Aggregates Builders

Overview

In this guide, you can learn how to use the Aggregates class which provides static factory methods that build aggregation pipeline stages in the MongoDB Kotlin driver.

For a more thorough introduction to Aggregation, see our Aggregation guide.

The examples on this page assume imports for methods of the following classes:

• Aggregates

• Filters

• Projections

• Sorts

• Accumulators

import com.mongodb.client.model.Aggregates
import com.mongodb.client.model.Filters
import com.mongodb.client.model.Projections
import com.mongodb.client.model.Sorts
import com.mongodb.client.model.Accumulators

Use these methods to construct pipeline stages and specify them in your aggregation as a list:

val matchStage = Aggregates.match(Filters.eq("someField", "someCriteria"))
val sortByCountStage = Aggregates.sortByCount("\$someField")
val results = collection.aggregate(
    listOf(matchStage, sortByCountStage)).toList()

Many Aggregation examples in this guide use the Atlas sample_mflix.movies dataset. The documents in this collection are modeled by the following Movie data class for use with the Kotlin driver:

data class Movie(
    val title: String,
    val year: Int,
    val genres: List<String>,
    val rated: String,
    val plot: String,
    val runtime: Int,
    val imdb: IMDB
){
    data class IMDB(
        val rating: Double
    )
}

Match

Use the match() method to create a $match pipeline stage that matches incoming documents against the specified query filter, filtering out documents that do not match.

The following example creates a pipeline stage that matches all documents in the movies collection where the title field is equal to "The Shawshank Redemption":

Aggregates.match(Filters.eq(Movie::title.name, "The Shawshank Redemption"))

Project

Use the project() method to create a $project pipeline stage that project specified document fields. Field projection in aggregation follows the same rules as field projection in queries.

The following example creates a pipeline stage that includes the title and plot fields but excludes the _id field:

Aggregates.project(
    Projections.fields(
        Projections.include(Movie::title.name, Movie::plot.name),
        Projections.excludeId())
)

Aggregates.project(
    Projections.fields(
        Projections.computed("rating", "\$${Movie::rated.name}"),
        Projections.excludeId()
    )
)

Documents

Use the documents() method to create a $documents pipeline stage that returns literal documents from input values.

The following example creates a pipeline stage that creates sample documents in the movies collection with a title field:

Aggregates.documents(
    listOf(
        Document(Movie::title.name, "Steel Magnolias"),
        Document(Movie::title.name, "Back to the Future"),
        Document(Movie::title.name, "Jurassic Park")
    )
)

val docsStage = database.aggregate<Document>( // ... )

Sample

Use the sample() method to create a $sample pipeline stage to randomly select documents from input.

The following example creates a pipeline stage that randomly selects 5 documents from the movies collection:

Aggregates.sample(5)

Sort

Use the sort() method to create a $sort pipeline stage to sort by the specified criteria.

The following example creates a pipeline stage that sorts in descending order according to the value of the year field and then in ascending order according to the value of the title field:

Aggregates.sort(
    Sorts.orderBy(
        Sorts.descending(Movie::year.name),
        Sorts.ascending(Movie::title.name)
    )
)

Skip

Use the skip() method to create a $skip pipeline stage to skip over the specified number of documents before passing documents into the next stage.

The following example creates a pipeline stage that skips the first 5 documents in the movies collection:

Aggregates.skip(5)

Limit

Use the $limit pipeline stage to limit the number of documents passed to the next stage.

The following example creates a pipeline stage that limits the number of documents returned from the movies collection to 4:

Aggregates.limit(4)

Lookup

Use the lookup() method to create a $lookup pipeline stage to perform joins and uncorrelated subqueries between two collections.

Aggregates.lookup(
    "comments",
    "_id",
    "movie_id",
    "joined_comments"
)

data class Order(
    @BsonId val id: Int,
    val customerId: Int,
    val item: String,
    val ordered: Int
)
data class Inventory(
    @BsonId val id: Int,
    val stockItem: String,
    val inStock: Int
)

val variables = listOf(
    Variable("order_item", "\$item"),
    Variable("order_qty", "\$ordered")
)
val pipeline = listOf(
    Aggregates.match(
        Filters.expr(
            Document("\$and", listOf(
                Document("\$eq", listOf("$\$order_item", "\$${Inventory::stockItem.name}")),
                Document("\$gte", listOf("\$${Inventory::inStock.name}", "$\$order_qty"))
            ))
        )
    ),
    Aggregates.project(
        Projections.fields(
            Projections.exclude(Order::customerId.name, Inventory::stockItem.name),
            Projections.excludeId()
        )
    )
)
val innerJoinLookup =
    Aggregates.lookup("warehouses", variables, pipeline, "stockData")

Group

Use the group() method to create a $group pipeline stage to group documents by a specified expression and output a document for each distinct grouping.

The following example creates a pipeline stage that groups documents in the orders collection by the value of the customerId field. Each group accumulates the sum and average of the values of the ordered field into the totalQuantity and averageQuantity fields:

Aggregates.group("\$${Order::customerId.name}",
    Accumulators.sum("totalQuantity", "\$${Order::ordered.name}"),
    Accumulators.avg("averageQuantity", "\$${Order::ordered.name}")
)

Learn more about accumulator operators from the Server manual section on Accumulators.

Pick-N Accumulators

The pick-n accumulators are aggregation accumulation operators that return the top and bottom elements given a specific ordering. Use one of the following builders to create an aggregation accumulation operator:

• minN()

• maxN()

• firstN()

• lastN()

• top()

• topN()

• bottom()

• bottomN()

Learn which aggregation pipeline stages you can use accumulator operators with from the Server manual section on Accumulators.

The pick-n accumulator examples use documents from the movies collection in the sample-mflix database.

Aggregates.group(
    "\$${Movie::year.name}",
    Accumulators.minN(
        "lowestThreeRatings",
        "\$${Movie::imdb.name}.${Movie.IMDB::rating.name}",
        3
    )
)

Aggregates.group(
    "\$${Movie::year.name}",
    Accumulators.maxN(
        "highestTwoRatings",
        "\$${Movie::imdb.name}.${Movie.IMDB::rating.name}",
        2
    )
)

Aggregates.group(
    "\$${Movie::year.name}",
    Accumulators.firstN(
        "firstTwoMovies",
        "\$${Movie::title.name}",
        2
    )
)

Aggregates.group(
    "\$${Movie::year.name}",
    Accumulators.lastN(
        "lastThreeMovies",
        "\$${Movie::title.name}",
        3
    )
)

Aggregates.group(
    "\$${Movie::year.name}",
    Accumulators.top(
        "topRatedMovie",
        Sorts.descending("${Movie::imdb.name}.${Movie.IMDB::rating.name}"),
        listOf("\$${Movie::title.name}", "\$${Movie::imdb.name}.${Movie.IMDB::rating.name}")
    )
)

Aggregates.group(
    "\$${Movie::year.name}",
    Accumulators.topN(
        "longestThreeMovies",
        Sorts.descending(Movie::runtime.name),
        listOf("\$${Movie::title.name}", "\$${Movie::runtime.name}"),
        3
    )
)

Aggregates.group(
    "\$${Movie::year.name}",
    Accumulators.bottom(
        "shortestMovies",
        Sorts.descending(Movie::runtime.name),
        listOf("\$${Movie::title.name}", "\$${Movie::runtime.name}")
    )
)

Aggregates.group(
    "\$${Movie::year.name}",
    Accumulators.bottom(
        "lowestRatedTwoMovies",
        Sorts.descending("${Movie::imdb.name}.${Movie.IMDB::rating.name}"),
        listOf("\$${Movie::title.name}", "\$${Movie::imdb.name}.${Movie.IMDB::rating.name}"),
    )
)

Unwind

Use the unwind() method to create an $unwind pipeline stage to deconstruct an array field from input documents, creating an output document for each array element.

The following example creates a document for each element in the lowestRatedTwoMovies array:

Aggregates.unwind("\$${"lowestRatedTwoMovies"}")

To preserve documents that have missing or null values for the array field, or where array is empty:

Aggregates.unwind(
    "\$${"lowestRatedTwoMovies"}",
    UnwindOptions().preserveNullAndEmptyArrays(true)
)

To include the array index (in this example, in a field called "position"):

Aggregates.unwind(
    "\$${"lowestRatedTwoMovies"}",
    UnwindOptions().includeArrayIndex("position")
)

Out

Use the out() method to create an $out pipeline stage that writes all documents to the specified collection in the same database.

The following example writes the results of the pipeline to the classic_movies collection:

Aggregates.out("classic_movies")

Merge

Use the merge() method to create a $merge pipeline stage that merges all documents into the specified collection.

The following example merges the pipeline into the nineties_movies collection using the default options:

Aggregates.merge("nineties_movies")

The following example merges the pipeline into the movie_ratings collection in the aggregation database using some non-default options that specify to replace the document if both year and title match, otherwise insert the document:

Aggregates.merge(
    MongoNamespace("aggregation", "movie_ratings"),
    MergeOptions().uniqueIdentifier(listOf("year", "title"))
            .whenMatched(MergeOptions.WhenMatched.REPLACE)
        .whenNotMatched(MergeOptions.WhenNotMatched.INSERT)
)

GraphLookup

Use the graphLookup() method to create a $graphLookup pipeline stage that performs a recursive search on a specified collection to match a specified field in one document to a specified field of another document.

The following example uses the contacts collection. The data is modeled using the following Kotlin data class:

data class Users(
    val name: String,
    val friends: List<String>?,
    val hobbies: List<String>?
)

The example computes the reporting graph for users in the contact collection, recursively matching the value in the friends field to the name field:

Aggregates.graphLookup(
    "contacts",
    "\$${Users::friends.name}", Users::friends.name, Users::name.name,
    "socialNetwork"
)

Using GraphLookupOptions, you can specify the depth to recurse as well as the name of the depth field, if desired. In this example, $graphLookup will recurse up to two times, and create a field called degrees with the recursion depth information for every document.

Aggregates.graphLookup(
    "contacts",
    "\$${Users::friends.name}", Users::friends.name, Users::name.name,
    "socialNetwork",
    GraphLookupOptions().maxDepth(2).depthField("degrees")
)

Using GraphLookupOptions, you can specify a filter that documents must match in order for MongoDB to include them in your search. In this example, only links with "golf" in their hobbies field will be included:

Aggregates.graphLookup(
    "contacts",
    "\$${Users::friends.name}", Users::friends.name, Users::name.name, "socialNetwork",
    GraphLookupOptions().maxDepth(1).restrictSearchWithMatch(
        Filters.eq(Users::hobbies.name, "golf")
    )
)

SortByCount

Use the sortByCount() method to create a $sortByCount pipeline stage that groups documents by a given expression and then sorts these groups by count in descending order.

[
    { "$group": { "_id": <expression to group on>, "count": { "$sum": 1 } } },
    { "$sort": { "count": -1 } }
]

The following example groups documents in the movies collection by the genres field and computes the count for each distinct value:

Aggregates.sortByCount("\$${Movie::genres.name}"),

ReplaceRoot

Use the replaceRoot() method to create a $replaceRoot pipeline stage that replaces each input document with the specified document.

The following example uses a fictional books collection that contains data modeled using the following Kotlin data class:

data class Libro(val titulo: String)
data class Book(val title: String, val spanishTranslation: Libro)

Each input document is replaced by the nested document in the spanishTranslation field:

Aggregates.replaceRoot("\$${Book::spanishTranslation.name}")

AddFields

Use the addFields() method to create an $addFields pipeline stage that adds new fields to documents.

The following example adds two new fields, watched and type, to the input documents in the movie collection:

Aggregates.addFields(
    Field("watched", false),
    Field("type", "movie")
)

Count

Use the count() method to create a $count pipeline stage that counts the number of documents that enter the stage, and assigns that value to a specified field name. If you do not specify a field, count() defaults the field name to "count".

{ "$group":{ "_id": 0, "count": { "$sum" : 1 } } }

The following example creates a pipeline stage that outputs the count of incoming documents in a field called "total":

Aggregates.count("total")

Bucket

Use the bucket() method to create a $bucket pipeline stage that automates the bucketing of data around predefined boundary values.

The following examples use data modeled with the following Kotlin data class:

data class Screen(
    val id: String,
    val screenSize: Int,
    val manufacturer: String,
    val price: Double
)

This example creates a pipeline stage that groups incoming documents based on the value of their screenSize field, inclusive of the lower boundary and exclusive of the upper boundary:

Aggregates.bucket("\$${Screen::screenSize.name}", listOf(0, 24, 32, 50, 70, 1000))

Use the BucketOptions class to specify a default bucket for values outside of the specified boundaries, and to specify additional accumulators.

The following example creates a pipeline stage that groups incoming documents based on the value of their screenSize field, counting the number of documents that fall within each bucket, pushing the value of screenSize into a field called matches, and capturing any screen sizes greater than "70" into a bucket called "monster" for monstrously large screen sizes:

Aggregates.bucket("\$${Screen::screenSize.name}", listOf(0, 24, 32, 50, 70),
    BucketOptions()
        .defaultBucket("monster")
        .output(
            Accumulators.sum("count", 1),
            Accumulators.push("matches", "\$${Screen::screenSize.name}")
        )
)

BucketAuto

Use the bucketAuto() method to create a $bucketAuto pipeline stage that automatically determines the boundaries of each bucket in its attempt to distribute the documents evenly into a specified number of buckets.

The following examples use data modeled with the following Kotlin data class:

data class Screen(
    val id: String,
    val screenSize: Int,
    val manufacturer: String,
    val price: Double
)

This example creates a pipeline stage that will attempt to create and evenly distribute documents into 5 buckets using the value of their price field:

Aggregates.bucketAuto("\$${Screen::screenSize.name}", 5)

Use the BucketAutoOptions class to specify a preferred number based scheme to set boundary values, and specify additional accumulators.

The following example creates a pipeline stage that will attempt to create and evenly distribute documents into 5 buckets using the value of their price field, setting the bucket boundaries at powers of 2 (2, 4, 8, 16, ...). It also counts the number of documents in each bucket, and calculates their average price in a new field called avgPrice:

Aggregates.bucketAuto(
    "\$${Screen::price.name}", 5,
    BucketAutoOptions()
        .granularity(BucketGranularity.POWERSOF2)
        .output(Accumulators.sum("count", 1), Accumulators.avg("avgPrice", "\$${Screen::price.name}"))
        )

Facet

Use the facet() method to create a $facet pipeline stage that allows for the definition of parallel pipelines.

The following examples use data modeled with the following Kotlin data class:

data class Screen(
    val id: String,
    val screenSize: Int,
    val manufacturer: String,
    val price: Double
)

This example creates a pipeline stage that executes two parallel aggregations:

• The first aggregation distributes incoming documents into 5 groups according to their screenSize field.

• The second aggregation counts all manufacturers and returns their count, limited to the top 5.

Aggregates.facet(
    Facet(
        "Screen Sizes",
        Aggregates.bucketAuto(
            "\$${Screen::screenSize.name}",
            5,
            BucketAutoOptions().output(Accumulators.sum("count", 1))
        )
    ),
    Facet(
        "Manufacturer",
        Aggregates.sortByCount("\$${Screen::manufacturer.name}"),
        Aggregates.limit(5)
    )
)

SetWindowFields

Use the setWindowFields() method to create a $setWindowFields pipeline stage that allows using window operators to perform operations on a specified span of documents in a collection.

The following example uses a fictional weather collection using data modeled with the following Kotlin data class:

data class Weather(
    val localityId: String,
    val measurementDateTime: LocalDateTime,
    val rainfall: Double,
    val temperature: Double
)

The example creates a pipeline stage that computes the accumulated rainfall and the average temperature over the past month for each locality from more fine-grained measurements presented in the rainfall and temperature fields:

val pastMonth = Windows.timeRange(-1, MongoTimeUnit.MONTH, Windows.Bound.CURRENT)
val resultsFlow = weatherCollection.aggregate<Document>(
    listOf(
       Aggregates.setWindowFields("\$${Weather::localityId.name}",
           Sorts.ascending(Weather::measurementDateTime.name),
           WindowOutputFields.sum(
               "monthlyRainfall",
               "\$${Weather::rainfall.name}",
               pastMonth
           ),
           WindowOutputFields.avg(
               "monthlyAvgTemp",
               "\$${Weather::temperature.name}",
               pastMonth
           )
       )
    )

Densify

Use the densify() method to create a $densify pipeline stage that generates a sequence of documents to span a specified interval.

Consider the following documents retrieved from the Atlas sample weather dataset that contain measurements for a similar position field, spaced one hour apart:

Document{{ _id=5553a..., position=Document{{type=Point, coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:00:00 EST 1984, ... }}
Document{{ _id=5553b..., position=Document{{type=Point, coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 09:00:00 EST 1984, ... }}

These documents are modeled using the following Kotlin data class:

data class Weather(
    @BsonId val id: ObjectId = ObjectId(),
    val position: Point,
    val ts: LocalDateTime
)

Suppose you needed to create a pipeline stage that performs the following actions on these documents:

• Add a document at every 15-minute interval for which a ts value does not already exist.

• Group the documents by the position field.

The call to the densify() aggregation stage builder that accomplishes these actions should resemble the following:

Aggregates.densify(
    "ts",
    DensifyRange.partitionRangeWithStep(15, MongoTimeUnit.MINUTE),
    DensifyOptions.densifyOptions().partitionByFields("Position.coordinates")
)

The following output highlights the documents generated by the aggregate stage which contain ts values every 15 minutes between the existing documents:

Document{{ _id=5553a..., position=Document{{type=Point, coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:00:00 EST 1984, ... }}
Document{{ position=Document{{coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:15:00 EST 1984 }}
Document{{ position=Document{{coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:30:00 EST 1984 }}
Document{{ position=Document{{coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:45:00 EST 1984 }}
Document{{ _id=5553b..., position=Document{{type=Point, coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 09:00:00 EST 1984, ... }}

See the densify package API documentation for more information.

Fill

Use the fill() method to create a $fill pipeline stage that populates null and missing field values.

Consider the following documents that contain temperature and air pressure measurements at an hourly interval:

Document{{_id=6308a..., hour=1, temperature=23C, air_pressure=29.74}}
Document{{_id=6308b..., hour=2, temperature=23.5C}}
Document{{_id=6308c..., hour=3, temperature=null, air_pressure=29.76}}

These documents are modeled using the following Kotlin data class:

data class Weather(
    @BsonId val id: ObjectId = ObjectId(),
    val hour: Int,
    val temperature: String?,
    val air_pressure: Double?
)

Suppose you needed to populate missing temperature and air pressure data points in the documents as follows:

• Populate the air_pressure field for hour "2" using linear interpolation to calculate the value.

• Set the missing temperature value to "23.6C" for hour "3".

The call to the fill() aggregation stage builder that accomplishes these actions resembles the following:

val resultsFlow = weatherCollection.aggregate<Weather>(
    listOf(
        Aggregates.fill(
            FillOptions.fillOptions().sortBy(Sorts.ascending(Weather::hour.name)),
            FillOutputField.value(Weather::temperature.name, "23.6C"),
            FillOutputField.linear(Weather::air_pressure.name)
        )
    )
)
resultsFlow.collect { println(it) }

Weather(id=6308a..., hour=1, temperature=23C, air_pressure=29.74)
Weather(id=6308b..., hour=2, temperature=23.5C, air_pressure=29.75)
Weather(id=6308b..., hour=3, temperature=23.6C, air_pressure=29.76)

See the fill package API documentation for more information.

Atlas Full-Text Search

Use the search() method to create a $search pipeline stage that specifies a full-text search of one or more fields.

The following example creates a pipeline stage that searches the title field in the movies collection for text that contains the word "Future":

Aggregates.search(
    SearchOperator.text(
        SearchPath.fieldPath(Movie::title.name), "Future"
    ),
    SearchOptions.searchOptions().index("title")
)

Learn more about the builders from the search package API documentation.

Atlas Search Metadata

Use the searchMeta() method to create a $searchMeta pipeline stage which returns only the metadata part of the results from Atlas full-text search queries.

The following example shows the count metadata for an Atlas search aggregation stage:

Aggregates.searchMeta(
    SearchOperator.near(1985, 2, SearchPath.fieldPath(Movie::year.name)),
    SearchOptions.searchOptions().index("year")
)

Learn more about this helper from the searchMeta() API documentation.

Atlas Vector Search

Use the vectorSearch() method to create a $vectorSearch pipeline stage that specifies a semantic search. A semantic search is a type of search that locates pieces of information that are similar in meaning.

To use this feature when performing an aggregation on a collection, you must create a vector search index and index your vector embeddings. To learn how to set up search indexes in MongoDB Atlas, see How to Index Vector Embeddings for Vector Search in the Atlas documentation.

The example in this section uses data modeled with the following Kotlin data class:

data class MovieAlt(
    val title: String,
    val year: Int,
    val plot: String,
    val plotEmbedding: List<Double>
)

This example shows how to build an aggregation pipeline that uses the vectorSearch() method to perform an exact vector search with the following specifications:

• Searches plotEmbedding field values by using vector embeddings of a string value

• Uses the mflix_movies_embedding_index vector search index

• Returns 1 document

• Filters for documents in which the year value is at least 2016

Aggregates.vectorSearch(
    SearchPath.fieldPath(MovieAlt::plotEmbedding.name),
    listOf(-0.0072121937, -0.030757688, -0.012945653),
    "mflix_movies_embedding_index",
    1.toLong(),
    exactVectorSearchOptions().filter(Filters.gte(MovieAlt::year.name, 2016))
)

To learn more about this helper, see the vectorSearch() API documentation.