Recent Posts
Archives

Posts Tagged ‘Java’

PostHeaderIcon Efficient Inter-Service Communication with Feign and Spring Cloud in Multi-Instance Microservices

In a world where systems are becoming increasingly distributed and cloud-native, microservices have emerged as the de facto architecture. But as we scale
microservices horizontally—running multiple instances for each service—one of the biggest challenges becomes inter-service communication.

How do we ensure that our services talk to each other reliably, efficiently, and in a way that’s resilient to failures?

Welcome to the world of Feign and Spring Cloud.


The Challenge: Multi-Instance Microservices

Imagine you have a user-service that needs to talk to an order-service, and your order-service runs 5 instances behind a
service registry like Eureka. Hardcoding URLs? That’s brittle. Manual load balancing? Not scalable.

You need:

  • Service discovery to dynamically resolve where to send the request
  • Load balancing across instances
  • Resilience for timeouts, retries, and fallbacks
  • Clean, maintainable code that developers love

The Solution: Feign + Spring Cloud

OpenFeign is a declarative web client. Think of it as a smart HTTP client where you only define interfaces — no more boilerplate REST calls.

When combined with Spring Cloud, Feign becomes a first-class citizen in a dynamic, scalable microservices ecosystem.

✅ Features at a Glance:

  • Declarative REST client
  • Automatic service discovery (Eureka, Consul)
  • Client-side load balancing (Spring Cloud LoadBalancer)
  • Integration with Resilience4j for circuit breaking
  • Easy integration with Spring Boot config and observability tools

Step-by-Step Setup

1. Add Dependencies

[xml][/xml]

If using Eureka:

[xml][/xml]


2. Enable Feign Clients

In your main Spring Boot application class:

[java]@SpringBootApplication
@EnableFeignClients
public <span>class <span>UserServiceApplication { … }
[/java]


3. Define Your Feign Interface

[java]
@FeignClient(name = "order-service")
public interface OrderClient { @GetMapping("/orders/{id}")
OrderDTO getOrder(@PathVariable("id") Long id); }
[/java]

Spring will automatically:

  • Register this as a bean
  • Resolve order-service from Eureka
  • Load-balance across all its instances

4. Add Resilience with Fallbacks

You can configure a fallback to handle failures gracefully:

[java]

@FeignClient(name = "order-service", fallback = OrderClientFallback.class)
public interface OrderClient {
@GetMapping("/orders/{id}") OrderDTO getOrder(@PathVariable Long id);
}[/java]

The fallback:

[java]

@Component
public class OrderClientFallback implements OrderClient {
@Override public OrderDTO getOrder(Long id) {
return new OrderDTO(id, "Fallback Order", LocalDate.now());
}
}[/java]


⚙️ Configuration Tweaks

Customize Feign timeouts in application.yml:

[yml]

feign:

    client:

       config:

           default:

                connectTimeout:3000

                readTimeout:500

[/yml]

Enable retry:

[xml]
feign:
client:
config:
default:
retryer:
maxAttempts: 3
period: 1000
maxPeriod: 2000
[/xml]


What Happens Behind the Scenes?

When user-service calls order-service:

  1. Spring Cloud uses Eureka to resolve all instances of order-service.
  2. Spring Cloud LoadBalancer picks an instance using round-robin (or your chosen strategy).
  3. Feign sends the HTTP request to that instance.
  4. If it fails, Resilience4j (or your fallback) handles it gracefully.

Observability & Debugging

Use Spring Boot Actuator to expose Feign metrics:

[xml]

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency[/xml]

And tools like Spring Cloud Sleuth + Zipkin for distributed tracing across Feign calls.


Beyond the Basics

To go even further:

  • Integrate with Spring Cloud Gateway for API routing and external access.
  • Use Spring Cloud Config Server to centralize configuration across environments.
  • Secure Feign calls with OAuth2 via Spring Security and OpenID Connect.

✨ Final Thoughts

Using Feign with Spring Cloud transforms service-to-service communication from a tedious, error-prone task into a clean, scalable, and cloud-native solution.
Whether you’re scaling services across zones or deploying in Kubernetes, Feign ensures your services communicate intelligently and resiliently.

PostHeaderIcon Java’s Emerging Role in AI and Machine Learning: Bridging the Gap to Production

While Python dominates in model training, Java is becoming increasingly vital for deploying and serving AI/ML models in production. Its performance, stability, and enterprise integration capabilities make it a strong contender.

Java Example: Real-time Object Detection with DL4J and OpenCV

[java]
import …

public class ObjectDetection {

public static void main(String[] args) {
String modelPath = "yolov3.weights";
String configPath = "yolov3.cfg";
String imagePath = "image.jpg";
Net net = Dnn.readNet(modelPath, configPath);
Mat image = imread(imagePath);
Mat blob = Dnn.blobFromImage(image, 1 / 255.0, new Size(416, 416), new Scalar(0, 0, 0), true, false);

net.setInput(blob);

MatVector detections = net.forward(); // Inference

// Process detections (bounding boxes, classes, confidence)
// … (complex logic for object detection results)
// Draw bounding boxes on the image
// … (OpenCV drawing functions)
imwrite("detected_objects.jpg", image);
}
}

[/java]

Python Example: Similar Object Detection with OpenCV and YOLO

[python]

import numpy as np

net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
image = cv2.imread("image.jpg")
blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
detections = net.forward()

# Process detections (bounding boxes, classes, confidence)
# … (simpler logic, NumPy arrays)
# Draw bounding boxes on the image
# … (OpenCV drawing functions)
cv2.imwrite("detected_objects.jpg", image)
[/python]

Comparison and Insights:

  • Syntax and Readability: Python’s syntax is generally more concise and readable for data science and AI tasks. Java, while more verbose, offers strong typing and better performance for production deployments.
  • Library Ecosystem: Python’s ecosystem (NumPy, OpenCV, TensorFlow, PyTorch) is more mature and developer-friendly for AI/ML development. Java, with libraries like DL4J, is catching up, but its strength lies in enterprise integration and performance.
  • Performance: Java’s performance is often superior to Python’s, especially for real-time inference and high-throughput applications.
  • Enterprise Integration: Java’s ability to seamlessly integrate with existing enterprise systems (databases, message queues, APIs) is a significant advantage.
  • Deployment: Java’s deployment capabilities are more robust, making it suitable for mission-critical AI applications.

Key Takeaways:

  • Python is excellent for rapid prototyping and model training.
  • Java excels in deploying and serving AI/ML models in production environments, where performance and reliability are paramount.
  • The choice between Java and Python depends on the specific use case and requirements.

PostHeaderIcon CTO Perspective: Choosing a Tech Stack for Mainframe Rebuild

Original post

From LinkedIn: https://www.linkedin.com/posts/matthias-patzak_cto-technology-activity-7312449287647375360-ogNg?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAWqBcBNS5uEX9jPi1JPdGxlnWwMBjXwaw

Summary of the question

As CTO for a mainframe rebuild (core banking/insurance/retail app, 100 teams/1000 people with Cobol expertise), considering Java/Kotlin, TypeScript/Node.js, Go, and Python. Key decision criteria are technical maturity/stability, robust community, and innovation/adoption. The CTO finds these criteria sound and seeks a language recommendation.

TL;DR: my response

  • Team, mainframe rebuild: Java/Kotlin are frontrunners due to maturity, ecosystem, and team’s Java-adjacent skills. Go has niche potential. TypeScript/Node.js and Python less ideal for core.
  • Focus now: deep PoC comparing Java (Spring Boot) vs. Kotlin on our use cases. Evaluate developer productivity, readability, interoperability, performance.
  • Develop comprehensive Java/Kotlin training for our 100 Cobol-experienced teams.
  • Strategic adoption plan (Java, Kotlin, or hybrid) based on PoC and team input is next.
  • This balances proven stability with modern practices on the JVM for our core.

My detailed opinion

As a CTO with experience in these large-scale transformations, my priority remains a solution that balances technical strength with the pragmatic realities of our team’s current expertise and long-term maintainability.

While Go offers compelling performance characteristics, the specific demands of our core business application – be it in banking, insurance, or retail – often prioritize a mature ecosystem, robust enterprise patterns, and a more gradual transition path for our significant team. Given our 100 teams deeply skilled in Cobol, the learning curve and the availability of readily transferable concepts become key considerations.

Therefore, while acknowledging Go’s strengths in certain cloud-native scenarios, I want to emphasize the strategic advantages of the Java/Kotlin ecosystem for our primary language choice, with a deliberate hesitation and deeper exploration between these two JVM-based options.

Re-emphasizing Java and Exploring Kotlin More Deeply:

  • Java’s Enduring Strength: Java’s decades of proven stability in building mission-critical enterprise systems cannot be overstated. The JVM’s resilience, the vast array of mature libraries and frameworks (especially Spring Boot), and the well-established architectural patterns provide a solid and predictable foundation. Moreover, the sheer size of the Java developer community ensures a deep pool of talent and readily available support for our teams as they transition. For a core system in a regulated industry, this level of established maturity significantly mitigates risk.

  • Kotlin’s Modern Edge and Interoperability: Kotlin presents a compelling evolution on the JVM. Its modern syntax, null safety features, and concise code can lead to increased developer productivity and reduced boilerplate – benefits I’ve witnessed firsthand in JVM-based projects. Crucially, Kotlin’s seamless interoperability with Java is a major strategic advantage. It allows us to:

    • Gradually adopt Kotlin: Teams can start by integrating Kotlin into existing Java codebases, allowing for a phased learning process without a complete overhaul.
    • Leverage the entire Java ecosystem: Kotlin developers can effortlessly use any Java library or framework, giving us access to the vast resources of the Java world.
    • Attract modern talent: Kotlin’s growing popularity can help us attract developers who are excited about working with a modern, yet stable, language on a proven platform.

Why Hesitate Between Java and Kotlin?

The decision of whether to primarily adopt Java or Kotlin (or a strategic mix) requires careful consideration of our team’s specific needs and the long-term vision:

  • Learning Curve: While Kotlin is designed to be approachable for Java developers, there is still a learning curve associated with its new syntax and features. We need to assess how quickly our large Cobol-experienced team can become proficient in Kotlin.
  • Team Preference and Buy-in: Understanding our developers’ preferences and ensuring buy-in for the chosen language is crucial for successful adoption.
  • Long-Term Ecosystem Evolution: While both Java and Kotlin have strong futures on the JVM, we need to consider the long-term trends and the level of investment in each language within the enterprise space.
  • Specific Use Cases: Certain parts of our system might benefit more from Kotlin’s conciseness or specific features, while other more established components might initially remain in Java.

Proposed Next Steps (Revised Focus):

  1. Targeted Proof of Concept (PoC) – Deep Dive into Java and Kotlin: Instead of a broad PoC including Go, let’s focus our initial efforts on a detailed comparison of Java (using Spring Boot) and Kotlin on representative use cases from our core business application. This PoC should specifically evaluate:
    • Developer Productivity: How quickly can teams with a Java-adjacent mindset (after initial training) develop and maintain code in both languages?
    • Code Readability and Maintainability: How do the resulting codebases compare in terms of clarity and ease of understanding for a large team?
    • Interoperability Scenarios: How seamlessly can Java and Kotlin code coexist and interact within the same project?
    • Performance Benchmarking: While the JVM provides a solid base, are there noticeable performance differences for our specific workloads?
  2. Comprehensive Training and Upskilling Program: We need to develop a detailed training program that caters to our team’s Cobol background and provides clear pathways for learning both Java and Kotlin. This program should include hands-on exercises and mentorship opportunities.
  3. Strategic Adoption Plan: Based on the PoC results and team feedback, we’ll develop a strategic adoption plan that outlines whether we’ll primarily focus on Java, Kotlin, or a hybrid approach. This plan should consider the long-term maintainability and talent acquisition goals.

While Go remains a valuable technology for specific niches, for the core of our mainframe rebuild, our focus should now be on leveraging the mature and evolving Java/Kotlin ecosystem and strategically determining the optimal path for our large and experienced team. This approach minimizes risk while embracing modern development practices on a proven platform.

PostHeaderIcon A Tricky Java Question

Here’s a super tricky Java interview question that messes with developer intuition:

❓ Weird Question:

“What will be printed when executing the following code?”

import java.util.*;
public class TrickyJava {
 public static void main(String[] args) {
 List list = Arrays.asList("T-Rex", "Velociraptor", "Dilophosaurus");
 list.replaceAll(s -> s.toUpperCase());
 System.out.println(list);
 }
 }

The Trap:

At first glance, everything looks normal:

Arrays.asList(...) creates a List.
replaceAll(...) is a method in List that modifies elements using a function.
Strings are converted to uppercase.
Most developers will expect this output:

[T-REX, VELOCIRAPTOR, DILOPHOSAURUS]

But surprise! This code sometimes throws an UnsupportedOperationException.

 

✅ Correct Answer:

The output depends on the JVM implementation!

It might work and print:

[T-REX, VELOCIRAPTOR, DILOPHOSAURUS]

Or it might crash with:

Exception in thread "main" java.lang.UnsupportedOperationException
at java.util.AbstractList$Itr.remove(AbstractList.java:572)
at java.util.AbstractList.remove(AbstractList.java:212)
at java.util.AbstractList$ListItr.remove(AbstractList.java:582)
at java.util.List.replaceAll(List.java:500)

Why?

Arrays.asList(...) does not return a regular ArrayList, but rather a fixed-size list backed by an array.
The replaceAll(...) method attempts to modify the list in-place, which is not allowed for a fixed-size list.
Some JVM implementations optimize this internally, making it work, but it is not guaranteed to succeed.

Key Takeaways

Arrays.asList(...) returns a fixed-size list, not a modifiable ArrayList.
Modifying it directly (e.g., add(), remove(), replaceAll()) can fail with UnsupportedOperationException.
Behavior depends on the JVM implementation and internal optimizations.

How to Fix It?

To ensure safe modification, wrap the list in a mutable ArrayList:

List list = new ArrayList<>(Arrays.asList("T-Rex", "Velociraptor", "Dilophosaurus"));
list.replaceAll(s -> s.toUpperCase());
System.out.println(list); // ✅ Always works!

PostHeaderIcon [DevoxxBE2024] Java Language Futures by Gavin Bierman

Gavin Bierman, from Oracle’s Java Platform Group, captivated attendees at Devoxx Belgium 2024 with a forward-looking talk on Java’s evolution under Project Amber. Focusing on productivity-oriented language features, Gavin outlined recent additions like records, sealed classes, and pattern matching, while previewing upcoming enhancements like simplified main methods and flexible constructor bodies. His session illuminated Java’s design philosophy—prioritizing readability, explicit programmer intent, and compatibility—while showcasing how these features enable modern, data-oriented programming paradigms suited for today’s microservices architectures.

Project Amber’s Mission: Productivity and Intent

Gavin introduced Project Amber as a vehicle for delivering smaller, productivity-focused Java features, leveraging the six-month JDK release cadence to preview and finalize enhancements. Unlike superficial syntax changes, Amber emphasizes exposing programmer intent to improve code readability and reduce bugs. Compatibility is paramount, with breaking changes minimized, as Java evolves to address modern challenges distinct from its 1995 origins. Gavin highlighted how features like records and sealed classes make intent explicit, enabling the compiler to enforce constraints and provide better error checking, aligning with the needs of contemporary applications.

Records: Simplifying Data Carriers

Records, introduced to streamline data carrier classes, were a key focus. Gavin demonstrated how a Point class with two integers requires verbose boilerplate (constructors, getters, equals, hashCode) that obscures intent. Records (record Point(int x, int y)) eliminate this by auto-generating a canonical constructor, accessor methods, and value-based equality, ensuring immutability and transparency. This explicitness allows the compiler to enforce a contract: constructing a record from its components yields an equal instance. Records also support deserialization via the canonical constructor, ensuring domain-specific constraints, making them safer than traditional classes.

Sealed Classes and Pattern Matching

Sealed classes, shipped in JDK 17, allow developers to restrict class hierarchies explicitly. Gavin showed a Shape interface sealed to permit only Circle and Rectangle implementations, preventing unintended subclasses at compile or runtime. This clarity enhances library design by defining precise interfaces. Pattern matching, enhanced in JDK 21, further refines this by enabling type patterns and record patterns in instanceof and switch statements. For example, a switch over a sealed Shape interface requires exhaustive cases, eliminating default clauses and reducing errors. Nested record patterns allow sophisticated data queries, handling nulls safely without exceptions.

Data-Oriented Programming with Amber Features

Gavin illustrated how records, sealed classes, and pattern matching combine to support data-oriented programming, ideal for microservices exchanging pure data. He reimagined the Future class’s get method, traditionally complex due to multiple control paths (success, failure, timeout, interruption). By modeling the return type as a sealed AsyncReturn interface with four record implementations (Success, Failure, Timeout, Interrupted), and using pattern matching in a switch, developers handle all cases uniformly. This approach simplifies control flow, ensures exhaustiveness, and leverages Java’s type safety, contrasting with error-prone exception handling in traditional designs.

Future Features: Simplifying Java for All

Looking ahead, Gavin previewed features in JDK 23 and beyond. Simplified main methods allow beginners to write void main() without boilerplate, reducing cognitive load while maintaining full Java compatibility. The with expression for records enables concise updates (e.g., doubling a component) without redundant constructor calls, preserving domain constraints. Flexible constructor bodies (JEP 482) relax top-down initialization, allowing pre-super call logic to validate inputs, addressing issues like premature field access in subclass constructors. Upcoming enhancements include patterns for arbitrary classes, safe template programming, and array pattern matching, promising further productivity gains.

Links:

PostHeaderIcon [DevoxxBE2024] Project Panama in Action: Building a File System by David Vlijmincx

At Devoxx Belgium 2024, David Vlijmincx delivered an engaging session on Project Panama, demonstrating its power by building a custom file system in Java. This practical, hands-on talk showcased how Project Panama simplifies integrating C libraries into Java applications, replacing the cumbersome JNI with a more developer-friendly approach. By leveraging Fuse, virtual threads, and Panama’s memory management capabilities, David walked attendees through creating a functional file system, highlighting real-world applications and performance benefits. His talk emphasized the ease of integrating C libraries and the potential to build high-performance, innovative solutions.

Why Project Panama Matters

David began by addressing the challenges of JNI, which many developers find frustrating due to its complexity. Project Panama, part of OpenJDK, offers a modern alternative for interoperating with native C libraries. With a vast ecosystem of specialized C libraries—such as io_uring for asynchronous file operations or libraries for AI and keyboard communication—Panama enables Java developers to access functionality unavailable in pure Java. David demonstrated this by comparing file reading performance: using io_uring with Panama, he read files faster than Java’s standard APIs (e.g., BufferedReader or Channels) in just two nights of work, showcasing Panama’s potential for performance-critical applications.

Building a File System with Fuse

The core of David’s demo was integrating the Fuse (Filesystem in Userspace) library to create a custom file system. Fuse acts as a middle layer, intercepting commands like ls from the terminal and passing them to a Java application via Panama. David explained how Fuse provides a C struct that Java developers can populate with pointers to Java methods, enabling seamless communication between C and Java. This struct, filled with method pointers, is mounted to a directory (e.g., ~/test), allowing the Java application to handle file system operations transparently to the user, who sees only the terminal output.

Memory Management with Arenas

A key component of Panama is its memory management via arenas, which David used to allocate memory for passing strings to Fuse. He demonstrated using Arena.ofShared(), which allows memory sharing across threads and explicit lifetime control via try-with-resources. Other arena types, like Arena.ofConfined() (single-threaded) or Arena.global() (unbounded lifetime), were mentioned for context. David allocated a memory segment to store pointers to a string array (e.g., ["-f", "-d", "~/test"]) and used Arena.allocateFrom() to create C-compatible strings. This ensured safe memory handling when interacting with Fuse, preventing leaks and simplifying resource management.

Downcalls and Upcalls: Bridging Java and C

David detailed the process of making downcalls (Java to C) and upcalls (C to Java). For downcalls, he created a function descriptor mirroring the C method’s signature (e.g., fuse_main_real, returning an int and taking parameters like string arrays and structs). Using Linker.nativeLinker(), he generated a platform-specific linker to invoke the C method. For upcalls, he recreated Fuse’s struct in Java using MemoryLayout.structLayout, populating it with pointers to Java methods like getattr. Tools like JExtract simplified this by generating bindings automatically, reducing boilerplate code. David showed how JExtract creates Java classes from C headers, though it requires an additional abstraction layer for user-friendly APIs.

Implementing File System Operations

David implemented two file system operations: reading files and creating directories. For reading, he extracted the file path from a memory segment using MemorySegment.getString(), checked if it was a valid file, and copied file contents into a buffer with MemorySegment.reinterpret() to handle size constraints. For directory creation, he added paths to a map, demonstrating simplicity. Running the application mounted the file system to ~/test, where commands like mkdir and echo worked seamlessly, with Fuse calling Java methods via upcalls. David unmounted the file system, showing its clean integration. Performance tips included reusing method handles and memory segments to avoid overhead, emphasizing careful memory management.

Links:

PostHeaderIcon [DevoxxUK2024] Enter The Parallel Universe of the Vector API by Simon Ritter

Simon Ritter, Deputy CTO at Azul Systems, delivered a captivating session at DevoxxUK2024, exploring the transformative potential of Java’s Vector API. This innovative API, introduced as an incubator module in JDK 16 and now in its eighth iteration in JDK 23, empowers developers to harness Single Instruction Multiple Data (SIMD) instructions for parallel processing. By leveraging Advanced Vector Extensions (AVX) in modern processors, the Vector API enables efficient execution of numerically intensive operations, significantly boosting application performance. Simon’s talk navigates the intricacies of vector computations, contrasts them with traditional concurrency models, and demonstrates practical applications, offering developers a powerful tool to optimize Java applications.

Understanding Concurrency and Parallelism

Simon begins by clarifying the distinction between concurrency and parallelism, a common source of confusion. Concurrency involves tasks that overlap in execution time but may not run simultaneously, as the operating system may time-share a single CPU. Parallelism, however, ensures tasks execute simultaneously, leveraging multiple CPUs or cores. For instance, two users editing documents on separate machines achieve parallelism, while a single-core CPU running multiple tasks creates the illusion of parallelism through time-sharing. Java’s threading model, introduced in JDK 1.0, facilitates concurrency via the Thread class, but coordinating data sharing across threads remains challenging. Simon highlights how Java evolved with the concurrency utilities in JDK 5, the Fork/Join framework in JDK 7, and parallel streams in JDK 8, each simplifying concurrent programming while introducing trade-offs, such as non-deterministic results in parallel streams.

The Essence of Vector Processing

The Vector API, distinct from the legacy java.util.Vector class, enables true parallel processing within a single execution unit using SIMD instructions. Simon explains that vectors in mathematics represent sets of values, unlike scalars, and the Vector API applies this concept by storing multiple values in wide registers (e.g., 256-bit AVX2 registers). These registers, divided into lanes (e.g., eight 32-bit integers), allow a single operation, such as adding a constant, to process all lanes in one clock cycle. This contrasts with iterative loops, which process elements sequentially. Historical context reveals SIMD’s roots in 1960s supercomputers like the ILLIAC IV and Cray-1, with modern implementations in Intel’s MMX, SSE, and AVX instructions, culminating in AVX-512 with 512-bit registers. The Vector API abstracts these complexities, enabling developers to write cross-platform code without targeting specific microarchitectures.

Leveraging the Vector API

Simon illustrates the Vector API’s practical application through its core components: Vector, VectorSpecies, and VectorShape. The Vector class, parameterized by type (e.g., Integer), supports operations like addition and multiplication across all lanes. Subclasses like IntVector handle primitive types, offering methods like fromArray to populate vectors from arrays. VectorShape defines register sizes (64 to 512 bits or S_MAX for the largest available), ensuring portability across architectures like Intel and ARM. VectorSpecies combines type and shape, specifying, for example, an IntVector with eight lanes in a 256-bit register. Simon demonstrates a loop processing a million-element array, using VectorSpecies to calculate iterations based on lane count, and employs VectorMask to handle partial arrays, ensuring no side effects from unused lanes. This approach optimizes performance for numerically intensive tasks, such as matrix computations or data transformations.

Performance Insights and Trade-offs

The Vector API’s performance benefits shine in specific scenarios, particularly when autovectorization by the JIT compiler is insufficient. Simon references benchmarks from Tomas Zezula, showing that explicit Vector API usage outperforms autovectorization for small arrays (e.g., 64 elements) due to better register utilization. However, for larger arrays (e.g., 2 million elements), memory access latency—100+ cycles for RAM versus 3-5 for L1 cache—diminishes gains. Conditional operations, like adding only even-valued elements, further highlight the API’s value, as the C2 JIT compiler often fails to autovectorize such cases. Azul’s Falcon JIT compiler, based on LLVM, improves autovectorization, but explicit Vector API usage remains superior for complex operations. Simon emphasizes that while the API offers significant flexibility through masks and shuffles, its benefits wane with large datasets due to memory bottlenecks.

Links:

PostHeaderIcon [DevoxxUK2024] Project Leyden: Capturing Lightning in a Bottle by Per Minborg

Per Minborg, a seasoned member of Oracle’s Core Library team, delivered an insightful session at DevoxxUK2024, unveiling the ambitions of Project Leyden, a transformative initiative to enhance Java application performance. Focused on slashing startup time, accelerating warmup, and reducing memory footprint, Per’s talk explores how Java can evolve to meet modern demands while preserving its dynamic nature. By strategically shifting computations to optimize execution, Project Leyden introduces innovative techniques like condensers and enhanced Class Data Sharing (CDS). This session provides a roadmap for developers seeking to harness Java’s potential in high-performance environments, balancing flexibility with efficiency.

The Vision of Project Leyden

Per begins by outlining the core objectives of Project Leyden: improving startup time, warmup time, and memory footprint. Startup time, the duration from launching an application to its first meaningful output (e.g., a “Hello World” or serving a web request), is critical for user experience. Warmup time, the period until an application reaches peak performance through JIT compilation, can hinder responsiveness in dynamic systems. Footprint, encompassing memory and storage use, impacts scalability, especially in cloud environments. Per emphasizes that the best approach is to eliminate unnecessary computations, but when that’s not feasible, shifting them temporally—either earlier to compile time or later to runtime—can yield significant gains. This philosophy underpins Leyden’s strategy to refine Java’s execution model.

Shifting Computations for Efficiency

A cornerstone of Project Leyden is the concept of temporal computation shifting. Per explains that Java’s dynamic nature—encompassing dynamic class loading, JIT compilation, and runtime optimizations—enables expressive programming but can inflate startup and warmup times. By moving computations to build time, such as through constant folding or ahead-of-time (AOT) compilation, Leyden reduces runtime overhead. Alternatively, lazy evaluation postpones non-critical tasks, streamlining startup. Per introduces condensers, a novel mechanism that transforms program representations by shifting computations earlier, adding metadata, or imposing constraints on dynamism. Condensers are composable, meaning-preserving, and selectable, allowing developers to tailor optimizations based on application needs. For instance, a condenser might precompile lambda expressions into bytecode at build time, slashing runtime costs.

Enhancing Class Data Sharing (CDS)

Per delves into Class Data Sharing (CDS), a long-standing Java feature that Project Leyden enhances to achieve dramatic performance boosts. CDS allows pre-initialized JDK classes to be stored in a file, bypassing costly class loading during startup. With CDS++, Leyden extends this to include application classes, compiled code, and resolved constant pool references. Per shares compelling benchmarks: a test compiling 100 small Java files achieved a 2x startup improvement, while an XML parsing workload saw an 8x boost. For the Spring Pet Clinic benchmark, Leyden’s optimizations, including early class loading and cached compiled code, yielded up to 4x faster startup. These gains stem from a training run approach, where a representative execution gathers profiling data to inform optimizations, ensuring compatibility across platforms.

Balancing Dynamism and Performance

Java’s dynamism—encompassing dynamic typing, class loading, and reflection—empowers developers but complicates optimization. Per proposes selective constraints to balance this trade-off. For example, developers can restrict dynamic class loading for specific modules, enabling aggressive optimizations without sacrificing Java’s flexibility. The stable value feature, initially part of Leyden but now a standalone JEP, allows delayed initialization of final fields while maintaining performance akin to compile-time constants. Per illustrates this with a Fibonacci computation example, where memoization using stable values drastically reduces recursive overhead. By offering a “mixer board” of concessions, Leyden empowers developers to fine-tune performance, ensuring compatibility and preserving program semantics across diverse use cases.

Links:

PostHeaderIcon Understanding Dependency Management and Resolution: A Look at Java, Python, and Node.js

Understanding Dependency Management and Resolution: A Look at Java, Python, and Node.js

Mastering how dependencies are handled can define your project’s success or failure. Let’s explore the nuances across today’s major development ecosystems.

Introduction

Every modern application relies heavily on external libraries. These libraries accelerate development, improve security, and enable integration with third-party services. However, unmanaged dependencies can lead to catastrophic issues — from version conflicts to severe security vulnerabilities. That’s why understanding dependency management and resolution is absolutely essential, particularly across different programming ecosystems.

What is Dependency Management?

Dependency management involves declaring external components your project needs, installing them properly, ensuring their correct versions, and resolving conflicts when multiple components depend on different versions of the same library. It also includes updating libraries responsibly and securely over time. In short, good dependency management prevents issues like broken builds, “dependency hell”, or serious security holes.

Java: Maven and Gradle

In the Java ecosystem, dependency management is an integrated and structured part of the build lifecycle, using tools like Maven and Gradle.

Maven and Dependency Scopes

Maven uses a declarative pom.xml file to list dependencies. A particularly important notion in Maven is the dependency scope.

Scopes control where and how dependencies are used. Examples include:

  • compile (default): Needed at both compile time and runtime.
  • provided: Needed for compile, but provided at runtime by the environment (e.g., Servlet API in a container).
  • runtime: Needed only at runtime, not at compile time.
  • test: Used exclusively for testing (JUnit, Mockito, etc.).
  • system: Provided by the system explicitly (deprecated practice).

<dependency>
  <groupId>junit</groupId>
  <artifactId>junit</artifactId>
  <version>4.13.2</version>
  <scope>test</scope>
</dependency>
    

This nuanced control allows Java developers to avoid bloating production artifacts with unnecessary libraries, and to fine-tune build behaviors. This is a major feature missing from simpler systems like pip or npm.

Gradle

Gradle, offering both Groovy and Kotlin DSLs, also supports scopes through configurations like implementation, runtimeOnly, testImplementation, which have similar meanings to Maven scopes but are even more flexible.


dependencies {
    implementation 'org.springframework.boot:spring-boot-starter'
    testImplementation 'org.springframework.boot:spring-boot-starter-test'
}
    

Python: pip and Poetry

Python dependency management is simpler, but also less structured compared to Java. With pip, there is no formal concept of scopes.

pip

Developers typically separate main dependencies and development dependencies manually using different files:

  • requirements.txt – Main project dependencies.
  • requirements-dev.txt – Development and test dependencies (pytest, tox, etc.).

This manual split is prone to human error and lacks the rigorous environment control that Maven or Gradle enforce.

Poetry

Poetry improves the situation by introducing a structured division:


[tool.poetry.dependencies]
requests = "^2.31"

[tool.poetry.dev-dependencies]
pytest = "^7.1"
    

Poetry brings concepts closer to Maven scopes, but they are still less fine-grained (no runtime/compile distinction, for instance).

Node.js: npm and Yarn

JavaScript dependency managers like npm and yarn allow a simple distinction between regular and development dependencies.

npm

Dependencies are declared in package.json under different sections:

  • dependencies – Needed in production.
  • devDependencies – Needed only for development (e.g., testing libraries, linters).

{
  "dependencies": {
    "express": "^4.18.2"
  },
  "devDependencies": {
    "mocha": "^10.2.0"
  }
}
    

While convenient, npm’s dependency management lacks Maven’s level of strictness around dependency resolution, often leading to version mismatches or “node_modules bloat.”

Key Differences Between Ecosystems

When switching between Java, Python, and Node.js environments, developers must be aware of the following fundamental differences:

1. Formality of Scopes

Java’s Maven/Gradle ecosystem defines scopes formally at the dependency level. Python (pip) and JavaScript (npm) ecosystems use looser, file- or section-based categorization.

2. Handling of Transitive Dependencies

Maven and Gradle resolve and include transitive dependencies automatically with sophisticated conflict resolution strategies (e.g., nearest version wins). pip historically had weak transitive dependency handling, leading to issues unless careful pinning is done. npm introduced better nested module flattening with npm v7+ but conflicts still occur in complex trees.

3. Lockfiles

npm/yarn and Python Poetry use lockfiles (package-lock.json, yarn.lock, poetry.lock) to ensure consistent dependency installations across machines. Maven and Gradle historically did not need lockfiles because they strictly followed declared versions and scopes. However, Gradle introduced lockfile support with dependency locking in newer versions.

4. Dependency Updating Strategy

Java developers often manually manage dependency versions inside pom.xml or use dependencyManagement blocks for centralized control. pip requires updating requirements.txt or regenerating them via pip freeze. npm/yarn allows semver rules (“^”, “~”) but auto-updating can lead to subtle breakages if not careful.

Best Practices Across All Languages

  • Pin exact versions wherever possible to avoid surprise updates.
  • Use lockfiles and commit them to version control (Git).
  • Separate production and development/test dependencies explicitly.
  • Use dependency scanners (e.g., OWASP Dependency-Check, Snyk, npm audit) regularly to detect vulnerabilities.
  • Prefer stable, maintained libraries with good community support and recent commits.

Conclusion

Dependency management, while often overlooked early in projects, becomes critical as applications scale. Maven and Gradle offer the most fine-grained controls via dependency scopes and conflict resolution. Python and JavaScript ecosystems are evolving rapidly, but require developers to be much more careful manually. Understanding these differences, and applying best practices accordingly, will ensure smoother builds, faster delivery, and safer production systems.

Interested in deeper dives into dependency vulnerability scanning, SBOM generation, or automatic dependency update pipelines? Subscribe to our blog for more in-depth content!

PostHeaderIcon [DevoxxBE2023] How Sand and Java Create the World’s Most Powerful Chips

Johan Janssen, an architect at ASML, captivated the DevoxxBE2023 audience with a deep dive into the intricate process of chip manufacturing and the role of Java in optimizing it. Johan, a seasoned speaker and JavaOne Rock Star, explained how ASML’s advanced lithography machines, powered by Java-based software, enable the creation of cutting-edge computer chips used in devices worldwide.

From Sand to Silicon Wafers

Johan began by demystifying chip production, starting with silica sand, an abundant resource transformed into silicon ingots and sliced into wafers. These wafers, approximately 30 cm in diameter, serve as the foundation for chips, hosting up to 600 chips per wafer or thousands for smaller sensors. He passed around a wafer adorned with Java’s mascot, Duke, illustrating the physical substrate of modern electronics.

The process involves printing multiple layers—up to 200—onto wafers using extreme ultraviolet (EUV) lithography machines. These machines, requiring four Boeing 747s for transport, achieve precision at the nanometer scale, with transistors as small as three nanometers. Johan likened this to driving a car 300 km and retracing the path with only 2 mm deviation, highlighting the extraordinary accuracy required.

The Role of EUV Lithography

Johan detailed the EUV lithography process, where tin droplets are hit by a 40-kilowatt laser to generate plasma at sun-like temperatures, producing EUV light. This light, directed by ultra-flat mirrors, patterns wafers through reticles costing €250,000 each. The process demands cleanroom environments, as even a single dust particle can ruin a chip, and involves continuous calibration to maintain precision across thousands of parameters.

ASML’s machines, some over 30 years old, remain in use for producing sensors and less advanced chips, demonstrating their longevity. Johan also previewed future advancements, such as high numerical aperture (NA) machines, which will enable even smaller transistors, further enhancing chip performance and energy efficiency.

Java-Powered Analytics Platform

At the heart of Johan’s talk was ASML’s Java-based analytics platform, which processes 31 terabytes of data weekly to optimize chip production. Built on Apache Spark, the platform distributes computations across worker nodes, supporting plugins for data ingestion, UI customization, and processing. These plugins allow departments to integrate diverse data types, from images to raw measurements, and support languages like Julia and C alongside Java.

The platform, running on-premise to protect sensitive data, consolidates previously disparate applications, improving efficiency and user experience. Johan highlighted a machine learning use case where the platform increased defect detection from 70% to 92% without slowing production, showcasing Java’s role in handling complex computations.

Challenges and Solutions in Chip Manufacturing

Johan discussed challenges like layer misalignment, which can cause short circuits or defective chips. The platform addresses these by analyzing wafer plots to identify correctable errors, such as adjusting subsequent layers to compensate for misalignments. Non-correctable errors may result in downgrading chips (e.g., from 16 GB to 8 GB RAM), ensuring minimal waste.

He emphasized a pragmatic approach to tool selection, starting with REST endpoints and gradually adopting Kafka for streaming data as needs evolved. Johan also noted ASML’s collaboration with tool maintainers to enhance compatibility, such as improving Spark’s progress tracking for customer feedback.

Future of Chip Manufacturing

Looking ahead, Johan highlighted the industry’s push to diversify chip production beyond Taiwan, driven by geopolitical and economic factors. However, building new factories, or “fabs,” costing $10–20 billion, faces challenges like equipment backlogs and the need for highly skilled operators. ASML’s customer support teams, working alongside clients like Intel, underscore the specialized knowledge required.

Johan concluded by stressing the importance of a forward-looking mindset, with ASML’s roadmap prioritizing innovation over rigid methodologies. This approach, combined with Java’s robustness, ensures the platform’s scalability and adaptability in a rapidly evolving industry.

Links: