Fix High CPU Usage In Pod Test-app:8001

by Elias Adebayo 40 views

Hey guys! We've got a situation with the test-app:8001 pod, and it looks like we're wrestling with some serious CPU spikes. Let's dive into what's going on and how we can fix it. We're going to break down the issue, propose a solution, and map out the next steps. Think of this as our game plan to get things running smoothly again.

Pod Information

First, let's nail down the essentials. We're dealing with:

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

This gives us the exact location of the problem so we know where to focus our efforts. Knowing the pod name and namespace is like having the GPS coordinates to our troubleshooting destination.

Analysis

Alright, here’s the scoop. The logs are showing that our application is behaving as expected in many ways, but there's a recurring theme: high CPU usage. This isn't just a little blip; it's enough to cause the pod to restart, which, as we know, isn't ideal for keeping things stable. We need to understand why our CPU is getting maxed out.

Digging deeper, the root cause appears to be nestled in the cpu_intensive_task() function. This function is where things get interesting – and problematic. It's running an unoptimized brute-force shortest path algorithm. Imagine trying to find the quickest route across a massive city without a map, and you’re checking every single street. That's what our function is doing, but with graphs. And these aren’t small graphs; we're talking potentially large ones. The function doesn’t have any rate limiting or timeout controls, meaning it can run wild if it hits a complex scenario.

Here’s the kicker: the cpu_intensive_task() function runs continuously as long as cpu_spike_active is true. This means that, under the right (or wrong) conditions, it can consume 100% CPU across multiple threads. That's like flooring the gas pedal in your car and never letting up – eventually, something's going to break or overheat. In our case, it’s the CPU hitting its limit and the pod restarting. To really put it in perspective, consider a scenario where the graph size is huge, the algorithm is complex, and there are no brakes on how long it can run. This is a recipe for CPU overload.

So, to sum it up, the high CPU usage stems from an intensive, unoptimized algorithm that's allowed to run unchecked. It's like having a runaway train on our system, and we need to lay down some tracks to guide it safely.

Proposed Fix

Okay, team, let's talk solutions. We need to tame this CPU-hungry beast without sacrificing the functionality of our simulation. The goal is to optimize the cpu_intensive_task() so it plays nice with our resources. Our strategy involves a multi-pronged approach, making several key adjustments to how the algorithm operates. These changes are aimed at reducing the load on the CPU while still allowing the task to perform its core function.

Here’s the plan:

  1. Reduce the Graph Size: Instead of working with massive graphs of 20 nodes, we're scaling it down to 10 nodes. This immediately cuts down the complexity of the problem the algorithm has to solve. Think of it like reducing the size of the city we're trying to navigate – fewer streets mean less searching.
  2. Add Rate Limiting: We're introducing a 100ms sleep between iterations. This is like putting a governor on an engine, preventing it from running at full throttle all the time. This small pause allows the CPU to breathe and handle other tasks, preventing saturation.
  3. Implement a Timeout: We're setting a 5-second timeout per iteration. This is a crucial safety net. If an iteration takes too long, we'll break out of it. It’s like saying, “We’ll search for a maximum of 5 seconds, and if we don’t find anything, we’ll move on.” This prevents the algorithm from getting stuck in endless loops.
  4. Reduce Maximum Path Depth: We're reducing the maximum path depth from 10 to 5. This limits the amount of recursion, which can be a major CPU hog. It's like saying, “We’ll only explore paths up to 5 steps long,” which significantly reduces the search space.

These changes are designed to maintain the simulation's core functionality while ensuring it doesn't consume excessive CPU. We're essentially giving the algorithm guardrails to prevent it from running wild. The idea is to make it more efficient and less resource-intensive.

Code Change

Here’s the code snippet showcasing 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

Let's walk through the key tweaks:

  • Reduced Graph Size: We've shrunk graph_size from a potentially larger number down to 10. This means fewer nodes and edges for the algorithm to process, directly reducing the computational load. This is the first line of defense against CPU overload.
  • Rate Limiting Sleep: time.sleep(0.1) introduces a 100ms pause between each iteration. This is a simple but effective way to prevent the CPU from being bombarded continuously. It's like giving the CPU a chance to catch its breath between sprints.
  • Timeout Implementation: The if elapsed > 5: check ensures that each iteration has a maximum runtime of 5 seconds. If an iteration exceeds this limit, it's terminated, preventing the algorithm from getting stuck indefinitely. This is a critical failsafe to avoid runaway CPU usage.
  • Reduced Maximum Path Depth: The max_depth=5 parameter in the brute_force_shortest_path function call limits the depth of the search. This prevents excessive recursion, which can quickly consume CPU resources. It's like setting a boundary on how far we're willing to explore, preventing us from getting lost in the maze.

By implementing these changes, we’re not just tweaking the code; we’re fundamentally altering how the algorithm behaves. We’re shifting from an aggressive, resource-intensive approach to a more controlled and efficient one. This is about making smart trade-offs to ensure our application remains performant and stable.

File to Modify

The file we need to modify is:

main.py

This is where the cpu_intensive_task() function lives, so it’s the epicenter of our fix. Knowing the exact file makes the implementation process straightforward and reduces the chance of errors. It's like having the exact address for the repair job.

Next Steps

Alright, so what’s the game plan from here? We’re not done yet, but we’re on the right track. The immediate next step is to:

Create a pull request (PR) with the proposed fix.

This PR will contain the code changes we’ve discussed, allowing the team to review, test, and provide feedback. It’s a collaborative process to ensure we’re making the best possible changes. Think of the PR as our proposal to the team – it lays out the problem, the solution, and the reasoning behind it. Once the PR is created, the team can review the changes, run tests, and provide valuable input. This collaborative approach ensures that we’re not just fixing the problem but also improving the overall quality of our codebase.

After the review process, the changes will be merged into the main codebase, and we can deploy the updated version of the application. We'll then monitor the pod's CPU usage to ensure our fix is effective. It’s a cycle of continuous improvement, and we’re committed to keeping our application running smoothly. So, stay tuned for updates as we move through these next steps. We’re in this together, and we’ll get this sorted out!