5 Common Kafka Admin Problems (And How KCPilot Detects Them)

Fri, 07 Nov 2025 08:52:00 +0000
Kafka, Apache Kafka, KCPilot, Diagnostics Tool

Were you ever responsible for managing a Kafka cluster running in production, assuming that it’s not trivial and handles thousands or maybe millions of messages daily, you probably had many sleepless nights where you were thinking how big of a mess you would need to solve next day at work?

Not to mention situations where you were simply on call and, with proper monitoring setup, you had to solve them straight away that very night...

At SoftwareMill, we manage multiple Kafka clusters for our clients, often with on-call situations, and, needless to say, it’s often not a pleasurable experience. Kafka is powerful but also really complex.

Simple configuration mistakes can have serious consequences, and in situations where we inherit existing setups, and often when we try to fix some basics of those done wrongly from the beginning, we have a period of time where we need to give 110% of our experience and Developer/DevOps brains to make things work as they should.

Kafka is a tool that has been in existence for over 10 years and offers a plethora of tools to make life easier for developers and others setting up clusters. However, even with good monitoring, you still only see symptoms and not the root causes.

KCPilot explained: How can our tool help you?

Being firm believers in the quote work smarter, not harder, we try to leverage automation to reduce manual effort. For this very purpose, KCPilot was created. KCPilot is a tool that, although still in its early development stage, promises to make existing Kafka cluster setups check faster by identifying common problems that require your attention.

In short, KCPilot is basically a set of tasks that analyzes, with the help of LLM, the data associated with the running cluster to find common mistakes people make when setting it up. The tool first scans your cluster configuration, logs, and metrics (some of which also with the help of LLM) and executes predefined tasks to search for potential issues.

Retrieving the data to be analyzed is a complex task on its own, mainly because Kafka deployments can differ significantly in their architecture, the way brokers are started, and how logs and metrics are handled. There is no single standard or unified approach, and every production setup tends to do things slightly differently.

On top of that, Kafka itself doesn’t provide any simple commands or binary responses that would directly tell you whether the cluster is healthy or not. A good example of this complexity are logs, which require a multi-step process to locate and collect from the right locations (more on that later).

Except for logs, we are trying to get the system info, environment variables, and other information on each broker and store that data for the use of the analysis step. Finding this bit of information is usually done by any person analysing existing Kafka clusters so that it can be evaluated and corrections (if needed) can be made. The tool significantly...