Engineering With Java: Digest #93
Fresh Java reads, so your backlog stays stale. 😄
👋 Java Devs! Welcome to this week’s addition! I hope you’re all doing great.
This week, we cover essential insights on:
One Quick Announcement:
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Articles Of The Week
The Hidden Tradeoffs of Event-Driven Design : This article shares hard-earned lessons from building large-scale event-driven systems, showing that the biggest production challenges often come from state management, consumer bottlenecks, and cache consistency, not Kafka itself. It explains practical trade-offs between local caches, Redis, synchronous communication, and event streams, arguing that successful architectures combine asynchronous and synchronous patterns rather than relying on event-driven design everywhere.
Hardwood Promises High-Speed JVM Apache Parquet Processing : Hardwood is a new open-source Java library that reimagines Apache Parquet reading with near-zero mandatory dependencies and a multi-threaded architecture, delivering faster performance than the traditional parquet-java implementation. Designed by Gunnar Morling, it provides both row- and column-oriented APIs while avoiding the heavy Hadoop dependency tree, making it ideal for modern JVM data processing workloads.
Save Memory in Java by Making Memory Efficiency Your Top Priority : This article argues that memory efficiency should be a first-class design goal in Java, as reducing object overhead, choosing the right data structures, and optimizing object layouts often delivers bigger gains than GC tuning. Drawing on real-world experience and Eclipse Collections, Don Raab demonstrates how measuring memory with tools like JOL and making small, deliberate optimizations can dramatically improve scalability without sacrificing performance.
Dead Letter Queue Patterns in Apache Flink : This article explains why Dead Letter Queues (DLQs) are essential in Apache Flink to prevent a single "poison message" from repeatedly crashing a streaming job. It recommends production-ready patterns such as side outputs, exponential backoff retries, tiered DLQs, durable Kafka/S3 sinks, monitoring, and dedicated replay pipelines to ensure bad records are isolated, debuggable, and safely reprocessable.
Resilience4j Circuit Breakers in Spring Boot That Actually Trip : This article explains that Resilience4j circuit breakers only trip after collecting enough failure data—they don't block traffic on the first error. It walks through how sliding windows, minimum call thresholds, failure/slow-call rates, and the CLOSED → OPEN → HALF_OPEN state transitions determine when requests are rejected, helping developers understand why breakers sometimes appear to "never trip."
Continuous Development with Claude Code on GitHub : This article demonstrates how to turn Claude Code into a continuous AI contributor on GitHub by integrating the Claude GitHub App with GitHub Actions, allowing it to implement issues, review pull requests, and submit tested code automatically. Using a Spring Boot sample project, it shows how AI-driven development workflows can automate much of the feature implementation and review cycle while keeping developers in control through GitHub's existing collaboration model.
Building an AI-Powered Operations Assistant with Spring AI and MongoDB Atlas : This article completes the AI Operations Assistant series by adding stateful workflows, checkpointing, and human-in-the-loop approvals, enabling multi-step investigations to pause, resume, and survive application restarts. Built with Spring AI and MongoDB Atlas, it combines tool calling, persistent workflow state, audit trails, and operator approval to create production-ready AI agents that are reliable, transparent, and safe for operational tasks.
Quarkus REST with Apache Camel and Keycloak : This article demonstrates how to build a secure Quarkus REST API using Apache Camel's REST DSL, Hibernate Panache, and Keycloak for OIDC authentication, all with minimal configuration. It also highlights Quarkus Dev Services, automatic Keycloak provisioning, and the integration of Swagger/OpenAPI with Camel routes, making it easy to develop, test, and document secured REST services.
Videos Of The Week
A Decade of Redefining Java Performance : This talk traces the evolution of Java garbage collection, showing how ZGC transformed Java from suffering long GC pauses to delivering sub-millisecond pause times suitable for low-latency applications. It explains ZGC’s concurrent, generational design, its scalability to terabyte-sized heaps, and recent improvements in JDK 21–26 that make it easier to use, more efficient, and ready for future innovations like automatic heap sizing.
SIMD Vectors in the HotSpot JVM : Java is steadily improving its ability to leverage modern CPUs through auto-vectorization and the Vector API, enabling multiple data elements to be processed with a single instruction for significant performance gains. This talk demonstrates how these technologies accelerate common algorithms like reductions and dot products while keeping Java portable across hardware. It also highlights ongoing OpenJDK work to expand automatic optimizations and mature the Vector API for AI, scientific computing, cryptography, and other compute-intensive workloads.
The Spring AI Ecosystem in 2026: From Foundations to Agents : Spring AI is evolving from simple prompt pipelines into a complete framework for building AI applications with memory, RAG, guardrails, structured outputs, tool calling, and agentic workflows using a pluggable Advisor pattern. It also embraces Model Context Protocol (MCP), making it easier for LLMs to securely interact with external systems, APIs, databases, and enterprise tools through standardized connectors. Together, Spring AI and MCP provide the foundation for building observable, extensible, production-ready AI agents in the Java ecosystem.
The New @RedisListener Simplifies Everything : Spring Boot 4.1 introduces first-class Redis Pub/Sub listeners with @RedisListener, eliminating the boilerplate of manually configuring RedisMessageListenerContainer and MessageListenerAdapter. Developers can now consume typed JSON payloads directly, making Redis messaging feel as simple and consistent as @KafkaListener or @RabbitListener. This significantly reduces setup code while improving readability and developer experience.
In-house Blogs
Spring Boot Interview Question — Real-Time Seat Map Updates
Spring Boot Interview Question - Kafka Consumer Became a Zombie
Java Interview Question - Find Minimum Version in Rotated Release History
Thats all for this week friends! Thanks for reading this far. If you liked it please share with your network.
Happy Coding 🚀
Suraj
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