Sparse Indexes

Sparse Index and Incomplete Results

If a sparse index would result in an incomplete result set for queries and sort operations, MongoDB will not use that index unless a hint() explicitly specifies the index.

For example, the query { x: { $exists: false } } will not use a sparse index on the x field unless explicitly hinted. See Sparse Index On A Collection Cannot Return Complete Results for an example that details the behavior.

If you include a hint() that specifies a sparse index when you perform a count() of all documents in a collection (i.e. with an empty query predicate), the sparse index is used even if the sparse index results in an incorrect count.

db.collection.insertOne( { _id: 1, y: 1 } );
db.collection.createIndex( { x: 1 }, { sparse: true } );
db.collection.find().hint( { x: 1 } ).count();

To obtain the correct count, do not hint() with a sparse index when performing a count of all documents in a collection.

db.collection.find().count();
db.collection.createIndex( { y: 1 } );
db.collection.find().hint( { y: 1 } ).count();

Indexes that are Sparse by Default

The following index types are always sparse:

• 2d

• 2dsphere (version 2)

• Geohaystack

• Text

• Wildcard

Sparse Compound Indexes

Compound indexes can contain different types of sparse indexes. The combination of index types determines how the compound index matches documents.

This table summarizes the behavior of a compound index that contains different types of sparse indexes:

sparse and unique Properties

An index that is both sparse and unique prevents a collection from having documents with duplicate values for a field but allows multiple documents that omit the key.

Create a Sparse Index On A Collection

Consider a collection scores that contains the following documents:

{ "_id" : ObjectId("523b6e32fb408eea0eec2647"), "userid" : "newbie" }
{ "_id" : ObjectId("523b6e61fb408eea0eec2648"), "userid" : "abby", "score" : 82 }
{ "_id" : ObjectId("523b6e6ffb408eea0eec2649"), "userid" : "nina", "score" : 90 }

The collection has a sparse index on the field score:

db.scores.createIndex( { score: 1 } , { sparse: true } )

Then, the following query on the scores collection uses the sparse index to return the documents that have the score field less than ($lt) 90:

db.scores.find( { score: { $lt: 90 } } )

Because the document for the userid "newbie" does not contain the score field and thus does not meet the query criteria, the query can use the sparse index to return the results:

{ "_id" : ObjectId("523b6e61fb408eea0eec2648"), "userid" : "abby", "score" : 82 }

Sparse Index On A Collection Cannot Return Complete Results

Consider a collection scores that contains the following documents:

{ "_id" : ObjectId("523b6e32fb408eea0eec2647"), "userid" : "newbie" }
{ "_id" : ObjectId("523b6e61fb408eea0eec2648"), "userid" : "abby", "score" : 82 }
{ "_id" : ObjectId("523b6e6ffb408eea0eec2649"), "userid" : "nina", "score" : 90 }

The collection has a sparse index on the field score:

db.scores.createIndex( { score: 1 } , { sparse: true } )

Because the document for the userid "newbie" does not contain the score field, the sparse index does not contain an entry for that document.

Consider the following query to return all documents in the scores collection, sorted by the score field:

db.scores.find().sort( { score: -1 } )

Even though the sort is by the indexed field, MongoDB will not select the sparse index to fulfill the query in order to return complete results:

{ "_id" : ObjectId("523b6e6ffb408eea0eec2649"), "userid" : "nina", "score" : 90 }
{ "_id" : ObjectId("523b6e61fb408eea0eec2648"), "userid" : "abby", "score" : 82 }
{ "_id" : ObjectId("523b6e32fb408eea0eec2647"), "userid" : "newbie" }

To use the sparse index, explicitly specify the index with hint():

db.scores.find().sort( { score: -1 } ).hint( { score: 1 } )

The use of the index results in the return of only those documents with the score field:

{ "_id" : ObjectId("523b6e6ffb408eea0eec2649"), "userid" : "nina", "score" : 90 }
{ "_id" : ObjectId("523b6e61fb408eea0eec2648"), "userid" : "abby", "score" : 82 }

Sparse Index with Unique Constraint

Consider a collection scores that contains the following documents:

{ "_id" : ObjectId("523b6e32fb408eea0eec2647"), "userid" : "newbie" }
{ "_id" : ObjectId("523b6e61fb408eea0eec2648"), "userid" : "abby", "score" : 82 }
{ "_id" : ObjectId("523b6e6ffb408eea0eec2649"), "userid" : "nina", "score" : 90 }

You could create an index with a unique constraint and sparse filter on the score field using the following operation:

db.scores.createIndex( { score: 1 } , { sparse: true, unique: true } )

This index would permit the insertion of documents that had unique values for the score field or did not include a score field. As such, given the existing documents in the scores collection, the index permits the following insert operations:

db.scores.insertMany( [
   { "userid": "newbie", "score": 43 },
   { "userid": "abby", "score": 34 },
   { "userid": "nina" }
] )

However, the index would not permit the addition of the following documents since documents already exists with score value of 82 and 90:

db.scores.insertMany( [
   { "userid": "newbie", "score": 82 },
   { "userid": "abby", "score": 90 }
] )

Sparse and Non-Sparse Unique Indexes

Starting in MongoDB 5.0, unique sparse and unique non-sparse indexes with the same key pattern can exist on a single collection.

db.scoreHistory.createIndex( { score : 1 }, { name: "unique_index", unique: true } )
db.scoreHistory.createIndex( { score : 1 }, { name: "unique_sparse_index", unique: true, sparse: true } )

db.scoreHistory.createIndex( { score : 1 }, { name: "sparse_index", sparse: true } )
db.scoreHistory.createIndex( { score : 1 }, { name: "basic_index" } )