Java’s performance is often a topic of discussion, particularly its startup time and memory footprint. GraalVM Native Image has emerged as a powerful tool to address these concerns, allowing developers to compile Java code ahead-of-time (AOT) into native executables. With the release of GraalVM 24.1.0, several enhancements further boost the performance of native images, making them even more attractive for various applications.

This latest release doesn’t introduce a single, monolithic feature called “Java Native Image Performance Enhancements.” Instead, it incorporates a collection of optimizations across the compilation and runtime stages that contribute to overall performance gains. Let’s explore some of these key improvements:

Well, it depends! Let’s start with the models.

It feels like a new model is released pretty much every month claiming to be “best in class” and having superior results to competitor models.

Can Large Language Models (LLMs) Write Compilable Code?

Large language models (LLMs) have demonstrated impressive capabilities in generating human-like text, translating languages, and even writing different kinds of creative content. But can these powerful AI tools also write code that’s actually compilable and functional? The answer, in short, is a qualified yes, but with important caveats.

As a data engineer, you’re likely familiar with the challenges of error handling in Apache Beam Java applications. Traditional approaches can lead to verbose code, making it difficult to read and maintain. The Asgarde library offers a solution by providing a way to write less code and produce more concise and expressive code.

What is Asgarde?

Asgarde is an open-source library that simplifies error handling in Apache Beam Java applications. It accomplishes this by wrapping common error handling patterns into reusable components. This can save you time and effort when writing Beam pipelines, and it can also make your code easier to read and understand.

Java 20 was released on March 21, 2023. It is a short-term release supported for six months, following the September 20 release of JDK 19. JDK 21, due in September, will be a long-term support (LTS) release, backed by multiple years of support.

New features in Java 20:

• Record Patterns (Second Preview): This feature enhances the Java programming language with record patterns to deconstruct record values. Record patterns and type patterns can be nested to enable a powerful, declarative, and composable form of data navigation and processing.
• Pattern Matching for Switch Statements and Expressions (Fourth Preview): This feature extends pattern matching to express more sophisticated, composable data queries in switch statements and expressions.
• Foreign Function and Memory API (Second Preview): This API introduces an API by which Java programs can interoperate with code and data outside of the Java runtime.
• Scoped Values (Incubator): This feature introduces scoped values, which enable the sharing of immutable data within and across threads.
• Virtual Threads (Incubator): This feature provides a lightweight, non-blocking thread abstraction that can be used to improve the performance and scalability of concurrent applications.
• Vector API (Proposal): This API provides a high-performance, efficient way to operate on arrays of primitive data types.
• Unicode 15.0 Support: This release includes support for Unicode 15.0, which includes new characters and emoji.
• Improved Control of G1 Concurrent Refinement Threads: This release includes improvements to the G1 garbage collector, which can improve the performance of concurrent applications.
• Support for CLDR Version 42: This release includes support for CLDR Version 42, which provides locale data for over 150 languages.

More info

• OpenJDK is the reference implementation of the Java Virtual Machine (JVM). It is free and open-source, and is available for a variety of platforms.
• Oracle JDK is a commercial implementation of the JVM. It is developed and supported by Oracle, and includes additional features and performance optimizations over OpenJDK.
• GraalVM is a high-performance JVM that is designed for modern applications. It includes a number of features that can improve the performance of Java applications, such as ahead-of-time (AOT) compilation and native image generation.
• IKVM.NET is a JVM that runs on the .NET Framework. It allows Java code to be run on .NET-based platforms, such as Windows and macOS.
• JRockit is a JVM that is designed for high-performance computing applications. It includes a number of features that can improve the performance of Java applications, such as garbage collection tuning and thread affinity.
• Azul VM is a high-performance Java Virtual Machine (JVM) developed by Azul Systems. It is based on the OpenJDK codebase, but includes a number of performance optimizations and features that can improve the performance of Java applications.

These are just a few of the many VMs that are available for Java. The best VM for a particular application will depend on the specific requirements of the application.

Java ByteBuffers, a core component of the Java NIO (New Input/Output) API, offer a powerful and versatile way to manage data in your Java applications. They provide significant advantages in terms of efficiency, flexibility, and portability, making them a valuable tool for a wide range of tasks. This article explores the key benefits of using ByteBuffers and highlights specific use cases where they shine.

Why Use Java ByteBuffers?

• Efficiency for Data Manipulation and I/O: ByteBuffers excel in data manipulation and input/output operations. They allow direct reading and writing of data to and from memory, eliminating the need to copy data to intermediate buffers. This direct access, often referred to as “zero-copy,” significantly boosts performance, especially when dealing with large datasets or high-throughput I/O. This efficiency gain is crucial for applications where performance is paramount.
• Flexibility with Diverse Data Types: ByteBuffers offer remarkable flexibility in handling various data types. They can represent integers, floats, strings, and even raw binary data within a single unified structure. This versatility makes them a valuable asset for tasks like network programming, file I/O, cryptography, and any scenario requiring manipulation of different data formats. You can work with different views of the same underlying data (e.g., asIntBuffer(), asFloatBuffer()).
• Portability Across JVMs: As a standard part of the Java NIO API, ByteBuffers are supported across all Java Virtual Machines (JVMs). This portability ensures that your code remains consistent and functional across different Java environments, simplifying development and deployment.

Practical Use Cases for ByteBuffers:

Chronicle Queue and Aeron are both high-performance messaging systems, but they have different strengths and weaknesses.

• Chronicle Queue is designed for low latency and high throughput messaging within a single machine or cluster. It uses a shared memory ring buffer to store messages, which can achieve very low latency (<1 microsecond) for messages that are sent and received on the same machine. Chronicle Queue also supports persistence, so messages can be written to disk and recovered in the event of a crash.

Pseudo-Random Number Generators (PRNGs) are getting a major update in Java with the release of JEP 356. New interfaces and implementations make it easier to use different algorithms interchangeably and offer better support for stream-based programming. This is a great improvement for Java developers who require randomness in their applications. 

The JDK is constantly evolving and improving, and part of that process is ensuring that internal APIs are properly encapsulated. JEP 403 represents a step in that direction, by removing the –illegal-access flag. This will prevent JDK users from accessing internal APIs, except for critical ones like sun.misc.Unsafe.  

A couple of options I’ve used and seen: 

• Java Native interface - watch out for segfaults! 
• Project Panama - early access
• GraalVM - still really new.
• zt-exec - call the native library as an external process
• remotetea - an old favourite if it’s legacy C++ code

Great talk by by Peter Lawrey regarding latency in micro-services.  https://www.infoq.com/presentations/latency-sensitive-microservices/

1. Download the latest jars from http://www.sencha.com/products/extgwt/download/
2. Follow the “setup.txt” create a eclipse project.
3. Add all the folders in samples/**/src as source folder
4. Expand the samples/examples.war into the “war” folder in your eclipse dir
5. Delete the old “gxt.jar” from the WEB-INF/lib and replace with the gxt-2.2.3-gwt22.jar (seems like an old version is bundled with the samples.war)
6. Run As, Web app, explorer.html, cross fingers…

This has really helped in the Coherence projects I’ve been working on. Nothing quite like real world experience. More resources here.