Fix High CPU Usage On Pod Test-app:8001
Guys, let's dive deep into this CPU usage analysis for our pod test-app:8001. We've got a situation where the application's behaving normally in terms of logic, but the CPU usage is through the roof, causing the pod to restart. It's like trying to run a marathon in flip-flops – doable, but not sustainable!
CPU Usage Analysis for test-app:8001
Understanding the Problem
We need to get to the bottom of this high CPU usage. It's not just about knowing the pod's name; it's about understanding why our little digital buddy is working so hard. High CPU usage can lead to all sorts of problems, from slow response times to complete application crashes. So, let's put on our detective hats and figure this out, shall we?
Pod Information
Before we get too deep, let's lay out the facts:
- Pod Name:
test-app:8001 - Namespace:
default
This gives us the specific context. We're talking about a particular instance of our application running in the default namespace. Think of it as identifying the patient in the emergency room – now we can start diagnosing!
Root Cause Analysis: The Culprit Revealed
After some digging, the logs are telling us a story. Our main suspect is the cpu_intensive_task() function. This function is running an unoptimized brute force shortest path algorithm. Imagine trying to find the best route across a city without using a map – you'd try every street, every alley, and probably end up exhausted! That’s what our function is doing, but with graphs instead of streets.
The real kicker? This function is doing all this on large graphs and without any brakes! No rate limiting, no resource constraints – it's a free-for-all CPU party, and our pod is the overwhelmed host. The function is spinning up multiple CPU-intensive threads, like having a dozen mini-marathons running simultaneously. No wonder our pod is gasping for air!
Proposed Fix: Taming the CPU Beast
Okay, so we've found our culprit. Now, how do we fix this? Our proposed fix focuses on optimizing that cpu_intensive_task() function. We're not trying to eliminate the function; we just need to teach it some manners – some resource-friendly behavior.
Optimizations
Here's the game plan:
- Reduce the Graph Size: We're shrinking the graph size from 20 nodes to 10 nodes. Think of it as reducing the size of the city our algorithm has to navigate. Smaller city, less exploring.
- Add Rate Limiting: We're introducing a
time.sleep(0.1)between iterations. This is like telling our algorithm to take a breather every so often. No need to rush; we're not trying to break any speed records here. - Maximum Execution Time Check: We're setting a 5-second limit per iteration. If an iteration takes too long, we're cutting it off. This prevents any single iteration from hogging the CPU indefinitely.
- Reduce Maximum Path Depth: We're lowering the maximum path depth from 10 to 5 in the shortest path algorithm. This limits the algorithm's search scope, preventing it from going down endless rabbit holes.
These changes are designed to prevent those massive CPU spikes while still allowing the simulation functionality to work. It's about finding a balance – like Goldilocks finding the porridge that's just right.
Code Changes: The Nitty-Gritty
Here's the code we're changing:
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
Line-by-Line Breakdown
Let's break down this code snippet. First, we reduce the graph size to graph_size = 10. This is crucial because it directly reduces the computational complexity. Think of it as downsizing from a 1000-piece puzzle to a 100-piece one. The algorithm still runs, but on a much smaller scale, making it significantly less CPU-intensive. We're not just reducing the load; we're making the task fundamentally easier.
Next, we implement rate limiting with time.sleep(0.1). This seemingly small addition is like a traffic light for our CPU. Without it, the algorithm would run at full throttle, potentially overwhelming the system. The sleep function pauses execution for a tenth of a second, giving the CPU a chance to breathe and handle other tasks. It's a simple yet effective way to prevent CPU spikes and maintain system stability. This also helps in distributing the workload more evenly over time, which is beneficial in the long run.
We also introduce a maximum execution time check with if elapsed > 5. This acts as a safety net, preventing any single iteration from consuming excessive resources. If an iteration takes longer than 5 seconds, it's terminated. This is particularly important in scenarios where the algorithm might get stuck in a loop or encounter an unusually complex case. By setting a time limit, we ensure that the system doesn't get bogged down by a single, runaway process. It’s like setting a kitchen timer to prevent overcooking – ensuring that things don’t get too hot.
Lastly, we reduce the maximum path depth to max_depth=5. This optimization targets the core of the shortest path algorithm. By limiting the depth of the search, we drastically reduce the number of paths the algorithm needs to explore. It's like saying,
