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The State of Coding the Future with Java and AI – May 2025
Software development is changing fast, and Java developers are right in the middle of it - especially when it comes to using Artificial Intelligence (AI) in their apps. This report brings together feedback from 647 Java professionals to show where things stand and what is possible as Java and AI come together. One of the biggest takeaways is this: Java developers do not need to be experts in AI, machine learning, or Python. With tools like the Model Context Protocol (MCP) Java SDK, Spring AI, and LangChain4j, they can start adding smart features to their apps using the skills they already have. Whether it is making recommendations, spotting fraud, supporting natural language search or a world of possibilities, AI can be part of everyday Java development. The report walks through real-world approaches that Java developers are already using - things like Retrieval-Augmented Generation (RAG), vector databases, embeddings, and AI agents. These are not just buzzwords - they help teams build apps that work well at scale, stay secure, and are easier to manage over time. For teams figuring out where to start, the report includes guidance and simple workflows to make things easier. In short, Java is well-positioned to keep leading in enterprise software. This is an invitation to Java architects, tech leads, decision-makers, and developers to explore what is next and build smarter, more connected apps with AI. Introduction The world of software development is changing fast. Over the past two years, we have seen a major shift – not just in tools and frameworks, but in how developers think about building software. Artificial Intelligence is now part of the everyday conversation – helping developers rethink what their applications can do and how quickly they can build them. In the middle of all this change, it helps to pause and look at where we are. Java developers are especially exploring how to add intelligence to their existing applications or build new ones that can learn, adapt, and scale. But with so many innovative ideas and so much information out there, the real question is – what are developers doing? To answer that, we reached out directly to Java professionals across the world. We wanted to understand their thinking, what they are trying, and what they need to move forward with confidence. Our invitation was simple - "Calling all Java pros – share your insights to help simplify AI-powered apps 👉 aka.ms/java-ai." The response was strong. A total of 647 Java professionals took part: 587 have experience with AI - representing a wide range of perspectives and levels of AI knowledge. 60 have not yet explored AI - but are curious and eager to learn what is possible. Among all respondents: Two-thirds (67%) had 4 to 10 years of Java experience. One-third (33%) had more than 10 years of experience. This report highlights what we learned – and what it means for the future of Java and AI. The Scenario We Asked Java Pros to Imagine “Picture yourself adding an AI-driven feature to an existing Java-based app or building a brand-new intelligent application. This feature might improve customer experience – such as personalized recommendations – optimize business processes – like fraud detection – or enhance product searches using natural language. Your goal is to seamlessly integrate this feature, ensuring it is easy to develop, scalable, and maintainable.” An impressive 97 percent of respondents said they would choose Java for building this type of intelligent application. A Common Misconception 90 percent of respondents believed that building intelligent Java apps would require deep experience with AI, Machine Learning, or Python. Developers Can Start to Deliver Production-Grade Intelligent Java Apps without AI, ML, or Python Skills Myth of AI/ML and Java Java developers already have what they need – today – to build intelligent applications using modern Java-first frameworks such as Model Context Protocol (MCP) Java SDK, Spring AI, or LangChain4j. No prior experience in Python or Machine Learning is required for Java developers to begin adding intelligent features to their apps. Connecting a Java application to backend AI systems – including Large Language Models and Vector Databases – is conceptually like working with REST APIs or traditional SQL and NoSQL databases. Modern libraries like MCP Java SDK, Spring AI, and LangChain4j make it easier for developers to build and enhance AI-powered Java applications. These frameworks offer support for: Retrieval-Augmented Generation (RAG) Conversational memory Conversation logging Integration with vector stores Secure, observable, and safe-by-default interactions Streamed outputs and structured reasoning Java continues to play a leading role in enterprise software. This gives Java developers a natural advantage – and a unique opportunity – to lead the way in delivering intelligent features inside core business applications. It is also important to note that tasks requiring deep AI and Data Science knowledge are best left to specialists. Java developers can focus on app logic, integration, and delivering business value without needing to become AI experts themselves. In-Process vs HTTP-Based - A Common Misstep in AI Application Design AI-powered applications can be built in different ways - and one of the patterns is to embed the model directly within the same app that handles business logic and exposes the API. This is known as the in-process approach. In this setup, the model is loaded at runtime, using local weights and often relying on a GPU for inference. It is a convenient option - especially when working with models you have created from scratch or downloaded for use in your own application. The Shift to Model-as-a-Service - A Simple History Before foundation models were made available as services, most AI models were custom-built for specific use cases – like classifying documents, detecting anomalies, or predicting demand. These models were typically developed in Python using frameworks such as TensorFlow or PyTorch. Because development and usage happened in the same environment, it was natural to load the model directly into the application’s memory using local weights, and to rely on a local GPU for inference. This model-in-app pattern made sense when the app and the model were designed together. Many popular Python-based libraries, including PyTorch, TensorFlow, and Hugging Face Transformers, encourage this in-process setup by default. As a result, the model often becomes a local function call - tightly coupled to the application’s logic and runtime. However, that convenience introduces scaling and maintenance challenges. Every application instance must run on a machine with GPU access. You must allocate GPU resources per app, even when the app is idle. As demand grows, this leads to higher infrastructure costs, lower resource efficiency, and architectural rigidity. If you scale the app, you are often forced to scale GPU capacity along with it - even if only the app's business logic needs scaling. The Rise of HTTP-Based Integration with Model-as-a-Service The introduction of foundation models like GPT-4, available through services such as OpenAI and Azure OpenAI, brought a shift in how models are used in applications. These models are designed to handle a wide range of tasks and are offered as cloud-hosted APIs - a model-as-a-service approach. Instead of embedding the model into each application, you send a request over HTTP and receive a response. This change enables a new design pattern: the application and the model are treated as separate services. The app handles business logic, while the model service handles inference. This pattern brings clear advantages - modularity, cleaner separation of concerns, centralized control over GPU infrastructure, and the ability to reuse models across many applications. The diagram above illustrates this shift. On the left, the in-process setup binds the model tightly to the application, requiring direct GPU access and local weights. On the right, the HTTP-based setup enables applications written in any language stack - such as Java, Python, JavaScript, .NET, or Go - to interact with a shared model endpoint over HTTP. This separation makes it easier to update models, manage infrastructure, including GPU infrastructure, and scale intelligently. It also reflects how most modern AI platforms are now built. HTTP-based integration is scalable, cost-effective, and designed for modern application environments. It reduces operational complexity and gives developers the flexibility to choose the architecture that fits their needs - without being locked into one stack, tools, models, or setup. Myth about Python As we listened to Java developers across the community, a familiar pattern emerged. Many shared their experiences - and sometimes frustrations - when working with AI technologies in Java. These were not just passing remarks. They reflected real challenges - especially when it came to building or training machine learning models, where Python has long been the preferred environment. Here is a glimpse into what we heard: “Java has fewer AI-specific libraries compared to Python. Libraries like TensorFlow and PyTorch are more mature and feature-rich in Python, making it easier to implement complex AI models.” “Working on AI-powered applications with Java presents challenges, especially when building or training models. The ecosystem is not as deep as Python’s, which has tools like Scikit-learn and notebooks like Jupyter.” “Even though Java can be used for AI, the support for GPU acceleration is not as seamless as it is in Python. You need extra setup and tuning.” “There are fewer Java developers with strong AI backgrounds. It is harder to find or grow a team when Python seems to be the go-to language for most ML engineers.” These are all honest, valid observations. If the job-to-be-done is building foundation models, training models from scratch, or fine-tuning existing models, then Python is a natural choice. It offers the right tools, libraries, and ecosystem support to do that job well. But here is what really matters today: Most AI application developers - including those working in Java - are not training or fine-tuning models. They are not building models from the ground up or optimizing low-level GPU workloads. Instead, they are focused on a different job: Connecting to existing foundation models. Calling AI services over REST APIs. Using AI libraries like Spring AI and LangChain4j to orchestrate intelligent workflows. Querying vector databases. Embedding AI capabilities into production-grade enterprise applications. This distinction is clearly reflected in the diagram above. On the right side, you see “Model Training and Development” as a separate, specialized job. It is critical work - best handled by teams with deep expertise in data science and model engineering. On the left side, you see the application architecture most Java developers work with every day: REST APIs, business logic, database integration, and calls to external AI models and vector stores using AI libraries. This is where Java fits. Java developers are not building models - they are building apps on top of foundation models. And with tools like MCP Java SDK, Spring AI, and LangChain4j, they are not playing catch-up - they are building what matters, integrate AI into existing apps and capabilities. You do not need to train models. You just need to wire up the right parts, connect to the services that make AI possible, and deliver intelligent functionality where it belongs - inside the applications your organization already depends on. “You can be an AI application developer - in less than 2 minutes. Minute 1: Sign up for access to an LLM - Azure OpenAI, OpenAI, whatever. Get yourself an API key. Minute 2: Head to https://start.spring.io, specify `OpenAI` (or whatever), hit `Generate`, and then open your new Spring Boot + Spring AI project in your IDE. In your `application.properties`, you’ll need to specify your API key. Then, inject the auto-configured `ChatClient` somewhere in your code. Use the `ChatClient` to make a request to your model. Congratulations - you’re an AI application developer!” -- Josh Long, Spring Developer Advocate, Broadcom "Is Java still relevant in this new era of AI?" "How do I, with my years of Java expertise, even begin to work with these Large Language Models today?" These are questions I have heard time and again at community events and industry conferences. Today, Java developers are at a pivotal moment. Our existing skills are not just still relevant - they are the foundation for building the next generation of AI-powered applications. Thanks to frameworks like Quarkus, Langchain4j, and MCP integration, we can bridge the world of traditional enterprise development with the fast-growing world of AI - all without giving up the strengths and familiarity of Java.” – Daniel Oh, Senior Principal Developer Advocate, Red Hat The future of AI in software development will be defined by who integrates AI well into applications - both existing and new apps. Most AI-related development will be connecting models to solve real problems inside real applications. And in that space, Java is already strong. From financial services to healthcare, logistics to manufacturing - Java powers the business logic and workflows that now need to become intelligent. This is where Java developers shine. And with the right tools, they are more than ready to lead. Crucial Elements for Java AI Applications As we looked deeper into the survey results, one thing became clear – Java developers are not just interested in adding AI for the sake of it. They are focused on building practical, enterprise-ready features that are reliable, secure, and easy to maintain. 98% of respondents highlighted a core set of approaches or elements that they see as essential for any AI-powered Java application: Retrieval-Augmented Generation (RAG) – Bringing real-time, context-aware answers by grounding responses in trusted data. This is especially useful in enterprise scenarios where accuracy and context matter. Embeddings and Vector Databases – Enabling efficient semantic search and advanced knowledge retrieval. Developers recognize this as the key to making applications “understand” the meaning behind user inputs. Function Calling or Tool Calling – Allowing AI models to interact with APIs, pull in real-time data, or trigger backend workflows. This is where AI starts to act – not just suggest – making it a true part of the application logic. AI Agents – These are not just chatbots. Agents are intelligent programs that can automate or assist with tasks on behalf of users or teams. They combine reasoning, memory, and action – gathering information and triggering responses dynamically. For many developers, agents represent the next step toward intelligent automation inside business-critical workflows. Fundamentals for Enterprise-Grade AI Applications When building applications for the enterprise, developers know that intelligence alone is not enough. Trust, safety, and integration matter just as much. These are the foundational features Java developers called out: Security and Access Control – Making sure AI features respect user roles, protect sensitive data, and fit into enterprise identity systems. Safety and Compliance – Filtering outputs to align with internal policies, legal regulations, and brand standards. This is especially important for customer-facing features. Observability – Tracking how AI decisions are made, logging user and AI interactions, and making sure there is a clear record of what happened – and why. Structured Outputs – AI responses need to work within the system, not outside it. Structured formats – like JSON or XML – ensure smooth handoffs between the AI component and the rest of the application. Reasoning and Explainability – Developers want AI features that can explain their answers, show their sources, and help users trust the output – especially in domains like finance, healthcare, or compliance. Representative Scenarios and Business Impact To make things more concrete, let us look at two sample scenarios. These are not the only ones – just representative examples to help spark ideas. There is a broad and growing range of real-world situations where Java developers can use AI to create business value. Scenario One – Intelligent Workflow Automation Imagine a production manager at an auto manufacturer - say, Mercedes-Benz or Ford - who needs to align the assembly line schedule with real-time component availability and constantly shifting order priorities. The manager’s question is urgent and complex: “How can I adjust the production schedule based on today’s parts inventory and current orders?” Answering means pulling in data from ERP systems, supply chain feeds, vendor dashboards, and manufacturing operations - a level of complexity that can overwhelm even experienced teams. This is where AI steps in as a true copilot - working alongside the human decision-maker to gather data, flag supply constraints, and highlight scheduling options. Together, they can plan faster, adapt more confidently, and respond to change in real time. For Java developers, this is an opportunity to build intelligent systems that bring together data from ERP, inventory, and order management applications - enabling AI models to interact with information and collaborate with decision-makers. These systems do not rely on AI alone; they depend on strong data integration and reliable workflows - all of which can be designed, secured, and scaled within the Java ecosystem. In this way, AI becomes part of a co-working loop - and Java developers are the ones who make that loop real. Scenario Two – AI-Powered Process Assistants Picture a logistics manager at a major shipping company - FedEx, UPS, or DHL - facing cascading delays due to severe weather across multiple regions. The manager is under pressure to reroute packages efficiently while minimizing disruptions to downstream delivery schedules. The question is urgent: “What is the fastest rerouting option for delayed packages due to weather?” Answering requires combining live weather feeds, traffic data, delivery schedules, hub capacities, and driver availability - all in real time. AI acts as a true copilot at this moment, working alongside the manager to collect relevant signals, flag risk zones, and generate rerouting recommendations. Together, they respond with speed and clarity, keeping shipments moving and customers informed. For Java teams, this is a practical opportunity to build intelligent systems that embed AI into logistics, supply chain, and delivery operations - not by rebuilding everything, but by integrating the right data streams, APIs, and business logic. The real value lies in data orchestration, not just algorithms. Java developers are key to enabling these AI-powered assistants by securing connections across systems and building workflows that help humans and AI collaborate effectively under pressure. More Scenarios – World of Possibilities These two scenarios only scratch the surface. Developers across industries – from healthcare and finance to retail and public services – are finding ways to integrate AI that solve meaningful problems, reduce complexity, and improve how their systems perform. Java and AI Technology Stack So far, we have looked at what developers want to build and how AI is changing the way applications are designed. Now, let us look at the platform behind it all – the technology stack that powers intelligent Java applications. To bring these ideas to life and impact, Java developers need a foundation that connects data, apps, and AI services. We call this an AI application platform. It is not a specific product – it is an integrated platform made up of components that most developer teams already use or are familiar with. The goal of this platform is to make developers more productive while building intelligent features into their applications. It gives teams the freedom to choose familiar tools – while making it easier to bring in AI capabilities when and where they are needed. We group this platform into three areas: The app platform The data platform The AI platform Let us break it down using the numbered diagram: Developer Services: These are the core tools that developers use every day – IDEs, coding assistants, build tools, testing frameworks, CI/CD pipelines. They help you write, debug, and manage application code across your team. Container Services | Platform-as-a-Service: This is the runtime layer – where your applications are deployed and scaled. Whether using containers or a managed platform, this layer handles traffic, performance, and operational efficiency. Data Platform: This is where your application data lives – databases, data lakes, and other storage services. It connects structured data, business logic, and real-time events. AI Platform: This is where intelligence is added. It includes access to large language models, embeddings, vector search, and other tools that support natural language interactions, automation, and decision-making. Together, these four parts form the foundation for building, deploying, and managing AI-powered Java applications. Technology Stacks for Spring Boot and Quarkus Applications To make this more relatable, we highlighted two of the most widely adopted Java frameworks – Spring Boot and Quarkus. These stacks represent popular combinations that many Java teams are already using today for building cloud-native applications. That said, these are just representative examples. There are many valid combinations that developers, platform teams, and organizations can choose – based on their existing tools, workloads, and team preferences. Representative Spring Boot Stack App Platform: App hosting service of choice AI Library: Spring AI AI Platform: OpenAI Business Data: PostgreSQL Vector Database: PostgreSQL Representative Quarkus Stack App Platform: App hosting service of choice AI Library: LangChain4j AI Platform: OpenAI Business Data: PostgreSQL Vector Database: PostgreSQL Both stacks support the core capabilities needed for building intelligent apps – from secure model access and real-time data integration to observability and system-level debugging. But the opportunity does not stop there. Traditional App Servers - Tomcat, WebLogic, JBoss EAP or WebSphere Many enterprise applications continue to run on Tomcat, WebLogic, JBoss EAP, or WebSphere. These are stable platforms that power core business systems – and they are very much part of the AI journey. If you are running on one of these platforms, you can still bring intelligence into your applications. By using a Java library of choice (Spring AI or LangChain4j) , you can connect these applications to Large Language Models (LLMs) and Model Context Protocol (MCP) servers – without needing to rewrite or migrate them. This means that intelligence can be added, not just rebuilt – a powerful approach for teams with large existing investments in Java EE or Jakarta EE applications. Whether your Java app is built with Spring Boot, Quarkus, or deployed on a traditional app server, the tools are here – and the path to intelligent applications is open. You do not have to start from scratch. You can start from where you are. Java and MCP – The Bridge to Intelligent Applications One of the most important parts of the Java and AI story is MCP - the Model Context Protocol. MCP is an open, flexible, and interoperable standard that allows large language models to connect with the outside world - and more importantly, with real applications and real data. At its core, MCP is a bridge - a structured way for models to access enterprise data, invoke tools, and collaborate with AI agents. It gives developers control over how data moves, how decisions are made, and how actions are triggered. The result is safer, more predictable AI behavior inside real-world systems. MCP servers can be implemented in any language stack – such as Java, C#, Python, and NodeJS - and integrated into any AI-powered application, regardless of how that application is written. That interoperability makes MCP especially valuable for teams working across systems and languages. If you are building an MCP server using Java, the official MCP Java SDK maintained by Anthropic provides the right starting point. You can also use frameworks like Spring or Quarkus to implement MCP servers with full enterprise capabilities. For those building applications using Spring AI or LangChain4j, both libraries support connecting to any MCP server - whether running locally or remotely - to orchestrate tools, call functions, and manage agent behavior as part of the runtime flow. In addition, ready-to-use implementations like the Azure MCP Server make it easier to add intelligence to backends, orchestrate workflows, and shape AI agent behavior without starting from scratch. Authentication and Authorization Security is a critical part of any enterprise-grade solution - and MCP is no exception. In collaboration with Anthropic, Microsoft proposed a new authorization specification for MCP. This specification has now been finalized and is being implemented across MCP clients and servers to ensure that all interactions are secure, policy-driven, and consistent across environments. This continued investment in standards, tooling, and security is helping MCP mature into a core enabler of intelligent applications - especially for enterprise Java teams looking to move fast without compromising on trust or control. Preferred Libraries – What Java Developers Are Choosing As part of our outreach to 647 Java professionals, we asked a key question: “Which AI frameworks or libraries would you consider for building or integrating intelligence into Java applications?” Here is what they told us: Spring AI - selected by 43 percent. LangChain4j – preferred by 37 percent. These two libraries clearly lead the way. They reflect the maturity of the Java ecosystem and show strong alignment with the two most active communities – Spring and Jakarta EE. Spring AI and LangChain4j offer higher-level abstractions that simplify how developers connect to AI services, manage context, interact with models, and build intelligent features. For developers already working in Spring Boot or Quarkus, these libraries feel familiar – and that lowers the barrier to adding intelligent capabilities into existing codebases. Other Developer Preferences At the same time, a considerable number of developers – 37 percent and 29 percent – also shared that they would prefer to work directly with AI service provider libraries or call REST APIs. This is not a surprise. In fact, it is a healthy signal. Many teams use these lower-level integrations to gain early access to new features or customize interactions in ways that higher-level libraries may not yet support. It is important that these developers know: You are not wrong - but you are not alone either. While direct API integration offers flexibility, AI libraries like Spring AI and LangChain4j are designed to make those experiences easier. They wrap the complexity, manage context, offer tested patterns that align with enterprise application needs - like observability, security, and structured outputs – plug your code into the Spring and Java ecosystem of possibilities for deep integration. Evolving Together As AI services evolve, day-zero support will almost always appear first in the service provider’s native SDKs and REST APIs. That is expected. But as those capabilities stabilize, AI libraries like Spring AI and LangChain4j will latch on – offering developers a smoother, more consistent programming experience. The result: Developers get to start fast with APIs – and scale confidently with higher level libraries and frameworks. Top Challenges and Areas Needing Improvement As part of our research, we asked 647 Java professionals to share the biggest challenges they face – and where they believe improvements would help the most. The answers reflected where the community is today – eager to build but still facing some friction points. Top Challenges Java Developers Encounter Lack of clear starting points and step-by-step guidance. Feeling overwhelmed by the variety of AI models, libraries, and tools. Misconceptions that machine learning expertise is required. Complexity with integrating AI features into existing applications - particularly introduced by suboptimal patterns such as directly calling REST APIs through low-level HTTP libraries, invoking Python-based routines through external OS processes, or loading models into the application’s memory at runtime using local weights and GPU resources. Missing features in some of the current libraries and frameworks. Uncertainty about scaling applications and safely using private models on the cloud. Areas Developers Believe Need the Most Improvement Clear and practical step-by-step workflows. Guidance on how to securely integrate private models. Examples that show how to use chat models for function calling and streaming completions. Educational content that explains completions, reasoning, and data validation. Tools and how-to guides for embedding-based search and question answering. Tutorials on how to leverage external data to improve model output. Java Developers – Familiar with AI vs. New to AI Among all respondents: 87 percent are familiar with AI. 13 percent are newer to AI and just getting started. Java Developers New to AI These developers are exploring use cases, evaluating models, and building early prototypes. Their top challenges are: Lack of clear starting points. Too many options in terms of tools and models. Need for simple, practical guidance. The top areas that will benefit them: Step-by-step development workflows. Examples of using chat models and completions. Simple breakdowns of key concepts like reasoning and validation. Java Developers Familiar with AI These developers are further along – often in development or production stages – and face a wide range of challenges. A top need for them is: Secure ways to integrate private models into Java apps. They also benefit from deeper technical content, patterns, and advanced tooling support. Moving Forward with Confidence This space is evolving faster than anyone expected – and that can feel overwhelming. But the top challenges Java developers face are real, and the community is actively addressing them. It is important not to worry about the number of tools, models, or libraries. That diversity is a sign of progress. Models will keep evolving. New ones will arrive. And this is exactly where higher-level Java libraries step in. Spring AI, LangChain4j, and the MCP Java SDK are designed to simplify the path forward. These libraries create a layer of abstraction that shields your application code from constant changes. You can build once – and switch models or providers as needed, without rewriting core logic. And these libraries are alive. You can see them in action on GitHub – through open issues, pull requests, and rapid updates. If you see a missing feature, open an issue. If you want to contribute, send a pull request. These are responsive communities that welcome participation. “AI, for most people today, effectively means "sending human-language sentences to an HTTP endpoint." 80% of the noise right now is about the artful and scalable connection of these models with your data and business logic - data guarded by services whose business logic is statistically implemented in Spring Boot and Spring AI. We, as JVM developers, are uniquely well-positioned to expand the AI universe. Don’t delay - start (spring.io) today!” -- Josh Long, Spring Developer Advocate, Broadcom “By combining Quarkus’s speed, Langchain4j’s AI orchestration, and MCP’s unified tool access, Java developers are in a unique position to lead this transformation - building intelligent, resilient applications using the tools and approaches we know well. Working with AI is no longer a distant specialty - it is becoming a natural part of modern Java development” -- Daniel Oh, Senior Principal Developer Advocate, Red Hat AI Concepts for App Developers As you build your first intelligent Java applications, it helps to become familiar with key AI concepts such as foundation models, chat models, embedding models, prompts, inference endpoints, context windows, and vector search (see diagram below for a curated list). These concepts are not just theory - they directly shape how your applications interact with AI systems. To make it easier, we created a simple learning prompt that you can use with your favorite Chat Model like ChatGPT and Claude: // Prompt Template for Java Developers Learning AI Concepts // (Replace <TERM> with the topic you want to learn.) I am a Java enterprise app developer focused on building AI-powered Java applications. I do not fully understand what '<TERM>' means. Please explain it to me so simply that I can think about it like a Java library or service I would naturally use. Use examples from enterprise Java (like APIs, search, summarization, customer service bots) that I would typically build. If possible, give a mental model I can remember and relate to Java patterns. Also, suggest a small sample of how I would use '<TERM>' via an API in a Java app, if that helps understanding. You can replace <TERM> with any concept you want to learn, such as "Embedding Model" or "Inference Endpoint," and get a focused, practical explanation that fits how you already think as a Java developer. By practicing with this method, you can quickly build second-nature familiarity with the terms and ideas behind AI development - without needing a research background. With the strong foundation you already have in Java, you will be ready to confidently integrate, adapt, and innovate as AI tools continue to evolve. AI Is Now a Java Developer’s Game You do not need to train your own models to start building intelligent applications. You can do it today - using popular, production-ready foundation models - all within the Java ecosystem you already know and trust. These are still Java applications at their core. What sets you apart is not just your Java expertise - it is your deep understanding of how real business processes work and how to bring them to life in code. You know how to build, secure, scale, and operate reliable systems - and now, you can apply those same skills to deliver AI-powered solutions that run in the environments your teams already use, including Microsoft Azure. AI-Assisted App Development – A Powerful Companion to Building Intelligent Java Apps No discussion about the future of software development is complete without acknowledging the rise of AI-assisted development. This is a separate - but equally important - path alongside building intelligent applications. And it is transforming how developers write, upgrade, and manage code. At the center of this shift is tools like GitHub Copilot - a tool that is reshaping how developers approach their daily work. Developers using GitHub Copilot report coding up to 55 percent faster, freeing up time to focus on design decisions, solving business problems, and writing less boilerplate code. But the benefits go deeper than speed - 75 percent of developers say that Copilot makes their work more satisfying. Today, 46 percent of all code on GitHub is written with AI assistance, and over 20,000 organizations are already embracing these tools to improve development workflows and accelerate delivery. Built Into the Tools You Already Use GitHub Copilot works where Java developers already build – in IDEs like Visual Studio Code, IntelliJ, and Eclipse. It brings contextual, customizable assistance, powered by the latest models such as gpt-4o and Claude 3.5 Sonnet. Whether it is suggesting code snippets, auto-completing functions, or helping enforce best practices, GitHub Copilot enhances the quality of code and the productivity of developers – all while keeping them in control. Helping Modernize and Maintain Java Codebases One of the most exciting capabilities is GitHub Copilot’s growing role in modernizing Java applications. Late last year, the GitHub Copilot App Modernization – Java Upgrade feature entered private preview. This tool is designed to support large, mission-critical tasks like: Upgrading Java versions and frameworks Refactoring code Updating dependencies Aligning to cloud-first practices The process starts with your local Java project. Copilot provides an AI-generated upgrade assistant, and developers stay in the loop to approve changes step-by-step. The goal is to take the heavy lifting out of routine upgrades while ensuring everything remains safe and aligned with your architecture. Beyond App Code – Towards Cloud-Ready Modernization Technology providers like Microsoft are investing deeply in this space to bring additional capabilities into developer workflows – including: Secure identity handling through passwordless authentication Certificate management and secrets handling Integration with PaaS services for storage, data, caching, and observability All of this reduces the time it takes to bring legacy apps forward and prepare them for modern, scalable deployments – so teams can spend more time building intelligent features and less time managing technical debt. Two Paths – One Goal AI-assisted development, including upgrading and modernizing apps with AI, and building intelligent apps are not the same – but together, they form a powerful foundation. One helps you write and modernize code faster, the other helps you deliver smarter features inside your apps. For Java developers, this means there is support at every step – from idea to implementation to impact. Start Today and Move the Java Community Forward The message from 647 Java professionals is clear: Java developers are ready – and the tools they need to build intelligent applications are already here. If you are a Java developer and have not started your AI journey yet now is the right time. You do not need to become an AI expert. You do not need to change your language, tools, or working style. Modern Java frameworks and libraries like Spring AI, LangChain4j, and the MCP Java SDK are designed to work the way you already build – while making it easier to add intelligence, automation, and smart experiences to your applications. You can start with what you know – and grow into what is next: aka.ms/spring-ai and aka.ms/langchain4j. To Java Ecosystem Leaders We also want to speak directly to those shaping the Java ecosystem – community leaders, experienced developers, and technical influencers. Your role is more important than ever. We invite you to: Show what is possible – share real examples of how AI features can be integrated into Java applications with minimal friction. Promote best practices – use meetups, blogs, workshops, and developer forums to spread practical guidance and patterns that others can follow. Improve the experience – contribute documentation, examples, and even code to help close the gaps developers face when starting their AI journeys. Push frameworks forward – help identify and implement missing features that can simplify Java + AI integration and speed up real-world adoption. This is not just about tools – it is about people helping people move forward. Many of you already helped make this research possible – by spreading the word on LinkedIn, sharing the survey, and encouraging others to contribute. Your support made a difference. And now, these findings belong to the entire Java ecosystem – so we can act on them together. To the Java Developers Who Participated Thank you. Your input – your time, your thoughts, your challenges, your ideas – shaped this entire report. You told us what is working, what is missing, and what you need next. We hope this reflection of your voices is helpful – to you and to the broader Java community. The road ahead is exciting – and Java is ready to lead. AI Learning Resources for Java App Developers Azure AI Services documentation Azure AI Services quick starts – like Chat Completions and Use Your Data Build Enterprise Agents using Java and Spring OpenAI RAG with Java, LangChain4j and Quarkus Spring AI Learn how to build effective agents with Spring AI Spring AI reference documentation Prompt Engineering Techniques with Spring AI Spring AI GitHub repo Spring AI examples Spring AI updates The Seamless Path for Spring Developer to the World of Generative AI LangChain4j Supercharge your Java application with the power of LLMs LangChain4j GitHub repo LangChain4j Examples Quarkus LangChain4j Quarkus LangChain4j Workshop LangChain4j updates
Asir-Selvasingh
May 08, 2025 Place Apps on Azure Blog
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Week 4 . Microsoft Agents Hack Online Events and Readiness Resources
Readiness and skilling events for Week 4: Microsoft AI Agents Hack Register Now at https://aka.ms/agentshack 2025 is the year of AI agents! But what exactly is an agent, and how can you build one? Whether you're a seasoned developer or just starting out, this FREE three-week virtual hackathon is your chance to dive deep into AI agent development. 🔥 Learn from expert-led sessions streamed live on YouTube, covering top frameworks like Semantic Kernel, Autogen, the new Azure AI Agents SDK and the Microsoft 365 Agents SDK. Week 4: April 28th-30th Live and On Demand Topic Track Irresponsible AI Agents Java Securing AI agents on Azure Python The Art of AI Embodiment: Real-Time Interactive Experiences with Azure OpenAI GPT-4o and 3D Avatars Python Evaluating Agents Python 🌟 Join the Conversation on Azure AI Foundry Discussions! 🌟 Have ideas, questions, or insights about AI? Don't keep them to yourself! Share your thoughts, engage with experts, and connect with a community that’s shaping the future of artificial intelligence. 🧠✨ 👉 Click here to join the discussion!
Lee_Stott
Apr 28, 2025 Place Microsoft Developer Community Blog
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Keep Your Azure Functions Up to Date: Identify Apps Running on Retired Versions
Running Azure Functions on retired language versions can lead to security risks, performance issues, and potential service disruptions. While Azure Functions Team notifies users about upcoming retirements through the portal, emails, and warnings, identifying affected Function Apps across multiple subscriptions can be challenging. To simplify this, we’ve provided Azure CLI scripts to help you: ✅ Identify all Function Apps using a specific runtime version ✅ Find apps running on unsupported or soon-to-be-retired versions ✅ Take proactive steps to upgrade and maintain a secure, supported environment Read on for the full set of Azure CLI scripts and instructions on how to upgrade your apps today! Why Upgrading Your Azure Functions Matters Azure Functions supports six different programming languages, with new stack versions being introduced and older ones retired regularly. Staying on a supported language version is critical to ensure: Continued access to support and security updates Avoidance of performance degradation and unexpected failures Compliance with best practices for cloud reliability Failure to upgrade can lead to security vulnerabilities, performance issues, and unsupported workloads that may eventually break. Azure's language support policy follows a structured deprecation timeline, which you can review here. How Will You Know When a Version Is Nearing its End-of-Life? The Azure Functions team communicates retirements well in advance through multiple channels: Azure Portal notifications Emails to subscription owners Warnings in client tools and Azure Portal UI when an app is running on a version that is either retired, or about to be retired in the next 6 months Official Azure Functions Supported Languages document here To help you track these changes, we recommend reviewing the language version support timelines in the Azure Functions Supported Languages document. However, identifying all affected apps across multiple subscriptions can be challenging. To simplify this process, I've built some Azure CLI scripts below that can help you list all impacted Function Apps in your environment. Linux* Function Apps with their language stack versions: az functionapp list --query "[?siteConfig.linuxFxVersion!=null && siteConfig.linuxFxVersion!=''].{Name:name, ResourceGroup:resourceGroup, OS:'Linux', LinuxFxVersion:siteConfig.linuxFxVersion}" --output table *Running on Elastic Premium and App Service Plans Linux* Function Apps on a specific language stack version: Ex: Node.js 18 az functionapp list --query "[?siteConfig.linuxFxVersion=='Node|18'].{Name:name, ResourceGroup:resourceGroup, OS: 'Linux', LinuxFxVersion:siteConfig.linuxFxVersion}" --output table *Running on Elastic Premium and App Service Plans Windows Function Apps only: az functionapp list --query "[?!contains(kind, 'linux')].{Name:name, ResourceGroup:resourceGroup, OS:'Windows'}" --output table Windows Function Apps with their language stack versions: az functionapp list --query "[?!contains(kind, 'linux')].{name: name, resourceGroup: resourceGroup}" -o json | ConvertFrom-Json | ForEach-Object { $appSettings = az functionapp config appsettings list -n $_.name -g $_.resourceGroup --query "[?name=='FUNCTIONS_WORKER_RUNTIME' || name=='WEBSITE_NODE_DEFAULT_VERSION']" -o json | ConvertFrom-Json $siteConfig = az functionapp config show -n $_.name -g $_.resourceGroup --query "{powerShellVersion: powerShellVersion, netFrameworkVersion: netFrameworkVersion, javaVersion: javaVersion}" -o json | ConvertFrom-Json $runtime = ($appSettings | Where-Object { $_.name -eq 'FUNCTIONS_WORKER_RUNTIME' }).value $version = switch($runtime) { 'node' { ($appSettings | Where-Object { $_.name -eq 'WEBSITE_NODE_DEFAULT_VERSION' }).value } 'powershell' { $siteConfig.powerShellVersion } 'dotnet' { $siteConfig.netFrameworkVersion } 'java' { $siteConfig.javaVersion } default { 'Unknown' } } [PSCustomObject]@{ Name = $_.name ResourceGroup = $_.resourceGroup OS = 'Windows' Runtime = $runtime Version = $version } } | Format-Table -AutoSize Windows Function Apps running on Node.js runtime: az functionapp list --query "[?!contains(kind, 'linux')].{name: name, resourceGroup: resourceGroup}" -o json | ConvertFrom-Json | ForEach-Object {     $appSettings = az functionapp config appsettings list -n $_.name -g $_.resourceGroup --query "[?name=='FUNCTIONS_WORKER_RUNTIME' || name=='WEBSITE_NODE_DEFAULT_VERSION']" -o json | ConvertFrom-Json         $runtime = ($appSettings | Where-Object { $_.name -eq 'FUNCTIONS_WORKER_RUNTIME' }).value     if ($runtime -eq 'node') {         $version = ($appSettings | Where-Object { $_.name -eq 'WEBSITE_NODE_DEFAULT_VERSION' }).value                 [PSCustomObject]@{             Name = $_.name             ResourceGroup = $_.resourceGroup             OS = 'Windows'             Runtime = $runtime             Version = $version         }     } } | Format-Table -AutoSize Windows Function Apps running on a specific language version: Ex: Node.js 18 az functionapp list --query "[?!contains(kind, 'linux')].{name: name, resourceGroup: resourceGroup}" -o json | ConvertFrom-Json | ForEach-Object {     $appSettings = az functionapp config appsettings list -n $_.name -g $_.resourceGroup --query "[?name=='FUNCTIONS_WORKER_RUNTIME' || name=='WEBSITE_NODE_DEFAULT_VERSION']" -o json | ConvertFrom-Json         $runtime = ($appSettings | Where-Object { $_.name -eq 'FUNCTIONS_WORKER_RUNTIME' }).value     $nodeVersion = ($appSettings | Where-Object { $_.name -eq 'WEBSITE_NODE_DEFAULT_VERSION' }).value         if ($runtime -eq 'node' -and $nodeVersion -eq '~18') {         [PSCustomObject]@{             Name = $_.name             ResourceGroup = $_.resourceGroup             OS = 'Windows'             Runtime = $runtime             Version = $nodeVersion         }     } } | Format-Table -AutoSize All windows Apps running on unsupported language runtimes: (as of March 2025) az functionapp list --query "[?!contains(kind, 'linux')].{name: name, resourceGroup: resourceGroup}" -o json | ConvertFrom-Json | ForEach-Object {     $appSettings = az functionapp config appsettings list -n $_.name -g $_.resourceGroup --query "[?name=='FUNCTIONS_WORKER_RUNTIME' || name=='WEBSITE_NODE_DEFAULT_VERSION']" -o json | ConvertFrom-Json     $siteConfig = az functionapp config show -n $_.name -g $_.resourceGroup --query "{powerShellVersion: powerShellVersion, netFrameworkVersion: netFrameworkVersion}" -o json | ConvertFrom-Json         $runtime = ($appSettings | Where-Object { $_.name -eq 'FUNCTIONS_WORKER_RUNTIME' }).value     $version = switch($runtime) {         'node' {             $nodeVer = ($appSettings | Where-Object { $_.name -eq 'WEBSITE_NODE_DEFAULT_VERSION' }).value             if ([string]::IsNullOrEmpty($nodeVer)) { 'Unknown' } else { $nodeVer }         }         'powershell' { $siteConfig.powerShellVersion }         'dotnet' { $siteConfig.netFrameworkVersion }         default { 'Unknown' }     }         # Check if runtime version is unsupported     $isUnsupported = switch($runtime) {         'node' {             $ver = $version -replace '~',''             [double]$ver -le 16         }         'powershell' {             $ver = $version -replace '~',''             [double]$ver -le 7.2         }         'dotnet' {             $ver = $siteConfig.netFrameworkVersion             $ver -notlike 'v7*' -and $ver -notlike 'v8*'         }         default { $false }     }         if ($isUnsupported) {         [PSCustomObject]@{             Name = $_.name             ResourceGroup = $_.resourceGroup             OS = 'Windows'             Runtime = $runtime             Version = $version         }     } } | Format-Table -AutoSize Take Action Now By using these scripts, you can proactively identify and update Function Apps before they reach end-of-support status. Stay ahead of runtime retirements and ensure the reliability of your Function Apps. For step-by-step instructions to upgrade your Function Apps, check out the Azure Functions Language version upgrade guide. For more details on Azure Functions' language support lifecycle, visit the official documentation. Have any questions? Let us know in the comments below!
madhurabharadwaj
Apr 23, 2025 Place Apps on Azure Blog
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Capture a JVM heap dump for Java apps running on Azure Container Apps

croffz
Apr 10, 2025 Place Apps on Azure Blog
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Construa, inove e #Hacktogether!
🛠️ Construa, inove e #Hacktogether! 🛠️ 2025 é o ano dos agentes de IA! Mas o que exatamente é um agente? E como você pode criar um? Seja você um desenvolvedor experiente ou esteja apenas começando, este hackathon virtual GRATUITO de três semanas é sua chance de mergulhar no desenvolvimento de agentes de IA. 🔥 Aprenda com mais de 20 sessões lideradas por especialistas, transmitidas ao vivo no YouTube, abordando os principais frameworks, como Semantic Kernel, Autogen, o novo Azure AI Agents SDK e o Microsoft 365 Agents SDK. 💡 Coloque a mão na massa, explore sua criatividade e crie agentes de IA poderosos! Depois, envie seu projeto e concorra a prêmios incríveis! 💸 Datas importantes: Sessões com especialistas: 8 de abril de 2025 – 30 de abril de 2025 Prazo para envio do hack: 30 de abril de 2025, 23:59 PST Não perca essa oportunidade—junte-se a nós e comece a construir o futuro da IA! 🔥 Inscrição 🎟️ Garanta sua vaga agora! Preencha o formulário para confirmar sua participação no hackathon. Em seguida, confira a programação das transmissões ao vivo e inscreva-se nas sessões que mais te interessam. Após se inscrever, apresente-se e procure por colegas de equipe! Submissão de Projetos 🚀 Leia atentamente as regras oficiais e certifique-se de entender os requisitos. Quando seu projeto estiver pronto, siga o processo de submissão. 📝 Prêmios e Categorias 🏅 Os projetos serão avaliados por um painel de jurados, incluindo engenheiros da Microsoft, gerentes de produto e defensores de desenvolvedores. Os critérios de avaliação incluirão inovação, impacto, usabilidade técnica e alinhamento com a categoria correspondente do hackathon. Cada equipe vencedora nas categorias abaixo receberá um prêmio. 💸 Melhor Agente Geral - $20,000 Melhor Agente em Python - $5,000 Melhor Agente em C# - $5,000 Melhor Agente em Java - $5,000 Melhor Agente em JavaScript/TypeScript - $5,000 Melhor Agente Copilot (usando Microsoft Copilot Studio ou Microsoft 365 Agents SDK) - $5,000 Melhor Uso do Azure AI Agent Service - $5,000 Cada equipe pode ganhar em apenas uma categoria. Todos os participantes que submeterem um projeto receberão um badge digital. Transmissões 📅 Português Inscreva-se em todas as sessões em português Dia/Horário Tópico Recursos 4/8 12:00 PM PT Bem-vindo ao AI Agents Hackathon - 4/10 12:00 PM PT Crie um aplicativo com o Azure AI Agent Service - 4/17 06:00 AM PT Seu primeiro agente de IA em JavaScript com o Azure AI Agent Service - Outros Idiomas Teremos mais de 30 transmissões em inglês, além de transmissões em espanhol e chinês. Veja a página principal para mais detalhes. 🕒 Horário de Suporte Técnico Precisa de ajuda com seu projeto? Participe do Horário de Suporte Técnico no canal de Discord de IA e receba orientação de especialistas! 🚀 Aqui estão os horários de atendimento já agendados: Dia/Horário Tópico/Anfitriões Toda quinta-feira, 12:30 PM PT Python + IA (Inglês) Toda segunda-feira, 03:00 PM PT Python + IA (Espanhol) Recursos de Aprendizado 📚 Acesse os recursos aqui! Junte-se ao TheSource EHub para explorar os principais recursos, incluindo treinamentos, transmissões ao vivo, repositórios, guias técnicos, blogs, downloads, certificações e muito mais, atualizados mensalmente. A seção de Agentes de IA oferece recursos essenciais para criar agentes de IA, enquanto outras seções fornecem insights sobre IA, ferramentas de desenvolvimento e linguagens de programação. Você também pode postar perguntas em nosso fórum de discussões ou conversar com outros participantes no canal do Discord.
Lee_Stott
Apr 07, 2025 Place Microsoft Developer Community Blog
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Meet your hosts for JDConf 2025!
JDConf 2025 is right around the corner and is set to be a global gathering for Java developers passionate about Cloud, AI, and the future of Java. With 22+ sessions and 10+ hours of live content streaming from April 9 - 10, plus additional on-demand sessions, this year’s event dives into app modernization, intelligent apps, frameworks, and AI-powered development with tools like Copilot and more. We are excited to invite you to join us with our three distinguished hosts: Bruno Borges, Sandra Ahlgrimm, and Rory Preddy. Meet Bruno Borges - Your host for JDConf 2025 Americas Bruno Borges is a seasoned professional with a rich background in the tech industry. Currently serving as Principal Product Manager at Microsoft, he focuses on Java developers' experience on Azure and beyond. With over two decades of experience, Bruno has been instrumental in bridging the gap between Java communities and Microsoft technologies and services, ensuring seamless integration, optimal developer productivity, and application performance and efficiency. His dedication to enhancing developer experiences has made him a respected figure in the Java ecosystem. Meet Sandra Ahlgrimm - Your host for JDConf 2025 EMEA Sandra Ahlgrimm is a Senior Cloud Advocate at Microsoft, specializing in Java and AI. With over fifteen years of experience as a Java developer, she is highly engaged with various communities. She co-leads the local Java User Group (JUG) and Docker Meetup. Her role on the Program Advisory Board at Oracle's GraalVM community highlights her interest in cutting-edge Java and sustainability. Meet Rory Preddy - Your host for JDConf 2025 Asia-Pacific Rory Preddy is a Principal Cloud Advocate at Microsoft, where he helps professional cloud developers discover and successfully use Microsoft’s platforms. He is a seasoned speaker whose talks are both meaningful and humorous, and he speaks around the world empowering developers to achieve more. Rory has over 25 years of experience in software development, working with various technologies such as Java, AI and Azure. Prepare for learning and some entertainment! Under the guidance of these accomplished hosts, JDConf 2025 promises to be an enlightening experience. The conference will feature a technical keynote and 22 breakout sessions, showcasing 26 Java influencers and community leaders from organizations such as Microsoft, Broadcom, Oracle, Red Hat, and IBM. Live sessions are scheduled to accommodate attendees worldwide, fostering a global networking opportunity for Java professionals and enthusiasts. With Bruno, Sandra, and Rory as your hosts, you will be entertained throughout the live stream as they invite each speaker to share more insights and learning, with Q&A after each session that attendees find very helpful. Bruno's engaging style, Sandra's enthusiasm, and Rory's humor make learning fun and interactive. Their energy and passion as hosts ensure a delightful experience for everyone. Here are few memories from last year’s event. Join the JDConf experience and earn Microsoft Rewards! We are thrilled to offer Microsoft Rewards points to participants who register or attend the JDConf! ⭐ Registration Rewards: Participants who register for one of the JDConf 2025 – America, Europe or Asia – will receive 100 Microsoft Rewards points. ⭐ Attendance Rewards: The first 300 attendees to check-in live for one of the JDConf - America, Europe or Asia - will receive 5000 Microsoft Rewards points Don't miss this chance to learn from industry leaders, connect with peers, and explore the latest developments in Java, Cloud, and AI. Join us at JDConf 2025 and be part of the future of Java development. 👉 RSVP Now at JDConf.com Learn more about our hosts: You can hear from Bruno in his recent keynote at JavaOne 2025. You can Sandra's videos here Get to know Rory's work here
sonjagu
Apr 07, 2025 Place Apps on Azure Blog
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Code the Future with Java and AI – Join Me at JDConf 2025
JDConf 2025 is just around the corner, and whether you’re a Java developer, architect, team leader, or decision maker I hope you’ll join me as we explore how Java is evolving with the power of AI and how you can start building the next generation of intelligent applications today. Why JDConf 2025? With over 22 expert-led sessions and 10+ hours of live content, JDConf is packed with learning, hands-on demos, and real-world solutions. You’ll hear from Java leaders and engineers on everything from modern application design to bringing AI into your Java stack. It’s free, virtual and your chance to connect from wherever you are. (On-demand sessions will also be available globally from April 9–10, so you can tune in anytime from anywhere.) Bring AI into Java Apps At JDConf 2025, we are going beyond buzzwords. We’ll show you how to bring AI into real Java apps, using patterns and tools that work today. First, we’ll cover Retrieval-Augmented Generation (RAG), a design pattern where your app retrieves the right business data in real time, and combines it with AI models to generate smart, context-aware responses. Whether it is answering support queries, optimizing schedules, or generating insights, RAG enables your app to think in real time. Second, we’ll introduce AI agents -- software entities that do more than respond. They act. Think about automating production line scheduling at an auto manufacturer or rebooking delayed flights for passengers. These agents interact with APIs, reason over data, and make decisions, all without human intervention. Third, we’ll explore the complete AI application platform on Azure. It is built to work with the tools Java developers already know - from Spring Boot to Quarkus - and includes OpenAI and many other models, vector search with PostgreSQL, and libraries like Spring AI and LangChain4j. Here are just two example stacks: Spring Boot AI Stack: any app hosting services like Azure Container Apps or App Service + Spring AI + OpenAI + PostgreSQL for business data and vector data store. Quarkus AI Stack: any app hosting services like Azure Container Apps or App Service + LangChain4j + OpenAI + PostgreSQL for business data and vector data store. This is how you turn existing Java apps into intelligent, interactive systems, without reinventing everything. Whether you are an experienced developer or just starting out, JDConf offers valuable opportunities to explore the latest advancements in Java, cloud, and AI technologies; gain practical insights; and connect with Java experts from across the globe – including Java 25, Virtual Threads, Spring Boot, Jakarta EE 12, AI developer experiences, Spring AI, LangChain4j, combining data and AI, automated refactoring to Java app code modernization. We’ll also show you how GitHub Copilot helps you modernize faster. GitHub Copilot's new “upgrade assistant” can help refactor your project, suggest dependency upgrades, and guide you through framework transitions, freeing you up to focus on innovation. Get the Right Fit for Your Java App And what if your apps run on JBoss, WebLogic, or Tomcat? We will walk you through how to map those apps to the right Azure service: Monoliths (JAR, WAR, EAR) → Deploy to App Service Microservices or containers → Use Azure Container Apps or AKS WebLogic & WebSphere → Lift and shift to Azure Virtual Machines JBoss EAP containers → Run on Azure Red Hat OpenShift You’ll get clear guidance on where your apps fit and how to move forward, with no guesswork or dead ends. Let's Code the Future, Together I’ll be there, along with Josh Long from the Spring AI community and Lize Raes from the LangChain4j community, delivering a technical keynote packed with practical insights. If you haven’t started building intelligent Java apps, you can start with JDConf. If you’ve already started on the journey, tune in to learn how you can enrich your experiences with the latest in tech. So, mark your calendar. Spread the word. Bring your team. JDConf 2025 is your place to build what is next with Java and AI. 👉 Register now at jdconf.com. Check out the 20+ exclusive sessions brought to you by Java experts from across the globe in all major time zones.
AmandaKSilver
Apr 03, 2025 Place Apps on Azure Blog
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Understanding 'Always On' vs. Health Check in Azure App Service
The 'Always On' feature in Azure App Service helps keep your app warm by ensuring it remains running and responsive, even during periods of inactivity with no incoming traffic. As this feature pings to root URI after every 5 minutes. On Other hand Health-check feature helps pinging configured path every minute to monitor the application availability on each instance. What is 'Always On' in Azure App Service? The Always On feature ensures that the host process of your web app stays running continuously. This results in better responsiveness after idle periods since the app doesn’t need to cold boot when a request arrives. How to enable Always On: Navigate to the Azure Portal and open your Web App. Go to Configuration > General Settings. Toggle Always On to On. What is Health Check in Azure App Service? Health check increases your application's availability by rerouting requests away from instance where application is marked unhealthy and replacing instances if they remain unhealthy. How to enable Health-Check: Navigate to the Azure Portal and open your Web App. Under Monitoring, select Health check. Select Enable and provide a valid URL path for your application, such as /health or /api/health. Select Save. So, is it still necessary to enable the 'Always On' feature when Health Check is already pinging your application every minute? -> Yes, please find below explanation for the same. Test App scenario: Health Check enabled (pointing to /health_check path) and Always On disabled. Started the app and sent some user requests. Observations from the Test: After the application starts up, health check pings begin following the end user's request. Please find below table representing Health-check pings following user's request to root URI. Time Bucket URL Status Request Count 2025-03-20 07:00:00.0000000 / 200 6 2025-03-20 07:00:00.0000000 /health_check 200 30 2025-03-20 07:30:00.0000000 /health_check 200 30 Subsequent Health-check pings will continue, even in the absence of user requests. However, after restarting the app and in the absence of any user requests, we observed that Health Check requests were not initiated. This indicates that Health Check does not start automatically unless application is actively running and serving requests. Conclusion: Always On ensures that the app is proactively kept warm by sending root URI pings, even post-restart. The health-check feature is useful for monitoring application availability when the application is active. However, after a restart, if the application isn't active due to a lack of requests, Health-check pings won't initiate. Therefore, it is highly recommended to enable Always On, particularly for applications that need continuous availability and to avoid application process unload events. Recommendation: Enable Always On alongside Health Check to ensure optimal performance and reliability.
Samiksha
Apr 02, 2025 Place Apps on Azure Blog
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Observe Quarkus Apps with Azure Application Insights using OpenTelemetry
Overview This blog shows you how to observe Red Hat Quarkus applications with Azure Application Insights using OpenTelemetry. The application is a "to do list" with a JavaScript front end and a REST endpoint. Azure Database for PostgreSQL Flexible Server provides the persistence layer for the app. The app utilizes OpenTelemetry to instrument, generate, collect, and export telemetry data for observability. The blog guides you to test your app locally, deploy it to Azure Container Apps and observe its telemetry data with Azure Application Insights. Prerequisites An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Prepare a local machine with Unix-like operating system installed - for example, Ubuntu, macOS, or Windows Subsystem for Linux. Install a Java SE implementation version 17 - for example, Microsoft build of OpenJDK. Install Maven, version 3.9.8 or higher. Install Docker for your OS. Install the Azure CLI to run Azure CLI commands. Sign in to the Azure CLI by using the az login command. To finish the authentication process, follow the steps displayed in your terminal. For other sign-in options, see Sign into Azure with Azure CLI. When you're prompted, install the Azure CLI extension on first use. For more information about extensions, see Use and manage extensions with the Azure CLI. Run az version to find the version and dependent libraries that are installed. To upgrade to the latest version, run az upgrade. This blog requires at least version 2.65.0 of Azure CLI. Prepare the Quarkus app Run the following commands to get the sample app app-insights-quarkus from GitHub: git clone https://github.com/Azure-Samples/quarkus-azure cd quarkus-azure git checkout 2024-11-27 cd app-insights-quarkus Here's the file structure of the application, with important files and directories: ├── src/main/ │ ├── java/io/quarkus/sample/ │ │ └── TodoResource.java │ └── resources/ │ ├── META-INF/resources/ │ ├── application.properties ├── pom.xml The directory src/main/resources/META-INF/resources contains the front-end code for the application. It's a Vue.js front end where you can view, add, update, and delete todo items. The src/main/java/io/quarkus/sample/TodoResource.java file implements the REST resource for the application. It uses the Jakarta REST API to expose the REST endpoints for the front end. The invocation of the REST endpoints is automatically instrumented by OpenTelemetry tracing. Besides, each REST endpoint uses the org.jboss.logging.Logger to log messages, which are collected by OpenTelemetry logging. For example, the GET method for the /api endpoint that returns all todo items is shown in the following code snippet: package io.quarkus.sample; import jakarta.inject.Inject; import jakarta.transaction.Transactional; import jakarta.validation.Valid; import jakarta.ws.rs.*; import jakarta.ws.rs.core.Response; import jakarta.ws.rs.core.Response.Status; import java.util.List; import org.jboss.logging.Logger; @Path("/api") public class TodoResource { private static final Logger LOG = Logger.getLogger(TodoResource.class); @Inject TodoRepository todoRepository; @GET public List<Todo> getAll() { List<Todo> todos = todoRepository.findAll(); LOG.info("Found " + todos.size() + " todos"); return todos; } } The pom.xml file contains the project configuration, including the dependencies for the Quarkus application. The application uses the following extensions to support OpenTelemetry: <dependency> <groupId>io.quarkus</groupId> <artifactId>quarkus-opentelemetry</artifactId> </dependency> <dependency> <groupId>io.opentelemetry.instrumentation</groupId> <artifactId>opentelemetry-jdbc</artifactId> </dependency> <dependency> <groupId>io.opentelemetry</groupId> <artifactId>opentelemetry-exporter-logging</artifactId> </dependency> The src/main/resources/application.properties file contains the configuration for the Quarkus application. The configuration includes database connection properties for production, such as the JDBC URL and username. The configuration also includes the OpenTelemetry properties, such as enabling OpenTelemetry including logs and JDBC instrumentation at build time, using logging as exporter in development mode, and specifying the endpoint for the OpenTelemetry Protocol (OTLP) exporter in production mode. The following example shows the configuration for the OpenTelemetry: quarkus.otel.enabled=true quarkus.otel.logs.enabled=true quarkus.datasource.jdbc.telemetry=true %dev.quarkus.otel.logs.exporter=logging %dev.quarkus.otel.traces.exporter=logging %prod.quarkus.otel.exporter.otlp.endpoint=${OTEL_EXPORTER_OTLP_ENDPOINT} Run the Quarkus app locally Quarkus supports the automatic provisioning of unconfigured services in development mode. For more information, see Dev Services Overview in the Quarkus documentation. Now, run the following command to enter Quarkus dev mode, which automatically provisions a PostgreSQL database as a Docker container for the app: mvn clean package quarkus:dev The output should look like the following example: 2025-03-17 11:14:32,880 INFO [io.qua.dat.dep.dev.DevServicesDatasourceProcessor] (build-26) Dev Services for default datasource (postgresql) started - container ID is 56acc7e1cb46 2025-03-17 11:14:32,884 INFO [io.qua.hib.orm.dep.dev.HibernateOrmDevServicesProcessor] (build-4) Setting quarkus.hibernate-orm.database.generation=drop-and-create to initialize Dev Services managed database __ ____ __ _____ ___ __ ____ ______ --/ __ \/ / / / _ | / _ \/ //_/ / / / __/ -/ /_/ / /_/ / __ |/ , _/ ,< / /_/ /\ \ --\___\_\____/_/ |_/_/|_/_/|_|\____/___/ 2025-03-17 11:14:36,202 INFO [io.ope.exp.log.LoggingSpanExporter] (JPA Startup Thread) 'quarkus' : 80437b598962f82bffd0735bbf00e9f1 aa86f0553056a8c9 CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=set client_min_messages = WARNING, db.system=postgresql}, capacity=128, totalAddedValues=6} 1970-01-01T00:00:00Z INFO ''quarkus' : 80437b598962f82bffd0735bbf00e9f1 aa86f0553056a8c9 CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=set client_min_messages = WARNING, db.system=postgresql}, capacity=128, totalAddedValues=6}' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.lineno=-1, log.logger.namespace="org.jboss.logmanager.Logger", thread.id=122, thread.name="JPA Startup Thread"} 2025-03-17 11:14:36,236 INFO [io.ope.exp.log.LoggingSpanExporter] (JPA Startup Thread) 'DROP table quarkus' : 6b732661c29a9f0966403d49db9e4cff d86f29284f0d8eac CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.operation=DROP table, db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=drop table if exists Todo cascade, db.system=postgresql}, capacity=128, totalAddedValues=7} 1970-01-01T00:00:00Z INFO ''DROP table quarkus' : 6b732661c29a9f0966403d49db9e4cff d86f29284f0d8eac CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.operation=DROP table, db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=drop table if exists Todo cascade, db.system=postgresql}, capacity=128, totalAddedValues=7}' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.lineno=-1, log.logger.namespace="org.jboss.logmanager.Logger", thread.id=122, thread.name="JPA Startup Thread"} 2025-03-17 11:14:36,259 INFO [io.ope.exp.log.LoggingSpanExporter] (JPA Startup Thread) 'CREATE table quarkus' : 54df3edf9f523a71bc85d0106a57016c bb43aa63ec3526ed CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.operation=CREATE table, db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=create table Todo (completed boolean not null, ordering integer, id bigint generated by default as identity, title varchar(?) unique, url varchar(?), primary key (id)), db.system=postgresql}, capacity=128, totalAddedValues=7} 1970-01-01T00:00:00Z INFO ''CREATE table quarkus' : 54df3edf9f523a71bc85d0106a57016c bb43aa63ec3526ed CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.operation=CREATE table, db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=create table Todo (completed boolean not null, ordering integer, id bigint generated by default as identity, title varchar(?) unique, url varchar(?), primary key (id)), db.system=postgresql}, capacity=128, totalAddedValues=7}' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.lineno=-1, log.logger.namespace="org.jboss.logmanager.Logger", thread.id=122, thread.name="JPA Startup Thread"} 2025-03-17 11:14:36,438 INFO [io.quarkus] (Quarkus Main Thread) quarkus-todo-demo-app-insights 1.0.0-SNAPSHOT on JVM (powered by Quarkus 3.16.3) started in 7.409s. Listening on: http://localhost:8080 1970-01-01T00:00:00Z INFO 'quarkus-todo-demo-app-insights 1.0.0-SNAPSHOT on JVM (powered by Quarkus 3.16.3) started in 7.409s. Listening on: http://localhost:8080' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.function="printStartupTime", code.lineno=109, code.namespace="io.quarkus.bootstrap.runner.Timing", log.logger.namespace="org.jboss.logging.Logger", thread.id=112, thread.name="Quarkus Main Thread"} 2025-03-17 11:14:36,441 INFO [io.quarkus] (Quarkus Main Thread) Profile dev activated. Live Coding activated. 1970-01-01T00:00:00Z INFO 'Profile dev activated. Live Coding activated.' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.function="printStartupTime", code.lineno=113, code.namespace="io.quarkus.bootstrap.runner.Timing", log.logger.namespace="org.jboss.logging.Logger", thread.id=112, thread.name="Quarkus Main Thread"} 2025-03-17 11:14:36,443 INFO [io.quarkus] (Quarkus Main Thread) Installed features: [agroal, cdi, hibernate-orm, hibernate-validator, jdbc-postgresql, narayana-jta, opentelemetry, rest, rest-jackson, smallrye-context-propagation, vertx] 1970-01-01T00:00:00Z INFO 'Installed features: [agroal, cdi, hibernate-orm, hibernate-validator, jdbc-postgresql, narayana-jta, opentelemetry, rest, rest-jackson, smallrye-context-propagation, vertx]' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.function="printStartupTime", code.lineno=115, code.namespace="io.quarkus.bootstrap.runner.Timing", log.logger.namespace="org.jboss.logging.Logger", thread.id=112, thread.name="Quarkus Main Thread"} -- Tests paused Press [e] to edit command line args (currently ''), [r] to resume testing, [o] Toggle test output, [:] for the terminal, [h] for more options> The output shows that the Quarkus app is running in development mode. The app is listening on http://localhost:8080. The PostgreSQL database is automatically provisioned as a Docker container for the app. The OpenTelemetry instrumentation for Quarkus and JDBC is enabled, and the telemetry data is exported to the console. Access the application GUI at http://localhost:8080. You should see a similar Todo app with an empty todo list, as shown in the following screenshot: Switch back to the terminal where Quarkus dev mode is running, you should see more OpenTelemetry data exported to the console. For example, the following output shows the OpenTelemetry logging and tracing data for the GET method for the /api endpoint: 2025-03-17 11:15:13,785 INFO [io.qua.sam.TodoResource] (executor-thread-1) Found 0 todos 1970-01-01T00:00:00Z INFO 'Found 0 todos' : 7cf260232ff81caf90abc354357c16ab c48a4a02e74e1901 [scopeInfo: io.quarkus.opentelemetry:] {code.function="getAll", code.lineno=25, code.namespace="io.quarkus.sample.TodoResource", log.logger.namespace="org.jboss.logging.Logger", parentId="c48a4a02e74e1901", thread.id=116, thread.name="executor-thread-1"} 2025-03-17 11:15:13,802 INFO [io.ope.exp.log.LoggingSpanExporter] (vert.x-eventloop-thread-1) 'GET /api' : 7cf260232ff81caf90abc354357c16ab c48a4a02e74e1901 SERVER [tracer: io.quarkus.opentelemetry:] AttributesMap{data={http.response.status_code=200, url.scheme=http, server.port=8080, server.address=localhost, client.address=0:0:0:0:0:0:0:1, user_agent.original=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36 Edg/134.0.0.0, url.path=/api/, code.namespace=io.quarkus.sample.TodoResource, http.request.method=GET, code.function=getAll, http.response.body.size=2, http.route=/api}, capacity=128, totalAddedValues=12} 1970-01-01T00:00:00Z INFO ''GET /api' : 7cf260232ff81caf90abc354357c16ab c48a4a02e74e1901 SERVER [tracer: io.quarkus.opentelemetry:] AttributesMap{data={http.response.status_code=200, url.scheme=http, server.port=8080, server.address=localhost, client.address=0:0:0:0:0:0:0:1, user_agent.original=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36 Edg/134.0.0.0, url.path=/api/, code.namespace=io.quarkus.sample.TodoResource, http.request.method=GET, code.function=getAll, http.response.body.size=2, http.route=/api}, capacity=128, totalAddedValues=12}' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.function="export", code.lineno=65, code.namespace="io.opentelemetry.exporter.logging.LoggingSpanExporter", log.logger.namespace="org.jboss.logmanager.Logger", thread.id=126, thread.name="vert.x-eventloop-thread-1"} Then return to the web browser and interact with the Todo app, try to add a new todo item by typing in the text box and pressing ENTER, selecting the checkbox to mark the todo item as completed, or selecting Clear completed to remove all completed todo items. You can also delete a todo item by selecting the x icon when you hover over it. The app should work as expected. Finally, switch back to the terminal and press q to exit Quarkus dev mode. Create the Azure resources The steps in this section show you how to create the following Azure resources to run the Quarkus sample app on Azure: Azure Database for PostgreSQL Flexible Server Azure Container Registry Azure Container Apps environment Azure Application Insights First, define the following variables in your bash shell by replacing the placeholders with your own values. They will be used throughout the example: UNIQUE_VALUE=<your unique value> LOCATION=eastus2 RESOURCE_GROUP_NAME=${UNIQUE_VALUE}rg DB_SERVER_NAME=${UNIQUE_VALUE}db DB_NAME=demodb REGISTRY_NAME=${UNIQUE_VALUE}reg ACA_ENV=${UNIQUE_VALUE}env APP_INSIGHTS=${UNIQUE_VALUE}appinsights ACA_NAME=${UNIQUE_VALUE}aca Next, create the resource group to host Azure resources: az group create \ --name $RESOURCE_GROUP_NAME \ --location $LOCATION Then, create the Azure resources in the resource group by following the steps below. Create an Azure Database for PostgreSQL flexible server instance: az postgres flexible-server create \ --name $DB_SERVER_NAME \ --resource-group $RESOURCE_GROUP_NAME \ --database-name $DB_NAME \ --public-access None \ --sku-name Standard_B1ms \ --tier Burstable \ --active-directory-auth Enabled Create the Azure Container Registry and get the login server: az acr create \ --resource-group $RESOURCE_GROUP_NAME \ --location ${LOCATION} \ --name $REGISTRY_NAME \ --sku Basic LOGIN_SERVER=$(az acr show \ --name $REGISTRY_NAME \ --query 'loginServer' \ --output tsv) Create the Azure Container Apps environment: az containerapp env create \ --resource-group $RESOURCE_GROUP_NAME \ --location $LOCATION \ --name $ACA_ENV Create an Azure Application Insights instance: logAnalyticsWorkspace=$(az monitor log-analytics workspace list \ -g $RESOURCE_GROUP_NAME \ --query "[0].name" -o tsv | tr -d '\r') az monitor app-insights component create \ --resource-group $RESOURCE_GROUP_NAME \ --location $LOCATION \ --app $APP_INSIGHTS \ --workspace $logAnalyticsWorkspace Use the created Application Insights instance as the destination service to enable the managed OpenTelemetry agent for the Azure Container Apps environment: appInsightsConn=$(az monitor app-insights component show \ --app $APP_INSIGHTS \ -g $RESOURCE_GROUP_NAME \ --query 'connectionString' -o tsv | tr -d '\r') az containerapp env telemetry app-insights set \ --name $ACA_ENV \ --resource-group $RESOURCE_GROUP_NAME \ --connection-string $appInsightsConn \ --enable-open-telemetry-logs true \ --enable-open-telemetry-traces true When you deploy the Quarkus app to the Azure Container Apps environment later in this blog, the OpenTelemetry data is automatically collected by the managed OpenTelemetry agent and exported to the Application Insights instance. Deploy the Quarkus app to Azure Container Apps You have set up all the necessary Azure resources to run the Quarkus app on Azure Container Apps. In this section, you containerize the Quarkus app and deploy it to Azure Container Apps. First, use the following command to build the application. This command uses the Jib extension to build the container image. Quarkus instrumentation works both in JVM and native modes. In this blog, you build the container image for JVM mode, to work with Microsoft Entra ID authentication for Azure Database for PostgreSQL flexible server. TODO_QUARKUS_IMAGE_NAME=todo-quarkus-app-insights TODO_QUARKUS_IMAGE_TAG=${LOGIN_SERVER}/${TODO_QUARKUS_IMAGE_NAME}:1.0 mvn clean package -Dquarkus.container-image.build=true -Dquarkus.container-image.image=${TODO_QUARKUS_IMAGE_TAG} Next, sign in to the Azure Container Registry and push the Docker image to the registry: az acr login --name $REGISTRY_NAME docker push $TODO_QUARKUS_IMAGE_TAG Then, use the following command to create a Container Apps instance to run the app after pulling the image from the Container Registry: az containerapp create \ --resource-group $RESOURCE_GROUP_NAME \ --name $ACA_NAME \ --image $TODO_QUARKUS_IMAGE_TAG \ --environment $ACA_ENV \ --registry-server $LOGIN_SERVER \ --registry-identity system \ --target-port 8080 \ --ingress 'external' \ --min-replicas 1 Finally, connect the Azure Database for PostgreSQL Flexible Server instance to the container app using Service Connector: # Install the Service Connector passwordless extension az extension add --name serviceconnector-passwordless --upgrade --allow-preview true az containerapp connection create postgres-flexible \ --resource-group $RESOURCE_GROUP_NAME \ --name $ACA_NAME \ --target-resource-group $RESOURCE_GROUP_NAME \ --server $DB_SERVER_NAME \ --database $DB_NAME \ --system-identity \ --container $ACA_NAME \ --yes Wait for a while until the application is deployed, started and running. Then get the application URL and open it in a browser: QUARKUS_URL=https://$(az containerapp show \ --resource-group $RESOURCE_GROUP_NAME \ --name $ACA_NAME \ --query properties.configuration.ingress.fqdn -o tsv) echo $QUARKUS_URL You should see the similar Todo app when you ran the app locally before. Interact with the app by adding, completing and removing todo items, which generates telemetry data and sends it to Azure Application Insights. Observe the Quarkus app with Azure Application Insights Open the Azure Portal and navigate to the Azure Monitor Application Insights resource you created earlier. You can monitor the application with different views backed by the telemetry data sent from the application. For example: Investigate > Application map: Shows the application components and their dependencies. Investigate > Failures: Shows the failures and exceptions in the application. Investigate > Performance: Shows the performance of the application. Monitoring > Logs: Shows the logs and traces of the application. You may notice that metrics are not observed in the Application Insights in this blog, that's because the Application Insights endpoint used in the managed OpenTelemetry agent doesn't accept metrics yet, which is listed as a known limitation. This is also the reason why Quarkus metrics is not enabled in the configuration file with quarkus.otel.metrics.enabled=true above. Alternatively, you can consider using Quarkus Opentelemetry Exporter for Microsoft Azure in your Quarkus apps to export the telemetry data directly to Azure Application Insights. Clean up resources To avoid Azure charges, you should clean up unneeded resources. When the resources are no longer needed, use the az group delete command to remove the resource group and all Azure resources within it: az group delete \ --name $RESOURCE_GROUP_NAME \ --yes \ --no-wait Next steps In this blog, you observe the Quarkus app with Azure Application Insights using OpenTelemetry. To learn more, explore the following resources: OpenTelemetry on Azure Collect and read OpenTelemetry data in Azure Container Apps (preview) Application Insights overview Using OpenTelemetry Deploy a Java application with Quarkus on an Azure Container Apps Secure Quarkus applications with Microsoft Entra ID using OpenID Connect Deploy a Java application with Quarkus on an Azure Kubernetes Service cluster Deploy serverless Java apps with Quarkus on Azure Functions Jakarta EE on Azure
Jianguo_Ma
Mar 27, 2025 Place Apps on Azure Blog
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Getting Started with Java WebJobs on Azure App Service
Getting Started with Linux WebJobs on App Service - Java WebJobs Intro WebJobs is a feature of Azure App Service that enables you to run a program or script in the same instance as a web app. All app service plans support WebJobs. There's no extra cost to use WebJobs. This sample uses a Triggered (scheduled) WebJob to output the system time once every 15 minutes. Create Web App Before creating our WebJobs, we need to create an App Service webapp. If you already have an App Service Web App, skip to the next step Otherwise, in the portal, select App Services > Create > Web App. After following the create instructions and selecting one of the Java runtime stacks, create your App Service Web App. The stack must be Java, since we plan on writing our WebJob using Java and a bash startup script. Next, we’ll add a basic WebJob to our app. Create WebJob Before we do anything else, let’s write our WebJob. This will execute every time our WebJob is triggered. WebJobs on App Service can be run on a Triggered (Scheduled) basis or Continuously. This example uses a Triggered WebJob. For this example, we’ll need to compress two files into a zip archive that we’ll upload to our App Service Web App. We’ll need a startup script and an executable jar. The full code for this sample is available at: https://github.com/Azure-Samples/App-Service-Java-WebJobs-QuickStart Digging into the code, our Java project located at project/src/main/java/webjob/HelloWorld.java is relatively simple. It just outputs a message & the current time to the console. import java.time.LocalDateTime; public class HelloWorld { public static void main(String[] args) { System.out.println("------------------------------------------------------------"); System.out.println("Hello World from WebJob: " + LocalDateTime.now()); System.out.println("------------------------------------------------------------"); } } The run.sh script located at the root of our git repo, just runs a jar with the name that we’ve set in our maven configuration. This script will run when our WebJob is triggered, and it is the job of this script to kick off our Java executable (jar). java -jar webjob-artifact-1.0.0.jar Now we need to compile our Java project to produce the executable jar. There are multiple ways to do this, but for this example, we’ll use Maven. Run the following commands from the project/ directory: mvn install mvn package After successfully building our app with mvn package, our jar file will be located at project/target/webjob-artifact-1.0.0.jar. Now we have everything we need to assemble our zip file. Again, there are multiple ways to do this, but for this demo we’ll use the zip CLI utility to create our zip file called webjob.zip. First move your jar file to the root of the git repo with project/target/webjob-artifact-1.0.0.jar. Then run the zip command to package our application as a zip file. zip webjob.zip run.sh webjob-artifact-1.0.0.jar Now it’s time to create our WebJob and upload our zip file. Create WebJob in Portal Start by entering your Web App overview page. Then, under Settings select WebJobs. Here we can create and manage our App Service WebJobs for this Web App. Click Add to create a new WebJob. Now we can name our WebJob, upload our zip from the previous step, and choose our execution type. Under Type, select Triggered. Under CRON Expression, enter the following to trigger our WebJob once every 15 minutes. 0 */15 * * * * Note: These are NCRONTAB expressions, not standard Linux CRONTAB expressions. An important distinction. Now click Create WebJob to finish making our new WebJob. Let’s test it out now. Run Manually or Scheduled To manually test our WebJob, we can click the play button under Run. A status of Completed means that WebJob is finished. Confirm Results in Logs We can check the logs to confirm that the console print statements from our Java file were output to the console. While this is a basic example, WebJobs are a powerful and easy to use feature that have incredible utility for running scheduled (or continuous) actions in conjunction with your App Service Web Apps at no additional cost. Learn more about WebJobs on App Service
denverbrittain
Mar 26, 2025 Place Apps on Azure Blog
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State of Coding the Future with Java and AI – May 2025","conversation":{"__ref":"Conversation:conversation:4411210"},"id":"message:4411210","revisionNum":11,"uid":4411210,"depth":0,"board":{"__ref":"Blog:board:AppsonAzureBlog"},"author":{"__ref":"User:user:888651"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":560},"postTime":"2025-05-07T10:07:39.828-07:00","lastPublishTime":"2025-05-07T17:03:56.055-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Software development is changing fast, and Java developers are right in the middle of it - especially when it comes to using Artificial Intelligence (AI) in their apps. This report brings together feedback from 647 Java professionals to show where things stand and what is possible as Java and AI come together. \n One of the biggest takeaways is this: Java developers do not need to be experts in AI, machine learning, or Python. With tools like the Model Context Protocol (MCP) Java SDK, Spring AI, and LangChain4j, they can start adding smart features to their apps using the skills they already have. Whether it is making recommendations, spotting fraud, supporting natural language search or a world of possibilities, AI can be part of everyday Java development. \n The report walks through real-world approaches that Java developers are already using - things like Retrieval-Augmented Generation (RAG), vector databases, embeddings, and AI agents. These are not just buzzwords - they help teams build apps that work well at scale, stay secure, and are easier to manage over time. For teams figuring out where to start, the report includes guidance and simple workflows to make things easier. \n In short, Java is well-positioned to keep leading in enterprise software. This is an invitation to Java architects, tech leads, decision-makers, and developers to explore what is next and build smarter, more connected apps with AI. \n Introduction \n The world of software development is changing fast. Over the past two years, we have seen a major shift – not just in tools and frameworks, but in how developers think about building software. Artificial Intelligence is now part of the everyday conversation – helping developers rethink what their applications can do and how quickly they can build them. \n In the middle of all this change, it helps to pause and look at where we are. Java developers are especially exploring how to add intelligence to their existing applications or build new ones that can learn, adapt, and scale. But with so many innovative ideas and so much information out there, the real question is – what are developers doing? \n To answer that, we reached out directly to Java professionals across the world. We wanted to understand their thinking, what they are trying, and what they need to move forward with confidence. \n Our invitation was simple - \n \"Calling all Java pros – share your insights to help simplify AI-powered apps 👉 aka.ms/java-ai.\" \n \n The response was strong. A total of 647 Java professionals took part: \n \n 587 have experience with AI - representing a wide range of perspectives and levels of AI knowledge. \n 60 have not yet explored AI - but are curious and eager to learn what is possible. \n \n Among all respondents: \n \n Two-thirds (67%) had 4 to 10 years of Java experience. \n One-third (33%) had more than 10 years of experience. \n \n \n This report highlights what we learned – and what it means for the future of Java and AI. \n The Scenario We Asked Java Pros to Imagine \n “Picture yourself adding an AI-driven feature to an existing Java-based app or building a brand-new intelligent application. This feature might improve customer experience – such as personalized recommendations – optimize business processes – like fraud detection – or enhance product searches using natural language. Your goal is to seamlessly integrate this feature, ensuring it is easy to develop, scalable, and maintainable.” \n An impressive 97 percent of respondents said they would choose Java for building this type of intelligent application. \n \n A Common Misconception \n 90 percent of respondents believed that building intelligent Java apps would require deep experience with AI, Machine Learning, or Python. \n \n Developers Can Start to Deliver Production-Grade Intelligent Java Apps without AI, ML, or Python Skills \n Myth of AI/ML and Java \n \n Java developers already have what they need – today – to build intelligent applications using modern Java-first frameworks such as Model Context Protocol (MCP) Java SDK, Spring AI, or LangChain4j. \n No prior experience in Python or Machine Learning is required for Java developers to begin adding intelligent features to their apps. \n Connecting a Java application to backend AI systems – including Large Language Models and Vector Databases – is conceptually like working with REST APIs or traditional SQL and NoSQL databases. \n Modern libraries like MCP Java SDK, Spring AI, and LangChain4j make it easier for developers to build and enhance AI-powered Java applications. These frameworks offer support for:\n \n Retrieval-Augmented Generation (RAG) \n Conversational memory \n Conversation logging \n Integration with vector stores \n Secure, observable, and safe-by-default interactions \n Streamed outputs and structured reasoning \n \n \n Java continues to play a leading role in enterprise software. This gives Java developers a natural advantage – and a unique opportunity – to lead the way in delivering intelligent features inside core business applications. \n It is also important to note that tasks requiring deep AI and Data Science knowledge are best left to specialists. Java developers can focus on app logic, integration, and delivering business value without needing to become AI experts themselves. \n \n In-Process vs HTTP-Based - A Common Misstep in AI Application Design \n AI-powered applications can be built in different ways - and one of the patterns is to embed the model directly within the same app that handles business logic and exposes the API. This is known as the in-process approach. In this setup, the model is loaded at runtime, using local weights and often relying on a GPU for inference. It is a convenient option - especially when working with models you have created from scratch or downloaded for use in your own application. \n The Shift to Model-as-a-Service - A Simple History \n Before foundation models were made available as services, most AI models were custom-built for specific use cases – like classifying documents, detecting anomalies, or predicting demand. These models were typically developed in Python using frameworks such as TensorFlow or PyTorch. Because development and usage happened in the same environment, it was natural to load the model directly into the application’s memory using local weights, and to rely on a local GPU for inference. \n This model-in-app pattern made sense when the app and the model were designed together. Many popular Python-based libraries, including PyTorch, TensorFlow, and Hugging Face Transformers, encourage this in-process setup by default. As a result, the model often becomes a local function call - tightly coupled to the application’s logic and runtime. \n However, that convenience introduces scaling and maintenance challenges. Every application instance must run on a machine with GPU access. You must allocate GPU resources per app, even when the app is idle. As demand grows, this leads to higher infrastructure costs, lower resource efficiency, and architectural rigidity. If you scale the app, you are often forced to scale GPU capacity along with it - even if only the app's business logic needs scaling. \n \n The Rise of HTTP-Based Integration with Model-as-a-Service \n The introduction of foundation models like GPT-4, available through services such as OpenAI and Azure OpenAI, brought a shift in how models are used in applications. These models are designed to handle a wide range of tasks and are offered as cloud-hosted APIs - a model-as-a-service approach. Instead of embedding the model into each application, you send a request over HTTP and receive a response. \n This change enables a new design pattern: the application and the model are treated as separate services. The app handles business logic, while the model service handles inference. This pattern brings clear advantages - modularity, cleaner separation of concerns, centralized control over GPU infrastructure, and the ability to reuse models across many applications. \n The diagram above illustrates this shift. On the left, the in-process setup binds the model tightly to the application, requiring direct GPU access and local weights. On the right, the HTTP-based setup enables applications written in any language stack - such as Java, Python, JavaScript, .NET, or Go - to interact with a shared model endpoint over HTTP. \n This separation makes it easier to update models, manage infrastructure, including GPU infrastructure, and scale intelligently. It also reflects how most modern AI platforms are now built. \n HTTP-based integration is scalable, cost-effective, and designed for modern application environments. It reduces operational complexity and gives developers the flexibility to choose the architecture that fits their needs - without being locked into one stack, tools, models, or setup. \n Myth about Python \n As we listened to Java developers across the community, a familiar pattern emerged. Many shared their experiences - and sometimes frustrations - when working with AI technologies in Java. These were not just passing remarks. They reflected real challenges - especially when it came to building or training machine learning models, where Python has long been the preferred environment. \n Here is a glimpse into what we heard: \n \n “Java has fewer AI-specific libraries compared to Python. Libraries like TensorFlow and PyTorch are more mature and feature-rich in Python, making it easier to implement complex AI models.” \n “Working on AI-powered applications with Java presents challenges, especially when building or training models. The ecosystem is not as deep as Python’s, which has tools like Scikit-learn and notebooks like Jupyter.” \n “Even though Java can be used for AI, the support for GPU acceleration is not as seamless as it is in Python. You need extra setup and tuning.” \n “There are fewer Java developers with strong AI backgrounds. It is harder to find or grow a team when Python seems to be the go-to language for most ML engineers.” \n \n These are all honest, valid observations. If the job-to-be-done is building foundation models, training models from scratch, or fine-tuning existing models, then Python is a natural choice. It offers the right tools, libraries, and ecosystem support to do that job well. \n But here is what really matters today: \n Most AI application developers - including those working in Java - are not training or fine-tuning models. They are not building models from the ground up or optimizing low-level GPU workloads. Instead, they are focused on a different job: \n \n Connecting to existing foundation models. \n Calling AI services over REST APIs. \n Using AI libraries like Spring AI and LangChain4j to orchestrate intelligent workflows. \n Querying vector databases. \n Embedding AI capabilities into production-grade enterprise applications. \n \n \n This distinction is clearly reflected in the diagram above. \n On the right side, you see “Model Training and Development” as a separate, specialized job. It is critical work - best handled by teams with deep expertise in data science and model engineering. On the left side, you see the application architecture most Java developers work with every day: REST APIs, business logic, database integration, and calls to external AI models and vector stores using AI libraries. \n This is where Java fits. \n Java developers are not building models - they are building apps on top of foundation models. \n And with tools like MCP Java SDK, Spring AI, and LangChain4j, they are not playing catch-up - they are building what matters, integrate AI into existing apps and capabilities. \n You do not need to train models. \n You just need to wire up the right parts, connect to the services that make AI possible, and deliver intelligent functionality where it belongs - inside the applications your organization already depends on. \n “You can be an AI application developer - in less than 2 minutes. \n Minute 1: Sign up for access to an LLM - Azure OpenAI, OpenAI, whatever. Get yourself an API key. \n Minute 2: Head to https://start.spring.io, specify `OpenAI` (or whatever), hit `Generate`, and then open your new Spring Boot + Spring AI project in your IDE. In your `application.properties`, you’ll need to specify your API key. Then, inject the auto-configured `ChatClient` somewhere in your code. Use the `ChatClient` to make a request to your model. \n Congratulations - you’re an AI application developer!” -- Josh Long, Spring Developer Advocate, Broadcom \n \n \"Is Java still relevant in this new era of AI?\" \n \"How do I, with my years of Java expertise, even begin to work with these Large Language Models today?\" \n These are questions I have heard time and again at community events and industry conferences. \n Today, Java developers are at a pivotal moment. Our existing skills are not just still relevant - they are the foundation for building the next generation of AI-powered applications. Thanks to frameworks like Quarkus, Langchain4j, and MCP integration, we can bridge the world of traditional enterprise development with the fast-growing world of AI - all without giving up the strengths and familiarity of Java.” – Daniel Oh, Senior Principal Developer Advocate, Red Hat \n \n The future of AI in software development will be defined by who integrates AI well into applications - both existing and new apps. Most AI-related development will be connecting models to solve real problems inside real applications. And in that space, Java is already strong. From financial services to healthcare, logistics to manufacturing - Java powers the business logic and workflows that now need to become intelligent. \n \n This is where Java developers shine. And with the right tools, they are more than ready to lead. \n Crucial Elements for Java AI Applications \n As we looked deeper into the survey results, one thing became clear – Java developers are not just interested in adding AI for the sake of it. They are focused on building practical, enterprise-ready features that are reliable, secure, and easy to maintain. \n 98% of respondents highlighted a core set of approaches or elements that they see as essential for any AI-powered Java application: \n \n Retrieval-Augmented Generation (RAG) – Bringing real-time, context-aware answers by grounding responses in trusted data. This is especially useful in enterprise scenarios where accuracy and context matter. \n Embeddings and Vector Databases – Enabling efficient semantic search and advanced knowledge retrieval. Developers recognize this as the key to making applications “understand” the meaning behind user inputs. \n Function Calling or Tool Calling – Allowing AI models to interact with APIs, pull in real-time data, or trigger backend workflows. This is where AI starts to act – not just suggest – making it a true part of the application logic. \n AI Agents – These are not just chatbots. Agents are intelligent programs that can automate or assist with tasks on behalf of users or teams. They combine reasoning, memory, and action – gathering information and triggering responses dynamically. For many developers, agents represent the next step toward intelligent automation inside business-critical workflows. \n \n \n Fundamentals for Enterprise-Grade AI Applications \n When building applications for the enterprise, developers know that intelligence alone is not enough. Trust, safety, and integration matter just as much. These are the foundational features Java developers called out: \n \n Security and Access Control – Making sure AI features respect user roles, protect sensitive data, and fit into enterprise identity systems. \n Safety and Compliance – Filtering outputs to align with internal policies, legal regulations, and brand standards. This is especially important for customer-facing features. \n Observability – Tracking how AI decisions are made, logging user and AI interactions, and making sure there is a clear record of what happened – and why. \n Structured Outputs – AI responses need to work within the system, not outside it. Structured formats – like JSON or XML – ensure smooth handoffs between the AI component and the rest of the application. \n Reasoning and Explainability – Developers want AI features that can explain their answers, show their sources, and help users trust the output – especially in domains like finance, healthcare, or compliance. \n \n Representative Scenarios and Business Impact \n To make things more concrete, let us look at two sample scenarios. These are not the only ones – just representative examples to help spark ideas. There is a broad and growing range of real-world situations where Java developers can use AI to create business value. \n Scenario One – Intelligent Workflow Automation \n Imagine a production manager at an auto manufacturer - say, Mercedes-Benz or Ford - who needs to align the assembly line schedule with real-time component availability and constantly shifting order priorities. \n \n The manager’s question is urgent and complex: “How can I adjust the production schedule based on today’s parts inventory and current orders?” Answering means pulling in data from ERP systems, supply chain feeds, vendor dashboards, and manufacturing operations - a level of complexity that can overwhelm even experienced teams. This is where AI steps in as a true copilot - working alongside the human decision-maker to gather data, flag supply constraints, and highlight scheduling options. Together, they can plan faster, adapt more confidently, and respond to change in real time. \n For Java developers, this is an opportunity to build intelligent systems that bring together data from ERP, inventory, and order management applications - enabling AI models to interact with information and collaborate with decision-makers. These systems do not rely on AI alone; they depend on strong data integration and reliable workflows - all of which can be designed, secured, and scaled within the Java ecosystem. In this way, AI becomes part of a co-working loop - and Java developers are the ones who make that loop real. \n Scenario Two – AI-Powered Process Assistants \n Picture a logistics manager at a major shipping company - FedEx, UPS, or DHL - facing cascading delays due to severe weather across multiple regions. The manager is under pressure to reroute packages efficiently while minimizing disruptions to downstream delivery schedules. \n \n The question is urgent: “What is the fastest rerouting option for delayed packages due to weather?” Answering requires combining live weather feeds, traffic data, delivery schedules, hub capacities, and driver availability - all in real time. AI acts as a true copilot at this moment, working alongside the manager to collect relevant signals, flag risk zones, and generate rerouting recommendations. Together, they respond with speed and clarity, keeping shipments moving and customers informed. \n For Java teams, this is a practical opportunity to build intelligent systems that embed AI into logistics, supply chain, and delivery operations - not by rebuilding everything, but by integrating the right data streams, APIs, and business logic. The real value lies in data orchestration, not just algorithms. Java developers are key to enabling these AI-powered assistants by securing connections across systems and building workflows that help humans and AI collaborate effectively under pressure. \n More Scenarios – World of Possibilities \n These two scenarios only scratch the surface. Developers across industries – from healthcare and finance to retail and public services – are finding ways to integrate AI that solve meaningful problems, reduce complexity, and improve how their systems perform. \n Java and AI Technology Stack \n So far, we have looked at what developers want to build and how AI is changing the way applications are designed. Now, let us look at the platform behind it all – the technology stack that powers intelligent Java applications. \n To bring these ideas to life and impact, Java developers need a foundation that connects data, apps, and AI services. We call this an AI application platform. It is not a specific product – it is an integrated platform made up of components that most developer teams already use or are familiar with. \n The goal of this platform is to make developers more productive while building intelligent features into their applications. It gives teams the freedom to choose familiar tools – while making it easier to bring in AI capabilities when and where they are needed. \n We group this platform into three areas: \n \n The app platform \n The data platform \n The AI platform \n \n Let us break it down using the numbered diagram: \n \n \n Developer Services: These are the core tools that developers use every day – IDEs, coding assistants, build tools, testing frameworks, CI/CD pipelines. They help you write, debug, and manage application code across your team. \n Container Services | Platform-as-a-Service: This is the runtime layer – where your applications are deployed and scaled. Whether using containers or a managed platform, this layer handles traffic, performance, and operational efficiency. \n Data Platform: This is where your application data lives – databases, data lakes, and other storage services. It connects structured data, business logic, and real-time events. \n AI Platform: This is where intelligence is added. It includes access to large language models, embeddings, vector search, and other tools that support natural language interactions, automation, and decision-making. \n \n Together, these four parts form the foundation for building, deploying, and managing AI-powered Java applications. \n Technology Stacks for Spring Boot and Quarkus Applications \n To make this more relatable, we highlighted two of the most widely adopted Java frameworks – Spring Boot and Quarkus. These stacks represent popular combinations that many Java teams are already using today for building cloud-native applications. \n That said, these are just representative examples. There are many valid combinations that developers, platform teams, and organizations can choose – based on their existing tools, workloads, and team preferences. \n Representative Spring Boot Stack \n \n App Platform: App hosting service of choice \n AI Library: Spring AI \n AI Platform: OpenAI \n Business Data: PostgreSQL \n Vector Database: PostgreSQL \n \n Representative Quarkus Stack \n \n App Platform: App hosting service of choice \n AI Library: LangChain4j \n AI Platform: OpenAI \n Business Data: PostgreSQL \n Vector Database: PostgreSQL \n \n Both stacks support the core capabilities needed for building intelligent apps – from secure model access and real-time data integration to observability and system-level debugging. \n But the opportunity does not stop there. \n Traditional App Servers - Tomcat, WebLogic, JBoss EAP or WebSphere \n Many enterprise applications continue to run on Tomcat, WebLogic, JBoss EAP, or WebSphere. These are stable platforms that power core business systems – and they are very much part of the AI journey. \n If you are running on one of these platforms, you can still bring intelligence into your applications. By using a Java library of choice (Spring AI or LangChain4j) , you can connect these applications to Large Language Models (LLMs) and Model Context Protocol (MCP) servers – without needing to rewrite or migrate them. \n This means that intelligence can be added, not just rebuilt – a powerful approach for teams with large existing investments in Java EE or Jakarta EE applications. \n Whether your Java app is built with Spring Boot, Quarkus, or deployed on a traditional app server, the tools are here – and the path to intelligent applications is open. You do not have to start from scratch. You can start from where you are. \n Java and MCP – The Bridge to Intelligent Applications \n One of the most important parts of the Java and AI story is MCP - the Model Context Protocol. MCP is an open, flexible, and interoperable standard that allows large language models to connect with the outside world - and more importantly, with real applications and real data. \n At its core, MCP is a bridge - a structured way for models to access enterprise data, invoke tools, and collaborate with AI agents. It gives developers control over how data moves, how decisions are made, and how actions are triggered. The result is safer, more predictable AI behavior inside real-world systems. \n MCP servers can be implemented in any language stack – such as Java, C#, Python, and NodeJS - and integrated into any AI-powered application, regardless of how that application is written. That interoperability makes MCP especially valuable for teams working across systems and languages. \n If you are building an MCP server using Java, the official MCP Java SDK maintained by Anthropic provides the right starting point. You can also use frameworks like Spring or Quarkus to implement MCP servers with full enterprise capabilities. \n For those building applications using Spring AI or LangChain4j, both libraries support connecting to any MCP server - whether running locally or remotely - to orchestrate tools, call functions, and manage agent behavior as part of the runtime flow. \n In addition, ready-to-use implementations like the Azure MCP Server make it easier to add intelligence to backends, orchestrate workflows, and shape AI agent behavior without starting from scratch. \n Authentication and Authorization \n Security is a critical part of any enterprise-grade solution - and MCP is no exception. \n In collaboration with Anthropic, Microsoft proposed a new authorization specification for MCP. This specification has now been finalized and is being implemented across MCP clients and servers to ensure that all interactions are secure, policy-driven, and consistent across environments. \n This continued investment in standards, tooling, and security is helping MCP mature into a core enabler of intelligent applications - especially for enterprise Java teams looking to move fast without compromising on trust or control. \n Preferred Libraries – What Java Developers Are Choosing \n As part of our outreach to 647 Java professionals, we asked a key question: \n “Which AI frameworks or libraries would you consider for building or integrating intelligence into Java applications?” \n Here is what they told us: \n \n Spring AI - selected by 43 percent. \n LangChain4j – preferred by 37 percent. \n \n \n These two libraries clearly lead the way. They reflect the maturity of the Java ecosystem and show strong alignment with the two most active communities – Spring and Jakarta EE. \n Spring AI and LangChain4j offer higher-level abstractions that simplify how developers connect to AI services, manage context, interact with models, and build intelligent features. For developers already working in Spring Boot or Quarkus, these libraries feel familiar – and that lowers the barrier to adding intelligent capabilities into existing codebases. \n Other Developer Preferences \n At the same time, a considerable number of developers – 37 percent and 29 percent – also shared that they would prefer to work directly with AI service provider libraries or call REST APIs. \n This is not a surprise. In fact, it is a healthy signal. Many teams use these lower-level integrations to gain early access to new features or customize interactions in ways that higher-level libraries may not yet support. \n It is important that these developers know: You are not wrong - but you are not alone either. \n While direct API integration offers flexibility, AI libraries like Spring AI and LangChain4j are designed to make those experiences easier. They wrap the complexity, manage context, offer tested patterns that align with enterprise application needs - like observability, security, and structured outputs – plug your code into the Spring and Java ecosystem of possibilities for deep integration. \n Evolving Together \n As AI services evolve, day-zero support will almost always appear first in the service provider’s native SDKs and REST APIs. That is expected. But as those capabilities stabilize, AI libraries like Spring AI and LangChain4j will latch on – offering developers a smoother, more consistent programming experience. \n The result: Developers get to start fast with APIs – and scale confidently with higher level libraries and frameworks. \n Top Challenges and Areas Needing Improvement \n As part of our research, we asked 647 Java professionals to share the biggest challenges they face – and where they believe improvements would help the most. The answers reflected where the community is today – eager to build but still facing some friction points. \n \n Top Challenges Java Developers Encounter \n \n Lack of clear starting points and step-by-step guidance. \n Feeling overwhelmed by the variety of AI models, libraries, and tools. \n Misconceptions that machine learning expertise is required. \n Complexity with integrating AI features into existing applications - particularly introduced by suboptimal patterns such as directly calling REST APIs through low-level HTTP libraries, invoking Python-based routines through external OS processes, or loading models into the application’s memory at runtime using local weights and GPU resources. \n Missing features in some of the current libraries and frameworks. \n Uncertainty about scaling applications and safely using private models on the cloud. \n \n Areas Developers Believe Need the Most Improvement \n \n Clear and practical step-by-step workflows. \n Guidance on how to securely integrate private models. \n Examples that show how to use chat models for function calling and streaming completions. \n Educational content that explains completions, reasoning, and data validation. \n Tools and how-to guides for embedding-based search and question answering. \n Tutorials on how to leverage external data to improve model output. \n \n Java Developers – Familiar with AI vs. New to AI \n Among all respondents: \n \n 87 percent are familiar with AI. \n 13 percent are newer to AI and just getting started. \n \n Java Developers New to AI \n These developers are exploring use cases, evaluating models, and building early prototypes. Their top challenges are: \n \n Lack of clear starting points. \n Too many options in terms of tools and models. \n Need for simple, practical guidance. \n \n The top areas that will benefit them: \n \n Step-by-step development workflows. \n Examples of using chat models and completions. \n Simple breakdowns of key concepts like reasoning and validation. \n \n Java Developers Familiar with AI \n These developers are further along – often in development or production stages – and face a wide range of challenges. A top need for them is: \n \n Secure ways to integrate private models into Java apps. \n They also benefit from deeper technical content, patterns, and advanced tooling support. \n \n Moving Forward with Confidence \n This space is evolving faster than anyone expected – and that can feel overwhelming. But the top challenges Java developers face are real, and the community is actively addressing them. \n It is important not to worry about the number of tools, models, or libraries. That diversity is a sign of progress. Models will keep evolving. New ones will arrive. And this is exactly where higher-level Java libraries step in. \n Spring AI, LangChain4j, and the MCP Java SDK are designed to simplify the path forward. These libraries create a layer of abstraction that shields your application code from constant changes. You can build once – and switch models or providers as needed, without rewriting core logic. \n And these libraries are alive. You can see them in action on GitHub – through open issues, pull requests, and rapid updates. If you see a missing feature, open an issue. If you want to contribute, send a pull request. These are responsive communities that welcome participation. \n “AI, for most people today, effectively means \"sending human-language sentences to an HTTP endpoint.\" 80% of the noise right now is about the artful and scalable connection of these models with your data and business logic - data guarded by services whose business logic is statistically implemented in Spring Boot and Spring AI. We, as JVM developers, are uniquely well-positioned to expand the AI universe. \n Don’t delay - start (spring.io) today!” -- Josh Long, Spring Developer Advocate, Broadcom \n \n “By combining Quarkus’s speed, Langchain4j’s AI orchestration, and MCP’s unified tool access, Java developers are in a unique position to lead this transformation - building intelligent, resilient applications using the tools and approaches we know well. Working with AI is no longer a distant specialty - it is becoming a natural part of modern Java development” -- Daniel Oh, Senior Principal Developer Advocate, Red Hat \n AI Concepts for App Developers \n As you build your first intelligent Java applications, it helps to become familiar with key AI concepts such as foundation models, chat models, embedding models, prompts, inference endpoints, context windows, and vector search (see diagram below for a curated list). These concepts are not just theory - they directly shape how your applications interact with AI systems. \n \n To make it easier, we created a simple learning prompt that you can use with your favorite Chat Model like ChatGPT and Claude: \n \n // Prompt Template for Java Developers Learning AI Concepts // (Replace <TERM> with the topic you want to learn.) I am a Java enterprise app developer focused on building AI-powered Java applications. I do not fully understand what '<TERM>' means. Please explain it to me so simply that I can think about it like a Java library or service I would naturally use. Use examples from enterprise Java (like APIs, search, summarization, customer service bots) that I would typically build. If possible, give a mental model I can remember and relate to Java patterns. Also, suggest a small sample of how I would use '<TERM>' via an API in a Java app, if that helps understanding. \n \n You can replace <TERM> with any concept you want to learn, such as \"Embedding Model\" or \"Inference Endpoint,\" and get a focused, practical explanation that fits how you already think as a Java developer. \n By practicing with this method, you can quickly build second-nature familiarity with the terms and ideas behind AI development - without needing a research background. With the strong foundation you already have in Java, you will be ready to confidently integrate, adapt, and innovate as AI tools continue to evolve. \n AI Is Now a Java Developer’s Game \n You do not need to train your own models to start building intelligent applications. You can do it today - using popular, production-ready foundation models - all within the Java ecosystem you already know and trust. These are still Java applications at their core. \n What sets you apart is not just your Java expertise - it is your deep understanding of how real business processes work and how to bring them to life in code. You know how to build, secure, scale, and operate reliable systems - and now, you can apply those same skills to deliver AI-powered solutions that run in the environments your teams already use, including Microsoft Azure. \n AI-Assisted App Development – A Powerful Companion to Building Intelligent Java Apps \n No discussion about the future of software development is complete without acknowledging the rise of AI-assisted development. This is a separate - but equally important - path alongside building intelligent applications. And it is transforming how developers write, upgrade, and manage code. \n At the center of this shift is tools like GitHub Copilot - a tool that is reshaping how developers approach their daily work. \n Developers using GitHub Copilot report coding up to 55 percent faster, freeing up time to focus on design decisions, solving business problems, and writing less boilerplate code. But the benefits go deeper than speed - 75 percent of developers say that Copilot makes their work more satisfying. \n Today, 46 percent of all code on GitHub is written with AI assistance, and over 20,000 organizations are already embracing these tools to improve development workflows and accelerate delivery. \n Built Into the Tools You Already Use \n GitHub Copilot works where Java developers already build – in IDEs like Visual Studio Code, IntelliJ, and Eclipse. It brings contextual, customizable assistance, powered by the latest models such as gpt-4o and Claude 3.5 Sonnet. \n \n Whether it is suggesting code snippets, auto-completing functions, or helping enforce best practices, GitHub Copilot enhances the quality of code and the productivity of developers – all while keeping them in control. \n Helping Modernize and Maintain Java Codebases \n One of the most exciting capabilities is GitHub Copilot’s growing role in modernizing Java applications. Late last year, the GitHub Copilot App Modernization – Java Upgrade feature entered private preview. \n This tool is designed to support large, mission-critical tasks like: \n \n Upgrading Java versions and frameworks \n Refactoring code \n Updating dependencies \n Aligning to cloud-first practices \n \n The process starts with your local Java project. Copilot provides an AI-generated upgrade assistant, and developers stay in the loop to approve changes step-by-step. The goal is to take the heavy lifting out of routine upgrades while ensuring everything remains safe and aligned with your architecture. \n Beyond App Code – Towards Cloud-Ready Modernization \n Technology providers like Microsoft are investing deeply in this space to bring additional capabilities into developer workflows – including: \n \n Secure identity handling through passwordless authentication \n Certificate management and secrets handling \n Integration with PaaS services for storage, data, caching, and observability \n \n All of this reduces the time it takes to bring legacy apps forward and prepare them for modern, scalable deployments – so teams can spend more time building intelligent features and less time managing technical debt. \n Two Paths – One Goal \n AI-assisted development, including upgrading and modernizing apps with AI, and building intelligent apps are not the same – but together, they form a powerful foundation. One helps you write and modernize code faster, the other helps you deliver smarter features inside your apps. \n For Java developers, this means there is support at every step – from idea to implementation to impact. \n Start Today and Move the Java Community Forward \n The message from 647 Java professionals is clear: Java developers are ready – and the tools they need to build intelligent applications are already here. \n If you are a Java developer and have not started your AI journey yet now is the right time. You do not need to become an AI expert. You do not need to change your language, tools, or working style. Modern Java frameworks and libraries like Spring AI, LangChain4j, and the MCP Java SDK are designed to work the way you already build – while making it easier to add intelligence, automation, and smart experiences to your applications. \n You can start with what you know – and grow into what is next: aka.ms/spring-ai and aka.ms/langchain4j. \n \n To Java Ecosystem Leaders \n We also want to speak directly to those shaping the Java ecosystem – community leaders, experienced developers, and technical influencers. Your role is more important than ever. \n We invite you to: \n \n Show what is possible – share real examples of how AI features can be integrated into Java applications with minimal friction. \n Promote best practices – use meetups, blogs, workshops, and developer forums to spread practical guidance and patterns that others can follow. \n Improve the experience – contribute documentation, examples, and even code to help close the gaps developers face when starting their AI journeys. \n Push frameworks forward – help identify and implement missing features that can simplify Java + AI integration and speed up real-world adoption. \n \n This is not just about tools – it is about people helping people move forward. \n Many of you already helped make this research possible – by spreading the word on LinkedIn, sharing the survey, and encouraging others to contribute. Your support made a difference. And now, these findings belong to the entire Java ecosystem – so we can act on them together. \n To the Java Developers Who Participated \n Thank you. \n Your input – your time, your thoughts, your challenges, your ideas – shaped this entire report. You told us what is working, what is missing, and what you need next. We hope this reflection of your voices is helpful – to you and to the broader Java community. \n The road ahead is exciting – and Java is ready to lead. \n AI Learning Resources for Java App Developers \n Azure AI Services documentation \n Azure AI Services quick starts – like Chat Completions and Use Your Data \n Build Enterprise Agents using Java and Spring \n OpenAI RAG with Java, LangChain4j and Quarkus \n Spring AI \n \n Learn how to build effective agents with Spring AI \n Spring AI reference documentation \n Prompt Engineering Techniques with Spring AI \n Spring AI GitHub repo \n Spring AI examples \n Spring AI updates \n The Seamless Path for Spring Developer to the World of Generative AI \n \n LangChain4j \n \n Supercharge your Java application with the power of LLMs \n LangChain4j GitHub repo \n LangChain4j Examples \n Quarkus LangChain4j \n Quarkus LangChain4j Workshop \n LangChain4j updates \n 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,"solved":false},"Blog:board:AzureDevCommunityBlog":{"__typename":"Blog","id":"board:AzureDevCommunityBlog","displayId":"AzureDevCommunityBlog","nodeType":"board","conversationStyle":"BLOG","title":"Microsoft Developer Community Blog","shortTitle":"Microsoft Developer Community Blog","parent":{"__ref":"Category:category:Azure"}},"User:user:210546":{"__typename":"User","uid":210546,"login":"Lee_Stott","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yMTA1NDYtODM5MjVpMDI2ODNGQTMwMzAwNDFGQQ"},"id":"user:210546"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA4ODU4LWR5dWZleA?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA4ODU4LWR5dWZleA?revision=1","title":"AiAgentsImages_FriendlyRobot.png","associationType":"COVER","width":1920,"height":1080,"altText":""},"BlogTopicMessage:message:4408858":{"__typename":"BlogTopicMessage","subject":"Week 4 . Microsoft Agents Hack Online Events and Readiness Resources","conversation":{"__ref":"Conversation:conversation:4408858"},"id":"message:4408858","revisionNum":1,"uid":4408858,"depth":0,"board":{"__ref":"Blog:board:AzureDevCommunityBlog"},"author":{"__ref":"User:user:210546"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":92},"postTime":"2025-04-28T13:21:44.987-07:00","lastPublishTime":"2025-04-28T13:21:44.987-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Readiness and skilling events for Week 4: Microsoft AI Agents Hack Register Now at https://aka.ms/agentshack \n \n 2025 is the year of AI agents! But what exactly is an agent, and how can you build one? Whether you're a seasoned developer or just starting out, this FREE three-week virtual hackathon is your chance to dive deep into AI agent development. \n 🔥 Learn from expert-led sessions streamed live on YouTube, covering top frameworks like Semantic Kernel, Autogen, the new Azure AI Agents SDK and the Microsoft 365 Agents SDK. \n \n Week 4: April 28th-30th Live and On Demand \n Topic Track Irresponsible AI Agents Java Securing AI agents on Azure Python The Art of AI Embodiment: Real-Time Interactive Experiences with Azure OpenAI GPT-4o and 3D Avatars Python Evaluating Agents Python \n 🌟 Join the Conversation on Azure AI Foundry Discussions! 🌟 Have ideas, questions, or insights about AI? Don't keep them to yourself! Share your thoughts, engage with experts, and connect with a community that’s shaping the future of artificial intelligence. 🧠✨ 👉 Click here to join the discussion! ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1182","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA4ODU4LWR5dWZleA?revision=1\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA4ODU4LWR5dWZleA?revision=1"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4398187":{"__typename":"Conversation","id":"conversation:4398187","topic":{"__typename":"BlogTopicMessage","uid":4398187},"lastPostingActivityTime":"2025-04-23T13:22:39.654-07:00","solved":false},"User:user:360851":{"__typename":"User","uid":360851,"login":"madhurabharadwaj","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0zNjA4NTEtMjUyNjg5aTcxMkE1Njk5MjBBM0JFODU"},"id":"user:360851"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTg3LUZNRFd3SQ?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTg3LUZNRFd3SQ?revision=3","title":"FunctionsLogopng.png","associationType":"COVER","width":600,"height":315,"altText":""},"BlogTopicMessage:message:4398187":{"__typename":"BlogTopicMessage","subject":"Keep Your Azure Functions Up to Date: Identify Apps Running on Retired Versions","conversation":{"__ref":"Conversation:conversation:4398187"},"id":"message:4398187","revisionNum":3,"uid":4398187,"depth":0,"board":{"__ref":"Blog:board:AppsonAzureBlog"},"author":{"__ref":"User:user:360851"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"Ensure your Azure Functions stay supported and secure with these Azure CLI scripts, helping you identify and upgrade apps running on soon-to-be-retired or unsupported language versions.","metrics":{"__typename":"MessageMetrics","views":2044},"postTime":"2025-03-27T14:13:33.387-07:00","lastPublishTime":"2025-03-27T14:13:33.387-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Running Azure Functions on retired language versions can lead to security risks, performance issues, and potential service disruptions. While Azure Functions Team notifies users about upcoming retirements through the portal, emails, and warnings, identifying affected Function Apps across multiple subscriptions can be challenging. \n To simplify this, we’ve provided Azure CLI scripts to help you: ✅ Identify all Function Apps using a specific runtime version ✅ Find apps running on unsupported or soon-to-be-retired versions ✅ Take proactive steps to upgrade and maintain a secure, supported environment \n Read on for the full set of Azure CLI scripts and instructions on how to upgrade your apps today! \n Why Upgrading Your Azure Functions Matters \n Azure Functions supports six different programming languages, with new stack versions being introduced and older ones retired regularly. Staying on a supported language version is critical to ensure: \n \n Continued access to support and security updates \n Avoidance of performance degradation and unexpected failures \n Compliance with best practices for cloud reliability \n \n Failure to upgrade can lead to security vulnerabilities, performance issues, and unsupported workloads that may eventually break. Azure's language support policy follows a structured deprecation timeline, which you can review here. \n How Will You Know When a Version Is Nearing its End-of-Life? \n The Azure Functions team communicates retirements well in advance through multiple channels: \n \n Azure Portal notifications \n Emails to subscription owners \n Warnings in client tools and Azure Portal UI when an app is running on a version that is either retired, or about to be retired in the next 6 months \n Official Azure Functions Supported Languages document here \n \n To help you track these changes, we recommend reviewing the language version support timelines in the Azure Functions Supported Languages document. \n However, identifying all affected apps across multiple subscriptions can be challenging. To simplify this process, I've built some Azure CLI scripts below that can help you list all impacted Function Apps in your environment. \n \n Linux* Function Apps with their language stack versions: \n \n az functionapp list --query \"[?siteConfig.linuxFxVersion!=null && siteConfig.linuxFxVersion!=''].{Name:name, ResourceGroup:resourceGroup, OS:'Linux', LinuxFxVersion:siteConfig.linuxFxVersion}\" --output table \n \n *Running on Elastic Premium and App Service Plans \n \n Linux* Function Apps on a specific language stack version: Ex: Node.js 18 \n \n az functionapp list --query \"[?siteConfig.linuxFxVersion=='Node|18'].{Name:name, ResourceGroup:resourceGroup, OS: 'Linux', LinuxFxVersion:siteConfig.linuxFxVersion}\" --output table \n \n *Running on Elastic Premium and App Service Plans \n \n Windows Function Apps only: \n \n az functionapp list --query \"[?!contains(kind, 'linux')].{Name:name, ResourceGroup:resourceGroup, OS:'Windows'}\" --output table \n \n \n Windows Function Apps with their language stack versions: \n \n az functionapp list --query \"[?!contains(kind, 'linux')].{name: name, resourceGroup: resourceGroup}\" -o json | ConvertFrom-Json | ForEach-Object { \n $appSettings = az functionapp config appsettings list -n $_.name -g $_.resourceGroup --query \"[?name=='FUNCTIONS_WORKER_RUNTIME' || name=='WEBSITE_NODE_DEFAULT_VERSION']\" -o json | ConvertFrom-Json \n $siteConfig = az functionapp config show -n $_.name -g $_.resourceGroup --query \"{powerShellVersion: powerShellVersion, netFrameworkVersion: netFrameworkVersion, javaVersion: javaVersion}\" -o json | ConvertFrom-Json \n \n $runtime = ($appSettings | Where-Object { $_.name -eq 'FUNCTIONS_WORKER_RUNTIME' }).value \n $version = switch($runtime) { \n 'node' { ($appSettings | Where-Object { $_.name -eq 'WEBSITE_NODE_DEFAULT_VERSION' }).value } \n 'powershell' { $siteConfig.powerShellVersion } \n 'dotnet' { $siteConfig.netFrameworkVersion } \n 'java' { $siteConfig.javaVersion } \n default { 'Unknown' } \n } \n \n [PSCustomObject]@{ \n Name = $_.name \n ResourceGroup = $_.resourceGroup \n OS = 'Windows' \n Runtime = $runtime \n Version = $version \n } \n } | Format-Table -AutoSize \n \n \n Windows Function Apps running on Node.js runtime: \n \n az functionapp list --query \"[?!contains(kind, 'linux')].{name: name, resourceGroup: resourceGroup}\" -o json | ConvertFrom-Json | ForEach-Object { \n     $appSettings = az functionapp config appsettings list -n $_.name -g $_.resourceGroup --query \"[?name=='FUNCTIONS_WORKER_RUNTIME' || name=='WEBSITE_NODE_DEFAULT_VERSION']\" -o json | ConvertFrom-Json \n     \n     $runtime = ($appSettings | Where-Object { $_.name -eq 'FUNCTIONS_WORKER_RUNTIME' }).value \n     if ($runtime -eq 'node') { \n         $version = ($appSettings | Where-Object { $_.name -eq 'WEBSITE_NODE_DEFAULT_VERSION' }).value \n         \n         [PSCustomObject]@{ \n             Name = $_.name \n             ResourceGroup = $_.resourceGroup \n             OS = 'Windows' \n             Runtime = $runtime \n             Version = $version \n         } \n     } \n } | Format-Table -AutoSize \n \n \n Windows Function Apps running on a specific language version: Ex: Node.js 18 \n \n az functionapp list --query \"[?!contains(kind, 'linux')].{name: name, resourceGroup: resourceGroup}\" -o json | ConvertFrom-Json | ForEach-Object { \n     $appSettings = az functionapp config appsettings list -n $_.name -g $_.resourceGroup --query \"[?name=='FUNCTIONS_WORKER_RUNTIME' || name=='WEBSITE_NODE_DEFAULT_VERSION']\" -o json | ConvertFrom-Json \n     \n     $runtime = ($appSettings | Where-Object { $_.name -eq 'FUNCTIONS_WORKER_RUNTIME' }).value \n     $nodeVersion = ($appSettings | Where-Object { $_.name -eq 'WEBSITE_NODE_DEFAULT_VERSION' }).value \n     \n     if ($runtime -eq 'node' -and $nodeVersion -eq '~18') { \n         [PSCustomObject]@{ \n             Name = $_.name \n             ResourceGroup = $_.resourceGroup \n             OS = 'Windows' \n             Runtime = $runtime \n             Version = $nodeVersion \n         } \n     } \n } | Format-Table -AutoSize \n \n \n All windows Apps running on unsupported language runtimes: (as of March 2025) \n \n az functionapp list --query \"[?!contains(kind, 'linux')].{name: name, resourceGroup: resourceGroup}\" -o json | ConvertFrom-Json | ForEach-Object { \n     $appSettings = az functionapp config appsettings list -n $_.name -g $_.resourceGroup --query \"[?name=='FUNCTIONS_WORKER_RUNTIME' || name=='WEBSITE_NODE_DEFAULT_VERSION']\" -o json | ConvertFrom-Json \n     $siteConfig = az functionapp config show -n $_.name -g $_.resourceGroup --query \"{powerShellVersion: powerShellVersion, netFrameworkVersion: netFrameworkVersion}\" -o json | ConvertFrom-Json \n     \n     $runtime = ($appSettings | Where-Object { $_.name -eq 'FUNCTIONS_WORKER_RUNTIME' }).value \n     $version = switch($runtime) { \n         'node' { \n             $nodeVer = ($appSettings | Where-Object { $_.name -eq 'WEBSITE_NODE_DEFAULT_VERSION' }).value \n             if ([string]::IsNullOrEmpty($nodeVer)) { 'Unknown' } else { $nodeVer } \n         } \n         'powershell' { $siteConfig.powerShellVersion } \n         'dotnet' { $siteConfig.netFrameworkVersion } \n         default { 'Unknown' } \n     } \n     \n     # Check if runtime version is unsupported \n     $isUnsupported = switch($runtime) { \n         'node' { \n             $ver = $version -replace '~','' \n             [double]$ver -le 16 \n         } \n         'powershell' { \n             $ver = $version -replace '~','' \n             [double]$ver -le 7.2 \n         } \n         'dotnet' { \n             $ver = $siteConfig.netFrameworkVersion \n             $ver -notlike 'v7*' -and $ver -notlike 'v8*' \n         } \n         default { $false } \n     } \n     \n     if ($isUnsupported) { \n         [PSCustomObject]@{ \n             Name = $_.name \n             ResourceGroup = $_.resourceGroup \n             OS = 'Windows' \n             Runtime = $runtime \n             Version = $version \n         } \n     } \n } | Format-Table -AutoSize \n \n Take Action Now \n By using these scripts, you can proactively identify and update Function Apps before they reach end-of-support status. Stay ahead of runtime retirements and ensure the reliability of your Function Apps. \n For step-by-step instructions to upgrade your Function Apps, check out the Azure Functions Language version upgrade guide. \n For more details on Azure Functions' language support lifecycle, visit the official documentation. \n Have any questions? Let us know in the comments below! ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"9963","kudosSumWeight":1,"repliesCount":2,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTg3LUZNRFd3SQ?revision=3\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk4MTg3LUZNRFd3SQ?revision=3"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4275753":{"__typename":"Conversation","id":"conversation:4275753","topic":{"__typename":"BlogTopicMessage","uid":4275753},"lastPostingActivityTime":"2025-04-09T18:58:07.826-07:00","solved":false},"User:user:2719338":{"__typename":"User","uid":2719338,"login":"croffz","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-11.svg?time=0"},"id":"user:2719338"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mjc1NzUzLU9tYmZaRA?revision=16\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mjc1NzUzLU9tYmZaRA?revision=16","title":"image.png","associationType":"COVER","width":1758,"height":993,"altText":""},"BlogTopicMessage:message:4275753":{"__typename":"BlogTopicMessage","subject":"Capture a JVM heap dump for Java apps running on Azure Container Apps","conversation":{"__ref":"Conversation:conversation:4275753"},"id":"message:4275753","revisionNum":16,"uid":4275753,"depth":0,"board":{"__ref":"Blog:board:AppsonAzureBlog"},"author":{"__ref":"User:user:2719338"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":269},"postTime":"2025-03-12T19:01:43.491-07:00","lastPublishTime":"2025-04-09T18:58:07.826-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Overview \n Capturing a JVM heap dump is one of the most widely used debugging techniques for troubleshooting memory issues in Java applications. It might be not that straight-forward when it comes to cloud services, but in Azure Container Apps, you can do a JVM heap dump easily with the help of the debug console. \n Connect to the debug console of a running Azure Container Apps instance \n With the Azure CLI and the latest Azure Container Apps extension installed, run the following command to connect to the debug console. \n az containerapp debug \\ --resource-group <resource-group> \\ --name <container-app-name> \n To use JDK built-in tools to capture a JVM heap dump, run the following command to install the JDK after connected to the debug console: \n root [ / ]# setup-jdk \n Then, use arrow up (↑) and down (↓) to select the JDK version to install. We detect your app runtime environment and recommend a JDK version for you. For example: \n Select a JDK to install: [temurin-8-jdk]: Eclipse Temurin JDK 8 [msopenjdk-11]: Microsoft Build OpenJDK 11 [msopenjdk-17]: Microsoft Build OpenJDK 17 >[msopenjdk-21]: Microsoft Build OpenJDK 21 (Recommended) \n [Optional] Switch user context \n In the debug console session, a Java app is usually the process 1, with its files stored at /proc/1, if no customizations are made in the image. (Otherwise, you might need to get the correct process ID of the Java application). However, if the Java app to be profiled is run by a non-root user (which is also defined in the image), you cannot access anything in /proc/1 directly given that the debug console starts as root by default. \n root [ / ]# ls /proc/1/root ls: cannot access '/proc/1/root': Permission denied \n To work this around this issue, use the following command to switch the user context. \n root [ / ]# switch-user Switching to user app... app [ / ]$ \n The user context will then be changed to the one that runs the Java application, and you can capture a JVM heap dump with it. \n Capture a JVM heap dump \n jmap and jcmd are both built-in tools in the JDK to help you capture a JVM heap dump. They are all located under the bin folder inside the JDK home directory. \n To capture a JVM heap dump by using jmap, run the following command in the debug console: \n app [ / ]$ jmap -dump:live,format=b,file=/tmp/dump.hprof 1 \n To capture a JVM heap dump by using jcmd, run the following command in the debug console: \n app [ / ]$ jcmd 1 GC.heap_dump /tmp/dump.hprof \n Either way, you can then access the dump file at /proc/1/root/tmp/dump.hprof. \n Download the dump file to local \n Transferring files from cloud services to local could be tricky sometimes. But in the Azure Container Apps debug console, you can download the dump file with the help of Azure Cloud Storage. There are several options: \n \n Mount an Azure Storage File Share as a volume to Azure Container Apps. \n Send an HTTP request to Azure Blob Storage to upload the file from the debug console session. \n Use AzCopy to save the effort of constructing an upload HTTP request and use Azure authentication instead. \n \n In this article, we focus on the 3rd approach. \n \n Step 1: Install AzCopy in the debug console session. Note that this step needs to be done as the root user. If the current user is not root, type \"exit\" to get back to the root context. \n root [ / ]# tdnf install -y azcopy \n Step 2: Authenticate with Azure Entra ID. \n root [ / ]# azcopy login \n Step 3: Copy the dump file to Azure Blob Storage. \n root [ / ]# azcopy copy \"/proc/1/tmp/dump.hprof\" \"https://<storage-account-name>.blob.core.windows.net/<blob-storage-name>/dump.hprof\" \n Step 4: Download the dump file from Azure Blob Storage by using the Azure Portal or the Azure CLI on the local machine. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"3809","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mjc1NzUzLU9tYmZaRA?revision=16\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mjc1NzUzLU9tYmZaRA?revision=16"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4401708":{"__typename":"Conversation","id":"conversation:4401708","topic":{"__typename":"BlogTopicMessage","uid":4401708},"lastPostingActivityTime":"2025-04-07T11:58:20.228-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNzA4LUFzV3NpZg?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNzA4LUFzV3NpZg?revision=2","title":"Agenthack.png","associationType":"COVER","width":1300,"height":500,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNzA4LVo5VjdoWQ?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNzA4LVo5VjdoWQ?revision=2","title":"clipboard_image-1-1744051959313.png","associationType":"BODY","width":1300,"height":300,"altText":""},"BlogTopicMessage:message:4401708":{"__typename":"BlogTopicMessage","subject":"Construa, inove e #Hacktogether!","conversation":{"__ref":"Conversation:conversation:4401708"},"id":"message:4401708","revisionNum":2,"uid":4401708,"depth":0,"board":{"__ref":"Blog:board:AzureDevCommunityBlog"},"author":{"__ref":"User:user:210546"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":63},"postTime":"2025-04-07T11:55:14.537-07:00","lastPublishTime":"2025-04-07T11:58:20.228-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n \n \n 🛠️ Construa, inove e #Hacktogether! 🛠️ \n 2025 é o ano dos agentes de IA! Mas o que exatamente é um agente? E como você pode criar um? Seja você um desenvolvedor experiente ou esteja apenas começando, este hackathon virtual GRATUITO de três semanas é sua chance de mergulhar no desenvolvimento de agentes de IA. \n 🔥 Aprenda com mais de 20 sessões lideradas por especialistas, transmitidas ao vivo no YouTube, abordando os principais frameworks, como Semantic Kernel, Autogen, o novo Azure AI Agents SDK e o Microsoft 365 Agents SDK. \n 💡 Coloque a mão na massa, explore sua criatividade e crie agentes de IA poderosos! Depois, envie seu projeto e concorra a prêmios incríveis! 💸 \n Datas importantes: \n \n Sessões com especialistas: 8 de abril de 2025 – 30 de abril de 2025 \n Prazo para envio do hack: 30 de abril de 2025, 23:59 PST \n \n Não perca essa oportunidade—junte-se a nós e comece a construir o futuro da IA! 🔥 \n Inscrição 🎟️ \n Garanta sua vaga agora! Preencha o formulário para confirmar sua participação no hackathon. Em seguida, confira a programação das transmissões ao vivo e inscreva-se nas sessões que mais te interessam. \n Após se inscrever, apresente-se e procure por colegas de equipe! \n Submissão de Projetos 🚀 \n Leia atentamente as regras oficiais e certifique-se de entender os requisitos. \n Quando seu projeto estiver pronto, siga o processo de submissão. 📝 \n Prêmios e Categorias 🏅 \n Os projetos serão avaliados por um painel de jurados, incluindo engenheiros da Microsoft, gerentes de produto e defensores de desenvolvedores. Os critérios de avaliação incluirão inovação, impacto, usabilidade técnica e alinhamento com a categoria correspondente do hackathon. \n Cada equipe vencedora nas categorias abaixo receberá um prêmio. 💸 \n \n Melhor Agente Geral - $20,000 \n Melhor Agente em Python - $5,000 \n Melhor Agente em C# - $5,000 \n Melhor Agente em Java - $5,000 \n Melhor Agente em JavaScript/TypeScript - $5,000 \n Melhor Agente Copilot (usando Microsoft Copilot Studio ou Microsoft 365 Agents SDK) - $5,000 \n Melhor Uso do Azure AI Agent Service - $5,000 \n \n Cada equipe pode ganhar em apenas uma categoria. Todos os participantes que submeterem um projeto receberão um badge digital. \n Transmissões 📅 \n Português \n Inscreva-se em todas as sessões em português \n Dia/Horário Tópico Recursos 4/8 12:00 PM PT Bem-vindo ao AI Agents Hackathon - 4/10 12:00 PM PT Crie um aplicativo com o Azure AI Agent Service - 4/17 06:00 AM PT Seu primeiro agente de IA em JavaScript com o Azure AI Agent Service - \n Outros Idiomas \n Teremos mais de 30 transmissões em inglês, além de transmissões em espanhol e chinês. Veja a página principal para mais detalhes. \n 🕒 Horário de Suporte Técnico \n Precisa de ajuda com seu projeto? Participe do Horário de Suporte Técnico no canal de Discord de IA e receba orientação de especialistas! 🚀 \n \n \n Aqui estão os horários de atendimento já agendados: \n Dia/Horário Tópico/Anfitriões Toda quinta-feira, 12:30 PM PT Python + IA (Inglês) Toda segunda-feira, 03:00 PM PT Python + IA (Espanhol) \n Recursos de Aprendizado 📚 \n Acesse os recursos aqui! \n Junte-se ao TheSource EHub para explorar os principais recursos, incluindo treinamentos, transmissões ao vivo, repositórios, guias técnicos, blogs, downloads, certificações e muito mais, atualizados mensalmente. A seção de Agentes de IA oferece recursos essenciais para criar agentes de IA, enquanto outras seções fornecem insights sobre IA, ferramentas de desenvolvimento e linguagens de programação. \n Você também pode postar perguntas em nosso fórum de discussões ou conversar com outros participantes no canal do Discord. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"3832","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNzA4LUFzV3NpZg?revision=2\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNzA4LVo5VjdoWQ?revision=2\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNzA4LUFzV3NpZg?revision=2"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4401664":{"__typename":"Conversation","id":"conversation:4401664","topic":{"__typename":"BlogTopicMessage","uid":4401664},"lastPostingActivityTime":"2025-04-07T10:00:00.017-07:00","solved":false},"User:user:968497":{"__typename":"User","uid":968497,"login":"sonjagu","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS05Njg0OTctNDIzNzE2aUNEMDY3NkM0NjAxRDkwOTM"},"id":"user:968497"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LWtBc1VtZg?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LWtBc1VtZg?revision=1","title":"Meet-Your-JDConf-Hosts.jpg","associationType":"COVER","width":8001,"height":4501,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LUJaNlc4Sg?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LUJaNlc4Sg?revision=1","title":"clipboard_image-2-1744042367246.png","associationType":"BODY","width":316,"height":313,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LWE1MGZDNQ?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LWE1MGZDNQ?revision=1","title":"clipboard_image-4-1744042367297.jpeg","associationType":"BODY","width":363,"height":483,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LTZ0OGlxVQ?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LTZ0OGlxVQ?revision=1","title":"clipboard_image-5-1744042367303.jpeg","associationType":"BODY","width":375,"height":351,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LWRaUUl5eg?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LWRaUUl5eg?revision=1","title":"clipboard_image-6-1744042367427.png","associationType":"BODY","width":881,"height":500,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LVB3Z2FjTQ?revision=1\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxNjY0LVB3Z2FjTQ?revision=1","title":"clipboard_image-7-1744042367436.png","associationType":"BODY","width":728,"height":90,"altText":""},"BlogTopicMessage:message:4401664":{"__typename":"BlogTopicMessage","subject":"Meet your hosts for JDConf 2025!","conversation":{"__ref":"Conversation:conversation:4401664"},"id":"message:4401664","revisionNum":1,"uid":4401664,"depth":0,"board":{"__ref":"Blog:board:AppsonAzureBlog"},"author":{"__ref":"User:user:968497"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":98},"postTime":"2025-04-07T10:00:00.017-07:00","lastPublishTime":"2025-04-07T10:00:00.017-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" JDConf 2025 is right around the corner and is set to be a global gathering for Java developers passionate about Cloud, AI, and the future of Java. With 22+ sessions and 10+ hours of live content streaming from April 9 - 10, plus additional on-demand sessions, this year’s event dives into app modernization, intelligent apps, frameworks, and AI-powered development with tools like Copilot and more. We are excited to invite you to join us with our three distinguished hosts: Bruno Borges, Sandra Ahlgrimm, and Rory Preddy. \n Meet Bruno Borges - Your host for JDConf 2025 Americas \n \n Bruno Borges is a seasoned professional with a rich background in the tech industry. Currently serving as Principal Product Manager at Microsoft, he focuses on Java developers' experience on Azure and beyond. With over two decades of experience, Bruno has been instrumental in bridging the gap between Java communities and Microsoft technologies and services, ensuring seamless integration, optimal developer productivity, and application performance and efficiency. His dedication to enhancing developer experiences has made him a respected figure in the Java ecosystem. \n Meet Sandra Ahlgrimm - Your host for JDConf 2025 EMEA \n \n Sandra Ahlgrimm is a Senior Cloud Advocate at Microsoft, specializing in Java and AI. With over fifteen years of experience as a Java developer, she is highly engaged with various communities. She co-leads the local Java User Group (JUG) and Docker Meetup. Her role on the Program Advisory Board at Oracle's GraalVM community highlights her interest in cutting-edge Java and sustainability. \n Meet Rory Preddy - Your host for JDConf 2025 Asia-Pacific \n \n Rory Preddy is a Principal Cloud Advocate at Microsoft, where he helps professional cloud developers discover and successfully use Microsoft’s platforms. He is a seasoned speaker whose talks are both meaningful and humorous, and he speaks around the world empowering developers to achieve more. Rory has over 25 years of experience in software development, working with various technologies such as Java, AI and Azure. \n Prepare for learning and some entertainment! \n Under the guidance of these accomplished hosts, JDConf 2025 promises to be an enlightening experience. The conference will feature a technical keynote and 22 breakout sessions, showcasing 26 Java influencers and community leaders from organizations such as Microsoft, Broadcom, Oracle, Red Hat, and IBM. Live sessions are scheduled to accommodate attendees worldwide, fostering a global networking opportunity for Java professionals and enthusiasts. \n With Bruno, Sandra, and Rory as your hosts, you will be entertained throughout the live stream as they invite each speaker to share more insights and learning, with Q&A after each session that attendees find very helpful. Bruno's engaging style, Sandra's enthusiasm, and Rory's humor make learning fun and interactive. Their energy and passion as hosts ensure a delightful experience for everyone. Here are few memories from last year’s event. \n \n Join the JDConf experience and earn Microsoft Rewards! \n We are thrilled to offer Microsoft Rewards points to participants who register or attend the JDConf! \n ⭐ Registration Rewards: Participants who register for one of the JDConf 2025 – America, Europe or Asia – will receive 100 Microsoft Rewards points. \n ⭐ Attendance Rewards: The first 300 attendees to check-in live for one of the JDConf - America, Europe or Asia - will receive 5000 Microsoft Rewards points \n Don't miss this chance to learn from industry leaders, connect with peers, and explore the latest developments in Java, Cloud, and AI. Join us at JDConf 2025 and be part of the future of Java development. 👉 RSVP Now at JDConf.com \n Learn more about our hosts: \n \n You can hear from Bruno in his recent keynote at JavaOne 2025. \n You can Sandra's videos here \n Get to know Rory's work here \n \n 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(1).jpg","associationType":"BODY","width":8001,"height":4500,"altText":""},"BlogTopicMessage:message:4400230":{"__typename":"BlogTopicMessage","subject":"Code the Future with Java and AI – Join Me at JDConf 2025","conversation":{"__ref":"Conversation:conversation:4400230"},"id":"message:4400230","revisionNum":8,"uid":4400230,"depth":0,"board":{"__ref":"Blog:board:AppsonAzureBlog"},"author":{"__ref":"User:user:2133155"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":148},"postTime":"2025-04-03T11:00:00.011-07:00","lastPublishTime":"2025-04-03T16:48:40.606-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" JDConf 2025 is just around the corner, and whether you’re a Java developer, architect, team leader, or decision maker I hope you’ll join me as we explore how Java is evolving with the power of AI and how you can start building the next generation of intelligent applications today. \n Why JDConf 2025? \n With over 22 expert-led sessions and 10+ hours of live content, JDConf is packed with learning, hands-on demos, and real-world solutions. You’ll hear from Java leaders and engineers on everything from modern application design to bringing AI into your Java stack. It’s free, virtual and your chance to connect from wherever you are. (On-demand sessions will also be available globally from April 9–10, so you can tune in anytime from anywhere.) \n Bring AI into Java Apps \n At JDConf 2025, we are going beyond buzzwords. We’ll show you how to bring AI into real Java apps, using patterns and tools that work today. \n First, we’ll cover Retrieval-Augmented Generation (RAG), a design pattern where your app retrieves the right business data in real time, and combines it with AI models to generate smart, context-aware responses. Whether it is answering support queries, optimizing schedules, or generating insights, RAG enables your app to think in real time. \n Second, we’ll introduce AI agents -- software entities that do more than respond. They act. Think about automating production line scheduling at an auto manufacturer or rebooking delayed flights for passengers. These agents interact with APIs, reason over data, and make decisions, all without human intervention. \n Third, we’ll explore the complete AI application platform on Azure. It is built to work with the tools Java developers already know - from Spring Boot to Quarkus - and includes OpenAI and many other models, vector search with PostgreSQL, and libraries like Spring AI and LangChain4j. \n Here are just two example stacks: \n \n Spring Boot AI Stack: any app hosting services like Azure Container Apps or App Service + Spring AI + OpenAI + PostgreSQL for business data and vector data store. \n \n \n Quarkus AI Stack: any app hosting services like Azure Container Apps or App Service + LangChain4j + OpenAI + PostgreSQL for business data and vector data store. \n \n \n This is how you turn existing Java apps into intelligent, interactive systems, without reinventing everything. \n Whether you are an experienced developer or just starting out, JDConf offers valuable opportunities to explore the latest advancements in Java, cloud, and AI technologies; gain practical insights; and connect with Java experts from across the globe – including Java 25, Virtual Threads, Spring Boot, Jakarta EE 12, AI developer experiences, Spring AI, LangChain4j, combining data and AI, automated refactoring to Java app code modernization. We’ll also show you how GitHub Copilot helps you modernize faster. GitHub Copilot's new “upgrade assistant” can help refactor your project, suggest dependency upgrades, and guide you through framework transitions, freeing you up to focus on innovation. \n Get the Right Fit for Your Java App \n And what if your apps run on JBoss, WebLogic, or Tomcat? We will walk you through how to map those apps to the right Azure service: \n \n Monoliths (JAR, WAR, EAR) → Deploy to App Service \n \n \n Microservices or containers → Use Azure Container Apps or AKS \n \n \n WebLogic & WebSphere → Lift and shift to Azure Virtual Machines \n \n \n JBoss EAP containers → Run on Azure Red Hat OpenShift \n \n \n You’ll get clear guidance on where your apps fit and how to move forward, with no guesswork or dead ends. \n Let's Code the Future, Together \n I’ll be there, along with Josh Long from the Spring AI community and Lize Raes from the LangChain4j community, delivering a technical keynote packed with practical insights. If you haven’t started building intelligent Java apps, you can start with JDConf. If you’ve already started on the journey, tune in to learn how you can enrich your experiences with the latest in tech. \n So, mark your calendar. Spread the word. Bring your team. JDConf 2025 is your place to build what is next with Java and AI. \n \n 👉 Register now at jdconf.com. Check out the 20+ exclusive sessions brought to you by Java experts from across the globe in all major time zones. \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"4451","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAwMjMwLWpZWkhOaQ?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAwMjMwLTVoV0RjUA?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAwMjMwLTdQdXE3bw?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAwMjMwLXNKZ2JZcQ?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAwMjMwLUR4VWN2NA?revision=8\"}"}}],"totalCount":5,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAwMjMwLWpZWkhOaQ?revision=8"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4399899":{"__typename":"Conversation","id":"conversation:4399899","topic":{"__typename":"BlogTopicMessage","uid":4399899},"lastPostingActivityTime":"2025-04-02T08:02:48.250-07:00","solved":false},"User:user:2970231":{"__typename":"User","uid":2970231,"login":"Samiksha","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-3.svg?time=0"},"id":"user:2970231"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk5ODk5LUNHbE0wWA?revision=4\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk5ODk5LUNHbE0wWA?revision=4","title":"image.png","associationType":"BODY","width":1651,"height":588,"altText":""},"BlogTopicMessage:message:4399899":{"__typename":"BlogTopicMessage","subject":"Understanding 'Always On' vs. Health Check in Azure App Service","conversation":{"__ref":"Conversation:conversation:4399899"},"id":"message:4399899","revisionNum":4,"uid":4399899,"depth":0,"board":{"__ref":"Blog:board:AppsonAzureBlog"},"author":{"__ref":"User:user:2970231"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"Always On vs. Health Check: Do We Need Both?\n","metrics":{"__typename":"MessageMetrics","views":581},"postTime":"2025-04-02T07:50:52.366-07:00","lastPublishTime":"2025-04-02T08:02:48.250-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n The 'Always On' feature in Azure App Service helps keep your app warm by ensuring it remains running and responsive, even during periods of inactivity with no incoming traffic. As this feature pings to root URI after every 5 minutes. \n On Other hand Health-check feature helps pinging configured path every minute to monitor the application availability on each instance. \n \n What is 'Always On' in Azure App Service? \n The Always On feature ensures that the host process of your web app stays running continuously. This results in better responsiveness after idle periods since the app doesn’t need to cold boot when a request arrives. \n How to enable Always On: \n \n Navigate to the Azure Portal and open your Web App. \n Go to Configuration > General Settings. \n Toggle Always On to On. \n \n What is Health Check in Azure App Service? \n Health check increases your application's availability by rerouting requests away from instance where application is marked unhealthy and replacing instances if they remain unhealthy. \n How to enable Health-Check: \n \n Navigate to the Azure Portal and open your Web App. \n Under Monitoring, select Health check. \n Select Enable and provide a valid URL path for your application, such as /health or /api/health. \n Select Save. \n \n \n So, is it still necessary to enable the 'Always On' feature when Health Check is already pinging your application every minute? \n -> Yes, please find below explanation for the same. \n \n Test App scenario: \n \n Health Check enabled (pointing to /health_check path) and Always On disabled. \n Started the app and sent some user requests. \n \n Observations from the Test: \n \n After the application starts up, health check pings begin following the end user's request. \n Please find below table representing Health-check pings following user's request to root URI. \n \n \n Time Bucket \n \n URL \n \n Status \n \n Request Count \n \n 2025-03-20 07:00:00.0000000 \n \n / \n \n 200 \n \n 6 \n \n 2025-03-20 07:00:00.0000000 \n \n /health_check \n \n 200 \n \n 30 \n \n 2025-03-20 07:30:00.0000000 \n \n /health_check \n \n 200 \n \n 30 \n \n \n \n Subsequent Health-check pings will continue, even in the absence of user requests. \n However, after restarting the app and in the absence of any user requests, we observed that Health Check requests were not initiated. \n This indicates that Health Check does not start automatically unless application is actively running and serving requests. \n \n Conclusion: \n \n Always On ensures that the app is proactively kept warm by sending root URI pings, even post-restart. \n The health-check feature is useful for monitoring application availability when the application is active. However, after a restart, if the application isn't active due to a lack of requests, Health-check pings won't initiate. \n Therefore, it is highly recommended to enable Always On, particularly for applications that need continuous availability and to avoid application process unload events. \n \n \n Recommendation: Enable Always On alongside Health Check to ensure optimal performance and reliability. 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The application is a \"to do list\" with a JavaScript front end and a REST endpoint. Azure Database for PostgreSQL Flexible Server provides the persistence layer for the app. The app utilizes OpenTelemetry to instrument, generate, collect, and export telemetry data for observability. The blog guides you to test your app locally, deploy it to Azure Container Apps and observe its telemetry data with Azure Application Insights. \n Prerequisites \n \n An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. \n Prepare a local machine with Unix-like operating system installed - for example, Ubuntu, macOS, or Windows Subsystem for Linux. \n Install a Java SE implementation version 17 - for example, Microsoft build of OpenJDK. \n Install Maven, version 3.9.8 or higher. \n Install Docker for your OS. \n Install the Azure CLI to run Azure CLI commands.\n \n Sign in to the Azure CLI by using the az login command. To finish the authentication process, follow the steps displayed in your terminal. For other sign-in options, see Sign into Azure with Azure CLI. \n When you're prompted, install the Azure CLI extension on first use. For more information about extensions, see Use and manage extensions with the Azure CLI. \n Run az version to find the version and dependent libraries that are installed. To upgrade to the latest version, run az upgrade. This blog requires at least version 2.65.0 of Azure CLI. \n \n \n \n Prepare the Quarkus app \n Run the following commands to get the sample app app-insights-quarkus from GitHub: \n git clone https://github.com/Azure-Samples/quarkus-azure\ncd quarkus-azure\ngit checkout 2024-11-27\ncd app-insights-quarkus \n Here's the file structure of the application, with important files and directories: \n ├── src/main/\n│ ├── java/io/quarkus/sample/\n│ │ └── TodoResource.java\n│ └── resources/\n│ ├── META-INF/resources/\n│ ├── application.properties\n├── pom.xml \n The directory src/main/resources/META-INF/resources contains the front-end code for the application. It's a Vue.js front end where you can view, add, update, and delete todo items. \n The src/main/java/io/quarkus/sample/TodoResource.java file implements the REST resource for the application. It uses the Jakarta REST API to expose the REST endpoints for the front end. The invocation of the REST endpoints is automatically instrumented by OpenTelemetry tracing. Besides, each REST endpoint uses the org.jboss.logging.Logger to log messages, which are collected by OpenTelemetry logging. For example, the GET method for the /api endpoint that returns all todo items is shown in the following code snippet: \n package io.quarkus.sample;\n\nimport jakarta.inject.Inject;\nimport jakarta.transaction.Transactional;\nimport jakarta.validation.Valid;\nimport jakarta.ws.rs.*;\nimport jakarta.ws.rs.core.Response;\nimport jakarta.ws.rs.core.Response.Status;\n\nimport java.util.List;\n\nimport org.jboss.logging.Logger;\n\n@Path(\"/api\")\npublic class TodoResource {\n\n private static final Logger LOG = Logger.getLogger(TodoResource.class);\n\n @Inject\n TodoRepository todoRepository;\n\n @GET\n public List<Todo> getAll() {\n List<Todo> todos = todoRepository.findAll();\n LOG.info(\"Found \" + todos.size() + \" todos\");\n return todos;\n }\n} \n The pom.xml file contains the project configuration, including the dependencies for the Quarkus application. The application uses the following extensions to support OpenTelemetry: \n <dependency>\n <groupId>io.quarkus</groupId>\n <artifactId>quarkus-opentelemetry</artifactId>\n</dependency>\n<dependency>\n <groupId>io.opentelemetry.instrumentation</groupId>\n <artifactId>opentelemetry-jdbc</artifactId>\n</dependency>\n<dependency>\n <groupId>io.opentelemetry</groupId>\n <artifactId>opentelemetry-exporter-logging</artifactId>\n</dependency> \n The src/main/resources/application.properties file contains the configuration for the Quarkus application. The configuration includes database connection properties for production, such as the JDBC URL and username. The configuration also includes the OpenTelemetry properties, such as enabling OpenTelemetry including logs and JDBC instrumentation at build time, using logging as exporter in development mode, and specifying the endpoint for the OpenTelemetry Protocol (OTLP) exporter in production mode. The following example shows the configuration for the OpenTelemetry: \n quarkus.otel.enabled=true\nquarkus.otel.logs.enabled=true\nquarkus.datasource.jdbc.telemetry=true\n%dev.quarkus.otel.logs.exporter=logging\n%dev.quarkus.otel.traces.exporter=logging\n%prod.quarkus.otel.exporter.otlp.endpoint=${OTEL_EXPORTER_OTLP_ENDPOINT} \n Run the Quarkus app locally \n Quarkus supports the automatic provisioning of unconfigured services in development mode. For more information, see Dev Services Overview in the Quarkus documentation. \n Now, run the following command to enter Quarkus dev mode, which automatically provisions a PostgreSQL database as a Docker container for the app: \n mvn clean package quarkus:dev \n The output should look like the following example: \n 2025-03-17 11:14:32,880 INFO [io.qua.dat.dep.dev.DevServicesDatasourceProcessor] (build-26) Dev Services for default datasource (postgresql) started - container ID is 56acc7e1cb46\n2025-03-17 11:14:32,884 INFO [io.qua.hib.orm.dep.dev.HibernateOrmDevServicesProcessor] (build-4) Setting quarkus.hibernate-orm.database.generation=drop-and-create to initialize Dev Services managed database\n__ ____ __ _____ ___ __ ____ ______\n --/ __ \\/ / / / _ | / _ \\/ //_/ / / / __/\n -/ /_/ / /_/ / __ |/ , _/ ,< / /_/ /\\ \\\n--\\___\\_\\____/_/ |_/_/|_/_/|_|\\____/___/\n2025-03-17 11:14:36,202 INFO [io.ope.exp.log.LoggingSpanExporter] (JPA Startup Thread) 'quarkus' : 80437b598962f82bffd0735bbf00e9f1 aa86f0553056a8c9 CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=set client_min_messages = WARNING, db.system=postgresql}, capacity=128, totalAddedValues=6}\n\n1970-01-01T00:00:00Z INFO ''quarkus' : 80437b598962f82bffd0735bbf00e9f1 aa86f0553056a8c9 CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=set client_min_messages = WARNING, db.system=postgresql}, capacity=128, totalAddedValues=6}' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.lineno=-1, log.logger.namespace=\"org.jboss.logmanager.Logger\", thread.id=122, thread.name=\"JPA Startup Thread\"}\n2025-03-17 11:14:36,236 INFO [io.ope.exp.log.LoggingSpanExporter] (JPA Startup Thread) 'DROP table quarkus' : 6b732661c29a9f0966403d49db9e4cff d86f29284f0d8eac CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.operation=DROP table, db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=drop table if exists Todo cascade, db.system=postgresql}, capacity=128, totalAddedValues=7}\n1970-01-01T00:00:00Z INFO ''DROP table quarkus' : 6b732661c29a9f0966403d49db9e4cff d86f29284f0d8eac CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.operation=DROP table, db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=drop table if exists Todo cascade, db.system=postgresql}, capacity=128, totalAddedValues=7}' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.lineno=-1, log.logger.namespace=\"org.jboss.logmanager.Logger\", thread.id=122, thread.name=\"JPA Startup Thread\"}\n2025-03-17 11:14:36,259 INFO [io.ope.exp.log.LoggingSpanExporter] (JPA Startup Thread) 'CREATE table quarkus' : 54df3edf9f523a71bc85d0106a57016c bb43aa63ec3526ed CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.operation=CREATE table, db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=create table Todo (completed boolean not null, ordering integer, id bigint generated by default as identity, title varchar(?) unique, url varchar(?), primary key (id)), db.system=postgresql}, capacity=128, totalAddedValues=7}\n1970-01-01T00:00:00Z INFO ''CREATE table quarkus' : 54df3edf9f523a71bc85d0106a57016c bb43aa63ec3526ed CLIENT [tracer: io.opentelemetry.jdbc:2.8.0-alpha] AttributesMap{data={db.operation=CREATE table, db.name=quarkus, server.port=59406, server.address=localhost, db.connection_string=postgresql://localhost:59406, db.statement=create table Todo (completed boolean not null, ordering integer, id bigint generated by default as identity, title varchar(?) unique, url varchar(?), primary key (id)), db.system=postgresql}, capacity=128, totalAddedValues=7}' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.lineno=-1, log.logger.namespace=\"org.jboss.logmanager.Logger\", thread.id=122, thread.name=\"JPA Startup Thread\"}\n\n2025-03-17 11:14:36,438 INFO [io.quarkus] (Quarkus Main Thread) quarkus-todo-demo-app-insights 1.0.0-SNAPSHOT on JVM (powered by Quarkus 3.16.3) started in 7.409s. Listening on: http://localhost:8080\n1970-01-01T00:00:00Z INFO 'quarkus-todo-demo-app-insights 1.0.0-SNAPSHOT on JVM (powered by Quarkus 3.16.3) started in 7.409s. Listening on: http://localhost:8080' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.function=\"printStartupTime\", code.lineno=109, code.namespace=\"io.quarkus.bootstrap.runner.Timing\", log.logger.namespace=\"org.jboss.logging.Logger\", thread.id=112, thread.name=\"Quarkus Main Thread\"}\n2025-03-17 11:14:36,441 INFO [io.quarkus] (Quarkus Main Thread) Profile dev activated. Live Coding activated.\n1970-01-01T00:00:00Z INFO 'Profile dev activated. Live Coding activated.' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.function=\"printStartupTime\", code.lineno=113, code.namespace=\"io.quarkus.bootstrap.runner.Timing\", log.logger.namespace=\"org.jboss.logging.Logger\", thread.id=112, thread.name=\"Quarkus Main Thread\"}\n2025-03-17 11:14:36,443 INFO [io.quarkus] (Quarkus Main Thread) Installed features: [agroal, cdi, hibernate-orm, hibernate-validator, jdbc-postgresql, narayana-jta, opentelemetry, rest, rest-jackson, smallrye-context-propagation, vertx]\n1970-01-01T00:00:00Z INFO 'Installed features: [agroal, cdi, hibernate-orm, hibernate-validator, jdbc-postgresql, narayana-jta, opentelemetry, rest, rest-jackson, smallrye-context-propagation, vertx]' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.function=\"printStartupTime\", code.lineno=115, code.namespace=\"io.quarkus.bootstrap.runner.Timing\", log.logger.namespace=\"org.jboss.logging.Logger\", thread.id=112, thread.name=\"Quarkus Main Thread\"}\n\n--\nTests paused\nPress [e] to edit command line args (currently ''), [r] to resume testing, [o] Toggle test output, [:] for the terminal, [h] for more options> \n The output shows that the Quarkus app is running in development mode. The app is listening on http://localhost:8080. The PostgreSQL database is automatically provisioned as a Docker container for the app. The OpenTelemetry instrumentation for Quarkus and JDBC is enabled, and the telemetry data is exported to the console. \n Access the application GUI at http://localhost:8080. You should see a similar Todo app with an empty todo list, as shown in the following screenshot: \n \n Switch back to the terminal where Quarkus dev mode is running, you should see more OpenTelemetry data exported to the console. For example, the following output shows the OpenTelemetry logging and tracing data for the GET method for the /api endpoint: \n 2025-03-17 11:15:13,785 INFO [io.qua.sam.TodoResource] (executor-thread-1) Found 0 todos\n1970-01-01T00:00:00Z INFO 'Found 0 todos' : 7cf260232ff81caf90abc354357c16ab c48a4a02e74e1901 [scopeInfo: io.quarkus.opentelemetry:] {code.function=\"getAll\", code.lineno=25, code.namespace=\"io.quarkus.sample.TodoResource\", log.logger.namespace=\"org.jboss.logging.Logger\", parentId=\"c48a4a02e74e1901\", thread.id=116, thread.name=\"executor-thread-1\"}\n2025-03-17 11:15:13,802 INFO [io.ope.exp.log.LoggingSpanExporter] (vert.x-eventloop-thread-1) 'GET /api' : 7cf260232ff81caf90abc354357c16ab c48a4a02e74e1901 SERVER [tracer: io.quarkus.opentelemetry:] AttributesMap{data={http.response.status_code=200, url.scheme=http, server.port=8080, server.address=localhost, client.address=0:0:0:0:0:0:0:1, user_agent.original=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36 Edg/134.0.0.0, url.path=/api/, code.namespace=io.quarkus.sample.TodoResource, http.request.method=GET, code.function=getAll, http.response.body.size=2, http.route=/api}, capacity=128, totalAddedValues=12}\n1970-01-01T00:00:00Z INFO ''GET /api' : 7cf260232ff81caf90abc354357c16ab c48a4a02e74e1901 SERVER [tracer: io.quarkus.opentelemetry:] AttributesMap{data={http.response.status_code=200, url.scheme=http, server.port=8080, server.address=localhost, client.address=0:0:0:0:0:0:0:1, user_agent.original=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36 Edg/134.0.0.0, url.path=/api/, code.namespace=io.quarkus.sample.TodoResource, http.request.method=GET, code.function=getAll, http.response.body.size=2, http.route=/api}, capacity=128, totalAddedValues=12}' : 00000000000000000000000000000000 0000000000000000 [scopeInfo: io.quarkus.opentelemetry:] {code.function=\"export\", code.lineno=65, code.namespace=\"io.opentelemetry.exporter.logging.LoggingSpanExporter\", log.logger.namespace=\"org.jboss.logmanager.Logger\", thread.id=126, thread.name=\"vert.x-eventloop-thread-1\"} \n Then return to the web browser and interact with the Todo app, try to add a new todo item by typing in the text box and pressing ENTER, selecting the checkbox to mark the todo item as completed, or selecting Clear completed to remove all completed todo items. You can also delete a todo item by selecting the x icon when you hover over it. The app should work as expected. \n Finally, switch back to the terminal and press q to exit Quarkus dev mode. \n Create the Azure resources \n The steps in this section show you how to create the following Azure resources to run the Quarkus sample app on Azure: \n \n Azure Database for PostgreSQL Flexible Server \n Azure Container Registry \n Azure Container Apps environment \n Azure Application Insights \n \n First, define the following variables in your bash shell by replacing the placeholders with your own values. They will be used throughout the example: \n UNIQUE_VALUE=<your unique value> \nLOCATION=eastus2 \nRESOURCE_GROUP_NAME=${UNIQUE_VALUE}rg \nDB_SERVER_NAME=${UNIQUE_VALUE}db \nDB_NAME=demodb \nREGISTRY_NAME=${UNIQUE_VALUE}reg \nACA_ENV=${UNIQUE_VALUE}env \nAPP_INSIGHTS=${UNIQUE_VALUE}appinsights \nACA_NAME=${UNIQUE_VALUE}aca \n Next, create the resource group to host Azure resources: \n az group create \\\n --name $RESOURCE_GROUP_NAME \\\n --location $LOCATION \n Then, create the Azure resources in the resource group by following the steps below. \n Create an Azure Database for PostgreSQL flexible server instance: \n az postgres flexible-server create \\\n --name $DB_SERVER_NAME \\\n --resource-group $RESOURCE_GROUP_NAME \\\n --database-name $DB_NAME \\\n --public-access None \\\n --sku-name Standard_B1ms \\\n --tier Burstable \\\n --active-directory-auth Enabled \n Create the Azure Container Registry and get the login server: \n az acr create \\\n --resource-group $RESOURCE_GROUP_NAME \\\n --location ${LOCATION} \\\n --name $REGISTRY_NAME \\\n --sku Basic\nLOGIN_SERVER=$(az acr show \\\n --name $REGISTRY_NAME \\\n --query 'loginServer' \\\n --output tsv) \n Create the Azure Container Apps environment: \n az containerapp env create \\\n --resource-group $RESOURCE_GROUP_NAME \\\n --location $LOCATION \\\n --name $ACA_ENV \n Create an Azure Application Insights instance: \n logAnalyticsWorkspace=$(az monitor log-analytics workspace list \\\n -g $RESOURCE_GROUP_NAME \\\n --query \"[0].name\" -o tsv | tr -d '\\r')\naz monitor app-insights component create \\\n --resource-group $RESOURCE_GROUP_NAME \\\n --location $LOCATION \\\n --app $APP_INSIGHTS \\\n --workspace $logAnalyticsWorkspace \n Use the created Application Insights instance as the destination service to enable the managed OpenTelemetry agent for the Azure Container Apps environment: \n appInsightsConn=$(az monitor app-insights component show \\\n --app $APP_INSIGHTS \\\n -g $RESOURCE_GROUP_NAME \\\n --query 'connectionString' -o tsv | tr -d '\\r')\naz containerapp env telemetry app-insights set \\\n --name $ACA_ENV \\\n --resource-group $RESOURCE_GROUP_NAME \\\n --connection-string $appInsightsConn \\\n --enable-open-telemetry-logs true \\\n --enable-open-telemetry-traces true \n When you deploy the Quarkus app to the Azure Container Apps environment later in this blog, the OpenTelemetry data is automatically collected by the managed OpenTelemetry agent and exported to the Application Insights instance. \n Deploy the Quarkus app to Azure Container Apps \n You have set up all the necessary Azure resources to run the Quarkus app on Azure Container Apps. In this section, you containerize the Quarkus app and deploy it to Azure Container Apps. \n First, use the following command to build the application. This command uses the Jib extension to build the container image. Quarkus instrumentation works both in JVM and native modes. In this blog, you build the container image for JVM mode, to work with Microsoft Entra ID authentication for Azure Database for PostgreSQL flexible server. \n TODO_QUARKUS_IMAGE_NAME=todo-quarkus-app-insights\nTODO_QUARKUS_IMAGE_TAG=${LOGIN_SERVER}/${TODO_QUARKUS_IMAGE_NAME}:1.0\nmvn clean package -Dquarkus.container-image.build=true -Dquarkus.container-image.image=${TODO_QUARKUS_IMAGE_TAG} \n Next, sign in to the Azure Container Registry and push the Docker image to the registry: \n az acr login --name $REGISTRY_NAME\ndocker push $TODO_QUARKUS_IMAGE_TAG \n Then, use the following command to create a Container Apps instance to run the app after pulling the image from the Container Registry: \n az containerapp create \\\n --resource-group $RESOURCE_GROUP_NAME \\\n --name $ACA_NAME \\\n --image $TODO_QUARKUS_IMAGE_TAG \\\n --environment $ACA_ENV \\\n --registry-server $LOGIN_SERVER \\\n --registry-identity system \\\n --target-port 8080 \\\n --ingress 'external' \\\n --min-replicas 1 \n Finally, connect the Azure Database for PostgreSQL Flexible Server instance to the container app using Service Connector: \n # Install the Service Connector passwordless extension\naz extension add --name serviceconnector-passwordless --upgrade --allow-preview true\n\naz containerapp connection create postgres-flexible \\\n --resource-group $RESOURCE_GROUP_NAME \\\n --name $ACA_NAME \\\n --target-resource-group $RESOURCE_GROUP_NAME \\\n --server $DB_SERVER_NAME \\\n --database $DB_NAME \\\n --system-identity \\\n --container $ACA_NAME \\\n --yes \n Wait for a while until the application is deployed, started and running. Then get the application URL and open it in a browser: \n QUARKUS_URL=https://$(az containerapp show \\\n --resource-group $RESOURCE_GROUP_NAME \\\n --name $ACA_NAME \\\n --query properties.configuration.ingress.fqdn -o tsv)\necho $QUARKUS_URL \n You should see the similar Todo app when you ran the app locally before. Interact with the app by adding, completing and removing todo items, which generates telemetry data and sends it to Azure Application Insights. \n Observe the Quarkus app with Azure Application Insights \n Open the Azure Portal and navigate to the Azure Monitor Application Insights resource you created earlier. You can monitor the application with different views backed by the telemetry data sent from the application. For example: \n \n Investigate > Application map: Shows the application components and their dependencies. \n Investigate > Failures: Shows the failures and exceptions in the application. \n Investigate > Performance: Shows the performance of the application. \n Monitoring > Logs: Shows the logs and traces of the application. \n \n You may notice that metrics are not observed in the Application Insights in this blog, that's because the Application Insights endpoint used in the managed OpenTelemetry agent doesn't accept metrics yet, which is listed as a known limitation. This is also the reason why Quarkus metrics is not enabled in the configuration file with quarkus.otel.metrics.enabled=true above. Alternatively, you can consider using Quarkus Opentelemetry Exporter for Microsoft Azure in your Quarkus apps to export the telemetry data directly to Azure Application Insights. \n Clean up resources \n To avoid Azure charges, you should clean up unneeded resources. When the resources are no longer needed, use the az group delete command to remove the resource group and all Azure resources within it: \n az group delete \\\n --name $RESOURCE_GROUP_NAME \\\n --yes \\\n --no-wait \n Next steps \n In this blog, you observe the Quarkus app with Azure Application Insights using OpenTelemetry. 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There's no extra cost to use WebJobs. This sample uses a Triggered (scheduled) WebJob to output the system time once every 15 minutes. \n Create Web App \n Before creating our WebJobs, we need to create an App Service webapp. If you already have an App Service Web App, skip to the next step Otherwise, in the portal, select App Services > Create > Web App. After following the create instructions and selecting one of the Java runtime stacks, create your App Service Web App. The stack must be Java, since we plan on writing our WebJob using Java and a bash startup script. \n Next, we’ll add a basic WebJob to our app. \n Create WebJob \n Before we do anything else, let’s write our WebJob. This will execute every time our WebJob is triggered. WebJobs on App Service can be run on a Triggered (Scheduled) basis or Continuously. This example uses a Triggered WebJob. \n For this example, we’ll need to compress two files into a zip archive that we’ll upload to our App Service Web App. We’ll need a startup script and an executable jar. \n The full code for this sample is available at: https://github.com/Azure-Samples/App-Service-Java-WebJobs-QuickStart \n Digging into the code, our Java project located at project/src/main/java/webjob/HelloWorld.java is relatively simple. It just outputs a message & the current time to the console. \n import java.time.LocalDateTime;\npublic class HelloWorld {\n public static void main(String[] args) {\n System.out.println(\"------------------------------------------------------------\");\n System.out.println(\"Hello World from WebJob: \" + LocalDateTime.now());\n System.out.println(\"------------------------------------------------------------\");\n }\n} \n The run.sh script located at the root of our git repo, just runs a jar with the name that we’ve set in our maven configuration. This script will run when our WebJob is triggered, and it is the job of this script to kick off our Java executable (jar). \n java -jar webjob-artifact-1.0.0.jar \n Now we need to compile our Java project to produce the executable jar. There are multiple ways to do this, but for this example, we’ll use Maven. Run the following commands from the project/ directory: \n mvn install\nmvn package \n After successfully building our app with mvn package, our jar file will be located at project/target/webjob-artifact-1.0.0.jar. \n Now we have everything we need to assemble our zip file. Again, there are multiple ways to do this, but for this demo we’ll use the zip CLI utility to create our zip file called webjob.zip. First move your jar file to the root of the git repo with project/target/webjob-artifact-1.0.0.jar. Then run the zip command to package our application as a zip file. \n zip webjob.zip run.sh webjob-artifact-1.0.0.jar \n Now it’s time to create our WebJob and upload our zip file. \n Create WebJob in Portal \n Start by entering your Web App overview page. Then, under Settings select WebJobs. Here we can create and manage our App Service WebJobs for this Web App. Click Add to create a new WebJob. \n \n \n \n \n \n Now we can name our WebJob, upload our zip from the previous step, and choose our execution type. Under Type, select Triggered. Under CRON Expression, enter the following to trigger our WebJob once every 15 minutes. \n \n 0 */15 * * * * \n \n Note: These are NCRONTAB expressions, not standard Linux CRONTAB expressions. An important distinction. \n Now click Create WebJob to finish making our new WebJob. Let’s test it out now. \n \n Run Manually or Scheduled \n To manually test our WebJob, we can click the play button under Run. A status of Completed means that WebJob is finished. \n Confirm Results in Logs \n We can check the logs to confirm that the console print statements from our Java file were output to the console. \n \n \n \n While this is a basic example, WebJobs are a powerful and easy to use feature that have incredible utility for running scheduled (or continuous) actions in conjunction with your App Service Web Apps at no additional cost. \n Learn more about WebJobs on App Service \n \n 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