Taming the Noise: Decoding Kubernetes Logs for Humans

Some projects emerge from necessity, others from curiosity. Hallucino, my Kubernetes log analyser, came from a rare chance to set aside deadlines, deliverables, and sprint boards in favor of learning and exploration. During an Engineering Development Day event—a two-day pause to focus on personal growth—I dove into the challenge of crafting an “intelligent” tool that could make sense of the noisy, chaotic world of Kubernetes logs.

Kubernetes logs are a goldmine of information, but also a labyrinth. In large, distributed systems, logs pour in from countless containers, pods, and nodes. Amid this flood are hidden gems—critical insights that can resolve outages, optimise performance, or detect brewing problems. But the sheer volume often buries their value. I wanted to explore the possibilities of combining modern Large Language Models (LLMs) with Go’s concurrency capabilities to transform unstructured logs into something a little more human readable.

For me, tackling this problem wasn’t just about building a tool; it was an opportunity to sharpen skills. I saw this as a chance to push myself beyond my comfort zone, to deepen my understanding of Go’s concurrency model, and to navigate the complexities of memory management in a real-world context.

The Problem

Logs are supposed to tell a story, but they rarely read like one. They’re scattered fragments from many narrators, speaking in technical jargon, error codes, and timestamps. Manually sifting through them is time-consuming, and traditional tools can only go so far. I wondered: Could LLMs help to make sense of log data, highlight patterns, and offer useful insights?

The challenge wasn’t just technical; it was practical. The tool needed to process logs efficiently in real time, handle large volumes without choking on memory, and support users navigating their Kubernetes clusters.

The Solution

The result of my efforts was Hallucino, a CLI tool for analysing Kubernetes logs, built in Go and powered by an LLM backend. The core idea was straightforward: fetch logs from Kubernetes, break them down into manageable chunks using Go’s concurrency features, and run them through an LLM model for summarization and pattern detection.

Building Hallucino involved designing three key components:

  1. Kubernetes Integration: Using the Kubernetes client-go library, the tool connects to a cluster, retrieves logs, and lets users specify the scope of their analysis—whether it’s a particular pod, container, or namespace.
  2. Concurrent Log Processing: With Goroutines and channels, logs are parsed concurrently, maximizing efficiency and minimizing memory usage. The tool handles even high log volumes gracefully, maintaining performance under pressure.
  3. LLM-Powered Analysis: Logs are passed to a large language model (deployed on-premise via Azure OpenAI) for processing. The model identifies errors, warnings, and trends, and provides recommendations in a user-friendly Markdown summary.

The Journey

One of the most rewarding parts of this project was stepping out of my usual routine to focus on pure learning and exploration. In just two days, I touched on multiple areas: enhancing my understanding of Kubernetes, deepening my Go expertise, and experimenting with LLM integrations in practical applications.

Go’s concurrency model, while elegant, is apparently tricky to master. Debugging Goroutines and understanding synchronization with sync.WaitGroup tested my patience but ultimately left me more confident in handling real-world performance challenges. Similarly, designing the LLM integration forced me to think critically about unstructured data and the nuances of summarisation. Logs aren’t neatly written essays—they’re messy, context-dependent snippets. Encouraging the LLM to make sense of that complexity was an art as much as a science.

What I Learned

Every project teaches something, but Hallucino was particularly instructive.

  • Concurrency Is Powerful (and Hard): Breaking logs into chunks and processing them in parallel felt like a cheat code for speed—until things broke. Balancing speed and safety in concurrent programming is a skill I’ll keep refining.
  • Kubernetes is Deep: Even with prior experience, I found new challenges in working with Kubernetes logs, namespaces, and containers. There’s always more to learn.
  • LLMs Can Help (Sometimes): Large language models aren’t magic bullets. While they excel at summarising patterns and errors, their usefulness depends on how well the data is pre-processed and how clear the prompts are.

But perhaps the most valuable takeaway wasn’t technical. It was the joy of uninterrupted learning—of picking a problem, diving into it headfirst, and building something from scratch without worrying about perfection. This event reminded me why I started building things in the first place. In a work place, where deadlines and bureaucracy often stifle creativity, building Hallucino provided a much-needed break. By taking a step back to experiment, learn, and create without immediate pressure, I gained invaluable insight into what truly drives me - the joy of bringing ideas to life.

What’s Next

Hallucino is still a work in progress. There’s plenty of room for improvement, from fine-tuning its AI capabilities to expanding its customization options. But for now, it’s a solid foundation—and a personal milestone in both learning and building.

If you’re curious about the details, including the codebase and installation instructions, you can find them here. Feedback, ideas, or pull requests are always welcome.

Here’s to decoding the noise, one project at a time.