Ever spent three hours tracing a single NullPointerException across five different microservices? You grep through endless terminal windows, match timestamps, and manually piece together the puzzle. It’s exhausting.
As a full-stack developer working heavily with Spring Boot, I realized that while microservices solve scalability issues, they introduce a massive debugging headache. I wanted a tool that didn't just show me the logs, but actually helped me fix them in real-time.
Since I couldn't find a lightweight, developer-friendly solution that fit my exact needs, I built one. Meet LogIQ.
What is LogIQ? 🧠
LogIQ is a centralized observability SaaS platform and a custom Java SDK designed to streamline the debugging workflow. It captures telemetry data, tracks server health in real-time, and uses an AI Neural Engine to analyze complex stack traces and provide actionable code fixes.
Instead of navigating through different log files, everything is streamed directly into a modern, cyber-tech inspired dashboard.
Core Features at a Glance:
- Plug-and-Play SDK: A custom Spring Boot starter that integrates in under 2 minutes.
- Real-Time Health Monitoring: Zero-lag live server status tracking via WebSockets.
- AI-Powered Debugging: Instant stack-trace analysis and fix suggestions powered by LLMs.
- Secure Team Collaboration: Built-in Role-Based Access Control (RBAC) to manage Owner/Developer access.
- Cyber-Tech UI: A sleek Glassmorphism interface built with React, Tailwind CSS, and Framer Motion.
The Architecture: How It All Fits Together 🏗️
I wanted LogIQ to have zero performance bottleneck on the host microservices. To achieve this, I separated the system into three main components:
- The Client SDK (
logiq-spring-boot-starter): Runs on the target microservices, collecting logs and health metrics asynchronously. - The Core Engine (Spring Boot Backend): Acts as the secure broker, handling JWT authentication, database persistence (MySQL), and routing messages to the AI provider.
- The Dashboard (React Frontend): Consumes REST APIs and listens to WebSocket channels for live updates.
Here is the high-level system architecture:
flowchart TB
subgraph Target Microservices
S1["Spring Boot App 1<br/>(LogIQ SDK)"]
S2["Spring Boot App 2<br/>(LogIQ SDK)"]
end
subgraph LogIQ Core Server
API["REST APIs<br/>(Auth, Projects)"]
WS["WebSocket Broker<br/>(STOMP)"]
AI["AI Processing Engine"]
end
subgraph Client Browser
UI["React Dashboard<br/>(Tailwind + Framer Motion)"]
end
S1 -->|"Async Telemetry Stream"| API
S2 -->|"Async Telemetry Stream"| API
API <--> AI
API -->|"Publish Events"| WS
WS -->|"Real-time Health & Logs"| UIBuilding the Custom Spring Boot SDK 📦
One of the most exciting parts of this project was building my own custom SDK. I wanted the integration process to be frictionless for other developers.
By creating a custom spring-boot-starter, a developer only needs to add a single dependency to their pom.xml:
<dependency>
<groupId>com.bytecodelk</groupId>
<artifactId>logiq-spring-boot-starter</artifactId>
<version>1.0.0</version>
</dependency>
Then, simply configure the project credentials in application.properties:
logiq.api.key=YOUR_PROJECT_API_KEY
logiq.server.url=http://localhost:8080
Behind the scenes, the SDK automatically hooks into the Spring environment, intercepts application exceptions, and pushes them to the LogIQ backend asynchronously so it never slows down the main application thread.
The Magic: Real-Time WebSockets & AI Analysis 🪄
1. Zero-Lag Telemetry
Instead of relying on heavy polling mechanisms, I implemented WebSockets (STOMP) in the Spring Boot backend. When the backend receives a new log entry or health ping from the SDK, it broadcasts the payload to a specific project topic. The React frontend listens to this topic and updates the UI instantly.
2. The AI Neural Engine
Reading a 50-line Java stack trace is never fun. In the LogIQ dashboard, if an error is flagged as critical, you can click "Analyze with AI".
The Spring Boot backend securely formats a strict prompt containing the exact stack trace and context, sends it to the LLM via an API call, and returns a human-readable explanation and code fix. It ensures that sensitive environment variables are not unnecessarily exposed, keeping the debugging process secure.
The Frontend: Cyber-Tech Aesthetics 🌌
Developers stare at screens all day, so the UI had to be easy on the eyes but visually striking. I opted for a dark-themed, industrial cyber-tech aesthetic.
Using React, Tailwind CSS, and Framer Motion, I built a responsive Glassmorphism UI. I even integrated React Three Fiber to render some subtle 3D depth elements on the landing page to give it that futuristic command-center feel.
Conclusion & Next Steps
Building LogIQ has been an incredible journey that pushed my boundaries in full-stack development, API design, and real-time data handling.
Currently, the SDK is exclusively available for Spring Boot, but I plan to expand support to Node.js and Python microservices in the near future.
If you are a developer looking for an easier way to debug your distributed systems, or just curious about the code, I'd love for you to check it out!
🔗 Check out the source code on GitHub:
If you find the architecture interesting or the tool helpful, please consider dropping a ⭐️ on the repositories. I’m also open to feedback, contributions, and ideas for new features!
What’s your current go-to strategy for debugging microservices? Let me know in the comments below! 👇
Top comments (2)
Hey everyone! 👋 Thanks for reading. I'm really curious—what tools are you currently using to debug your microservices? Have you ever thought about integrating AI into your logging workflow before? Let me know! 👇
When the backend is unavailable, where does the SDK put telemetry that was sent asynchronously?