Aggregation Framework Support

Spring Data MongoDB provides support for the Aggregation Framework introduced to MongoDB in version 2.2.

For further information, see the full reference documentation of the aggregation framework and other data aggregation tools for MongoDB.

Basic Concepts

The Aggregation Framework support in Spring Data MongoDB is based on the following key abstractions: Aggregation, AggregationOperation, and AggregationResults.

  • Aggregation

    An Aggregation represents a MongoDB aggregate operation and holds the description of the aggregation pipeline instructions. Aggregations are created by invoking the appropriate newAggregation(…) static factory method of the Aggregation class, which takes a list of AggregateOperation and an optional input class.

    The actual aggregate operation is executed by the aggregate method of the MongoTemplate, which takes the desired output class as a parameter.

  • AggregationOperation

    An AggregationOperation represents a MongoDB aggregation pipeline operation and describes the processing that should be performed in this aggregation step. Although you could manually create an AggregationOperation, we recommend using the static factory methods provided by the Aggregate class to construct an AggregateOperation.

  • AggregationResults

    AggregationResults is the container for the result of an aggregate operation. It provides access to the raw aggregation result, in the form of a Document to the mapped objects and other information about the aggregation.

    The following listing shows the canonical example for using the Spring Data MongoDB support for the MongoDB Aggregation Framework:

    import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

Aggregation agg = newAggregation(
    pipelineOP1(),
    pipelineOP2(),
    pipelineOPn()
);

AggregationResults<OutputType> results = mongoTemplate.aggregate(agg, "INPUT_COLLECTION_NAME", OutputType.class);
List<OutputType> mappedResult = results.getMappedResults();

Note that, if you provide an input class as the first parameter to the newAggregation method, the MongoTemplate derives the name of the input collection from this class. Otherwise, if you do not not specify an input class, you must provide the name of the input collection explicitly. If both an input class and an input collection are provided, the latter takes precedence.

Supported Aggregation Operations

The MongoDB Aggregation Framework provides the following types of aggregation operations:

  • Pipeline Aggregation Operators

  • Group Aggregation Operators

  • Boolean Aggregation Operators

  • Comparison Aggregation Operators

  • Arithmetic Aggregation Operators

  • String Aggregation Operators

  • Date Aggregation Operators

  • Array Aggregation Operators

  • Conditional Aggregation Operators

  • Lookup Aggregation Operators

  • Convert Aggregation Operators

  • Object Aggregation Operators

At the time of this writing, we provide support for the following Aggregation Operations in Spring Data MongoDB:

Table 1. Aggregation Operations currently supported by Spring Data MongoDB

Pipeline Aggregation Operators

bucket, bucketAuto, count, facet, geoNear, graphLookup, group, limit, lookup, match, project, replaceRoot, skip, sort, unwind

Set Aggregation Operators

setEquals, setIntersection, setUnion, setDifference, setIsSubset, anyElementTrue, allElementsTrue

Group Aggregation Operators

addToSet, first, last, max, min, avg, push, sum, (*count), stdDevPop, stdDevSamp

Arithmetic Aggregation Operators

abs, add (*via plus), ceil, divide, exp, floor, ln, log, log10, mod, multiply, pow, sqrt, subtract (*via minus), trunc

String Aggregation Operators

concat, substr, toLower, toUpper, stcasecmp, indexOfBytes, indexOfCP, split, strLenBytes, strLenCP, substrCP, trim, ltrim, rtim

Comparison Aggregation Operators

eq (*via: is), gt, gte, lt, lte, ne

Array Aggregation Operators

arrayElementAt, arrayToObject, concatArrays, filter, in, indexOfArray, isArray, range, reverseArray, reduce, size, slice, zip

Literal Operators

literal

Date Aggregation Operators

dayOfYear, dayOfMonth, dayOfWeek, year, month, week, hour, minute, second, millisecond, dateToString, dateFromString, dateFromParts, dateToParts, isoDayOfWeek, isoWeek, isoWeekYear

Variable Operators

map

Conditional Aggregation Operators

cond, ifNull, switch

Type Aggregation Operators

type

Convert Aggregation Operators

convert, toBool, toDate, toDecimal, toDouble, toInt, toLong, toObjectId, toString

Object Aggregation Operators

objectToArray, mergeObjects

  • The operation is mapped or added by Spring Data MongoDB.

Note that the aggregation operations not listed here are currently not supported by Spring Data MongoDB. Comparison aggregation operators are expressed as Criteria expressions.

Projection Expressions

Projection expressions are used to define the fields that are the outcome of a particular aggregation step. Projection expressions can be defined through the project method of the Aggregation class, either by passing a list of String objects or an aggregation framework Fields object. The projection can be extended with additional fields through a fluent API by using the and(String) method and aliased by using the as(String) method. Note that you can also define fields with aliases by using the Fields.field static factory method of the aggregation framework, which you can then use to construct a new Fields instance. References to projected fields in later aggregation stages are valid only for the field names of included fields or their aliases (including newly defined fields and their aliases). Fields not included in the projection cannot be referenced in later aggregation stages. The following listings show examples of projection expression:

Example 1. Projection expression examples
// generates {$project: {name: 1, netPrice: 1}}
project("name", "netPrice")

// generates {$project: {thing1: $thing2}}
project().and("thing1").as("thing2")

// generates {$project: {a: 1, b: 1, thing2: $thing1}}
project("a","b").and("thing1").as("thing2")
Example 2. Multi-Stage Aggregation using Projection and Sorting
// generates {$project: {name: 1, netPrice: 1}}, {$sort: {name: 1}}
project("name", "netPrice"), sort(ASC, "name")

// generates {$project: {name: $firstname}}, {$sort: {name: 1}}
project().and("firstname").as("name"), sort(ASC, "name")

// does not work
project().and("firstname").as("name"), sort(ASC, "firstname")

More examples for project operations can be found in the AggregationTests class. Note that further details regarding the projection expressions can be found in the corresponding section of the MongoDB Aggregation Framework reference documentation.

Faceted Classification

As of Version 3.4, MongoDB supports faceted classification by using the Aggregation Framework. A faceted classification uses semantic categories (either general or subject-specific) that are combined to create the full classification entry. Documents flowing through the aggregation pipeline are classified into buckets. A multi-faceted classification enables various aggregations on the same set of input documents, without needing to retrieve the input documents multiple times.

Buckets

Bucket operations categorize incoming documents into groups, called buckets, based on a specified expression and bucket boundaries. Bucket operations require a grouping field or a grouping expression. You can define them by using the bucket() and bucketAuto() methods of the Aggregate class. BucketOperation and BucketAutoOperation can expose accumulations based on aggregation expressions for input documents. You can extend the bucket operation with additional parameters through a fluent API by using the with…() methods and the andOutput(String) method. You can alias the operation by using the as(String) method. Each bucket is represented as a document in the output.

BucketOperation takes a defined set of boundaries to group incoming documents into these categories. Boundaries are required to be sorted. The following listing shows some examples of bucket operations:

Example 3. Bucket operation examples
// generates {$bucket: {groupBy: $price, boundaries: [0, 100, 400]}}
bucket("price").withBoundaries(0, 100, 400);

// generates {$bucket: {groupBy: $price, default: "Other" boundaries: [0, 100]}}
bucket("price").withBoundaries(0, 100).withDefault("Other");

// generates {$bucket: {groupBy: $price, boundaries: [0, 100], output: { count: { $sum: 1}}}}
bucket("price").withBoundaries(0, 100).andOutputCount().as("count");

// generates {$bucket: {groupBy: $price, boundaries: [0, 100], 5, output: { titles: { $push: "$title"}}}
bucket("price").withBoundaries(0, 100).andOutput("title").push().as("titles");

BucketAutoOperation determines boundaries in an attempt to evenly distribute documents into a specified number of buckets. BucketAutoOperation optionally takes a granularity value that specifies the preferred number series to use to ensure that the calculated boundary edges end on preferred round numbers or on powers of 10. The following listing shows examples of bucket operations:

Example 4. Bucket operation examples
// generates {$bucketAuto: {groupBy: $price, buckets: 5}}
bucketAuto("price", 5)

// generates {$bucketAuto: {groupBy: $price, buckets: 5, granularity: "E24"}}
bucketAuto("price", 5).withGranularity(Granularities.E24).withDefault("Other");

// generates {$bucketAuto: {groupBy: $price, buckets: 5, output: { titles: { $push: "$title"}}}
bucketAuto("price", 5).andOutput("title").push().as("titles");

To create output fields in buckets, bucket operations can use AggregationExpression through andOutput() and SpEL expressions through andOutputExpression().

Note that further details regarding bucket expressions can be found in the $bucket section and $bucketAuto section of the MongoDB Aggregation Framework reference documentation.

Multi-faceted Aggregation

Multiple aggregation pipelines can be used to create multi-faceted aggregations that characterize data across multiple dimensions (or facets) within a single aggregation stage. Multi-faceted aggregations provide multiple filters and categorizations to guide data browsing and analysis. A common implementation of faceting is how many online retailers provide ways to narrow down search results by applying filters on product price, manufacturer, size, and other factors.

You can define a FacetOperation by using the facet() method of the Aggregation class. You can customize it with multiple aggregation pipelines by using the and() method. Each sub-pipeline has its own field in the output document where its results are stored as an array of documents.

Sub-pipelines can project and filter input documents prior to grouping. Common use cases include extraction of date parts or calculations before categorization. The following listing shows facet operation examples:

Example 5. Facet operation examples
// generates {$facet: {categorizedByPrice: [ { $match: { price: {$exists : true}}}, { $bucketAuto: {groupBy: $price, buckets: 5}}]}}
facet(match(Criteria.where("price").exists(true)), bucketAuto("price", 5)).as("categorizedByPrice"))

// generates {$facet: {categorizedByCountry: [ { $match: { country: {$exists : true}}}, { $sortByCount: "$country"}]}}
facet(match(Criteria.where("country").exists(true)), sortByCount("country")).as("categorizedByCountry"))

// generates {$facet: {categorizedByYear: [
//     { $project: { title: 1, publicationYear: { $year: "publicationDate"}}},
//     { $bucketAuto: {groupBy: $price, buckets: 5, output: { titles: {$push:"$title"}}}
// ]}}
facet(project("title").and("publicationDate").extractYear().as("publicationYear"),
      bucketAuto("publicationYear", 5).andOutput("title").push().as("titles"))
  .as("categorizedByYear"))

Note that further details regarding facet operation can be found in the $facet section of the MongoDB Aggregation Framework reference documentation.

Sort By Count

Sort by count operations group incoming documents based on the value of a specified expression, compute the count of documents in each distinct group, and sort the results by count. It offers a handy shortcut to apply sorting when using mongo.aggregation.facet. Sort by count operations require a grouping field or grouping expression. The following listing shows a sort by count example:

Example 6. Sort by count example
// generates { $sortByCount: "$country" }
sortByCount("country");

A sort by count operation is equivalent to the following BSON (Binary JSON):

{ $group: { _id: <expression>, count: { $sum: 1 } } },
{ $sort: { count: -1 } }

Spring Expression Support in Projection Expressions

We support the use of SpEL expressions in projection expressions through the andExpression method of the ProjectionOperation and BucketOperation classes. This feature lets you define the desired expression as a SpEL expression. On query execution, the SpEL expression is translated into a corresponding MongoDB projection expression part. This arrangement makes it much easier to express complex calculations.

Complex Calculations with SpEL expressions

Consider the following SpEL expression:

1 + (q + 1) / (q - 1)

The preceding expression is translated into the following projection expression part:

{ "$add" : [ 1, {
    "$divide" : [ {
        "$add":["$q", 1]}, {
        "$subtract":[ "$q", 1]}
    ]
}]}

You can see examples in more context in mongo.aggregation.examples.example5 and mongo.aggregation.examples.example6. You can find more usage examples for supported SpEL expression constructs in SpelExpressionTransformerUnitTests. The following table shows the SpEL transformations supported by Spring Data MongoDB:

Table 2. Supported SpEL transformations
SpEL Expression Mongo Expression Part

a == b

{ $eq : [$a, $b] }

a != b

{ $ne : [$a , $b] }

a > b

{ $gt : [$a, $b] }

a >= b

{ $gte : [$a, $b] }

a < b

{ $lt : [$a, $b] }

a ⇐ b

{ $lte : [$a, $b] }

a + b

{ $add : [$a, $b] }

a - b

{ $subtract : [$a, $b] }

a * b

{ $multiply : [$a, $b] }

a / b

{ $divide : [$a, $b] }

a^b

{ $pow : [$a, $b] }

a % b

{ $mod : [$a, $b] }

a && b

{ $and : [$a, $b] }

a || b

{ $or : [$a, $b] }

!a

{ $not : [$a] }

In addition to the transformations shown in the preceding table, you can use standard SpEL operations such as new to (for example) create arrays and reference expressions through their names (followed by the arguments to use in brackets). The following example shows how to create an array in this fashion:

// { $setEquals : [$a, [5, 8, 13] ] }
.andExpression("setEquals(a, new int[]{5, 8, 13})");

Aggregation Framework Examples

The examples in this section demonstrate the usage patterns for the MongoDB Aggregation Framework with Spring Data MongoDB.

Aggregation Framework Example 1

In this introductory example, we want to aggregate a list of tags to get the occurrence count of a particular tag from a MongoDB collection (called tags) sorted by the occurrence count in descending order. This example demonstrates the usage of grouping, sorting, projections (selection), and unwinding (result splitting).

class TagCount {
 String tag;
 int n;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

Aggregation agg = newAggregation(
    project("tags"),
    unwind("tags"),
    group("tags").count().as("n"),
    project("n").and("tag").previousOperation(),
    sort(DESC, "n")
);

AggregationResults<TagCount> results = mongoTemplate.aggregate(agg, "tags", TagCount.class);
List<TagCount> tagCount = results.getMappedResults();

The preceding listing uses the following algorithm:

  1. Create a new aggregation by using the newAggregation static factory method, to which we pass a list of aggregation operations. These aggregate operations define the aggregation pipeline of our Aggregation.

  2. Use the project operation to select the tags field (which is an array of strings) from the input collection.

  3. Use the unwind operation to generate a new document for each tag within the tags array.

  4. Use the group operation to define a group for each tags value for which we aggregate the occurrence count (by using the count aggregation operator and collecting the result in a new field called n).

  5. Select the n field and create an alias for the ID field generated from the previous group operation (hence the call to previousOperation()) with a name of tag.

  6. Use the sort operation to sort the resulting list of tags by their occurrence count in descending order.

  7. Call the aggregate method on MongoTemplate to let MongoDB perform the actual aggregation operation, with the created Aggregation as an argument.

Note that the input collection is explicitly specified as the tags parameter to the aggregate Method. If the name of the input collection is not specified explicitly, it is derived from the input class passed as the first parameter to the newAggreation method.

Aggregation Framework Example 2

This example is based on the Largest and Smallest Cities by State example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return the smallest and largest cities by population for each state by using the aggregation framework. This example demonstrates grouping, sorting, and projections (selection).

class ZipInfo {
   String id;
   String city;
   String state;
   @Field("pop") int population;
   @Field("loc") double[] location;
}

class City {
   String name;
   int population;
}

class ZipInfoStats {
   String id;
   String state;
   City biggestCity;
   City smallestCity;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<ZipInfo> aggregation = newAggregation(ZipInfo.class,
    group("state", "city")
       .sum("population").as("pop"),
    sort(ASC, "pop", "state", "city"),
    group("state")
       .last("city").as("biggestCity")
       .last("pop").as("biggestPop")
       .first("city").as("smallestCity")
       .first("pop").as("smallestPop"),
    project()
       .and("state").previousOperation()
       .and("biggestCity")
          .nested(bind("name", "biggestCity").and("population", "biggestPop"))
       .and("smallestCity")
          .nested(bind("name", "smallestCity").and("population", "smallestPop")),
    sort(ASC, "state")
);

AggregationResults<ZipInfoStats> result = mongoTemplate.aggregate(aggregation, ZipInfoStats.class);
ZipInfoStats firstZipInfoStats = result.getMappedResults().get(0);

Note that the ZipInfo class maps the structure of the given input-collection. The ZipInfoStats class defines the structure in the desired output format.

The preceding listings use the following algorithm:

  1. Use the group operation to define a group from the input-collection. The grouping criteria is the combination of the state and city fields, which forms the ID structure of the group. We aggregate the value of the population property from the grouped elements by using the sum operator and save the result in the pop field.

  2. Use the sort operation to sort the intermediate-result by the pop, state and city fields, in ascending order, such that the smallest city is at the top and the biggest city is at the bottom of the result. Note that the sorting on state and city is implicitly performed against the group ID fields (which Spring Data MongoDB handled).

  3. Use a group operation again to group the intermediate result by state. Note that state again implicitly references a group ID field. We select the name and the population count of the biggest and smallest city with calls to the last(…) and first(…​) operators, respectively, in the project operation.

  4. Select the state field from the previous group operation. Note that state again implicitly references a group ID field. Because we do not want an implicitly generated ID to appear, we exclude the ID from the previous operation by using and(previousOperation()).exclude(). Because we want to populate the nested City structures in our output class, we have to emit appropriate sub-documents by using the nested method.

  5. Sort the resulting list of StateStats by their state name in ascending order in the sort operation.

Note that we derive the name of the input collection from the ZipInfo class passed as the first parameter to the newAggregation method.

Aggregation Framework Example 3

This example is based on the States with Populations Over 10 Million example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return all states with a population greater than 10 million, using the aggregation framework. This example demonstrates grouping, sorting, and matching (filtering).

class StateStats {
   @Id String id;
   String state;
   @Field("totalPop") int totalPopulation;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<ZipInfo> agg = newAggregation(ZipInfo.class,
    group("state").sum("population").as("totalPop"),
    sort(ASC, previousOperation(), "totalPop"),
    match(where("totalPop").gte(10 * 1000 * 1000))
);

AggregationResults<StateStats> result = mongoTemplate.aggregate(agg, StateStats.class);
List<StateStats> stateStatsList = result.getMappedResults();

The preceding listings use the following algorithm:

  1. Group the input collection by the state field and calculate the sum of the population field and store the result in the new field "totalPop".

  2. Sort the intermediate result by the id-reference of the previous group operation in addition to the "totalPop" field in ascending order.

  3. Filter the intermediate result by using a match operation which accepts a Criteria query as an argument.

Note that we derive the name of the input collection from the ZipInfo class passed as first parameter to the newAggregation method.

Aggregation Framework Example 4

This example demonstrates the use of simple arithmetic operations in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .and("netPrice").plus(1).as("netPricePlus1")
        .and("netPrice").minus(1).as("netPriceMinus1")
        .and("netPrice").multiply(1.19).as("grossPrice")
        .and("netPrice").divide(2).as("netPriceDiv2")
        .and("spaceUnits").mod(2).as("spaceUnitsMod2")
);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Note that we derive the name of the input collection from the Product class passed as first parameter to the newAggregation method.

Aggregation Framework Example 5

This example demonstrates the use of simple arithmetic operations derived from SpEL Expressions in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .andExpression("netPrice + 1").as("netPricePlus1")
        .andExpression("netPrice - 1").as("netPriceMinus1")
        .andExpression("netPrice / 2").as("netPriceDiv2")
        .andExpression("netPrice * 1.19").as("grossPrice")
        .andExpression("spaceUnits % 2").as("spaceUnitsMod2")
        .andExpression("(netPrice * 0.8  + 1.2) * 1.19").as("grossPriceIncludingDiscountAndCharge")

);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();
Aggregation Framework Example 6

This example demonstrates the use of complex arithmetic operations derived from SpEL Expressions in the projection operation.

Note: The additional parameters passed to the addExpression method can be referenced with indexer expressions according to their position. In this example, we reference the first parameter of the parameters array with [0]. When the SpEL expression is transformed into a MongoDB aggregation framework expression, external parameter expressions are replaced with their respective values.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

double shippingCosts = 1.2;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .andExpression("(netPrice * (1-discountRate)  + [0]) * (1+taxRate)", shippingCosts).as("salesPrice")
);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Note that we can also refer to other fields of the document within the SpEL expression.

Aggregation Framework Example 7

This example uses conditional projection. It is derived from the $cond reference documentation.

public class InventoryItem {

  @Id int id;
  String item;
  String description;
  int qty;
}

public class InventoryItemProjection {

  @Id int id;
  String item;
  String description;
  int qty;
  int discount
}
import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<InventoryItem> agg = newAggregation(InventoryItem.class,
  project("item").and("discount")
    .applyCondition(ConditionalOperator.newBuilder().when(Criteria.where("qty").gte(250))
      .then(30)
      .otherwise(20))
    .and(ifNull("description", "Unspecified")).as("description")
);

AggregationResults<InventoryItemProjection> result = mongoTemplate.aggregate(agg, "inventory", InventoryItemProjection.class);
List<InventoryItemProjection> stateStatsList = result.getMappedResults();

This one-step aggregation uses a projection operation with the inventory collection. We project the discount field by using a conditional operation for all inventory items that have a qty greater than or equal to 250. A second conditional projection is performed for the description field. We apply the Unspecified description to all items that either do not have a description field or items that have a null description.

As of MongoDB 3.6, it is possible to exclude fields from the projection by using a conditional expression.

Example 7. Conditional aggregation projection
TypedAggregation<Book> agg = Aggregation.newAggregation(Book.class,
  project("title")
    .and(ConditionalOperators.when(ComparisonOperators.valueOf("author.middle")     (1)
        .equalToValue(""))                                                          (2)
        .then("$$REMOVE")                                                           (3)
        .otherwiseValueOf("author.middle")                                          (4)
    )
	.as("author.middle"));
1 If the value of the field author.middle
2 does not contain a value,
3 then use $$REMOVE to exclude the field.
4 Otherwise, add the field value of author.middle.