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Sparse Indexes¶
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Sparse indexes only contain entries for documents that have the indexed field, even if the index field contains a null value. The index skips over any document that is missing the indexed field. The index is “sparse” because it does not include all documents of a collection. By contrast, non-sparse indexes contain all documents in a collection, storing null values for those documents that do not contain the indexed field.
Important
Changed in version 3.2: Starting in MongoDB 3.2, MongoDB provides the option to create partial indexes. Partial indexes offer a superset of the functionality of sparse indexes. If you are using MongoDB 3.2 or later, partial indexes should be preferred over sparse indexes.
Create a Sparse Index¶
To create a sparse index, use the
db.collection.createIndex() method with the sparse option
set to true. For example, the following operation in the
mongo shell creates a sparse index on the xmpp_id field
of the addresses collection:
db.addresses.createIndex( { "xmpp_id": 1 }, { sparse: true } )
The index does not index documents that do not include the xmpp_id
field.
Note
Do not confuse sparse indexes in MongoDB with block-level indexes in other databases. Think of them as dense indexes with a specific filter.
Behavior¶
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.
Changed in version 3.4.
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.insert({ _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¶
2dsphere (version 2), 2d,
geoHaystack, and text indexes are always sparse.
sparse Compound Indexes¶
Sparse compound indexes that only contain ascending/descending index keys will index a document as long as the document contains at least one of the keys.
For sparse compound indexes that contain a geospatial key (i.e. 2dsphere, 2d, or geoHaystack index keys) along with ascending/descending index key(s), only the existence of the geospatial field(s) in a document determine whether the index references the document.
For sparse compound indexes that contain text
index keys along with ascending/descending index keys, only the
existence of the text index field(s) determine whether the index
references a document.
Examples¶
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 }
See also
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.insert( { "userid": "AAAAAAA", "score": 43 } )
db.scores.insert( { "userid": "BBBBBBB", "score": 34 } )
db.scores.insert( { "userid": "CCCCCCC" } )
db.scores.insert( { "userid": "DDDDDDD" } )
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.insert( { "userid": "AAAAAAA", "score": 82 } )
db.scores.insert( { "userid": "BBBBBBB", "score": 90 } )