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

What we would normally achieve with statements is now encoded as method calls. If we loved this style of programming, we would not have statements in our programming language and merrily code in Lisp. For a HTTP server the unit of concurrency is the request itself.

These threads cannot handle the level of concurrency required by applications developed nowadays. For instance, an application would easily allow up to millions of tasks execution concurrently, which is not near the number of threads handled by the operating system. The Internet is required to connect people in different parts of the world.

Why go to this trouble, instead of just adopting something like ReactiveX at the language level? The answer is both to make it easier for developers to understand, and to make it easier to move the universe of existing code. For example, data store drivers can be more easily transitioned to the new model. Although JavaRX is a powerful and potentially high-performance approach to concurrency, it is not without drawbacks.

Google solved that aviation problem by turning it into a computer problem. Winds blow in different directions and at different speeds in different layers of the stratosphere. As a smaller balloon inside the main one inflates or deflates, they can rise or fall to seek out the winds that will send them where Google wants them to go.

Project Loon was previously a research and development project that had been started by X, which was formerly known as Google X. It later developed into a company of its own, now known as Loon LLC. Users of the service connected to the balloon network using a special Internet antenna attached to their building. The signal travelled through the balloon network from balloon https://globalcloudteam.com/ to balloon, then to a ground-based station connected to an Internet service provider , then into the global Internet. As you build your distributed system, write your tests using the simulation framework. For shared datastructures that see accesses from multiple threads, one could write unit tests which check that properties are maintained using the framework.

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The balloons can also endure huge fluctuations in temperature ranging from temperatures as low as -90°C to high temperatures right up to150°C. Google ran a pilot experiment in Christchurch, Canterbury, New Zealand with 30 antenna-equipped balloons on 16 June, 2013. It was launched in collaboration with the Civil Aviation Authority from the Tekapo area of the South Island. There were at least 50 local families in the Christchurch area who tested the connection.

Billions of people could get online for the first time thanks to helium balloons that Google will soon send over many places cell towers don’t reach. Project Loon reached a huge milestone in 2019, where Alphabet Inc.’s hot air balloons covered over 40 million kilometres. The balloon also created a record of its flight hours for a total of one million hours. The altitude of the stratospheric balloon is controlled by the ballonet, which is a smaller inner balloon.

Altogether, Google has introduced the first major changes the balloon industry has seen in decades, says Mahesh ­Krishnaswamy, who oversees manufacturing for Project Loon and previously worked on Apple’s manufacturing operations. In the summer of 2013, Loon balloons lasted only eight days before having to be brought down, says ­Krishnaswamy. Today balloons last on average over 100 days, with most exceeding that time in flight; a handful last as long as 130 days. Google’s plan is to release hundreds of literal balloons into the air over the least-connected regions in the world.

There is plenty of good information in the 2020 blog post ‘State of Loom’ although details have changed in the last two years. It’s typical to test the consistency protocols of distributed systems via randomized failure testing. Two approaches which sit at different ends of the spectrum are Jepsen and the simulation mechanism pioneered by FoundationDB.

It’s all directed by software in a Google data center that incorporates wind forecasts from the U.S. National Oceanic and Atmospheric Administration into a simulation of stratospheric airflow. “The idea is to find a way through the maze of the winds,” says Johan Mathe, a software engineer working on Loon’s navigation system.

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

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• For example, there are many potential failure modes for RPCs that must be considered; network failures, retries, timeouts, slowdowns etc; we can encode logic that accounts for a realistic model of this.
• The answer is both to make it easier for developers to understand, and to make it easier to move the universe of existing code.
• Ideally, the handleOrder() method should fail if any subtask fails.
• Let’s look at some examples that show the power of virtual threads.
• The town’s residents used a satellite Internet service in 2009, but found that the service could reach costs of up to $1000 per month.
• The stratosphere, which typically is used only by weather balloons and spy planes, is safely above clouds, storms, and commercial flights.

JVM, being the application, gets the total control over all the virtual threads and the whole scheduling process when working with Java. The virtual threads play an important role in serving concurrent requests from users and other applications. Now the familar fixed thread pool will schedule virtual threads from the factory, in the same way as it has always done.

Whats New With Project Loon Progress?

Vert.x is one such library that helps Java developers write code in a reactive manner. This would be accomplished via virtual threads, delimited continuations and tail calls. Candidates include Java server software like Tomcat, Undertow, and Netty; and web frameworks like Spring and Micronaut. I expect most Java web technologies to migrate to virtual threads from thread pools. Java web technologies and trendy reactive programming libraries like RxJava and Akka could also use structured concurrency effectively.

This asynchronous programming style is great for servers, allowing them to handily support millions of concurrent requests. To combat the above problems asynchronous apis were introduced in the java.nio package. The thread is returned to the thread pool for the duration of the blocking call . There is an interrupt triggered when response is made available from the external call and the callback is invoked on another thread from the thread pool. A balloon that was intentionally burst in a test is checked for flaws.

What The Heck Is Project Loom For Java?

The simulation model therefore infects the entire codebase and places large constraints on dependencies, which makes it a difficult choice. Let’s use a simple Java example, where we have a thread that kicks off some concurrent work, does some work for itself, and then waits for the initial work to finish. As a white box tool for bug detection, Jepsen is fantastic. If you’ve written the database in question, Jepsen leaves something to be desired.

Combined with the Thread.yield() primitive, we can also influence the points at which code becomes deschedulable. Yes – all subsystems same as in production No – detailed simulations, but test doubles relied upon Useful for debugging No – distributed systems failures never fun to debug. Yes – since deterministic, all failures replayable Java’s Project Loom considerably shakes up this tradeoff. In this article, we will be looking into Project Loom and how this concurrent model works. We will be discussing the prominent parts of the model such as the virtual threads, Scheduler, Fiber class and Continuations.

Virtual threads, as the primary part of the Java Loom, are currently targeted to be included in JDK 19 as a preview feature. If it gets the expected response, the preview status of the virtual threads will then be removed by the time of the release of JDK21. Most concurrent applications developed in Java require some level of synchronization between threads for every request to work properly. It is required due to the high frequency of threads working concurrently. Hence, context switching takes place between the threads, which is an expensive task affecting the execution of the application.

Project Documents

Loom proposes to move this limit towards million of threads. The implications of this for Java server scalability are breathtaking, as standard request processing is married to thread count. The downside is that Java threads are mapped directly to the threads in the OS. This places a hard limit on the scalability of concurrent Java apps. Not only does it imply a one-to-one relationship between app threads and operating system threads, but there is no mechanism for organizing threads for optimal arrangement. For instance, threads that are closely related may wind up sharing different processes, when they could benefit from sharing the heap on the same process.

Suppose we’re trying to test the correctness of a buggy version of Guava’s Suppliers.memoize function. I will give a simplified description of what I find exciting about this. If it needs to pause for some reason, the thread will be paused, and will resume when it is able to.

They built mocks of networks, filesystems, hosts, which all worked similarly to those you’d see in a real system but with simulated time and resources allowing injection of failures. Project Loom introduces lightweight and efficient virtual threads called fibers, massively increasing resource efficiency while preserving the same simple thread abstraction for developers. Project Loon, which became one of Google’s “moonshot projects” in 2011, began launching balloons by 2013 and partnered with Telkom Kenya in 2018. Following this deal, the solar-powered balloons were tested on 35,000 customers covering over 50,000 square kilometers. The goal was to provide adequate connectivity to underserved and disadvantaged communities, beginning with Kenya.

About 50 local users in and around Christchurch and the Canterbury region tested connections to the aerial network using special antennas. After this initial trial, Google planned on sending up 300 balloons around the world at the 40th parallel south that would provide coverage to New Zealand, Australia, Chile, and Argentina. Google hoped to eventually have thousands of balloons flying in the stratosphere. My main claim is that the team that follows this path would find themselves to have commercial advantages over a more traditionally tested database. These costs are particularly high for distributed systems.