Scaling Streams: Mastering Virtual Threads in Spring Boot 4 and Java 25

As a software architect, I’ve seen the industry shift from heavy platform threads to reactive streams, and finally to the “best of both worlds”: Virtual Threads. With the recent release of Spring Boot 4.0 and Java 25 (LTS), Project Loom’s innovations have officially become the bedrock of high-concurrency enterprise Java.

Today, we’re going to look at a modern architectural challenge: scaling intelligent data pipelines using Spring Boot 4, Spring AI, and DL4J by “injecting” virtual threads and managing state with Scoped Values.

Why Spring Boot 4 and Java 25?

Spring Boot 4.0 is designed from the ground up for the Java 25 ecosystem. While Spring Boot 3 introduced initial support, version 4.0 treats Virtual Threads as a first-class citizen, enabling them by default for most I/O-bound operations. This allows us to handle millions of concurrent tasks—like LLM orchestrations—without the cognitive overhead of reactive programming (Project Reactor).

The Problem: The “I/O Wall” in Streams

Java’s standard parallel streams use the ForkJoinPool.commonPool(). If your stream performs blocking I/O—such as calling an LLM via Spring AI or running a multi-layered prediction via DL4J—the common pool quickly saturates. This leads to thread starvation and brings your entire application to a crawl.

// Traditional Parallel Stream (Dangerous for I/O)
list.parallelStream()
    .map(data -> springAiClient.generate(data)) // Blocks common pool threads!
    .collect(Collectors.toList());

The Solution: Seamless Virtual Thread Injection

In Java 25, we can maintain the declarative beauty of Streams but offload the “heavy” part of the pipeline to Virtual Threads. Spring Boot 4 makes this incredibly easy.

1. Enable Virtual Threads

In Spring Boot 4, virtual threads are often enabled by default if the JVM supports them, but you can ensure it in your application.yml:

spring:
  threads:
    virtual:
      enabled: true

2. Context Management with Scoped Values

When spawning millions of virtual threads, ThreadLocal is an anti-pattern due to memory overhead and potential leaks. Java 25’s Scoped Values provide a lightweight, immutable, and thread-safe alternative for sharing context (like Tenant IDs or Security Tokens).

public class SecurityContext {
    // ScopedValue is the modern, lightweight replacement for ThreadLocal
    public static final ScopedValue<String> TENANT_ID = ScopedValue.newInstance();
}

3. The Stream Chain with Spring AI and DL4J

Here is how we integrate Spring AI for summarization and DL4J for deep learning inference, all while keeping our virtual threads context-aware.

@Service
public class IntelligenceService {

    private final ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();
    
    @Autowired
    private ChatClient chatClient; // Spring AI

    public List<AnalysisResult> processIntelligencePipeline(List<Document> docs, String tenantId) {
        // Bind the context using ScopedValue
        return ScopedValue.where(SecurityContext.TENANT_ID, tenantId).call(() -> 
            docs.stream()
                .filter(doc -> !doc.isEmpty())
                
                // Step 1: Offload LLM Summarization to a Virtual Thread
                .map(doc -> CompletableFuture.supplyAsync(() -> {
                    String currentTenant = SecurityContext.TENANT_ID.get();
                    // Call Spring AI (Blocking I/O is now "free")
                    String summary = chatClient.prompt(doc.getContent()).call().content();
                    return new IntermediateResult(doc.getId(), summary);
                }, executor))
                
                .toList().stream()
                .map(CompletableFuture::join)
                
                // Step 2: Offload DL4J Deep Learning Inference
                .map(res -> CompletableFuture.supplyAsync(() -> {
                    // DL4J Model Prediction
                    INDArray tensor = prepareTensor(res.getSummary());
                    return new AnalysisResult(res.getId(), model.predict(tensor));
                }, executor))
                
                .toList().stream()
                .map(CompletableFuture::join)
                .collect(Collectors.toList())
        );
    }
}

Deep Dive: Breaking Down the Intelligence Pipeline

To truly appreciate the architectural elegance of this pattern, let’s break down the processIntelligencePipeline method step by step:

The Context Wrapper (ScopedValue.where)

The entire stream is wrapped in a ScopedValue.where(...).call(...) block. In Java 25, this binds the tenantId to the current execution scope. Unlike ThreadLocal, which is inherited (and copied) by child threads, Scoped Values are efficiently shared with the Virtual Threads spawned within this block. This is critical when you have 100,000+ concurrent requests; the memory savings are massive.

The First Concurrency Injection (Spring AI)

We use .map(...) to transform each Document into a CompletableFuture. By passing our virtualThreadExecutor to supplyAsync, we ensure that the high-latency call to Spring AI (which might take 500ms or more) runs on a Virtual Thread.

• Architectural Benefit: While the Virtual Thread waits for the LLM response, it “unmounts” from its carrier thread, allowing the CPU to process other tasks.

The Barrier Sync (toList().stream().map(join))

Because standard Java Streams are lazy, we must call .toList() to trigger the execution of all asynchronous tasks. We then immediately reopen the stream and call join(). This acts as a non-blocking barrier, ensuring all AI summaries are completed before proceeding to the next stage.

The Second Concurrency Injection (DL4J)

Once we have our summaries, we repeat the pattern for the DL4J inference. Deep learning predictions can be CPU-intensive (tensor preparation) or I/O-intensive (if offloading to a GPU/Remote Server).

• Why Virtual Threads here? By using them again, we decouple the “Data Science” logic from the main application flow, ensuring that even if one model prediction takes longer, it doesn’t block the rest of the stream elements.

The Final Collection

The final .collect(Collectors.toList()) returns the fully hydrated AnalysisResult objects to the caller. The beauty of this approach is that the caller sees a simple, synchronous method signature, while underneath, the JVM has orchestrated a highly concurrent, context-aware AI pipeline.

Key Architectural Takeaways

1. Reactive vs. Virtual: With Spring Boot 4 and Java 25, the need for WebFlux and Project Reactor is diminishing for most business applications. You get the same scalability with simple, imperative code.
2. Memory Efficiency: Replacing ThreadLocal with ScopedValue is non-negotiable when dealing with the high thread counts that Virtual Threads enable.
3. The “Wait-State” is Free: Because Virtual Threads unmount from the carrier thread during I/O (like waiting for a Spring AI response), your CPU stays busy doing actual work instead of waiting for network packets.
4. DL4J Integration: Even with compute-heavy ML libraries like DL4J, using Virtual Threads for the pre-processing and post-processing I/O steps ensures that the GPU or CPU-bound inference isn’t bottlenecked by data ingestion.

Conclusion

Spring Boot 4.0 and Java 25 have fundamentally changed how we design high-throughput systems. By leveraging Virtual Threads and Scoped Values, we can build sophisticated AI-integrated pipelines that are easy to write, easy to debug, and incredibly fast.