Volume Management in Kubernetes

Kubernetes introduces a component known as volume storage, which connects a physical hard drive to your pod. This storage solution can either be locally attached to the node or hosted remotely on a cloud server outside the Kubernetes cluster.

The use of volumes significantly enhances the data persistence of pods. Because volumes are associated independently, your data remains intact even if the pods are restarted or crash. This illustrates an essential lesson: Kubernetes by itself cannot address data persistence issues; it is up to you, the user, to attach volumes accordingly.

Kubernetes defines volumes as “a directory, possibly containing data, which is accessible to the containers in a pod.” To specify the necessary volumes for a pod, you will define them in the .spec.volumes file, and indicate where to mount these volumes in .spec.containers[*].volumeMounts.

There are various types of volumes supported by Kubernetes, including:

• awsElasticBlockStore: This volume type mounts an AWS EBS (Amazon Web Services Elastic Block Store) volume into your pod, but it is restricted to nodes running on AWS EC2 instances. When a pod is terminated, the contents of the EBS volume remain intact, allowing for data to persist even after unmounting.
• azureDisk: This volume type enables the mounting of Microsoft Azure Data Disk to your pod.
• azureFile: Similar to traditional file shares, Azure Files is a fully managed cloud-based file sharing service that utilizes the azureFile volume type to mount an Azure File volume onto a pod’s storage.
• Cinder: Cinder is part of the OpenStack project and provides permanent data storage to cloud computing applications. It manages block devices and mounts the OpenStack Cinder volume into your pod.

Scaling Applications in Kubernetes

With a basic understanding of volumes, let’s now examine how Kubernetes manages pod scaling.

There may be instances when your application experiences a surge in traffic, leading to potential slowdowns or crashes. Such scenarios result in downtime, which is detrimental, particularly in production environments.

For example, if our NodeJS application encounters a crash, it becomes unavailable to users. A solution is to create a new pod that replicates the NodeJS application and links it to the same service that the original application uses. In this setup, both NodeJS applications share the same IP address, connected through the service.

The service also functions as a load balancer, automatically routing requests based on pod states. If one pod faces a high volume of requests, the service will redirect new traffic to the replica pod. Additionally, if one pod crashes, the traffic will be rerouted to the operational pod.

However, in large-scale tech companies, managing pods individually becomes impractical, especially when deploying hundreds across various regions. Instead, you will create a blueprint detailing the number of replicas required, known as a deployment. Deployments are essential Kubernetes components that facilitate pod scaling.

Managing Stateful Applications

Now, a pressing question arises: can the same scaling principles apply to databases? Unlike applications, databases maintain states, which complicates the replication process. For instance, if you create a replica of a MongoDB database, inconsistencies may occur if one pod writes to the first database while fetching data from a second one.

To address this, Kubernetes offers StatefulSets for applications requiring state management. StatefulSets handle the replication and scaling of pods, ensuring no inconsistencies occur during database transactions.

Due to their complexity, deploying StatefulSets can be challenging. As a result, many developers opt to deploy databases outside the Kubernetes cluster to mitigate potential issues.