The shift to virtual threads represents one of the most significant changes in Java concurrency in years. For developers maintaining existing applications, the migration process can seem daunting. I’ve spent considerable time working through these transitions, and the benefits are profound when approached methodically. Virtual threads allow us to handle massive concurrency without rewriting our entire codebase in reactive patterns.
Let’s start with the most straightforward change: replacing traditional executor services. In my applications, I often found thread pools sized arbitrarily—usually between 50 and 200 threads. These numbers were chosen based on hardware limitations and guesswork rather than actual requirements. With virtual threads, we can eliminate this constraint entirely.
The change is remarkably simple. Where you previously used Executors.newFixedThreadPool(), you can now use Executors.newVirtualThreadPerTaskExecutor(). This creates a new virtual thread for each submitted task, allowing your application to scale to thousands or even millions of concurrent operations. The best part? Your existing Runnable and Callable implementations continue working unchanged.
ThreadLocal variables present a particular challenge during migration. While they still work with virtual threads, they can lead to memory issues when used extensively. Each virtual thread has its own copy of ThreadLocal data, and with potentially millions of virtual threads, this can quickly consume significant memory.
I’ve found ScopedValue to be an excellent replacement. It provides similar functionality to ThreadLocal but with better memory characteristics. Scoped values are inherited by child threads within a structured concurrency scope, making them ideal for request context propagation. The migration requires some code changes, but the memory benefits are substantial.
Synchronized blocks require special attention. When a virtual thread enters a synchronized block, it becomes “pinned” to its carrier thread. This prevents the virtual thread from being unmounted during blocking operations, reducing the scalability benefits. I’ve learned to identify these critical sections through careful profiling.
Replacing synchronized with ReentrantLock solves the pinning issue. The lock acquisition and release work the same way, but virtual threads can be unmounted while waiting for the lock. This change requires careful testing—especially around exception handling and lock acquisition patterns—but the concurrency improvements are worth the effort.
Many applications adopted complex asynchronous programming patterns to work around platform thread limitations. These patterns often involved intricate chains of CompletableFuture callbacks that were difficult to debug and maintain. Virtual threads allow us to simplify this code significantly.
Instead of breaking operations into multiple asynchronous stages, we can write straightforward blocking code. The virtual thread scheduler handles the suspension and resumption transparently. I’ve successfully converted complex async workflows into simple sequential code that’s easier to read and maintain while achieving better performance.
Database connection pools need reconfiguration when migrating to virtual threads. Traditional pools were sized to match the maximum number of platform threads, typically ranging from 20 to 100 connections. With virtual threads, we can handle thousands of concurrent database operations.
I recommend increasing connection pool sizes substantially—often by an order of magnitude. However, this requires coordination with your database administrators. The database must be able to handle the increased connection load. Monitoring database performance during this transition is crucial to avoid overwhelming your storage systems.
Identifying blocking operations that pin virtual threads is an essential migration step. The JVM provides helpful debugging flags that highlight when virtual threads cannot be unmounted. I enable these flags during development and testing to catch pinning issues early.
The djdk.tracePinnedThreads flag outputs stack traces when virtual threads become pinned. This helps identify synchronized methods, native calls, or other operations that prevent unmounting. Addressing these pinning points often yields the most significant performance improvements.
Structured concurrency provides a powerful paradigm for managing virtual threads. It ensures that related operations complete together and properly handle errors and cancellation. I’ve found it particularly valuable for request processing workflows where multiple operations must complete before returning a response.
The StructuredTaskScope API allows spawning multiple virtual threads that share a common lifecycle. If any operation fails, all related operations can be cancelled automatically. This eliminates many common concurrency bugs and makes error handling more robust.
Monitoring virtual thread behavior requires new approaches. Traditional thread dumps show carrier threads rather than virtual threads, making debugging more challenging. I configure additional JVM flags to gain visibility into virtual thread scheduling and behavior.
The djdk.virtualThreadScheduler.parallelism flag controls how many carrier threads are available. This should typically match the number of processor cores. Monitoring carrier thread utilization helps identify whether the scheduler has sufficient resources or if virtual threads are being pinned excessively.
Third-party libraries may not immediately support virtual threads. Some libraries create their own threads or use synchronization patterns that can cause pinning. I test each library with virtual threads during the migration process.
Many modern libraries already work well with virtual threads. For others, you may need to provide a virtual thread factory or wait for library updates. I’ve found that most popular frameworks have been quick to add virtual thread support once they understand the performance implications.
A gradual migration strategy often works best. Rather than converting everything at once, I start with I/O-bound operations that benefit most from virtual threads. CPU-intensive operations often remain on platform threads since they don’t benefit from virtual thread scheduling.
Hybrid approaches allow mixing virtual and platform threads during transition periods. This lets you gain benefits where they matter most while maintaining stability in other areas. I typically migrate web request handling first, then gradually address other parts of the application.
The migration process requires careful planning and testing. I start with development environments, thoroughly testing each component with virtual threads. Performance testing under load reveals pinning issues and other problems that might not appear during normal operation.
Production deployment should be gradual as well. I often enable virtual threads for a small percentage of traffic initially, monitoring performance and error rates closely. This phased approach minimizes risk while providing valuable real-world performance data.
Memory usage patterns change with virtual threads. While individual virtual threads are lightweight, applications may create millions of them. This requires attention to memory allocation and garbage collection tuning. I monitor heap usage and garbage collection behavior closely during migration.
Exception handling remains largely unchanged, but stack traces become much longer due to virtual thread nesting. This actually improves debugging in many cases, as the full execution path becomes visible. However, log management systems may need configuration adjustments to handle larger stack traces.
Testing strategies should include concurrency testing at scale. Traditional unit tests may not reveal virtual thread-specific issues. I implement integration tests that simulate high concurrency scenarios to ensure the application behaves correctly under load.
Debugging virtual threads requires updated tools and techniques. IDE debuggers now support virtual thread awareness, showing both virtual threads and their carrier threads. Understanding this relationship is crucial for effective debugging during migration.
The performance benefits can be dramatic. Applications that were previously limited by platform thread constraints can often handle order-of-magnitude increases in concurrent users. Response times improve significantly for I/O-bound operations due to reduced context switching overhead.
Resource utilization changes substantially. CPU usage may increase slightly due to the additional scheduling overhead, but memory usage often decreases because virtual threads are more efficient than platform threads. The overall system capacity typically increases significantly.
Monitoring and observability tools need updates to properly track virtual threads. Traditional thread-based metrics become less meaningful. I work with operations teams to ensure our monitoring systems can track virtual thread creation, execution, and completion metrics.
Error reporting and logging should include virtual thread identifiers. This helps correlate operations across different parts of the system. I’ve found that including both virtual thread and carrier thread information in logs provides the most complete picture during debugging.
The migration ultimately delivers not just performance improvements but also developer productivity gains. Code becomes simpler and more maintainable without complex asynchronous patterns. The mental model shifts from managing thread pools to writing straightforward blocking code.
This transformation represents a fundamental improvement in how we build concurrent applications in Java. The investment in migration pays dividends in application performance, reliability, and maintainability. The techniques I’ve described provide a practical path to achieving these benefits in existing applications.
