Kubernetes Control Plane and Data Plane Concepts: Difference between revisions
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[https://kubernetes.io/docs/reference/glossary/?fundamental=true#term-control-plane Control Plane] | [https://kubernetes.io/docs/reference/glossary/?fundamental=true#term-control-plane Control Plane] | ||
The control plane is the collective name for a cluster's [[#Master_Node|master nodes]]. The control plane exposes the API via the [[#API_Server|API Server]] and contains the [[#Cluster_Store|cluster store]], which stores state in [[Etcd|etcd]], [[#Controller_Manager|controller manager]], [[#Cloud_Controller_Manager|cloud controller manager]], [[#Scheduler|scheduler]] and other management components. The control plane makes workload scheduling decisions, performs monitoring and responds to external and internal events. These components can be run as traditional operating system services (daemons) or as containers. In production environments, the control plane usually runs across multiple computers, providing fault-tolerance and high availability. | The control plane is the collective name for a cluster's [[#Master_Node|master nodes]]. The control plane consists of cluster control systems. The control plane exposes the API via the [[#API_Server|API Server]] and contains the [[#Cluster_Store|cluster store]], which stores state in [[Etcd|etcd]], [[#Controller_Manager|controller manager]], [[#Cloud_Controller_Manager|cloud controller manager]], [[#Scheduler|scheduler]] and other management components. The control plane makes workload scheduling decisions, performs monitoring and responds to external and internal events. These components can be run as traditional operating system services (daemons) or as containers. In production environments, the control plane usually runs across multiple computers, providing fault-tolerance and high availability. | ||
==<span id='Master'></span>Master Node== | ==<span id='Master'></span>Master Node== | ||
Revision as of 16:18, 27 September 2020
External
Internal
Overview
When Kubernetes is deployed, you get a cluster.
Cluster
A Kubernetes cluster consists of a set of nodes, which all run containerized applications. Of those, a small number are running applications that manage the cluster. They are referred to as master nodes, also collectively known as the control plane. The rest of the nodes, a potentially relatively larger number, but at least one, are the worker nodes. They run the cluster's workload and are collectively known as the data plane.
Node
A node is a Linux host that can run as a VM, a bare-metal device or an instance in a private or public cloud. A node can be a master or worker. In most cases, the term "node" means worker node. The controller manager includes a node controller. Pods are scheduled on nodes.
kubelet
Each node runs an agent called kubelet. For more details see:
Container Runtime
Kube-proxy
The Kube-proxy is a process running on each node of the cluster. It is responsible with local networking. Kube-proxy makes sure each node gets its own unique IP address and implements local iptables or IPVS rules to handle routing and traffic on the pod network.
Control Plane
The control plane is the collective name for a cluster's master nodes. The control plane consists of cluster control systems. The control plane exposes the API via the API Server and contains the cluster store, which stores state in etcd, controller manager, cloud controller manager, scheduler and other management components. The control plane makes workload scheduling decisions, performs monitoring and responds to external and internal events. These components can be run as traditional operating system services (daemons) or as containers. In production environments, the control plane usually runs across multiple computers, providing fault-tolerance and high availability.
Master Node
A master node is a collection of system services that manage the Kubernetes cluster. The master nodes are sometimes called heads or head nodes, and most often simply masters. Collectively, they represent the control plane. While it is possible to execute user workloads on the master node, this is generally not recommended. This approach frees up the master nodes' resources to be exclusively used for cluster management activities.
HA Master Nodes
The recommended configuration includes 3 or 5 replicated masters.
Control Plane System Services
API Server
The API server is the control plane front-end service. All components (internal system components and external user components) communicate exclusively via the API Server and use the same API. The most common operation is to POST a manifest as part of a REST API invocation - once the invocation is authenticated and authorized, the manifest content is validated, then persisted into the cluster store and various controllers kick in to insure that the cluster state matches the desired state expressed in the manifest. The API Server runtime is represented by the kube-apiserver binary. The API server has admission controllers compiled into its binary.
Admission Controllers
An admission controller is a piece of code that intercepts requests to the API Server prior to persistence of the object, but after the request is authenticated and authorized. Some of these controllers allow dynamic admission control, implemented as extensible functionality that can run as webhooks configured at runtime. More details:
Cluster Store
The cluster store is the service that persistently stores the entire configuration and state of the cluster. The cluster store is the only stateful part of the control plane, and the single source of truth for the cluster. The current implementation is based on etcd. Productions deployments run in a HA configuration, with 3 - 5 replicas. etcd prefers consistence over availability, and it will halt updates to the cluster in split brain situations to maintain consistency. However, if etcd becomes unavailable, the applications running on the cluster will continue to work.
Controller Manager
The controller manager implements multiple specialized and independent control loops that monitor the state of the cluster and respond to events, insured that the current state of the cluster matches the desired state, as declared to the API server, thus implementing the declarative approach to operations. The controller manager is shipped as a monolithic binary. The controller manager is a "controller of controllers", including a node controller, an endpoints controller, a replicaset controller, a persistent volume controller, etc.
The logic implemented by each control loop consists of obtaining the desired state, observing the current state, determining differences and, if differences exist, reconciling differences. The terms "control loop" are used interchangeably with "watch loop" and "reconciliation loop".
Cloud Controller Manager
The cloud controller manager is a system service that manages integration with the underlying cloud technology and services such as storage and load balancers. It is only present if Kubernetes runs on a cloud like AWS, Azure or GCP.
Scheduler
The scheduler is a system service whose job is to distribute pods to nodes for execution. An individual pod can be scheduled on one node and one node only, and the target node is chosen by the scheduler as result of the evaluation of a set of predicates (affinity and anti-affinity rules, resource availability, etc.), followed by ranking according to criteria such as whether the node has the image or not, how many pods are already running, etc. The highest ranking node is chosen to run the pod. If the scheduler cannot find a suitable node, the pod goes into a "Pending" state.
Data Plane
The data plane is the set of worker nodes, constituting a layer that provides resources such as CPU, memory, network and storage so that containers can run and connect to network.
Worker Node
A worker node, most often referred simply as "node" (as opposite to master), is where the application services run. Collectively, the worker nodes make up the data plane. A worker node constantly watches for new work assignments, which materialize in the form of pods, which are the components of the application workload.