operators used can affect database performance for the node that mongot runs on.

Highly complex queries, such as queries with multiple clauses that use the compound operator, or queries which use the regex (regular expression) or the wildcard operator, are resource-intensive.

Compound Queries

If your query includes multiple nested compound statements, ensure that these are not redundant. If the clauses are added programmatically, consider implementing the logic in the application to avoid inclusion of redundant clauses in the queries. Every score calculation per field that mongot performs, such as for the must and should clauses, increases execution time. To optimize performance, place non-scoring operators such as equals, range, and in in the filter clause to avoid unnecessary scoring operations.

Faceted Search

You can use the MongoDB Search facet (MongoDB Search Operator) collector to extract metadata and avoid running multiple queries for search results and metadata. You can use $search.facet to retrieve both documents and metadata in a single query. If you only need metadata, use $searchMeta.facet to avoid document lookup. For an example, see the Metadata and Search Results example.

Use $limit Before $facet

Using a $limit aggregation pipeline stage after a $facet aggregation pipeline stage might negatively impact query performance. To avoid performance bottlenecks, use $limit before $facet.

Example

{
  {
    "$search": {...}
  },
  { "$limit": 20 },
  {
    "$facet": {
      "results": [],
      "totalCount": $$SEARCH_META
    }
  }
}

For a demonstration, see the following examples:

Minimize Additional MQL Aggregation Stages

Try to encapsulate the entire search logic within the $search stage itself and minimize using additional blocking stages, such as $group, $count, $match, or $sort. This optimizes the MongoDB Search index usage, and reduces the need for additional database operations in mongod.

Use compound.filter Instead of $match

For queries that require multiple filtering operations, use the compound Operator operator with filter clauses. If you must use the $match stage in your aggregation pipeline, consider using the storedSource option to store only the fields that your $match condition needs. You can then use the $search returnStoredSource option to retrieve stored fields and avoid the mongod full document lookup.

Tip

Use facet Instead of $group

If you use $group to get basic counts for field aggregations, you can use facet (MongoDB Search Operator) inside the $search stage. If you need only metadata results, you can use facet (MongoDB Search Operator) inside inside the $searchMeta stage instead.

Use count Instead of $count

If you use $count to get a count of the number of documents, we recommend that you use count inside the $search or $searchMeta stage instead.

Use sort, near, or returnStoredSource Instead of $sort

Avoid Use of $skip After $search

Using $skip and $limit to retrieve results non-sequentially might be slow if the results for your query are large. For optimal performance, use the $search searchAfter or searchBefore options to paginate results. To learn more, see Paginate the Results.

To return non-sequential results, such as jumping from page 2 to page 5, you can use the following pipeline stages:

  • $search searchAfter the last result on Page 2

  • $skip documents on Pages 3 and 4

  • $limit results for Page 5

Here, your query is optimized to skip only 2 pages of results, instead of skipping 4 pages if you didn't use searchAfter. For a demonstration of this, see Paginate the Results.

Use $search Instead of $text or $regex

For applications that rely heavily on MongoDB $text and $regex queries, use the following recommendations to determine whether to refactor your applications or migrate your applications to MongoDB Search $search. The $search aggregation pipeline stage provides features that are either not available through the MongoDB operators or are available through the MongoDB operators but not as performant as MongoDB Search $search.

The following table shows how MongoDB $regex, $text, and MongoDB Search $search address your application's requirements.

If your application requires...
Use...
Because...

Datastore to respect write concerns

For transactions with heavy reads after writes, we recommend $regex. For $search, reads after writes should be rare.

Cluster optimized for write performance

MongoDB Search indexes don't degrade cluster write performance.

Searching through large data sets

MongoDB Search uses an inverted index, which enables fast document retrieval at very large scales.

Language awareness

MongoDB Search supports many language analyzers that can tokenize (create searchable terms) languages, remove stopwords, and interpret diacritics for improved search relevance.

Case-insensitive text search

$search offers more capabilities than $regex.

Highlighting result text

MongoDB Search highlighting allows you to contextualize the documents in the results, which is essential for natural language queries.

Geospatial-aware search queries

MongoDB $regex and MongoDB Search $search treat geospatial parameters differently. In MongoDB, lines between coordinates are spherical, which is well-suited for coordinates for long distance such as air flight. MongoDB Search uses Lucene, which draws a straight line between coordinates and is well-suited for short distance.

Autocompletion of search queries

For autocomplete of characters (nGrams), MongoDB Search includes edgeGrams for left-to-right autocomplete, nGrams for autocomplete of languages that don't have whitespace, and rightEdgeGram for autocomplete of languages that you write and read right-to-left.

For autocomplete of words (wordGrams), MongoDB Search includes shingle token filter, which supports word-based autocomplete by concatenating adjacent words to create a single token.

Fuzzy matching on text input

MongoDB Search text and autocomplete operators support fuzzy matching to filter on input text and address misspelled words (typos).

Filtering based on multiple strings

MongoDB Search compound supports filtering based on multiple strings.

Relevance score sorted search

MongoDB Search uses the BM25 algorithm for determining the search relevance score of documents. It supports advanced configuration through boost expressions like multiply and gaussian decay, as well as analyzers, search operators, and synonyms. To learn more, see Modify the Score of the Documents in the Results.

Partial indexes

MongoDB Search supports partial indexing by using a View with a $match expression. To learn more, see Example: Filter Documents.

Patial match

MongoDB Search wildcard and autocomplete operators support partial match queries.

Single compound index on arrays

MongoDB Search term indexes are intersected in a single MongoDB Search index and eliminate the need for compound indexes for filtering on arrays.

Synonyms search

MongoDB Search supports synonyms defined in a separate collection, which you can reference in your search index for use. To learn more, see the How to Use Synonyms with MongoDB Search tutorial.

Faceting for counts

MongoDB Search provides fast counts of documents based on text criteria, and also supports faceted search for numbers and dates. To learn more, see How to Use Facets with MongoDB Search.

Extract metadata

MongoDB Search facet (MongoDB Search Operator) collector returns metadata and doesn't require you to run multiple queries for retrieving metadata. To learn more, see the How to Use Facets with MongoDB Search tutorial.

Custom analyzers

MongoDB Search supports custom analyzers to suit your specific indexing requirements. For example, you can index and search email addresses and HTTP or HTTPS URLs using custom analyzers.

Searching phrases or multiple words

MongoDB Search phrase operator supports searching for a sequence of terms.

Searching with regular expression

MongoDB Search provides improved performance when you use the MongoDB Search autocomplete operator instead.

Tip

Learn More

Continue Learning

Follow along with this video to learn how to understand, iterate, and improve your MongoDB Search results using explain and $search score details.

Duration: 5 Minutes

Consider Query Operators and Query Complexity

The complexity level of MongoDB Search queries and the type of operators used can affect database performance for the node that mongot runs on.

Highly complex queries, such as queries with multiple clauses that use the compound operator, or queries which use the regex (regular expression) or the wildcard operator, are resource-intensive.

Compound Queries

If your query includes multiple nested compound statements, ensure that these are not redundant. If the clauses are added programmatically, consider implementing the logic in the application to avoid inclusion of redundant clauses in the queries. Every score calculation per field that mongot performs, such as for the must and should clauses, increases execution time. To optimize performance, place non-scoring operators such as equals, range, and in in the filter clause to avoid unnecessary scoring operations.

Faceted Search

You can use the MongoDB Search facet (MongoDB Search Operator) collector to extract metadata and avoid running multiple queries for search results and metadata. You can use $search.facet to retrieve both documents and metadata in a single query. If you only need metadata, use $searchMeta.facet to avoid document lookup. For an example, see the Metadata and Search Results example.

Use $limit Before $facet

Using a $limit aggregation pipeline stage after a $facet aggregation pipeline stage might negatively impact query performance. To avoid performance bottlenecks, use $limit before $facet.

Example

{
  {
    "$search": {...}
  },
  { "$limit": 20 },
  {
    "$facet": {
      "results": [],
      "totalCount": $$SEARCH_META
    }
  }
}

For a demonstration, see the following examples:

Minimize Additional MQL Aggregation Stages

Try to encapsulate the entire search logic within the $search stage itself and minimize using additional blocking stages, such as $group, $count, $match, or $sort. This optimizes the MongoDB Search index usage, and reduces the need for additional database operations in mongod.

Use compound.filter Instead of $match

For queries that require multiple filtering operations, use the compound Operator operator with filter clauses. If you must use the $match stage in your aggregation pipeline, consider using the storedSource option to store only the fields that your $match condition needs. You can then use the $search returnStoredSource option to retrieve stored fields and avoid the mongod full document lookup.

Tip

Use facet Instead of $group

If you use $group to get basic counts for field aggregations, you can use facet (MongoDB Search Operator) inside the $search stage. If you need only metadata results, you can use facet (MongoDB Search Operator) inside inside the $searchMeta stage instead.

Use count Instead of $count

If you use $count to get a count of the number of documents, we recommend that you use count inside the $search or $searchMeta stage instead.

Use sort, near, or returnStoredSource Instead of $sort

Avoid Use of $skip After $search

Using $skip and $limit to retrieve results non-sequentially might be slow if the results for your query are large. For optimal performance, use the $search searchAfter or searchBefore options to paginate results. To learn more, see Paginate the Results.

To return non-sequential results, such as jumping from page 2 to page 5, you can use the following pipeline stages:

  • $search searchAfter the last result on Page 2

  • $skip documents on Pages 3 and 4

  • $limit results for Page 5

Here, your query is optimized to skip only 2 pages of results, instead of skipping 4 pages if you didn't use searchAfter. For a demonstration of this, see Paginate the Results.

Use $search Instead of $text or $regex

For applications that rely heavily on MongoDB $text and $regex queries, use the following recommendations to determine whether to refactor your applications or migrate your applications to MongoDB Search $search. The $search aggregation pipeline stage provides features that are either not available through the MongoDB operators or are available through the MongoDB operators but not as performant as MongoDB Search $search.

The following table shows how MongoDB $regex, $text, and MongoDB Search $search address your application's requirements.

If your application requires...
Use...
Because...

Datastore to respect write concerns

For transactions with heavy reads after writes, we recommend $regex. For $search, reads after writes should be rare.

Cluster optimized for write performance

MongoDB Search indexes don't degrade cluster write performance.

Searching through large data sets

MongoDB Search uses an inverted index, which enables fast document retrieval at very large scales.

Language awareness

MongoDB Search supports many language analyzers that can tokenize (create searchable terms) languages, remove stopwords, and interpret diacritics for improved search relevance.

Case-insensitive text search

$search offers more capabilities than $regex.

Highlighting result text

MongoDB Search highlighting allows you to contextualize the documents in the results, which is essential for natural language queries.

Geospatial-aware search queries

MongoDB $regex and MongoDB Search $search treat geospatial parameters differently. In MongoDB, lines between coordinates are spherical, which is well-suited for coordinates for long distance such as air flight. MongoDB Search uses Lucene, which draws a straight line between coordinates and is well-suited for short distance.

Autocompletion of search queries

For autocomplete of characters (nGrams), MongoDB Search includes edgeGrams for left-to-right autocomplete, nGrams for autocomplete of languages that don't have whitespace, and rightEdgeGram for autocomplete of languages that you write and read right-to-left.

For autocomplete of words (wordGrams), MongoDB Search includes shingle token filter, which supports word-based autocomplete by concatenating adjacent words to create a single token.

Fuzzy matching on text input

MongoDB Search text and autocomplete operators support fuzzy matching to filter on input text and address misspelled words (typos).

Filtering based on multiple strings

MongoDB Search compound supports filtering based on multiple strings.

Relevance score sorted search

MongoDB Search uses the BM25 algorithm for determining the search relevance score of documents. It supports advanced configuration through boost expressions like multiply and gaussian decay, as well as analyzers, search operators, and synonyms. To learn more, see Modify the Score of the Documents in the Results.

Partial indexes

MongoDB Search supports partial indexing by using a View with a $match expression. To learn more, see Example: Filter Documents.

Patial match

MongoDB Search wildcard and autocomplete operators support partial match queries.

Single compound index on arrays

MongoDB Search term indexes are intersected in a single MongoDB Search index and eliminate the need for compound indexes for filtering on arrays.

Synonyms search

MongoDB Search supports synonyms defined in a separate collection, which you can reference in your search index for use. To learn more, see the How to Use Synonyms with MongoDB Search tutorial.

Faceting for counts

MongoDB Search provides fast counts of documents based on text criteria, and also supports faceted search for numbers and dates. To learn more, see How to Use Facets with MongoDB Search.

Extract metadata

MongoDB Search facet (MongoDB Search Operator) collector returns metadata and doesn't require you to run multiple queries for retrieving metadata. To learn more, see the How to Use Facets with MongoDB Search tutorial.

Custom analyzers

MongoDB Search supports custom analyzers to suit your specific indexing requirements. For example, you can index and search email addresses and HTTP or HTTPS URLs using custom analyzers.

Searching phrases or multiple words

MongoDB Search phrase operator supports searching for a sequence of terms.

Searching with regular expression

MongoDB Search provides improved performance when you use the MongoDB Search autocomplete operator instead.

Tip

Learn More

Continue Learning

Follow along with this video to learn how to understand, iterate, and improve your MongoDB Search results using explain and $search score details.

Duration: 5 Minutes

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