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Use Atlas Search for Full-Text Search Queries

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If your queries rely on inefficient regex matching, Atlas Search queries that use the $search aggregation pipeline stage can significantly improve the performance of text queries and offer more options for customizing query parameters.

If you frequently run case-insensitive regex queries (utilizing the i option), we recommend Atlas Search queries that use the $search aggregation pipeline stage. Alternatively, you can create a case-insensitive index to support your queries. You can also specify a collation on an index to define language-specific rules for string comparison, such as rules for lettercase and accent marks. However, collation can cause some functionality loss compared to Atlas Search queries. While a case-insensitive index improves performance for case-insensitive queries, Atlas Search queries more significiantly improve the performance of text queries and offer more options for customizing query parameters.

Consider an employees collection with the following documents. This collection has no indexes besides the default _id index:

// employees collection
{
"_id": 1,
"first_name": "Hannah",
"last_name": "Simmons",
"dept": "Engineering"
},
{
"_id": 2,
"first_name": "Michael",
"last_name": "Hughes",
"dept": "Security"
},
{
"_id": 3,
"first_name": "Wendy",
"last_name": "Crawford",
"dept": "Human Resources"
},
{
"_id": 4,
"first_name": "MICHAEL",
"last_name": "FLORES",
"dept": "Sales"
}

If your application frequently queries the first_name field, you may want to run case-insensitive regex queries to more easily find matching names. Case-insensitive regex also matches against differing data formats, as in the example above where you have first_names of both "Michael" and "MICHAEL". However, we recommend Atlas Search queries that use the $search aggregation pipeline stage.

If a user searches for the string "michael", the application may run the following query:

db.employees.find( { first_name: { $regex: /michael/i } } )

Since this query specifies the $regex option i, it is case-insensitive. The query returns the following documents:

{ "_id" : 2, "first_name" : "Michael", "last_name" : "Hughes", "dept" : "Security" }
{ "_id" : 4, "first_name" : "MICHAEL", "last_name" : "FLORES", "dept" : "Sales" }

Although this query does return the expected documents, case-insensitive regex queries with no index support are not very performant.

To improve performance, create an Atlas Search index. While you can create a case-insensitive index on the first_name field, Atlas Search queries more significantly improve the performance of text queries and offer more options for customizing query parameters.

Regex Index
Atlas Search Index
db.employees.createIndex(
{ first_name: 1 },
{ collation: { locale: 'en', strength: 2 } }
)
{
"mappings": {
"dynamic": true
}
}

Collation can cause some functionality loss. When the strength field of an index's collation document is 1 or 2, the index is case-insensitive. For a detailed description of the collation document and the different strength values, see Collation Document.

For the application to use the case-insesitive index, you must also specify the same collation document from the index in the regex query. While you can remove the $regex operator from the previous find() method and use the newly created index, we recommend that you use an Atlas Search query that uses the $search aggregation pipeline stage.

Regex Query
Atlas Search Query
db.employees.find( { first_name: "michael" } ).collation( { locale: 'en', strength: 2 } )
db.employees.aggregate([
{
$search: {
"index": "default",
"text": {
"path": "first_name",
"query": "michael"
}
}
}
])

Important

Do not use the $regex operator when using a case-insensitive index for your query. The $regex implementation is not collation-aware and cannot utilize case-insensitive indexes. Instead, we recommend Atlas Search queries that use the $search aggregation pipeline stage.

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