Taming the Noise: Decoding Kubernetes Logs for Humans

4 min read

Hallucino, my Kubernetes log analyser, came out of a rare two days with no deadlines attached. During an Engineering Development Day event, a pause to focus on personal growth, I had a go at building an “intelligent” tool that could make sense of the noisy, chaotic world of Kubernetes logs.

Kubernetes logs are a labyrinth. In large, distributed systems, logs pour in from countless containers, pods, and nodes, and the lines that would actually help you resolve an outage are buried under everything else. I wanted to see whether a Large Language Model plus Go’s concurrency could turn that stream into something a little more human readable.

I also wanted the excuse. Two days is enough to push on Go’s concurrency model properly, and to find out what memory management looks like when there’s a real volume of text moving through it.

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 practical constraints mattered as much as the technical ones. The tool had to process logs in real time and handle large volumes without choking on memory.

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, so nothing has to hold the whole stream in memory at once.
  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 writes the result out as a Markdown summary.

The Journey

The best part was stepping out of my usual routine to focus on learning. Two days covered a lot of ground: Kubernetes, Go, and getting an LLM to do something useful in a real application.

Go’s concurrency model, while elegant, is apparently tricky to master. Debugging Goroutines and understanding synchronisation with sync.WaitGroup tested my patience, but I came out of it more confident. Designing the LLM integration forced me to think about unstructured data and how you summarise it. Logs aren’t neatly written essays; they’re messy, context-dependent snippets. Getting the LLM to make sense of that took a lot of trial and error on the prompts.

What I Learned

Three things stuck with me.

  • 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.

The best thing I took away wasn’t technical. It was two uninterrupted days on a problem I’d picked myself, building something from scratch without worrying about whether it was any good. Deadlines and bureaucracy have a way of grinding that out of you, and I’d forgotten how much I like it.

What’s Next

Hallucino is still a work in progress. The prompts need work, and there’s no way to configure much of anything yet. But it runs, and it does the thing I built it to do.

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.