Using Java’s Project Loom To Build More Reliable Distributed Systems

Once a blocking call has completed, the request in question will be continued, using a thread again. This model makes much more efficient use of the threads resource for IO-bound workloads, unfortunately at the price of a more involved programming model, which doesn’t feel familiar to many developers. Also aspects like debuggability or observability can be more challenging with reactive models, as described in the Loom JEP.

java loom release date

When I cranked up the rate of timeouts and failures , I saw closer to 15k requests per second processed and when I made performance uniformly excellent, I saw single core throughput as high as 85k Raft rounds per second. This represents simulating hundreds of thousands of individual RPCs per second, and represents 2.5M Loom context switches per second on a single core. For the actual Raft implementation, I follow a thread-per-RPC model, similar to many web applications. My application has HTTP endpoints (via Palantir’s Conjure RPC framework) for implementing the Raft protocol, and requests are processed in a thread-per-RPC model similar to most web applications. Local state is held in a store , which for purposes of demonstration is implemented solely in memory.

Project Loom Early

I’ve found Jepsen and FoundationDB to apply two similar in idea but different in implementation testing methodologies in an extremely interesting way. Java’s Project Loom makes fine grained control over execution easier than ever before, enabling a hybridized approach to be cheaply invested in. I believe that there’s a competitive advantage to be had for a development team that uses simulation to guide their development, and usage of Loom should allow a team to dip in and out where the approach is and isn’t beneficial. Historically this approach was viable, but a gamble, since it led to large compromises elsewhere in the stack.

java loom release date

We can achieve the same functionality with structured concurrency using the code below. If the thread executing handleOrder() is interrupted, the interruption is not propagated to the subtasks. In this case updateInventory() and updateOrder() will leak and continue to run in the background. Imagine that updateInventory() is an expensive long-running operation and updateOrder() throws an error. The handleOrder() task will be blocked on inventory.get() even though updateOrder() threw an error.

Using Java’s Project Loom To Build More Reliable Distributed Systems

Works on any device using Loom’s desktop and mobile apps or Chrome extension. If a log entry is committed in a given term, then that entry will be present in the logs of the leaders for all higher-numbered terms. Subsystems could be tested in isolation against the simulation . // Thread.yield will pause the thread and cause it to become schedulable again. Error handling with short-circuiting — If either the updateInventory() or updateOrder() fails, the other is canceled unless its already completed. This is managed by the cancellation policy implemented by ShutdownOnFailure(); other policies are possible.

Virtual Threads: New Foundations for High-Scale Java Applications –

Virtual Threads: New Foundations for High-Scale Java Applications.

Posted: Fri, 23 Sep 2022 09:04:53 GMT [source]

Kotlin and Clojure offer these as the preferred communication model for their coroutines. Instead of shared, mutable state, they rely on immutable messages that are written to a channel and received from there by the receiver. Whether channels will become part of Project Loom, however, is still open. For example, the experimental “Fibry” is an actor library for Loom. Virtual threads may be new to Java, but they aren’t new to the JVM.

Project Loom

While they were all started at the same time, for the first two seconds only eight of them were actually executed, followed by the next eight, and so on. The problem with the classic thread-per-request model is that only scales up to a certain point. Threads managed by the operating system are a costly resource, which means you can typically have at most a few thousands of them, but not hundreds of thousands, or even millions.

java loom release date

This creates an executor that always launches a new lightweight thread instead of a limited sized thread pool . So in a thread-per-request model, the throughput will be limited by the number of OS threads available, which depends on the number of physical cores/threads available on the hardware. To work around this, you have to use shared thread pools or asynchronous concurrency, both of which have their drawbacks. Thread pools have many limitations, like thread leaking, deadlocks, resource thrashing, etc. Asynchronous concurrency means you must adapt to a more complex programming style and handle data races carefully. There are also chances for memory leaks, thread locking, etc.

Inside Java

Continuations have a justification beyond virtual threads and are a powerful construct to influence the flow of a program. Project Loom includes an API for working with continuations, but it’s not meant for application development and is locked away in the jdk.internal.vm package. It’s the low-level construct that makes virtual threads possible. However, those who want to experiment with it have the option, see listing 3. As virtual threads are a preview feature, you will need to add special flags —- enable-preview and–releasewhen compiling your program.

When that task is run by the executor, if the thread needs to block, the submitted runnable will exit, instead of pausing. When the thread can be unblocked, a new runnable is submitted to the same executor to pick up where the previous Runnable left off. Here, interleaving is much, much easier, since we are passed each piece of runnable work as it becomes runnable. Combined with the Thread.yield() primitive, we can also influence the points at which code becomes deschedulable. If the ExecutorService involved is backed by multiple operating system threads, then the task will not be executed in a deterministic fashion because the operating system task scheduler is not pluggable. If instead it is backed by a single operating system thread, it will deadlock.

It should be easy enough though to bring this back if the current behavior turns out to be problematic. By tweaking latency properties I could easily ensure that the software continued to work in the presence of e.g. RPC failures or slow servers, and I could validate the testing quality by introducing obvious bugs (e.g. if the required quorum size is set too low, it’s not possible to make progress).

The simulation model therefore infects the entire codebase and places large constraints on dependencies, which makes it a difficult choice. The tests could be made extremely fast because the test doubles enabled skipping work. For example, suppose that a task needs to wait for a second. Instead of implementing by actually waiting for a second, the simulation could possibly increment a number by a second . Instead of actually using the TCP stack, a simulation could be used which does not require any operating system collaboration.

Structured Concurrency

Because Java’s implementation of virtual threads is so general, one could also retrofit the system onto their pre-existing system. A loosely coupled system which uses a ‘dependency injection’ style for construction where different subsystems can be replaced with test stubs as necessary would likely find it easy to get started . A tightly coupled system which uses lots of static singletons would likely need some refactoring before the model could be attempted.

Since it runs on its own thread, it can complete successfully. But now we have an issue with a mismatch in inventory and order. In that case, we are just wasting the resources for nothing, and we will have to write some sort of guard logic to revert the updates done to order as our overall operation has failed.

  • The tests were deterministic and so any test failure naturally had a baked in reproduction (one can simply re-run the test to observe the same result).
  • ScyllaDB documents their testing strategy here and while the styles of testing might vary between different vendors, the strategis have mostly coalesced around this approach.
  • Library authors will see huge performance and scalability improvements while simplifying the codebase and making it more maintainable.
  • Early-access functionality might never make it into a general-availability release.

I think that there’s room for a library to be built that provides standard Java primitives in a way that can admits straightforward simulation . Now it is time to convert your own program to use virtual threads. If you are already using the executor service pattern for scheduling tasks, you can just easily replace your executor definition by using the new util function Executors.newVirtualThreadPerTaskExecutor().

Thanks to the changed libraries, which are then using virtual threads. When the FoundationDB team set out to build a distributed database, they didn’t start by building a distributed database. Instead, they built a deterministic simulation of a distributed database.

Ideally, we would like the handleOrder() task to cancel updateInventory() when a failure occurs in updateOrder() so that we are not wasting time. Imagine that updateInventory() fails and throws an exception. Then, the handleOrder() method throws an exception when calling inventory.get().

Update: Huobi Will List Both $coti Native And $coti Erc20

Now there’s not much point in overcommitting to more threads than physically supported by a given CPU anyways for CPU-bound code . But in any case it’s worth pointing out that CPU-bound code may behavior differently with virtual threads than with classic OS-level threads. This may come at a suprise for Java developers, in particular if authors of such code are not in charge of selecting the thread executor/scheduler actually used by an application. Reactive programming models address this limitation by releasing threads upon blocking operations such as file or network IO, allowing other requests to be processed in the meantime.

Loom For Enterprise

As the author of the database, we have much more access to the database if we so desire, as shown by FoundationDB. You can reach us directly at or you can also ask us on the forum. Deepu is a polyglot developer, Java Champion, and OSS aficionado. Check out these additional resources to learn more about Java, multi-threading, and Project Loom. Cancellation propagation — If the thread running handleOrder() is interrupted before or during the call to join(), both forks are canceled automatically when the thread exits the scope. Due to limited intellectual property protection and enforcement in certain countries, the source code may only be distributed to an authorized list of countries.

Virtual threads could be a no-brainer replacement for all use cases where you use thread pools today. This will increase performance and scalability in most cases based on the benchmarks out there. Structured concurrency Java Loom can help simplify the multi-threading or parallel processing use cases and make them less fragile and more maintainable. First, let’s see how many platform threads vs. virtual threads we can create on a machine.

In a production environment, there would then be two groups of threads in the system. Suppose that we either have a large server farm or a large amount of time and have detected the bug somewhere in our stack of at least tens of thousands of lines of code. If there is some kind of smoking gun in the bug report or a sufficiently small set of potential causes, this might just be the start of an odyssey. Jepsen is a software framework and blog post series which attempts to find bugs in distributed databases, especially although not exclusively around partition tolerance. The database is installed in a controlled environment and operations are issued and their results recorded.

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