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FastAPI framework, high performance, easy to learn, fast to code, ready for production

Manage security and best-practices with policies.

This page describes the maximum number of volumes that can be attached to a Node for various cloud providers. Cloud providers like Google, Amazon, and Microsoft typically have a limit on how many volumes can be attached to a Node. It is important for Kubernetes to respect those limits. Otherwise, Pods scheduled on a Node could get stuck waiting for volumes to attach. Kubernetes default limits The Kubernetes scheduler has default limits on the number of volumes that can be attached to a Node:

On-disk files in a container are ephemeral, which presents some problems for non-trivial applications when running in containers. One problem occurs when a container crashes or is stopped. Container state is not saved so all of the files that were created or modified during the lifetime of the container are lost. During a crash, kubelet restarts the container with a clean state. Another problem occurs when multiple containers are running in a Pod and need to share files.

FastAPI framework, high performance, easy to learn, fast to code, ready for production

FastAPI framework, high performance, easy to learn, fast to code, ready for production

When several users or teams share a cluster with a fixed number of nodes, there is a concern that one team could use more than its fair share of resources. Resource quotas are a tool for administrators to address this concern. A resource quota, defined by a ResourceQuota object, provides constraints that limit aggregate resource consumption per namespace. It can limit the quantity of objects that can be created in a namespace by type, as well as the total amount of compute resources that may be consumed by resources in that namespace.

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Node affinity is a property of Pods that attracts them to a set of nodes (either as a preference or a hard requirement). Taints are the opposite -- they allow a node to repel a set of pods. Tolerations are applied to pods. Tolerations allow the scheduler to schedule pods with matching taints. Tolerations allow scheduling but don't guarantee scheduling: the scheduler also evaluates other parameters as part of its function.