Troubleshoot High CPU In Pod Test-app 8001: A Fix Guide

Aug 13, 2025 by Henrik Larsen 56 views

CPU Usage Analysis in Pod test-app 8001 Troubleshooting High CPU Utilization

Hey everyone! Today, we're diving deep into a real-world scenario: troubleshooting high CPU utilization in a Kubernetes pod. Specifically, we'll be looking at the test-app:8001 pod and how we tackled its CPU issues. So, buckle up and let's get started!

Understanding the Problem

Our journey begins with identifying the problem. The test-app:8001 pod, residing in the default namespace, was experiencing high CPU usage. The telltale signs? Frequent restarts. While the application logs seemed normal, the pod was clearly struggling. This is where the detective work begins, guys!

To really nail this, we need to talk about why high CPU usage is a problem. When a pod consumes too much CPU, it hogs resources, potentially starving other applications and services in the cluster. This can lead to performance degradation, application unresponsiveness, and, as we saw, pod restarts. In short, high CPU usage is a symptom of something not quite right, and it's our job to figure out what that "something" is.

The initial analysis pointed towards the cpu_intensive_task() function as the culprit. This function was running an unoptimized brute force shortest path algorithm. Imagine trying to find the best route across a massive city without a map – that's essentially what this algorithm was doing! The algorithm was operating on a large graph (20 nodes, to be precise) without any safeguards like rate limiting or timeouts. This meant the function could run indefinitely, consuming CPU cycles like there's no tomorrow. No wonder our pod was feeling the heat!

The core issue here is algorithmic complexity. A brute-force approach, by its nature, explores all possible solutions, which can become computationally expensive very quickly as the problem size increases. In our case, searching for the shortest path in a 20-node graph without any optimization is like trying to find a needle in a haystack the size of Texas. It's just not efficient! This inefficiency translates directly to high CPU usage, which, in turn, leads to our pod's woes.

Moreover, the absence of rate limiting and timeout controls exacerbated the problem. Without rate limiting, the function could continuously hammer the CPU without any pauses, preventing other processes from getting a fair share of resources. The lack of timeouts meant that if the algorithm got stuck in a particularly complex search, it would keep running indefinitely, further driving up CPU usage. It's like leaving a tap running – eventually, the tank will run dry. In our case, the "tank" is the available CPU resources.

The Root Cause: An Unoptimized Algorithm

Delving deeper, the root cause was traced back to the cpu_intensive_task() function. This function was executing an unoptimized brute force shortest path algorithm on a large graph (20 nodes) without any rate limiting or timeout controls. Imagine trying to find the shortest route in a city by exploring every single street, one by one – that’s the essence of a brute force approach!

This method, while simple to implement, becomes incredibly resource-intensive as the graph size grows. The function was essentially stuck in an infinite loop of computation, hogging the CPU and eventually causing the pod to restart. It's like trying to push a car uphill without any gas – you'll just burn out!

The unoptimized algorithm was the prime suspect, but let's break down why it was so problematic. Brute-force algorithms, while straightforward, have a nasty habit of scaling poorly. This means that as the input size (in our case, the graph size) increases, the computational cost skyrockets. The function was essentially caught in a combinatorial explosion, where the number of possible paths to explore grew exponentially with the number of nodes in the graph. It's like trying to count grains of sand – eventually, you'll lose track.

Furthermore, the lack of rate limiting meant that the function could run continuously without any pauses, preventing other processes from getting a fair share of CPU resources. Think of it as someone constantly talking without taking a breath – eventually, everyone else will tune out. In our case, the "tuning out" manifested as pod restarts.

Finally, the absence of timeout controls meant that if the algorithm got stuck in a particularly complex search, it would keep running indefinitely, further driving up CPU usage. It's like getting lost in a maze – without a way to stop and re-evaluate, you'll just keep wandering aimlessly.

The Proposed Fix: A Multi-Pronged Approach

Our proposed solution tackles the CPU hog head-on by optimizing the cpu_intensive_task() function. We’re employing a multi-pronged approach, targeting different aspects of the function’s behavior to reduce CPU load. The goal is to make the function more efficient and prevent it from monopolizing CPU resources.

First, we're reducing the graph size from 20 to 10 nodes. This might seem like a small change, but it has a significant impact on the algorithm's complexity. Remember, the brute-force approach scales poorly with input size. By halving the graph size, we drastically reduce the number of possible paths the algorithm needs to explore. It's like shrinking a maze – there are simply fewer paths to get lost in.

Second, we're adding a 100ms rate-limiting sleep between iterations. This is crucial for preventing the function from monopolizing the CPU. By introducing a short pause, we give other processes a chance to run, preventing resource starvation. Think of it as taking a break during a marathon – it helps you conserve energy and avoid burning out.

Third, we're introducing a 2-second timeout check. This acts as a safety net, preventing the algorithm from running indefinitely if it gets stuck in a complex search. If the algorithm takes longer than 2 seconds to find a path, it will break out of the loop, freeing up CPU resources. It's like having a fire alarm – it alerts you when things are getting out of control.

Finally, we're reducing the max_depth parameter to 5 for the path-finding algorithm. This limits the maximum length of the paths the algorithm explores, further reducing the computational cost. It's like setting a limit on how far you're willing to walk – you'll eventually turn back if you haven't found what you're looking for.

These changes, when combined, significantly reduce CPU usage while preserving the core functionality of the task. We're not eliminating the CPU-intensive nature of the task entirely, but we're making it much more manageable and preventing it from causing pod restarts.

Here's a breakdown of each fix and why it matters:

Reducing Graph Size: This is perhaps the most impactful change. By shrinking the graph, we dramatically reduce the number of possible paths the algorithm needs to explore. This directly translates to lower CPU usage.
Adding Rate Limiting: The time.sleep(0.1) introduces a 100ms pause between iterations. This might seem small, but it's enough to allow other processes to run, preventing CPU starvation and improving overall system responsiveness.
Introducing Timeout Check: The if elapsed > 2.0: block acts as a circuit breaker. If the algorithm takes longer than 2 seconds, it breaks out of the loop, preventing runaway CPU consumption. This is especially important in scenarios where the algorithm might get stuck in a particularly complex search.
Reducing Max Depth: Limiting the maximum path depth to 5 further constrains the search space, reducing the computational cost. It's like telling the algorithm not to explore paths that are likely to be too long, saving valuable CPU cycles.

Code Changes: The Heart of the Solution

def cpu_intensive_task():
    print(f"[CPU Task] Starting CPU-intensive graph algorithm task")
    iteration = 0
    while cpu_spike_active:
        iteration += 1
        # Reduced graph size and added rate limiting
        graph_size = 10
        graph = generate_large_graph(graph_size)
        
        start_node = random.randint(0, graph_size-1)
        end_node = random.randint(0, graph_size-1)
        while end_node == start_node:
            end_node = random.randint(0, graph_size-1)
        
        print(f"[CPU Task] Iteration {iteration}: Running optimized shortest path algorithm")
        
        start_time = time.time()
        path, distance = brute_force_shortest_path(graph, start_node, end_node, max_depth=5)
        elapsed = time.time() - start_time
        
        if path:
            print(f"[CPU Task] Found path with {len(path)} nodes and distance {distance} in {elapsed:.2f} seconds")
        else:
            print(f"[CPU Task] No path found after {elapsed:.2f} seconds")
            
        # Add rate limiting sleep
        time.sleep(0.1)
        
        # Break if taking too long
        if elapsed > 2.0:
            print(f"[CPU Task] Task taking too long, breaking early")
            break

This code snippet showcases the optimized cpu_intensive_task() function. Notice the key changes:

Reduced graph_size: The graph size is now set to 10, down from 20.
Rate limiting: The time.sleep(0.1) call introduces a 100ms delay.
Timeout check: The if elapsed > 2.0: condition breaks the loop if the algorithm runs for more than 2 seconds.
Reduced max_depth: The max_depth parameter in the brute_force_shortest_path function is set to 5.

These modifications work together to significantly reduce the CPU footprint of the function. It’s like giving our engine a tune-up – it runs smoother, consumes less fuel (CPU), and is less likely to overheat (restart).

The file targeted for modification is main.py, where the original cpu_intensive_task() function resides. This is the operating room where we'll perform our surgical fix!

Next Steps: From Fix to Implementation

The final step is to create a pull request with the proposed fix. This will allow for code review and ensure that the changes are properly tested before being merged into the main codebase. Think of it as getting a second opinion from a doctor before undergoing surgery – it’s always a good idea to get another perspective.

The pull request will contain the modified main.py file with the optimized cpu_intensive_task() function. It will also include a detailed description of the changes made and the rationale behind them. This ensures that anyone reviewing the code understands the problem, the solution, and the potential impact of the changes.

Once the pull request is reviewed and approved, the changes will be merged into the main codebase and deployed to the Kubernetes cluster. This will effectively address the high CPU utilization issue in the test-app:8001 pod and prevent future restarts. It's like finally getting that nagging engine problem fixed – the car runs smoothly, and you can drive without worry!

And that's a wrap, folks! We've successfully diagnosed and proposed a solution for high CPU utilization in our Kubernetes pod. By understanding the problem, identifying the root cause, and implementing targeted fixes, we can keep our applications running smoothly and efficiently. Keep an eye out for the pull request, and let's get this fixed!