The Java Runtime Wars Are Over (We Won)

I still remember the panic in 2018. You probably do too. The emails from legal departments, the frantic audits of which JDK version was running on which server, and the sudden realization that “free Java” wasn’t exactly guaranteed anymore. It was a mess. Developers just wanted to write code, but we got dragged into licensing discussions.

Fast forward to late 2025, and the landscape is… quiet. Wonderfully, beautifully quiet.

I was looking at some infrastructure stats recently, and it hit me: we stopped arguing about which JDK distribution to use. The community coalesced. We have massive adoption of open, vendor-neutral builds like Eclipse Temurin, with download numbers that would have seemed impossible five years ago. We’re talking hundreds of millions of downloads. The “fragmentation” everyone feared? It didn’t break us. It actually made the ecosystem stronger.

Why “Boring” is the Best Feature

When I pick a runtime for production today, I don’t want excitement. I want boring. I want a JDK that passes the TCK (Technology Compatibility Kit) without drama, generates an SBOM (Software Bill of Materials) so my security team stays off my back, and runs on everything from my MacBook M3 to the ancient Linux boxes in the basement.

The stability of modern OpenJDK builds has allowed us to focus on the actual language features. And honestly, that’s where the real fun is. Since we aren’t fighting the runtime, we can finally leverage the aggressive release cadence we’ve been getting.

Take Virtual Threads. They’ve been standard for a while now, but I still see codebases full of reactive spaghetti or massive thread pools. If you’re running on a modern Temurin build, you have a high-performance runtime that handles this natively. You don’t need to be afraid of blocking code anymore.

Here is a quick reminder of how clean concurrency looks in 2025 using Structured Concurrency. No more callback hell.

import java.util.concurrent.StructuredTaskScope;
import java.util.concurrent.ExecutionException;
import java.util.function.Supplier;

public class UserDashboard {

    // A simple record to hold our aggregated data
    public record UserView(UserProfile profile, List<Order> recentOrders) {}

    public UserView fetchDashboard(String userId) throws InterruptedException, ExecutionException {
        // This try-with-resources block ensures all threads are cleaned up automatically
        try (var scope = new StructuredTaskScope.ShutdownOnFailure()) {
            
            // Forking tasks is cheap now. Really cheap.
            Supplier<UserProfile> profileTask = scope.fork(() -> userService.findById(userId));
            Supplier<List<Order>> ordersTask = scope.fork(() -> orderService.getRecent(userId));

            // Wait for all to finish or one to fail
            scope.join(); 
            scope.throwIfFailed();

            // Construct the result. Simple, readable, synchronous-looking code.
            return new UserView(profileTask.get(), ordersTask.get());
        }
    }
}

That code is readable. It looks synchronous. But under the hood, the JVM is handling the scheduling with an efficiency that old thread-per-request models couldn’t touch. This only works reliably because the underlying runtime engineering—the stuff provided by these open builds—is rock solid.

Security Isn’t Just a Patch Tuesday Thing

Another reason the industry flocked to these open builds is the supply chain security aspect. It’s not just about fixing CVEs anymore; it’s about proving the binary hasn’t been tampered with between the build server and your production cluster.

I used to manually verify checksums (when I remembered). Now, the tooling does it. The major distributions have standardized on robust signing and reproducible builds. This trust allows us to push updates faster. I remember waiting months to upgrade Java versions because we were scared of breaking changes. Now? I bump the version in my Dockerfile, run the CI pipeline, and if it passes, we ship.

Speaking of modern features we can use safely now, let’s talk about Data-Oriented Programming. With the pattern matching features fully matured in the recent LTS versions, our domain logic has become incredibly expressive.

I wrote a processor recently that handles different payment types. Five years ago, this was a Visitor pattern nightmare or a chain of if-else checks with casting. Today, it’s just a switch expression.

public sealed interface PaymentMethod permits CreditCard, PayPal, Crypto {}

public record CreditCard(String last4, String token) implements PaymentMethod {}
public record PayPal(String email) implements PaymentMethod {}
public record Crypto(String walletAddress, String chain) implements PaymentMethod {}

public class PaymentProcessor {

    public String authorize(PaymentMethod pm, double amount) {
        // The compiler enforces that we cover all cases because the interface is sealed
        return switch (pm) {
            case CreditCard cc when amount > 5000 -> 
                "High value auth required for card ending in " + cc.last4();
                
            case CreditCard cc -> 
                "Standard auth for card " + cc.last4();
                
            case PayPal pp -> 
                "Redirecting to PayPal for " + pp.email();
                
            // Pattern matching deconstructs the record automatically
            case Crypto(var wallet, var chain) -> 
                "Initiating " + chain + " transaction for wallet " + wallet;
        };
    }
}

Look at that when guard. Look at the record deconstruction in the Crypto case. This is Java. It’s concise, type-safe, and because we’re running on a modern, widely-supported runtime, it’s performant.

The Stream API Finally Grew Up

One last thing that’s been a joy to use in these newer releases is the enhancement to Streams. For a decade, if you wanted to do something complex in a stream—like a moving window or fixed-size batching—you had to write a custom collector or use a third-party library. It was annoying.

With the introduction of Gatherers (which stabilized nicely), we can finally compose complex stream operations natively. This is huge for data processing pipelines where you don’t want to spin up a full Spark job but need more than just map and filter.

import java.util.stream.Stream;
import java.util.stream.Gatherers;
import java.util.List;

public class LogAnalyzer {

    public void analyzeLogBatches(Stream<String> logLines) {
        // Imagine processing millions of log lines
        logLines.filter(line -> line.contains("ERROR"))
                // Group into batches of 100 items automatically
                .gather(Gatherers.windowFixed(100))
                .forEach(batch -> {
                    // 'batch' is a List<String> of size 100 (or less for the last one)
                    sendToAlertSystem(batch);
                });
    }

    private void sendToAlertSystem(List<String> batch) {
        System.out.println("Processing batch of size: " + batch.size());
    }
}

The gather method is the extension point we were missing. It keeps the pipeline declarative but opens the door to custom intermediate operations.

The Ecosystem Wins

The success of distributions like Temurin proves that open governance works. We aren’t beholden to a single vendor’s roadmap anymore. The sheer volume of adoption ensures that bugs get found and squashed quickly. It’s a herd immunity of sorts for software.

If you’re still running Java 8 or 11 because you’re worried about “stability” or “licensing,” you’re fighting a war that ended years ago. The new runtimes are better, faster, and frankly, safer. Upgrade the JDK, change the Docker base image, and start deleting all that boilerplate code you wrote in 2016. You won’t miss it.