Project Loom: Broadening Concurrency

Project Loom has landed in Java 19 as a preview feature. It promises to simplify concurrency the way garbage collection simplifies memory management and improves applications' scalability.

It's about time to closely examine the value proposition and how it is implemented to understand what Loom can provide for your application. We will dive into technical details to understand concurrency, look at examples of how they behave, and how we can debug virtual thread code. You will learn what to watch out for and how project Loom can help you to write simple, concurrent code to get the most out of your runtime environment.

Transcript

1. Project Loom Broadening Concurrency
2. Mark Paluch Spring Data Project Lead @ VMware R2DBC Spec Lead Lettuce Redis Driver Maintainer Paramedic & SAR Dogs
3. Sept 2020, https://paluch.biz/blog/182-experimenting-with-project-loom-eap-and-spring-webmvc.html „We’ve been there, already“
4. Tomcat + Virtual Threads Tomcat + Kernel Threads
5. What is Project Loom • Initiative to introduce virtual threads to the Java Platform. Virtual threads are lightweight threads that dramatically reduce the effort of writing, maintaining, and observing high-throughput concurrent applications.
6. What is Project Loom • Initiative to introduce Introduction of virtual threads and concurrency API to the Java Platform. Virtual threads are lightweight threads that eliminate I/O wait. dramatically reduce the effort of writing, maintaining, and observing high-throughput concurrent applications.
7. What Project Loom actually is • Introduction of virtual threads and concurrency API to the Java Platform. Virtual threads are lightweight threads that eliminate I/O wait.
8. Dramatically reduce the effort of writing, maintaining, and observing concurrent applications • True if you run on a Virtual Thread • Future.get(), Thread.sleep(), Lock.lock() are good friends again • No need for future chaining, using complex reactive or asynchronous API • Not so, if you accidentally run on a Kernel Thread
9. Overview
10. Platform Thread • Renamed from Kernel Thread • new Thread(…)
11. Virtual Thread • Package-private subtype of java.lang.Thread • Provides customized behavior • Subject to be executed on carrier Thread
12. Carrier Threads • Separate Fork/Join Pool of Platform Threads • Default size: Runtime.availableProcessors() • Configurable through system properties (jdk.virtualThreadScheduler.parallelism, maxPoolSize, minRunnable)
13. Virtual Threads API • Extended Thread API Thread.ofVirtual().start(() -> {…}); Executors.newVirtualThreadPerTaskExecutor() Thread.currentThread().isVirtual() • Implemented almost entirely in Java
14. try(var e = Executors.newVirtualThreadPerTaskExecutor()){ for (int j = 0; j < 100; j++) { e.execute(() -> Thread.sleep(100)); } } // implicit wait
16. Parking & Unparking • On each blocking operation • If blocked, capture stack and store it • Carrier thread free to perform other work (unpark next virtual thread)
18. Blocking in synchronized code • Leads to Carrier Thread Pinning • Carrier Thread bound to virtual thread until unblock • synchronized is a JVM behavior, cannot be controlled from Java code • I/O, Locks, Future.get() in synchronized leads to Thread Pinning
19. Thread Type Selection • I/O-heavy, shared-resources with Java Locks, Concurrency Utils • Virtual Threads • Computation-heavy, synchronized workloads • Platform Threads
20. Observability, Tooling, Trouble Shooting • Java Flight Recorder Events • Virtual Thread start/end, pinned, submission failed • Debugging: IntelliJ announced tooling support for later this year • Pinned thread monitoring • -Djdk.tracePinnedThreads=full|short
21. Demo
22. Virtual Thread Usage
23. Usage of Virtual Thread API • In every piece that could block or wait in Java • Thread.sleep • Locks, ConcurrentHashMap • I/O (Socket) • Rewrite of several components as pre-requisite for Loom to avoid thread pinning
24. Usage Pattern • Kernel Threads („Platform Threads“) • Expensive • Thread Pooling • Virtual Threads • Cheap • New Virtual Thread per Runnable
25. Benchmarks
26. JMH Benchmarks Benchmark Mode Cnt Score Error Units VirtualThreadsBenchmark.createVirtualThreads thrpt 5 14488555,637 ± 1127513,241 ops/s VirtualThreadsBenchmark.runAndAwait thrpt 5 455494,235 ± 22033,286 ops/s PlatformThreadsBenchmark.createPlatformThreads thrpt 5 19094,736 ± 677,739 ops/s PlatformThreadsBenchmark.runAndAwait thrpt 5 15807,802 ± 7337,906 ops/s
27. JMH Benchmarks Benchmark Mode Cnt Score Error Units VirtualThreadsBenchmark.createVirtualThreads thrpt 5 14488555,637 ± 1127513,241 ops/s VirtualThreadsBenchmark.runAndAwait thrpt 5 455494,235 ± 22033,286 ops/s PlatformThreadsBenchmark.createPlatformThreads thrpt 5 19094,736 ± 677,739 ops/s PlatformThreadsBenchmark.runAndAwait thrpt 5 15807,802 ± 7337,906 ops/s ThreadPoolExecutorBenchmark.runAndAwait thrpt 5 461335,862 ± 54427,010 ops/s
28. Conclusion
29. What does that mean? • If the CPU availability is the limit: You cannot increase concurrency • If the Runnable wait is the limit: You can increase concurrency
30. Loom Overhead • Hard to say. What’s the typical application?
31. Scalability Boundaries • I/O in synchronized Code • Connection Pool Sizes • Databases • HTTP/JMS/… Connection Pools • File handles • number of open files
32. Pitfalls • System.out.println(…) • Attempts to obtain a lock on the I/O • Measurement and benchmark correctness („What do you measure?“) • Visible and invisible Thread Pinning
33. Client/Server Workloads • Workload to be served by virtual thread (carrier threads must be available) • System must have sufficient resources to serve the request (e.g. connection pools). Also a constraint for platform thread usage. • Request to server via I/O • During wait, virtual thread is parked • Client must not pin kernel thread (see carrier thread availability)
34. Computation Workloads • Incoming request/event to be served by virtual thread (carrier threads must be available) • System performs a computation based on in-memory resources • Response is returned
35. Call to Action • Investigate: What is limiting your application scalability? • Investigate: What workloads do you run? • Experiment: Run your application on Virtual Threads and benchmark it! • Observe: What libraries lead to thread pinning?
36. Maybe. „Will Loom help with concurrency and scalability in my app?“
37. Thank You. github.com/mp911de @mp911de paluch.biz