Fixing High CPU Usage In Kubernetes Pod

by Felix Dubois 40 views

Hey guys! Let's dive deep into this CPU usage analysis for the test-app:8001 pod. We're going to break down what's causing those pesky restarts and how we can fix it. Think of this as our troubleshooting playbook!

Pod Information

First, let's get the basics straight. We're dealing with a pod named test-app:8001 residing in the default namespace. Knowing this helps us pinpoint exactly where the problem lies within our Kubernetes cluster. This is like knowing the exact room in a house where the lights are flickering – super helpful!

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

Analysis: Decoding the CPU Spike

Alright, so the heart of the matter is that our pod is experiencing high CPU usage, which unfortunately leads to those annoying restarts. Nobody likes unexpected reboots, right? The logs are showing normal application behavior until the CPU spikes, which is our key clue. After digging a bit, it seems the culprit is the cpu_intensive_task() function.

This function is running an unoptimized brute-force shortest path algorithm on, get this, large graphs. Imagine trying to find the best route through a massive city without a map – that's essentially what this algorithm is doing! To make matters worse, it's doing this without any rate limiting or timeout controls. Think of it like flooring the gas pedal in your car with no brakes – eventually, something's gotta give.

To add fuel to the fire, this function is running continuously in multiple threads – specifically, twice the number of CPU cores. That's like having a bunch of tiny robots all trying to solve the same impossible puzzle at the same time, without any breaks. No wonder our CPU is screaming for help! So, in a nutshell, the root cause is an unoptimized, resource-hogging algorithm running without any safeguards. We need to tame this beast!

Proposed Fix: Taming the CPU Beast

Okay, so we've identified the problem – now let's talk solutions! Our goal is to optimize this CPU-intensive task so it doesn't hog all the resources and cause restarts. We're essentially going to give our little robots some rules to follow and make the puzzle a bit easier.

Here's the game plan to optimize the CPU-intensive task: Reduce the graph size from 20 to 10 nodes. Imagine shrinking that massive city map by half – suddenly, finding the best route becomes a lot easier! This significantly reduces the search space for our algorithm.

Add a 0.5s sleep between iterations for rate limiting. This is like telling our robots to take a breather between puzzle attempts. It prevents them from going into overdrive and gives the CPU some breathing room. Implement a 5-second timeout per iteration. This is like setting a timer for each robot's puzzle attempt. If they can't solve it within 5 seconds, they stop and try again later. This prevents individual iterations from running indefinitely and hogging resources. Reduce the max_depth parameter in path finding from 10 to 5. This limits the search depth of our algorithm, preventing it from exploring overly long and complex paths. Think of it as telling our robots to focus on the most direct routes first.

These changes, when combined, will significantly reduce CPU load while still allowing the core functionality of the task to be maintained. It's all about finding that sweet spot between performance and resource usage. We're essentially tuning our engine so it runs smoothly without overheating. By applying these fixes, we expect to see a dramatic decrease in CPU spikes and a much more stable pod. This means fewer restarts and a happier application!

Code Change: The Nitty-Gritty

Now, let's get into the code! Here’s the modified cpu_intensive_task() function with our proposed fixes baked in:

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(0.5)
 
 # Break if taking too long
 if elapsed > 5:
 print(f"[CPU Task] Task taking too long, breaking iteration")
 break

Let's break down the key changes:

  1. graph_size = 10: We've reduced the graph size, making the problem much less complex.
  2. time.sleep(0.5): This introduces a 0.5-second pause between iterations, rate-limiting the task.
  3. if elapsed > 5: break: We've added a timeout, so iterations don't run forever.
  4. max_depth=5: We've limited the search depth for paths, reducing the computational load. You see reducing the graph size reduces the overall complexity, while adding the sleep introduces rate limiting. The timeout ensures no single iteration runs wild, and capping the max depth prevents exhaustive searches. Each tweak works in concert to tame the CPU beast! This is the heart of our fix – making smart, targeted changes to the code to address the root cause of the problem. It's like a surgeon performing a delicate operation, precisely targeting the issue while minimizing any side effects.

File to Modify: Location, Location, Location

To apply these changes, we need to modify the main.py file. Knowing the exact file is crucial – it's like having the precise address for where to deliver the fix. This ensures we're making the changes in the right place and avoid any accidental modifications to other parts of the application.

Next Steps: From Analysis to Action

So, what’s next? We're not stopping here! The next logical step is to create a pull request (PR) with the proposed fix. Think of a PR as a formal proposal for changes. It allows others to review the code, provide feedback, and ensure everything looks good before merging it into the main codebase. This is a crucial step in any collaborative software development process. A pull request acts like a safety net, catching any potential issues before they make their way into production.

This PR will contain all the code changes we discussed above, along with a clear explanation of the problem and the proposed solution. It's like presenting our case to a jury – we want to make sure everyone understands the issue and why our fix is the right one. Once the PR is reviewed and approved, the changes can be merged, and we can deploy the updated code to our pod. Hopefully, this will put an end to those pesky CPU spikes and restarts. Fingers crossed!