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

Hey guys! Let's dive into this CPU usage analysis for the test-app:8001 pod. We're going to break down what's causing the high CPU usage and how we can fix it. This is super important for keeping our applications running smoothly in Kubernetes. We'll cover everything from the initial problem to the proposed solution, making sure it's all clear and easy to understand.

Pod Information

First, let's get some context. We need to know exactly which pod we're talking about.

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

So, we're dealing with the test-app:8001 pod in the default namespace. Keeping track of these details is crucial for pinpointing the issue and applying the fix correctly. Understanding the environment helps us ensure that our solutions are targeted and effective.

Analysis

Okay, let's get to the heart of the matter. CPU usage analysis revealed that while the application seems to be behaving normally, the pod is experiencing high CPU usage, which is causing it to restart. Nobody wants constant restarts! After digging into the logs, it looks like the culprit is the cpu_intensive_task() function. This function is running an unoptimized, brute-force shortest path algorithm on large graphs. Think of it like trying to find the best route on a massive, complicated map without using GPS – super inefficient!

The main issues are:

1. The function is creating graphs with 20 nodes, which is quite large for this kind of task.
2. It's running continuous iterations without any breaks. Imagine running a marathon without stopping for water – you're going to crash eventually.
3. There's no rate limiting or resource constraints in place. This means the function can hog all the CPU it wants, leading to saturation.

In simpler terms, the cpu_intensive_task() function is working way too hard without any breaks or limits, causing the pod to overheat (figuratively speaking) and restart. This is a classic case of an algorithm running wild and needs some taming. Let's look at how we can fix this.

Proposed Fix

Alright, let's talk solutions! To fix this high CPU usage issue, we need to optimize the cpu_intensive_task() function. We're going to make a few key changes to keep it from going overboard. The goal here is to maintain the functionality of the task while preventing it from consuming too much CPU. Think of it as giving the function a chill pill so it doesn't get so worked up.

Here’s the plan:

1. Reduce graph size: Instead of using graphs with 20 nodes, we'll scale it down to 10 nodes. This immediately reduces the complexity of the calculations. It's like going from a huge city map to a smaller town map – much easier to navigate.
2. Add a 100ms sleep between iterations: This introduces a small pause between each run of the algorithm, preventing CPU saturation. It's like giving the CPU a mini-break to cool down before tackling the next iteration.
3. Add a 5-second timeout per iteration: This ensures that no single iteration runs for too long. If an iteration takes more than 5 seconds, we'll stop it. This prevents the function from getting stuck in an endless loop and hogging resources. It's like setting a timer to make sure you don't spend too long on one task.
4. Reduce max_depth parameter: This limits the recursion depth in the shortest path algorithm, further reducing its complexity. Think of it as limiting how many wrong turns you can take before stopping to ask for directions.
5. Add early termination: If processing takes too long overall, we'll stop the task. This is a safety net to prevent the function from running indefinitely and consuming excessive resources. It's like having a kill switch for when things get out of hand.

These changes are designed to keep the functionality intact while ensuring the task doesn't overload the CPU. It's all about finding the right balance between performance and resource usage. Now, let's see the code changes in action.

Code Change

Okay, let's get into the code! This is where the magic happens. We're going to modify the cpu_intensive_task() function to include the optimizations we discussed. Here's the updated code:

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 break down these changes:

• We reduced the graph size from 20 to 10 nodes. This significantly reduces the computational load.
• We added time.sleep(0.1) to introduce a 100ms sleep between iterations. This prevents the function from running continuously and hogging the CPU.
• We implemented a 5-second timeout for each iteration. If an iteration takes longer than 5 seconds, it will be terminated.
• We set max_depth=5 to limit the recursion depth in the brute_force_shortest_path function.
• We added an early termination condition that breaks the iteration if it takes too long. This prevents the function from getting stuck in an infinite loop.

These modifications ensure that the cpu_intensive_task() function is more efficient and doesn't overload the CPU. It's like giving the function a set of rules to follow so it doesn't overwork itself.

File to Modify

Now that we have the code changes, we need to know where to apply them. The file we need to modify is:

main.py

This is where the cpu_intensive_task() function lives, so this is where we'll make our changes. Knowing the exact file makes the deployment process smooth and ensures we're targeting the right code.

Next Steps

So, what's next? We've identified the issue, proposed a fix, and even shown the code changes. The next step is to:

A pull request will be created with the proposed fix.

This means we'll submit our changes for review and testing. A pull request is like saying,