---
title: Persistent Storage Strategies and Consulting for Kubernetes
description: Discover persistent storage strategies and solutions for various container storage interfaces and managed storage classes for Kubernetes.
image: https://www.xenonstack.com/hubfs/persistent-storage-services.png
---

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# Persistent Storage Strategies and Consulting for Kubernetes

[Navdeep Singh Gill](https://www.xenonstack.com/blog/author/navdeep-singh-gill) | 02 November 2024

Persistent Storage Strategies and Consulting for Kubernetes

24:03

## Overview of Persistent Storage

 Persistent Storage is critical for running stateful containers. [Kubernetes](https://kubernetes.io/) is an open-source system for automating the deployment and management of containerized applications. There are many storage options available. In this blog, we are going to discuss the most widely used storage, which can be used on-premises or in the cloud, such as GlusterFS, CephFS, Ceph RBD, OpenEBS, NFS, [GCE Persistent Storage](https://cloud.google.com/persistent-disk), AWS EBS, NFS, and Azure Disk.

---

> Open Source Distributed Object Storage Server written in Go, designed for Private Cloud infrastructure providing S3 storage functionality. Click to explore about, [Minio Distributed Object Storage Architecture and Performance](https://www.xenonstack.com/insights/minio)

### Why Persistent Storage Solutions are important?

 To follow this guide, you need -

- Kubernetes
- Kubectl
- DockerFile
- Container Registry
- Storage technologies that will be used - 
    - OpenEBS
    - CephFS
    - GlusterFS
    - AWS EBS
    - Azure Disk
    - GCE persistent storage
    - CephRBD

### Kubernetes

Kubernetes is one of the best open-source orchestration platforms for deploying, autoscaling and managing containerized applications.

#### Kubectl

Kubectl is a command-line utility for managing Kubernetes clusters remotely or locally. Use this link to configure Kubectl.

### Container Registry

 Container Registry is the repository where we store and distribute docker images. Several repositories are available online, including DockerHub, Google Cloud, Microsoft Azure, and [AWS Elastic](https://aws.amazon.com/what-is/elasticsearch/) Container Registry (ECR). Container storage is ephemeral, meaning all the data in the container is removed when it crashes or restarted. Persistent storage is necessary for stateful containers to run applications like MySQL, Apache, PostgreSQL, etc., so we don’t lose our data when a container stops.

> Running Containers at any real-world scale requires container orchestration, and scheduling platform like Docker Swarm, Apache Mesos, and AWS ECS. Click to explore about, [Laravel Docker Application Development](https://www.xenonstack.com/blog/laravel-docker)

## 3 Types of Storage

- **Block Storage is the most commonly used storage and is very flexible. It stores chunks of data in blocks, each identified by its address. Because of its performance, block storage is mostly used for databases**.
- **File Storage stores data as files; each file is referenced by a filename and has attributes associated with it. NFS is the most commonly used file system. We can use file storage when** we want to share data with multiple containers.
- **Object Storage**: Object storage is different from file storage and block storage. In object storage, data is stored as an object and is referenced by object ID. It is massively scalable and provides more flexibility than block storage, but performance is slower. Amazon S3, Swift and Ceph Object Storage are the most commonly used object storage.

![Emerging Storage Technologies](https://www.xenonstack.com/hubfs/xenonstack-object-storage-kubernetes.png)

---

## 7 Emerging Storage Technologies

The below highlighted are the 7 Emerging Storage Technologies:

### OpenEBS Container Storage

[OpenEBS](https://openebs.io/) is a pure container-based storage platform available for Kubernetes. Using OpenEBS, we can easily use persistent storage for stateful containers, and the process of provisioning a disk is automated. It is a scalable storage solution that can run anywhere, from the cloud to on-premises hardware.

### Ceph Storage Cluster

[Ceph](https://ceph.com/en/) is an advanced and scalable software-defined storage system that fits best with today’s requirements, providing object storage, block storage, and file systems on a single platform. Ceph can also be used with Kubernetes. We can either use CephFS or CephRBD for persistent storage for Kubernetes pods.

- Ceph RBD is the block storage we assign to each pod. In read-write mode, CephRBD can’t be shared with two pods at a time.
- CephFS is a POSIX-compliant file system service which stores data on top Ceph cluster. We can share CephFS with multiple pods at the same time. CephFS is now announced as stable in the latest Ceph release.

### GlusterFS Storage Cluster

[GlusterFS](https://www.gluster.org/) is a scalable network file system suitable for cloud storage. It is also a software-defined storage which runs on commodity hardware just like Ceph but it only provides File systems, and it is similar to CephFS. Glusterfs provides more speed than Ceph as it uses larger block size as compared to ceph i.e Glusterfs uses a block size of 128kb whereas ceph uses a block size of 64Kb.

### AWS EBS Block Storage

[Amazon EBS](https://aws.amazon.com/ebs/) provides persistent block storage volumes which are attached to EC2 instances. AWS provides various options for EBS, so we can choose the storage according to requirements depending on parameters like the number of IOPS, storage type(SSD/HDD), etc. We mount AWS EBS with Kubernetes pods for persistent block storage using AWSElasticBlockStore. EBS disks are automatically replicated over multiple AZ’s for durability and high availability.

### GCEPersistentDisk Storage

 GCEPersistentDisk is a durable and high-performance block storage used with the Google Cloud Platform. We can use it either with [Google Compute Engine](https://cloud.google.com/products/compute?hl=en) or Google Container Engine. We can choose from HDD or SSD and increase the volume disk size as the need increases. GCEPersistentDisks are automatically replicated across multiple data centres for durability and high availability. We mount GCEPersistentDisk with Kubernetes pods for persistent block storage using GCEPersistentDisk.

### Azure Disk Storage

 An [Azure Disk](https://azure.microsoft.com/en-in/products/storage/disks/) is also durable and high-performance block storage like AWS EBS and GCEPersistentDisk. It provides the option to choose from SSD or HDD for your environment and features like Point-in-time backup, easy migration, etc. An AzureDiskVolume is used to mount an Azure Data Disk into a Pod. Azure Disks are replicated within multiple data centres for high availability and durability.

### Network File System Storage

 NFS is one of the oldest file systems, providing the facility to share a single file system on the network with multiple machines. There are several NAS devices available for high performance, or we can make our system a NAS. We use NFS for persistent storage for pods, and data can be shared with multiple instances.

---

> RAID storage uses different disks to provide fault tolerance, to improve overall performance, and to increase storage size in a system. Click to explore about, [Types of RAID Storage for Databases](https://www.xenonstack.com/blog/raid-storage-databases)

## Continuous Deployment of Storage Solutions

 Now, we will walk through the deployments of storage solutions described above, starting with Ceph.

### Ceph Deployment

 For Ceph, we need an existing Ceph cluster. Either we have to deploy the Ceph cluster on Bare Metal or we can use [Docker Containers](https://ceph.com/geen-categorie/bootstrap-your-ceph-cluster-in-docker/). Then, we install the Ceph client on a Kubernetes host. For CephRBD, we have to create a separate pool and user for the created pool.

- Creating new/separate pool

 \# ceph osd pool create Kube 200 200

- Creating a user with full access to the Kube pool

 \# ceph auth get-or-create client.kubep mon 'allow r' osd 'allow class-read object\_prefix rbd\_children, allow rwx pool=kube' -o /etc/ceph/ceph.client.kube.keyring

- Get the authentication key from the Ceph cluster for the user client.kubep

 \# ceph --cluster ceph auth get-key client.kubep

- Creating a new secret in default namespace in Kubernetes

\# kubectl create secret generic ceph-secret-kube --type="kubernetes.io/rbd" --from- literal=key='AQBvPvNZwfPoIBAAN9EjWaou6S4iLVg/meA0YA==’ --namespace=default

- kube-controller-manager must have the privilege to provision storage and it needs admin key from Ceph to do that. For that, we must get admin key

 \# sudo ceph --cluster ceph auth get-key client.admin

- Creating a new secret for admin in default namespace in Kubernetes

\# kubectl create secret generic ceph-secret --type="kubernetes.io/rbd" --from-literal=key='AQAbM/NZAA0KHhAAdpCHwG62kE0zKGHnGybzgg==' --namespace=ceph-storage

- After adding secrets, we have to define a new Storage Class by copying the following content in the file named ceph-rbd-storage.yml

```
apiVersion: storage.k8s.io / v1
kind: StorageClass
metadata:
 name: rbd
provisioner: kubernetes.io / rbd
parameters:
 monitors: 192.168 .122 .110: 6789
adminID: admin
adminSecretName: ceph - secret
pool: kube
userId: kubep
userSecretName: ceph - secret - kube
fsType: ext4
imageFormat: "2"
imageFeatures: "layering"
```

 \# kubectl create -f ceph-rbd-storage.yml --namespace=ceph-storage

- Creating a volume using rbd StorageClass in the file named it ceph-vc.yml -

```
apiVersion: v1
kind: PersistentVolume
metadata:
 name: apache - pv
namespace: ceph - storage
spec:
 capacity:
 storage: 100 Mi
accessModes:
 -ReadWriteMany
rbd:
 monitors:
 -192.168 .122 .110: 6789
pool: kube
image: myvol
user: admin
secretRef:
 name: ceph - secret
fsType: ext4
readOnly: false
```

 \# kubectl create -f ceph-vc.yml --namespace=ceph-storage

- Creating a volume claim using rbd StorageClass in the file named it ceph-pvc.yml -

```
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
  name: apache-pvc
spec:
  accessModes:
    - ReadWriteMany
  resources:
    requests:
      Storage: 500Mi
```

 \# kubectl create -f ceph-pvc.yml --namespace=ceph-storage

- Now, we are going to launch Apache Pod using the claimed volume. Create a new file with the following content -

```
apiVersion: v1
kind: ReplicationController
metadata:
 name: apache
spec:
 replicas: 1
selector:
 app: apache
template:
 metadata:
 name: apache
labels:
 app: apache
spec:
 containers:
 -name: apache
image: bitnami / apache
ports:
 -containerPort: 80
volumeMounts:
 -mountPath: /var/www / html
name: apache - vol
volumes:
 -name: apache - vol
persistentVolumeClaim:
 claimName: apache - pvc
```

 \# kubectl create -f apache-pod.yml --namespace=ceph-storage

- For CephFS, we are going to create a Ceph pool -

 \# ceph osd pool create cephfs\_data 200 # ceph osd pool create cephfs\_metadata 200 # ceph fs new newfs cephfs\_metadata cephfs\_data

- A new secret for the Ceph admin user is being created in the default namespace in Kubernetes. Using

 \# sudo ceph --cluster ceph auth get-key client.admin

\# kubectl create secret generic ceph-secret --type="kubernetes.io/rbd" --from-literal=key='AQDkTeBZLDwlORAA6clp1vUBTGbaxaax/Mwpew==' --namespace=default

- After adding secrets, we have to copy the following content in the file named ceph-fs-storage.yml

```
apiVersion: v1
kind: ReplicationController
metadata:
 name: apache
spec:
 replicas: 1
selector:
 app: apache
template:
 metadata:
 name: apache
labels:
 app: apache
spec:
 containers:
 -name: apache
image: apache
ports:
 -containerPort: 80
volumeMounts:
 -mountPath: /var/www / html
name: mypvc
volumes:
 -name: mypvc
cephfs:
 monitors:
 -192.168 .100 .26: 6789
user: admin
secretRef:
 name: ceph - secret
```

---

### GlusterFS Deployment

 We can deploy the Glusterfs cluster on [Bare Metal](https://docs.gluster.org/en/latest/Quick-Start-Guide/Quickstart/) servers or containers using Heketi and create a GlusterFS volume afterward.

- Creating the following directory on the servers where we want to keep the data.

 \# mkdir -p /data/brick1/myvol

- Then, we will create a volume using -

 \# gluster volume create myvol replica 2 node1:/data/brick1/myvol node2:/data/brick1/myvol # gluster volume start gv0

- Now, we have to create cluster endpoints for Kubernetes. By adding the content to a file named gluster-endpoint.yaml

```
kind: Endpoints
apiVersion: v1
metadata:
 name: glusterfs - cluster
subsets:
 -addresses:
 -ip: 10.240 .106 .152
ports:
 -port: 1 - addresses:
 -ip: 10.240 .79 .157
ports:
 -port: 1
```

 \< # kubectl create -f gluster-endpoint.yaml

- Create the cluster service in Kubernetes by adding the following content in glusterfs-service.yaml

```
kind: Service
apiVersion: v1
metadata:
  name: glusterfs-cluster
spec:
  ports:
  - port: 1
```

 \# kubectl create -f glusterfs-service.yaml

- Then we will launch Apache pod using gluster as backend storage and add the following content to file apache-pod.yaml -

```
apiVersion: v1
kind: Pod
metadata:
  name: glusterfs
spec:
  containers:
  - name: glusterfs
    image: apache
    volumeMounts:
    - mountPath: "/var/www/html"
      name: glusterfsvol
  volumes:
  - name: glusterfsvol
    glusterfs:
      endpoints: glusterfs-cluster
      path: kube_vol
      readOnly: true
```

 \# kubectl create -f apache-pod.yaml

---

### NFS Deployment

 For the NFS server to be consumed by the Kubernetes pod. First, we will create persistent volume by adding the following content in nfs-pv.yaml.

```
apiVersion: v1
kind: PersistentVolume
metadata:
  name: nfs-pv 
spec:
  capacity:
    storage: 10Gi
  accessModes:
    - ReadWriteMany 
  persistentVolumeReclaimPolicy: Retain 
  nfs: 
    path: /data/nfs 
    server: nfs.server1
    readOnly: false
```

 \# kubectl create -f nfs.pv.yaml

- Creating persistent volume claim by adding the following content in nfs-pvc.yaml -

```
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
 name: nfs - pvc
spec:
 accessModes:
 -ReadWriteMany
resources:
 requests:
 storage: 10 Gi
```

 \# kubectl create -f nfs-pvc.yaml

- Now we will launch web-server pod with NFS persistent volume by adding the following content in apache-server.yaml

```
apiVersion: v1
kind: ReplicationController
metadata:
 name: apache
spec:
 replicas: 1
selector:
 app: apache
template:
 metadata:
 name: apache
labels:
 app: apache
spec:
 containers:
 -name: apache
image: apache
ports:
 -containerPort: 80
volumeMounts:
 -mountPath: /var/www / html
name: nfs - vol
volumes:
 -name: nfs - vol
persistentVolumeClaim:
 claimName: nfs - pvc
```

 \# kubectl create -f apache-server.yaml

### AWS EBS Deployment

 For using Amazon EBS in Kubernetes pod first, we have to make sure that -

- The nodes on which Kubernetes pods are running are Amazon EC2 instances.
- EC2 instances need to be in the same region and AZ as of EBS.

- First, we have to create storage class in kubernetes for EBS disk by adding the following content in awsebs-storage.yaml

```
kind: StorageClass
apiVersion: storage.k8s.io / v1
metadata:
 name: slow
provisioner: kubernetes.io / aws - ebs
parameters:
 type: io1
zones: us - east - 1 d, us - east - 1 c
iopsPerGB: "10"
```

 \# kubectl create -f aws-ebs-storage.yaml

- Then, we will create PVC by adding the following content in aws-ebs.yaml -

```
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
 name: aws - ebs
annotations:
 volume.beta.kubernetes.io / storage - class: standard

spec:
 accessModes:
 -ReadWriteOnce
resources:
 requests:
 storage: 10 Gi
storageClassName: io1
```

 \# kubectl create -f aws-ebs.yaml

- Now we will launch apache-webserver pod with AWS EBS as persistent storage by adding the following content apache-web-ebs.yaml

```
apiVersion: v1
kind: ReplicationController
metadata:
 name: apache
spec:
 replicas: 1
selector:
 app: apache
template:
 metadata:
 name: apache
labels:
 app: apache
spec:
 containers:
 -name: apache
image: apache
ports:
 -containerPort: 80
volumeMounts:
 -mountPath: /var/www / html
name: aws - ebs - storage
volumes:
 -name: aws - ebs - storage
persistentVolumeClaim:
 claimName: aws - ebs
```

 \# kubectl create -f apache-web-ebs.yaml

---

### Azure Disk Deployment

- This is for using the Azure disk as persistent storage for Kubernetes pods. We have to create a storage class by adding the following content in the sc-azure file.yaml

```
kind: StorageClass
apiVersion: storage.k8s.io / v1beta1
metadata:
 name: slow
provisioner: kubernetes.io / azure - disk
parameters:
 skuName: Standard_LRS
location: eastus
```

 \# kubectl create -f sc-azure.yaml

- After creating storage class we are going to create Persistent Volume claim by adding the following content in azure-pvc.yaml.

```
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
 name: azure - disk - storage
annotations:
 volume.beta.kubernetes.io / storage - class: slow
spec:
 accessModes:
 -ReadWriteOnce
resources:
 requests:
 storage: 10 Gi
```

 \# kubectl create -f azure-pvc.yaml

- Now we will launch Apache-webserver pod with AZURE DISK as persistent storage by adding the following content apache-web-azure.yaml.

```
apiVersion: v1
kind: ReplicationController
metadata:
 name: apache
spec:
 replicas: 1
selector:
 app: apache
template:
 metadata:
 name: apache
labels:
 app: apache
spec:
 containers:
 -name: apache
image: apache
ports:
 -containerPort: 80
volumeMounts:
 -mountPath: /var/www / html
name: azure - disk
volumes:
 -name: azure - disk
persistentVolumeClaim:
 claimName: azure - disk - storage
```

 \# kubectl create -f apache-web-azure.yaml

### GCEPersistantDisk Deployment

 For using GCEPersistantDisk in Kubernetes pod first, we have to make sure that.

- The nodes on which Kubernetes pods are running are GCE instances.
- EC2 instances must be in the same GCE project and zone as the PD.

- First, we have to create a storage class in Kubernetes for the GCEPersistantDisk disk by adding the following content in gcepd-storage.yaml.

```
kind: StorageClass
apiVersion: storage.k8s.io / v1beta1
metadata:
 name: fast
provisioner: kubernetes.io / gce - pd
parameters:
 type: pd - ssd
```

 \# kubectl create -f gcepd-storage.yaml

- We will create a PVC by adding the following content in gcepd-pvc.yaml.

```
apiVersion: extensions / v1beta1
kind: Deployment
metadata:
 name: apache
spec:
 template:
 metadata:
 name: apache
labels:
 app: apache
spec:
 containers:
 -name: apache
image: bitnami / apache
ports:
 -containerPort: 80
volumeMounts:
 -mountPath: /opt/bitnami / apache / htdocs
name: apache - vol
volumes:
 -name: apache - vol
gcePersistentDisk:
 pdName: gce - disk
fsType: ext4
```

 \# kubectl create -f apache-gce.yaml

- After creating a persistent disk, we are going to use it to store web data for the web server pod by adding the following content to the file named "web-data."

### OpenEBS Deployment

- For OpenEBS cluster setup, click on this link for the [setup guide](https://readthedocs.org/). First, we are going to start the OpenEBS Services   
  using Operator by -

`# kubectl create -f https://github.com/openebs/openebs/blob/master/k8s/openebs-operator.yaml`

- Then, we are going to create some default storage classes by -

`# kubectl create -f https://raw.githubusercontent.com/openebs/openebs/master/k8s/openebs-storageclasses.yaml`<https://raw.githubusercontent.com/openebs/openebs/master/k8s/openebs-storageclasses.yaml>

- Now, we are going to launch jupyter with OpenEBS persistent volume by adding the following content in the file named demo-openebs-jupyter.yaml -

```
apiVersion: apps
kind: Deployment
metadata:
 name: jupyter - server
namespace: default
spec:
 replicas: 1
template:
 metadata:
 labels:
 name: jupyter - server
spec:
 containers:
 -name: jupyter - server
imagePullPolicy: Always
image: satyamz / docker - jupyter: v0 .4
ports:
 -containerPort: 8888
env:
 -name: GIT_REPO
value: https: //github.com/vharsh/plot-demo.git
 volumeMounts:
 -name: data - vol
mountPath: /mnt/data
volumes:
 -name: data - vol
persistentVolumeClaim:
 claimName: jupyter - data - vol - claim
 -- -
 kind: PersistentVolumeClaim
apiVersion: v1
metadata:
 name: jupyter - data - vol - claim
spec:
 storageClassName: openebs - jupyter
accessModes:
 -ReadWriteOnce
resources:
 requests:
 storage: 5 G
 -- -
 apiVersion: v1
kind: Service
metadata:
 name: jupyter - service
spec:
 ports:
 -name: ui
port: 8888
nodePort: 32424
protocol: TCP
selector:
 name: jupyter - server
sessionAffinity: None
type: NodePort

```

 \# kubectl create -f demo-openebs-juypter.yaml This will provision the PV and PVC automatically for the Jupyter pod.

## Features Comparison for Storage Solutions

| Storage Technologies | Read Write Once | Read Only Many | Read Write Many | Deployed On | Internal Provisioner | Format | Provider | Scalability Capacity Per Disk | Network Intensive |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CephFS | Yes | Yes | Yes | On-Premises/Cloud | No | File | Ceph Cluster | Upto Petabytes | Yes |
| CephRBD | Yes | Yes | No | On-Premises/Cloud | Yes | Block | Ceph Cluster | Upto Petabytes | Yes |
| GlusterFS | Yes | Yes | Yes | On-Premises/Cloud | Yes | File | GlusterFS Cluster | Upto Petabytes | Yes |
| AWS EBS | Yes | No | No | AWS | Yes | Block | AWS | 16TB | No |
| Azure Disk | Yes | No | No | Azure | Yes | Block | Azure | 4TB | No |
| GCE Persistent Storage | Yes | No | No | Google Cloud | Yes | Block | Google Cloud | 64TB/4TB (Local SSD) | No |
| OpenEBS | Yes | Yes | No | On-Premises/Cloud | Yes | Block | OpenEBS Cluster | Depends on the Underlying Disk | Yes |
| NFS | Yes | Yes | Yes | On-Premises/Cloud | No | File | NFS Server | Depends on the Shared Disk | Yes |

## Storage Class in Kubernetes

First, we need to understand the Kubernetes volume concept to understand the storage class concept. In simple terms, a Kubernetes volume is a directory where our data is stored; containers inside pods in Kubernetes use this volume to persist their data. Kubernetes supports many types of volumes, such as persistent, Ephemeral, and Projected Volumes.

Here, the Storage class concept will be around persistent volume type, which we will discuss in this document. In persistence volume, we have two things to discuss before moving towards storage class, i.e., two APIs provided by Kubernetes PersistentVolumeClaim and PersistenceVolume; the reason for providing two APIs instead of directly provisioning storage to the pod is to hide some information like from where storage has been provided and where it is used.

> An open-source system, developed by Google, an orchestration engine for managing containerized applications over a cluster of machines. Taken From Article, [Kubernetes Deployment Tools and Best Practices](https://www.xenonstack.com/insights/kubernetes-deployment)

Persistent Volume is either created by the administrator or dynamically provisioned with the help of the Storage class; this resource is available at the cluster level, just like the node and any other resources available at the cluster level.

PersistentVolumeClaim is the resource from which we request the storage PersistenVolumeClaim uses PersistencVolume just like pod uses node resources. PersistentVolumeClaim allows users to utilize abstract storage provided by the administrator in the form of PersistentVolume.

### What is Storage Class in Kubernetes?

The storage class is the Kubernetes resource from which we achieve dynamic, persistent volume provisioning. The storage class concept in Kubernetes allows the user(administrator) to provide the type of storage they want to offer. To do all this, the administrator needs to create the class.

Below is the way we can create the storage class in Kubernetes.

`apiVersion: storage.k8s.io/v1`

`kind: StorageClass`

`Metadata:`

`name: standard`

`provisioner: kubernetes.io/aws-ebs`

`Parameters:`

`type: gp2`

`reclaimPolicy: Retain`

`allowVolumeExpansion: true`

`Mount options:`

`- debug`

`volumeBindingMode: Immediate`

Provisioner, parameters, and reclaimPolicy: these three fields must be in every storage class the administrator makes.

In the Provisioner section, we assign different volume plugins; our PVs are created using this plugin. In the above example, we have used aws-ebs

## Importance of Storage Class in Kubernetes?

In Kubernetes, storage classes are an essential concept that provides a way to define different types of storage that can be used by persistent volume claims (PVCs) in a cluster.

Here are some of the critical reasons why storage classes are essential in Kubernetes:

- Flexibility: Storage classes allow the definition of multiple types of storage, such as network-attached storage (NAS), block storage, and object storage, and make them available to applications. This gives the flexibility to choose the type of storage that best fits an application's needs.
- Automation: Kubernetes storage classes can be automated, which means that when a user creates a new PVC, Kubernetes can dynamically provision a new volume using the appropriate storage class. This can save time and reduce errors, as the user cannot manually create and manage the volumes.
- Performance: Storage classes can also define performance characteristics such as disk speed, IOPS, and latency. This helps ensure that the storage resources used by the application meet the necessary performance requirements.
- Storage Management: Storage classes can also be used to manage the lifecycle of storage resources, such as by defining retention policies and backup schedules. This makes it easier to manage and monitor storage resources in the cluster.

> Serverless Framework is serverless computing to build and run applications and services without thinking about the servers. Taken From Article, [Kubeless - Kubernetes Native Serverless Framework](https://www.xenonstack.com/insights/kubeless)

### Types of Storage Classes

There are several different types of storage classes in Kubernetes, each with its own set of features and use cases. Let's explore each of these types in more detail.

### Standard Storage Class

The standard storage class is the default storage class in Kubernetes. It is used when no other storage class is specified. This storage class uses the default settings of the underlying infrastructure, which may be a cloud provider, a local disk, or a network-attached storage device. The standard storage class is ideal for applications that do not require specific performance or availability requirements.

### Managed Storage Class

The managed storage class provides managed storage resources, such as Amazon Elastic Block Store (EBS) or Google Cloud Persistent Disks. The cloud provider manages These storage resources and offers high availability and durability. Managed storage classes are ideal for applications that require reliable storage with high availability and durability.

## Types of Managed Storage Classes in Kubernetes?

Let's see some managed storage classes in Kubernetes:

### AWS EBS Storage Class

The AWS EBS storage class is used to provision and manage storage volumes in the AWS Elastic Block Store. It is commonly used by developers deploying their applications on the Amazon Web Services (AWS) cloud platform.

### Azure Disk Storage Class

The Azure Disk storage class provides and manages storage volumes in Microsoft Azure Disk Storage. This storage class is commonly used by developers deploying their applications on the Microsoft Azure cloud platform.

### GCE Persistent Disk Storage Class

The GCE Persistent Disk storage class is used to provision and manage storage volumes in the Google Compute Engine Persistent Disk. This storage class is commonly used by developers deploying their applications on the Google Cloud Platform (GCP).

### Network File System (NFS) Storage Class

The Network File System (NFS) storage class provides shared storage accessible from multiple nodes in the cluster. This storage class is ideal for shared storage applications like databases or file servers. However, to prevent data loss, the NFS server must be highly available and reliable.

### Managing Storage Classes in Kubernetes

Kubernetes provides various tools for managing storage classes in a Kubernetes cluster. Let's examine some of the standard management tasks that need to be performed on storage classes in Kubernetes.

### Creating a Storage Class

To create a storage class in Kubernetes, one must define the storage class specifications in a YAML file and then use the kubectl to apply the command to create the class.

For example, here is a sample YAML file that defines an AWS EBS storage class:

`apiVersion: storage.k8s.io/v1`

`kind: StorageClass`

`Metadata:`

`name: aws-ebs`

`provisioner: kubernetes.io/aws-ebs`

`Parameters:`

`type: gp2`

### Modifying a Storage Class

To modify a storage class in Kubernetes, you need to update the specifications in the YAML file and then use the kubectl apply command to apply the changes.

For example, if someone wants to change an AWS EBS storage class type from gp2 to io1, one can update the parameters section in the YAML file and then apply the changes using the kubectl apply command.

### Deleting a Storage Class

To delete a storage class in Kubernetes, use the kubectl delete command and specify the name of the storage class you want to delete.

For example, if suppose one wants to delete an NFS storage class named nfs-storage, then one can use the following command:

`kubectl delete storage-class nfs-storage`

<https://www.xenonstack.com/insights/local-vs-session-storage-vs-cookie>

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## Table of Contents

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## Explore Related Topics

[Decision Intelligence](https://www.xenonstack.com/blog/tag/decision-intelligence)

[Cloud Native Applications](https://www.xenonstack.com/blog/tag/cloud-native-applications)

[Generative AI](https://www.xenonstack.com/blog/tag/generative-ai)

[Big Data Engineering](https://www.xenonstack.com/blog/tag/big-data-engineering)

[FinOps](https://www.xenonstack.com/blog/tag/finops)

[Data Foundry](https://www.xenonstack.com/blog/tag/data-foundry)

[XAI](https://www.xenonstack.com/blog/tag/xai)

[Autonomous Agents](https://www.xenonstack.com/blog/tag/autonomous-agents)

[MetaSecure AI](https://www.xenonstack.com/blog/tag/metasecure-ai)

![navdeep-singh-gill](https://www.xenonstack.com/hubfs/navdeep-gill-ceo-xenonstack.svg)

## Navdeep Singh Gill

Global CEO and Founder of XenonStack

Navdeep Singh Gill is serving as Chief Executive Officer and Product Architect at XenonStack. He holds expertise in building SaaS Platform for Decentralised Big Data management and Governance, AI Marketplace for Operationalising and Scaling. His incredible experience in AI Technologies and Big Data Engineering thrills him to write about different use cases and its approach to solutions.

[Explore More by Navdeep Singh Gill ![cta-blue-arrow](https://www.xenonstack.com/hubfs/cta-arrow-blue.svg)](https://www.xenonstack.com/blog/author/navdeep-singh-gill)

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## Related Articles

![CI/CD Pipeline Tools and its Use Cases](https://www.xenonstack.com/hs-fs/hubfs/cd-pipeline-tools.png?width=1200&height=675&name=cd-pipeline-tools.png)

### [CI/CD Pipeline Tools and its Use Cases](https://www.xenonstack.com/blog/ci/cd-pipeline-tools)

07 August 2024

![Microservices Deployment at the Edge and its Best Practices](https://www.xenonstack.com/hs-fs/hubfs/microservices-deployment-at-edge.png?width=1200&height=675&name=microservices-deployment-at-edge.png)

### [Microservices Deployment at the Edge and its Best Practices](https://www.xenonstack.com/blog/microservices-deployment-at-edge)

07 March 2025

![Canary Release vs Blue-green Deployment | Quick Guide](https://www.xenonstack.com/hs-fs/hubfs/canary-vs-blue-green-deployment.png?width=1200&height=675&name=canary-vs-blue-green-deployment.png)

### [Canary Release vs Blue-green Deployment | Quick Guide](https://www.xenonstack.com/blog/canary-vs-blue-green-deployment)

16 August 2024

![xenonstack-logo](https://www.xenonstack.com/hubfs/xenonstack-new-logo-release.svg)

XenonStack Agentic Foundry powers enterprise agentic systems with unified infra, analytics, workflows, and security to drive automation and compliance.

[![Youtube](https://www.xenonstack.com/hubfs/xs-footer-social-icons/youtube-icon.svg)](https://www.youtube.com/c/XenonStackOfficial) [![LinkedIn](https://www.xenonstack.com/hubfs/xs-footer-social-icons/linkedin-icon.svg)](https://www.linkedin.com/company/xenonstack/) [![Github](https://www.xenonstack.com/hubfs/xs-footer-social-icons/github-icon.svg)](https://github.com/xenonstack) [![Twitter](https://www.xenonstack.com/hubfs/xs-footer-social-icons/twitter-icon.svg)](https://twitter.com/xenonstack) [![Medium](https://www.xenonstack.com/hubfs/xs-footer-social-icons/medium-icon.svg)](https://medium.com/@xenonstack) [![Instagram](https://www.xenonstack.com/hubfs/xs-footer-social-icons/instagram-icon.svg)](https://www.instagram.com/teamxenonstack/)

![iso-9001-certified](https://www.xenonstack.com/hubfs/iso-9001-2015-certified.svg) ![iso-27001-certified](https://www.xenonstack.com/hubfs/iso-27001-2022-certified.svg) ![soc-certified](https://www.xenonstack.com/hubfs/soc-certified-org.svg) ![power-bi-partner](https://www.xenonstack.com/hubfs/power-bi-2.svg) ![kubernetes-certified-partner](https://www.xenonstack.com/hubfs/kubernetes-certified.svg)

![advance-tier-competency](https://www.xenonstack.com/hubfs/xenonstack-competency/aws-advanced-tier-service.svg) ![managed-service-competency](https://www.xenonstack.com/hubfs/xenonstack-competency/aws-managed-service.svg) ![ml-service-competency](https://www.xenonstack.com/hubfs/xenonstack-competency/aws-ml-competency.svg) ![devops-service-competency](https://www.xenonstack.com/hubfs/xenonstack-competency/aws-devops-competency.svg) ![amazon-kinesis-delivery](https://www.xenonstack.com/hubfs/xenonstack-competency/amazon-kinesis.svg)

## AI Engineering

[Composite AI](https://www.xenonstack.com/artificial-intelligence/composite-ai/) [Decision AI](https://www.xenonstack.com/artificial-intelligence/decision-ai/) [AI Quality](https://www.xenonstack.com/artificial-intelligence/ai-quality/) [Generative AI](https://www.xenonstack.com/artificial-intelligence/generative-ai/) [Multimodal AI](https://www.xenonstack.com/artificial-intelligence/multimodal-ai/) [AI Assurance](https://www.xenonstack.com/artificial-intelligence/explainable-ai/) [MLOps](https://www.xenonstack.com/artificial-intelligence/mlops/) [Physical AI](https://www.xenonstack.com/artificial-intelligence/physical-ai/) [Augmented Engineering](https://www.xenonstack.com/artificial-intelligence/ai-augmented-software-development/) [Generative BI](https://www.xenonstack.com/artificial-intelligence/generative-bi/)

## Data Foundry

[Streaming Data Platform](https://www.xenonstack.com/dataops/streaming-data-platform/) [Data Lakehouse](https://www.xenonstack.com/dataops/delta-lake/) [Data Catalog](https://www.xenonstack.com/dataops/data-catalog/) [Data Observability](https://www.xenonstack.com/dataops/data-observability/) [Cloud Data Warehouse](https://www.xenonstack.com/dataops/cloud-data-warehouse/) [Data Engineering](https://www.xenonstack.com/dataops/data-engineering/) [MetaData Management](https://www.xenonstack.com/dataops/metadata-management/) [Data Quality](https://www.xenonstack.com/dataops/augmented-data-quality/) [Real Time Analytics](https://www.xenonstack.com/dataops/real-time-analytics/) [Data Modernization](https://www.xenonstack.com/dataops/data-modernization/)

## Platform Engineering

[Cloud Native](https://www.xenonstack.com/cloud-native/platform-engineering/) [Automation As Code](https://www.xenonstack.com/cloud-native/automation-as-code/) [Observability](https://www.xenonstack.com/cloud-native/observability/) [FinOps](https://www.xenonstack.com/cloud-native/finops/) [Application Modernization](https://www.xenonstack.com/cloud-native/application-modernization/) [DevSecOps](https://www.xenonstack.com/cloud-native/devsecops/) [Site Reliability Engineering](https://www.xenonstack.com/cloud-native/site-reliability-engineering/) [Progressive Delivery](https://www.xenonstack.com/cloud-native/progressive-delivery/) [GitOps](https://www.xenonstack.com/cloud-native/gitops/) [Compliance as code](https://www.xenonstack.com/cloud-native/compliance-as-code/) [Value Stream Management](https://www.xenonstack.com/cloud-native/value-stream-management/) [Policy as Code](https://www.xenonstack.com/cloud-native/policy-as-code/) [Telemetry Pipeline](https://www.xenonstack.com/cloud-native/telemetry-pipeline/)

## Agentic AI

[Agentic Analytics](https://www.xenonstack.com/agentic-ai/enterprise-systems/) [Agentic AI Systems](https://www.xenonstack.com/agentic-ai/agentic-ai-system/) [Process Intelligence](https://www.xenonstack.com/agentic-ai/business-process-operations/) [Developer Experience](https://www.xenonstack.com/agentic-ai/developer-experience-platform/) [Autonomous Operations](https://www.xenonstack.com/agentic-ai/autonomous-operations/) [AI Vision at EDGE](https://www.xenonstack.com/agentic-ai/edge-and-vision-ai/) [Compound AI System](https://www.xenonstack.com/agentic-ai/compound-ai-system/) [GUI Agents](https://www.xenonstack.com/agetic-ai/gui-agents/) [CMDB Management](https://www.xenonstack.com/agentic-ai/cmdb-management/) [ITSM](https://www.xenonstack.com/agentic-ai/itsm/) [Network Automation](https://www.xenonstack.com/agentic-ai/network-automation/)

## AI Agents

[DataBricks AI Agents](https://www.xenonstack.com/ai-agents/databricks-ai-agents/) [SnowFlake AI Agents](https://www.xenonstack.com/ai-agents/snowflake-ai-agents/) [ServiceNow AI Agents](https://www.xenonstack.com/ai-agents/servicenow-ai-agents/) [AWS AI Agents](https://www.xenonstack.com/ai-agents/aws-ai-agents/) [Microsoft Azure AI Agents](https://www.xenonstack.com/ai-agents/microsoft-azure-ai-agents-actions/) [SalesForce AI Agents](https://www.xenonstack.com/ai-agents/salesforce-agents-actions/) [MySQL AI Agents](https://www.xenonstack.com/ai-agents/mysql-agents-actions/) [PostgreSQL AI Agents](https://www.xenonstack.com/ai-agents/postgresql-agents-actions/) [Datadog AI Agents](https://www.xenonstack.com/ai-agents/datadog-agents-actions/) [DynaTrace AI Agents](https://www.xenonstack.com/ai-agents/dynatrace-agents-actions/) [Splunk AI Agents](https://www.xenonstack.com/ai-agents/splunk-agents-actions/) [BigQuery AI Agents](https://www.xenonstack.com/ai-agents/bigquery-agents-actions/) [SAP AI Agents](https://www.xenonstack.com/ai-agents/sap-agents-actions/) [Infor AI Agents](https://www.xenonstack.com/ai-agents/infor-agents-actions/) [Oracle AI Agents](https://www.xenonstack.com/ai-agents/oracle-agents-actions/) [WorkDay AI Agents](https://www.xenonstack.com/ai-agents/workday-agents-actions/) [Jira AI Agents](https://www.xenonstack.com/ai-agents/jira-agents-actions/)

## Industry

[Aerospace and Aviation](https://www.xenonstack.com/industries/aerospace/) [Financial Services](https://www.xenonstack.com/industries/banking/) [Automotive And Industrial](https://www.xenonstack.com/industries/automotive/) [Consumer Tech](https://www.xenonstack.com/industries/consumer-technology/) [Technology, Media and Telco](https://www.xenonstack.com/industries/enterprise-technology/) [Digital Supply Chain](https://www.xenonstack.com/industries/digital-supply-chain/) [Hospitality and Tourism](https://www.xenonstack.com/industries/travel-hospitality/) [Discrete Manufacturing](https://www.xenonstack.com/industries/automotive/) [Education](https://www.xenonstack.com/industries/education/) [Media and Entertainment](https://www.xenonstack.com/industries/media-entertainment/) [Oil and Gas](https://www.xenonstack.com/industries/oil-and-gas/) [Energy and Utilities](https://www.xenonstack.com/industries/energy-and-utilities/)

## Enterprise Support

[AI Managed Services](https://www.xenonstack.com/managed-services/ai-managed-services/) [Kubernetes Managed Services](https://www.xenonstack.com/managed-services/kubernetes/) [SRE as a Service](https://www.xenonstack.com/managed-services/site-reliability-engineering/) [Data Managed Services](https://www.xenonstack.com/managed-services/big-data/) [Analytics Managed Services](https://www.xenonstack.com/managed-services/analytics-managed-services/) [Data Protection](https://www.xenonstack.com/readiness-assessment/data-protection/) [On-Premise AI](https://www.xenonstack.com/managed-services/on-premise-ai-cluster/)

## Solutions

[Private Cloud](https://www.xenonstack.com/solutions/private-cloud/) [Internal Developer Platform](https://www.xenonstack.com/solutions/internal-developer-platform/) [AI Inference](https://www.xenonstack.com/solutions/ai-inference/) [Open-Source Data Platform](https://www.xenonstack.com/solutions/open-source-data-platform/) [AI Trust Score](https://www.xenonstack.com/solutions/ai-trust-score/) [Autonomous SoC](https://www.xenonstack.com/solutions/autonomous-soc/) [Digital Twin](https://www.xenonstack.com/solutions/digital-twin/) [Readiness Assessment](https://www.xenonstack.com/readiness-assessment/) [Talk To Specialist](https://www.xenonstack.com/talk-to-specialist/)

## Company

[About Us](https://www.xenonstack.com/about-us/) [Leadership Team](https://www.xenonstack.com/about-us/leadership-team/) [TAO of XenonStack](https://www.xenonstack.com/about-us/tao-of-xenonstack/) [How We Grow](https://www.xenonstack.com/about-us/how-we-grow/) [How We Work](https://www.xenonstack.com/about-us/how-we-work/) [Careers](https://www.xenonstack.com/careers/) [XA - QSIR](https://www.xenonstack.com/xenonstack-academy/) [Contact Us](https://www.xenonstack.com/contact-us/) [Book Demo](https://demo.xenonstack.com/)

## Resources

[Blog](https://www.xenonstack.com/blog) [Insights](https://www.xenonstack.com/insights/) [Use Cases](https://www.xenonstack.com/use-cases) [Case Study](https://www.xenonstack.com/case-studies) [Videos](https://www.xenonstack.com/videos/) [EBooks](https://www.xenonstack.com/e-book) [Presentations](https://www.xenonstack.com/presentations/)

@2026 XenonStack - A Stack Innovator!

[Privacy Policy](https://www.xenonstack.com/privacy-policy/) [Terms and Conditions](https://www.xenonstack.com/terms-and-conditions/)

Global Presence :

![india-flag-icon](https://www.xenonstack.com/hubfs/united-states.svg)

USA

![india-flag-icon](https://www.xenonstack.com/hubfs/uae-flag-icon.svg)

Dubai

![india-flag-icon](https://www.xenonstack.com/hubfs/india-flag-icon.svg)

India

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UK

![india-flag-icon](https://www.xenonstack.com/hubfs/australia-flag-icon.svg)

Australia

✕

## Agent SRE for Reliability and Observability Solutions

 AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability

- ![Performance Icon](https://www.xenonstack.com/hubfs/performance.svg)Proactive detection of performance and availability issues
- ![Root Cause Icon](https://www.xenonstack.com/hubfs/root-cause.svg)Root-cause analysis across microservices and environments
- ![Remediation Icon](https://www.xenonstack.com/hubfs/remediation.svg)Automated remediation playbooks to reduce MTTR

[Explore Agent SRE](https://agentsre.ai/)

![akira-ai-banner-illustration](https://www.xenonstack.com/hubfs/akira-ai-banner-illustration.svg)

✕

## Physical Surveillance with Vision AI Agent Technology

 AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly

- ![Motion Icon](https://www.xenonstack.com/hubfs/motion.svg)Real-time detection of suspicious motion or intrusion
- ![Video Search Icon](https://www.xenonstack.com/hubfs/video-search.svg)Natural language video search and instant playback
- ![Summary Icon](https://www.xenonstack.com/hubfs/summary.svg)Smart summaries for audits, investigations, and compliance

[See Vision AI in Action](https://www.xenonstack.ai/)

![physical-surveillance](https://www.xenonstack.com/hubfs/xai-banner-image.svg)

✕

## Agentic Data Intelligence Across Your Full Data Stack

 Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics

- ![Connects warehouses](https://www.xenonstack.com/hubfs/data-icon.svg)Connects to warehouses, lakes, and streaming sources
- ![Question Answering](https://www.xenonstack.com/hubfs/answers.svg)Question-answering in natural language
- ![Continuous monitoring](https://www.xenonstack.com/hubfs/monitoring-2.svg)Continuous monitoring for anomalies and KPI deviations

[See in Action](https://elixirdata.co/)

![agentic-data-intelligence](https://www.xenonstack.com/hubfs/empowerment-of-analysts.svg)

✕

## Intelligent Diagnostic for Self-Healing System Automation

 Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment

- ![Diagnostics Icon](https://www.xenonstack.com/hubfs/diagnostic.svg)Automated diagnostics for recurring errors
- ![Playbook Icon](https://www.xenonstack.com/hubfs/playbook.svg)Playbook execution: restart services, scale pods, clear queues
- ![Feedback Icon](https://www.xenonstack.com/hubfs/feedback.svg)Feedback loop for improving remediation strategies

[See in Action](https://agentanalyst.ai/)

![intelligent-diagnostic](https://www.xenonstack.com/hubfs/dataops-reimagined-banner-image.svg)

✕

## Agentic GRC - Monitoring Risk and Compliance Controls

 AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready

- ![Controls Icon](https://www.xenonstack.com/hubfs/controls.svg)Continuous control checks across infrastructure and SaaS
- ![Audit Icon](https://www.xenonstack.com/hubfs/audit.svg)Automated evidence collection for audits
- ![Risk Icon](https://www.xenonstack.com/hubfs/risk.svg)Risk scoring and prioritized remediation recommendations

[Explore Agent GRC](https://agentgrc.ai/)

![monitoring-risk-and-compliance](https://www.xenonstack.com/hubfs/enhanced-security-measures.svg)

✕

## Agentic Finance and Procurement Intelligent Agents

 Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter

- ![Visibility Icon](https://www.xenonstack.com/hubfs/visibility.svg)Real-time visibility into spend and commitments
- ![Anomaly Icon](https://www.xenonstack.com/hubfs/anomaly.svg)Anomaly detection on invoices and vendor performance
- ![Workflow Icon](https://www.xenonstack.com/hubfs/workflow.svg)Intelligent workflows for approvals and sourcing decisions

[Optimize Finance & Procurement](https://www.xenonify.ai/)

![agentic-finance-and-procurement](https://www.xenonstack.com/hubfs/responsible-ai-aviators-banner-illustration.svg)

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