The Detroit Post
Tuesday, 19 October, 2021

Rust For Jvm

Earl Hamilton
• Friday, 11 December, 2020
• 10 min read

This library provides two modes of attachment, each ensuring the thread is promptly detached: The thread will automatically detach itself once the returned guard is dropped.

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If you have an undefined scope where you need to use Ancient and cannot keep the AttachGuard, consider attaching the thread permanently. Remember that the native thread attached to the VM must manage the local references properly, i.e., do not allocate an excessive number of references and release them promptly when they are no longer needed to enable the GC to collect them.

It is recommended to set JAVA_HOME to have reproducible builds, especially, in case of multiple VMs installed. At application run time, you must specify the path to the JVM library so that the loader can locate it.

You must explicitly use attach_current_thread… methods (refer to Attaching Native Threads section). The returned AttachGuard can be referenced to a Ancient and automatically detaches the thread when dropped.

Calling this in a thread that is already attached is a no-op, and will neither change its daemon status nor prematurely detach it. If you use it frequently in the same threads, consider either, or, if the scope where you need the Ancient is well-defined, keeping the returned guard.

Pass TCK Refactor & Rewrite Divide into several crates, build a collection of modular and reusable vm technologies Well, it's a long term plan, Sun spent 30 years to improve VM, Oracle continue doing it.

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In this previous article I implemented a small, enterprise benchmark to compare the concept of garbage collection used by Kotlin / JVM with the concept of Rust, which claims to not have any garbage collector at all. My conclusion was, that for a moderate increase in the complexity of the programming language the benchmark in Rust performed roughly 3 times faster than using Kotlin on the JVM.

A colleague of mine, who uses Rust for some time now, looked at the code and gave me some hints for further speed improvements. My benchmark consists of a computation on a large set of randomly generated entities.

I also made a proc_pure for Skill since I have some skills which are called often (Fahrenheit, a weapon, has damage scale with quanta (read: MANA) in real time) so buffs go through that path where they don't take a mutable borrow on the game state. But I was able to get the farthest by throwing i32 s around since most game state is inside FxHashMap s. Heavy use of hash maps still competing with JS is nice, since those engines do so much to try to optimize that code path.

I had to change some semantics due to the call stack throwing much sooner than in JS land. Now skills push attack requests to a DEC<(i32, i32)> & afterwards I iterate this vector (which may grow while I do so).

I opted to do a let but n = 0; while n < DEC.Len() {n+=1} DEC.clear() loop to avoid the copying DEC.remove(0) would cause or the increase in code size bringing in Vendée would cause. I'd like to eventually have Card in JS land get its data from the was blob rather than pulling in Cards.Jason to parse it itself.

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But Card gets used throughout code base, so having a working top level import first would be nice I figured I should write this up since people seem to like this sort of things while I usually don't bother sharing experiences.

I’ve written an event sourcing bank simulation in Clojure (a lisp build for Java virtual machines or Jams) called open-bank-mark, which you are welcome to read about in my previous blog post explaining the story behind this open source example. As a next step, specifically for this article I’ve added SSL and combined some topics together, using the subject name strategy option of Confluent Schema Registry, making it more production like, adding security, and making it possible to put multiple kinds of commands on one topic.

We will examine how the application works, and what was needed to change one of the components from Clojure to Rust. We’ll also take a look at some performance tests to see if Rust might be a viable alternative for Java applications using Apache Kafka ®.

In this example, the balance of two accounts have been changed, creating a balance-changed event and putting it on another topic. Here, the orange pieces are part of the Confluent Platform, while the gray ones are small Clojure or Rust applications.

The blue parts represent PostgreSQL databases, and turquoise is a Nginx web server. The events are handled by the command handler, which is the part of the system that has been ported to Rust.

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Using Java interop, these classes make working with the data in Clojure easier. They offer several functions that are included in Topology and can be used in different applications, such as getting a Java UUID from the fixed bytes ID in the schema.

Although Rust is a system programming language, you can indeed use it to write applications at a level that are relatively on par with that of Java. It behaves a lot like you were using Java, since it can auto-import, make it easy to rename functions, and has been validated.

It’s also easy to run Clippy from IntelliJ, a linter that suggests improvements, which is especially useful while learning Rust. The first version was written in Clojure using a separate Kafka consumer and producer with Java interop.

Using the UUID from the command, they try to find the entry; if they do, they send the same kind of response back as earlier. Given this, it is important to validate the token send with the command, and make sure that the balance won’t drop below the minimum amount.

Both libraries offer a consumer and a producer but are a bit more low-level than the Java clients. When a potential error is detected, most libraries return the Result type.


For example, it’s missing LZ4 compression support, which depending on the Kafka configuration could make it unusable. Support for SSL is present but requires some additional code, not just setting properties.

Work is being done to support more features, and recently admin client functions were added. Because it’s based on librdkafka, the configuration is pretty similar to the Java client.

Include the correct Afro serialized depending on the type of client. For making REST calls to Schema Registry, I used a Rust library based on curl and across for Afro serialization.

Make sure it is indeed an ID and that the Value matches the expected type Fixed, with 16 bytes. However, once you start using more complex ones, the across library won’t handle serialization of both fixed and ENIM values correctly.

In Rust, it’s not possible to dynamically create data objects this way. Certain times on the JVM, an object will be checked for garbage collection eligibility, which comes down to whether or not it has non-weak references.


In this example, we need a mutable producer that can be shared, which is isn’t allowed by the borrow checker. A multi-producer, single consumer FIFO queue communication allows you to use one producer from multiple threads.

This is part of the standard library and makes it possible to send the same kind of objects from different threads to one receiver. Creating a Docker image to run a Java application is easy.

With Rust, it’s slightly more complicated since by default it will compile for the current environment. Part of the configuration for both the JVM and Rust variants can be set using environment variables.

It falls back to default values when they are not present to run them more easily without Docker. By keeping the names for the Docker image the same, only a few changes are needed to run the open bank application with Rust instead of Clojure.

With Docker builders, you don’t even need to have Rust installed locally. The laptop used for the benchmarks was an Apple MacBook Pro 2008 with 2.6 GHz i7 and 16 GB of memory.

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The test focused on end-to-end latency and measured it by adding or subtracting money from the opened account, followed by a check every 20 ms to see if the balance was properly updated. While testing the latency, the load on the system is increased by letting the heartbeat service produce more messages.

The raw data of the performance tests can be found on GitHub or online via the background tab. While average latency does provide some information, if some responses are really slow, it can still hinder the user experience a lot.

As shown above, there are some spikes, which could be explained by small hiccups in one of the tests that bring the score up. For Rust native, code was added to simulate the behavior of for the producer, and .poll(ms) for the consumer.

The main reason for doing this was that on an earlier, similar kind of test, it would already fail at about 220 messages. This is simple using the FIFO queue, but you do lose some time, because all available messages need to be serialized before being sent.

The two Kafka clients don’t appear to be different, which makes sense since most of the code is the same, and the advantage of asynchronous might only prove relevant for high loads. This makes sense as the JIT compiler is generating and optimizing byte code.

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The asynchronous client needs more memory and significantly more CPU than the sync variant. Aside from the initial starting spike for Clojure, all the languages are pretty linear in relation to load.

The big difference is in the small runtime of Rust, which doesn’t use a garbage collector. In an earlier test, another JVM implementation with Kotlin and Spring Boot was using about twice as much memory as Clojure.

I hope my experience of writing a service in Rust and comparison of its performance to functionally equivalent JVM ones are useful to you. As you can see, Rust can be used as a replacement to Java applications that use Kafka, and you can use Schema Registry in almost the same way you’d use it on a JVM.

I personally really enjoyed working with Rust and would like to keep making contributions that help take things forward. He uses his knowledge of Kafka to solve infrastructure and implementation challenges both internally and for clients, the most recent being Marconi.

He previously worked at Axial building a Kafka-based platform for Rabobank, and he is the maintainer of crate, making it easy to use the Confluent Schema Registry with Rust.

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