Fixing High CPU Usage In Kubernetes Pods
Hey guys! Ever faced the dreaded high CPU usage issue in your Kubernetes pods? It's a common problem, but fear not! This article breaks down a real-world scenario, offering a step-by-step analysis and practical solutions. We'll dive into a case study involving a pod named test-app:8001 and walk through identifying the root cause, implementing a fix, and the next steps to ensure smooth sailing for your applications.
Understanding the Scenario
In this specific case, the test-app:8001 pod, residing in the default namespace, was experiencing high CPU utilization, leading to frequent restarts. The logs indicated normal application behavior, but the persistent CPU spikes pointed to an underlying issue within the application's code. Let's dig deeper into the analysis.
Pod Information
Before we get into the nitty-gritty, let's quickly recap the key details of the affected pod:
- Pod Name:
test-app:8001 - Namespace:
default
These details help us pinpoint the exact resource we're dealing with in our Kubernetes cluster. Now, onto the real detective work!
Root Cause Analysis: Unraveling the Mystery
So, what was causing this high CPU usage? After a thorough investigation, the culprit was identified as the cpu_intensive_task() function. This function was running an unoptimized brute-force path finding algorithm on a large graph (20 nodes!). Imagine searching for the shortest route in a massive maze – that's essentially what this algorithm was doing. To make matters worse, there were no rate-limiting mechanisms or timeout controls in place. This meant the function could run indefinitely, consuming CPU resources like there's no tomorrow.
The problem was further compounded by multiple threads running this CPU-intensive task simultaneously. Think of it like multiple people trying to solve the same maze at the same time – it just adds to the chaos and resource consumption.
To summarize, the root cause was a combination of:
- An unoptimized algorithm
- A large problem size (20 nodes)
- Lack of rate limiting
- No timeout controls
- Multiple threads competing for resources
This perfect storm led to the high CPU usage and subsequent pod restarts.
The Proposed Fix: A Multi-Pronged Approach
Now that we've identified the culprit, let's talk about the solution. The proposed fix focuses on optimizing the cpu_intensive_task() function to reduce its CPU footprint while maintaining its core functionality. We're employing a multi-pronged approach, targeting each aspect of the problem:
- Reducing Graph Size: The first step is to reduce the complexity of the problem. We're decreasing the graph size from 20 nodes to 10 nodes. This significantly reduces the search space for the path finding algorithm, making it less computationally expensive.
- Adding Rate Limiting: To prevent the function from hogging the CPU, we're introducing a 100ms sleep between iterations. This acts as a
