Fixing High CPU Usage In Pod Test-app-8001

by Mei Lin 43 views

Pod Information

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

Analysis

Alright, so here's the scoop. The logs are showing everything running smoothly on the application side, but our poor pod is getting hammered with high CPU usage, which is causing it to restart. Not ideal, right? After digging around, it looks like the culprit is the cpu_intensive_task() function. This little guy is running an unoptimized, brute-force shortest path algorithm. Imagine trying to find the best route across the city without a map – that’s what this function is doing, but with a massive graph of 20 nodes.

Here's the breakdown of what's going wrong:

  • Unoptimized Algorithm: The algorithm is basically trying every single possible path to find the shortest one. This is like checking every street in the city instead of using a GPS – super inefficient!
  • Large Graph Size: With 20 nodes, the number of possible paths explodes. It’s like trying to navigate a maze the size of a small town. The bigger the maze, the more CPU power we need.
  • No Rate Limiting: The function is just churning away continuously without any breaks. It's like a marathon runner sprinting the entire race – bound to burn out.
  • No Timeout Controls: There's no “escape hatch” if the calculation takes too long. It keeps going and going, hogging all the CPU resources. It is really important to implement this kind of checks in these cases, as any infinite loops, or very long running executions might impact the application overall performance.

This all adds up to a major CPU overload, which is why our pod is getting the jitters and restarting. We need to give this function a bit of a chill pill and make it play nicer with our system. We need to find a way to reduce the amount of work the algorithm does, without losing the functionallity and desired outcome.

Proposed Fix

Okay, so how do we solve this CPU-hogging problem? Our plan is to optimize the cpu_intensive_task() function with a few key tweaks. Think of it as giving our algorithm a smart makeover. The goal here is to maintain the functionality of the algorithm, but prevent those crazy CPU spikes that are causing chaos. We'll achieve this by making the function more efficient and less resource-intensive. We want it to be a sprint, not an endless marathon.

Here’s the game plan:

  1. Reduce Graph Size: We're cutting the graph size from 20 nodes down to 10. This is like shrinking our maze to a manageable size. Fewer nodes mean fewer paths to check, which means less work for the CPU.
  2. Add Rate Limiting: We're adding a 100ms sleep between iterations. This is like giving our marathon runner a short water break. It lets the CPU catch its breath and prevents it from getting overloaded. Imagine the impact, even just a tenth of a second pause will do!
  3. Add a 5-Second Timeout: We're setting a 5-second timeout per calculation. This is our “escape hatch.” If the algorithm takes longer than 5 seconds to find a path, it throws in the towel. This stops it from getting stuck in endless loops and hogging resources indefinitely. Without this timeout, we're basically giving the function a blank check to run forever, and that’s a recipe for disaster. It's like saying, “Hey, you’ve got 5 seconds to find the path, or we’re moving on.”
  4. Reduce Max Path Depth: We're reducing the maximum path depth from 10 to 5 for the shortest path algorithm. This is like telling our maze runner, “You only need to go five turns deep.” It limits the search area and reduces the complexity of the calculation. By reducing the depth, we’re essentially telling the algorithm to focus on the most likely paths and not waste time exploring every single dead end. It's a smart shortcut that can significantly reduce CPU load.

These changes together are going to make a big difference. By reducing the complexity of the problem, adding breaks, and setting time limits, we’re making sure our cpu_intensive_task() function is a responsible citizen of our system. It can still do its job, but it won’t be hogging all the resources and causing those annoying restarts. It’s all about finding the right balance between functionality and performance.

Code Change

Here’s the code snippet that shows how we’re implementing these changes. It’s pretty straightforward, but these small tweaks are going to have a big impact on our CPU usage. The changes are well documented, so you can see exactly what we’re doing and why. We added a bunch of comments in the code itself to explain what's going on. Think of it as a roadmap for our optimization journey!

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 > 5:
            break

File to Modify

main.py

Next Steps

So, what’s next? We’re going to create a pull request with this fix. This will allow the team to review the changes, make sure everything looks good, and then merge it into the main codebase. It’s like getting a second opinion from your friends before you make a big decision. We want to make sure we’re not introducing any new problems while we’re fixing the old ones. After the code has been reviewed and approved, we can deploy the updated version of the application and say goodbye to those pesky CPU spikes. It’s all about teamwork and making sure our system is running smoothly!