#GraalVM native image

[javadevtech]

Java Runtime Efficiency: Ahead-of-Time (AOT) Compilation with Project Leyden OpenJdk

Estimated reading time: 5 minutes Introduction: The Cold Start Problem in Modern Java Java has long been celebrated for its runtime performance thanks to Just-In-Time (JIT) compilation. However, in the era of containerization, serverless functions, and microservices, slow startup and warm-up times have become significant drawbacks. In response, the Java community is increasingly adopting…

[govindhtech]

Spring AI 1.0 and Google Cloud to Build Intelligent Apps

Spring AI 1.0

After extensive development, Spring AI 1.0 provides a robust and dependable AI engineering solution for your Java ecosystem. This is calculated to position Java and Spring at the forefront of the AI revolution, not just another library.

Spring Boot is used by so many enterprises that integrating AI into business logic and data has never been easier. Spring AI 1.0 lets developers effortlessly integrate cutting-edge AI models into their apps, bringing up new possibilities. Prepare to implement smart JVM app features!

Spring AI 1.0 is a powerful and comprehensive Java AI engineering solution. Its goal is to lead the AI revolution with Java and Spring. Spring AI 1.0 integrates AI into business logic and data without the integration issues many Spring Boot-using enterprises confront. It lets developers use cutting-edge AI models in their apps, expanding possibilities.

Spring AI supports multiple AI models:

Images produced by text-command image models.

Audio-to-text transcription models.

Vectors are formed by embedding models that transform random data into them for semantic similarity search.

Chat models can edit documents and write poetry, but they are tolerant and easily sidetracked.

The following elements in Spring AI 1.0 enable conversation models overcome their limits and improve:

Use system prompts to set and manage model behaviour.

Memory is added to the model to capture conversational context and memory.

Making tool calling feasible for AI models to access external features.

Including confidential information in the request with rapid filling.

Retrieval Augmented Generation (RAG) uses vector stores to retrieve and use business data to inform the model's solution.

Evaluation to ensure output accuracy employs a different model.

Linking AI apps to other services using the Model Context Protocol (MCP), which works with all programming languages, to develop agentic workflows for complex tasks.

Spring AI integrates seamlessly with Spring Boot and follows Spring developers' convention-over-configuration setup by providing well-known abstractions and startup dependencies via Spring Initialisation. This lets Spring Boot app developers quickly integrate AI models utilising their logic and data.

When using Gemini models in Vertex AI, Google Cloud connectivity is required. A Google Cloud environment must be created by establishing or selecting a project, enabling the Vertex AI API in the console, billing, and the gcloud CLI.

Use gcloud init, config set project, and auth application-default login to configure local development authentication.

The Spring Initialiser must generate GraalVM Native Support, Spring Web, Spring Boot Actuator, Spring Data JDBC, Vertex AI Gemini, Vertex AI Embeddings, PGvector Vector Database, MCP Client, and Docker Compose Support to build a Spring AI and Google Cloud application. The site recommends using the latest Java version, especially GraalVM, which compiles code into native images instead of JRE-based apps to save RAM and speed up startup. Set application properties during configuration.characteristics for application name, database connection options, Vertex AI project ID and location for chat and embedding models (gemini-2.5-pro-preview-05-06), actuator endpoints, Docker Compose administration, and PGvector schema initialisation.

PostgreSQL database with a vector type plugin that stores data with Spring AI's VectorStore abstraction? Database schema and data can be initialised on startup using schema.sql and data.sql files, and Spring Boot's Docker Compose can start the database container automatically. Spring Data JDBC creates database interaction and data access entities.

The ChatClient manages chat model interactions and is a one-stop shop. ChatClients need autoconfigured ChatModels like Google's Gemini. Developers can create several ChatClients with different parameters and conditions using ChatClient.Builder. ChatClients can be used with PromptChatMemoryAdvisor or QuestionAnswerAdvisor to handle chat memory or VectorStore data for RAG.

Spring AI simplifies tool calls by annotating methods and arguments with @Tool and @ToolParam. The model evaluates tool relevance using annotation descriptions and structure. New default tools can be added to ChatClient.

Spring AI also integrates with the Model Context Protocol (MCP), which separates tools into services and makes them available to LLMs in any language.

For Google Cloud production deployments, Spring AI apps work with pgVector-supporting databases like Google Cloud SQL or AlloyDB. AlloyDB is built for AI applications with its high performance, availability (99.99% SLA including maintenance), and scalability.

FAQ

How is spring AI?

The Spring AI application framework simplifies Spring ecosystem AI application development. It allows Java developers to easily integrate AI models and APIs without retraining, inspired by LangChain and LlamaIndex.

Essentials and Ideas:

AI integration:

Spring AI integrates AI models with enterprise data and APIs.

Abstraction and Portability:

Its portable APIs work across vector database and AI model manufacturers.

Spring Boot compatibility:

It integrates with Spring Boot and provides observability tools, starters, and autoconfiguration.

Support the Model:

It supports text-to-image, embedding, chat completion, and other AI models.

Quick Templates:

Template engines let Spring AI manage and produce AI model prompts.

Vector databases:

It uses popular vector database providers to store and retrieve embeddings.

Tools and Function Calling:

Models can call real-time data access functions and tools.

Observability:

It tracks AI activity with observability solutions.

Assessing and Preventing Hallucinations:

Spring AI helps evaluate content and reduce hallucinations.

[sybrenbolandit]

Are you looking for a way to build super fast starting images with a low memory footprint? Use GraalVM’s native image and make the difference!

[brilworks]

What’s New in Spring Boot 3 for Java Developers in 2023

A guide to the latest Spring Boot versions, as well as the addition of new features and enhancements to the latest update.

The Spring team released Spring Boot 3 in November 2022; it includes GraalVM native image support, enhanced Log4j2, Improved @ConstructorBinding, and a handful of new features. This article examines what’s new for Java web development in Spring Boot 3.

What is Spring Boot?

It is an open-source Java development framework that incorporates conventional Spring MVC features allowing developers to leverage Spring framework’s functionalities. If we talk about features, it has robust tools for developing production-grade, robust, asynchronous, and non-blocking web apps. In addition to this, developers can explore a range of configured templates, pre-built modules for services, and security, allowing developers to developer to develop applications at a faster pace.

The Spring Boot 2.X line was a remarkable journey for Java developers as it delivered 95 distinct releases and new functionalities throughout its 4-year timeframe. Nonetheless, the Spring team has ensured open-source support for the 2.7 version until November 2023, and commercial support will continue till 2025.

What’s New in Spring Boot 3?

Moving on to the new features, Spring Boot 3 has some significant additions, including GraalVM native image support, which optimizes application performance. Developers can leverage this new feature to create native images for their Spring Boot applications, leading to faster startup times and lower memory footprints. Additionally, the latest version includes several other updates and enhancements that improve the development experience and provide a better user interface.

1. GraalVM Native Image Support

Furthermore, it’s worth noting that Spring Boot 3 now supports GraalVM native images. This development allows developers to convert Spring Boot applications into native images that have a lesser memory footprint and faster compilation.

For those who don’t know, GraalVM is a runtime environment that allows for the faster execution of Java code. You can learn more about GraalVM in Spring by exploring the resources available here.

2. Log4j2 enhancement

Log4j2, the logging tool available in the Spring Boot framework, includes a few incremental improvements, which are as follows:

Profile-specific Configuration

Environment Properties Lookup

Log4j2 System Properties

For more details, you can check the documentation page.

3. Improved @ConstructorBinding Detection

These improvements in constructor binding will make it easier to use constructor binding with @ConfigurationProperties classes and simplify the code.

When you use constructor-bound @ConfigurationProperties, Spring Boot no longer requires the @ConstructorBinding annotation if the class has a single parameterized constructor. However, if you have more than one constructor, you still need to use @ConstructorBinding to inform Spring Boot which one to use.

For most users, this updated logic will allow for simpler @ConfigurationProperties classes. However, if you have a @ConfigurationProperties and want to inject beans into the constructor rather than binding it, you’ll now need to add an @Autowired annotation.

4. Micrometer Updates

Micrometer, a monitoring tool for Spring applications, includes several enhancements and improvements.

Auto-configuration for Micrometer Observation API

Auto-configuration for Micrometer Tracing

Auto-configuration for Micrometer’s OtlpMeterRegistry

5. Prometheus Support

In Spring Boot 3, Auto-Configuration for Prometheus Exemplars and Push Gateway can be configured to perform a PUT on shutdown. Furthermore, Spring Boot has deprecated the existing push setting and now recommends using post instead.

6. Miscellaneous

Auto-configuration for the new Elasticsearch Java Client has been introduced.

Apache HTTP client a JdkClientHttpConnector will now be auto-configured

The @SpringBootTest annotation can now use the main of any discovered @SpringBootConfiguration class if it’s available. This means that tests can now pick up any custom SpringApplication configuration performed by your main method.

Read more at https://www.brilworks.com/blog/whats-new-in-spring-boot-3-for-java-developers-in-2023/

[devsnews]

Spring Boot 3 now supports native images powered by GraalVM. Native image support enables developers to compile a Spring Boot application into a native executable, providing a fast and low-memory footprint deployment environment. It also provides improved startup times, better performance, and a more secure runtime environment. GraalVM is an open-source virtual machine that can compile Java applications into native images. The Spring Boot team has worked hard to make the native image support feature as easy as possible, with minimal configuration and setup.

[strategictech]

Spring Boot 3 and Spring Framework 6 Use Java 17 and Jakarta EE 9, Support Native Java with GraalVM

VMware released Spring Framework 6 and Spring Boot 3, a new generation for the Spring ecosystem. Spring Framework 6 requires Java 17 and Jakarta EE 9. It also embeds observability through Micrometer with tracing and metrics. Spring Boot 3 requires Spring Framework 6. It has built-in support for creating native executables through static Ahead-of-Time (AOT) compilation with GraalVM Native Image.

[technsavi]

JEP Updates, GraalVM Code to OpenJDK, Return of JavaOne

JEP Updates, GraalVM Code to OpenJDK, Return of JavaOne

This week’s Java roundup for October 17th, 2022 features news from OpenJDK, JDK 19, JDK 20, JavaFX 20, Generational ZGC Build 20, Oracle Labs, Liberica JDK and Native Image Kit, Spring milestone, point and release candidates, EclipseLink 4.0, Quarkus 2.13.3, Micronaut 3.7.2, Hibernate Reactive 1.1.9, JHipster Lite 0.20, Apache Commons CVE, Groovy 4.0.6 and 2.5.29 and the return of…

[hackernewsrobot]

Compile the Minecraft Server (Java Edition) to Native with GraalVM Native Image

https://github.com/hpi-swa/native-minecraft-server Comments

[sizzlenut]

Quarkus - a Kubernetes based framework

Quarkus is a Java framework tailored for deployment on Kubernetes. Key technology components surrounding it are OpenJDK HotSpot and GraalVM. The goal of Quarkus is to make Java a leading platform in Kubernetes and serverless environments while offering developers a unified reactive and imperative programming model to optimally address a wider range of distributed application architectures. Quarkus also offers near-instant scale-up and high-density utilisation in container orchestration platforms such as Kubernetes. Many more application instances can be run given the same hardware resources. After its initial debut, Quarkus underwent several enhancements over the next few months, culminating in a 1.0 release within the open source community in October 2019. As a new framework, Quarkus does not need to attempt to retrofit new patterns and principles into an existing codebase. Instead, it can focus on innovation.

Java applications are called WORA (Write Once Run Anywhere). This means a programmer can develop Java code on one system and can expect it to run on any other Java-enabled system without any adjustment. This is all possible because of JVM. The Java VM or Java Virtual Machine resides on the RAM. During execution, using the class loader the class files are brought on the RAM. The BYTE code is verified for any security breaches. Next, the execution engine will convert the Bytecode into Native machine code. 

Traditional Java stacks were engineered for monolithic applications with long start-up times and large memory requirements in a world where the cloud, containers, and Kubernetes did not exist. Java frameworks needed to evolve to meet the needs of this new world. 

Quarkus was created to enable Java developers to create applications for a modern, cloud-native world. Quarkus is a Kubernetes-native Java framework tailored for GraalVM and HotSpot, crafted from best-of-breed Java libraries and standards. The goal is to make Java the leading platform in Kubernetes and serverless environments while offering developers a framework to address a wider range of distributed application architectures. Quarkus was built from the ground up for Kubernetes making it easy to deploy applications without having to understand all of the complexities of the platform. Quarkus allows developers to automatically generate Kubernetes resources including building and deploying container images without having to manually create YAML files. Quarkus provides a cohesive, fun to use, full-stack framework by leveraging a growing list of hundreds of best-of-breed libraries that you love and use. All wired on a standard backbone.

One of the major productivity problems that face most Java developers is traditional Java development workflow. For most web developers this will generally be:

Write Code → Compile → Deploy → Refresh Browser → Repeat

This can be a major drain on productivity, as the compile + redeploy cycle can often take up to a minute or more. Quarkus aims to solve this problem with its Live Coding feature. When running in development mode the workflow is simply:

Write Code → Refresh Browser → Repeat

RESULTS

The above figure shows us the docker stats of the two containers, one running without (app-access) Quarkus the other with (app-access-jars) respectively. We can see the the docker container running with Quarkus takes up less CPU and memory utilisation.

We can see that the throughput with Quarkus is almost double than that without Quarkus. The more the throughput the better; throughput signifies the number of requests that can be sent per second.

CONCLUSION

As claimed by the Quarkus developer, Red Hat, we were able to see some difference in the response time and memory imprint taken by the API though not as gigantic difference as claimed by Red Hat.

[updatesnews]

GraalVM speeds up native image builds

GraalVM speeds up native image builds

GraalVM 22.1, the latest release of the Oracle-built polyglot runtime, features an early iteration of quick build mode for Native Image, the platform’s technology for ahead-of-time (AOT) compilation of Java code into a standalone executable. GraalVM 22.1 was introduced April 26. With quick build mode, the compiler works in economy mode with fewer optimizations, resulting in much faster…

[kaizencb]

Native Image(GraalVM) is an innovative technology that compiles Java code into a standalone binary executable or a native shared library.

Visit our website:- www.kaizenbootcamp.com

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[divergentsl]

Native Image(GraalVM) Native Image is an innovative technology that compiles Java code into a standalone binary executable or a native shared library.

[digital-dynasty]

Java Virtual Machine: GraalVM 21.3 baut auf Java 17 und bindet Schleifen um

Neben dem Update auf die aktuelle Java-Version bringt Oracles JVM-Implementierung Optimierungen beim Kompilieren und für native Images. Read more www.heise.de/news/…... www.digital-dynasty.net/de/blogs/team-blogs/…

http://www.digital-dynasty.net/de/blogs/team-blogs/35974-java-virtual-machine-graalvm-21-3-baut-auf-java-17-und-bindet-schleifen-um.html

[grimrose7]

ScalaでGraalVM native-imageを作ってAWS Lambdaで動かす

[trendingnewstracker]

Spring Native turns Spring apps into native executables

Spring Native, for compiling Spring Java applications to standalone executables called native images, is now available as a beta release. Native images promise faster startup times and lower runtime memory overhead compared to the JVM. Launched March 11 and available on start.spring.io, the Spring Native beta compiles Spring applications to native images using the GraalVM…

https://is.gd/nst8I3