{"id":34745,"date":"2024-09-13T10:10:19","date_gmt":"2024-09-13T08:10:19","guid":{"rendered":"https:\/\/www.dbi-services.com\/blog\/?p=34745"},"modified":"2024-10-11T11:23:33","modified_gmt":"2024-10-11T09:23:33","slug":"control-m-for-kubernetes-overview","status":"publish","type":"post","link":"https:\/\/www.dbi-services.com\/blog\/control-m-for-kubernetes-overview\/","title":{"rendered":"Control-M for Kubernetes – Overview"},"content":{"rendered":"\n

At a customer, I implemented Control-M for Kubernetes\/OpenShift, as the customer has a hybrid environment between VM, on-premise\/Cloud Kubernetes\/OpenShift. This is the first blog of a series, in which I will share with you from A to Z how to implement Control-M for Kubernetes and OpenShift. <\/p>\n\n\n\n\n\n\n\n

Control-M<\/a> for Kubernetes <\/a>enables you to execute Control-M job(s) to run one or more pods to completion in a Kubernetes cluster. This enables you to integrate Control-M capabilities, such as advanced scheduling criteria, status monitoring, and SLA management.<\/p>\n\n\n\n

In fact, my story with Control-M began before Kubernetes existed, today, as a Control-M and Kubernetes expert I will be able to help you to understand and implement Control-M for Kubernetes. This first blog will be more a quick overview on these technologies and understand the implementation.<\/p>\n\n\n\n

To be honest, you will need at least intermediate level on Control-M and Kubernetes to implement smoothly this solution. If you want to know more about Docker and Kubernetes, I would recommend this training<\/a>.<\/p>\n\n\n\n

Control-M Overview<\/h2>\n\n\n\n

Control-M is a workload automation solution that enables you to automate the scheduling and processing the business workflows across various platforms and applications from a single point of control. Nowadays, those platforms and application could be on VM, Kubernetes cluster, Cloud, etc.<\/p>\n\n\n\n

Today, Control-M allow you to execute jobs in almost every platform, but what is a job?<\/p>\n\n\n\n

A job is an execution unit, each job needs specific run information to be executed, such as scheduling criteria, post processing actions, and also job dependencies as shown in the following:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

On “legacy” systems, a job could be a script or command, that is executed at the operating system level. On Kubernetes, a job take another role, which allow you at the end to deploy pods in Kubernetes. Let’s get a quick overview on Kubernetes and understand pods \ud83d\ude09<\/p>\n\n\n\n

Kubernetes Overview<\/h2>\n\n\n\n

Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. The smallest unit of a Kubernetes application is a pod!<\/p>\n\n\n\n

A Kubernetes pod <\/a>is a collection of one or more Linux containers. Any given pod can be composed of multiple, tightly coupled containers or just a single container.<\/p>\n\n\n\n

Control-M for Kubernetes Overview<\/h2>\n\n\n\n

Now, we understood Control-M jobs and Kubernetes pods, let see how those will be connected.<\/p>\n\n\n\n

The following diagram demonstrates how the components of Control-M for Kubernetes are incorporated into a Kubernetes cluster and how they enable you to run one or more pods to completion.<\/p>\n\n\n\n

In this example, two Control-M\/Agents are deployed as cluster pods.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

This setup has the following main features:<\/p>\n\n\n\n