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This article explains why Java thread pools need tuning, walks through the seven core ThreadPoolExecutor parameters, provides formula‑based sizing, offers configuration templates for different workloads, shows monitoring and dynamic adjustment techniques, and highlights common pitfalls with concrete code examples.
This article explains how to differentiate memory leaks from out‑of‑memory errors, identifies classic GC‑based leak signals, introduces a toolchain (jstat, jmap, MAT, Arthas, JProfiler), walks through a step‑by‑step investigation workflow, lists common leak patterns, presents a real‑world ThreadLocal leak case, and offers preventive measures such as monitoring, regular heap dumps, code review, and stress testing.
When a Java application suddenly hits 100% CPU and becomes unresponsive, this guide walks through a three-step investigation—identifying the hottest process, the most CPU-intensive thread, and the offending code—using tools like top, jstack, and Arthas, and presents common causes and preventive measures.
This article launches a Java performance tuning series, explaining why GC logs are the starting point, reviewing the JVM memory model, showing how to enable and read GC logs, dissecting minor and full GC entries, comparing common GC algorithms, and introducing visualization tools to help pinpoint memory issues.
This article examines the three core challenges—stability, cost, and observability—of running Java AI services in production and presents concrete solutions such as timeout and retry policies, circuit‑breaker fallback, token‑monitoring, caching, tracing, custom metrics, and Docker‑based containerization.
This article explains why LangChain4j is needed for advanced Java AI agents, compares its capabilities with Spring AI, walks through project setup, configuration, defining tools and memory, assembling the agent, and demonstrates a complete smart‑customer service example with testing commands.
This article explains how Function Calling lets large language models invoke Java methods via Spring AI, walks through the four‑step workflow, shows declarative and programmatic tool definitions, and demonstrates a complete customer‑service chatbot with code examples and best‑practice guidelines.
This article explains how to equip a Java application with Retrieval‑Augmented Generation (RAG) so large language models can access private PDFs, Word files, and internal documents, covering the core architecture, two implementation paths using LangChain4j and Spring AI, vector‑store options, and practical tuning techniques.
This article explores Spring AI's core design patterns, advanced ChatClient usage, dynamic PromptTemplate creation, few‑shot prompting, structured output parsing, and declarative function calling with @Tool annotations, providing code examples, advisor mechanisms, and testing tips for Java developers.
This tutorial walks Java backend developers through the mature Java LLM ecosystem, comparing frameworks like Spring AI and LangChain4j, and demonstrates step‑by‑step how to create a Spring Boot application with a chat endpoint, streaming responses, and dynamic model switching among OpenAI, Tongyi Qwen, and Ollama.
