Fixing High CPU Usage In Kubernetes Pod Test-app:8001

Aug 19, 2025 by Kenji Nakamura 54 views

Analysis of CPU Usage in Pod test-app 8001 and Proposed Fix

Hey guys! Let's dive into a recent CPU usage analysis we did for the test-app:8001 pod. We'll break down what we found, the fixes we're proposing, and how we plan to roll them out. This analysis falls under the Discussion category and was flagged by vaibhav541 through the kubernetes-agent.

Pod Information

Pod Name: test-app:8001
Namespace: default

Analysis of CPU Consumption

So, what's the deal? Our analysis revealed that the test-app:8001 pod was experiencing some serious CPU hogging, leading to frequent restarts. We dug into the logs and found that the application was behaving normally in terms of functionality, but the CPU usage was through the roof. The culprit? A function called cpu_intensive_task(). This function is where things get interesting, or rather, problematic. It turns out this function was running an unoptimized, brute-force path-finding algorithm. Imagine trying to find the best route through a city using only trial and error – that’s kind of what this algorithm was doing, but with a massive graph of 20 nodes. To make matters worse, there were no rate limits or timeout controls in place. This meant the function could run wild, consuming excessive CPU resources through aggressive thread usage and unbounded computation time. Think of it like a runaway train with no brakes! This unrestrained CPU consumption is the primary reason why the pod was restarting.

Digging deeper into this CPU usage analysis, the issue boils down to a combination of factors within the cpu_intensive_task() function. First, the sheer size of the graph (20 nodes) made the brute-force path-finding algorithm incredibly resource-intensive. This algorithm essentially explores every possible path until it finds the shortest one, which can take an exponential amount of time as the graph size increases. Second, the absence of any rate limiting meant that the function was running in a tight loop, constantly hammering the CPU with new path-finding calculations. There was no pause or delay between iterations, allowing the CPU to cool down or handle other tasks. Third, the lack of a timeout mechanism meant that an iteration could potentially run indefinitely if it got stuck in a complex part of the graph. This is like sending a search party into a vast forest with no time limit – they could spend days wandering around without finding anything, while consuming valuable resources. Finally, the aggressive thread usage likely amplified the problem. By spawning multiple threads to perform these calculations, the function was able to saturate the CPU cores and prevent other processes from getting their fair share of processing time. In essence, the cpu_intensive_task() function was designed in a way that made it almost guaranteed to overload the CPU under certain conditions. We needed to find a way to tame this beast without sacrificing the functionality it provides.

To fully understand the impact of this high CPU usage, it’s important to consider the broader context of the Kubernetes environment. When a pod consumes excessive CPU resources, it can lead to a number of negative consequences. First and foremost, it can cause the pod to become unresponsive or crash, leading to service disruptions. In the case of test-app:8001, the frequent restarts were a clear indication that the pod was struggling to cope with the CPU load. Second, high CPU usage can impact the performance of other pods running on the same node. Kubernetes attempts to distribute resources fairly among pods, but a single pod that is hogging the CPU can still starve other pods of processing time. This can lead to cascading failures, where one pod’s performance issues trigger problems in other pods. Third, excessive CPU usage can increase the overall cost of running the application. Cloud providers typically charge based on resource consumption, so a pod that is constantly maxing out the CPU will rack up a higher bill. Finally, high CPU usage can make it difficult to diagnose and troubleshoot other issues in the application. When the CPU is constantly overloaded, it becomes harder to collect metrics, analyze logs, and identify the root cause of problems. In the case of test-app:8001, the CPU usage issue was masking other potential problems that might have been present in the application. Therefore, addressing this CPU hog is crucial not only for the stability of the pod itself, but also for the overall health and performance of the application and the Kubernetes cluster.

Proposed Solution to Reduce CPU Load

Okay, so we've identified the problem. Now, let's talk solutions! Our proposed fix focuses on optimizing the cpu_intensive_task() function to reduce its CPU footprint. We're tackling this in several ways:

Reducing Graph Size: We're cutting the graph size from 20 nodes down to 10 nodes. This significantly reduces the complexity of the path-finding algorithm. Think of it like navigating a small town versus a sprawling metropolis – fewer roads mean fewer possibilities to explore.
Adding a Delay: We're introducing a 0.5-second delay between iterations. This gives the CPU a breather and prevents it from being constantly bombarded with calculations. It's like taking a short break between sprints to catch your breath.
Implementing a Timeout: We're setting a 2-second timeout per iteration. If an iteration takes longer than 2 seconds, it'll be terminated. This prevents the algorithm from getting stuck in long, unproductive loops. It's like setting a timer for your search party – if they don't find anything within the allotted time, they return to base.
Limiting Path Depth: We're reducing the maximum path depth from 10 to 5. This further limits the computational complexity of the algorithm. It's like telling your search party to only explore the areas closest to their starting point, rather than venturing deep into the wilderness.

These changes are designed to maintain the simulation capability of the function while preventing the excessive CPU usage that was causing the pod restarts. We believe these adjustments strike a good balance between performance and resource consumption. The core idea behind our proposed fix is to introduce some constraints and controls into the cpu_intensive_task() function. By reducing the graph size, adding a delay between iterations, implementing a timeout, and limiting the path depth, we're effectively putting guardrails in place to prevent the function from running wild. These changes should prevent the pod restarts and improve the overall stability of the application. It's important to note that we're not aiming to completely eliminate the CPU load generated by this function. The function is, after all, designed to be CPU-intensive. Our goal is to bring the CPU usage down to a manageable level that doesn't jeopardize the pod's health or the performance of other applications in the cluster. We want to ensure that the function can still perform its intended task without causing resource exhaustion. This requires a careful balancing act between functionality and efficiency. We believe that the proposed changes achieve this balance by making the algorithm more efficient without fundamentally altering its behavior. By making the path-finding process less computationally demanding, we can significantly reduce the CPU load without sacrificing the core functionality of the function. This approach aligns with the principles of resource optimization and efficient software design, where we strive to achieve the desired outcome with the least amount of resources possible. The proposed fix is a targeted intervention that addresses the root cause of the problem without introducing unnecessary complexity or side effects. By focusing on the specific aspects of the cpu_intensive_task() function that were contributing to the high CPU usage, we can achieve a significant improvement in performance and stability with minimal disruption to the application as a whole.

Code Modification Details

Here's the code snippet showcasing the proposed changes:

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 on graph with {graph_size} nodes from node {start_node} to {end_node}")
        
        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 delay between iterations to reduce CPU load
        time.sleep(0.5)
        
        # Break if iteration takes too long
        if elapsed > 2.0:
            print(f"[CPU Task] Iteration took too long ({elapsed:.2f}s), breaking loop")
            break

Target File for Modification

main.py

Next Steps

We're gearing up to create a pull request with this proposed fix. This will allow for a thorough review and testing process before we merge the changes into the main codebase. Stay tuned for updates!