Merged master into dev-1.18 to keep in sync

Signed-off-by: vineeth <vineethpothulapati@outlook.com>
This commit is contained in:
vineeth
2020-01-19 10:09:18 +05:30
49 changed files with 956 additions and 6118 deletions
@@ -443,7 +443,7 @@ Each feature gate is designed for enabling/disabling a specific feature:
- `ServerSideApply`: Enables the [Sever Side Apply (SSA)](/docs/reference/using-api/api-concepts/#server-side-apply) path at the API Server.
- `ServiceLoadBalancerFinalizer`: Enable finalizer protection for Service load balancers.
- `ServiceNodeExclusion`: Enable the exclusion of nodes from load balancers created by a cloud provider.
A node is eligible for exclusion if labelled with "`alpha.service-controller.kubernetes.io/exclude-balancer`" key (when `LegacyNodeRoleBehavior` is on) or `node.kubernetes.io/exclude-from-external-load-balancers`.
A node is eligible for exclusion if labelled with "`alpha.service-controller.kubernetes.io/exclude-balancer`" key or `node.kubernetes.io/exclude-from-external-load-balancers`.
- `StartupProbe`: Enable the [startup](/docs/concepts/workloads/pods/pod-lifecycle/#when-should-you-use-a-startup-probe) probe in the kubelet.
- `StorageObjectInUseProtection`: Postpone the deletion of PersistentVolume or
PersistentVolumeClaim objects if they are still being used.
@@ -4,14 +4,14 @@ id: cluster
date: 2019-06-15
full_link:
short_description: >
A set of machines, called nodes, that run containerized applications managed by Kubernetes. A cluster has at least one worker node and at least one master node.
A set of worker machines, called nodes, that run containerized applications. Every cluster has at least one worker node.
aka:
tags:
- fundamental
- operation
---
A set of machines, called nodes, that run containerized applications managed by Kubernetes. A cluster has at least one worker node and at least one master node.
A set of worker machines, called nodes, that run containerized applications. Every cluster has at least one worker node.
<!--more-->
The worker node(s) host the pods that are the components of the application. The master node(s) manages the worker nodes and the pods in the cluster. Multiple master nodes are used to provide a cluster with failover and high availability.
The worker node(s) host the pods that are the components of the application. The Control Plane manages the worker nodes and the pods in the cluster. In production environments, the Control Plane usually runs across multiple computers and a cluster usually runs multiple nodes, providing fault-tolerance and high availability.
@@ -7,6 +7,7 @@ short_description: >
An API resource that automatically scales the number of pod replicas based on targeted CPU utilization or custom metric targets.
aka:
- HPA
tags:
- operation
---
@@ -4,14 +4,14 @@ id: kube-controller-manager
date: 2018-04-12
full_link: /docs/reference/command-line-tools-reference/kube-controller-manager/
short_description: >
Component on the master that runs controllers.
Control Plane component that runs controller processes.
aka:
tags:
- architecture
- fundamental
---
Component on the master that runs {{< glossary_tooltip text="controllers" term_id="controller" >}}.
Control Plane component that runs {{< glossary_tooltip text="controller" term_id="controller" >}} processes.
<!--more-->
@@ -4,15 +4,15 @@ id: kube-scheduler
date: 2018-04-12
full_link: /docs/reference/generated/kube-scheduler/
short_description: >
Component on the master that watches newly created pods that have no node assigned, and selects a node for them to run on.
Control Plane component that watches for newly created pods with no assigned node, and selects a node for them to run on.
aka:
tags:
- architecture
---
Component on the master that watches newly created pods that have no node assigned, and selects a node for them to run on.
Control Plane component that watches for newly created pods with no assigned node, and selects a node for them to run on.
<!--more-->
Factors taken into account for scheduling decisions include individual and collective resource requirements, hardware/software/policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference and deadlines.
Factors taken into account for scheduling decisions include individual and collective resource requirements, hardware/software/policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference and deadlines.