For the most recent version of the reference documentation, see our MongoDB Java Driver documentation site.
- Builders
- Aggregates
Aggregates
The Aggregates
class provides static factory methods that build aggregation
pipeline operators. Each method returns an instance of the
Bson
type, which can in turn be passed to the aggregate
method of MongoCollection
.
For brevity, you may choose to import the methods of the Aggregates
class statically:
import static com.mongodb.client.model.Aggregates.*;
All the examples below assume this static import.
Match
The $match
pipeline stage passes all documents matching the
specified filter to the next stage. Though the filter can be an instance of any class that implements Bson
, it’s convenient to
combine with use of the Filters
class. In the example below, it’s assumed that the
eq
method of the Filters
class has been statically imported.
This example creates a pipeline stage that matches all documents where the author
field is equal to "Dave"
:
match(eq("author", "Dave"))
Project
The $project
pipeline stage passes the projected fields of all
documents to the next stage. Though the projection can be an instance of any class that implements Bson
, it’s convenient to combine
with use of the Projections
class. In the example below, it’s assumed that the
include
, excludeId
, and fields
methods of the Projections
class have been statically imported.
This example creates a pipeline stage that excludes the _id
field but includes the title
and author
fields:
project(fields(include("title", "author"), excludeId()))
Projecting Computed Fields
The $project
stage can project computed fields as well.
This example simply projects the qty
field into a new field called quantity
. In other words, it renames the field:
project(computed("quantity", "$qty"))
Sample
The $sample
pipeline stage randomly select N documents from its input.
This example creates a pipeline stage that randomly selects 5 documents from the collection:
sample(5)
Sort
The $sort
pipeline stage passes all documents to the next stage,
sorted by the specified sort criteria. Though the sort criteria can be an instance of any class that implements Bson
, it’s convenient to
combine with use of the Sorts
class. In the example below, it’s assumed that the
descending
, ascending
, and orderBy
methods of the Sorts
class have been statically imported.
This example creates a pipeline stage that sorts in descending order according to the value of the age
field and then in ascending order
according to the value of the posts
field:
sort(orderBy(descending("age"), ascending("posts")))
Skip
The $skip
pipeline stage skips over the specified number of
documents that pass into the stage and passes the remaining documents to the next stage.
This example skips the first 5
documents:
skip(5)
Limit
The $limit
pipeline stage limits the number of documents passed
to the next stage.
This example limits the number of documents to 10
:
limit(10)
Lookup
Starting in 3.2, MongoDB provides a new $lookup
pipeline stage
that performs a left outer join with another collection to filter in documents from the joined collection for processing.
This example performs a left outer join on the fromCollection
collection, joining the local
field to the from
field and outputted in
the joinedOutput
field:
lookup("fromCollection", "local", "from", "joinedOutput")
Starting in 3.6, the $lookup
pipeline stage also supports uncorrelated subqueries between two collections as well as allows other join
conditions besides a single equality match.
For example, given a collection orders
with the following documents:
{ "_id" : 1, "item" : "almonds", "price" : 12, "ordered" : 2 }
{ "_id" : 2, "item" : "pecans", "price" : 20, "ordered" : 1 }
{ "_id" : 3, "item" : "cookies", "price" : 10, "ordered" : 60 }
and another collection warehouses
with the following documents:
{ "_id" : 1, "stock_item" : "almonds", "warehouse": "A", "instock" : 120 }
{ "_id" : 2, "stock_item" : "pecans", "warehouse": "A", "instock" : 80 }
{ "_id" : 3, "stock_item" : "almonds", "warehouse": "B", "instock" : 60 }
{ "_id" : 4, "stock_item" : "cookies", "warehouse": "B", "instock" : 40 }
{ "_id" : 5, "stock_item" : "cookies", "warehouse": "A", "instock" : 80 }
The following $lookup
stage, executed in an aggregation against the orders
collection, joins with the warehouses
collection by the
item and whether the quantity in stock is sufficient to cover the ordered quantity:
List<Variable<?>> variables = asList(
new Variable<>("order_item", "$item"),
new Variable<>("order_qty", "$ordered"));
List<Bson> pipeline = asList(
match(expr(new Document("$and",
asList(new Document("$eq", asList("$stock_item", "$$order_item")),
new Document("$gte", asList("$instock", "$$order_qty")))))),
project(fields(exclude("stock_item"), excludeId())));
lookup("warehouses", variables, pipeline, "stockdata");
The aggregation produces the following documents:
{ "_id" : 1, "item" : "almonds", "price" : 12, "ordered" : 2,
"stockdata" : [ { "warehouse" : "A", "instock" : 120 }, { "warehouse" : "B", "instock" : 60 } ] }
{ "_id" : 2, "item" : "pecans", "price" : 20, "ordered" : 1,
"stockdata" : [ { "warehouse" : "A", "instock" : 80 } ] }
{ "_id" : 3, "item" : "cookies", "price" : 10, "ordered" : 60,
"stockdata" : [ { "warehouse" : "A", "instock" : 80 } ] }
Group
The $group
pipeline stage groups documents by some specified
expression and outputs to the next stage a document for each distinct grouping. A group consists of an _id
which specifies the
expression on which to group, and zero or more
accumulators which are evaluated for each
grouping. To simplify the expression of accumulators, the driver includes an
Accumulators
class with static factory methods for each of the supported
accumulators. In the example below, it’s assumed that the sum
and avg
methods of the Accumulators
class have been statically
imported.
This example groups documents by the value of the customerId
field, and for each group accumulates the sum and average of the values of
the quantity
field into the totalQuantity
and averageQuantity
fields, respectively.
group("$customerId", sum("totalQuantity", "$quantity"), avg("averageQuantity", "$quantity"))
Unwind
The $unwind
pipeline stage deconstructs an array field from the
input documents to output a document for each element.
This example outputs, for each document, a document for each element in the sizes
array:
unwind("$sizes")
Available with MongoDB 3.2, this example also includes any documents that have missing or null
values for the $sizes
field or where
the $sizes
list is empty:
unwind("$sizes", new UnwindOptions().preserveNullAndEmptyArrays(true))
Available with MongoDB 3.2, this example unwinds the sizes
array and also outputs the array index into the $position
field:
unwind("$sizes", new UnwindOptions().includeArrayIndex("$position"))
Out
The $out
pipeline stage outputs all documents to the specified
collection. It must be the last stage in any aggregate pipeline:
This example writes the pipeline to the authors
collection:
out("authors")
GraphLookup
The $graphLookup
pipeline stage performs a recursive search on a specified collection to match field A of one document to some field B of the other documents. For the matching documents, the stage repeats the search to match field A from the matching documents to the field B of the remaining documents until no new documents are encountered or until a specified depth. To each output document, $graphLookup
adds a new array field that contains the traversal results of the search for that document.
The following example computes the social network graph for users in the contacts
collection, recursively matching the value in the friends
field to the name
field, up to recursive depth of 1.
graphLookup("contacts", "$friends", "friends", "name", "socialNetwork",
new GraphLookupOptions().maxDepth(1))
Using GraphLookupOptions
, the output can be tailored to restrict the depth of the recursion as well to inject a field containing the depth of the recursion at which a document was included.
The recursive search can be filtered by specifying additional conditions.
graphLookup("contacts", "$friends", "friends", "name", "socialNetwork",
new GraphLookupOptions().maxDepth(1).restrictSearchWithMatch(Filters.eq("hobbies","golf"))
SortByCount
The $sortByCount
stage groups documents by a given expression and then sorts these groups by count in descending order. The sortByCount
outputs documents that contains an _id
field, which contains the discrete values of the given expression, and the count
field that contains the number of documents that fall into that group.
The following example groups documents by the truncated value of the field x
and computes the count for each distinct value of x
.
sortByCount(new Document("$floor", "$x"))
ReplaceRoot
The $replaceRoot
pipeline stage replaces each input document to the stage with the specified document. All existing fields, including the _id
field, are replaced.
If each input document to the replaceRoot
stage has a field a1
that contains a field b
whose value is a document, the following operation replaces each input document with the document in the b
field.
replaceRoot("$a1.b")
AddFields
The $addFields
pipeline stage adds new fields to documents. The stage outputs documents that contain all existing fields from the input documents and the newly added fields.
This example adds two new fields, myNewField
and z
to the input documents; myNewField
has the value {c: 3, d: 4}
, z
has the value 5.
addFields(new Field("myNewField", new Document("c", 3).append("d", 4)),
new Field("z", 5))
These new fields do not need be statically defined. The following example shows how to add a new field which is a function of the current document’s values. In this case, a new field alt3
is added with a value of true
if the current value of the field a
is less than 3. Otherwise, alt3
will be false
in the new field.
addFields(new Field("alt3", new Document("$lt", asList("$a", 3))))
Count
The $count
pipeline stage specifies the name of the field that will contain the number of documents that enter this stage. The $count
stage is syntactic sugar for: {$group:{_id:null, count:{$sum:1}}}
There are two ways to invoke this stage. The first way is to explicitly name the resulting field as in the two following examples:
count("count")
count("total")
These two invocations will put the count in the count
and total
fields respectively. If count
is the field name to be used, this can be shortened with the following convenience method:
count()
This invocation defaults the field name to count
.
Bucket
The $bucket
pipeline stage automates the bucketing of data around predefined boundary values.
The following example shows a basic $bucket
stage:
bucket("$screenSize", [0, 24, 32, 50, 70, 200])
This will result in output that looks like this:
[_id:0, count:1]
[_id:24, count:2]
[_id:32, count:1]
[_id:50, count:1]
[_id:70, count:2]
The default output is simply the lower bound as the _id
and a single field containing the size of that bucket. This output can be modified using the BucketOptions
class. The above example can be expanded to look like this:
bucket("$screenSize", [0, 24, 32, 50, 70], new BucketOptions()
.defaultBucket("monster")
.output(sum("count", 1), push("matches", "$screenSize")))
The optional value defaultBucket
defines the name of the bucket for values that fall outside defined bucket boundaries. If defaultBucket
is undefined and values exist outside of the defined bucket boundaries, the stage will produce an error. The other value is the output
field which defines the shape of the document output for each bucket. The output of this stage looks something like this:
[[_id: 0, count: 1, matches: [22]],
[_id: 24, count: 2, matches: [24, 30]],
[_id: 32, count: 1, matches: [42]],
[_id: 50, count: 1, matches: [55]],
[_id: monster, count: 2, matches: [75, 155]]]
This output contains not only the size of the bucket but also the values in the bucket. Notice the enormous screen sizes are found in the synthetic bucket named monster
reflecting the outrageously large screen sizes.
BucketAuto
The $bucketAuto
pipeline stage automatically determines the boundaries of each bucket in its attempt to distribute the documents evenly into a specified number of buckets. Depending on the input documents, the number of buckets may be less than the specified number of buckets.
For example, this stage creates 10 buckets:
bucketAuto("$price", 10)
This results in output that looks something like this:
[[_id: [min: 2, max: 30], count: 14],
[_id: [min: 30, max: 58], count: 14],
[_id: [min: 58, max: 86], count: 14],
[_id: [min: 86, max: 114], count: 14],
[_id: [min: 114, max: 142], count: 14],
[_id: [min: 142, max: 170], count: 14],
[_id: [min: 170, max: 198], count: 14],
[_id: [min: 198, max: 226], count: 14],
[_id: [min: 226, max: 254], count: 14],
[_id: [min: 254, max: 274], count: 11]]
Note the uniformity of bucket sizes except for the last bucket. For a more precise scheme of bucket definition, the BucketAutoOptions
class exposes the opportunity to use a preferred number based scheme to determine those boundary values. As with BucketOptions
, the output document shape can be defined using the output
value on BucketAutoOptions
. An example of these options is shown below:
bucketAuto("$price", 10, new BucketAutoOptions()
.granularity(BucketGranularity.POWERSOF2)
.output(sum("count", 1), avg("avgPrice", "$price")))
Facet
The $facet
pipeline stage allows for the definition of a faceted search. The stage is defined with a set of names and nested aggregation pipelines which define each particular facet. For example, to return to the example of the television screen size search, the following $facet
will return a document that groups televisions by size and manufacturer:
facet(
new Facet("Screen Sizes",
unwind("$attributes"),
bucketAuto("$attributes.screen_size", 5, new BucketAutoOptions()
.output(sum("count", 1)))),
new Facet("Manufacturer",
sortByCount("$attributes.manufacturer"),
limit(5))
)
This stage returns a document that looks like this:
{
"Manufacturer": [
{"_id": "Vizio", "count": 17},
{"_id": "Samsung", "count": 17},
{"_id": "Sony", "count": 17}
],
"Screen Sizes": [
{"_id": {"min": 35, "max": 45}, "count": 10},
{"_id": {"min": 45, "max": 55}, "count": 10},
{"_id": {"min": 55, "max": 65}, "count": 10},
{"_id": {"min": 65, "max": 75}, "count": 10},
{"_id": {"min": 75, "max": 85}, "count": 11}
]
}
Creating a Pipeline
The above pipeline operators are typically combined into a list and passed to the aggregate
method of a MongoCollection
. For instance:
collection.aggregate(Arrays.asList(match(eq("author", "Dave")),
group("$customerId", sum("totalQuantity", "$quantity"),
avg("averageQuantity", "$quantity"))
out("authors")));