Java 16: Records and Vector API

Java 16: Records Finalized and Performance Features
Java 16 (March 2021) finalized records as a standard language feature, moving them out of preview. It also introduced the Vector API for SIMD operations and continued the pattern matching evolution.
1. Records - Now Standard (Finalized)
Records achieved final status, ready for production use.
// Standard feature - no preview needed!
public record Employee(long id, String name, String department, double salary) {
// Compact constructor for validation
public Employee {
if (salary < 0) {
throw new IllegalArgumentException("Salary cannot be negative");
}
name = name.trim();
}
// Methods can be added
public boolean isSalariedAbove(double threshold) {
return salary > threshold;
}
// Static members allowed
public static Employee dummy() {
return new Employee(0, "Unknown", "N/A", 0);
}
}
// Usage is clean and intuitive
Employee emp = new Employee(1, "Alice", "Engineering", 95000);
System.out.println(emp.name()); // Direct field access
System.out.println(emp.isSalariedAbove(90000)); // true
System.out.println(emp); // toString() auto-generated
// Equals and hashCode work perfectly
Employee emp2 = new Employee(1, "Alice", "Engineering", 95000);
System.out.println(emp.equals(emp2)); // true - perfect for collections
// Destructuring (future enhancement)
// var (id, name, dept, salary) = emp; // Coming in future Java versions2. Vector API (Incubating)
SIMD (Single Instruction, Multiple Data) operations for high-performance computing.
// Vector operations for parallel computation
import jdk.incubator.vector.*;
public class VectorExample {
static final VectorSpecies<Integer> SPECIES = IntVector.SPECIES_PREFERRED;
// Traditional loop - processes one element at a time
public static void addScalarLoop(int[] a, int[] b, int[] result) {
for (int i = 0; i < a.length; i++) {
result[i] = a[i] + b[i]; // Sequential
}
}
// Vector operation - processes multiple elements in parallel
public static void addVector(int[] a, int[] b, int[] result) {
int i = 0;
// Process chunks using SIMD
for (; i < SPECIES.loopBound(a.length); i += SPECIES.length()) {
IntVector va = IntVector.fromArray(SPECIES, a, i);
IntVector vb = IntVector.fromArray(SPECIES, b, i);
IntVector sum = va.add(vb);
sum.intoArray(result, i);
}
// Handle remainder
for (; i < a.length; i++) {
result[i] = a[i] + b[i];
}
}
// Benefits: 10-100x faster for large datasets!
public static void benchmark() {
int[] a = new int[10000];
int[] b = new int[10000];
int[] result = new int[10000];
long start = System.nanoTime();
addVector(a, b, result);
long duration = System.nanoTime() - start;
System.out.println("Vector operation: " + duration + " ns");
}
}3. Pattern Matching for instanceof (Finalized)
Pattern matching in instanceof statements is now standard.
// Standard feature - not preview
public void processObject(Object obj) {
// Clean pattern matching
if (obj instanceof String str) {
System.out.println("String length: " + str.length());
} else if (obj instanceof Integer num) {
System.out.println("Integer value: " + num);
} else if (obj instanceof Employee emp) {
System.out.println("Employee: " + emp.name());
}
}
// Pattern matching with logical operators
public boolean validateInput(Object input) {
return input instanceof String str &&
str.length() > 0 &&
!str.isBlank();
}
// Nested patterns (coming in future versions)
public void analyzeData(Object data) {
if (data instanceof Employee emp &&
emp.isSalariedAbove(100000)) {
System.out.println("High earner: " + emp.name());
}
}4. Sealed Classes - More Refinement
Sealed classes receive additional polishing and features.
// Sealed classes work perfectly with records
public sealed interface Shape permits Circle, Square, Rectangle {}
public record Circle(double radius) implements Shape {}
public record Square(double side) implements Shape {}
public record Rectangle(double width, double height) implements Shape {}
// Exhaustive pattern matching
public double calculateArea(Shape shape) {
return switch (shape) {
case Circle(double r) -> Math.PI * r * r;
case Square(double s) -> s * s;
case Rectangle(double w, double h) -> w * h;
};
}
// Sealed classes enforce domain constraints
public sealed class PaymentMethod permits CreditCard, PayPal {}
public final class CreditCard extends PaymentMethod {}
public final class PayPal extends PaymentMethod {}5. Stream API Enhancements
New stream methods for better data processing.
// mapMulti: Replace elements with multiple values efficiently
List<Integer> numbers = Arrays.asList(1, 2, 3);
numbers.stream()
.mapMulti((n, consumer) -> {
consumer.accept(n);
consumer.accept(n * 10);
})
.forEach(System.out::println);
// Output: 1, 10, 2, 20, 3, 30
// Practical example
public Stream<String> allWords(Stream<String> lines) {
return lines.mapMulti((line, consumer) -> {
for (String word : line.split("\\s+")) {
if (!word.isEmpty()) {
consumer.accept(word);
}
}
});
}6. Strong Encapsulation of JDK Internals
Further encapsulation of internal APIs.
import sun.misc.Unsafe;
// ❌ This and other internal APIs are now inaccessible
// Throws: java.lang.reflect.InaccessibleObjectException
// Need to use: --add-opens java.base/sun.misc=ALL-UNNAMED
// Recommended: Use public APIs instead
// sun.misc.Unsafe -> java.util.concurrent, VarHandle, etc.Developer Impact
Positive:
- Records Standard: Can use in production applications
- Performance: Vector API for HPC applications
- Pattern Matching: More natural control flow
- Sealed Classes: Enforced design constraints
Challenges:
- Vector API Incubating: Still experimental and may change
- Learning Curve: Multiple new features to master
- Performance Gains: Vector API requires careful optimization
Pros and Cons
Pros ✅
- Records Standard: Production-ready immutable data classes
- Pattern Matching (instanceof): Standard feature for clean type checks
- Vector API: Massive performance for compute-intensive code
- Sealed Classes Stability: Now well-established
- Stream enhancements: More flexible data processing
- Finalization: Java moving toward stable feature set
Cons ❌
- Vector API Incubating: Limited stability guarantees
- Learning Curve: Multiple features reaching maturity simultaneously
- No LTS: Non-LTS release - next LTS is Java 17
- JDK Internals Encapsulation: May break legacy code
- Platform-Specific: Vector API performance varies by CPU
Performance Example
// Vector API for financial calculations
public double[] vectorizedMovingAverage(double[] prices, int window) {
double[] result = new double[prices.length - window + 1];
for (int i = 0; i < result.length; i++) {
double sum = 0;
// Replace with vector operations for better performance
for (int j = 0; j < window; j++) {
sum += prices[i + j];
}
result[i] = sum / window;
}
return result;
}
// With records for clean data
record PriceData(double[] prices, LocalDate date) {}
record MovingAverage(double[] values, int window) {}Use Cases
Great for:
- Production applications using records for data classes
- Machine learning and data science (Vector API)
- Financial modeling and calculations
- Image processing and scientific computing
Code Example:
// Modern Java 16 application structure
public sealed interface DataSource permits DatabaseSource, APISource, FileSource {}
public record DatabaseSource(String connection) implements DataSource {}
public record APISource(String endpoint) implements DataSource {}
public record FileSource(Path filePath) implements DataSource {}
// Type-safe data loading
public String loadData(DataSource source) {
if (source instanceof DatabaseSource db) {
return queryDatabase(db.connection());
} else if (source instanceof APISource api) {
return fetchFromAPI(api.endpoint());
} else if (source instanceof FileSource file) {
return readFile(file.filePath());
}
return "";
}Conclusion
Java 16 represents significant maturation of Java's modern features. With records finalized and pattern matching progressing, Java becomes increasingly expressive. The Vector API opens doors for high-performance computing workloads in Java. This release is a bridge to Java 17 LTS, which incorporates these stable features.
Recommendation:
- Use Java 16 to experiment with Vector API for performance-critical code
- Adopt finalized records in all new projects
- Use pattern matching for cleaner type checks
- Plan migration path to Java 17 LTS for stability