Fixing High CPU Usage In Kubernetes Pod: A Step-by-Step Analysis

Aug 16, 2025 by Henrik Larsen 65 views

Analysis: CPU Usage in Pod test-app:8001

Hey guys! Let's dive into this CPU usage analysis for the test-app:8001 pod in our Kubernetes cluster. We've got some interesting stuff to unpack, so let's get started!

Pod Information

First, let's nail down the basics. We're talking about:

Pod Name: test-app:8001
Namespace: default

Knowing this helps us stay oriented as we dig deeper.

Analysis

Okay, so here’s the scoop: the logs are showing that our application is behaving normally, but we're seeing high CPU usage. This is causing the pod to restart, which isn't ideal. After some digging, it looks like the culprit is the cpu_intensive_task() function. This function is running a brute-force shortest path algorithm on some pretty large graphs without any kind of rate limiting or resource constraints. Think of it like trying to find the best route across a massive city without a map or any traffic lights – it's gonna take a while and use a lot of energy!

This function is creating multiple CPU-intensive threads, and these threads are basically overwhelming the system. Imagine a bunch of tiny workers all trying to do heavy lifting at the same time – things are bound to get chaotic. The key here is to figure out how to make this task less of a resource hog without sacrificing the core functionality.

Deep Dive into the CPU Spike Scenario

To really understand the issue, let's break down what's happening in more detail. The cpu_intensive_task() function, as the name suggests, is designed to push the CPU to its limits. This is often done to simulate real-world scenarios where applications might face heavy loads. However, in this case, the task is a bit too intensive, leading to the problems we're seeing.

The brute-force shortest path algorithm is inherently resource-intensive. It explores every possible path in a graph to find the shortest one, which can be extremely time-consuming and CPU-intensive, especially with large graphs. The original implementation was running this algorithm on graphs with 20 nodes, which is a significant size for this kind of exhaustive search. Additionally, the lack of rate limiting meant that the function was running these calculations continuously, without any breaks, further exacerbating the CPU usage.

Furthermore, the function was creating multiple threads to perform these calculations concurrently. While multithreading can improve performance in many cases, it can also lead to resource contention if not managed carefully. In this scenario, the threads were all competing for CPU time, leading to spikes in CPU usage and, ultimately, the restarts we observed.

It's like having a team of chefs all trying to cook in the same kitchen at the same time, without coordinating their efforts. They'll likely get in each other's way, and the kitchen will become a chaotic mess. Similarly, the uncoordinated threads were causing a CPU bottleneck, leading to the application's instability. The goal of the proposed fix is to introduce some order and efficiency into this process, ensuring that the CPU is used effectively without being overwhelmed.

Proposed Fix

Alright, so how do we fix this mess? Our proposed solution involves optimizing the CPU-intensive task by making a few key changes. We’re aiming to reduce the load on the CPU while still keeping the core functionality intact. Here’s the plan:

Reduce the graph size: We’re cutting the graph size from 20 nodes down to 10. Think of this as reducing the size of the city we're trying to navigate. Smaller city, fewer routes to check.
Add rate limiting: We’re adding a 100ms sleep between iterations. This is like putting in traffic lights, giving the system a chance to breathe between calculations.
Add a timeout: We’re implementing a 5-second timeout per iteration. If a calculation takes too long, we’ll break it off. This prevents any single iteration from hogging the CPU for too long.
Reduce maximum path depth: We’re reducing the maximum path depth for the shortest path algorithm from 10 to 5. This limits the complexity of the search, making it faster and less CPU-intensive.

These changes should prevent those CPU spikes while still allowing the application to do its thing. It’s all about finding that sweet spot between performance and resource usage.

Diving Deeper into the Optimization Strategies

Let's break down each of these optimizations a bit further to understand why they're effective:

Reducing the Graph Size: The complexity of the brute-force shortest path algorithm grows exponentially with the size of the graph. This means that even a small reduction in graph size can have a significant impact on CPU usage. By cutting the graph size in half, from 20 to 10 nodes, we're drastically reducing the number of possible paths the algorithm needs to explore. This is a crucial step in reducing the overall CPU load.
Adding Rate Limiting: The 100ms sleep between iterations is a form of rate limiting. It prevents the function from running continuously at full speed, giving the CPU a chance to recover and handle other tasks. This is especially important in a multithreaded environment, where multiple tasks might be competing for CPU time. By introducing a small pause, we're ensuring that the CPU doesn't get overwhelmed, preventing those spikes in usage.
Adding a Timeout: The 5-second timeout per iteration is a safety net. If the algorithm gets stuck in a particularly complex calculation, the timeout will interrupt it, preventing it from consuming CPU resources indefinitely. This is important because some graphs might have paths that are very difficult to find, leading the algorithm to run for a long time. The timeout ensures that these edge cases don't cause the entire system to slow down.
Reducing Maximum Path Depth: The maximum path depth limits how far the algorithm will search for a path. By reducing this limit from 10 to 5, we're reducing the number of paths the algorithm needs to consider. This is another way to control the complexity of the search and reduce CPU usage. In many cases, the shortest path is likely to be found within a relatively short depth, so reducing the maximum depth doesn't significantly impact the accuracy of the results while providing a noticeable performance improvement.

Together, these optimizations form a comprehensive strategy for reducing the CPU load of the cpu_intensive_task() function. They address the root causes of the high CPU usage, such as the complexity of the algorithm, the lack of rate limiting, and the potential for long-running calculations. By implementing these changes, we can ensure that the application runs smoothly and efficiently, without overwhelming the system's resources.

Code Change

Here’s the code snippet with our 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 rate limiting sleep
        time.sleep(0.1)
        
        # Break if taking too long
        if elapsed > 5:
            print(f"[CPU Task] Task taking too long, breaking iteration")
            break

File to Modify

main.py

Next Steps

We’re going to create a pull request with this fix. This will allow us to review the changes, test them thoroughly, and then merge them into the main codebase. It’s all about making sure our application runs smoothly and efficiently!

Elaborating on the Next Steps

Creating a pull request (PR) is a standard practice in software development for proposing changes to a codebase. It allows other developers to review the changes, provide feedback, and ensure that the changes are safe and effective before they are integrated into the main codebase.

In this case, the pull request will contain the code changes we discussed earlier, including the optimizations to the cpu_intensive_task() function. The PR will also include a description of the problem we're addressing, the proposed solution, and the reasoning behind the changes. This helps reviewers understand the context of the changes and make informed decisions.

Once the PR is created, it will be assigned to one or more reviewers. These reviewers will carefully examine the code, looking for potential issues, bugs, or areas for improvement. They might also run tests to ensure that the changes don't introduce any regressions or unexpected behavior.

The review process is a collaborative effort. Reviewers might leave comments on the PR, asking questions, suggesting alternative approaches, or pointing out potential problems. The author of the PR, in this case, the person who implemented the fix, will respond to these comments, clarifying their reasoning, making adjustments to the code, or providing additional information.

This back-and-forth process continues until the reviewers are satisfied that the changes are safe and effective. Once the PR has been approved by the reviewers, it can be merged into the main codebase. This means that the changes will become part of the official version of the application.

After the merge, it's important to continue monitoring the application to ensure that the fix is working as expected and that no new issues have been introduced. This might involve checking CPU usage metrics, reviewing logs, and gathering feedback from users.

The entire process, from identifying the problem to implementing the fix and merging it into the codebase, is a testament to the importance of careful analysis, thoughtful design, and collaborative development practices. By following these steps, we can ensure that our applications are robust, efficient, and reliable.