Collectors.averagingDouble(ToDoubleFunction<? super T> mapper)

Collectors.averagingDouble calculates the average of stream element as double data type. It returns a Collector instance. The collect method of stream can accept the Collector instance and will return the average value calculated by Collectors.averagingDouble.

List<String> list = Arrays.asList("A", "BB", "CCC", "DDDD", "CCCCC");
double result = list.stream().collect(Collectors.averagingDouble(s -> s.length()));
System.out.println(result);

								

output:

3.0
								

Collectors.groupingBy(Function<? super T,? extends K> classifier)

A "group by" operation on input elements of type T, grouping elements according to a classification function, and returning the results in a map.

public class Employee {
    private String name;
    private String department;

    public Employee(String n, String d) {
        name = n;
        department = d;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public String getDepartment() {
        return department;
    }

    public void setDepartment(String department) {
        this.department = department;
    }

    @Override
    public String toString() {
        return getName() + " from " + getDepartment() + " dept";
    }
}
								

A common database operation is to group items in a set, based on one or more properties. It can be easily translated in a single, very readable statement by rewriting it in a more functional style as encouraged by Java 8.0. Suppose you want to group employees in a company based on their department. You can easily perform this task using a collector returned by the Collectors.groupingBy factory method as follows:

Employee e1 = new Employee("Mikalai", "Development");
Employee e2 = new Employee("Volha", "HR");
Employee e3 = new Employee("Anastasia", "Management");
Employee e4 = new Employee("Daria", "Management");
Employee e5 = new Employee("Ivan", "Management");

Stream<Employee> str = Stream.of(e1, e2, e3, e4, e5);

// Group Employees by department
Map<String, List<Employee>> map = str.collect(Collectors.groupingBy(Employee::getDepartment));

System.out.println(map.get("Management"));

								

Here, we passing to the groupingBy method a Function (expressed in the form of a method reference) extracting the corresponding department for each Employee in the stream. We call this Function a classification function because it is used to classify the elements of the stream into different groups. The result of this grouping operation is a java.util.Map having as map key the value returned by the classification function and as corresponding map value a list of all the items in the stream having that classified value:

[Anastasia from Management dept, Daria from Management dept, Ivan from Management dept]
								

It is possible to pass a second collector to the groupingBy method to achieve a multilevel grouping. For instance, it is possible to count the number of employees in each department, by passing the Collectors.counting() collector as a second argument to the groupingBy collector:

// Counting Employees by department
Map<String, Long> map = str.collect(Collectors.groupingBy(Employee::getDepartment, Collectors.counting()));
System.out.println(map.get("Management"));

								

output:

3
								

Collectors.joining()

The collector returned by the Collectors.joining() factory method concatenates into a single string all strings resulting from invoking the toString() method on each object in the stream. This means you can concatenate the names of all the employees in the company as follows:

String names = str.map(Employee::getName).collect(Collectors.joining());
								

Note that joining() internally makes use of a StringBuilder to append the generated strings into one. It produces the following string:

MikalaiVolhaAnastasiaDariaIvan
								

which is not very readable.

Fortunately, the joining factory method has an overloaded version that accepts a delimiter string between two consecutive elements, so you can obtain a comma-separated list of the employees' names with:

String names = str.map(Employee::getName).collect(Collectors.joining(", "));
								

It produces the following string:

Mikalai, Volha, Anastasia, Daria, Ivan
								

	Collectors.joining("") is equivalent to Collectors.joining()

Collectors.partitioningBy(Predicate<? super T> predicate)

Partitions the input elements according to a Predicate.

When the classifier function is a Predicate function (that is, a function returning a boolean value), the stream elements are partitioned into two lists: those where the function returns true and false. In this case, it is more efп¬Ѓcient to use partitioningBy instead of groupingBy. For example, here we split all employees into those that are managers, and all others:

Employee e1 = new Employee("Mikalai", "Development");
Employee e2 = new Employee("Volha", "HR");
Employee e3 = new Employee("Anastasia", "Management");
Employee e4 = new Employee("Daria", "Management");
Employee e5 = new Employee("Ivan", "Management");
Stream<Employee> str = Stream.of(e1, e2, e3, e4, e5);

// Parition Employees to managers and others
Map<Boolean, List<Employee>> map = str.collect(Collectors.partitioningBy(e -> "Management".equals(e.getDepartment())));

System.out.println(map.get(Boolean.TRUE));

								

resulting in:

[Anastasia from Management dept, Daria from Management dept, Ivan from Management dept]
								

You can think of partitioning as a special case of grouping. Partitioning may only use Boolean as Map key, so you can have maximum two entries (each entry represented by a list of stream elements) in the resulting map.