Let’s create a custom service account with ClusterRole. By default, the driver pod is automatically assigned the default service account in the namespace specified by, if no service account is specified when the pod gets created. Specifically, at minimum, the service account must be granted a Role or ClusterRole that allows driver pods to create pods and services. The service account used by the driver pod must have the appropriate permission for the driver to be able to do its work. The Spark driver pod uses a Kubernetes service account to access the Kubernetes API server to create and watch executor pods. In Kubernetes clusters with RBAC enabled, we can configure Kubernetes RBAC roles and service accounts used by the various Spark on Kubernetes components to access the Kubernetes API server. Once the Docker is up and running click the Docker icon and go to Preferences window as shown below.Īdditionally, we can use the docker push option available to save the docker image to a Docker repository, which in turn will enable production kubernetes to pull the docker image from the configured Docker repository.Double-click Docker.app in the Applications folder to start Docker.The steps to configure Kubernetes on Docker are as follows: dmg file, follow the standard installation steps. The Kubernetes server runs locally within the Docker instance, is not configurable, and is a single-node cluster.ĭownload the Docker from the official download page. This includes a standalone Kubernetes server and client, as well as Docker CLI integration. Kubernetes is available in Docker for Mac 17.12 CE Edge and higher, and 18.06 Stable and higher. Prerequisitesīefore we begin, we must ensure that we have the following pre-requisites installed on the local machine. We are not going to write any code for this, few examples are already available within Apache Spark distribution and we are going to use the same example jar and run SparkPi program from it onto our kubernetes cluster. In this post we will take a look on how to setup Kubernetes cluster using Docker on Mac on local machine and how to run apache spark job on top of it. Best of all, it requires no changes or new installations on your Kubernetes cluster simply create a container image and set up the right RBAC roles for your Spark Application and we’re all set. Apache Spark workloads can make direct use of Kubernetes clusters for multi-tenancy and sharing through Namespaces and Quotas, as well as administrative features such as Pluggable Authorization and Logging. Starting with Spark 2.3, we can run Spark workloads in an existing Kubernetes 1.7+ cluster and take advantage of Apache Spark’s ability to manage distributed data processing tasks. As a matter of fact, Kubernetes is the standard for managing containerized environments, hence it is a natural fit to have support for Kubernetes APIs within Spark. Data scientists are adopting containers to improve their workflows because of benefits such as packaging of dependencies and creating reproducible artifacts. Apache Spark on Kubernetes (Docker for Mac)Īpache Spark 2.3 with native Kubernetes support combines the best of the two prominent open source projects - Apache Spark, a framework for large-scale data processing and Kubernetes allows easy container management.Īpache Spark is an essential tool for data scientists, offering a robust platform for a variety of applications ranging from large scale data transformation to analytics to machine learning.
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