Limit Number of Text Index Entries Scanned on Self-Managed Deployments

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Procedure
Results
View Number of Documents Examined
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If you perform text search queries on a large dataset, a single-field text index may scan a large number of entries to return results, which can result in slow queries.

To improve query performance, you can create a compound text index and include an equality match in your text search queries. If the compound index contains the field used in your equality match, the index scans fewer entries and returns results faster.

About this Task

In this example, a store manager queries an inventory collection that contains these documents:

db.inventory.insertMany( [
   { _id: 1, department: "tech", description: "lime green computer" },
   { _id: 2, department: "tech", description: "wireless red mouse" },
   { _id: 3, department: "kitchen", description: "green placemat" },
   { _id: 4, department: "kitchen", description: "red peeler" },
   { _id: 5, department: "food", description: "green apple" },
   { _id: 6, department: "food", description: "red potato" }
] )

The manager performs text search queries for items within a specific department.

A compound text index on the department and description fields limits the index keys scanned to only documents within the specified department. The compound text index provides improved performance compared to a single-field text index on the description field.

Procedure

Create a compound index on the inventory collection that contains the following fields:

An ascending or descending index key on the department field
A text index key on the description field

db.inventory.createIndex(
   {
     department: 1,
     description: "text"
   }
)

Results

After you create the compound index, text search queries only scan documents that match a specified equality condition on the department field.

For example, the following query scans documents with department equal to kitchen where the description field contains the string green:

db.inventory.find( { department: "kitchen", $text: { $search: "green" } } )

Output:

[ { _id: 3, department: 'kitchen', description: 'green placemat' } ]

View Number of Documents Examined

To see how many documents were scanned to return the query, view the query's executionStats:

db.inventory.find(
   {
      department: "kitchen", $text: { $search: "green" }
   }
).explain("executionStats")

The number of index keys examined is indicated in the totalKeysExamined field. Queries that examine more index keys generally take longer to complete.

With the compound index on department and description, the query only examines one index key. There is only one document in the collection where department is kitchen and the description contains the string green.

However, if the query used a single-field text index only on the description field, the query would examine three index keys. There are three documents in the collection where the description field contains the string green.

In a small collection like the one used in the preceding example, there isn't a noticeable difference in performance between single-field and compound text indexes. However, in larger collections, increased index entry scans can noticeably hinder performance. For best performance, create text indexes that limit the number of index entries scanned to best fit your equality matches.

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