Jonathan Lalou's Blog

Posts Tagged ‘GraalVM’

Alina Yurenko 🇺🇦 , a developer advocate at Oracle Labs, captivated audiences at Devoxx France 2024 with her deep dive into GraalVM’s ahead-of-time (AOT) compilation for Java applications. With a passion for open-source and community engagement, Alina explored how GraalVM’s Native Image transforms Java applications into compact, high-performance native executables, ideal for cloud environments. Through demos and practical guidance, she addressed building, testing, and optimizing GraalVM applications, debunking myths and showcasing its potential. This post unpacks Alina’s insights, offering a roadmap for adopting GraalVM in production.

GraalVM and Native Image Fundamentals

Alina introduced GraalVM as both a high-performance JDK and a platform for AOT compilation via Native Image. Unlike traditional JVMs, GraalVM allows developers to run Java applications conventionally or compile them into standalone native executables that don’t require a JVM at runtime. This dual capability, built on over a decade of research at Oracle Labs, offers Java’s developer productivity alongside native performance benefits like faster startup and lower resource usage. Native Image, GA since 2019, analyzes an application’s bytecode at build time, identifying reachable code and dependencies to produce a compact executable, eliminating unused code and pre-populating the heap for instant startup.

The closed-world assumption underpins this process: all application behavior must be known at build time, unlike the JVM’s dynamic runtime optimizations. This enables aggressive optimizations but requires careful handling of dynamic features like reflection. Alina demonstrated this with a Spring Boot application, which started in 1.3 seconds on GraalVM’s JVM but just 47 milliseconds as a native executable, highlighting its suitability for serverless and microservices where startup speed is critical.

Benefits Beyond Startup Speed

While fast startup is a hallmark of Native Image, Alina emphasized its broader advantages, especially for long-running applications. By shifting compilation, class loading, and optimization to build time, Native Image reduces runtime CPU and memory usage, offering predictable performance without the JVM’s warm-up phase. A Spring Pet Clinic benchmark showed Native Image matching or slightly surpassing the JVM’s C2 compiler in peak throughput, a testament to two years of optimization efforts. For memory-constrained environments, Native Image excels, delivering up to 2–3x higher throughput per memory unit at heap sizes of 512MB to 1GB, as seen in throughput density charts.

Security is another benefit. By excluding unused code, Native Image reduces the attack surface, and dynamic features like reflection require explicit allow-lists, enhancing control. Alina also noted compatibility with modern Java frameworks like Spring Boot, Micronaut, and Quarkus, which integrate Native Image support, and a community-maintained list of compatible libraries on the GraalVM website, ensuring broad ecosystem support.

Building and Testing GraalVM Applications

Alina provided a practical guide for building and testing GraalVM applications. Using a Spring Boot demo, she showcased the Native Maven plugin, which streamlines compilation. The build process, while resource-intensive for large applications, typically stays within 2GB of memory for smaller apps, making it viable on CI/CD systems like GitHub Actions. She recommended developing and testing on the JVM, compiling to Native Image only when adding dependencies or in CI/CD pipelines, to balance efficiency and validation.

Dynamic features like reflection pose challenges, but Alina outlined solutions: predictable reflection works out-of-the-box, while complex cases may require JSON configuration files, often provided by frameworks or libraries like H2. A centralized GitHub repository hosts configs for popular libraries, and a tracing agent can generate configs automatically by running the app on the JVM. Testing support is robust, with JUnit and framework-specific tools like Micronaut’s test resources enabling integration tests in Native mode, often leveraging Testcontainers.

Optimizing and Future Directions

To achieve peak performance, Alina recommended profile-guided optimizations (PGO), where an instrumented executable collects runtime profiles to inform a final build, combining AOT’s predictability with JVM-like insights. A built-in ML model predicts profiles for simpler scenarios, offering 6–8% performance gains. Other optimizations include using the G1 garbage collector, enabling machine-specific flags, or building static images for minimal container sizes with distroless images.

Looking ahead, Alina highlighted two ambitious GraalVM projects: Layered Native Images, which pre-compile base images (e.g., JDK or Spring) to reduce build times and resource usage, and GraalOS, a platform for deploying native images without containers, eliminating container overhead. Demos of a LangChain for Java app and a GitHub crawler using Java 22 features showcased GraalVM’s versatility, running seamlessly as native executables. Alina’s session underscored GraalVM’s transformative potential, urging developers to explore its capabilities for modern Java applications.

Links:

• GraalVM website

• Oracle Labs website

• Devoxx France website

Hashtags: #GraalVM #NativeImage #Java #AOT #AlinaYurenko #DevoxxFR2024

At Devoxx Belgium 2023, Felipe Yanaga, a teaching assistant at the University of North Carolina at Chapel Hill and a Robertson Scholar, delivered a compelling presentation addressing the growing disconnect between the vibrant use of Java in industry and its outdated perception in academia. As a student with internships at Amazon and Google, and a fellow at UNC’s Computer Science Experience Lab, Felipe draws on his unique perspective to highlight how universities lag in teaching modern Java practices. His talk explores the reasons behind this divergence, the negative perceptions students hold about Java, and actionable steps to revitalize its presence in academic settings.

Java’s Strength in Industry

Felipe begins by emphasizing Java’s enduring relevance in the professional world. Far from the “Java is dead” narrative that periodically surfaces online, the language thrives in industry, powered by innovations like Quarkus, GraalVM, and a rapid six-month release cycle. Companies sponsoring Devoxx, such as Red Hat and Oracle, exemplify Java’s robust ecosystem, leveraging frameworks and tools that enhance developer productivity. For instance, Felipe references the keynote by Brian Goetz, which outlined Java’s roadmap, showcasing its adaptability to modern development needs by drawing inspiration from other languages. This continuous evolution ensures Java remains a cornerstone for enterprise applications, from microservices to large-scale systems.

However, Felipe points out a troubling trend: despite its industry strength, Java’s popularity is declining in metrics like GitHub’s language rankings and the TIOBE Index. While JavaScript and Python have surged, Java’s share of relevant Google searches has dropped from 26% in 2002 to under 10% by 2023. Felipe attributes this partly to a shift in academic settings, where the foundation for programming passion is often laid. The disconnect between industry innovation and university curricula is a critical issue that needs addressing to sustain Java’s future.

The Academic Lag: Java’s Outdated Image

In universities, Java’s reputation suffers from outdated teaching practices. Felipe notes that many institutions, including top U.S. universities, have shifted introductory courses from Java to Python, citing Java’s perceived complexity and age. A 2017 quote from a Stanford professor illustrates this sentiment, claiming Java “shows its age” and prompting a move to Python for introductory courses. Surveys of 70 leading U.S. universities confirm this trend, with Python now dominating as the primary teaching language, while Java is relegated to data structures or object-oriented programming courses.

Felipe’s own experience at UNC-Chapel Hill reflects this shift. A decade ago, Java dominated the curriculum, but by 2023, Python had overtaken introductory and database courses. This transition reinforces a perception among students that Java is verbose, bloated, and outdated. Felipe conducted a survey among 181 students in a software engineering course, revealing stark insights: 42% believed Python was in highest industry demand, 67% preferred Python for building REST APIs, and terms like “tedious,” “boring,” and “outdated” dominated a word cloud describing Java. One student even remarked that Java is suitable only for maintaining legacy code, a sentiment that underscores the stigma Felipe aims to dismantle.

The On-Ramp Challenge: Simplifying Java’s Introduction

A significant barrier to Java’s adoption in academia is its steep learning curve for beginners. Felipe contrasts Python’s straightforward “hello world” with Java’s intimidating boilerplate code, such as public static void main. This complexity overwhelms novices, who grapple with concepts like classes and static methods without clear explanations. Instructors often dismiss these as “magic,” which disengages students and fosters a negative perception. Felipe highlights Java’s JEP 445, which introduces unnamed classes and instance main methods to reduce boilerplate, as a promising step to make Java more accessible. By simplifying the initial experience, such innovations could align Java’s on-ramp with Python’s ease, engaging students early and encouraging exploration.

Beyond the language itself, the Java ecosystem poses additional challenges. Installing Java is daunting for beginners, with multiple Oracle websites offering conflicting instructions. Felipe recounts his own struggle as a student, only navigating this thanks to his father’s guidance. Tools like SDKMan and JBang simplify installation and scripting, but these are often unknown to students outside the Java community. Similarly, choosing an IDE—IntelliJ, Eclipse, or VS Code—adds another layer of complexity. Felipe advocates for clear, standardized guidance, such as recommending SDKMan and IntelliJ, to streamline the learning process and make Java’s ecosystem more approachable.

Bridging the Divide: Community and Mentorship

To reverse the declining trend in academia, Felipe proposes actionable steps centered on community engagement. He emphasizes the need for industry professionals to connect with universities, citing examples like Tom from Info Support, who collaborates with local schools to demonstrate Java’s real-world applications. By mentoring students and updating professors on modern tools like Maven, Gradle, and Quarkus, industry can reshape Java’s image. Felipe also encourages inviting students to Java User Groups (JUGs), where they can interact with professionals and discover tools that enhance Java development. These initiatives, he argues, plant seeds of enthusiasm that students will share with peers, amplifying Java’s appeal.

Felipe stresses that small actions, like a 10-minute conversation with a student, can make a significant impact. By demystifying stereotypes—such as Java being slow or bloated—and showcasing frameworks like Quarkus with hot reload capabilities, professionals can counter misconceptions. He also addresses the lack of Java-focused workshops compared to Python and JavaScript, urging the community to actively reach out to students. This collective effort, Felipe believes, is crucial to ensuring the next generation of developers sees Java as a vibrant, modern language, not a relic of the past.

Links:

• University of North Carolina at Chapel Hill

• Duke University

Hashtags: #Java #SoftwareDevelopment #Education #Quarkus #GraalVM #UNCChapelHill #DukeUniversity #FelipeYanaga