The Java Tutorials have been written for JDK 8. Examples and practices described in this page don't take advantage of improvements introduced in later releases and might use technology no longer available.
See Java Language Changes for a summary of updated language features in Java SE 9 and subsequent releases.
See JDK Release Notes for information about new features, enhancements, and removed or deprecated options for all JDK releases.
The section
Aggregate Operations describes the following pipeline of operations, which calculates the average age of all male members in the collection roster:
double average = roster
.stream()
.filter(p -> p.getGender() == Person.Sex.MALE)
.mapToInt(Person::getAge)
.average()
.getAsDouble();
The JDK contains many terminal operations (such as
average,
sum,
min,
max, and
count) that return one value by combining the contents of a stream. These operations are called reduction operations. The JDK also contains reduction operations that return a collection instead of a single value. Many reduction operations perform a specific task, such as finding the average of values or grouping elements into categories. However, the JDK provides you with the general-purpose reduction operations
reduce and
collect, which this section describes in detail.
This section covers the following topics:
You can find the code excerpts described in this section in the example
ReductionExamples.
The
Stream.reduce method is a general-purpose reduction operation. Consider the following pipeline, which calculates the sum of the male members' ages in the collection roster. It uses the
Stream.sum reduction operation:
Integer totalAge = roster
.stream()
.mapToInt(Person::getAge)
.sum();
Compare this with the following pipeline, which uses the Stream.reduce operation to calculate the same value:
Integer totalAgeReduce = roster
.stream()
.map(Person::getAge)
.reduce(
0,
(a, b) -> a + b);
The reduce operation in this example takes two arguments:
identity: The identity element is both the initial value of the reduction and the default result if there are no elements in the stream. In this example, the identity element is 0; this is the initial value of the sum of ages and the default value if no members exist in the collection roster.
accumulator: The accumulator function takes two parameters: a partial result of the reduction (in this example, the sum of all processed integers so far) and the next element of the stream (in this example, an integer). It returns a new partial result. In this example, the accumulator function is a lambda expression that adds two Integer values and returns an Integer value:
(a, b) -> a + b
The reduce operation always returns a new value. However, the accumulator function also returns a new value every time it processes an element of a stream. Suppose that you want to reduce the elements of a stream to a more complex object, such as a collection. This might hinder the performance of your application. If your reduce operation involves adding elements to a collection, then every time your accumulator function processes an element, it creates a new collection that includes the element, which is inefficient. It would be more efficient for you to update an existing collection instead. You can do this with the
Stream.collect method, which the next section describes.
Unlike the reduce method, which always creates a new value when it processes an element, the
collect method modifies, or mutates, an existing value.
Consider how to find the average of values in a stream. You require two pieces of data: the total number of values and the sum of those values. However, like the reduce method and all other reduction methods, the collect method returns only one value. You can create a new data type that contains member variables that keep track of the total number of values and the sum of those values, such as the following class,
Averager:
class Averager implements IntConsumer
{
private int total = 0;
private int count = 0;
public double average() {
return count > 0 ? ((double) total)/count : 0;
}
public void accept(int i) { total += i; count++; }
public void combine(Averager other) {
total += other.total;
count += other.count;
}
}
The following pipeline uses the Averager class and the collect method to calculate the average age of all male members:
Averager averageCollect = roster.stream()
.filter(p -> p.getGender() == Person.Sex.MALE)
.map(Person::getAge)
.collect(Averager::new, Averager::accept, Averager::combine);
System.out.println("Average age of male members: " +
averageCollect.average());
The collect operation in this example takes three arguments:
supplier: The supplier is a factory function; it constructs new instances. For the collect operation, it creates instances of the result container. In this example, it is a new instance of the Averager class.accumulator: The accumulator function incorporates a stream element into a result container. In this example, it modifies the Averager result container by incrementing the count variable by one and adding to the total member variable the value of the stream element, which is an integer representing the age of a male member.combiner: The combiner function takes two result containers and merges their contents. In this example, it modifies an Averager result container by incrementing the count variable by the count member variable of the other Averager instance and adding to the total member variable the value of the other Averager instance's total member variable.Note the following:
reduce operation.collect operations with parallel streams; see the section
Parallelism for more information. (If you run the collect method with a parallel stream, then the JDK creates a new thread whenever the combiner function creates a new object, such as an Averager object in this example. Consequently, you do not have to worry about synchronization.)Although the JDK provides you with the average operation to calculate the average value of elements in a stream, you can use the collect operation and a custom class if you need to calculate several values from the elements of a stream.
The collect operation is best suited for collections. The following example puts the names of the male members in a collection with the collect operation:
List<String> namesOfMaleMembersCollect = roster
.stream()
.filter(p -> p.getGender() == Person.Sex.MALE)
.map(p -> p.getName())
.collect(Collectors.toList());
This version of the collect operation takes one parameter of type
Collector. This class encapsulates the functions used as arguments in the collect operation that requires three arguments (supplier, accumulator, and combiner functions).
The
Collectors class contains many useful reduction operations, such as accumulating elements into collections and summarizing elements according to various criteria. These reduction operations return instances of the class Collector, so you can use them as a parameter for the collect operation.
This example uses the
Collectors.toList operation, which accumulates the stream elements into a new instance of List. As with most operations in the Collectors class, the toList operator returns an instance of Collector, not a collection.
The following example groups members of the collection roster by gender:
Map<Person.Sex, List<Person>> byGender =
roster
.stream()
.collect(
Collectors.groupingBy(Person::getGender));
The
groupingBy operation returns a map whose keys are the values that result from applying the lambda expression specified as its parameter (which is called a classification function). In this example, the returned map contains two keys, Person.Sex.MALE and Person.Sex.FEMALE. The keys' corresponding values are instances of List that contain the stream elements that, when processed by the classification function, correspond to the key value. For example, the value that corresponds to key Person.Sex.MALE is an instance of List that contains all male members.
The following example retrieves the names of each member in the collection roster and groups them by gender:
Map<Person.Sex, List<String>> namesByGender =
roster
.stream()
.collect(
Collectors.groupingBy(
Person::getGender,
Collectors.mapping(
Person::getName,
Collectors.toList())));
The
groupingBy operation in this example takes two parameters, a classification function and an instance of Collector. The Collector parameter is called a downstream collector. This is a collector that the Java runtime applies to the results of another collector. Consequently, this groupingBy operation enables you to apply a collect method to the List values created by the groupingBy operator. This example applies the collector
mapping, which applies the mapping function Person::getName to each element of the stream. Consequently, the resulting stream consists of only the names of members. A pipeline that contains one or more downstream collectors, like this example, is called a multilevel reduction.
The following example retrieves the total age of members of each gender:
Map<Person.Sex, Integer> totalAgeByGender =
roster
.stream()
.collect(
Collectors.groupingBy(
Person::getGender,
Collectors.reducing(
0,
Person::getAge,
Integer::sum)));
The
reducing operation takes three parameters:
identity: Like the Stream.reduce operation, the identity element is both the initial value of the reduction and the default result if there are no elements in the stream. In this example, the identity element is 0; this is the initial value of the sum of ages and the default value if no members exist.mapper: The reducing operation applies this mapper function to all stream elements. In this example, the mapper retrieves the age of each member.operation: The operation function is used to reduce the mapped values. In this example, the operation function adds Integer values.The following example retrieves the average age of members of each gender:
Map<Person.Sex, Double> averageAgeByGender = roster
.stream()
.collect(
Collectors.groupingBy(
Person::getGender,
Collectors.averagingInt(Person::getAge)));