One Stop Guide to Java Functional Interfaces

  • June 11, 2024
Table Of Contents

Introduction to Functional Programming

Functional programming is a paradigm that focuses on the use of functions to create clear and concise code. Instead of modifying data and maintaining state like in traditional imperative programming, functional programming treats functions as first-class citizens. That makes it possible to assign them to variables, pass as arguments, and return from other functions. This approach can make code easier to understand and reason about.

Example Code

This article is accompanied by a working code example on GitHub.

History of Functional Programming

Lambda calculus, also known as λ-calculus, is a formal system in mathematical logic used to express computation through function abstraction and application with variable binding and substitution. Alonzo Church, a mathematician, introduced it in the 1930s as part of his mathematical foundations research.

John McCarthy created Lisp, the initial high-level functional programming language, in the late 1950s at Massachusetts Institute of Technology (MIT) for the IBM 700/7000 series of scientific computers. Lisp introduced many concepts central to functional programming, like first-class functions and recursion. Lisp influenced many modern functional languages.

Moving forward, in the 1970s, languages like ML (Meta Language) and Scheme built on these ideas, introducing features like type inference and lazy evaluation. Haskell, another pivotal language introduced in the 1990s, brought pure functional programming to the forefront with its strong emphasis on immutability and function composition.

These languages laid the groundwork for many features we see in Java 8. Inspired by these pioneers, Java adopted functional programming principles to enhance its expressiveness and conciseness. Lambda expressions, method references, and a rich set of functional interfaces like Function, Predicate, and Consumer are now part of Java’s repertoire.

Competition from Scala and Python

Scala and Python are indeed strong competitors in the functional programming space.

Scala combines object-oriented and functional programming paradigms. It gained traction among Java developers looking for more powerful abstractions. Its compatibility with the JVM and expressive syntax made it a compelling alternative.

Python’s simplicity, readability, and support for functional programming through features like list comprehensions, lambda functions, and higher-order functions made it a favorite for many developers, especially in the fields of data science and web development.

By integrating functional programming features, Java aimed to provide its existing user base with modern tools without needing to switch languages, ensuring it remained a versatile and powerful choice for a wide range of applications.

Functional Programming in Java

In recent years, functional programming has gained popularity due to its ability to help manage complexity, especially in large-scale applications. It emphasizes immutability, avoiding side effects, and working with data in a more predictable and modular way. This makes it easier to test and maintain code.

Java, traditionally an object-oriented language, adopted functional programming features in Java 8. The following factors triggered this move:

  • Simplifying Code: Functional programming can reduce boilerplate code and make code more concise, leading to easier maintenance and better readability.

  • Concurrency and Parallelism: Functional programming works well with modern multicore architectures, enabling efficient parallel processing without worrying about shared state or side effects.

  • Expressiveness and Flexibility: By embracing functional interfaces and lambda expressions, Java gained a more expressive syntax, allowing us to write flexible and adaptable code.

Functional programming in Java revolves around several key concepts and idioms:

  • Lambda Expressions: Use these compact functions wherever we need to provide a functional interface. They help reduce boilerplate code.

  • Method References: These are a shorthand way to refer to methods, making code even more concise and readable.

  • Functional Interfaces: These are interfaces with a single abstract method, making them perfect for lambda expressions and method references. Common examples include Predicate, Function, Consumer, Supplier, and Operator.

Advantages and Disadvantages of Functional Programming

Functional programming in Java brings many advantages but also has its share of disadvantages and challenges.

One of the key benefits of functional programming is that it improves code readability. Functional code tends to be concise, thanks to lambda expressions and method references, leading to reduced boilerplate and easier code maintenance. This focus on immutability—where data structures remain unchanged after creation—helps to reduce side effects and prevents bugs caused by unexpected changes in state.

Another advantage is its compatibility with concurrency and parallelism. Since functional programming promotes immutability, operations can run in parallel without the usual risks of data inconsistency or race conditions. This results in code that’s naturally better suited for multithreaded environments.

Functional programming also promotes modularity and reusability. With functions being first-class citizens, we create small, reusable components, leading to cleaner, more maintainable code. The abstraction inherent in functional programming reduces overall complexity, allowing us to focus on the essential logic without worrying about implementation details.

However, these advantages come with potential drawbacks. The learning curve for functional programming can be steep, especially for us accustomed to imperative or object-oriented paradigms. Concepts like higher-order functions and immutability might require a significant mindset shift.

Performance overheads are another concern, particularly due to frequent object creation and additional function calls in functional programming. This could impact performance in resource-constrained environments. Debugging functional code can also be challenging due to the abstractions involved, and understanding complex lambda expressions might require a deeper understanding of functional concepts.

Compatibility issues may arise when integrating with legacy systems or libraries that aren’t designed for functional programming, potentially causing integration problems. Finally, functional programming’s focus on immutability and side-effect-free functions may reduce flexibility in scenarios that require mutability or complex object manipulations.

Ultimately, while functional programming offers significant benefits like improved readability and easier concurrency, it also comes with challenges. We need to consider both the advantages and disadvantages to determine how functional programming fits into our Java applications.

Understanding Functional Interfaces

The @FunctionalInterface annotation in Java is a special marker that makes an interface a functional interface. A functional interface is an interface with a single abstract method (SAM). That makes it possible to use it as a target for lambda expressions or method references.

This annotation serves as a way to document our intention for the interface and provides a layer of protection against accidental changes. By using @FunctionalInterface, we indicate that the interface should maintain its single-method structure. If we add more abstract methods, the compiler will generate an error, ensuring the functional interface’s integrity.

Functional interfaces are central to Java’s support for functional programming. They allow us to write cleaner, more concise code by using lambda expressions, reducing boilerplate code, and promoting reusability. Common examples of functional interfaces include Predicate, Consumer, Function, and Supplier.

Using the @FunctionalInterface annotation isn’t strictly necessary. Any interface with a single abstract method is inherently a functional interface. But it’s a good practice. It improves code readability, enforces constraints, and helps others to understand our intentions, contributing to better maintainability and consistency in our codebase.

Creating Custom Functional Interfaces

We now know that a functional interface in Java is an interface with a single abstract method.

Let’s consider a simple calculator example that takes two integers and returns the result of an arithmetic operation. To implement this, we can define a functional interface called ArithmeticOperation, which has a single method to perform the operation.

Here’s the definition of the functional interface:

interface ArithmeticOperation {
    int operate(int a, int b);

Consider the ArithmeticOperation interface, marked with @FunctionalInterface. This annotation makes it clear that the interface is functional, emphasizing that it should only contain one abstract method.

The ArithmeticOperation interface defines a single method, operate(), that takes two integers and returns an integer result. The use of this annotation documents that the interface is functional.

With this functional interface, we create different arithmetic operations, like addition, subtraction, multiplication, and division, using lambda expressions.

Let’s build a basic calculator with this setup:

void operate() {
  // Define operations
  ArithmeticOperation add = (a, b) -> a + b;
  ArithmeticOperation subtract = (a, b) -> a - b;
  ArithmeticOperation multiply = (a, b) -> a * b;
  ArithmeticOperation divide = (a, b) -> a / b;

  // Verify results
  assertEquals(15, add.operate(10, 5));
  assertEquals(5, subtract.operate(10, 5));
  assertEquals(50, multiply.operate(10, 5));
  assertEquals(2, divide.operate(10, 5));

The test operate() verifies if the defined arithmetic operations get accurate outcomes. Using the ArithmeticOperation functional interface, it begins by generating lambda expressions for the four fundamental arithmetic operations of addition, subtraction, multiplication, and division. After that, it uses assertions to confirm that the results of these operations on the integers 5 and 10 match the expected values.

Built-in Functional Interfaces

Here’s an overview of some of the most common built-in functional interfaces in Java 8, along with their typical use cases and examples:

Functional Interface Description Example Use Cases
Predicate<T> Represents a function that takes an input of type T and returns a boolean. Commonly used for filtering and conditional checks.
  • Checking if a number is even
  • Filtering a list of strings based on length
  • Validating user inputs
Function<T, R> Represents a function that takes an input of type T and returns a result of type R. Often used for transformation or mapping operations.
  • Converting a string to uppercase
  • Mapping employee objects to their salaries
  • Parsing a string to an integer
Consumer<T> Represents a function that takes an input of type T and performs an action, without returning a result. Ideal for side-effect operations like printing or logging.
  • Printing a list of numbers
  • Logging user actions
  • Updating object properties
Supplier<T> Represents a function that provides a value of type T without taking any arguments. Useful for lazy initialization and deferred computation.
  • Generating random numbers
  • Providing default values
  • Creating new object instances
UnaryOperator<T> Represents a function that takes an input of type T and returns a result of the same type. Often used for simple transformations or operations.
  • Negating a number
  • Reversing a string
  • Incrementing a value
BinaryOperator<T> Represents a function that takes two inputs of type T and returns a result of the same type. Useful for combining or reducing operations.
  • Adding two numbers
  • Concatenating strings
  • Finding the maximum of two values

These built-in functional interfaces in Java 8 provide a foundation for functional programming, enabling us to work with lambda expressions and streamline code. Due to their versatility, we can use them in a wide range of applications, from data transformation to filtering and beyond.

Lambda Expressions Explained

Lambda expressions are a key feature of Java 8, allowing us to create compact, anonymous functions in a clear and concise manner. They are a cornerstone of functional programming in Java and provide a way to represent functional interfaces in a simpler form.

The general syntax of a lambda expression is as follows:

(parameters) -> { body }

Parameters represent a comma-separated list of input parameters to the lambda function. If there’s only one parameter, we can omit the parentheses. The arrow operator separates the parameters from the body of the lambda expression. Finally, the body contains the function logic. If there’s only one statement, we can omit the braces. Typically, the logic in the body will be concise. But it can be complex, multiline logic as per requirements.

Example of crisp lambda expression:

Function<String, String> toUpper = s -> s == null ? null : s.toUpperCase();

Example of complex lambda expression:

IntToLongFunction factorial =
        n -> {
          long result = 1L;
          for (int i = 1; i <= n; i++) {
            result *= i;
          return result;

We can use lambda expressions to create anonymous functions. That allows us to write inline logic without the need for additional class definitions. We can use such anonymous functions where it requires us to pass functional interfaces.

Inner Workings of Lambda Expressions

Have you ever wondered what a lambda expression looks like in Java code and inside the JVM? It’s quite fascinating! In Java, we have two types of values: primitive types and object references. Now, lambdas are definitely not primitive types, which means they must be something else. Well, a lambda expression is actually a special kind of expression that returns an object reference. Isn’t that intriguing?

Let’s decode it. We start by writing a lambda expression in our source code.

For example:

public class Lambda {
  LongFunction<Double> squareArea = side -> (double) (side * side);

When we compile it and check its bytecode using javap command:

javap -c -p Lambda.class
Compiled from ""
public class Lambda {
java.util.function.LongFunction<java.lang.Double> squareArea;

public Lambda();
  0: aload_0
  1: invokespecial #1      // Method java/lang/Object."<init>":()V
  4: aload_0
  5: invokedynamic #7,0//InvokeDynamic #0:apply:()Ljava/util/function/LongFunction;
10: putfield      #11 // Field squareArea:Ljava/util/function/LongFunction;
13: return

private static java.lang.Double lambda$new$0(long);
  0: lload_0
  1: lload_0
  2: lmul
  3: l2d
  4: invokestatic  #17 // Method java/lang/Double.valueOf:(D)Ljava/lang/Double;
  7: areturn

Did you notice that the bytecode starts with a invokedynamic call? Imagine it as a call to a unique factory method. This method returns an instance of a type that implements Runnable. The compiler does not define the specific type in the bytecode and knowing the exact type is not important. It generates the type at runtime when needed, not during compilation.

  • Compilation: When we compile the code, the Java compiler transforms the lambda expression into a form that the Java Virtual Machine (JVM) can understand. Instead of generating a new anonymous inner class, the compiler uses a technique called invokedynamic introduced in Java 7.

  • InvokeDynamic: The invokedynamic bytecode instruction supports dynamic languages on the JVM. For lambdas, it allows the JVM to defer the decision of how to create the lambda instance until runtime. This provides more flexibility and efficiency compared to traditional anonymous inner classes.

  • Lambda Metafactory: When runtime encounters the invokedynamic instruction, it calls a special method called LambdaMetafactory.metafactory(). This method is responsible for creating the actual implementation of the lambda expression. The JVM uses this metafactory method to generate a lightweight class or method handle that represents the lambda.

  • Instance Creation: The LambdaMetafactory dynamically creates an instance of the lambda expression. This instance is typically a singleton if the lambda is stateless (i.e., it doesn’t capture any variables from the enclosing scope). If the lambda captures variables, it creates a new instance with those captured values.

  • Execution: It executes the lambda expression as if it were an instance of an anonymous inner class implementing the functional interface. The JVM ensures that the lambda conforms to the expected functional interface’s single abstract method.

Here are a few examples demonstrating how to use lambda expressions without relying on built-in functional interfaces:

Example 1: Implementing a Custom Functional Interface

We have already seen a custom functional interface for arithmetic operation:

interface ArithmeticOperation {
    int operate(int a, int b);

we create lambda expressions to implement this interface:

ArithmeticOperation add = (a, b) -> a + b;
ArithmeticOperation subtract = (a, b) -> a - b;

Example 2: Anonymous Comparator

It is not mandatory to define a custom functional interface and then use it to declare lambdas:

List<String> words = Arrays.asList("apple", "banana", "cherry");
Collections.sort(words, (s1, s2) ->, s2.length()));

In this example, we created an anonymous comparator to sort a list of strings by length.

Example 3: Runnable for a Thread

We can also use lambda expressions to create a Runnable for threads:

Thread thread = new Thread(() -> {
    System.out.println("Running in a lambda!");

This example demonstrates how we create an executable using lambda.

These examples demonstrate how we can use lambda expressions to define simple, concise functions without explicitly creating additional classes. They are powerful tools for streamlining code and making functional programming in Java more accessible and expressive.

Lambda Expressions and Var

You cannot also use var with lambda expressions because they require an explicit target type. The following assignment will fail:

var addAsVar = (a, b) -> a + b;

It gives the error: Cannot infer type: lambda expression requires an explicit target type.

The code is incorrect because we cannot use var to infer the type of lambda expression itself. We can use the var only for local variable type inference, not for lambda expressions or method return types.

Let’s now see how we can use var in lambda expressions:

ArithmeticOperation add = (var a, var b) -> a + b;

The lambda expression (var a, var b) -> a + b defines a lambda that takes two parameters a and b, both using the var keyword to indicate that the types should be inferred by the compiler. This lambda performs addition on the two parameters.

We can also use bean validation annotations:

ArithmeticOperation addNullSafe = (@NotNull var a, @NotNull var b) -> a + b;

Similar to the previous example, this lambda also takes two parameters with var. Additionally, it uses the @NotNull annotation from bean validation library. This ensures that the parameters a and b should not be null.

Method References

Method references are a shorthand way to refer to existing methods by their name. Instead of using lambda expressions, use method references to write code that is more concise and easier to read. Use method references to pass executable logic. Such deferred method invocation makes them ideal for functional programming scenarios and stream processing.

Java 8 provides four types of method references:

  1. Reference to a Static Method
  2. Reference to an Instance Method of a Particular Type
  3. Reference to an Instance Method of an Arbitrary Object of a Particular Type
  4. Reference to a Constructor

Let’s learn about them.

Reference to a Static Method

A static method reference refers to a static method in a class. It uses the class name followed by :: and the method name:


Let’s see an example of static reference:

public class MethodReferenceTest {
  void staticMethodReference() {
    List<Integer> numbers = List.of(1, -2, 3, -4, 5);
    List<Integer> positiveNumbers =;
    positiveNumbers.forEach(number -> Assertions.assertTrue(number > 0));

The test staticMethodReference in the MethodReferenceTest class verifies the use of a static method reference. It creates a list of integers, numbers, containing both positive and negative values. Using a stream, it applies the Math::abs method reference to convert each number to its absolute value, resulting in a new list, positiveNumbers. The test then checks that each element in positiveNumbers is positive.

Reference to an Instance Method of a Particular Type

This type of method reference refers to an instance method of a specific type.

There are two primary syntaxes for referencing instance methods: using a containing class or using a specific object instance.

Using a Containing Class:


The ContainingClass::instanceMethodName syntax denotes an instance method belonging to a particular class. This method reference is not for a specific object instance but rather signifies that any object of that class can use the method. We commonly use it in stream operations, where we know the object instance at runtime.

For example, we can use String::toLowerCase to refer to the toLowerCase() method on any String object. Use it in a stream operation like .map(String::toLowerCase) to apply it to each string in the stream.

Containing class instance method reference example:

void containingClassInstanceMethodReference() {
  List<String> numbers = List.of("One", "Two", "Three");
  List<Integer> numberChars =;
  numberChars.forEach(length -> Assertions.assertTrue(length > 0));

The containingClassInstanceMethodReference test verifies the use of an instance method reference. It creates a list of strings, numbers, containing “One”, “Two”, and “Three”. Using a stream, it applies the String::length method reference to convert each string into its length, resulting in a new list, numberChars. The test checks that each element in numberChars is greater than zero, ensuring that all strings have a positive length.

Using a Specific Object:


The syntax containingObject::instanceMethodName refers to an instance method of a specific object. It binds this method reference to a particular object, allowing us to call its method directly when needed.

For example, if we have an instance str of String, we can refer to its length() method with str::length. This approach is useful when we need to use a specific object’s method in a lambda expression or a stream operation.

Now let’s see how to use containing object method reference:

// Custom comparator
class StringNumberComparator implements Comparator<String> {
  public int compare(String o1, String o2) {
    if (o1 == null) {
      return o2 == null ? 0 : 1;
    } else if (o2 == null) {
      return -1;
    return o1.compareTo(o2);
void containingObjectInstanceMethodReference() {
  List<String> numbers = List.of("One", "Two", "Three");
  StringNumberComparator comparator = new StringNumberComparator();
  List<String> sorted =;
  List<String> expected = List.of("One", "Three", "Two");
  Assertions.assertEquals(expected, sorted);

The code snippet sorts a list of strings using an instance method reference. The StringNumberComparator class defines a comparison logic for strings. The comparator::compare is a method reference that references the compare method of the StringNumberComparator instance. It passes method reference to sorted(), allowing the stream to sort the numbers list according to the specified comparison logic. The test checks if the sorted list matches the expected order.

Comparison of two syntaxes: Both syntaxes are useful in different scenarios. The class-based method reference is more flexible, allowing us to reference methods without tying them to a specific object. The object-based method reference, on the other hand, is helpful when we want to use a method tied to a specific object instance. Both approaches provide a more concise way to call instance methods without the need for traditional anonymous classes or explicit lambda expressions.

Reference to an Instance Method of an Arbitrary Object of a Particular Type

This type also refers to an instance method, but it determines the exact object at runtime, allowing flexibility when dealing with collections or stream operations:

void instanceMethodArbitraryObjectParticularType() {
  List<Number> numbers = List.of(1, 2L, 3.0f, 4.0d);
  List<Integer> numberIntValues =;
  Assertions.assertEquals(List.of(1, 2, 3, 4), numberIntValues);

The instanceMethodArbitraryObjectParticularType test checks the use of method reference to instance method for an arbitrary object of a particular type. It creates a list of Number objects (numbers) containing various types of numeric values: a int, a long, a float, and a double.

Using a stream, it maps each Number to its integer value using the Number::intValue method reference, resulting in a list of integers (numberInvValues). The test then compares this list with the expected result.

Reference to a Constructor

A constructor reference refers to a class constructor, allowing us to create new instances through a method reference.

Its syntax is as follows:


The ContainingClass::new points to the constructor of a specific class, allowing us to create new instances.

Let’s now see how to use constructor reference:

void constructorReference() {
  List<String> numbers = List.of("1", "2", "3");
  Map<String, BigInteger> numberMapping =
          .collect(Collectors.toMap(BigInteger::toString, Function.identity()));
  Map<String, BigInteger> expected =
      new HashMap<>() {
          put("1", BigInteger.valueOf(1));
          put("2", BigInteger.valueOf(2));
          put("3", BigInteger.valueOf(3));
  Assertions.assertEquals(expected, numberMapping);

The constructorReference test demonstrates the use of a constructor reference in a stream operation. It creates a list of strings (numbers) containing “1”, “2”, and “3”. Using a stream, it maps each string to a BigInteger object by referencing the BigInteger constructor with BigInteger::new.

The test then collects the resulting BigInteger objects into a Map, where the keys are the original strings, and the values are the corresponding BigInteger instances. It uses Collectors.toMap with a lambda expression (BigInteger::toString) to create the keys and Function.identity() for the values.

Finally, the test compares it with an expected map (expected) containing the same key-value pairs.

Let’s summarize the use cases for method references, along with descriptions and examples:

Type of Method Reference Description Example
Reference to a Static Method Refers to a static method in a class. This type of method reference uses the class name followed by :: and the method name. Function<Integer, Integer> square = MathOperations::square;
Reference to an Instance Method of a Particular Object Refers to an instance method of a specific object. The instance must be explicitly defined before using the method reference. Supplier getMessage = stringUtils::getMessage;
Reference to an Instance Method of an Arbitrary Object of a Particular Type Refers to an instance method of an arbitrary object of a specific type. We commonly use this type in stream operations, where Java determines the object type at runtime. List uppercasedWords =
Reference to a Constructor Refers to a class constructor, allowing us to create new instances. This type is useful when we need to create objects without explicitly calling a constructor. Supplier carSupplier = Car::new;


Predicates are functional interfaces in Java that represent boolean-valued functions of a single argument. They are commonly used for filtering, testing, and conditional operations.

The Predicate functional interface is part of the java.util.function package and defines a functional method test(T t) that returns a boolean. It also provides default methods that allow combining two predicates:

public interface Predicate<T> {
    boolean test(T t);
    // default methods

The test() method evaluates the predicate on the input argument and determines whether it satisfies the condition defined by the predicate.

We often use predicates with the stream() API for filtering elements based on certain conditions. Pass them as arguments to methods like filter() to specify the criteria for selecting elements from a collection.

Let’s see filtering in action:

public class PredicateTest {
  void testFiltering() {
    List<Integer> numbers = List.of(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
    Predicate<Integer> isEven = num -> num % 2 == 0;
    List<Integer> actual =;
    List<Integer> expected = List.of(2, 4, 6, 8, 10);
    Assertions.assertEquals(expected, actual);

In the test testFiltering() method, first we populate a list of integers. Then we define a predicate isEven to check if a number is even. Using stream() and filter() methods, we filter the list to contain only even numbers. Finally, we compare the filtered list to the expected list.

Combining Predicates

We can combine predicates using logical operators such as and(), or(), negate() and not() to create complex conditions.

Let’s see how to combine the practices:

void testPredicate() {
  List<Integer> numbers = List.of(-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5);
  Predicate<Integer> isZero = num -> num == 0;
  Predicate<Integer> isPositive = num -> num > 0;
  Predicate<Integer> isNegative = num -> num < 0;
  Predicate<Integer> isOdd = num -> num % 2 == 1;

  Predicate<Integer> isPositiveOrZero = isPositive.or(isZero);
  Predicate<Integer> isPositiveAndOdd = isPositive.and(isOdd);
  Predicate<Integer> isNotPositive = Predicate.not(isPositive);
  Predicate<Integer> isNotZero = isZero.negate();
  Predicate<Integer> isAlsoZero = isPositive.negate().and(isNegative.negate());

  // check zero or greater
  Assertions.assertEquals(List.of(0, 1, 2, 3, 4, 5), 
  // check greater than zero and odd
  Assertions.assertEquals(List.of(1, 3, 5), 
  // check less than zero and negative
  Assertions.assertEquals(List.of(-5, -4, -3, -2, -1, 0), 
  // check not zero
  Assertions.assertEquals(List.of(-5, -4, -3, -2, -1, 1, 2, 3, 4, 5), 
  // check neither positive nor negative

In this test, we have combined predicates to filter a list of numbers. isPositiveOrZero combines predicates for positive numbers or zero. isPositiveAndOdd combines predicates for positive and odd numbers. isNotPositive negates the predicate for positive numbers. isNotZero negates the predicate for zero. isAlsoZero shows us how to chain predicates. We apply each combined predicate to the list, and verify the expected results.

Predicate Evaluation Order

When we combine multiple predicates, we need to pay attention to the order the predicates are evaluated.

Consider the following example:

Predicate<Integer> isPositive = x -> x > 0;
Predicate<Integer> isDiv3 = x -> x % 3 == 0;
Predicate<Integer> isOdd = x -> x % 2 != 0;

Following table explains the evaluation order.

Predicate Expression Evaluation Order Description
Predicate test1 = isPositive.or(isDiv3).and(isOdd)
  1. isPositive or isDiv3
  2. Result and isOdd
First checks if the number is positive or divisible by 3, then checks if the result is odd.
Predicate test2 = isPositive.and(isOdd).or(isDiv3)
  1. isPositive and isOdd
  2. Result or isDiv3
First checks if the number is positive and odd, then checks if the result is true or if the number is divisible by 3.
Predicate test3 = (isPositive.and(isOdd)).or(isDiv3)
  1. isPositive and isOdd
  2. Result or isDiv3
Same as test2, checks if the number is positive and odd, then checks if the result is true or if the number is divisible by 3.
Predicate test4 = isPositive.and((isOdd).or(isDiv3))
  1. isOdd or isDiv3
  2. isPositive and result
First checks if the number is odd or divisible by 3, then checks if the number is positive and the previous result is true.

Let’s deep dive into the evaluation order of the first test: Predicate<Integer> test1: isPositive.or(isDiv3).and(isOdd);

First, it evaluates the isPositive or isDiv3 condition. If isPositive is true, then the result is true. If isPositive is false, proceed to check isDiv3. Then, if isDiv3 is true, the result is true. Finally, if both isPositive and isDiv3 are false, the result is false.

Next, take the result from the first step and evaluate it with isOdd. If the result from the first step is true, check the isOdd condition. If the result from the first step is false, the final result is false.

Similarly, it evaluates the order for other predicates given in the table above.


The BiPredicate<T, U> takes two arguments of types T and U and returns a boolean result. It’s common to use them for testing conditions involving two parameters. For instance, we use BiPredicate to check if one value is greater than the other or if two objects satisfy a specific relationship. We may validate if a person’s age and income meet certain eligibility criteria for a financial service.

BiPredicate defines a test() method with two arguments, and it returns a boolean. It also provides default methods that allow combining two predicates:

public interface BiPredicate<T, U> {
    boolean test(T t, U u);
    // default methods

Let’s now learn how to use the BiPredicate:

public class PredicateTest {
  // C = Carpenter, W = Welder
  private Object[][] workers 
      = {{"C", 24}, {"W", 32}, {"C", 35}, {"W", 40}, {"C", 50}, {"W", 44}, {"C", 30}};

  void testBiPredicate() {

    BiPredicate<String, Integer> juniorCarpenterCheck =
        (worker, age) -> "C".equals(worker) && (age >= 18 && age <= 40);

    BiPredicate<String, Integer> juniorWelderCheck =
        (worker, age) -> "W".equals(worker) && (age >= 18 && age <= 40);

    long juniorCarpenterCount = ->
      juniorCarpenterCheck.test((String) person[0], (Integer) person[1])).count();
    Assertions.assertEquals(3L, juniorCarpenterCount);

    long juniorWelderCount = -> 
      juniorWelderCheck.test((String) person[0], (Integer) person[1])).count();
    Assertions.assertEquals(2L, juniorWelderCount);

In the test, first, we defined an array of workers with their respective ages. We have created two BiPredicate instances: juniorCarpenterCheck and juniorWelderCheck. These predicates evaluate if a worker is within a certain age range (18 to 40) based on their occupation (Carpenter or Welder). Then we use these predicates to filter the array of workers using the test() method. Finally, we count the workers meeting the criteria for junior carpenters and junior welders and verify if they match the expected counts.

Now let’s learn to use the default methods used to combine and negate:

  void testBiPredicateDefaultMethods() {
    // junior carpenters
    BiPredicate<String, Integer> juniorCarpenterCheck =
            (worker, age) -> "C".equals(worker) && (age >= 18 && age <= 40);
    // groomed carpenters
    BiPredicate<String, Integer> groomedCarpenterCheck =
            (worker, age) -> "C".equals(worker) && (age >= 30 && age <= 40);
    // all carpenters
    BiPredicate<String, Integer> allCarpenterCheck =
            (worker, age) -> "C".equals(worker) && (age >= 18);
    // junior welders
    BiPredicate<String, Integer> juniorWelderCheck =
            (worker, age) -> "W".equals(worker) && (age >= 18 && age <= 40);
    // junior workers
    BiPredicate<String, Integer> juniorWorkerCheck 
      = juniorCarpenterCheck.or(juniorWelderCheck);
    // junior groomed carpenters
    BiPredicate<String, Integer> juniorGroomedCarpenterCheck =
    // all welders
    BiPredicate<String, Integer> allWelderCheck = allCarpenterCheck.negate();

    // test or()
    long juniorWorkerCount = -> juniorWorkerCheck
                                   .test((String) person[0], (Integer) person[1]))
    Assertions.assertEquals(5L, juniorWorkerCount);

    // test and()
    long juniorGroomedCarpenterCount 
      = -> juniorGroomedCarpenterCheck
              .test((String) person[0], (Integer) person[1])).count();
    Assertions.assertEquals(2L, juniorGroomedCarpenterCount);

    // test negate()
    long allWelderCount = -> allWelderCheck
                                .test((String) person[0], (Integer) person[1]))
    Assertions.assertEquals(3L, allWelderCount);

The test demonstrates default methods in BiPredicate. It defines predicates for various worker conditions, like junior carpenters and welders. Using default methods or(), and(), and negate(), it creates new predicates for combinations like all junior workers, groomed carpenters, and non-carpenters. We apply these predicates to filter workers, and verify the counts. This showcases how default methods enhance the functionality of BiPredicate by enabling logical operations like OR, AND, and negation.


IntPredicate represents a predicate (boolean-valued function) that takes a single integer argument and returns a boolean result.

public interface IntPredicate {
    boolean test(int value);
    // default methods

This is the int-consuming primitive type specialization of Predicate.

Use IntPredicate to filter collections of primitive integer values or evaluate conditions based on integer inputs. It provides several default methods for composing predicates, including and(), or(), and negate(), allowing for logical combinations of predicates.

Here’s a simple example:

void testIntPredicate() {
  IntPredicate isZero = num -> num == 0;
  IntPredicate isPositive = num -> num > 0;
  IntPredicate isNegative = num -> num < 0;
  IntPredicate isOdd = num -> num % 2 == 1;

  IntPredicate isPositiveOrZero = isPositive.or(isZero);
  IntPredicate isPositiveAndOdd = isPositive.and(isOdd);
  IntPredicate isNotZero = isZero.negate();
  IntPredicate isAlsoZero = isPositive.negate().and(isNegative.negate());

  // check zero or greater
  Assertions.assertArrayEquals(new int[] {0, 1, 2, 3, 4, 5}, 
    IntStream.range(-5, 6).filter(isPositiveOrZero).toArray());

  // check greater than zero and odd
  Assertions.assertArrayEquals(new int[] {1, 3, 5}, 
    IntStream.range(-5, 6).filter(isPositiveAndOdd).toArray());

  // check not zero
  Assertions.assertArrayEquals(new int[] {-5, -4, -3, -2, -1, 1, 2, 3, 4, 5},
    IntStream.range(-5, 6).filter(isNotZero).toArray());

  // check neither positive nor negative
      IntStream.range(-5, 6).filter(isZero).toArray(),
      IntStream.range(-5, 6).filter(isAlsoZero).toArray());

The testIntPredicate() method demonstrates various scenarios using IntPredicate. Predicates like isZero, isPositive, and isNegative check specific conditions on integers. Combined predicates like isPositiveOrZero and isPositiveAndOdd perform logical operations. Tests verify filtering of integer ranges based on these predicates, ensuring correct outcomes for conditions like zero or greater, greater than zero and odd, not zero, and neither positive nor negative. Each assertion validates the filtering results against expected integer arrays, covering a wide range of scenarios.

Like IntPredicate, we also have LongPredicate and DoublePredicate. These can be used to handle long and double values.


The Function functional interface in Java represents a single-valued function that takes one argument and produces a result. It’s part of the java.util.function package.

The Function Interface and Its Variants

The Function interface contains a single abstract method called apply(), which takes an argument of type T and returns a result of type R.

public interface Function<T, R> {
  R apply(T t);
  // default methods

This interface enables developers to define and use functions that transform input values into output values, facilitating various data processing tasks. With Function we create reusable and composable transformations, making code more concise and expressive. We widely use it for mapping, filtering, and transforming data streams.

Function interface has several variants like BiFunction, IntFunction, and more. We’ll also learn about them in sections to follow.

Let’s witness the power of Function in action:

void simpleFunction() {
  Function<String, String> toUpper = s -> s == null ? null : s.toUpperCase();
  Assertions.assertEquals("JOY", toUpper.apply("joy"));

The test applies a Function to convert a string to uppercase. It asserts the converted value and also checks for null input handling.

Function Composition

Function composition is a process of combining multiple functions to create a new function. The compose() method in the Function interface combines two functions by applying the argument function first and then the caller function. Conversely, the andThen() method applies the caller function first and then the argument function.

For example, if we have two functions: one to convert a string to upper case and another to remove vowels from it, we can compose them using compose() or andThen(). If we use compose(), it first converts the string to uppercase and then removes vowels from it. Conversely, if we use andThen(), it first removes vowels from it and then converts the string to uppercase.

Let’s verify function composition:

void functionComposition() {
  Function<String, String> toUpper = s -> s == null ? null : s.toUpperCase();
  Function<String, String> replaceVowels =
      s ->
          s == null
              ? null
              : s.replace("A", "")
                  .replace("E", "")
                  .replace("I", "")
                  .replace("O", "")
                  .replace("U", "");
  Assertions.assertEquals("APPLE", toUpper.compose(replaceVowels).apply("apple"));
  Assertions.assertEquals("PPL", toUpper.andThen(replaceVowels).apply("apple"));

In the functionComposition test, we compose two functions to manipulate a string. The first function converts the string to uppercase, while the second one removes vowels. Using compose(), it first removes vowels and then converts to uppercase. Using andThen(), it first converts to uppercase and then removes vowels. We verify the results using assertion.


The BiFunction interface represents a function that accepts two arguments and produces a result. It’s similar to the Function interface, but it operates on two input parameters instead of one:

public interface BiFunction<T, U, R> {
  R apply(T t, U u);
  // default methods

This is the specialized version of Function with two arguments. It is a functional interface that defines the apply(Object, Object) functional method.

For example, suppose we have a BiFunction that takes two integers as input and returns the bigger number.

Let’s define it and test the results:

void biFunction() {
  BiFunction<Integer, Integer, Integer> bigger =
      (first, second) -> first > second ? first : second;
  Function<Integer, Integer> square = number -> number * number;

  Assertions.assertEquals(10, bigger.apply(4, 10));
  Assertions.assertEquals(100, bigger.andThen(square).apply(4, 10));

The BiFunction interface combines two input values and produces a result. In this test, bigger selects the larger of two integers. square then calculates the square of a number. The result of bigger is passed to square, which squares the larger integer.


The IntFunction interface represents a function that takes an integer as input and produces a result of any type.

public interface IntFunction<R> {
  R apply(int value);

This represents the int-consuming specialization for Function. It is a functional interface with a functional method named apply(int).

We can define custom logic based on integer inputs and return values of any type, making it versatile for various use cases in Java programming.

Let’s witness the IntFunction in action:

void intFunction() {
  IntFunction<Integer> square = number -> number * number;
  Assertions.assertEquals(100, square.apply(10));

The test applies a IntFunction to compute the square of an integer. It ensures that the square function correctly calculates the square of the input integer.

Similarly, we have LongFunction and DoubleFunction that accept long and double results respectively.


The IntToDoubleFunction interface represents a function that accepts an int-valued argument and produces a double-valued result.

public interface IntToDoubleFunction {
  double applyAsDouble(int value);

This is the specialized int-to-double conversion for the Function interface. It is a functional interface with a method called applyAsDouble(int).

Let’s explore the implementation of IntToDoubleFunction:

void intToDoubleFunction() {
  int principalAmount = 1000; // Initial investment amount
  double interestRate = 0.05; // Annual interest rate (5%)

  IntToDoubleFunction accruedInterest = principal -> principal * interestRate;
  Assertions.assertEquals(50.0, accruedInterest.applyAsDouble(principalAmount));

In this example, IntToDoubleFunction is used to define a function accruedInterest that calculates the interest accrued based on the principal amount provided as an integer input. Then the test verifies the calculated interest.

Similarly, we have IntToLongFunction, LongToIntFunction, LongToDoubleFunction, DoubleToIntFunction and DoubleToLongFunction to map the input to respective result types.

Functions and Stream Operations

Functional interfaces like IntToDoubleFunction and IntToLongFunction are particularly useful when working with streams of primitive data types. For instance, if we have a stream of integers, and we need to perform operations that require converting those integers to doubles or longs, we can use these functional interfaces within stream operations like mapToInt, mapToDouble, and mapToLong. This allows us to efficiently perform transformations on stream elements without the overhead of autoboxing and unboxing.


The ToIntFunction interface represents a function that produces an int-valued result.

public interface ToIntFunction<T> {
  int applyAsInt(T t);

This is the integer-producing primitive specialization for the Function interface. It provides a template for functions that take an argument and return a result. Its specialization, applyAsInt(Object), is a functional method specifically designed to produce an integer result. Its purpose is to allow for operations on data that return a primitive integer, thereby improving performance by avoiding unnecessary object wrappers. This specialization is an essential tool in functional programming paradigms within Java, allowing developers to write cleaner and more efficient code.

Let’s see how we can use the ToIntFunction in action:

void toIntFunction() {
  ToIntFunction<String> charCount = input -> input == null ? 0 : input.trim().length();

  Assertions.assertEquals(0, charCount.applyAsInt(null));
  Assertions.assertEquals(0, charCount.applyAsInt(""));
  Assertions.assertEquals(3, charCount.applyAsInt("JOY"));

This test counts the characters in a string using a function. It verifies the character count for null, empty string, and “JOY”, expecting 0, 0, and 3, respectively. The function handles null inputs gracefully, returning 0, and trims white space before counting characters.

Similarly, we have ToLongFunction and ToDoubleFunction that produce long and double results respectively.


The ToIntBiFunction interface represents a function that accepts two arguments and produces an int-valued result.

public interface ToIntBiFunction<T, U> {
  int applyAsInt(T t, U u);

The int-producing primitive specialization for BiFunction is a functional interface that contains a single abstract method called applyAsInt(), which takes two input parameters of type Object and returns a int.

Let’s discover how to use ToIntBiFunction:

void toIntBiFunction() {
  // discount on product
  ToIntBiFunction<String, Integer> discount =
      (season, quantity) -> "WINTER".equals(season) || quantity > 100 ? 40 : 10;

  Assertions.assertEquals(40, discount.applyAsInt("WINTER", 50));
  Assertions.assertEquals(40, discount.applyAsInt("SUMMER", 150));
  Assertions.assertEquals(10, discount.applyAsInt("FALL", 50));

This test calculates discounts based on the season and quantity. If it’s winter or the quantity exceeds 100, we apply a 40% discount, otherwise, it’s 10%. The test validates discounts for winter with 50 items, summer with 150 items, and fall with 50 items, expecting 40, 40, and 10, respectively.

Similarly, we have ToLongBiFunction and ToDoubleBiFunction that produce long and double results respectively.


We’ll now explore operators, fundamental functional interfaces in Java. We commonly use operators to perform operations on data, such as mathematical calculations, comparisons, or logical operations. Furthermore, we use operators to transform or manipulate data in our programs. These interfaces provide a way to encapsulate these operations, making our code more concise and readable. Whether it’s adding numbers, checking for equality, or combining conditions, operators play a crucial role in various programming scenarios, offering flexibility and efficiency in our code.

Let’s learn about unary and binary operators.


The UnaryOperator interface represents an operation on a single operand that produces a result of the same type as its operand.

public interface UnaryOperator<T> extends Function<T, T> {
  // helper methods

This is a specialization of Function for the case where the operand and result are of the same type. This is a functional interface whose functional method is apply(Object).

Let’s check out an example of UnaryOperator:

public class OperatorTest {
  void unaryOperator() {
    UnaryOperator<String> trim = value -> value == null ? null : value.trim();
    UnaryOperator<String> upperCase 
      = value -> value == null ? null : value.toUpperCase();
    Function<String, String> transform = trim.andThen(upperCase);

    Assertions.assertEquals("joy", trim.apply("  joy "));
    Assertions.assertEquals("  JOY ", upperCase.apply("  joy "));
    Assertions.assertEquals("JOY", transform.apply("  joy "));

In the OperatorTest, unary operators trim and convert strings. The transform function combines them, trimming white space and converting to uppercase. Tests verify individual and combined functionalities.


The IntUnaryOperator interface represents an operation on a single int-valued operand that produces an int-valued result.

public interface IntUnaryOperator {
  int applyAsInt(int operand);
  // helper methods

This represents the primitive type specialization of UnaryOperator for integers. It’s a functional interface featuring a method named applyAsInt(int).

Let’s learn how to use the IntUnaryOperator:

void intUnaryOperator() {
  // formula y = x^2 + 2x + 1
  IntUnaryOperator formula = x -> (x * x) + (2 * x) + 1;
  Assertions.assertEquals(36, formula.applyAsInt(5));

  IntStream input = IntStream.of(2, 3, 4);
  int[] result =;
  Assertions.assertArrayEquals(new int[] {9, 16, 25}, result);

  // the population doubling every 3 years, one fifth migrate and 10% mortality
  IntUnaryOperator growth = number -> number * 2;
  IntUnaryOperator migration = number -> number * 4 / 5;
  IntUnaryOperator mortality = number -> number * 9 / 10;
  IntUnaryOperator population = growth.andThen(migration).andThen(mortality);
  Assertions.assertEquals(1440000, population.applyAsInt(1000000));

This test defines an IntUnaryOperator to calculate a quadratic formula, then applies it to an array. It also models population growth, migration, and mortality rates, calculating the population size.

Similarly, we have LongUnaryOperator and DoubleUnaryOperator that produce long and double results respectively.


The BinaryOperator interface represents operation upon two operands of the same type, producing a result of the same type as the operands.

public interface BinaryOperator<T> extends BiFunction<T,T,T> {
  // helper methods

BiFunction is a specialized functional interface. We use it when the operands and the result are all the same type. It has a functional method called apply() that takes two objects as input and produces an object of the same type as the operands.

Let’s try out BinaryOperator:

void binaryOperator() {
  LongUnaryOperator factorial =
      n -> {
        long result = 1L;
        for (int i = 1; i <= n; i++) {
          result *= i;
        return result;
  // Calculate permutations
  BinaryOperator<Long> npr 
    = (n, r) -> factorial.applyAsLong(n) / factorial.applyAsLong(n - r);
  // Verify permutations
  // 3P2: the number of permutations of 2 that can be achieved from a choice of 3.
  Long result3P2 = npr.apply(3L, 2L);
  Assertions.assertEquals(6L, result3P2);

  // Add two prices
  BinaryOperator<Double> addPrices = Double::sum;
  // Apply discount
  UnaryOperator<Double> applyDiscount = total -> total * 0.9; // 10% discount
  // Apply tax
  UnaryOperator<Double> applyTax = total -> total * 1.07; // 7% tax
  // Composing the operation
  BiFunction<Double, Double, Double> finalCost =

  // Prices of two items
  double item1 = 50.0;
  double item2 = 100.0;
  // Calculate cost
  double cost = finalCost.apply(item1, item2);
  // Verify the calculated cost
  Assertions.assertEquals(144.45D, cost, 0.01);

In this test, we define a factorial function and use it to compute permutations (nPr). For pricing, we combine BinaryOperator<Double> for summing prices with UnaryOperator<Double> for applying discount and tax, and then validate the cost calculations.


The IntBinaryOperator interface represents an operation upon two int-valued operands and produces an int-valued result.

public interface IntBinaryOperator {
  int applyAsInt(int left, int right);

This is the primitive type specialization of BinaryOperator for numbers. It’s a special type of interface that has a functional method called applyAsInt(), which takes two numbers as input and returns an integer.

Here’s an example of how to use the IntBinaryOperator. Check it out:

void intBinaryOperator() {
  IntBinaryOperator add = Integer::sum;
  Assertions.assertEquals(10, add.applyAsInt(4, 6));

  IntStream input = IntStream.of(2, 3, 4);
  OptionalInt result = input.reduce(add);
  Assertions.assertEquals(OptionalInt.of(9), result);

In this test, we use IntBinaryOperator to sum two integers. We use it to add two numbers and apply it to a stream to sum all elements. We validate both operations. The reduce() method with IntBinaryOperator is useful for operations like summing, finding the maximum or minimum, or other cumulative operations on stream elements.

Similarly, we have LongBiaryOperator and DoubleBiaryOperator that produce long and double results respectively.


A Consumer is a functional interface that represents an operation that accepts a single input argument and returns no result. It is part of the java.util.function package. Unlike most other functional interfaces, we use it to perform side-effect operations on an input, such as printing, modifying state, or storing values.


The Consumer Represents an operation that accepts a single input argument and returns no result:

public interface Consumer<T> {
  void accept(T t);
  // default methods

Consumer is a unique functional interface that stands out from the rest because it operates through side effects. It performs actions rather than returning a value. The functional method of Consumer is accept(Object), which allows it to accept an object and perform some operation on it.

Consumers are particularly useful in functional programming and stream processing, where we perform operations on elements of collections or streams in a concise and readable manner. They enable us to focus on the action to be performed rather than the iteration logic.

Example showcasing use of Consumer:

void consumer() {
  Consumer<List<String>> trim =
      strings -> {
        if (strings != null) {
          strings.replaceAll(s -> s == null ? null : s.trim());
  Consumer<List<String>> upperCase =
      strings -> {
        if (strings != null) {
          strings.replaceAll(s -> s == null ? null : s.toUpperCase());

  List<String> input = null;
  input = Arrays.asList(null, "", " Joy", " Joy ", "Joy ", "Joy");
  Assertions.assertEquals(Arrays.asList(null, "", "Joy", "Joy", "Joy", "Joy"), input);

  input = Arrays.asList(null, "", " Joy", " Joy ", "Joy ", "Joy");
  Assertions.assertEquals(Arrays.asList(null, "", "JOY", "JOY", "JOY", "JOY"), input);

The test demonstrates the use of the Consumer interface to perform operations on a list of strings. The consumer trim trims white space from each string and the consumer upperCase converts them to uppercase. It shows the composition of consumers using andThen to chain operations.


The BiConsumer represents an operation that accepts two input arguments and returns no result.

public interface BiConsumer<T, U> {
  void accept(T t, U u);
  // default methods

This is the special version of Consumer that takes two arguments. Unlike other functional interfaces, BiConsumer results in side effects. It is a functional interface with a functional method called accept(Object, Object).

We’re going to figure out how to utilize BiConsumer in following example:

void biConsumer() {
  BiConsumer<List<Double>, Double> discountRule =
      (prices, discount) -> {
        if (prices != null && discount != null) {
          prices.replaceAll(price -> price * discount);
  BiConsumer<List<Double>, Double> bulkDiscountRule =
      (prices, discount) -> {
        if (prices != null && discount != null && prices.size() > 2) {
          // 20% discount cart has 2 items or more
          prices.replaceAll(price -> price * 0.80);

  double discount = 0.90; // 10% discount
  List<Double> prices = null;
  prices = Arrays.asList(20.0, 30.0, 100.0);
  discountRule.accept(prices, discount);
  Assertions.assertEquals(Arrays.asList(18.0, 27.0, 90.0), prices);

  prices = Arrays.asList(20.0, 30.0, 100.0);
  discountRule.andThen(bulkDiscountRule).accept(prices, discount);
  Assertions.assertEquals(Arrays.asList(14.4, 21.6, 72.0), prices);

This test demonstrates the use of the BiConsumer interface to apply discounts to a list of prices. The BiConsumer applies a standard discount and a bulk discount if there are more than two items in the list.

Next, we’ll explore various specializations of consumers and provide examples to illustrate their use cases.


The IntConsumer an operation that accepts a single int-valued argument and returns no result.

public interface IntConsumer {
  void accept(int value);
  // default methods

IntConsumer is a specialized type of Consumer for integers. Unlike most other functional interfaces, IntConsumer produces side effects. It is a functional interface with a method called accept(int).

Here is an illustration of how to use the IntConsumer interface:

  "15,Turning off AC.",
  "25,Turning on AC.",
  "52,Alert! Temperature not safe for humans."
void intConsumer(int temperature, String expected) {
  AtomicReference<String> message = new AtomicReference<>();
  IntConsumer temperatureSensor =
      t -> {
        if (t <= 20) {
          message.set("Turning off AC.");
        } else if (t >= 24 && t <= 50) {
          message.set("Turning on AC.");
        } else if (t > 50) {
          message.set("Alert! Temperature not safe for humans.");

  Assertions.assertEquals(expected, message.toString());

This test verifies a IntConsumer handling temperature sensor responses. Depending on the temperature, it sets a message indicating if the AC should be turned off, turned on, or if we need an alert. The @ParameterizedTest runs multiple scenarios, checking the expected message for each temperature input.

Similarly, we have LongConsumer and DoubleConsumer that consume long and double inputs respectively.


The ObjIntConsumer an operation that accepts an object-valued and an int-valued argument, and returns no result.

public interface ObjIntConsumer<T> {
  void accept(T t, int value);

ObjIntConsumer interface is a special type of BiConsumer. Unlike most other functional interfaces, ObjIntConsumer is designed to work by directly changing the input. Its functional method is accept(Object, int).

Let’s now check how to use ObjIntConsumer:

void objIntConsumer() {
  AtomicReference<String> result = new AtomicReference<>();
  ObjIntConsumer<String> trim =
      (input, len) -> {
        if (input != null && input.length() > len) {
          result.set(input.substring(0, len));

  trim.accept("123456789", 3);
  Assertions.assertEquals("123", result.get());

The test applies a ObjIntConsumer to trim a string if its length exceeds a given limit. It asserts the trimmed string.

Similarly, we have ObjLongConsumer and ObjDoubleConsumer that consume long and double inputs respectively.


The Supplier functional interface represents a supplier of results. Unlike other functional interfaces like Function or Consumer, the Supplier doesn’t accept any arguments. Instead, it provides a result of a specified type when called. This makes it particularly useful in scenarios where we need to generate or supply values without any input.

We commonly use suppliers for lazy evaluation to enhance performance by postponing expensive computations until necessary. We can use suppliers in factory methods to create new object instances, in dependency injection frameworks, or to encapsulate object creation logic. Suppliers also retrieve cached values, generate missing values, and store them in the cache. Additionally, suppliers provide default configurations, fallback values, or mock data for testing isolated components.


Supplier represents a supplier of results.

public interface Supplier<T> {
    T get();

Each time we invoke a supplier, it may return a distinct result or predefined result. This is a functional interface whose functional method is get().

Let’s consider a simple example where we generate a random number:

public class SupplierTest {
  void supplier() {
      // Supply random numbers
      Supplier<Integer> randomNumberSupplier = () -> new Random().nextInt(100);
      int result = randomNumberSupplier.get();
      Assertions.assertTrue(result >=0 && result < 100);

In this test, randomNumberSupplier generates a random number between 0 and 99. The test verifies that the generated number is within the expected range.

Lazy Initialization

Traditionally, we populate the needed data first and then pass it to the processing logic. With suppliers, that is no longer needed. We can now defer it to the point when it is actually needed. The supplier would generate the data when we call get() method on it. We may not use the input due to conditional logic. Sometimes such preparations are costly e.g., file resource, network connection. In such cases, we could even avoid such eager preparation of costly inputs.


IntSupplier represents a supplier of int-valued results.

public interface IntSupplier {
    int getAsInt();

This specialized version of Supplier produces int values. It offers the flexibility to return a distinct result for each invocation. As a functional interface, it provides the getAsInt() functional method as its core functionality.

Here is an example showcasing the use of IntSupplier:

void intSupplier() {
  IntSupplier nextWinner = () -> new Random().nextInt(100, 200);
  int result = nextWinner.getAsInt();
  Assertions.assertTrue(result >= 100 && result < 200);

In this test, nextWinner generates a random number between 100 and 199. The test verifies that the generated number is within this range by asserting the result is at least 100 and less than 200.

Similarly, we have LongSupplier, DoubleSupplier and BooleanSupplier that produce long, double and boolean results respectively.

BooleanSupplier Use Cases

While it’s true that a boolean value can only be true or false, a BooleanSupplier can be useful in scenarios where the boolean value needs to be determined dynamically based on some conditions or external factors. Here are a few practical use cases:

  • Feature Flags: In applications with feature toggles, use a BooleanSupplier to check whether a feature is on or off.
  • Conditional Execution: Use it to decide whether to execute certain logic based on dynamic conditions.
  • Health Checks: In microservices, determine the health status of a service or component using it.
  • Security: It can check if a user has the necessary permissions to access a resource or perform an action.


In this article, we learned functional interfaces, and how functional programming and lambda expressions bring a new level of elegance and efficiency to our code. We began by understanding the core concept of functional programming, where functions are first-class citizens, allowing us to pass and return them just like any other variable.

Then we dip dived into Function interfaces, which enable us to create concise and powerful transformations of data. Method references provided a shorthand for lambda expressions, making our code even cleaner and more readable.

Predicates, as powerful boolean-valued functions, helped us filter and match conditions seamlessly. We then moved on to operators, which perform operations on data, and consumers, which act on data without returning any result. This is particularly useful for processing lists and other collections in a streamlined manner.

Lastly, we explored suppliers, which generate data on demand, perfect for scenarios requiring dynamic data creation, such as random number generation or data sampling.

Each of these functional interfaces has shown us how to write more modular, reusable, and expressive code. By leveraging these idioms, we’ve learned to tackle complex tasks with simpler, more readable solutions. Embracing these concepts helps us become more effective Java developers, capable of crafting elegant and efficient code.

Happy coding! 🚀

Written By:

Sachin Raverkar

Written By:

Sachin Raverkar

Sachin is a Java enthusiast with over two decades of product development expertise. He enjoys architecting and delivering SAAS products as well as sharing expertise with people all over the world.

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