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Java News Roundup: GraalVM Build Tools, EclipseLink, Spring Milestones, Open Liberty, Quarkus

by Chief Editor
written by Chief Editor

Java’s Evolution: A Deep Dive into Recent Releases and Future Trends

The Java ecosystem is experiencing a period of rapid innovation, with recent releases signaling a strong push towards performance, developer productivity, and broader platform support. From the General Availability of GraalVM Native Build Tools to updates across Spring, Quarkus, and EclipseLink, the landscape is shifting. This article explores these developments and what they mean for the future of Java development.

GraalVM and the Rise of Native Image Technology

GraalVM continues to be a central force in Java’s evolution. The GA release of GraalVM Native Build Tools 1.0.0 streamlines the process of creating native executables from Java code. This is a significant step, as native images offer faster startup times and reduced memory footprint compared to traditional JVM-based applications. The January 2026 Oracle Critical Patch Update for GraalVM Community Edition (25.0.2) underscores Oracle’s commitment to security and stability within the GraalVM ecosystem.

Pro Tip: Consider using GraalVM Native Image for microservices or command-line applications where startup time and resource consumption are critical.

The example project demonstrating JPA with EclipseLink and GraalVM Native Image (available on GitHub) provides a practical starting point for developers looking to explore this technology. However, it’s important to note that Oracle GraalVM for JDK 24 was the last version supported as part of Oracle Java SE products, so users should be aware of licensing implications when considering Enterprise Edition options.

Framework Updates: Spring, Quarkus, and Open Liberty

The Spring ecosystem remains vibrant, with the fourth milestone release of Spring Boot 4.1.0 focusing on improvements to observability and integration with gRPC. Similarly, Spring Modulith and Spring AI are receiving regular updates, indicating a continued investment in modular architectures and AI-powered development tools. The fourth milestone release of Spring AI 2.0.0 adds support for Google Search and custom tooling for Gemini 3 models.

Quarkus 3.34.0 delivers bug fixes and deprecates older internal components, signaling a move towards a more streamlined and modern codebase. Open Liberty 26.0.0.3 introduces enhancements to user management and application startup optimization. These updates collectively demonstrate a commitment to improving developer experience and application performance across different Java frameworks.

Jakarta EE and EclipseLink: Modernizing Enterprise Java

The GA release of EclipseLink 5.0.0 marks a significant milestone, bringing full support for the Jakarta Persistence 3.2 specification under Jakarta EE 11. This includes improvements to the Jakarta Persistence Query Language (JPQL) and platform compatibility. GlassFish 8.0.1, the first maintenance release, further solidifies the Jakarta EE ecosystem with bug fixes and performance optimizations.

Infinispan and the Expanding Data Landscape

The first development release of Infinispan 16.2.0 showcases the project’s commitment to expanding its capabilities, particularly in the realm of data streaming and interoperability. The implementation of the Redis Serialization Protocol (RESP) and OpenAPI v3 in the Infinispan REST API demonstrates a desire to integrate with a wider range of data sources and systems.

Looking Ahead: Key Trends in Java Development

Several key trends are shaping the future of Java development:

  • Native Image Adoption: As GraalVM matures and tooling improves, we can expect to see wider adoption of native image technology, particularly in cloud-native environments.
  • Microservices Architectures: Frameworks like Spring Boot and Quarkus are well-suited for building microservices, and their continued development will drive innovation in this area.
  • AI Integration: The emergence of frameworks like Spring AI signals a growing interest in integrating AI capabilities into Java applications.
  • Jakarta EE Evolution: The Jakarta EE ecosystem is undergoing a modernization process, with recent specifications and implementations driving innovation in enterprise Java.
  • Observability and Monitoring: Improvements in observability, as seen in the Spring Boot 4.1.0 release, will develop into increasingly important as applications become more complex.

FAQ

Q: What is GraalVM Native Image?
A: GraalVM Native Image compiles Java code ahead of time into a standalone executable, resulting in faster startup times and reduced memory usage.

Q: What is Jakarta EE?
A: Jakarta EE is the open-source evolution of Java EE, providing a set of specifications for building enterprise Java applications.

Q: Is Oracle GraalVM still supported?
A: Oracle GraalVM for JDK 24 was the final version licensed and supported as part of Oracle Java SE products. Users should explore Oracle Software Delivery Cloud for updates to previously released versions.

Q: Where can I find more information about Spring Boot?
A: Visit the Spring Boot project website for documentation, tutorials, and release notes.

Did you realize? The Java ecosystem is one of the largest and most active open-source communities in the world, with a vast network of developers and contributors.

We encourage you to explore these new releases and consider how they can benefit your Java projects. Share your thoughts and experiences in the comments below!

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Inside Netflix’s Graph Abstraction: Handling 650TB of Graph Data in Milliseconds Globally

by Chief Editor
written by Chief Editor

Netflix’s Real-Time Graph: A Glimpse into the Future of Personalized Experiences

Netflix is no longer simply a streaming service; its expansion into gaming, live events, and advertising demands a sophisticated understanding of how users interact across its diverse ecosystem. To meet this challenge, Netflix engineers have developed Graph Abstraction, a high-throughput system capable of managing massive graph data in real time. This isn’t just about better recommendations – it’s a foundational shift in how Netflix understands and responds to user behavior.

The Challenge of Siloed Data

Traditionally, Netflix’s microservices architecture, while offering flexibility, created data silos. Video streaming data resided in one place, gaming data in another, and authentication information separately. Connecting these disparate pieces of information to create a unified view of the member experience proved difficult. Graph Abstraction addresses this by providing a centralized platform for representing relationships between users, content, and services.

How Graph Abstraction Works: Speed and Scale

The key to Graph Abstraction’s success lies in its design. It prioritizes speed and scalability, delivering single-digit millisecond latency for simple queries and under 50 milliseconds for more complex two-hop queries. This is achieved through several techniques, including restricting traversal depth, requiring a defined starting node, and leveraging caching strategies like write-aside and read-aside caching. The system stores the latest graph state in a Key Value abstraction and historical changes in a TimeSeries abstraction.

Global availability is ensured through asynchronous replication across regions, balancing latency, availability, and consistency. The platform utilizes a gRPC traversal API inspired by Gremlin, allowing services to chain queries and apply filters.

Beyond Recommendations: Diverse Use Cases

Graph Abstraction powers a variety of internal services. A real-time distributed graph captures interactions across all Netflix services. A social graph enhances Netflix Gaming by modeling user relationships. A service topology graph aids engineers in analyzing dependencies during incidents and identifying root causes. This versatility demonstrates the platform’s potential to support a wide range of applications beyond personalized recommendations.

The Rise of Graph Databases in the Streaming Era

Netflix’s investment in Graph Abstraction reflects a broader trend in the streaming industry. As services compete for user attention, the ability to deliver highly personalized experiences becomes paramount. Graph databases are uniquely suited to this task, enabling companies to model complex relationships and uncover hidden patterns in user behavior. This is particularly crucial as streaming platforms expand into new areas like interactive content and live events.

Future Trends: AI-Powered Graph Analytics

The integration of artificial intelligence (AI) with graph databases is poised to unlock even greater potential. Imagine a system that not only recommends content based on past viewing history but also predicts future preferences based on social connections and emerging trends. AI algorithms can analyze graph data to identify influential users, detect fraudulent activity, and optimize content distribution. The 2026 AI predictions report highlights the require for unified context engines, and Graph Abstraction provides a strong foundation for building such systems.

The Convergence of Real-Time and Historical Data

Netflix’s use of both a Key Value abstraction for current state and a TimeSeries abstraction for historical data is a significant development. This allows for both real-time personalization and long-term trend analysis. Future graph database systems will likely follow this pattern, offering a unified view of both current and historical relationships. This will enable more sophisticated analytics, auditing, and temporal queries.

Pro Tip:

When evaluating graph database solutions, consider the trade-offs between query flexibility and performance. For operational workloads that require high throughput and low latency, a system that prioritizes performance may be more suitable than a traditional graph database with extensive query capabilities.

FAQ

  • What is Graph Abstraction? Graph Abstraction is Netflix’s high-throughput system for managing large-scale graph data in real time.
  • What are the key benefits of Graph Abstraction? It provides millisecond-level query performance, global availability, and supports diverse use cases across Netflix.
  • How does Netflix ensure global availability? Through asynchronous replication of data across regions.
  • What types of queries does Graph Abstraction support? It supports traversals with defined starting nodes and limited depth, optimized for speed and scalability.

Did you know? Netflix’s Graph Abstraction platform manages roughly 650 TB of graph data.

Explore more about Netflix’s engineering innovations on the Netflix Tech Blog. Share your thoughts on the future of graph databases in the comments below!

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Stripe Engineers Deploy Minions, Autonomous Agents Producing Thousands of Pull Requests Weekly

by Chief Editor
written by Chief Editor

Stripe’s ‘Minions’ Signal a Modern Era of AI-Powered Coding

Engineers at Stripe have quietly launched a revolution in software development: autonomous coding agents dubbed “Minions.” These aren’t the yellow, banana-loving creatures, but sophisticated AI systems capable of generating production-ready pull requests with minimal human intervention. The implications for developer productivity and the future of coding are significant.

From Concept to 1,300 Pull Requests a Week

The Minions project began as an internal fork of Goose, a coding agent developed by Block. Stripe customized Goose for its specific LLM infrastructure and refined it to meet the demands of a large-scale payment processing system. The results are impressive. Currently, Minions generate over 1,300 pull requests per week, a figure that has climbed from 1,000 during initial trials. Crucially, all changes are reviewed by human engineers, ensuring quality and security.

This isn’t about replacing developers; it’s about augmenting their capabilities. The Minions handle tasks like configuration adjustments, dependency upgrades, and minor refactoring – the often-tedious but essential function that can consume a significant portion of a developer’s time.

One-Shot Agents: A Different Approach to AI Coding

What sets Minions apart from popular AI coding assistants like GitHub Copilot or Cursor? Minions operate on a “one-shot” basis, completing end-to-end tasks from a single instruction. Tasks can originate from various sources – Slack threads, bug reports, or feature requests – and are then orchestrated using “blueprints.” These blueprints combine deterministic code with flexible agent loops, allowing the system to adapt to different requirements.

This contrasts with interactive tools that require constant human guidance. Minions are designed to take a task description and deliver a complete, tested, and documented solution, ready for review.

Handling Complexity at Scale: $1 Trillion in Payments

The stakes are high. The code managed by Minions supports over $1 trillion in annual payment volume at Stripe. This means reliability and correctness are paramount. The system operates within a complex web of dependencies, navigating financial regulations and compliance obligations. Stripe reinforces reliability through robust CI/CD pipelines, automated tests, and static analysis.

Did you recognize? Stripe’s Minions are not just theoretical; they are actively managing critical infrastructure for a global payments leader.

The Rise of Agent-Driven Software Development

Stripe’s Minions are part of a broader trend toward agent-driven software development. LLM-based agents are becoming increasingly integrated with development environments, version control systems, and CI/CD pipelines. This integration promises to dramatically increase developer productivity while maintaining strict quality controls.

The key to success, according to Stripe engineers, lies in carefully defining tasks and utilizing blueprints to guide the agents. Blueprints act as a framework, weaving together agent skills with deterministic code to ensure both efficiency and adaptability.

Future Trends: What’s Next for AI Coding Agents?

The success of Minions suggests several potential future trends:

  • Increased Task Complexity: As agents become more sophisticated, they will be able to handle increasingly complex tasks, potentially automating entire features or modules.
  • Self-Improving Agents: Agents may learn from their successes and failures, continuously improving their performance and reducing the need for human intervention.
  • Domain-Specific Agents: We can expect to see the development of specialized agents tailored to specific industries or programming languages.
  • Enhanced Blueprinting Tools: Tools for creating and managing blueprints will become more user-friendly and powerful, allowing developers to easily define and orchestrate complex tasks.

FAQ

Q: Will AI coding agents replace developers?
A: No, the current focus is on augmenting developer productivity, not replacing developers entirely. Human review remains a critical part of the process.

Q: What are “blueprints” in the context of Stripe’s Minions?
A: Blueprints are workflows defined in code that specify how tasks are divided into subtasks and handled by either deterministic routines or the agent.

Q: How does Stripe ensure the reliability of code generated by Minions?
A: Stripe uses CI/CD pipelines, automated tests, and static analysis to ensure generated changes meet engineering standards before human review.

Q: What types of tasks are Minions best suited for?
A: Minions perform best on well-defined tasks such as configuration adjustments, dependency upgrades, and minor refactoring.

Pro Tip: Explore the Stripe developer blog for more in-depth technical details about the Minions project: https://stripe.dev/blog/minions-stripes-one-shot-end-to-end-coding-agents

What are your thoughts on the future of AI-powered coding? Share your insights in the comments below!

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QCon London 2026: Behind Booking.com’s AI Evolution: The Unpolished Story

by Chief Editor
written by Chief Editor

Booking.com’s AI Journey: Lessons for the Future of Data-Driven Platforms

Booking.com’s evolution from Perl scripts and MySQL databases to a sophisticated AI platform, as detailed at QCon London 2026 by Senior Principal Engineer Jabez Eliezer Manuel, offers valuable insights into the challenges and triumphs of scaling AI within a large organization. The presentation, “Behind Booking.com’s AI Evolution: The Unpolished Story,” highlighted a 20-year journey marked by pragmatic experimentation and a willingness to adapt.

The Power of Data-Driven DNA

In 2005, Booking.com began extensive A/B testing, running over 1,000 experiments concurrently and accumulating 150,000 total experiments. Despite a less than 25% success rate, the company prioritized rapid learning over immediate results, fostering a “Data-Driven DNA” that continues to shape its approach to innovation. This early commitment to experimentation laid the groundwork for future AI initiatives.

From Hadoop to a Unified Platform: A Migration Story

Booking.com initially leveraged Apache Hadoop for distributed storage and processing, building two on-premise clusters with approximately 60,000 cores and 200 PB of storage by 2011. However, limitations such as noisy neighbors, lack of GPU support, and capacity issues eventually led to a seven-year migration away from Hadoop. The migration strategy involved mapping the entire ecosystem, analyzing usage to reduce scope, applying the PageRank algorithm, migrating in waves, and finally phasing out Hadoop. A unified command center proved crucial to this complex undertaking.

The Evolution of the Machine Learning Stack

The company’s machine learning stack has undergone significant transformation, evolving from Perl and MySQL in 2005 to agentic systems in 2025. Key technologies along the way included Apache Oozie with Python, Apache Spark with MLlib, and H2O.ai. 2015 marked a turning point with the resolution of challenges in real-time predictions and feature engineering. As of 2024, the platform handles over 400 billion predictions daily with a latency of less than 20 milliseconds, powered by more than 480 machine learning models.

Domain-Specific AI Platforms

Booking.com has developed four distinct domain-specific machine learning platforms:

  • GenAI: Used for trip planning, smart filters, and review summaries.
  • Content Intelligence: Focused on image and review analysis, and text generation for detailed hotel content.
  • Recommendations: Delivering personalized content to customers.
  • Ranking: A complex platform optimizing for choice and value, exposure and growth, and efficiency and revenue.

The initial ranking formula, a simple function of bookings, views, and a random number, proved surprisingly resilient to machine learning replacements due to infrastructure limitations. The company adopted an interleaving technique for A/B testing, allowing for more variants with less traffic, followed by validation with traditional A/B testing.

Future Trends: What Lies Ahead?

Booking.com’s journey highlights several key trends likely to shape the future of AI-powered platforms:

  • Unified Orchestration Layers: The convergence of domain-specific AI platforms into a unified orchestration layer, as demonstrated by Booking.com, will become increasingly common. This allows for greater synergy and efficiency.
  • Pragmatic AI Adoption: The emphasis on learning from failures and iterating quickly, rather than striving for perfection, will be crucial for successful AI implementation.
  • Infrastructure as a Limiting Factor: Infrastructure limitations can significantly impact the effectiveness of even the most sophisticated algorithms. Investing in scalable and robust infrastructure is paramount.
  • The Importance of Data Management: Effective data management, including strategies for handling large datasets and ensuring data quality, remains a foundational element of any successful AI initiative.

FAQ

Q: What was the biggest challenge Booking.com faced during its AI evolution?
A: Migrating away from Hadoop proved to be a significant undertaking, requiring a seven-year phased approach.

Q: What is the current latency of Booking.com’s machine learning inference platform?
A: Less than 20 milliseconds.

Q: What is “interleaving” in the context of A/B testing?
A: A technique where 50% of experiments are interwoven into a single experiment, allowing for more variants with less traffic.

Q: What technologies did Booking.com use in its machine learning stack?
A: Perl, MySQL, Apache Oozie, Python, Apache Spark, MLlib, H2O.ai, deep learning, and GenAI.

Did you realize? Booking.com’s initial A/B testing experiments had a less than 25% success rate, but the focus was on learning, not immediate results.

Pro Tip: Don’t be afraid to experiment and fail quick. A culture of learning from mistakes is essential for successful AI adoption.

Want to learn more about the latest trends in AI and machine learning? Explore our other articles or subscribe to our newsletter for regular updates.

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Artist, Ignasi Monreal Spent 4 Months Covering His Entire Home in Gold Leaf

by Chief Editor
written by Chief Editor

The Golden Touch: How Luxury Interiors Are Redefining Home

Ignasi Monreal, a Barcelona-born artist now based in Rome, has recently unveiled a Madrid apartment that’s turning heads – and challenging conventional notions of home design. The space, lavishly finished with copper and gold, isn’t just a residence; it’s a statement. This bold move raises a key question: is this a fleeting trend, or a sign of a deeper shift in how we perceive and invest in our living spaces?

Beyond Beige: The Rise of Maximalist Interiors

For years, minimalist aesthetics dominated interior design. Clean lines, neutral palettes and a focus on functionality were the hallmarks of modern homes. However, a growing counter-movement is embracing maximalism – a celebration of color, texture, and personality. Monreal’s golden apartment exemplifies this trend, demonstrating a willingness to embrace opulence and individuality.

This shift isn’t simply about aesthetics. It reflects a broader cultural desire for self-expression and a rejection of cookie-cutter living. After years of prioritizing practicality, homeowners are increasingly seeking spaces that inspire joy and reflect their unique identities. The desire for ‘something peculiar’ as Monreal stated, is becoming more common.

The Allure of Precious Metals in Design

Gold, in particular, is experiencing a resurgence in interior design. Historically associated with royalty and luxury, gold adds a sense of warmth, sophistication, and timelessness to any space. Monreal’s decision to cover his apartment in a gold finish – reportedly the largest order of its kind in Europe – highlights the growing appeal of this precious metal.

While full-scale gold interiors may remain niche, we’re seeing gold accents appearing in everything from furniture and lighting to hardware and accessories. This trend extends beyond residential spaces, with high-finish hotels and restaurants also incorporating gold elements to create a luxurious and memorable experience. Rem Koolhaas’s use of 200,000 sheets of gold leaf for the Prada Foundation’s Haunted House in Milan demonstrates the impact of this material in architectural projects.

From Nomadic to Rooted: The Changing Role of ‘Home’

Monreal’s journey to creating his Madrid apartment is also revealing. Having previously led a nomadic life, he sought a fixed space to be closer to family. This reflects a broader trend of individuals re-evaluating their relationship with ‘home’ in a post-pandemic world. The desire for stability, connection, and a personal sanctuary has become more pronounced.

Investing in a home, as Monreal notes, represents a significant milestone – particularly for those who, like himself, have built a career through creative pursuits. This suggests that homes are increasingly viewed not just as financial assets, but as symbols of personal achievement and creative expression.

The Intersection of Art and Interior Design

Monreal’s background as a multidisciplinary artist – working in painting, digital art, scenography, and film – is evident in the meticulous design of his apartment. The space feels less like a purely functional living area and more like a curated art installation. This blurring of boundaries between art and interior design is another emerging trend.

Homeowners are increasingly commissioning artists to create bespoke pieces, incorporating unique artwork and design elements that reflect their personal tastes. This trend is fueled by a desire for authenticity and a rejection of mass-produced items. The inclusion of pieces like the Zaisu wooden chairs by Kenji Fujimori and Tomomi Fukuda, and glasswork by Sumida Yoriko, exemplifies this approach.

Frequently Asked Questions

Is gold a practical choice for interior design? Gold accents can be practical and add value. Full gold finishes, like Monreal’s, are more about artistic expression and require significant investment and maintenance.

What is maximalism in interior design? Maximalism is an aesthetic that embraces abundance, color, and personality, rejecting the minimalism of recent decades.

How is the pandemic influencing home design trends? The pandemic has increased the desire for comfortable, functional, and personalized living spaces, leading to a greater emphasis on home as a sanctuary.

What is trompe l’œil? Trompe l’œil is an art technique that uses realistic imagery to create the optical illusion that the depicted objects exist in three dimensions. Ignasi Monreal is known for his work in this style.

Did you realize? Ignasi Monreal’s work has been exhibited globally, from murals in New York and Shanghai to solo shows in Japan and the USA.

Pro Tip: When incorporating metallic accents, consider the undertones of your existing décor. Warm golds complement warmer palettes, while cooler golds pair well with cooler tones.

What are your thoughts on the golden apartment? Share your comments below!

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Java News Roundup: Lazy Constants, TornadoVM 3.0, NetBeans 29, Quarkus, JReleaser, Open Liberty

by Chief Editor
written by Chief Editor

Java’s Evolution: AI Acceleration, Performance Tweaks, and a Streamlined Developer Experience

The Java ecosystem continues its rapid evolution, with recent updates signaling a strong focus on performance, developer productivity, and emerging technologies like AI. February 23rd, 2026, marked a significant checkpoint with releases and advancements across several key projects, from core JDK improvements to specialized tools like TornadoVM and NetBeans.

Lazy Constants: A Step Towards More Efficient Java

OpenJDK’s JEP 531, now a Candidate release after previously being known as StableValues, introduces Lazy Constants. This feature aims to optimize performance by delaying the initialization of constants until they are actually needed. The latest preview removes the isInitialized() and orElse() methods, streamlining the interface and focusing on core functionality. A recent ofLazy() factory method allows for the creation of stable, pre-defined elements for Lists, Sets, and Maps. This subtle but impactful change promises to reduce application startup times and memory footprint.

GPU Acceleration Gains Momentum with TornadoVM 3.0

TornadoVM, a plugin for OpenJDK and GraalVM, is making significant strides in bringing Java applications to heterogeneous hardware. The recent 3.0 release focuses on stability and usability, with refactors to the IntelliJ project generation and GitHub Actions workflows. TornadoVM targets CPUs, GPUs (Intel, NVIDIA, AMD), and FPGAs, enabling developers to leverage the power of these accelerators for demanding workloads. It supports OpenCL, NVIDIA CUDA PTX assembly, and SPIR-V binary, offering flexibility in hardware choices.

Pro Tip: TornadoVM doesn’t replace the Java Virtual Machine (JVM); it complements it, allowing you to offload specific code sections to GPUs for faster processing. This is particularly useful for computationally intensive tasks like machine learning and data analysis.

NetBeans 29: Enhanced Developer Tools

Apache NetBeans 29 delivers a suite of improvements focused on stability and performance. Updates to the LazyProject class improve initialization speed, while fixes address warnings related to the NotificationCenterManager. Support for Codeberg projects has been added to the DefaultGitHyperlinkProvider class, expanding the IDE’s integration with popular code hosting platforms.

Quarkus, Micronaut, JReleaser, Chicory, and Jox: A Thriving Ecosystem

Beyond the major releases, several other projects saw updates. Quarkus 3.32 integrates with Project Leyden for improved service registration. Micronaut 4.10.9 provides bug fixes and updates to core modules. JReleaser 1.23.0 introduces path filtering for changelog generation. Chicory 1.7.0 advances WebAssembly support with GC and multi-memory proposals. Jox 1.1.2-channels adds non-blocking methods for integration with frameworks like Netty and Vert.x. These updates demonstrate the vibrant and active nature of the Java development community.

The Rise of WebAssembly and JVM Native Runtimes

Chicory’s advancements in WebAssembly support highlight a growing trend: bringing the power of the JVM to the web and beyond. WebAssembly offers a portable, efficient execution environment, and projects like Chicory are making it easier for Java developers to target this platform. This opens up new possibilities for building high-performance web applications and serverless functions.

Looking Ahead: AI, Heterogeneous Computing, and Developer Experience

These recent updates point to several key trends shaping the future of Java. AI acceleration, as exemplified by TornadoVM, is becoming increasingly important as developers seek to leverage GPUs for machine learning and data science. Heterogeneous computing, utilizing diverse hardware architectures, is gaining traction as a way to optimize performance and energy efficiency. Finally, a continued focus on developer experience, through tools like NetBeans and streamlined frameworks like Quarkus and Micronaut, is essential for attracting and retaining Java developers.

Did you know? TornadoVM supports multiple vendors, including NVIDIA, Intel, AMD, ARM, and even RISC-V hardware accelerators, offering developers a wide range of options for optimizing their applications.

FAQ

Q: What is JEP 531?
A: JEP 531, Lazy Constants, aims to improve Java performance by delaying the initialization of constants until they are actually used.

Q: What does TornadoVM do?
A: TornadoVM allows Java programs to run on GPUs and other specialized hardware, accelerating computationally intensive tasks.

Q: What is the benefit of using NetBeans 29?
A: NetBeans 29 offers improved performance, stability, and integration with popular code hosting platforms like Codeberg.

Q: What is WebAssembly and why is it important?
A: WebAssembly is a portable, efficient execution environment that allows Java applications to run in web browsers and other environments.

Explore the latest advancements in Java development and share your thoughts in the comments below! Don’t forget to subscribe to our newsletter for more in-depth analysis and updates on the Java ecosystem.

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[Video Podcast] Improving Valkey with Madelyn Olson

by Chief Editor
written by Chief Editor

Valkey: The Rising Star in In-Memory Databases and Its Future Trajectory

The landscape of in-memory databases is rapidly evolving, and Valkey is quickly establishing itself as a significant player. Born from a community fork of Redis, Valkey offers a compelling alternative for developers seeking a high-performance, scalable, and open-source caching and messaging solution. This article delves into the origins of Valkey, its current capabilities, and potential future trends shaping its development and adoption.

From Redis Fork to Independent Force

Valkey’s story is rooted in a pivotal moment within the Redis community. In 2024, Redis shifted its licensing from the permissive BSD license to a dual SSPL and proprietary licensing model. This change prompted a group of core Redis contributors – including engineers from Alibaba, Amazon, Ericsson, Google, Huawei, and Tencent – to fork the code and establish Valkey under the Linux Foundation. This move ensured the continuation of an open-source, community-driven project, appealing to developers who prioritize freedom and collaboration.

Madelyn Olson, a Principal Software Development Engineer at Amazon and a key maintainer of the Valkey project, highlights the collaborative spirit behind Valkey’s creation. The initial team comprised six engineers, and the project has since garnered support from numerous managed service providers like Amazon ElastiCache, Google Cloud’s Memorystore, Aiven, and Percona.

Seamless Migration and Compatibility

One of Valkey’s key strengths is its compatibility with existing Redis deployments. Valkey aims to be a drop-in replacement for Redis open source 7.2, simplifying the migration process for developers. This compatibility extends to client libraries, meaning applications using redis-py or Spring Data Redis can seamlessly transition to Valkey without significant code changes. The ease of migration is a major draw for organizations looking to avoid vendor lock-in and maintain control over their data infrastructure.

Pro Tip: Many users report a remarkably smooth transition to Valkey, often described as simply clicking a button in managed service consoles like Amazon ElastiCache.

The Core: More Than Just a Hash Map

While often described as a “hash map over TCP,” Valkey’s capabilities extend far beyond simple key-value storage. It supports a variety of abstract data structures, including strings, lists, maps, sets, sorted sets, HyperLogLogs, bitmaps, streams, and spatial indices. This versatility makes Valkey suitable for a wide range of applications, from caching and session management to real-time analytics and message queuing.

The recent focus on performance improvements, detailed in Madelyn Olson’s QCon San Francisco 2025 presentation, demonstrates Valkey’s commitment to pushing the boundaries of in-memory database performance. These improvements center around a complete rebuild of the hash table, optimizing memory allocation and leveraging modern hardware capabilities.

Performance Gains Through Architectural Refinements

Valkey 8 introduced significant changes to the underlying hash table, focusing on reducing memory overhead and improving throughput. Key optimizations included embedding key data directly within the hash table structure and adopting a “SwissTable” approach to collision resolution, utilizing CPU cache lines more efficiently. These changes resulted in substantial memory savings – up to 40% in some cases – and maintained, or even improved, performance.

The team prioritized maintaining performance during these architectural changes, focusing on throughput as a primary metric. Valkey aims to deliver approximately a quarter of a million requests per second per core, with a capacity of 1.2 million requests per second on a single key.

Future Trends and Potential Developments

Several trends are likely to shape Valkey’s future development:

  • Enhanced Scalability: Continued improvements in horizontal scalability will be crucial for handling increasingly large datasets and high-throughput workloads.
  • Advanced Data Structures: Expanding the range of supported data structures will broaden Valkey’s applicability to new use cases.
  • Improved Observability: Enhanced monitoring and observability tools will be essential for managing and troubleshooting Valkey deployments in production environments.
  • Plugin Ecosystem Growth: The Rust-based plugin extensibility system offers a promising avenue for community contributions and feature expansion.
  • Edge Computing Integration: As edge computing gains traction, Valkey’s low latency and small footprint could make it an ideal choice for deploying caching and data processing logic closer to conclude-users.

Valkey’s Open Source Governance Model

Valkey operates under a vendor-neutral governance model, guided by a Technical Steering Committee (TSC) comprised of representatives from the founding organizations. While the TSC currently consists of the original six contributors, there are plans to expand it to include more community members, fostering a more inclusive and collaborative development process.

FAQ

Q: Is Valkey a direct replacement for Redis?
A: Valkey aims to be a drop-in replacement for Redis open source 7.2, offering seamless migration for many use cases.

Q: What programming languages are supported by Valkey?
A: Valkey supports a wide range of languages through existing Redis client libraries.

Q: What are the key performance benefits of Valkey?
A: Valkey offers high throughput, low latency, and efficient memory utilization, making it suitable for demanding applications.

Q: Is Valkey actively maintained?
A: Yes, Valkey is actively maintained by a dedicated team of engineers and a growing community of contributors.

Did you know? Ericsson is utilizing Valkey in telecommunications equipment, showcasing its potential in specialized and demanding environments.

Explore the Valkey blog for in-depth technical articles and updates. Join the Valkey Slack community to connect with other users and contributors.

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Enhancing A/B Testing at DoorDash with Multi-Armed Bandits

by Chief Editor
written by Chief Editor

Beyond A/B Testing: How Multi-Armed Bandits are Revolutionizing Digital Experimentation

For years, A/B testing has been the gold standard for optimizing websites, apps, and digital experiences. But as companies like DoorDash are discovering, traditional A/B testing can be surprisingly slow and inefficient. A new approach, leveraging “multi-armed bandits” (MAB), is gaining traction, promising faster learning and reduced wasted opportunities.

The Problem with Traditional A/B Testing: Opportunity Cost and Slow Iteration

Imagine you’re testing two versions of a call-to-action button. With A/B testing, you typically split your audience 50/50 and wait until you reach statistical significance – often weeks or even months. But what if one version is clearly superior after just a few days? You’re still forcing traffic to the underperforming variant, incurring what’s known as “opportunity cost” or “regret.”

This regret compounds when running multiple experiments simultaneously. Teams often resort to sequential testing – running experiments one after another – to minimize regret, but this dramatically slows down the pace of innovation. A recent study by Optimizely found that companies running more than five concurrent A/B tests experience a 30% decrease in overall learning speed.

Enter the Multi-Armed Bandit: Adaptive Experimentation

The multi-armed bandit algorithm, inspired by a gambler facing multiple slot machines, offers a dynamic solution. Instead of fixed traffic splits, MABs adaptively allocate traffic to the better-performing options in real-time. As data flows in, the algorithm learns which “arms” (variants) are yielding the highest “rewards” (conversions, clicks, revenue, etc.) and shifts more traffic accordingly.

This isn’t about random chance. MABs balance exploration – trying out different options to gather data – with exploitation – maximizing rewards by focusing on the best-performing options. Think of Netflix recommending shows: they’re constantly exploring new content for you while simultaneously exploiting what they already know you like.

Pro Tip: MABs are particularly effective when dealing with rapidly changing user behavior or when the cost of serving a suboptimal experience is high.

DoorDash’s Success with Thompson Sampling

DoorDash engineers Caixia Huang and Alex Weinstein have seen significant benefits from implementing a MAB platform based on Thompson sampling, a Bayesian algorithm. Thompson sampling excels at handling delayed feedback and provides robust performance. They’ve reported a substantial reduction in experimentation costs and a faster iteration cycle, allowing them to evaluate more ideas quickly.

According to a case study published by Google, using MABs for ad campaign optimization resulted in a 20% increase in click-through rates compared to traditional A/B testing.

The Future of Bandits: Contextual Bandits and Beyond

While MABs offer a powerful upgrade to A/B testing, they aren’t without challenges. DoorDash highlights the difficulty of inferring metrics not directly included in the reward function. Furthermore, the dynamic allocation can lead to inconsistent user experiences.

The next evolution lies in contextual bandits, which incorporate user-specific information (location, demographics, past behavior) to personalize the experimentation process. Bayesian optimization is also being integrated to further refine the algorithm’s learning capabilities. Finally, “sticky” user assignment – ensuring a user consistently experiences the same variant during a session – is being explored to improve user experience.

Beyond these advancements, we’re seeing a convergence of MABs with reinforcement learning, creating even more sophisticated systems capable of optimizing complex, multi-stage user journeys. Companies like Amazon are already leveraging reinforcement learning to personalize product recommendations and optimize pricing strategies.

Will MABs Replace A/B Testing Entirely?

Not necessarily. A/B testing remains valuable for understanding the why behind user behavior. MABs excel at quickly identifying what works, but A/B testing provides deeper insights into the underlying reasons. The most effective approach is often a hybrid one – using A/B testing for initial exploration and hypothesis validation, then transitioning to MABs for rapid optimization and scaling.

Frequently Asked Questions (FAQ)

What is a “bandit” in multi-armed bandit algorithms?
A “bandit” refers to each variation being tested – like a slot machine with an unknown payout rate.
How do MABs handle the exploration-exploitation trade-off?
MABs use algorithms like Thompson sampling to dynamically balance trying new options (exploration) with focusing on the best-performing options (exploitation).
Are MABs more complex to implement than A/B testing?
Yes, MABs require more sophisticated statistical modeling and engineering effort than traditional A/B testing.
What types of businesses can benefit from using MABs?
Any business that relies on data-driven optimization, including e-commerce, online advertising, content platforms, and mobile apps.

Ready to dive deeper? Explore our article on advanced personalization techniques or the role of Bayesian statistics in marketing.

Don’t forget to share your thoughts in the comments below! What challenges are you facing with experimentation, and how do you see MABs fitting into your strategy?

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News

A Lahore Mosque Brought to Life Through the Revival of Traditional Islamic Tile-Making

by Rachel Morgan News Editor
written by Rachel Morgan News Editor

After nearly two decades of planning and construction, the Mian Salahuddin Community Mosque is complete. Located on a 650-square-metre site within a gated community, the mosque’s design was deeply influenced by poetry and spiritual reflection, specifically a poem entitled “Masjid-e-Qurtuba” by Allama Iqbal, the grandfather of the project’s namesake.

A Return to Traditional Design

Mian Salahuddin believes that older, traditional mosque architecture fosters a deeper connection to the divine than modern designs. He stated, “I believe that the architecture of traditional older mosques connects one with the divine far more than modern designs,” adding that it “symbolises the presence of the worshipper and the opening of one’s heart.” This belief, rooted in his grandfather’s verse – “Yet only that work of art is eternal, the one created by a man of God” – guided the project’s aesthetic.

Did You Know? The mosque’s construction took approximately two decades to complete.

Architect Taimoor Khan Mumtaz collaborated with his father, Kamil Khan Mumtaz, a renowned pioneer in the field, to ensure the design remained true to tradition. The team deliberately employed centuries-old construction methods, utilizing locally sourced burnt brick and a mixture of hydraulic and slaked lime mortar from Patoki, Kasur, and the Salt Range.

A key decision in the construction process was the avoidance of cement. According to Khan Mumtaz, “We didn’t use any cement because these factories are some of the biggest polluters in the world.” Lime was chosen instead, due to its longevity and its historical prevalence in the region for “hundreds of years.”

Expert Insight: The deliberate choice of traditional materials and construction techniques demonstrates a commitment to both cultural preservation and environmental responsibility. Prioritizing locally sourced, sustainable materials over modern alternatives reflects a growing awareness of the impact of construction on the environment and a desire to minimize that impact.

Looking Ahead

The completion of the Mian Salahuddin Community Mosque could inspire similar projects prioritizing traditional building methods and sustainable materials. It is likely that this mosque will serve as a model for future religious buildings seeking to balance architectural beauty with environmental consciousness. Further, the emphasis on the spiritual connection fostered by traditional design may lead to renewed interest in historical architectural styles.

Frequently Asked Questions

How long did it take to build the Mian Salahuddin Community Mosque?

The mosque took around two decades to complete, from initial planning to final construction.

What inspired the design of the mosque?

The design was heavily influenced by a poem, “Masjid-e-Qurtuba,” written by Allama Iqbal, the grandfather of Mian Salahuddin.

What materials were used to construct the mosque?

The mosque was built using locally sourced burnt brick and a mixture of hydraulic and slaked lime mortar, avoiding the use of cement.

What role does architectural design play in fostering a spiritual connection, and how might this influence future building projects?

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Tech

QCon AI NY 2025 – Becoming AI-Native Without Losing Our Minds To Architectural Amnesia

by Chief Editor
written by Chief Editor

The Looming “Agentic Debt”: Why AI’s Rise Demands Architectural Discipline

The relentless march of AI isn’t just about flashy new features and productivity gains. A critical warning, delivered at QCon AI NY 2025 by Tracy Bannon, suggests we’re sleepwalking into a new era of technical debt – “agentic debt” – if we don’t apply established software architecture principles to these increasingly autonomous systems. The core message? AI amplifies existing weaknesses, it doesn’t create entirely new ones.

Beyond Bots and Assistants: Understanding the Spectrum of AI Autonomy

Bannon’s talk highlighted a crucial distinction often lost in the AI hype: not all “AI” is created equal. She categorized AI systems into three broad types: bots (scripted responders), assistants (human-collaborative), and agents (goal-driven, autonomous actors). This isn’t merely semantic. Each category carries a vastly different risk profile. A simple chatbot responding to FAQs poses minimal risk, while an AI agent managing a supply chain or controlling critical infrastructure demands rigorous architectural oversight.

Consider a real-world example: a marketing team deploying an AI agent to automatically adjust ad spend based on performance. Without proper identity management and access controls, that agent could potentially drain the entire marketing budget into a single, poorly performing campaign – a scenario easily preventable with sound architectural practices.

The Autonomy Paradox: Faster Innovation, Greater Risk

The speed at which AI agents are being adopted is breathtaking. Forrester predicts a significant rise in technical debt severity in the near term, directly linked to this AI-driven complexity. But Bannon argues that the problem isn’t the AI itself, but our tendency to prioritize speed over foundational architectural principles. We’re chasing “visible activity metrics” – like lines of code deployed or features launched – while neglecting the “work that keeps systems healthy”: design, refactoring, validation, and threat modeling.

Pro Tip: Before deploying any AI agent, ask yourself: “What happens when it makes a mistake?” If you can’t answer that question quickly and confidently, you’re likely building agentic debt.

Agentic Debt: The Familiar Faces of Failure

Agentic debt manifests in ways that will sound eerily familiar to seasoned software engineers. Bannon identified key areas of concern: identity and permissions sprawl (who *is* this agent?), insufficient segmentation and containment (can it access things it shouldn’t?), missing lineage and observability (can we trace its actions?), and weak validation and safety checks (how do we know it’s doing the right thing?).

A recent report by Gartner found that 40% of organizations struggle with AI observability, meaning they lack the tools and processes to understand *why* their AI systems are making certain decisions. This lack of transparency is a breeding ground for agentic debt.

Identity as the Cornerstone of Agentic Security

Bannon emphasized identity as the foundational control for agentic systems. Every agent, she argued, must have a unique, revocable identity. Organizations need to be able to quickly answer three critical questions: what can the agent access, what actions has it taken, and how can it be stopped? She proposed a minimal identity pattern centered around an agent registry – a centralized repository of information about each agent operating within the system.

Did you know? The concept of least privilege – granting agents only the minimum necessary permissions – is even *more* critical in agentic systems, as their autonomous nature means they can potentially exploit broader access if compromised.

Decision-Making Discipline: Why, Not Just How

Bannon urged teams to shift their focus from *how* to implement AI agents to *why* they’re doing so. Every decision to increase autonomy should be a conscious tradeoff, explicitly acknowledging the potential downsides. She framed decisions as optimizations – improvements in one dimension always come at the expense of another (e.g., speed vs. quality, value vs. effort).

For example, an AI agent designed to automate customer support might improve response times (speed) but potentially at the cost of personalized service (quality). Understanding this tradeoff is crucial for responsible AI deployment.

The Architect’s Role: Preventing Architectural Amnesia

The call to action from Bannon’s talk was clear: architects and senior engineers must take ownership of AI agent integration. This means preventing “architectural amnesia” by designing governed agents, making risk and debt visible, and pursuing higher levels of autonomy only when demonstrably valuable. The good news? The core principles of software architecture remain valid. The challenge isn’t learning entirely new disciplines, but applying existing knowledge to a new context.

FAQ: Addressing Common Concerns

  • What is “agentic debt”? It’s the technical debt accumulated when AI agents are deployed without sufficient architectural discipline, leading to issues like identity sprawl and lack of observability.
  • Is AI inherently risky? No, but it amplifies existing risks in software systems.
  • What’s the first step to mitigating agentic debt? Focus on establishing a strong identity management system for all AI agents.
  • Do I need to rewrite all my existing code? Not necessarily, but you should carefully assess the architectural implications of integrating AI agents into existing workflows.

Want to learn more about building robust and secure AI systems? Explore additional resources from QCon AI and InfoQ. Recorded videos from the conference will be available starting January 15, 2026.

What are your biggest concerns about the rise of AI agents? Share your thoughts in the comments below!

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