This guide will walk you through a project built with Java 21, Spring Boot 3, and Gradle. Our goal isn’t just to write working code - we want to make sure the project is robust, fast, and easy to maintain in the future.

We’ll start with basic setup, then explore database JPA, version control with Liquibase, and Redis caching. After that, we’ll focus on “observability” - learning how to monitor your app’s health using Micrometer and OpenTelemetry (OTLP), and send all monitoring data to Grafana for analysis.

This guide is perfect for developers who want to learn modern Spring Boot 3 practices.

What is LGTM?

LGTM stands for Loki, Grafana, Tempo, and Metrics (usually Prometheus). It’s an open-source toolkit for monitoring your applications and infrastructure.

Think of it as a control center where you can:

• See all your logs in one place (Loki)
• View beautiful dashboards of your system’s performance (Grafana)
• Track requests as they move through your system (Tempo)
• Monitor key metrics like CPU, memory usage, and more (Prometheus)

Ollama is an open-source framework that allows you to easily run open-source large language models like Llama 3.3, Mistral, and Gemma 2. Google Cloud Run provides a containerized environment with GPU support, making it ideal for deploying AI inference services. This article will guide you through deploying Ollama on Cloud Run.

This article will guide you through setting up a clean Gradle multi-project structure from scratch.

Within Google’s Gemini product lineup, although both the Gemini API and Vertex AI Gemini are based on the Gemini model, they differ significantly in their target audiences and application scenarios:

• Gemini API:
  This API adopts an OpenAI-compatible interface, primarily targeting developers. It offers a lightweight and easy-to-use approach, enabling developers to quickly access and utilize the latest Gemini models via familiar OpenAI libraries or REST APIs—ideal for rapid prototyping and proof-of-concept projects.

• Vertex AI Gemini:
  Part of the Google Cloud Vertex AI suite, this service is geared toward enterprise use. In addition to offering Gemini models, it integrates advanced security, scalability, and comprehensive operational management features, making it well-suited for large-scale production environments and enterprise applications.

As a result, while Spring AI does not yet support native integration with the Gemini API, we can leverage the OpenAI compatibility mode (refer to the official documentation at https://ai.google.dev/gemini-api/docs/openai) to access Gemini models. This approach allows developers to utilize the familiar OpenAI interface within Spring AI for quick and efficient integration of Gemini API features.

Developers often face challenges in setting up consistent and reliable environments across teams. With the rapid pace of technological advancement, tools like VS Code DevContainer and Google Cloud Workstations have become invaluable in standardizing development setups. This guide walks you through creating a robust Java development environment tailored to modern needs.

All configurations and scripts mentioned in this post are available in the Java Development Environment Template repository. Feel free to explore and adapt it to your needs.

Adding Vue Flow to Your Project

This guide will walk you through integrating Vue Flow into your Monorepo project, including installing dependencies, setting up routing, and creating an editable flow page.

What is Vben Admin?

Vben Admin is a comprehensive solution for developing modern admin dashboards and management systems. It is built with Vue 3.0, Vite, and TypeScript, offering a robust and scalable foundation for enterprise applications.

Key Features:

• Cutting-Edge Technology Stack: Developed using the latest front-end technologies, including Vue 3, Vite, and TypeScript.
• Ready-to-Use: Comes with a complete set of admin dashboard features right out of the box.
• Multi-UI Framework Support: Compatible with popular UI libraries such as Ant Design Vue, Element Plus, and Naive UI.
• Full Internationalization: Includes a well-designed multi-language solution for seamless localization.

In today’s complex, microservices-driven landscape, understanding how requests flow through distributed systems is essential for maintaining performance, diagnosing issues, and optimizing applications. Distributed tracing provides a powerful tool for developers and operations teams to visualize and analyze the journey of requests across various services and components.

This comprehensive guide will take you through the process of integrating Spring Boot with OpenTelemetry using Micrometer to achieve robust distributed tracing. We’ll cover everything from setting up a local development environment to deploying your application on Google Cloud Platform (GCP) Cloud Run, ensuring you gain a thorough understanding of how to implement and leverage distributed tracing in both local and cloud environments.

This guide provides a step-by-step approach to integrating OpenTelemetry with Spring Boot and deploying it to Google Cloud Platform (GCP). The setup includes configuring the necessary dependencies, setting up OpenTelemetry Collector, creating a Docker environment, and deploying the application to GCP Cloud Run.

Important Notice: This article is for reference only. Please refer to the latest guide: Step-by-Step Guide to Integrating Spring Boot and OpenTelemetry with Micrometer on GCP for Distributed Tracing for up-to-date information and recommended practices.

It’s important to note that Micrometer is now the recommended framework by Spring for metrics and tracing in Spring Boot applications. The latest Spring Boot versions have integrated Micrometer as the default metrics and tracing facade, which can be easily configured to work with OpenTelemetry. For the most current approach to implementing observability in Spring Boot applications using Micrometer and OpenTelemetry, please refer to the guide linked above.