Java was the first programming language I was taught at University, and the language I used for the first decade of my career. It continues to be a reliable companion throughout my software development career. Unfortunately, not having developed with Java professionally for several years, I’ve found there are many aspects of the modern Java language that I’m simply not familiar with. To rectify this, I’ve collected the major improvements to the language beginning with Java 8, combined with a short explanation of how they work and how to use them. It assumes you know Java, but don’t really know Java. Hopefully, it can take you from experienced beginner to just plain experienced again.

Table of Contents

1 Lambda Expressions


Lambdas provide a concise shorthand for behaviour parameterization allowing you to pass executable code to a function that will be executed at a later point in time. Prior to Java 8, we used anonymous classes for this purpose, but anonymous classes are a somewhat unsatisfying solution. Technically speaking, lambdas in Java don’t actually let you do anything you couldn’t do before, but they vastly simplify the code and interface for passing code to methods.

For example, the following code uses an implementation of the Comparator interface to compare two students by their GPA.

Comparator<Student> byGpa = new Comparator<Student>() {
    public int compare(Student s1, Student s2){
        return s1.getGradePointAverage().compareTo(s2.getGradePointAverage());

With lambda expressions, this code can be simplified by removing the need for implementing a class and the compare method.

Comparator<Student> byGpa =
    (Student s1, Student s2) -> s1.getGradePointAverage().compareTo(s2.getGradePointAverage());

Functional Interfaces

Lambdas can be used wherever a functional interface is expected. In Java, a language not known to be functional, a functional interface means an interface with exactly one abstract method.

For example, consider the Runnable interface that should be implemented by any class that intends to have instances executed by a thread. To implement the interface correctly, the class must define a single method of no arguments called run.

public interface Runnable {
  void run();

The Runnable interface is a functional interface, and lambda expressions can be used to provide the implementation. They do this by treating the lambda expression as the implementation of the abstract method defined by the functional interface. The following example shows an implementation of the Runnable interface using a lambda expression. The lambda expression is treated as the concrete implementation of the single abstract method defined by the interface.

Implementation Runnable Using a Lambda Expression

For convenience, interfaces that specify a single abstract method can be marked with the @FunctionalInterface annotation. This annotation is useful for documentation, and the compiler will report an error if you try and mark an interface as functional that doesn’t match the “only one abstract method” requirement. The annotation isn’t mandatory, but is best practice to include it.

Lambda Syntax

The simplest lambda expression is a single parameter followed by an expression.

parameter -> expression

If you have more than one parameter, you can wrap them in parenthesis.

(parameter1, parameter2) -> expression

In the previous code blocks, the expression is immediately evaluated and the value is returned. This means that expressions cannot contain variables or control flow statements. To do something more complex you can define a lambda with a code block wrapped in curly braces instead of an expression. If the lambda expression needs to return a value, then the code block should include a return statement.

(parameter1, parameter2) -> { code block }

Using Lambda Expressions

Lambda expressions can be used wherever functional interface is expected, and we now know some of the syntax required to implement them. Let’s walk through a few examples of using them to get a sense for how they work.


The Consumer<T> interface is a functional interface with a single abstract method accept that performs an operation on the input item. The interface is implemented like this.

public interface Consumer<T> {

   * Performs this operation on the given argument.
   * @param t the input argument
  void accept(T t);

Anything implementing Iterable includes a default method forEach that takes as a parameter an action of the type Consumer<T>. Because this interface is functional, we can use lambda expressions to satisfy this parameter. The following code block defines a simple list and uses an expression to simply print out each element.

ArrayList<Integer> numbers = new ArrayList<Integer>();
// add numbers to array
numbers.forEach( n -> System.out.println(n));

We can also define this lambda using code blocks and an explicit return statement.

ArrayList<Integer> numbers = new ArrayList<Integer>();
// add numbers to array
numbers.forEach( n -> { return System.out.println(n); });

The Predicate<T> interface defines a single method test that accepts an input parameter and returns true or false.

public interface Predicate<T> {

   * Evaluates this predicate on the given argument.
   * @param t the input argument
   * @return {@code true} if the input argument matches the predicate,
   * otherwise {@code false}
  boolean test(T t);

The Predicate<T> interface is used by the filter function of the Stream<T> interface to remove elements from a list that don’t belong (i.e. that return false when tested).

Stream<Integer> positiveNumbers = -> n > 0);

A common pattern in GUI development is to write your logic using callbacks that respond to user input. For example, when a user clicks on a button, the GUI framework calls some function code you have implemented to respond to the input. This pattern is a natural fit for lambda expressions.

In JavaFX, the EventHandler interface is a functional interface defined for this callback purpose. The interface contains a single method handle accepting a parameter for the event to respond to.

public interface EventHandler<T extends Event> extends EventListener {
  void handle(T event);

The interface can be implemented using a lambda to respond to GUI events.

ToggleButton button = new ToggleButton("Click");
final StringProperty btnText = button.textProperty();
button.setOnAction((event) -> {    // lambda expression
   ToggleButton source = (ToggleButton) event.getSource();
   if(source.isSelected()) {
   } else {

Method References

Method references are a close cousin to lambda expressions that allow you to pass existing method definitions to functions as if they were defined as lambdas.

To create a method reference use the <class>::<method> syntax, where <class> is the target class where the method you wish to reference exists and <method> is the method your wish to reference from the class. As a concrete example, consider the simple getName() method of a fictional Student class.

public class Student {
  private String name;

  public String getName() {
    return name;

A method reference to the getName method is simply Student::getName. Functionally, Student::getName is equivalent to (Student s) -> s.getName(). The method reference or the lambda can be used interchangeably — it is typically a matter of preference and readability when deciding which one to use.

2 Streams

Stream Basics

Streams are an update to the Java API that let you interact with collections of data declaratively.

When using streams, you state what you want to achieve, as opposed to specifying how to implement an operation. Another way to think about this approach is that the iteration model is decoupled from the implementation — with streams, iteration is handled by the compiler, and the programmer focuses on the logic.

Another benefit of streams is the ability to chain together several building-block operations to express complicated data-processing pipelines while keeping your code readable and its intention clear.

The best way to get started using a stream is with an existing collection.

We start our discussion of streams with collections, because that’s the simplest way to begin working with streams. Collections in Java 8 support a new stream method that returns a stream. But what exactly is a stream?

A short definition from the book “Modern Java in Action” is “a sequence of elements from a source that supports data-processing operations.” Let’s break down this definition step-by-step:

  • Sequence of elements— Like a collection, a stream provides an interface to a sequenced set of values of a specific element type. Because collections are data structures, they’re mostly about storing and accessing elements with specific time/space complexities (for example, an ArrayList versus a LinkedList). But streams are about expressing computations such as filter, sorted, and map, which you saw earlier. Collections are about data; streams are about computations.
  • Source— Streams consume from a data-providing source such as collections, arrays, or I/O resources. Note that generating a stream from an ordered collection preserves the ordering. The elements of a stream coming from a list will have the same order as the list.
  • Data-processing operations— Streams support database-like operations and common operations from functional programming languages to manipulate data, such as filter, map, reduce, find, match, sort, and so on. Stream operations can be executed either sequentially or in parallel.

Modern Java in Action

The Streams API

The Streams API introduced in Java 8 and extended in Java 9 provides an extensive set of built-in operations allowing you to declaratively express fairly complex data processing operations. This article looks at how to use the Streams API to address some of the most common operations you might come across.

Use Case: Filtering

One common case when working with collections of data is selecting a subset of elements. Two ways to do this with the Streams API is by using filter and distinct

Stream<T> filter(Predicate<? super T> predicate);

The Stream interface supports this through the filter method. filter takes a single parameter, the predicate, which is a function that returns a boolean value, and it returns a Stream that includes all elements that the predicate evaluates as true, removing elements that evaluate as false. Because Predicate is a functional interface, you can also pass a lambda expression as the predicate parameter.

For example, the following block of code filters a list of Products to only include those priced greater than $3000 using a lambda expression.

class Product {
  int id;
  String name;
  float price;

  public Product(int id, String name, float price) { = id; = name;
    this.price = price;

public class StreamExample {
  public static void main(String[] args) {
    List<Product> productsList = new ArrayList<>();
    productsList.add(new Product(1, "HP Laptop", 2500f));
    productsList.add(new Product(2, "Dell Laptop", 3000f));
    productsList.add(new Product(3, "Lenevo Laptop", 2800f));
    productsList.add(new Product(4, "Sony Laptop", 2800f));
    productsList.add(new Product(5, "Apple Laptop", 5000f));

    List<Product> expensiveProducts =
            .filter(p -> p.price > 3000) // filtering by price
Stream<T> distinct();

distinct that is closely related to filter but returns a stream with unique elements only. Uniqueness is judged by the implementation of equals and hashcode methods of the objects in the stream. For example, the following code filters all even numbers from a list and then eliminates duplicates.

List<Integer> numbers = Arrays.asList(1, 2, 1, 3, 3, 2, 4);
   .filter(i -> i % 2 == 0) // filter event numbers
   .distinct() // return only unique numbers

Use Case: Slicing

Another common use case is to extract subsets of a list.

Stream<T> takeWhile(Predicate<? super T> predicate);

takeWhile allows you to create a slice of a stream by a taking elements until one is found that does not match predicate. For example, the following block of code creates a stream of integers, and takes from that stream until it finds an integer greater than or equal to 4 and predicate returns false.

    .takeWhile(i -> i < 4 )

// prints 123

Contrast this with filter. Given the same stream, filter will return all elements matching predicate.

    .filter(i -> i < 4 )
// prints 123321
Stream<T> dropWhile(Predicate<? super T> predicate);

dropWhile is the complement of takeWhile. It throws away the elements at the start where the predicate is false. Once the predicate evaluates to true it stops and returns everything that remains. For example, the following code removes elements from the beginning of the list that do not match the predicate.

    .dropWhile(i -> i < 4 )

// prints 45
Stream<T> limit(long maxSize);

limit(n) returns a stream that is truncated to the size n. It simply returns the first n elements the order they are encountered. For example, we can take a previous filter operation and restrict it to only return two elements.

    .filter(i -> i < 4 )

    // prints 12
Stream<T> skip(long n);

The skip(n) method is an intermediate operation that discards the first n elements of a stream. It is a nice complement to limit. For example, the following block of code filters all even numbers, but uses skip to skip the first two.

Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
    .filter(i -> i % 2 == 0)

    // prints 6810

Use Case: Mapping

A common theme in data processing is to select information or apply a function to certain elements of the data. The Streams API provides these idioms through the map and flatMap methods.

<R> Stream<R> map(Function<? super T, ? extends R> mapper);

map applies a function to each element of a stream and returns the result to create a new stream. map takes a single parameter mapper that is a functional interface of type Function.

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

The Function interface contains a single method apply that takes an element of type T and returns an element of type R. The most direct way to think of this interface is to relate it to math. The function $$ f(x) = y $$ is a function that takes an argument $$ x $$ and returns a result $$ y $$.

As a concrete example, we can take a stream of String and calculate the length of each of them by applying the String.length function on each:

List<String> words =
    Arrays.asList("Java", "For", "People", "Who", "Forgot", "They", "Knew", "Java");

List<Integer> wordLengths =


// prints [4, 3, 6, 3, 6, 4, 4, 4]
<R> Stream<R> flatMap(Function<? super T, ? extends Stream<? extends R>> mapper);

The flatMap operation is similar to map. However, flatMap flattens streams in addition to mapping the elements in those streams. It replaces each value of a stream with another stream and then concatenates all the generated streams into a single stream. That is a mouthful!! A simple example can help. More generally, flatmapping refers to the process of taking a nested collection and flattening it into a single collection:

List of lists: [[1, 2, 3], [4, 5, 6, 7]]
Flattened list: [1, 2, 3, 4, 5, 6, 7]

Let’s break this down a little by first looking at the mapper parameter. It’s type signature is Function<? super T, ? extends Stream<? extends R>> mapper which reads as a Function of two types: T and Stream<R>. Returning to our math example, the flatMap method takes a function similar to map, $$ f(x) = y $$. The difference in this case is that the return value $$ y $$ is a stream, and not a singular value.

Lastly, flatMap returns a Stream<R>. If our mapper parameter creates a Stream<R> with each invocation, our stream would look something like [Stream<R>, Stream<R>, Stream<R>]. What flatMap does is concatenate these results together to return a single stream.

At this point, an example is in order. Let’s start simply. Consider the case where our input is a list of lists:

List<List<String>> list = Arrays.asList(
// prints [[a], [b]]

Now, for each element of this list, we run the map function to create an underlying stream for each sub list. This returns an object of type Stream.

// prints [$Head@2af004b,$Head@248e319b]

We can use flatMap to concatenate these streams together to get a final result that strips the wrapping Stream from the previous version.

// prints [a, b]

As a more practical example, lets look at how you might create pairs of integers from two different streams. You could use two map operations to iterate on the two lists and then generate the pairs. Unfortunately, this would return a Stream<Stream<Integer[]>>. What you need to do is flatten the generated streams to return Stream<Integer[]>:

List<Integer> numbers1 = Arrays.asList(1, 2, 3);
List<Integer> numbers2 = Arrays.asList(3, 4);
List<int[]> pairs =
        .flatMap(i -> -> new int[] {i, j}))

Use Case: Matching

The matching use case determines if any items in a collection match a certain condition.

boolean anyMatch(Predicate<? super T> predicate);

anyMatch returns true if any item in the stream matches the predicate. anyMatch will return as soon as it finds an item matching the predicate condition and so it may not traverse all items in the list.

The following block of code checks if we have any students whose name starts with “D” using anyMatch.

List<Student> list = new ArrayList<>();
list.add(new Student("Daniel LaRusso", 3.8, 12));
list.add(new Student("Johnny Lawrence", 1.4, 13));

boolean result =
    .anyMatch(s -> s.getName().startsWith("D"));

boolean allMatch(Predicate<? super T> predicate);

allMatch returns true if all items in the stream match the predicate. allMatch will evaluate each item in the stream against the predicate, returning false if it encounters an item that doesn’t match. If it gets to the end of the stream without encountering a false match, it will return true.

We can use the same code example as before, but this time it evaluates to false because not all student names begin with the later “D”.

List<Student> list = new ArrayList<>();
list.add(new Student("Daniel LaRusso", 3.8, 12));
list.add(new Student("Johnny Lawrence", 1.4, 13));

boolean result =
    .allMatch(s -> s.getName().startsWith("D"));

boolean noneMatch(Predicate<? super T> predicate);

noneMatch is the complement to allMatch, returning false if all items in the stream do not match the predicate.

Replacing allMatch with noneMatch in our previous example still returns false because we do have at least one student whose name starts with letter “D”.

List<Student> list = new ArrayList<>();
list.add(new Student("Daniel LaRusso", 3.8, 12));
list.add(new Student("Johnny Lawrence", 1.4, 13));

boolean result =
    .noneMatch(s -> s.getName().startsWith("D"));


Use Case: Finding

Another common data algorithm for working with collections of data is to find and return elements that match a certain condition. Each of these operations are fairly straightforward.

Optional<T> findFirst();

findFirst simply returns the first item from the stream. stream. We use this method when we specifically want the first element from a sequence.

The following block of code returns “A”.

The return type is Optional, which will not contain a result if the input stream is empty.

List<String> list = Arrays.asList("A","B","C","D");

Optional<String> result =;
Optional<T> findAny();

The findAny operation returns an arbitrary item out of the current stream. Usually, findAny will return the first element, but if we are performing a operation or if the compiler makes certain optimizations, there is no guarantee which element will be returned.

The following block of code may return any one of “A”, “B”, “C”, or “D”.

List<String> list = Arrays.asList("A","B","C","D");

Optional<String> result =;

Use Case: Reducing

Reducing repeatedly combines all of the elements in the stream to produce a single value result.

T reduce(T identity, BinaryOperator<T> accumulator);

reduce takes two arguments. The first is an initial value, and the second a BinaryOperator<T> that combines two elements to produce a new value.

The canonical example of reduce is computing the sum of a list of integers.

int sum =, (a, b) -> a + b);

Conceptually, this works by successfully adding elements in our stream. Given a list [1, 2, 3, 4, 5] the reduce operation for summing our list can be visualized as:

0 + (1 + (2 + (3 + (4 + 5))))

Because of the generalized nature of reduce, we can easily adapt our sum operation to calculate a product by altering our initial condition and operator.

int product =, (a, b) -> a * b);

This can be visualized as:

1 * (1 * (2 * (3 * (4 * 5))))

Primitive Streams

Streams are great, but the come at a slight cost when dealing with primitive types. Namely, since the streams API deals with generic objects, when we use it with primitives such as integers or doubles we suffer unnecessary boxing and unboxing costs. Because of this, the Streams API provides a few interfaces specialized for dealing with primitive types: IntStream, DoubleStream, and LongStream.

Each of these interfaces include a few new methods for performing common numeric operations like sum and max that would otherwise be implemented using reduce.

To convert a stream into a primitive equivalent, use the mapTo family of functions. The following block of code uses getGradePointAverage to return a Double, and the mapToDouble method returns a Stream<Double> result. With this in hand, we can call the simple sum() function to gather our results.

To convert a primitive stream back to a generic stream, use the boxed() method:
  .boxed(); // Converts from primitive stream to object stream

Creating Streams

So far, we’ve created streams using the stream() method on a related Collection. The Streams API provides a few additional builders that are useful for creating new stream instances.


If you have values, you can create a stream directly from them.

Stream<String> stream = Stream.of("Java ", "For", "The", "Experienced", "Beginner");


The collect method is a terminal operation on a stream. We’ve used it already to convert a stream to a List to output the result of our stream pipeline.

There are many more ways to collect stream output than just a simple list. In fact, the collect interface takes a Collector as a parameter, and any implementation of this interface can be used to create the output you desire.

The Collectors API provides a number of built-in collectors that operate over streams we can take advantage of.

Use Case: Summarizing

Many of the collectors satisfy a similar use case as the reduce function to summarize data while providing a simplified API.

counting is a simple collector that counts the number of elements in the stream.;

You can use two collectors, Collectors.maxBy and Collectors.minBy to calculate the maximum or minimum in a stream. These functions require a Comparator that defines how to decide which element is greater than the other.

summingInt, summingDouble, summingLong, and their related functions averageInt, averageDouble, and averageLong do exactly what they say on the tin: calculate the sum or average of the stream.

double averageGpa =;

Collectors also provides a convenience function to calculate sums, counts, averages, and more with one operation. These functions are summarizingInt, summarizingDouble, and summarizingLong. They return a data structure holding the summary data computed for the stream.

DoubleSummaryStatistics stats =;

The joining function uses StringBuilder internally to append together each element of a Stream<String> and return the result as a single String. The joining function is overloaded to optionally take a separator that is used between elements of the joined stream.

String allNames =", "));

reducing is a generalized collector that runs user-defined reduce operations on the stream. You can often achieve what you want using the reduce intermediate operation or the reducing collector. The main difference between the two is that reduce is an immutable function whereas the reducing collector modifies a container that collects the result. This can become important if you want to run a parallel operation on a stream.

Use Case: Grouping

If you are familiar with SQL, you have likely used grouping operations to group results into a set based on certain properties.

The first method of grouping is using the groupingBy function. To make grouping work correctly, you pass groupingBy a function that returns the key you wish to group by. The return value of groupingBy is a map between the key we use for grouping and the resulting list of elements that matches the grouping key. As an example, you can group all students by the grade they are in using the following block of code.

Map<Student.Grade, List<Student>> studentsByGrade =

What if we want to continue processing each group of elements? We can use the filtering, or mapping functions in conjunction with our grouping to accomplish this. The following example groups the set of students by their grade, and in addition extracts the name of each student.

Map<Grade, List<String>> studentsByGrade =
      .collect(groupingBy(Student::getGrade, mapping(Student::getName, toList())));

This example used the overloaded groupingBy call that accepts a grouping and a downstream operation. We can also use this to perform a two-level grouping. This is done by passing a second groupingBy call defining second way to classify the items of a stream. In fact, you can pass any form of collector such as counting or summing to the second parameter of a groupingBy to continue processing the grouped elements.

Use Case: Partitioning

Partitioning is similar to grouping, but uses a predicate value called a partitioning function as a way of classifying items into groups. Since the partitioning function is a predicate, it splits the input elements into two groups: one for the true case and another for the false case.

The following example partitions a stream of integers into lists: one of elements greater than three and one of elements less than three.

Stream<Integer> s = Stream.of(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);

Map<Boolean, List<Integer>>
    map = s.collect(
        Collectors.partitioningBy(num -> num > 3));

Parallel Streams

Before Java 7, processing a collection of data in parallel was extremely cumbersome (if you ever have to do it, Java Concurrency in Practice is your friend). Because the Streams API takes iteration out of your hands and gives that task to the compiler, it makes executing operations in parallel on a collection of data easy.

To turn a collection into a parallel stream, invoke the parallelStream method on the collection. The following block of code does just that, and prints the current thread number for each value that is iterated over.

List<Integer> listOfNumbers = Arrays.asList(1, 2, 3, 4);
listOfNumbers.parallelStream().forEach(number ->
    System.out.println(number + " " + Thread.currentThread().getName())

Running this code, you might get something like this back (the results will vary depending on your setup). In my case, number 3 is processed first by the main thread, then numbers 4 and 2 by threads forked by the Streams API. Lastly, the first element, number 1, gets processed by the main thread.

3 main
4 ForkJoinPool.commonPool-worker-2
2 ForkJoinPool.commonPool-worker-1
1 main

In addition to the parallelStream method, you can convert a stream from parallel to sequential and back using the parallel and sequential methods.


Something to be aware of when using parallel streams is that any code executed during stream iteration cannot mutate any state (because that state would be shared between the threads of the parallel stream)..

You may be thinking, “If parallel streams are so simple to use, why don’t we make every stream parallel?” Although on the surface this seems like a good idea, in practice, it is not so easy. Some stream operations are simply more parallelizable than others. Whenever you invoke parallelization, the implementation needs to partition the stream, then assign an operation for substream to a different thread, and then finally combine the results of these different threads into a single return value.

It’s difficult to predict exactly the conditions that make parallel streams more performant than their sequential counterparts. One simple model that can help is the so-called $$ NQ $$. Here, $$ N $$ stands for the number of elements in the stream, and $$ Q $$ stands for the amount of computation done per element. The larger the product of $$ N \times Q $$, the more likely parallelization will improve performance.

3 Optional

Encountering a NullPointerException is a way of life for any Java developer. Unfortunately, the vast majority of these errors pop up at the worst possible time — during runtime. The Optional type was added to Java 8 to make explicit the case where a value does not exist. When an object is declared as Optional, you can no longer forget to check for null and wind up with a NullPointerException at the worst possible time. With Optional<T> checking for null cases is enforced by the type system and any errors will be surfaced at compile time.

So what is Optional. Conceptually, it is fairly straightforward — it models the case where a value may or may not exist. When a value is present, the Optional class wraps it under a get method. When a value is not presence, it is model with an empty optional created by Optional.empty, and attempting to call get on an empty Optional will throw an error.

You may wonder about the difference between a null reference and Optional.empty(). They seem very similar and trying to reference either will cause an exception: either NullPointerException for the null case or NoSuchElementException for the Optional case. The difference is that an empty Optional is a valid and usable typed object. By using Optional consistently, we create a clear distinction between a value that is missing but was supposed to be there and a value that is absent because of a bug or a data problem.

Creating Optionals

You can create Optionals in a few ways:

Optional.empty()  // with no value
Optional.of(object) // from an existing object
Optional.ofNullable(object) // from an existing object that may be null

Using Optionals


There are a number of different ways to unwrap an optional to get to the value inside.

The first, Optional.get is very similar to checking for null. If the value is absent, the code throws a NoSuchElementException. This use case doesn’t provide much additional benefit compared to checking for null, so in most cases it pays to look at other ways to access your value that take better advantage of the Optional data type.

String value = Optional.get();

orElse, orElseGet, or and orElseThrow

These methods have the following type signatures.

public T orElse(T other)

public T orElseGet(Supplier<? extends T> other)

public Optional<T> or(Supplier<? extends Optional<? extends T>> supplier)

public <X extends Throwable> T orElseThrow(Supplier<? extends X> exceptionSupplier) throws X

orElse takes a parameter of the same type as the Optional. When called, it returns the value in the Optional if it is present, otherwise it returns other. This construct is useful for the case of returning a default value if is not provided.

String name = Optional.of(user.firstName)

orElseGet takes a Supplier of the same type as the Optional. Supplier is a simple functional interface that has a sole method get returns an object of the specified type.

String name = Optional.of(user.firstName)
  .orElse(() -> getDefaultName();

The difference is subtle, but it can be significant.

  • orElse will always call the given function whether you want it or not, regardless of Optional.isPresent() value
  • orElseGet will only call the given function when the Optional.isPresent() == false

For example, the following block of code creates a new object when the optional is empty. Using orElse we create a new object no matter if the optional is present or empty, whereas using orElseGet acts lazily; the new function is called only if the optional is empty.

Optional<Foo> opt = ...
Foo x = opt.orElse( new Foo() ); // creates a Foo instance no matter what
Foo y = opt.orElseGet( Foo::new ); // creates a Foo instance only if opt is empty

or is very similar to orElseGet, but it explicitly returns another Optional.

Optional<String> name = Optional.of(user.firstName)

orElseThrow is equally familiar once you’ve covered the basics presented here.

Optional<String> name = Optional.of(user.firstName)

Two additional ormethods come in the form of theseThe or methods

Optional<String> name = Optional.of(user.firstName)

ifPresent and ifPresentOrElse

Optional.ifPresent(Consumer<? extends T> consumer)
Optional.ifPresentOrElse(Consumer<? extends T> action, Runnable emptyAction)

executes consumer if optional is present executes action if present or empty action if not present

Optionals as Streams

Beginning with JDK 9, Optionals can be treated much like the streams API using, Optional.flatMap, and Optional.filter. You can also convert an Optional<T> to a Stream<Optional<T>> with the method.

These methods can simplify and remove a lot of code for checking if an Optional has a value. For example, the following code block isn’t much different than checking for null before executing code:

Optional<Student> student = ...;

String name = null;
if (student.isPresent()) {
  name = student.get().getName();
  // do something
} else {
  name = "Anonymous";

It can be replaced with map to apply the specified function to the Optional if a value exists. Nested Optionals can be handled with flatMap.

String name ="Anonymous"); returns a stream with optional values that have been set. This method unwraps optionals and returns a stream of the underlying values. For example,

Stream<Optional<String>> stream = ...
Set<String> result = stream.flatMap(Optional::stream)

4 Collection Factories

Prior to Java 9, it was fairly tedious to create a small collection of elements, leading to code like the following from Modern Java in Action to create an immutable set of friends.

Set<String> friends =
  new HashSet<>(Arrays.asList("Raphael", "Olivia", Thibaut"));

Now, you can use newly added factory methods that support creating small immutable collections.

List<String> friends = List.of("Raphael", "Olivia", "Thibaut");


Set<String> friends = Set.of("Raphael", "Olivia", "Thibaut");

The factory methods added to the Collection API simplify creating simple collections.

5 Date and Time API

tldr; Use java.time! It provides thread safe, time-zone friendly dates and times with an easy to use API.

Java’s Date and Time APIs have been a historic weak spot in the language library. Prior to Java 8, most developers have used Joda-Time as the de facto date and time library. With the Java 8 release, the ideas and features in Joda-Time have been migrated into the Java core library under the java.time package.

All of this means, in modern Java projects you should use java.time and forget about anything in and java.util.calendar.

Rather than restate the API documentation you can find elsewhere, I will link out to the official documentation

6 Default Methods

Prior to Java 8, Any concrete implementation of an interface must provide an implementation for each method defined by the interface. The biggest problem this causes is when the author of an interface or library want to update an interface; whenever a new method is added to the interface, any existing concrete implementation will need to implement the new method. To help fix this problem, Java 8 introduced default methods that allow you to provide a default implementation of interface methods. In other words, interfaces can now provide a concrete implementation for methods.

With this change, any class implementing an interface automatically inherits any default implementations if they don’t provide one explicitly. In fact, Java 8 introduced some changes to the Collections API that include new methods like stream. Without a default method, any existing library authors who depend on Collections would need to implement these methods when upgrading to Java 8. With a default method, the addition of the stream method is transparent:

To mark a method as a default method, you use the default modifier. For example, the stream method is marked as default and any implementing classes will use this method for as long as it is not overridden.

default Stream<E> stream() {
    return, false);

Default methods make interfaces behave similar to abstract classes. So what is the difference? First, a class can only extend from one abstract class, while a class can implement multiple interfaces. Second, an abstract class can reference instance variables, while an interface can’t have instance variables.

Although quite rare in practice, it is now possible for a class to inherit more than one method implementation with the same signature. In this case, which method is used? There are three rules for method resolution that apply:

  1. Classes first. If a method is declared in a class (or superclass), that implementation takes priority over an interface’s default method.
  2. Subinterfaces second. If no class takes priority, the method with the same signature in the most specific interface is selected. For example, if B extends A, B is more specific than A and the method signature in B is chosen.
  3. Explicit last. If neither of the two previous cases resolves the issue, the class inheriting from multiple interfaces has to explicitly override the default implementation. It can then call the desired default method it wishes to use explicitly.

7 CompletableFuture

Java 8 introduced the CompletableFuture implementation of the Future to additional opportunities for executing method calls in parallel.

The Future interface, introduced in Java 5, models an asynchronous computation by providing a reference to its result that becomes available only when the computation is complete. The Thread that calls the future can continue doing useful work while it waits for the result of the computation to become complete.

Prior to Java 8, to work with a Future you have to wrap the time-consuming operation inside a Callable object that is submitted to an ExecutorService. For example, the following listing submits a Future. After creating the future, the method can continue on until the value of the future is retrieved with future.get(), which blocks until the long computation is complete and the value of the future is available.

ExecutorService executor = Executors.newFixedThreadPool(2);
Future<Double> future = executor.submit(new Callable<Double>() {
  public Double call() {
      return doSomethingLong();

/// do a bunch of things

Double result = future.get();  // blocks until doSomethingLong() completes

The Future interface is great for off-loading time consuming method calls from the main thread, but the way they are constructed and used makes it difficult to model dependencies among different futures. For example, modelling an operation like “do something long, and when it is done do this other long thing, and then when that is done combine the results” is very difficult with only the Future interface. CompletableFuture makes defining this type of operation possible with a simple declarative interface.

To change our simple example into a CompletableFuture implementation, we can start by using a simple lambda that is called by a new thread. The following block of code creates a CompletableFuture, then a new thread that is responsible for populating the value of the future using the complete method. While this thread runs, your code is able to continue until the point in time where you need to get the result from the computation.

CompletableFuture<Double> future = new CompletableFuture<>();
new Thread( () -> {
  Double result = doSomethingLong();

/// do a bunch of things

Double result = future.get();  // blocks until doSomethingLong() completes

Composing Futures

At first glance this code block is very similar to the early Future case without an ExecutorService. You can simplify this by using the supplyAsync static factory method to create a future with a lambda that implements the Supplier interface.

CompletableFuture<Double> future = CompletableFuture.supplyAsync(() -> doSomethingLong());

What’s more, you can chain CompletableFutures together using thenApply like so:

CompletableFuture<String> completableFuture
  = CompletableFuture.supplyAsync(() -> "Hello");

CompletableFuture<String> future = completableFuture
  .thenApply(s -> s + " World");

assertEquals("Hello World", future.get());

Similar methods exist for thenRun, thenAccept, thenCompose, and more to chain multiple futures together under a fluent API. What’s more, you can timeout a combination of futures by adding the orTimeout or completeOnTimeout calls to your call chain.

8 Reactive Programming with the Flow API

Reactive programming is programming that uses reactive streams — a standardized technique for processing data asynchronously based on a publish-subscribe protocol. Four interfaces represent the minimal set of features that a reactive stream must implement, and these are now included as part of Java 9 under the java.util.concurrent.Flow class.

The Flow API consists of three main concepts (Publisher, Subscriber, and Subscription) that follow a few defined rules as part of the specification:

  1. Publisher. Produce items consumed by one or more Subscribers. Publishers must send Subcribers no greater than the number of item requested (to facilitate backpressure). Publishers may terminate a Subscription by calling onComplete or onError.
  2. Subscriber. Registers as a listener of events published by a Publisher. Notifies the Publisher that it is ready to receive and process n elements from the stream. Must respond to onComplete and onError signals by not calling any other publisher methods and considering the subscription cancelled.
  3. Subscription. Manage the relationship between Producers and Subscribers. One subscription is shared by exactly one Publisher and one Subscriber. Each Subscription has an idempotent and thread-safe cancel() method. Once called, any other invocation on the Subscription has no effect.

The following diagram shows the typical lifecycle of publishers, subscribers, and subscriptions when implementing the Flow API

Flow API Lifecycle

Flow API Interfaces

The Publisher interface has a single subscribe method that takes a Subscriber that will receive events.

public interface Publisher<T> {
    void subscribe(Subscriber<? super T> s);

The Subscriber interface has four callback methods that are invoked by the Publisher when it produces the corresponding events.

public interface Subscriber<T> {
    void onSubscribe(Subscription s);
    void onNext(T t);
    void onError(Throwable t);
    void onComplete();

Lastly, the Subscription interface represents the 1-1 mapping between a Publisher and a Subscriber. A Subscriber can request the next n elements from the stream using the Subscription, and either a Subscriber or a Publisher can cancel the subscription.

public interface Subscription {
    void request(long n);
    void cancel();

The last interface that exists that we haven’t covered is the Processor interface. A Processor represents a transformation stage of the events processed through the reactive stream that turn the elements in the reactive stream from one type to another (similar to the map method on a stream).

public interface Processor<T, R> extends Subscriber<T>, Publisher<R> { }

Flow API Implementations

If you are doing reactive programming, it is great to understand the interfaces behind the Flow API and how to use them, but it should be rare that you need to implement these interfaces yourself unless you are creating a reactive framework yourself. Instead, you can use existing libraries and frameworks that implement the Flow API like Akka, Reactor, RxJava, and Vert.x.

9 Java Module System

The Java Module System was the main feature of the Java 9 release. Originally developed as part of Project Jigsaw, the designs for Java modules took almost a decade to complete!

The essence of the module system is providing an additional way to structure your programs that promotes separation of concerns and information hiding. This is done by introducing a new keyword module that makes up part of module descriptor including the dependencies the module requires and the types that the module exports.

The following example module descriptor includes the name of the module application, the modules it requires (in our case a sample utility clas), and the types that it exports.

module application {
  requires com.sookocheff.example.utilities;

  exports com.sookocheff.example;


Module descriptors lives in a special file called, which is in turn compiled to module-info.class.

| application
  | com
    | sookocheff
      | application

For libraries that haven’t been modularized, any JAR on the module path without a file becomes an automatic module. Automatic modules implicitly export all their packages and are given a name derived from the name of the JAR file.

In addition to the requires and exports keywords, modules may use requires-transitive, exports-to, open, opens, uses, and provides. Covering the entirety of this system is too much for an article like this, so if you are really interested in learning more, there is an entire book dedicated to it.

10 Local Variable Type Inference with var

In Java 10, the var keyword allows local variable type inference, which means the type for a local variable declared inside a method will be inferred by the compiler. This saves you the boilerplate of having to declare that type explicitly. For example, the following block of code uses local type inference to declare a String without writing the explicit String type.

String s1 = new String("Java 9 without var");

var s2 = new String("Java 10 with var"); // local type inference

In such a simple example, there is not much difference, but the var keyword does help when you are dealing with more complex types like Collection and Stream.

Java is still a statically typed language, and for the var keyword to work there needs be enough information for the compiler to infer the type of a local variable and once that type is known, it cannot be changed.

11 Switch Expressions

Java 12 introduced switch expressions, which (like all expressions) evaluate to a single value and can be used in statements. For example, the following block of code embeds a switch expression inside a println method. The result of the switch expression is returned as a value and printed.

Day day = Day.WEDNESDAY;
  switch (day) {
    case MONDAY, FRIDAY, SUNDAY -> 6;
    case TUESDAY                -> 7;
    case THURSDAY, SATURDAY     -> 8;
    case WEDNESDAY              -> 9;
    default -> throw new IllegalStateException("Invalid day: " + day);

Switch expressions also introduce the “arrow case” syntax -> that is used inside a case label. At runtime, any of the cases to the left of the arrow are matched with the switch value, and then the code on the right of the arrow runs. Case labels with the arrow syntax do not fall through to the next case, and so you do not need break statements.

Optionally, you can explicitly declare the return value of a switch expression using the yield statement.

int numLetters = switch (day) {
    yield 6;
  case TUESDAY -> {
    yield 7;
    yield 8;
  case WEDNESDAY -> {
    yield 9;
  default -> {
    throw new IllegalStateException("Invalid day: " + day);

12 Text Blocks and Multi Line Strings

Since Java 15, text blocks are a standard feature allowing you to easily construct multi line strings. Text blocks start with a tripe quotation marks """ followed by optional whitespaces and a newline. The text block is terminated with another pair of """. For example, a very simple example looks like:

String example = """
     Text block""";

Within the text we are free to use newlines and quote marks without having to escape them. The text block also respect any indentation and removes any leading whitespace when the String is built. This is really useful for keeping the formatting of source code.

String html = """

            <span>example text</span>

This format of multi-line strings is much more elegant than the existing alternatives using String concatenation or StringBuilder.

String s = "Lorem ipsum dolor sit amet, consectetur adipiscing elit\n"
  + "sed do eiusmod tempor incididunt ut labore et dolore magna\n"
  + "aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco\n"
  + "laboris nisi ut aliquip ex ea commodo consequat. Duis aute\n"
  + "irure dolor in reprehenderit in voluptate.";
String s = new StringBuilder()
  .append("Lorem ipsum dolor sit amet, consectetur adipiscing elit\n")
  .append("sed do eiusmod tempor incididunt ut labore et dolore magna\n")
  .append("aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco\n")
  .append("laboris nisi ut aliquip ex ea commodo consequat. Duis aute\n")
  .append("irure dolor in reprehenderit in voluptate.")

13 Record Classes

A Record is a special kind of Java class for defining immutable data-only classes with a concise syntax. A Record contains one or more data fields which correspond to member variables in a regular Java class.

With records, the Java compiler auto generates getter, toString(), hashcode() and equals() methods for these data fields, removing a lot of the boilerplate necessary in defining immutable Java classes. Since a Java Record is immutable, no setter methods are generated.

Prior to Java 14, if you wanted to create an immutable class you would create data classes with the following characteristics:

  1. a private final field for each piece of data
  2. a getter for each field
  3. a public constructor with a corresponding argument for each field
  4. an equals method that returns true for objects of the same class when all fields match
  5. a hashCode method that returns the same value when all fields match
  6. a toString method that includes the name of the class and the name of each field and its corresponding value
public final class Rectangle {
    private final double length;
    private final double width;

    public Rectangle(double length, double width) {
        this.length = length;
        this.width = width;

    double length() { return this.length; }
    double width()  { return this.width; }

    // Implementation of equals() and hashCode(), which specify
    // that two record objects are equal if they
    // are of the same type and contain equal field values.
    public boolean equals...
    public int hashCode...

    // An implementation of toString() that returns a string
    // representation of all the record class's fields,
    // including their names.
    public String toString() {...}

Writing this by hand is tedious, and using a tool like Lombok introduces an extra dependency to deal with.

With Java 14 and above, you can replace all of this boilerplate using the record keyword

record Rectangle(double length, double width) { }

The record syntax removes all of the boilerplate, and it makes the purpose of the class clear at a glance — this is a simple data class.

The record class generates a canonical constructor, but this can be overridden if we need to do any validation. Record constructors also allow us to remove constructor parameters since they can be implied by the record itself.

record Rectangle(double length, double width) {
    public Rectangle {
        if (length <= 0 || width <= 0) {
            throw new java.lang.IllegalArgumentException(
                String.format("Invalid dimensions: %f, %f", length, width));

You can also explicitly add methods to the class using regular method syntax.

record Rectangle(double length, double width) {

    // Public accessor method
    public double length() {
        System.out.println("Length is " + length);
        return length;

Records and Lombok

Project Lombok is a Java library that is often used to remove the boilerplate involved in writing Java classes. A Java record can be considered as equivalent to Lombok’s @Value annotation.

Unfortunately, Records are not a complete replacement for Lombok. For example, Records are for immutable data, whereas the @Data annotation in Lombok allows for mutation of data while still auto-generating most of the boilerplate. Lombok will continue to co-exist with records (and Java) for a longer time yet.

14 Pattern Matching

Pattern matching is a common technique in ML (Meta Language) derived languages that involves testing whether an object has a particular structure, and then extracting data from that object if there’s a match. You can already do this with plain old Java but the pattern matching syntax provides more concise and robust code.

Pattern matching comes in two flavours: matching with instanceof and matching with switch expressions.

Pattern Matching with instanceof

First, consider a simple block of code that checks whether an object is of a certain type:

if (shape instanceof Rectangle) {
  Rectangle r = (Rectangle) shape;
  return 2 * r.length() + 2 * r.width();

Checking whether an object is a certain type, then extracting the type, and using it in a computation is a common idiom in Java. We can simplify this with pattern matching.

In Java, a pattern is a combination of a target that is tested with a predicate, and a set of pattern variables that are assigned if the predicate is successful. In the following example, our target is the shape variable, the predicate is the instanceof operator, and the pattern variable is r.

if (shape instanceof Rectangle r) {
  return 2 * r.length() + 2 * r.width();

Pattern Matching with switch

Another common idiom in Java is using a switch statement to select the code block to execute. This version of pattern matching is quite similar to matching with instanceof — for each case statement we state the predicate and a pattern variable that is assigned when the predicate is true. This simplifies the resulting code by providing access to the pattern variable immediately in the case block.

return switch (shape) {
    case Rectangle r -> 2 * r.length() + 2 * r.width();
    default          -> throw new IllegalArgumentException("Unrecognized shape");

15 Sealed Classes

Sealed classes allow more fine-grained control over inheritance relationships. Before Java 15 and the introduction of sealed classes, Java assumed that code reuse is always a goal — every class was extendable by any number of subclasses. Now, a class or interface can define which classes can implement or extend it.

In contrast to creating package-private classes, sealed classes provide the possibility for a superclass to be widely accessible but not widely extensible.

To seal an interface, we use the sealed modifier in combination with the permits clause. The permits clause then specifies the classes that are allowed to implement the sealed interface:

public sealed interface Vehicle permits Car, Truck {

  // ...


Similar to interfaces, we can apply the same modifier classes. The only restriction is that the permits clause should be defined after any extends or implements clauses:

public abstract sealed class Vehicle permits Car, Truck {

  // ...


That’s really all there is to defining sealed classes with the exception of three important constraints on subclasses:

  • All permitted subclasses must belong to the same module as the sealed class.
  • Every permitted subclass must explicitly extend the sealed class.
  • Every permitted subclass must define a modifier: final, sealed, or non-sealed.

16 jwebserver

jwebserver, available since Java 18, provides a command-line tool to start a minimal web server that serves static files only. No CGI or servlet-like functionality is available.

This tool is useful for prototyping, ad-hoc coding, and testing.

You can start the web server with a simple command:

$ jwebserver

If startup is successful, the server prints a message to System.out listing the local address and the absolute path of the directory being served. For example:

$ jwebserver
Binding to loopback by default. For all interfaces use "-b" or "-b ::".
Serving /cwd and subdirectories on port 8000

By default, the files of the current directory are served, and every request is logged on the console.


For more information on these changes, consult the following resources.