The release of Java 21 marks another significant milestone in the evolution of one of the most popular programming languages in the world. With each iteration, Java continues to offer new features and improvements that enhance the development experience, performance, and security of applications. In this blog post, we'll dive into some of the key enhancements introduced in Java 21 and provide a practical example to demonstrate these advancements in action.
Key Enhancements in Java 21
Java 21 comes with a host of new features and updates that cater to the modern developer's needs. While the full list is extensive, here are some of the highlights:
- Project Loom Integration: One of the most anticipated features, Project Loom, is now fully integrated into Java 21. This project introduces lightweight, user-mode threads (fibers) that aim to simplify concurrent programming in Java by making it easier to write, debug, and maintain concurrent applications.
- Improved Pattern Matching: Java 21 enhances pattern matching capabilities, making code more readable and reducing boilerplate. This improvement is particularly beneficial in switch expressions and instanceof checks, allowing for more concise and type-safe code.
- Foreign Function & Memory API (Preview): Building on the work of Project Panama, Java 21 introduces a preview of the Foreign Function & Memory API, which simplifies the process of interacting with native code and memory. This feature is a boon for applications that need to interface with native libraries or require direct memory manipulation.
- Vector API (Third Incubator): The Vector API moves into its third incubator phase, offering a more stable and performant API for expressing vector computations that compile at runtime to optimal vector instructions. This promises significant performance improvements for applications that can leverage vectorized hardware instructions.
Practical Example: Using Project Loom for Concurrent Programming
To illustrate one of the standout features of Java 21, let's look at how Project Loom can transform the way we handle concurrent programming. We'll compare the traditional approach using threads with the new lightweight threads (fibers) introduced by Project Loom.
Traditional Thread-based Approach
In the traditional model, creating a large number of threads could lead to significant overhead and scalability issues due to the operating system's resources being consumed by each thread.
public class TraditionalThreadsExample {
public static void main(String[] args) {
for (int i = 0; i < 10; i++) {
new Thread(() -> {
System.out.println("Running in a traditional thread: " + Thread.currentThread().getName());
// Simulate some work
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
}).start();
}
}
}
Using Project Loom's Lightweight Threads (Fibers)
With Project Loom, we can use lightweight threads, or fibers, which are managed by the Java Virtual Machine (JVM) rather than the operating system. This allows for creating a large number of concurrent tasks with minimal overhead.
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.lang.Thread;
public class LoomExample {
public static void main(String[] args) {
ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor(); // Utilizes Project Loom
for (int i = 0; i < 10; i++) {
executor.submit(() -> {
System.out.println("Running in a lightweight thread: " + Thread.currentThread().getName());
// Simulate some work
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
});
}
executor.shutdown();
}
}
In this example, we use Executors.newVirtualThreadPerTaskExecutor() to create an executor service that manages our lightweight threads. This approach significantly simplifies concurrent programming, making it more efficient and scalable.
Improved Pattern Matching
With Java 21, the language continues to enhance its support for pattern matching, making code more readable and reducing boilerplate. Pattern matching for the instanceof operator was introduced in Java 16 as a preview feature and has since been evolving. Java 21 aims to build on this by streamlining the syntax further and possibly extending pattern matching capabilities to other areas of the language. Let's explore how pattern matching has been improved in Java 21 with a practical example.
Background on Pattern Matching
Pattern matching allows developers to query the type of an object in a more expressive and concise way than traditional methods. It eliminates the need for manual type checking and casting, which can clutter the code and introduce errors.
Pre-Java 16 Approach
Before pattern matching was introduced, checking and casting an object's type involved multiple steps:
Object obj = "Hello, Java 21!";
if (obj instanceof String) {
String str = (String) obj;
System.out.println(str.toUpperCase());
}
Java 16 to 20: Pattern Matching for instanceof
Java 16 introduced pattern matching for the instanceof operator, allowing developers to combine the type check and variable assignment into a single operation:
Object obj = "Hello, Java 21!";
if (obj instanceof String str) {
System.out.println(str.toUpperCase());
}
This syntax reduces boilerplate and makes the code cleaner and more readable.
Java 21: Enhanced Pattern Matching (Hypothetical Example)
Imagine Java 21 introduces further enhancements to pattern matching, such as extending it to switch expressions or providing more flexible pattern types. While specific details on enhancements in Java 21 are hypothetical in this context, let's explore a conceptual example that shows how pattern matching could be used in a switch expression:
Object obj = "Hello, Java 21!";
String result = switch (obj) {
case String s -> "String of length " + s.length();
case Integer i -> "Integer with value " + i;
default -> "Unknown type";
};
System.out.println(result);
In this example, the switch expression leverages pattern matching to not only check the type of obj but also to bind it to a variable that can be used directly within each case. This feature would greatly enhance the expressiveness and capabilities of switch expressions, making them more powerful for type checking and conditional logic.
Foreign Function & Memory API (Preview):
As of my last update in April 2023, the Foreign Function & Memory API was part of Project Panama, aiming to improve the connection between Java and native code. It's designed to replace the Java Native Interface (JNI) with a more performant and easier-to-use API. While specific details about new features in Java 21, including the Foreign Function & Memory API, would depend on the latest developments in Project Panama, I'll provide a conceptual example based on the progress as of my last update. This will illustrate how you might use the Foreign Function & Memory API to interact with native libraries in a hypothetical Java 21 environment.
Conceptual Example: Using the Foreign Function & Memory API
Suppose we want to call a simple C library function from Java that calculates the sum of two integers. The C function might look like this:
// sum.c
#include <stdint.h>
int32_t sum(int32_t a, int32_t b) {
return a + b;
}
To use this function in Java with the Foreign Function & Memory API, follow these steps:
-
Compile the C Code: First, compile the C code into a shared library (sum.so on Linux, sum.dylib on macOS, sum.dll on Windows).
-
Java Code to Call the Native Function:
import jdk.incubator.foreign.*;
import jdk.incubator.foreign.CLinker.*;
public class ForeignFunctionExample {
public static void main(String[] args) {
// Obtain a method handle for the sum function from the native library
MethodHandle sumHandle = CLinker.getInstance().downcallHandle(
LibraryLookup.ofLibrary("sum").lookup("sum").get(),
FunctionDescriptor.of(CLinker.C_INT, CLinker.C_INT, CLinker.C_INT)
);
// Call the native function
int result = (int) sumHandle.invokeExact(5, 7);
System.out.println("The sum is: " + result);
}
}
In this example, we're doing the following:
- Library Lookup: We use LibraryLookup.ofLibrary("sum") to locate and load the sum library.
- Obtaining a Method Handle: downcallHandle is used to obtain a handle to the native sum function. We specify the function's signature using FunctionDescriptor, indicating it takes two integers as parameters and returns an integer.
- Invoking the Native Function: Finally, we invoke the native function through the method handle with invokeExact, passing in two integer arguments and capturing the result.
Key Points:
- Safety and Performance: The Foreign Function & Memory API is designed to offer a safer and more performant alternative to JNI, reducing the boilerplate code and potential for errors.
- Incubation: As of the last update, these APIs were still in the incubator phase or preview. They might have been finalized or further evolved in Java 21. Always refer to the latest JDK Enhancement Proposals (JEPs) or the official Java documentation for current details.
Vector API (Third Incubator)
As of my last update, the Vector API was an evolving feature designed to provide a mechanism for expressing vector computations that compile at runtime to optimal vector instructions on supported CPU architectures. This allows Java programs to take full advantage of Data-Level Parallelism (DLP) for significant performance improvements in computations that can be vectorized. The Vector API has moved through several stages of incubation, with each iteration bringing enhancements and refinements based on developer feedback.
Conceptual Example: Using the Vector API for Vectorized Computations
Suppose we want to perform a simple vector operation: adding two arrays of integers element-wise and storing the result in a third array. Using the Vector API, we can achieve this with greater efficiency compared to a loop iteration for each element. Here's how it might look:
import jdk.incubator.vector.IntVector;
import jdk.incubator.vector.VectorSpecies;
public class VectorAPIExample {
public static void main(String[] args) {
// Define the length of the vectors
final int VECTOR_LENGTH = 256;
int[] array1 = new int[VECTOR_LENGTH];
int[] array2 = new int[VECTOR_LENGTH];
int[] result = new int[VECTOR_LENGTH];
// Initialize the arrays with some values
for (int i = 0; i < VECTOR_LENGTH; i++) {
array1[i] = i;
array2[i] = 2 * i;
}
// Preferred species for int vectors on the underlying CPU architecture
VectorSpecies<Integer> species = IntVector.SPECIES_PREFERRED;
// Perform the vector addition
for (int i = 0; i < VECTOR_LENGTH; i += species.length()) {
// Load vectors from the arrays
IntVector v1 = IntVector.fromArray(species, array1, i);
IntVector v2 = IntVector.fromArray(species, array2, i);
// Perform element-wise addition
IntVector vResult = v1.add(v2);
// Store the result back into the result array
vResult.intoArray(result, i);
}
// Output the result of the addition for verification
for (int i = 0; i < 10; i++) { // Just print the first 10 for brevity
System.out.println(result[i]);
}
}
}
Key Points of the Example:
- VectorSpecies: This is a key concept in the Vector API, representing a species of a vector that defines both its element type and length. The SPECIES_PREFERRED static variable is used to obtain the species that best matches the CPU's capabilities.
- Loading and Storing Vectors: IntVector.fromArray loads elements from an array into a new IntVector, according to the species. The intoArray method stores the vector's elements back into an array.
- Element-wise Operations: The add method performs element-wise addition between two vectors. The Vector API supports a variety of arithmetic operations, allowing for complex mathematical computations to be vectorized.
Conclusion:
Java 21 continues to push the boundaries of what's possible with Java, offering developers new tools and capabilities to build modern, efficient, and secure applications. The integration of Project Loom alone is a game-changer for concurrent programming, promising to simplify the development of highly concurrent applications. As Java evolves, it remains a robust, versatile, and future-proof choice for developers worldwide.