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It’s loaded with stats about the challenges Java developers are facing—and the tools and technologies they’re using to solve them. ... Perhaps the biggest shift noted in the 2025 Java Developer Productivity Report is a wholesale move from older JDK versions such as Java 8 and Java 11, to JDK versions for which Oracle currently offers long-term support (LTS). Those LTS versions are currently Java 17 and Java 21, with Java 25 coming out later in 2025. This is likely a continued response to a revised LTS cadence from Oracle. … One downside to cloud development is redeploy times. For many organizations, redeploys in local development environments are 2 minutes or less. That flips in cloud development environments, where 52% are 5+ minutes. That’s a massive inefficiency where developers are waiting 5 or even 10 minutes for a single redeploy. As a result, we’re seeing more and more customers identify inefficiencies around cloud development environments and actively look to address them. Back to top

Even in 2026, experienced Java developers continue to repeat the same architectural and design mistakes that silently damage scalability, reliability, and maintainability. In this video, we break down the most common and costly mistakes Java developers still make in real-world production systems, especially in microservices, concurrency, caching, logging, and async design. These are not beginner mistakes. These are problems seen in senior-level systems that pass reviews, work in staging, and fail under real load. We start by addressing premature microservices adoption. Many teams split systems before truly understanding the business domain, leading to distributed transactions, fragile data consistency, painful debugging, and unnecessary operational complexity. Microservices are not a future-proofing strategy. They are a response to real organizational and scaling pain. Next, we expose the illusion of async code that still blocks threads. Using async annotations with blocking I/O only shifts the problem to another thread pool. Under load, this leads to thread starvation, unpredictable latency, and production-only failures that are extremely hard to diagnose. We then dive into logging mistakes that either flood systems with useless data or provide no actionable context during incidents. Good logging is about decisions and traceability, not dumping objects or hiding errors. Poor logging often becomes the root cause of outages rather than the tool to resolve them. Caching is another area where teams routinely fail. Adding cache without a clear ownership and invalidation strategy introduces stale data bugs, cache stampedes, and trust issues that surface only at scale. Performance improvements mean nothing if correctness is compromised. We also cover concurrency mistakes, including lost exceptions in thread pools, misunderstood CompletableFuture behavior, and improper error handling in parallel execution. With modern Java features like structured concurrency, these problems are solvable but only if developers understand how failures propagate. Finally, we discuss architectural shortcuts like treating microservices as mini monoliths and blindly overusing Lombok without understanding the generated code. These choices create tight coupling, versioning nightmares, and subtle runtime bugs. This video is for developers who want to build systems that survive real traffic, real failures, and real growth, not just pass local tests or look good on resumes.

From legacy code to cloud-native architecture, performance tuning to staying current with the latest tools—what keeps you up at night? ... {ts:1716} with the for for something that doesn't have the expected result right at least I see I see I have seen some of these use some of these cases I don't know if you also had this similar experiences there's also the hype problem right hype {ts:1735} problem there's Most of the times there is not a clarity of which problem we're trying to solve or which goals we are trying to accomplish. … So maybe in his case the problem was not what type of {ts:1849} architect architecture this is not the problem that he has now. Yeah, maybe as he shared here uh if you work for example for a project a custom project a closed project probably you have much more challenge regarding to code and {ts:1869} cool stuff that we like probably you have that kind of things that usually we do not have in microser unfortunately this is what happen in most of the case of course there will be some small actually some bigger microser

☕ Java pain points 🤕 and fix 💡 ☕ Java pain points 🤕 and fix 💡 with Active projects in the OpenJDK community 🧵 Loom: Lightweight concurrency Pain point fixed: Threads are too expensive, don't scale Comparison: Go, Elixir 🗑️ ZGC: Sub-millisecond GC pauses Pain point: GC pauses too long Comparison: C, Rust 🏝️ Panama: Native code and memory interop SIMD Vector support Pain point: Using native librairies is too long. Numeric loops are too slow. Comparison: Python, C 🔥 Amber: Right-sizing language ceremony Pain point: Java is too verbose. Java is hard to teach. Comparison: C#, Kotlin ⚡ Leyden: Faster startup and warmup Pain point: Java starts up too slowly Comparison: Go ⛅ Valhalla: Value types and specialized generics Pain point: Cache misses are too expensive. Generics and primitives don't mix. Comparison: C, C#

## Virtual Threads finally without Pinning For nearly two years since their debut in JDK 21 (yes, JDK 21 was published 2 years ago), the results of Project Loom had one major “but” that effectively cooled enthusiasm in large codebases: pinning virtual threads on … block could pin them to a platform thread - destroying scalability where Java has the most legacy code… so in places where failures tend to surface at the least expected moment. Yes, the documentation warned about it, conference talks advised migrating to … Now virtual threads become compatible with idiomatic Java of the past, so migration stops being an architectural project and becomes a purely runtime decision. ... From the very beginning, the Stream API contained a certain contradiction. On the one hand, it offered an elegant, functional way to describe data processing - it is probably the widest used “new” Java feature. However, it was surprisingly closed to extension, which clashes strongly with what one would expect from this kind of functionality… especially people coming from more functional background were rolling eyes. For years, JVM developers kept coming back to the same problem: as long as … This was a quiet but very real structural problem. Every new JDK release meant a race: does ASM already support the new classfile format? Have the frameworks caught up? Will a preview feature break builds? The ecosystem lived in a state of permanent synchronization with a library that was formally “external” but, in practice, absolutely critical. … Even though most of you will never touch classfiles directly, this is a change with enormous significance for the long-term maintainability of the platform. Fewer dependencies, fewer risky upgrades, less “waiting for someone to release a new version of ASM.” But it’s also something more: the internalization of critical infrastructure as certain parts of the ecosystem are too fundamental to live outside the standard library. … The most important signal came early: GraalVM for JDK 24 is the last release licensed as part of Java SE. The experimental Graal JIT, which in Oracle JDK 23 and 24 could replace C2 as an alternative compiler, has been withdrawn. Native Image is no longer part of the Java SE offering, and Oracle is explicitly steering customers toward Project Leyden as the default path for JVM startup and runtime optimization. For many observers, this was a “wake-up moment”: GraalVM is not disappearing, but it is no longer the universal answer to all of Java’s problems that it seemed to be over the past decade. At the same time - and here lies the most interesting paradox - GraalVM has never been technologically stronger than it was in 2025. Instead of focusing on being a “better HotSpot,” the project fully embraced its unique identity: extremely aggressive AOT, a compiler as a research platform, and a proving ground for techniques that the classic JVM cannot adopt as quickly. … ... Now that it no longer has to “save Java,” it can focus on what it does best: experimenting faster than the mainline, bringing ML into compilation, and pushing the boundaries of AOT. And Java - paradoxically - comes out better for it, because instead of one tool for everything, it gains a coherent spectrum of solutions.

Perforce Software’s 2025 *Java Developer Productivity* report revealed barriers to Java developer productivity can take on many forms, but the most significant issue hurting their ability to work effectively was insufficient documentation (41%). Lacking robust documentation means devs have to spend more time initially getting to grips with the code, architecture, or tools they are working with, bogging down their ability to actually get to the task at hand. It can also feed into various further friction points when conducting maintenance and debugging, where the developer is not able to understand the previous decisions made during development and thus more time is taken to troubleshoot issues. Communication issues between teams were also a common problem cited by businesses, with 38% of developers reporting it was hurting their productivity. Bad communication can lead to improper implementation or developers misunderstanding the requirements of the project or feature they are working on, forcing them to spend time reworking and remediating mistakes. Other commonly cited barriers to Java developer productivity were mismanaged timelines (32%), long redeploy times (29%), developer turnover (26%), and insufficient developer tools (24%). … “About 40% of software developer time is toil not related to writing code – whether it’s waiting for builds to complete, being on call to troubleshoot, and resolving security issues,” he explained. “A lot of the perceived productivity problems developers face stem from process inefficiencies, bottlenecks during testing and quality control, and a lack of automation across the software delivery lifecycle (SLDC).” … The most popular method to boost developer productivity was using AI tools, cited by 50% of respondents, with code completion (60%) and refactoring (39%) being the top use cases highlighted by devs.

Key findings centered on respondents’ biggest barriers to Java productivity, with a combined 53% citing insufficient tooling and long redeploys, which cause disruption in developer workflow. Other barriers to Java productivity include insufficient documentation (41%), communication issues between teams (38%), mismanaged timelines (32%), and developer turnover (26%). **Cloud Redeploy Times Create Productivity Barrier** The survey also found that 70% of respondents are using remote, containerized, or cloud-based development environments. Within that contingent, however, 52% noted redeploys of over five minutes—far surpassing the 23% who noted redeploys of that length in local development environments. That pain point is even more pronounced for enterprise respondents, 54% of which noted remote deploys of five minutes or longer. In addition, more than 45% of respondents noted they had seen an increase in microservices startup times—with 22% of respondents noting an increase in their microservice startup times of 10% or more. “Cloud and microservices environments are becoming increasingly complex.

Written by Perforce CTO Rod Cope and developer tools exec Jeff Michael, the "2025 Java Developer Productivity Report " reveals a landscape undergoing significant shifts, particularly in hiring trends, tooling budgets, and the adoption of new Java versions. Although enterprises continue to prioritize Java for their mission-critical applications, the report's authors found, economic headwinds and the complexities of modern development environments are pushing teams to do more with less. … Headcount and Budget: A Sign of the Times One of the most telling takeaways from this year's report is the cooling of Java hiring. In 2024, 60% of companies planned to add Java developers, the authors found. That's a number that has dropped to 51% in 2025. Similarly, the percentage of companies planning to increase their developer tool budgets has fallen from 42% in 2024 to 34% this year. Those numbers underscore what appears to be a tightening in development spending, a trend reflective of broader economic concerns. … JDK Upgrades: The LTS Migration is in Full Swing Another key trend surfaced in this report is the accelerated shift toward long-term support (LTS) versions of Java. A majority of respondents report using Java 17 (61%) and Java 21 (45%), marking a significant departure from the era where Java 8 and Java 11 dominated. ... Notably, many organizations continue to use unsupported Oracle distributions, with 35% still on Java 8 and 32% on Java 11. ... While IntelliJ IDEA remains the dominant IDE (84%), VS Code has now overtaken Eclipse as the second most-used IDE, with 31% of respondents using it, compared to Eclipse’s 28%. ... 42% of respondents reported using more than one IDE, with many IntelliJ IDEA users also leveraging VS Code for certain tasks. ... Remote Development and Cloud Adoption: Productivity vs. Latency With 51% of respondents working in remote or containerized development environments, cloud-based Java development is clearly on the rise, the authors concluded. However, this transition is not without challenges. Redeploy times in cloud environments remain a major productivity bottleneck, with 27% of developers experiencing redeploys of 10+ minutes. That's a stark contrast with the sub-two-minute redeploy times common in local environments. Interestingly, cloud provider choice plays a role in these delays. 60% of AWS users reported redeploy times of five minutes or longer, whereas Microsoft Azure (53%) and Google Cloud Platform (43%) fared slightly better. This could indicate that companies need to consider not just cost and features when choosing a cloud provider but also the impact on developer efficiency, the authors warned … AI-powered coding tools are no longer a novelty. They’re becoming a staple in Java development. Only 12% of respondents reported not using AI tools, while another 12% said their companies prohibit AI use. The rest? They're embracing AI-powered assistance, with GitHub Copilot (42%) and ChatGPT (52%) being the most commonly used AI tools. Developers are primarily leveraging AI for code completion (60%) and refactoring (39%), with growing adoption for debugging, documentation generation, and automated testing. However, concerns about AI accuracy and security remain, particularly for enterprise developers, who show a clear preference for development-specific AI tools over generic AI chatbots. Final Thoughts: Productivity in an Era of Constraints The "2025 Java Developer Productivity Report "paints a picture of an industry facing economic constraints, cloud development challenges, and the growing complexity of modern architectures. But the report also highlights opportunities for teams willing to embrace AI, optimize their tooling, and strategically navigate their cloud and architecture choices." "While economic uncertainty remains, the value of investing in Java remains steadfast as the language celebrates its 30th birthday this year, Cope wrote.

Occasionally, I tried exploring Java but avoided diving deeper. Writing System.out.println("Hello World") just to print a single line felt unnecessarily verbose. On top of that, there was widespread negativity toward Java from YouTube content creators. People were constantly talking about Rust, Go, Ruby on Rails, and Zig, leaving Java to feel like an outdated relic. … - **Debugging Frustrations:** Now the first pain point for me was the fact that to make any changes I had to compile the class file and replace it in an .ear file and restart Tomcat server, the whole process would take about 5 mins. It was frustrating. In NodeJS I just had to use nodemon to get the changes reflected. - **Understanding OOP code:** Getting used to read OOP (Object Oriented Programming) based code was new to me. It got confusing sometimes, classes extending, implementing and what not. - **The Perception of Obsolescence:** My overall perception was that no one is starting new projects in Java in 2024. The most of the resources I find online are pretty outdated, they might be relevant but in JavaScript, you'll always find the most fresh content out there. ## Living with the Pain ### The pain of Debugging - If you aren't familiar above is a sample image of how stack trace in Java looks like. Initially it was really scary to search in the log file of 50K lines and seeing a lot of caused by chains. The above image paints a nice picture of how debugging in Java can be sometimes. but with time I realised that the useful message is in the bottom most caused by chain mostly. - But sometimes the root cause might be wildly unrelated to the error thrown but this is not something specific to Java, I have faced issues like this javascript as well. - I learned patience and to analyse problems deeply before taking any action to avoid the tedious process of compiling class files and replacing them in the ear archives deployed on Tomcat. ### Java has Evolved but has your organisation? One major feature of Java is its excellent backward compatibility. While this ensures stability, it also means that organizations tend to avoid updating older systems. For instance: - Using XML-based configurations for Spring Beans. - Relying on Enterprise Java Beans (EJBs). - Employing Ant for complex build processes. These older practices can make development unnecessarily complex. Modern tools like Spring Boot and Gradle are far more developer-friendly, yet many companies stick to the old ways. 🥲 … I believe that recent versions have picked up a lot of modern features (e.g. Functional Programming / FP style constructs a la map/filter/reduce etc) - but I'm not following it anymore, there's no incentive, and I don't really have a reason to. ... Ignoring the quarkus, spring, kotlin palliatives I think the main issue is the OOP itself, modern systems are less "heavy hierarchy of classes designed ahead by great luminaries" and more "dynamic smaller systems that can be upgraded on the go". Java works for the second as well, but seems clunkier

## Spending Is Slowing, But Investing in Java Tools Is More Important Than Ever Long-forecasted economic headwinds have arrived in force for nearly all industries. While it’s clear development spending has slowed across the board, enterprises (1,000+ employees) are still carving out budgets for the people and tools necessary to maintain their mission-critical business applications. Regardless of company size, development teams are now being tasked with doing more with less — and in increasingly complex development environments. Compounding that issue is tightened budgets and oftentimes blanket hiring freezes. Developers now have more to do and greater time burdens for those tasks, which makes investing in Java tools more important than ever. … **Source: ** **2025 Java Developer Productivity Report ** ... Respondents were most likely to turn to AI tools for code completion (60%) and refactoring (39%). Error detection (30%), documentation generation (28%), debugging assistance (26%), and automated testing (21%) were also ripe use cases. ... ## Remote Redeploy Times Are a Barrier to Java Productivity The 2025 Java Developer Productivity Report found that 70% of respondents are using remote, containerized, or cloud-based development environments. That stat should surprise exactly no one, given the flexibility that cloud affords. **Source: ** **2025 Java Developer Productivity Report ** That flexibility comes at a cost, however. 23% of respondents say their redeploys are five minutes or longer for local development environments, but 52% saw redeploys of that length for remote development environments. And 13% of respondents said that remote redeploys take 10-plus minutes.

{ts:1048} stuff right away and you don't have to like hunt around for it. Um for IO some {ts:1052} people saying that IO and Java is {ts:1054} cumbersome. So there's still some people who who program with buffered reader in {ts:1058} 2025. Don't do that. Um so the new IO

- I'll be honest - my first encounter with Java in 1999 wasn't love at first sight. It was during a university distributed systems course, and coming from a background in assembler and C, Java felt bloated, slow, and frankly untrustworthy. That "automatic memory handling" seemed like dangerous magic I couldn't control. After years of meticulously tracking every allocated memory block, the idea that the runtime would handle memory management felt like giving up control to forces I didn't understand. ... The team needed Java expertise because while they'd updated the JRE, the codebase wasn't using any features added since Java 1.4, including generics. I spent months helping add type information to collections throughout the system. We changed untyped Arrays to typed Lists, added types to Maps, and gradually eliminated the cognitive overhead of tracking what each collection contained. ... The real impact of generics went beyond type safety. Every time we encountered a List in the old codebase, we had to hunt through the code to figure out what it contained. With generics, the intent was immediately obvious. It helped us decrease the mental burden working with the code. This pattern has continued through Java's evolution. Features like records, pattern matching, and enhanced switch expressions all follow the same principle: make code that aligns with how developers naturally think about problems. … ... This code aligns perfectly with how our brains naturally categorize and handle different cases. The compiler ensures we've handled every possibility, reducing the mental overhead of "what if" scenarios. ... This backward compatibility isn't just a technical achievement; it's a career investment. The Java skills you learn today won't become obsolete in five years. They'll be refined, enhanced, and made more powerful, but they'll still be relevant. Oracle and the Java community have maintained this commitment to compatibility through: - Careful deprecation cycles that give developers time to adapt - Module system design that doesn't break existing code - Preview features that let developers experiment without commitment - LTS (Long Term Support) releases that provide stability for enterprise environments This means when you invest time learning Java, you're not just learning a language, you're investing in a platform that will support your career growth for decades.