Fix High CPU Usage In Pod Test-app:8001

by Viktoria Ivanova 40 views

Hey guys! Let's dive deep into a recent analysis of CPU usage in our Kubernetes pod, specifically test-app:8001. We've been seeing some high CPU usage, which led to restarts, and we need to get to the bottom of it. This article breaks down the issue, the root cause, the proposed solution, and the next steps. So, buckle up, and let's get started!

Pod Information

First, let’s get the basics down. We're talking about a specific pod here:

  • Pod Name: test-app:8001
  • Namespace: default

Knowing this helps us pinpoint exactly where the problem lies within our Kubernetes environment.

The Core of the Problem: Root Cause Analysis

CPU usage is a critical metric when monitoring application performance, and understanding the factors that lead to high consumption is essential for maintaining stability and reliability in any system. In our case, the logs initially showed normal application behavior, which made the high CPU usage and subsequent restarts quite puzzling. We needed to dig deeper to uncover the root cause.

Upon closer inspection, the root cause was traced back to the cpu_intensive_task() function. This function was designed to simulate a computationally heavy workload, but it inadvertently became the bottleneck due to several factors. The algorithm being used was an unoptimized brute-force approach to finding the shortest path in a graph. Now, this isn't usually an issue for small graphs, but the size we were dealing with—20 nodes—made the computation incredibly demanding. The brute-force method checks every possible path, which means the computational complexity increases exponentially with the number of nodes. This is a classic recipe for high CPU usage.

Furthermore, the function was running continuously in multiple threads, specifically two threads per CPU core. While multithreading can improve performance in many scenarios, in this case, it exacerbated the problem. Each thread was independently running the same computationally intensive algorithm, effectively multiplying the CPU load. There was no throttling mechanism in place, meaning the function would try to consume as much CPU as it could, leading to saturation.

To make matters worse, there were no rate limits or timeout controls. The function would run indefinitely, trying to find the shortest path, without any mechanism to stop it if it took too long. This meant that a single iteration could potentially consume a significant amount of CPU time, especially if no path was found quickly. The lack of a timeout also meant that the system could get stuck in an infinite loop if certain conditions were met, further driving up CPU usage.

In summary, the high CPU usage was a result of an unoptimized algorithm running on a large graph, compounded by multithreading and the absence of rate limiting or timeouts. Understanding these factors was crucial for devising an effective solution.

Proposed Fix: Taming the CPU Beast

Alright, so we've identified the problem – a hungry, CPU-guzzling function. Now, let's talk about the proposed fix. Our goal here is to optimize the cpu_intensive_task() function to reduce CPU load while maintaining its functionality. We don't want to break the app, just make it more efficient.

The fix involves a multi-pronged approach:

  1. Reducing Graph Size: First up, we're cutting the graph size in half, from 20 nodes down to 10 nodes. This might seem like a simple change, but it has a massive impact. Remember how the brute-force algorithm's complexity grows exponentially? Cutting the graph size significantly reduces the number of paths the algorithm needs to check. Less work for the CPU means less CPU usage.
  2. Adding a Delay: Next, we're introducing a 0.5-second delay between iterations. This is like giving the CPU a little breather. By pausing briefly between each run of the algorithm, we prevent it from running at 100% all the time. This helps avoid CPU saturation and gives other processes a chance to run smoothly.
  3. Implementing a Timeout: We're also adding a timeout. If an iteration takes more than 2 seconds, we're breaking the loop. This is a crucial safety net. It ensures that the function doesn't get stuck in an infinite loop and hog the CPU indefinitely. If a path isn't found within 2 seconds, we consider it a failed attempt and move on. This prevents the function from consuming excessive resources on a single iteration.
  4. Reducing max_depth: Finally, we're reducing the max_depth parameter to 5 for the path-finding algorithm. This parameter limits the maximum length of the paths that the algorithm considers. By reducing it, we're further reducing the computational complexity of each iteration. The algorithm will explore shorter paths first, which are often the most relevant anyway. This optimization reduces the overall workload and improves the efficiency of the algorithm.

These changes collectively ensure that the CPU-intensive task remains functional but consumes significantly less CPU. By reducing the graph size, adding a delay, implementing a timeout, and reducing the search depth, we're effectively throttling the function and preventing it from monopolizing CPU resources. The result is a more stable and responsive application.

Show Me the Code: The Nitty-Gritty

Okay, let's get to the good stuff – the code! Here's the proposed fix in action:

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

See those comments? They highlight the key changes: reducing the graph size, adding the delay with time.sleep(0.5), and implementing the timeout with the if elapsed > 2.0: check. These tweaks are designed to keep our CPU happy and our pod running smoothly.

File to Modify: Where the Magic Happens

To implement this fix, we need to modify one file:

  • main.py

This is where the cpu_intensive_task() function lives, so it's the perfect place to make our changes.

Next Steps: From Solution to Implementation

So, what's next? We've got a solid proposed fix, and we know where to implement it. The next step is to create a pull request (PR) with these changes. This allows the team to review the code, provide feedback, and ensure that everything looks good before we merge it into the main codebase.

Creating a PR is crucial for maintaining code quality and ensuring that everyone is on the same page. It provides a structured way to discuss the changes, identify potential issues, and make any necessary adjustments before deploying the code to production.

Once the PR is created, the team will review the code changes, test them thoroughly, and provide feedback. This collaborative process ensures that the fix is robust and addresses the root cause of the high CPU usage. After the review process, the changes can be merged into the main branch and deployed to the Kubernetes pod.

Conclusion: Wrapping It Up

CPU usage analysis is a critical part of maintaining application health, and addressing issues proactively is essential for preventing performance bottlenecks and downtime. In this case, we identified a root cause related to an unoptimized algorithm and implemented several key changes to mitigate the problem. By reducing the graph size, adding a delay, implementing a timeout, and reducing the search depth, we significantly reduced the CPU load while maintaining the functionality of the application.

This comprehensive approach not only fixes the immediate issue but also provides valuable insights for future development and optimization efforts. By understanding the factors that contribute to high CPU usage, we can design more efficient algorithms and implement best practices to ensure the stability and reliability of our applications.

By breaking down the problem, proposing a solution, and outlining the next steps, we're taking a proactive approach to managing our application's performance. And that's what it's all about, folks!