However, stateless web applications, which we call stateless applications, can be run on Kubernetes by scaling easily with deployment objects. For example, in some of the NoSQL databases, you must first initialize the master nodes and then the slave nodes. Operators ensure that situations that require human intervention, especially in stateful applications, databases, are predetermined by the operator object. The first is the method we have been using from other cluster managers, but the second is the Kubernetes world-specific and more preferred method. We can submit a spark application on Kubernetes in 2 ways. To summarize In the coming period, whether it is for Spark or other applications, even in databases, we will inevitably deal with container environments. Moreover, after long-term improvements since 2018, Spark announced that it is ready for production use (general availability) on Kubernetes with version 3.1 (March 2021). Maybe you can ignore container technologies for a while, but the trend is moving in this direction and more and more companies are starting to use Spark on K8s. Otherwise, other applications running in a microservice architecture are not a technical prerequisite for running Spark on Kubernetes. Of course, you can keep your other applications old-fashioned and set up a Kubernetes cluster only for Spark jobs, but it is debatable whether you can do this only for Spark. However, it may be possible to run only Spark applications on Kubernetes from now on. tools need to conform to the microservice approach. First of all, you need to start using a Kubernetes environment, migrating your apps to microservice architecture, all development, backup, monitoring, DevOps, etc.

So why is it still? Why is YARN still so prevalent when there is a hero like Kubernetes? It is not very quick for systems in the production environment to react to innovations, it takes some time. Until now, the dominant cluster manager was YARN, maybe it still is. With version 2.3, Kubernetes joined them. As we know, there were only 3 cluster managers until Spark 2.3: Hadoop YARN, Apache Mesos, and Spark Standalone. In this post, we are going to run a Spark application on a Kubernetes cluster.

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