309 lines
12 KiB
Markdown
309 lines
12 KiB
Markdown
---
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title: Pod 开销
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content_template: templates/concept
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weight: 20
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---
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{{% capture overview %}}
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{{< feature-state for_k8s_version="v1.18" state="beta" >}}
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<!--
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When you run a Pod on a Node, the Pod itself takes an amount of system resources. These
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resources are additional to the resources needed to run the container(s) inside the Pod.
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_Pod Overhead_ is a feature for accounting for the resources consumed by the Pod infrastructure
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on top of the container requests & limits.
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-->
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在节点上运行 Pod 时，Pod 本身占用大量系统资源。这些资源是运行 Pod 内容器所需资源的附加资源。
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_POD 开销_ 是一个特性，用于计算 Pod 基础设施在容器请求和限制之上消耗的资源。
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{{% /capture %}}
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{{% capture body %}}
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<!--
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## Pod Overhead
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-->
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## Pod 开销
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<!--
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In Kubernetes, the Pod's overhead is set at
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[admission](/docs/reference/access-authn-authz/extensible-admission-controllers/#what-are-admission-webhooks)
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time according to the overhead associated with the Pod's
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[RuntimeClass](/docs/concepts/containers/runtime-class/).
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-->
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在 Kubernetes 中，Pod 的开销是根据与 Pod 的 [RuntimeClass](/docs/concepts/containers/runtime-class/) 相关联的开销在
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[准入](/docs/reference/access-authn-authz/extensible-admission-controllers/#what-are-admission-webhooks) 时设置的。
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<!--
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When Pod Overhead is enabled, the overhead is considered in addition to the sum of container
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resource requests when scheduling a Pod. Similarly, Kubelet will include the Pod overhead when sizing
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the Pod cgroup, and when carrying out Pod eviction ranking.
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-->
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当启用 Pod 开销时，在调度 Pod 时，除了考虑容器资源请求的总和外，还要考虑 Pod 开销。类似地，Kubelet 将在确定 Pod cgroup 的大小和执行 Pod 驱逐排序时包含 Pod 开销。
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<!--
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## Enabling Pod Overhead {#set-up}
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-->
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## 启用 Pod 开销 {#set-up}
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<!--
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You need to make sure that the `PodOverhead`
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[feature gate](/docs/reference/command-line-tools-reference/feature-gates/) is enabled (it is on by default as of 1.18)
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across your cluster, and a `RuntimeClass` is utilized which defines the `overhead` field.
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-->
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您需要确保在集群中启用了 `PodOverhead` [特性门](/docs/reference/command-line-tools-reference/feature-gates/)（在 1.18 默认是开启的），以及一个用于定义 `overhead` 字段的 `RuntimeClass`。
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<!--
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## Usage example
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-->
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## 使用示例
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<!--
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To use the PodOverhead feature, you need a RuntimeClass that defines the `overhead` field. As
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an example, you could use the following RuntimeClass definition with a virtualizing container runtime
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that uses around 120MiB per Pod for the virtual machine and the guest OS:
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-->
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要使用 PodOverhead 特性，需要一个定义 `overhead` 字段的 RuntimeClass. 作为例子，可以在虚拟机和来宾操作系统中通过一个虚拟化容器运行时来定义 RuntimeClass 如下，其中每个 Pod 大约使用 120MiB:
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```yaml
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---
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kind: RuntimeClass
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apiVersion: node.k8s.io/v1beta1
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metadata:
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name: kata-fc
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handler: kata-fc
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overhead:
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podFixed:
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memory: "120Mi"
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cpu: "250m"
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```
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<!--
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Workloads which are created which specify the `kata-fc` RuntimeClass handler will take the memory and
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cpu overheads into account for resource quota calculations, node scheduling, as well as Pod cgroup sizing.
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Consider running the given example workload, test-pod:
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-->
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通过指定 `kata-fc` RuntimeClass 处理程序创建的工作负载会将内存和 cpu 开销计入资源配额计算、节点调度以及 Pod cgroup 分级。
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假设我们运行下面给出的工作负载示例 test-pod:
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```yaml
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apiVersion: v1
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kind: Pod
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metadata:
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name: test-pod
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spec:
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runtimeClassName: kata-fc
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containers:
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- name: busybox-ctr
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image: busybox
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stdin: true
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tty: true
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resources:
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limits:
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cpu: 500m
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memory: 100Mi
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- name: nginx-ctr
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image: nginx
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resources:
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limits:
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cpu: 1500m
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memory: 100Mi
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```
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<!--
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At admission time the RuntimeClass [admission controller](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/)
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updates the workload's PodSpec to include the `overhead` as described in the RuntimeClass. If the PodSpec already has this field defined,
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the Pod will be rejected. In the given example, since only the RuntimeClass name is specified, the admission controller mutates the Pod
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to include an `overhead`.
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-->
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在准入阶段 RuntimeClass [准入控制器](https://kubernetes.io/docs/reference/access-authn-authz/admission-controllers/) 更新工作负载的 PodSpec 以包含
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RuntimeClass 中定义的 `overhead`. 如果 PodSpec 中该字段已定义，该 Pod 将会被拒绝。在这个例子中，由于只指定了 RuntimeClass 名称，所以准入控制器更新了 Pod, 包含了一个 `overhead`.
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<!--
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After the RuntimeClass admission controller, you can check the updated PodSpec:
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-->
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在 RuntimeClass 准入控制器之后，可以检验一下已更新的 PodSpec:
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```bash
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kubectl get pod test-pod -o jsonpath='{.spec.overhead}'
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```
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<!--
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The output is:
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-->
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输出：
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```
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map[cpu:250m memory:120Mi]
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```
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<!--
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If a ResourceQuota is defined, the sum of container requests as well as the
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`overhead` field are counted.
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-->
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如果定义了 ResourceQuata, 则容器请求的总量以及 `overhead` 字段都将计算在内。
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<!--
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When the kube-scheduler is deciding which node should run a new Pod, the scheduler considers that Pod's
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`overhead` as well as the sum of container requests for that Pod. For this example, the scheduler adds the
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requests and the overhead, then looks for a node that has 2.25 CPU and 320 MiB of memory available.
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-->
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当 kube-scheduler 决定在哪一个节点调度运行新的 Pod 时，调度器会兼顾该 Pod 的 `overhead` 以及该 Pod 的容器请求总量。在这个示例中，调度器将资源请求和开销相加，然后寻找具备 2.25 CPU 和 320 MiB 内存可用的节点。
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<!--
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Once a Pod is scheduled to a node, the kubelet on that node creates a new {{< glossary_tooltip text="cgroup" term_id="cgroup" >}}
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for the Pod. It is within this pod that the underlying container runtime will create containers. -->
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一旦 Pod 调度到了某个节点， 该节点上的 kubelet 将为该 Pod 新建一个 {{< glossary_tooltip text="cgroup" term_id="cgroup" >}}. 底层容器运行时将在这个 pod 中创建容器。
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<!--
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If the resource has a limit defined for each container (Guaranteed QoS or Bustrable QoS with limits defined),
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the kubelet will set an upper limit for the pod cgroup associated with that resource (cpu.cfs_quota_us for CPU
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and memory.limit_in_bytes memory). This upper limit is based on the sum of the container limits plus the `overhead`
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defined in the PodSpec.
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-->
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如果该资源对每一个容器都定义了一个限制（定义了受限的 Guaranteed QoS 或者 Bustrable QoS），kubelet 会为与该资源（CPU 的 cpu.cfs_quota_us 以及内存的 memory.limit_in_bytes）
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相关的 pod cgroup 设定一个上限。该上限基于容器限制总量与 PodSpec 中定义的 `overhead` 之和。
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<!--
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For CPU, if the Pod is Guaranteed or Burstable QoS, the kubelet will set `cpu.shares` based on the sum of container
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requests plus the `overhead` defined in the PodSpec.
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-->
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对于 CPU, 如果 Pod 的 QoS 是 Guaranteed 或者 Burstable, kubelet 会基于容器请求总量与 PodSpec 中定义的 `overhead` 之和设置 `cpu.shares`.
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<!--
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Looking at our example, verify the container requests for the workload:
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-->
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请看这个例子，验证工作负载的容器请求：
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```bash
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kubectl get pod test-pod -o jsonpath='{.spec.containers[*].resources.limits}'
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```
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<!--
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The total container requests are 2000m CPU and 200MiB of memory:
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-->
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容器请求总计 2000m CPU 和 200MiB 内存：
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```
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map[cpu: 500m memory:100Mi] map[cpu:1500m memory:100Mi]
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```
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<!--
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Check this against what is observed by the node:
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-->
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对照从节点观察到的情况来检查一下：
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```bash
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kubectl describe node | grep test-pod -B2
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```
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<!--
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The output shows 2250m CPU and 320MiB of memory are requested, which includes PodOverhead:
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-->
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该输出显示请求了 2250m CPU 以及 320MiB 内存，包含了 PodOverhead 在内：
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```
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Namespace Name CPU Requests CPU Limits Memory Requests Memory Limits AGE
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--------- ---- ------------ ---------- --------------- ------------- ---
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default test-pod 2250m (56%) 2250m (56%) 320Mi (1%) 320Mi (1%) 36m
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```
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<!--
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## Verify Pod cgroup limits
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-->
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## 验证 Pod cgroup 限制
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<!--
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Check the Pod's memory cgroups on the node where the workload is running. In the following example, [`crictl`](https://github.com/kubernetes-sigs/cri-tools/blob/master/docs/crictl.md)
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is used on the node, which provides a CLI for CRI-compatible container runtimes. This is an
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advanced example to show PodOverhead behavior, and it is not expected that users should need to check
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cgroups directly on the node.
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First, on the particular node, determine the Pod identifier:ying
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-->
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在工作负载所运行的节点上检查 Pod 的内存 cgroups. 在接下来的例子中，将在该节点上使用具备 CRI 兼容的容器运行时命令行工具 [`crictl`](https://github.com/kubernetes-sigs/cri-tools/blob/master/docs/crictl.md).
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这是一个展示 PodOverhead 行为的进阶示例，用户并不需要直接在该节点上检查 cgroups.
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首先在特定的节点上确定该 Pod 的标识符：ying
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<!--
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```bash
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# Run this on the node where the Pod is scheduled
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-->
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```bash
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# 在该 Pod 调度的节点上执行如下命令：
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POD_ID="$(sudo crictl pods --name test-pod -q)"
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```
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<!--
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From this, you can determine the cgroup path for the Pod:
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-->
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可以依此判断该 Pod 的 cgroup 路径：
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<!--
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```bash
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# Run this on the node where the Pod is scheduled
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-->
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```bash
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# 在该 Pod 调度的节点上执行如下命令：
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sudo crictl inspectp -o=json $POD_ID | grep cgroupsPath
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```
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<!--
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The resulting cgroup path includes the Pod's `pause` container. The Pod level cgroup is one directory above.
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-->
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执行结果的 cgroup 路径中包含了该 Pod 的 `pause` 容器。Pod 级别的 cgroup 即上面的一个目录。
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```
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"cgroupsPath": "/kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2/7ccf55aee35dd16aca4189c952d83487297f3cd760f1bbf09620e206e7d0c27a"
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```
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<!--
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In this specific case, the pod cgroup path is `kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2`. Verify the Pod level cgroup setting for memory:
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-->
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在这个例子中，该 pod 的 cgroup 路径是 `kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2`。验证内存的 Pod 级别 cgroup 设置：
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<!--
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```bash
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# Run this on the node where the Pod is scheduled.
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# Also, change the name of the cgroup to match the cgroup allocated for your pod.
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-->
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```bash
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# 在该 Pod 调度的节点上执行这个命令。
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# 另外，修改 cgroup 的名称以匹配为该 pod 分配的 cgroup。
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cat /sys/fs/cgroup/memory/kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2/memory.limit_in_bytes
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```
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<!--
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This is 320 MiB, as expected:
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-->
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和预期的一样是 320 MiB
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```
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335544320
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```
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<!--
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### Observability
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-->
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### 可观察性
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<!--
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A `kube_pod_overhead` metric is available in [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics)
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to help identify when PodOverhead is being utilized and to help observe stability of workloads
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running with a defined Overhead. This functionality is not available in the 1.9 release of
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kube-state-metrics, but is expected in a following release. Users will need to build kube-state-metrics
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from source in the meantime.
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-->
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在 [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics) 中可以通过 `kube_pod_overhead` 指标来协助确定何时使用 PodOverhead 以及协助观察以一个既定开销运行的工作负载的稳定性。
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该特性在 kube-state-metrics 的 1.9 发行版本中不可用，不过预计将在后续版本中发布。在此之前，用户需要从源代码构建 kube-state-metrics.
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{{% /capture %}}
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{{% capture whatsnext %}}
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* [RuntimeClass](/docs/concepts/containers/runtime-class/)
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* [PodOverhead 设计](https://github.com/kubernetes/enhancements/blob/master/keps/sig-node/20190226-pod-overhead.md)
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{{% /capture %}}
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