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$sort (aggregation)

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  • Definition
  • Behavior
  • Examples
  • $sort Operator and Memory
  • $sort Operator and Performance
$sort

Sorts all input documents and returns them to the pipeline in sorted order.

The $sort stage has the following prototype form:

{ $sort: { <field1>: <sort order>, <field2>: <sort order> ... } }

$sort takes a document that specifies the field(s) to sort by and the respective sort order. <sort order> can have one of the following values:

Value
Description
1
Sort ascending.
-1
Sort descending.
{ $meta: "textScore" }
Sort by the computed textScore metadata in descending order. See Text Score Metadata Sort for an example.

If sorting on multiple fields, sort order is evaluated from left to right. For example, in the form above, documents are first sorted by <field1>. Then documents with the same <field1> values are further sorted by <field2>.

MongoDB does not store documents in a collection in a particular order. When sorting on a field which contains duplicate values, documents containing those values may be returned in any order.

If consistent sort order is desired, include at least one field in your sort that contains unique values. The easiest way to guarantee this is to include the _id field in your sort query.

Consider the following restaurant collection:

db.restaurants.insertMany( [
{ "_id" : 1, "name" : "Central Park Cafe", "borough" : "Manhattan"},
{ "_id" : 2, "name" : "Rock A Feller Bar and Grill", "borough" : "Queens"},
{ "_id" : 3, "name" : "Empire State Pub", "borough" : "Brooklyn"},
{ "_id" : 4, "name" : "Stan's Pizzaria", "borough" : "Manhattan"},
{ "_id" : 5, "name" : "Jane's Deli", "borough" : "Brooklyn"},
] );

The following command uses the $sort stage to sort on the borough field:

db.restaurants.aggregate(
[
{ $sort : { borough : 1 } }
]
)

In this example, sort order may be inconsistent, since the borough field contains duplicate values for both Manhattan and Brooklyn. Documents are returned in alphabetical order by borough, but the order of those documents with duplicate values for borough might not the be the same across multiple executions of the same sort. For example, here are the results from two different executions of the above command:

{ "_id" : 3, "name" : "Empire State Pub", "borough" : "Brooklyn" }
{ "_id" : 5, "name" : "Jane's Deli", "borough" : "Brooklyn" }
{ "_id" : 1, "name" : "Central Park Cafe", "borough" : "Manhattan" }
{ "_id" : 4, "name" : "Stan's Pizzaria", "borough" : "Manhattan" }
{ "_id" : 2, "name" : "Rock A Feller Bar and Grill", "borough" : "Queens" }
{ "_id" : 5, "name" : "Jane's Deli", "borough" : "Brooklyn" }
{ "_id" : 3, "name" : "Empire State Pub", "borough" : "Brooklyn" }
{ "_id" : 4, "name" : "Stan's Pizzaria", "borough" : "Manhattan" }
{ "_id" : 1, "name" : "Central Park Cafe", "borough" : "Manhattan" }
{ "_id" : 2, "name" : "Rock A Feller Bar and Grill", "borough" : "Queens" }

While the values for borough are still sorted in alphabetical order, the order of the documents containing duplicate values for borough (i.e. Manhattan and Brooklyn) is not the same.

To achieve a consistent sort, add a field which contains exclusively unique values to the sort. The following command uses the $sort stage to sort on both the borough field and the _id field:

db.restaurants.aggregate(
[
{ $sort : { borough : 1, _id: 1 } }
]
)

Since the _id field is always guaranteed to contain exclusively unique values, the returned sort order will always be the same across multiple executions of the same sort.

For the field or fields to sort by, set the sort order to 1 or -1 to specify an ascending or descending sort respectively, as in the following example:

db.users.aggregate(
[
{ $sort : { age : -1, posts: 1 } }
]
)

This operation sorts the documents in the users collection, in descending order according by the age field and then in ascending order according to the value in the posts field.

When comparing values of different BSON types, MongoDB uses the following comparison order, from lowest to highest:

  1. MinKey (internal type)
  2. Null
  3. Numbers (ints, longs, doubles, decimals)
  4. Symbol, String
  5. Object
  6. Array
  7. BinData
  8. ObjectId
  9. Boolean
  10. Date
  11. Timestamp
  12. Regular Expression
  13. MaxKey (internal type)

For details on the comparison/sort order for specific types, see Comparison/Sort Order.

For a pipeline that includes a $text search, you can sort by descending relevance score using the { $meta: "textScore" } expression. In the { <sort-key> } document, set the { $meta: "textScore" } expression to an arbitrary field name. The field name is ignored by the query system. For example:

db.users.aggregate(
[
{ $match: { $text: { $search: "operating" } } },
{ $sort: { score: { $meta: "textScore" }, posts: -1 } }
]
)

This operation uses the $text operator to match the documents, and then sorts first by the "textScore" metadata in descending order, and then by the posts field in descending order. The score field name in the sort document is ignored by the query system. In this pipeline, the "textScore" metadata is not included in the projection and is not returned as part of the matching documents. See $meta for more information.

When a $sort precedes a $limit and there are no intervening stages that modify the number of documents, the optimizer can coalesce the $limit into the $sort. This allows the $sort operation to only maintain the top n results as it progresses, where n is the specified limit, and ensures that MongoDB only needs to store n items in memory. This optimization still applies when allowDiskUse is true and the n items exceed the aggregation memory limit.

Optimizations are subject to change between releases.

The $sort stage has a limit of 100 megabytes of RAM for in-memory sorts. By default, if the stage exceeds this limit, $sort produces an error. To allow pipeline processing to take up more space, use the allowDiskUse option to enable aggregation pipeline stages to write data to temporary files.

Tip
See also:

The $sort operator can take advantage of an index if it's used in the first stage of a pipeline or if it's only preceeded by a $match stage.

When you use the $sort on a sharded cluster, each shard sorts its result documents using an index where available. Then the mongos or one of the shards performs a streamed merge sort.

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