Optimizing Spark Performance On Kubernetes

Kubernetes is an open-source framework for controlling and coordinating the execution of containerized workloads and services across multiple systems that are portable and flexible. Apache Spark, on the other hand, is a framework for performing processing tasks efficiently on very large data sets. As a result, when the two powers unite, a remarkable combination is created. Due to this rationale, as well as the other advantages of optimizing Spark performance on Kubernetes, Kubernetes is becoming a more common alternative for scheduling Spark tasks than YARN. Let’s take a look at the benefits that Kubernetes offers to Spark when it runs concurrently with Kubernetes.

Why Should You Use Spark On Kubernetes?

The performance of Spark on Kubernetes gives you the best of both worlds and here are some compelling reasons to use Spark on Kubernetes. Take a look!

Containerization

This is the most important reason to use Kubernetes. Containerization’s advantages over conventional software engineering also apply to big data and Spark. Containers make your programs more portable, minimize dependency packaging, and make building procedures more repeatable and reliable. They reduce DevOps stress and enable you to iterate on your code faster. You just have to create your dependence once, and it may then be used elsewhere. You also have the option of creating a new docker image for each app or using a smaller collection of docker images that package the majority of your required libraries and dynamically adding your application-specific code on top. 

You can also utilize the Kubernetes ecosystem’s benefits by including Kubernetes in your stack. For things like monitoring and logging, you may use Kubernetes add-ons. Because there is less maintenance and uplift to get started, most Spark engineers opted to deploy Spark workloads within an existing Kubernetes infrastructure that is utilized by the rest of the company.

Easy Deployment

Using Kubernetes to run Spark allows you to develop once and deploy everywhere, making a cloud-agnostic approach scalable. When you deploy Spark on Kubernetes, you get a lot of cool stuff for free, including multitenancy management with namespaces and quotas, and fine-grained security and data access with role-based access control. 

If you have a requirement that isn’t covered by k8s, the community is highly active, and you’re likely to discover a solution. If you currently use Kubernetes for the rest of your stack, this argument is more compelling because you can reuse your existing tooling. It also eliminates vendor lock-in and makes your data architecture more cloud-agnostic.

Affordable Option

As Kubernetes gains traction, more businesses and platform-as-a-service (PaaS) and software-as-a-service (SaaS) providers are deploying multi-tenant Kubernetes clusters for their workloads. As a result, a single cluster might be hosting applications from several teams, departments, clients, or environments. Kubernetes’ multi-tenancy allows businesses to manage a few big clusters rather than numerous smaller ones, resulting in better resource efficiency, elasticity, administrative control, and fragmentation reduction.

This elasticity is mirrored in the cloud pricing model, in which you only pay for what you use and may modify the number and kind of machines based on your workload and budget. Additionally, using Kubernetes to run Spark saves time. Time is crucial for data scientists and architects, and increasing efficiency in those roles and departments will result in even greater savings.

Better Than Standalone Approach

Hadoop YARN, Apache Mesos, or a standalone cluster were formerly used by Spark. However, after discovering that running Spark on Kubernetes requires less time, more organizations are adopting this strategy. By creating a Spark cluster in standalone mode, you can easily run Spark on Kubernetes. This implies you’re using Kubernetes to operate Spark Master and Workers, as well as the entire Spark cluster. This method is highly practical and may be used in a variety of situations. 

It has its own set of advantages, such as being a highly reliable solution to run Spark on Kubernetes and allowing you to take advantage of all of Kubernetes’ useful capabilities. In addition, there are a number of simple optimization methods available. Another disadvantage of the Standalone strategy is that it is difficult to control elasticity, which is something that the Standalone approach does not provide by default. In short, the performance of Spark on Kubernetes is better rather than following the standalone strategy.

In A Nutshell,

Running Spark on Kubernetes is becoming increasingly common. Its popularity stems from its ease of deployment, little dependence packing, and ability to regulate elasticity at a low cost. However, operating Spark on Kubernetes in a reliable, performant, cost-effective, and secure manner offers a few challenging problems. Even while Spark on Kubernetes is easier to administrate, more versatile, and maybe less expensive, that doesn’t mean it’s simple to set up and utilize. There can be resource waste, and it’s simple to make mistakes with setups if they’re not done by certified Apache Spark developers. Ksolves, being one of the best Apache Spark developers, helps in the efficient optimization of Spark performance on Kubernetes. With the help of Ksolves Apache Spark consulting services, you can easily eliminate out the risk of failures while using Spark on Kubernetes.

Contact Us for any Query

Email : sales@ksolves.com

Call : +91 8130704295

Read related articles:

Feeding Data To Apache Spark Streaming

Is Apache Spark enough to help you make great decisions?