Harnessing the Power of Distributed Databases on Kubernetes

Cloud-native technologies have ushered in a new era of database scalability and resilience requirements. To meet this demand, enterprises across multiple industries, from finance to retail, to healthcare, are turning to distributed databases to safely and effectively store data in multiple locations. 

Distributed databases provide consistency across availability zones and regions in the cloud, but some enterprises still question whether they should run their distributed database in Kubernetes. 

The Benefits of Running Distributed Databases on Kubernetes

Listed below are some of the key benefits of running distributed databases on Kubernetes.

Better Resource Utilization

One benefit of running distributed databases on Kubernetes is better resource utilization. Many companies are adopting microservices architectures for their modern applications. This shift tends to propagate a lot of smaller databases. Companies often have a finite set of nodes on which to place those databases. So, when companies decide to manage these databases, they’re left with a sub-optimal allocation of databases onto nodes. Running on Kubernetes allows the underlying system to determine the best places to put the databases while optimizing resource placement on those nodes. 

Kubernetes is best utilized when running a large number of databases in a multi-tenant environment. In this deployment scenario, companies save on costs and require fewer nodes to run the same sort of databases. These databases also have different footprints, CPU resources, memory, and disk requirements.

Elastic Scaling of Pod Resources Dynamically

Another benefit of running distributed databases on Kubernetes is the elastic scaling of pod resources dynamically. Running on Kubernetes enables enterprises to utilize resources more efficiently. The Kubernetes orchestration platform can resize pod resources dynamically. Specifically, to scale a database to meet demanding workloads, you can modify memory, CPU, and disk. Kubernetes makes it easy to scale up automatically without incurring any downtime through its horizontal pod autoscaler (HPA) and vertical pod autoscaler (VPA) operators. This is important for AI and ML workloads. Kubernetes enables teams to scale these workloads so they can handle extensive processing and training without interference. A distributed SQL database seamlessly manages data migration between pods, ensuring scalable and reliable data storage. For VPA, however, it’s worth noting that a database would need to have more than one instance to avoid downtime.

Consistency and Portability

And a final benefit is consistency and portability between clouds, on-premises, and the edge. Companies want to consistently build, deploy, and manage workloads at different locations. They also want to move workloads from one cloud to another, if needed. However, most organizations also have a large amount of legacy code they still run on-premises and are looking to move these installations up into the cloud.

Kubernetes allows you to deploy your infrastructure as code, in a consistent way, everywhere. This means you can write a bit of code that describes the resource requirements deployed to the Kubernetes engine, and the platform will take care of it. You now have the same level of control in the cloud that you have on bare metal servers in your data center or edge. This flexibility and the ability to simplify complex deployments are critical for enterprises as they work across distributed environments. Kubernetes’ built-in fault tolerance and self-healing features also support ML pipelines to ensure they operate smoothly, even when faced with technology failures or disruptions. 

Accelerating AI/ML Workloads Using Kubernetes

Kubernetes offers many benefits to enterprises, but in today’s AI-driven landscape, its ability to support and accelerate artificial intelligence (AI) and machine learning (ML) workloads is crucial. 

The proliferation of AI has caused business priorities to shift for many companies. They want to use AI to uplevel their technology and products, leading to enhanced productivity, better customer experiences, and greater revenue. 

Investment in AI, however, means higher stakes. Businesses must ensure databases and workloads are running smoothly to facilitate AI adoption. Deploying on Kubernetes can help teams guarantee their workloads are reliable and scalable – ultimately driving successful AI implementation.   

The Kubernetes Approach

Kubernetes has transformed how enterprises develop and deploy applications. Most established enterprises and cloud-born companies use Kubernetes in some form and it has become the de facto choice for container orchestration. 

In a distributed environment, however, no single database architecture fits all applications. Enterprises must determine the best choice for their current and future needs. I anticipate that cloud-native, geo-distributed databases will continue to grow in popularity as enterprises realize the value they provide and the ease of deployment in Kubernetes. 

This article was shared as part of DZone's media partnership with KubeCon + CloudNativeCon.

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