Troubleshooting Pods Stuck Pending State Insufficient CPU Resources AKS
Hey guys! Ever run into a situation where your pods are just sitting there in the Pending state, not doing anything? It's super frustrating, especially when you're trying to get things up and running on your Azure Kubernetes Service (AKS) cluster. One of the most common reasons for this is insufficient CPU resources. Let's break down how to troubleshoot this issue, focusing on a scenario where you're using AKS 1.28 and your cluster has three nodes. We’ll dive deep into the causes, how to diagnose the problem, and, most importantly, how to fix it. So, buckle up and let’s get started!
Understanding the Pending State and Resource Requests
First things first, let's understand what it means when a pod is in the Pending state. In Kubernetes, a pod is the smallest deployable unit, and it needs resources like CPU and memory to run. When you define a pod, you specify how much of these resources it needs using resource requests and limits. The resource request is the minimum amount of resources a pod needs to be scheduled on a node. The resource limit is the maximum amount of resources a pod can use. Think of the request as what the pod asks for, and the limit as the hard cap.
Now, when you create a pod, Kubernetes scheduler looks for a node that can satisfy the pod’s resource requests. If there isn't a node with enough available CPU or memory, the pod will stay in the Pending state. It’s like trying to fit a square peg in a round hole – if there’s no space, it just won’t fit. This is exactly what’s happening when you see that dreaded FailedScheduling event with the message "insufficient CPU resources".
To really get this, you need to visualize your cluster. Imagine your three nodes as three separate computers, each with a certain amount of CPU and memory. Your pods are like applications that need a certain amount of these resources to run. If you try to run too many resource-intensive applications on these computers, you’ll eventually run out of space. Kubernetes works in a similar way. It tries to schedule pods onto nodes where they can run efficiently, but if the demand exceeds the available resources, you’ll hit a bottleneck. It's crucial to specify resource requests appropriately. If you underestimate the resources your pods need, they might get throttled and perform poorly. If you overestimate, they might not get scheduled at all, leading to the Pending state.
Resource Requests and Limits
Let's delve a little deeper into resource requests and limits. When you define these in your pod's YAML file, you're essentially telling Kubernetes how much CPU and memory your pod needs. Here’s a quick example:
apiVersion: v1
kind: Pod
metadata:
name: my-pod
spec:
containers:
- name: my-container
image: nginx
resources:
requests:
cpu: 500m
memory: 512Mi
limits:
cpu: 1000m
memory: 1Gi
In this example, the pod requests 500 milliCPUs (0.5 CPU cores) and 512 MB of memory. It also has a limit of 1 CPU core and 1 GB of memory. Kubernetes uses the requests value to schedule the pod onto a node. The limits value ensures that the pod doesn't consume more resources than it's allowed to.
It's super important to set these values correctly. If you set the requests too low, your pod might get scheduled on a node but then get throttled if it tries to use more resources than it requested. This can lead to performance issues. On the other hand, if you set the requests too high, your pod might sit in the Pending state indefinitely if there's no node with enough available resources. Finding the right balance is key to ensuring your applications run smoothly and efficiently.
Diagnosing Insufficient CPU Resources on AKS
Okay, so your pod is stuck in Pending and the events say "insufficient CPU resources". What now? Don't worry, we've all been there. The first step is to confirm the issue and gather some information. Think of yourself as a detective, gathering clues to solve the mystery of the pending pod. Here’s a step-by-step approach to diagnosing the problem:
1. Check Pod Events
The first clue is usually in the pod’s events. You mentioned seeing FailedScheduling events, which is a dead giveaway. To view these events, use the following command:
kubectl describe pod <pod-name> -n <namespace>
Replace <pod-name> with the name of your pending pod and <namespace> with the namespace it’s in. Scroll through the output and look for the Events section. You should see something like this:
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Scheduled <unknown> default-scheduler Successfully assigned default/my-pod to aks-nodepool1-12345678-vmss000000
Warning FailedScheduling 2m default-scheduler 0/3 nodes are available: insufficient cpu.
This confirms that the scheduler couldn’t find a node with enough CPU to run your pod. The message "0/3 nodes are available: insufficient cpu" tells you that none of your three nodes have enough CPU capacity to meet the pod's request. This is our main clue, guys. We know the problem is CPU-related, so now we need to figure out why.
2. Check Node Capacity and Allocation
Now that we know the problem is CPU, let’s check the CPU capacity of your nodes and how much CPU is currently allocated to pods. This will give us a clear picture of how much CPU is available in your cluster. To do this, we'll use kubectl describe nodes command. But, we need to make it a bit more specific to get the CPU information we need. We can use grep to filter the output and focus on CPU-related metrics.
First, let’s get a list of your nodes:
kubectl get nodes
This will give you the names of your nodes. Then, for each node, run the following command, replacing <node-name> with the actual node name:
kubectl describe node <node-name> | grep -E 'CPU:|Allocatable:'
This command will output the CPU capacity, allocatable CPU, and CPU requests and limits for each node. The Capacity section shows the total CPU available on the node. The Allocatable section shows how much CPU is available for pods after accounting for system daemons and Kubernetes overhead. The Non-terminated Pods section shows the total CPU requests and limits for pods running on the node.
By comparing the Allocatable CPU with the total CPU requests, you can see how much CPU is still available on each node. If the total CPU requests are close to or exceed the allocatable CPU, you’ve found your problem. Your nodes are running close to their CPU capacity, which is why the scheduler can’t find a node to place your pod. This is like checking the fuel gauge in your car – if it’s near empty, you know you need to fill up (or, in this case, add more resources to your cluster).
3. Check Resource Requests and Limits of Existing Pods
Sometimes, the issue isn’t necessarily that your nodes are at full capacity, but rather that some pods might be requesting more CPU than they actually need. This can lead to a situation where resources are being reserved but not fully utilized, effectively starving other pods. To investigate this, we need to examine the resource requests and limits of the pods running in your cluster.
We can use kubectl get pods command along with some awk magic to get a summary of the CPU requests for each pod. Run the following command:
kubectl get pods --all-namespaces -o go-template='{{range .items}}{{"Namespace: "}}{{.metadata.namespace}}{{"\n"}}{{"Name: "}}{{.metadata.name}}{{"\n"}}{{"CPU Request: "}}{{.spec.containers.[0].resources.requests.cpu}}{{"\n---\n"}}{{end}}'
This command will list all pods across all namespaces and show their CPU requests. Go through the output and look for pods that have high CPU requests. You might find some pods that are requesting more CPU than they actually need. This is like having a leaky faucet – it might not seem like much, but over time, it can waste a lot of water (or, in this case, CPU).
Additionally, you can use tools like Kubernetes Resource Report or Kubewatch to monitor resource usage and identify potential resource hogs. These tools provide a more visual and comprehensive view of your cluster's resource utilization, making it easier to spot inefficiencies. Identifying and adjusting the resource requests of over-provisioned pods can free up significant CPU capacity and help resolve your Pending pod issue.
Solutions for Insufficient CPU Resources on AKS
Alright, detective work done! We've diagnosed the issue as insufficient CPU resources. Now for the exciting part: fixing it! There are several ways to tackle this, each with its own pros and cons. Let's explore the most common solutions, and remember, guys, there's no one-size-fits-all answer. The best solution will depend on your specific situation and long-term needs.
1. Increase the Number of Nodes in Your AKS Cluster
This is often the most straightforward solution. If your cluster is consistently running out of CPU, adding more nodes will increase the overall CPU capacity. Think of it like adding more lanes to a highway – more lanes mean more traffic can flow smoothly. In AKS, you can easily scale your node pools up or down using the Azure portal, Azure CLI, or infrastructure-as-code tools like Terraform.
To scale your node pool using the Azure CLI, you can use the following command:
az aks scale --resource-group <resource-group-name> --name <aks-cluster-name> --node-count <new-node-count> --node-pool <node-pool-name>
Replace <resource-group-name>, <aks-cluster-name>, <new-node-count>, and <node-pool-name> with your actual values. This command will increase the number of nodes in your specified node pool to the desired count. Keep in mind that scaling up your cluster will incur additional costs, so it’s important to monitor your resource usage and scale appropriately.
Before scaling, consider the type of workload you’re running. If you have bursty workloads that require more resources at certain times, autoscaling might be a better option. If you have a consistent workload, adding a fixed number of nodes might be more cost-effective. Also, think about your application’s architecture. Is it designed to scale horizontally? If not, simply adding more nodes might not solve your problem. You might need to refactor your application to take advantage of the additional resources.
2. Adjust Resource Requests and Limits
As we discussed earlier, incorrect resource requests and limits can lead to inefficient resource utilization. If pods are requesting more CPU than they need, they might be preventing other pods from being scheduled. On the other hand, if pods are requesting too little CPU, they might get throttled and perform poorly. It’s a balancing act, guys.
To adjust resource requests and limits, you’ll need to edit your pod’s YAML file. Identify the pods that are over-provisioning CPU and lower their requests. Similarly, if you have pods that are being throttled, consider increasing their requests. Remember to apply the changes by running kubectl apply -f <your-pod-definition.yaml>. After making changes, monitor your pods' performance to ensure they're running optimally. Tools like Prometheus and Grafana can be incredibly helpful for this.
It’s a good practice to conduct performance testing to determine the optimal resource requests and limits for your pods. This will help you avoid both over-provisioning and under-provisioning. Additionally, consider using Vertical Pod Autoscaling (VPA) in Kubernetes. VPA can automatically adjust the CPU and memory requests and limits of your pods based on their actual usage. This can help you optimize resource utilization and ensure your pods have the resources they need, when they need them.
3. Optimize Application Resource Consumption
Sometimes, the problem isn’t necessarily a lack of resources, but rather inefficient resource usage by your applications. Think of it like a car that’s guzzling gas – you might need to tune the engine to improve fuel efficiency. In the context of Kubernetes, this means optimizing your application code and configuration to reduce CPU consumption.
Start by profiling your application to identify performance bottlenecks and areas where CPU usage can be reduced. Common optimizations include reducing unnecessary computations, optimizing database queries, and caching frequently accessed data. For example, if your application is performing a lot of string manipulation, you might be able to improve performance by using more efficient string handling techniques. If your application is making a lot of database queries, you might be able to reduce CPU usage by optimizing your queries or adding indexes.
Another area to consider is your application's concurrency settings. If your application is spawning too many threads or processes, it can lead to excessive CPU usage. Try tuning the concurrency settings to match the available CPU resources. Also, ensure your application is using asynchronous operations where appropriate. Asynchronous operations allow your application to perform other tasks while waiting for a long-running operation to complete, which can improve overall throughput and reduce CPU usage.
4. Use Pod Priority and Preemption
Kubernetes offers a mechanism called Pod Priority and Preemption that can help you prioritize important pods and ensure they get scheduled even when resources are scarce. With pod priority, you can assign a priority class to your pods. Pods with higher priority are scheduled before pods with lower priority. Preemption takes this a step further – if a higher-priority pod needs resources, it can preempt (evict) lower-priority pods to free up those resources. Think of it like having VIP access – the VIPs get the best seats, even if it means someone else has to move.
To use pod priority and preemption, you first need to define priority classes. A priority class is a non-namespaced object that defines a mapping between a name and a priority value. You can create priority classes using YAML files like this:
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
name: high-priority
value: 1000000
globalDefault: false
description: