Engineering With Java: Digest #78
GraalVM native image, Reactive Java, Profiling, N+1 problem, Coding principle and more...
👋 Java Devs! Welcome to this week’s addition (#78)! I hope you’re all doing great.
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🗒️ Articles Of The Week (7)
GraalVM Native Image enables Java applications to be compiled ahead-of-time into standalone binaries, dramatically reducing startup time and memory usage, making Java competitive with Rust or Go for serverless, microservices, and edge workloads. While native images improve cold-start performance and scaling efficiency, they introduce trade-offs such as longer build times, limited dynamic features, and potentially lower peak throughput for long-running JVM workloads.
Project Reactor is presented as the core reactive programming library for Java, built on the Reactive Streams specification to enable non-blocking, backpressure-aware data pipelines. This article explains key abstractions like Flux and Mono, highlights when reactive architectures improve scalability, and stresses that reactive programming is about efficient resource usage rather than raw speed.
Unikernel: Profiling and Troubleshooting JVM on Nanos Unikernel
This article shows that running Java inside a unikernel does not limit observability or tooling, demonstrating how a JVM application on a Nanos unikernel can still be profiled using standard tools like JProfiler. By embedding the profiler agent and exposing the required ports, developers can inspect CPU, memory, GC, and threads exactly as with a regular JVM, proving that unikernels change the deployment model but not the JVM’s diagnostic capabilities.
Spring Data JPA can silently introduce the N+1 query problem when lazy-loaded relationships are accessed inside loops, causing excessive database round-trips and major performance degradation. The article explains when lazy loading is appropriate and outlines practical fixes such as JOIN FETCH, entity graphs, batching, or writing explicit SQL for read-heavy queries
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This article highlights practical coding principles for building maintainable Spring Boot applications, emphasizing clean architecture, proper layering, and separation of concerns. It stresses writing readable, testable code by keeping business logic out of controllers, handling exceptions centrally, and structuring services clearly to improve scalability and long-term maintainability.
This guide shows how to use Claude Code on Ollama to build applications that blend natural language interaction with local model execution, enabling developers to prototype AI‑driven features without cloud dependencies. It walks through setup, basic prompt workflows, and practical tips for integrating Claude Code into real projects for responsive, cost‑efficient AI functionality.
State of Java, AI, Cloud Costs, and the Future of the Java Ecosystem
The 2026 State of Java Survey of over 2,000 professionals highlights that Java remains central to enterprise development, with 62% using it for AI features and 31% saying half their apps now include AI functionality. It also shows rising concern about Oracle Java pricing, driving many organizations toward open‑source OpenJDK, and that cloud cost optimization with high‑performance Java platforms is a top strategic priority. Additionally, hidden technical debt from unused code and security noise continues to slow productivity.
🔥 Recently Published In-house Blogs (5)
Java Bug Fix Coding Question - Call to External API
Spring Boot Interview Question - Transactional Trap
Spring Boot Interview Question: Connection Pool Exhaustion
Spring Boot Interview Question: Batch Normalization API
Spring Data JPA Interview Question: Production Outage Due to ID Generation
▶️ Videos Of The Week (3)
This walkthrough demonstrates building a terminal UI in Java using the new Tamboo UI framework to streamline Spring project bootstrapping directly from the terminal. The tool integrates with Spring Initializr, automates project generation, opens the IDE, and runs post-generation commands, showing how small custom tools can meaningfully improve developer workflows. It highlights how modern Java tooling, native images, and AI assistants enable rapid creation of personalized productivity utilities.
This discussion revisits checked exceptions, arguing that instead of debating their existence, the focus should shift to improving their usability in APIs and the language. It highlights issues like inheritance-driven checkedness, awkward API design, and poor fit with deferred execution, while exploring ideas such as better compiler support, richer type systems, and selective use of alternatives like Either. The overall message is that effective error handling is inherently complex, and meaningful improvements require thoughtful language, library, and design evolution.
This demo showcases a Java-based AI assistant that combines Spring Boot, Spring AI, MCP, and OSQuery to create a natural-language interface for system diagnostics, even adding a voice UI built with JavaFX. By compiling the backend to a GraalVM native image, the application achieves extremely fast startup while using virtual threads and reactive streams for concurrent data gathering. The project illustrates how modern Java features can power responsive AI-driven developer tools without leaving the Java ecosystem.
🧑💻 Jobs Of The Week (37)
Thats all for this week! Thanks for reading this far. If you liked it please share with your network.
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Happy Coding 🚀
Suraj