How to get started, and achieve tasks, using Kubernetes

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Managing Compute Resources

When specifying a pod, you can optionally specify how much CPU and memory (RAM) each container needs. When containers have their resource requests specified, the scheduler is able to make better decisions about which nodes to place pods on; and when containers have their limits specified, contention for resources on a node can be handled in a specified manner. For more details about the difference between requests and limits, please refer to Resource QoS.

CPU and memory are each a resource type. A resource type has a base unit. CPU is specified in units of cores. Memory is specified in units of bytes.

CPU and RAM are collectively referred to as compute resources, or just resources. Compute resources are measureable quantities which can be requested, allocated, and consumed. They are distinct from API resources. API resources, such as pods and services are objects that can be written to and retrieved from the Kubernetes API server.

Resource Requests and Limits of Pod and Container

Each container of a pod can optionally specify one or more of the following:

Specifying resource requests and/or limits is optional. In some clusters, unset limits or requests may be replaced with default values when a pod is created or updated. The default value depends on how the cluster is configured. If the requests values are not specified, they are set to be equal to the limits values by default. Please note that limits must always be greater than or equal to requests.

Although requests/limits can only be specified on individual containers, it is convenient to talk about pod resource requests/limits. A pod resource request/limit for a particular resource type is the sum of the resource requests/limits of that type for each container in the pod, with unset values treated as zero (or equal to default values in some cluster configurations).

The following pod has two containers. Each has a request of 0.25 core of cpu and 64MiB (220 bytes) of memory and a limit of 0.5 core of cpu and 128MiB of memory. The pod can be said to have a request of 0.5 core and 128 MiB of memory and a limit of 1 core and 256MiB of memory.

apiVersion: v1
kind: Pod
  name: frontend
  - name: db
    image: mysql
        memory: "64Mi"
        cpu: "250m"
        memory: "128Mi"
        cpu: "500m"
  - name: wp
    image: wordpress
        memory: "64Mi"
        cpu: "250m"
        memory: "128Mi"
        cpu: "500m"

How Pods with Resource Requests are Scheduled

When a pod is created, the Kubernetes scheduler selects a node for the pod to run on. Each node has a maximum capacity for each of the resource types: the amount of CPU and memory it can provide for pods. The scheduler ensures that, for each resource type (CPU and memory), the sum of the resource requests of the containers scheduled to the node is less than the capacity of the node. Note that although actual memory or CPU resource usage on nodes is very low, the scheduler will still refuse to place pods onto nodes if the capacity check fails. This protects against a resource shortage on a node when resource usage later increases, such as due to a daily peak in request rate.

How Pods with Resource Limits are Run

When kubelet starts a container of a pod, it passes the CPU and memory limits to the container runner (Docker or rkt).

When using Docker:

TODO: document behavior for rkt

If a container exceeds its memory limit, it may be terminated. If it is restartable, it will be restarted by kubelet, as will any other type of runtime failure.

A container may or may not be allowed to exceed its CPU limit for extended periods of time. However, it will not be killed for excessive CPU usage.

To determine if a container cannot be scheduled or is being killed due to resource limits, see the “Troubleshooting” section below.

Monitoring Compute Resource Usage

The resource usage of a pod is reported as part of the Pod status.

If optional monitoring is configured for your cluster, then pod resource usage can be retrieved from the monitoring system.


My pods are pending with event message failedScheduling

If the scheduler cannot find any node where a pod can fit, then the pod will remain unscheduled until a place can be found. An event will be produced each time the scheduler fails to find a place for the pod, like this:

$ kubectl describe pod frontend | grep -A 3 Events
  FirstSeen	LastSeen	 Count	From          Subobject   PathReason			Message
  36s		5s		 6	    {scheduler }              FailedScheduling	Failed for reason PodExceedsFreeCPU and possibly others

In the case shown above, the pod “frontend” fails to be scheduled due to insufficient CPU resource on the node. Similar error messages can also suggest failure due to insufficient memory (PodExceedsFreeMemory). In general, if a pod or pods are pending with this message and alike, then there are several things to try:

You can check node capacities and amounts allocated with the kubectl describe nodes command. For example:

$ kubectl describe nodes gke-cluster-4-386701dd-node-ww4p
Name:			gke-cluster-4-386701dd-node-ww4p
[ ... lines removed for clarity ...]
 cpu:		1
 memory:	464Mi
 pods:		40
Allocated resources (total requests):
 cpu:		910m
 memory:	2370Mi
 pods:		4
[ ... lines removed for clarity ...]
Pods:				(4 in total)
  Namespace			Name								CPU(milliCPU)			Memory(bytes)
  frontend 			webserver-ffj8j							500 (50% of total)		2097152000 (50% of total)
  kube-system			fluentd-cloud-logging-gke-cluster-4-386701dd-node-ww4p		100 (10% of total)		209715200 (5% of total)
  kube-system			kube-dns-v8-qopgw						310 (31% of total)		178257920 (4% of total)
  CPU(milliCPU):		910 (91% of total)
  Memory(bytes):		2485125120 (59% of total)
[ ... lines removed for clarity ...]

Here you can see from the Allocated resources section that that a pod which ask for more than 90 millicpus or more than 1341MiB of memory will not be able to fit on this node.

Looking at the Pods section, you can see which pods are taking up space on the node.

The resource quota feature can be configured to limit the total amount of resources that can be consumed. If used in conjunction with namespaces, it can prevent one team from hogging all the resources.

My container is terminated

Your container may be terminated because it’s resource-starved. To check if a container is being killed because it is hitting a resource limit, call kubectl describe pod on the pod you are interested in:

[12:54:41] $ ./cluster/ describe pod simmemleak-hra99
Name:                           simmemleak-hra99
Namespace:                      default
Image(s):                       saadali/simmemleak
Node:                           kubernetes-node-tf0f/
Labels:                         name=simmemleak
Status:                         Running
Replication Controllers:        simmemleak (1/1 replicas created)
    Image:  saadali/simmemleak
      cpu:                      100m
      memory:                   50Mi
    State:                      Running
      Started:                  Tue, 07 Jul 2015 12:54:41 -0700
    Last Termination State:     Terminated
      Exit Code:                1
      Started:                  Fri, 07 Jul 2015 12:54:30 -0700
      Finished:                 Fri, 07 Jul 2015 12:54:33 -0700
    Ready:                      False
    Restart Count:              5
  Type      Status
  Ready     False
  FirstSeen                         LastSeen                         Count  From                              SubobjectPath                       Reason      Message
  Tue, 07 Jul 2015 12:53:51 -0700   Tue, 07 Jul 2015 12:53:51 -0700  1      {scheduler }                                                          scheduled   Successfully assigned simmemleak-hra99 to kubernetes-node-tf0f
  Tue, 07 Jul 2015 12:53:51 -0700   Tue, 07 Jul 2015 12:53:51 -0700  1      {kubelet kubernetes-node-tf0f}    implicitly required container POD   pulled      Pod container image "" already present on machine
  Tue, 07 Jul 2015 12:53:51 -0700   Tue, 07 Jul 2015 12:53:51 -0700  1      {kubelet kubernetes-node-tf0f}    implicitly required container POD   created     Created with docker id 6a41280f516d
  Tue, 07 Jul 2015 12:53:51 -0700   Tue, 07 Jul 2015 12:53:51 -0700  1      {kubelet kubernetes-node-tf0f}    implicitly required container POD   started     Started with docker id 6a41280f516d
  Tue, 07 Jul 2015 12:53:51 -0700   Tue, 07 Jul 2015 12:53:51 -0700  1      {kubelet kubernetes-node-tf0f}    spec.containers{simmemleak}         created     Created with docker id 87348f12526a

The Restart Count: 5 indicates that the simmemleak container in this pod was terminated and restarted 5 times.

You can call get pod with the -o go-template=... option to fetch the status of previously terminated containers:

[13:59:01] $ ./cluster/  get pod -o go-template='{{range.status.containerStatuses}}{{"Container Name: "}}{{.name}}{{"\r\nLastState: "}}{{.lastState}}{{end}}'  simmemleak-60xbc
Container Name: simmemleak
LastState: map[terminated:map[exitCode:137 reason:OOM Killed startedAt:2015-07-07T20:58:43Z finishedAt:2015-07-07T20:58:43Z containerID:docker://0e4095bba1feccdfe7ef9fb6ebffe972b4b14285d5acdec6f0d3ae8a22fad8b2]]

We can see that this container was terminated because reason:OOM Killed, where OOM stands for Out Of Memory.

Planned Improvements

The current system only allows resource quantities to be specified on a container. It is planned to improve accounting for resources which are shared by all containers in a pod, such as EmptyDir volumes.

The current system only supports container requests and limits for CPU and Memory. It is planned to add new resource types, including a node disk space resource, and a framework for adding custom resource types.

Kubernetes supports overcommitment of resources by supporting multiple levels of Quality of Service.

Currently, one unit of CPU means different things on different cloud providers, and on different machine types within the same cloud providers. For example, on AWS, the capacity of a node is reported in ECUs, while in GCE it is reported in logical cores. We plan to revise the definition of the cpu resource to allow for more consistency across providers and platforms.