A tinkering programmer and author of "Spring Cloud Microservices in Action"
The article recounts a startup’s painful experiment with 47 microservices, exposing hidden operational costs, deployment delays, and outage risks, and ultimately shows how consolidating back to a well‑structured monolith dramatically improved performance, cost, and delivery speed.
The article reveals how default Jackson JSON serialization can become a hidden CPU bottleneck in high‑traffic Spring Boot services, explains why full entity objects inflate serialization time, and presents practical optimizations such as @JsonView, custom ObjectMapper settings, hand‑written serializers, and alternative data formats to dramatically cut latency and cloud costs.
A recent study shows that deliberately shortening or misspelling Java variable names can reduce latency and increase throughput by up to 49%, revealing hidden performance costs in the JVM's string handling and prompting a data‑driven, selective naming strategy for high‑throughput systems.
This tutorial shows how to integrate UnifiedTTS's free Edge TTS service into a Spring Boot project, covering project setup, API key registration, configuration, request/response models, service implementation, unit testing, and runtime verification with sample code and images.
This article explains how enterprise architects can integrate ONNX‑based machine‑learning inference directly into Java applications, covering tokenizer integration, GPU acceleration, deployment patterns, and lifecycle management to achieve secure, scalable, and observable AI services without relying on Python runtimes.
This guide explains why Java struggles with high‑performance or data‑intensive workloads, introduces GPU acceleration with CUDA, compares integration options such as JCuda, JNI, and JNA, walks through a practical encryption use case with performance benchmarks, and provides production‑grade best practices for memory, threading, testing, security, and deployment.
This tutorial demonstrates how to integrate multiple large language models from different providers into a Spring Boot application using Spring AI, configure primary, secondary, and tertiary models, and implement a fallback mechanism with Spring Retry to ensure high availability of the chatbot.
This article explains how Netflix’s engineering teams shifted incident handling from a centralized SRE function to a company‑wide, engineer‑driven practice by selecting the right tooling, standardizing processes, and reshaping culture, enabling rapid, reliable responses for hundreds of millions of viewers.
This article explores how developers can evaluate the reliability of AI‑generated code by examining three key dimensions—probability of error, impact of mistakes, and detectability—and offers a structured approach to balance speed with safety.
Learn how to prevent duplicate execution of Spring Boot @Scheduled tasks in multi-instance deployments by integrating ShedLock, including Maven dependencies, H2 database setup, LockProvider configuration, and annotation usage to ensure each job runs only once across all nodes.
