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Use Indexes to Sort Query Results

Use Indexes to Sort Query Results¶

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In MongoDB, sort operations can obtain the sort order by retrieving documents based on the ordering in an index. If the query planner cannot obtain the sort order from an index, it will sort the results in memory. Sort operations that use an index often have better performance than those that do not use an index. In addition, sort operations that do not use an index will abort when they use 32 megabytes of memory.

Sort with a Single Field Index¶

If an ascending or a descending index is on a single field, the sort operation on the field can be in either direction.

For example, create an ascending index on the field a for a collection records:

db.records.createIndex( { a: 1 } )

This index can support an ascending sort on a:

db.records.find().sort( { a: 1 } )

The index can also support the following descending sort on a by traversing the index in reverse order:

db.records.find().sort( { a: -1 } )

Sort on Multiple Fields¶

Create a compound index to support sorting on multiple fields.

You can specify a sort on all the keys of the index or on a subset; however, the sort keys must be listed in the same order as they appear in the index. For example, an index key pattern { a: 1, b: 1 } can support a sort on { a: 1, b: 1 } but not on { b: 1, a: 1 }.

For a query to use a compound index for a sort, the specified sort direction for all keys in the cursor.sort() document must match the index key pattern or match the inverse of the index key pattern. For example, an index key pattern { a: 1, b: -1 } can support a sort on { a: 1, b: -1 } and { a: -1, b: 1 } but not on { a: -1, b: -1 } or {a: 1, b: 1}.

Sort and Index Prefix¶

If the sort keys correspond to the index keys or an index prefix, MongoDB can use the index to sort the query results. A prefix of a compound index is a subset that consists of one or more keys at the start of the index key pattern.

For example, create a compound index on the data collection:

db.data.createIndex( { a:1, b: 1, c: 1, d: 1 } )

Then, the following are prefixes for that index:

{ a: 1 }
{ a: 1, b: 1 }
{ a: 1, b: 1, c: 1 }

The following query and sort operations use the index prefixes to sort the results. These operations do not need to sort the result set in memory.

Example	Index Prefix
`db.data.find().sort( { a: 1 } )`	`{ a: 1 }`
`db.data.find().sort( { a: -1 } )`	`{ a: 1 }`
`db.data.find().sort( { a: 1, b: 1 } )`	`{ a: 1, b: 1 }`
`db.data.find().sort( { a: -1, b: -1 } )`	`{ a: 1, b: 1 }`
`db.data.find().sort( { a: 1, b: 1, c: 1 } )`	`{ a: 1, b: 1, c: 1 }`
`db.data.find( { a: { $gt: 4 } } ).sort( { a: 1, b: 1 } )`	`{ a: 1, b: 1 }`

Consider the following example in which the prefix keys of the index appear in both the query predicate and the sort:

db.data.find( { a: { $gt: 4 } } ).sort( { a: 1, b: 1 } )

In such cases, MongoDB can use the index to retrieve the documents in order specified by the sort. As the example shows, the index prefix in the query predicate can be different from the prefix in the sort.

Sort and Non-prefix Subset of an Index¶

An index can support sort operations on a non-prefix subset of the index key pattern. To do so, the query must include equality conditions on all the prefix keys that precede the sort keys.

For example, the collection data has the following index:

{ a: 1, b: 1, c: 1, d: 1 }

The following operations can use the index to get the sort order:

Example	Index Prefix
`db.data.find( { a: 5 } ).sort( { b: 1, c: 1 } )`	`{ a: 1 , b: 1, c: 1 }`
`db.data.find( { b: 3, a: 4 } ).sort( { c: 1 } )`	`{ a: 1, b: 1, c: 1 }`
`db.data.find( { a: 5, b: { $lt: 3} } ).sort( { b: 1 } )`	`{ a: 1, b: 1 }`

As the last operation shows, only the index fields preceding the sort subset must have the equality conditions in the query document; the other index fields may specify other conditions.

If the query does not specify an equality condition on an index prefix that precedes or overlaps with the sort specification, the operation will not efficiently use the index. For example, the following operations specify a sort document of { c: 1 }, but the query documents do not contain equality matches on the preceding index fields a and b:

db.data.find( { a: { $gt: 2 } } ).sort( { c: 1 } )
db.data.find( { c: 5 } ).sort( { c: 1 } )

These operations will not efficiently use the index { a: 1, b: 1, c: 1, d: 1 } and may not even use the index to retrieve the documents.

Index Use and Collation¶

To use an index for string comparisons, an operation must also specify the same collation. That is, an index with a collation cannot support an operation that performs string comparisons on the indexed fields if the operation specifies a different collation.

For example, the collection myColl has an index on a string field category with the collation locale "fr".

db.myColl.createIndex( { category: 1 }, { collation: { locale: "fr" } } )

The following query operation, which specifies the same collation as the index, can use the index:

db.myColl.find( { category: "cafe" } ).collation( { locale: "fr" } )

However, the following query operation, which by default uses the “simple” binary collator, cannot use the index:

db.myColl.find( { category: "cafe" } )

For a compound index where the index prefix keys are not strings, arrays, and embedded documents, an operation that specifies a different collation can still use the index to support comparisons on the index prefix keys.

For example, the collection myColl has a compound index on the numeric fields score and price and the string field category; the index is created with the collation locale "fr" for string comparisons:

db.myColl.createIndex(
   { score: 1, price: 1, category: 1 },
   { collation: { locale: "fr" } } )

The following operations, which use "simple" binary collation for string comparisons, can use the index:

db.myColl.find( { score: 5 } ).sort( { price: 1 } )
db.myColl.find( { score: 5, price: { $gt: NumberDecimal( "10" ) } } ).sort( { price: 1 } )

The following operation, which uses "simple" binary collation for string comparisons on the indexed category field, can use the index to fulfill only the score: 5 portion of the query:

db.myColl.find( { score: 5, category: "cafe" } )

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