Customized openshift operator: An introduction to a tool for MongoDB automation and its application

MongoDB is a widely adopted NoSQL database in modern Platform-as-a-Service (PaaS) environments due to its flexibility, scalability, and ability to handle large volumes of transactional data. In large enterprise platforms processing thousands of transactions per minute, the need to create new databases dynamically is frequent and time critical. Manual provisioning in such cases is inefficient and prone to human error. Therefore, this research aims to design and implement an automated MongoDB database provisioning and deployment framework to enhance operational efficiency, consistency, and reliability across development, testing, and production environments. The proposed solution leverages containerization and orchestration technologies, primarily Docker, Kubernetes (K8s), and OpenShift. To ensure continuous integration and delivery, Jenkins is incorporated as the CI/CD automation tool. This automation minimizes manual intervention and deployment risks while improving traceability and quality assurance. Building upon this foundation, the study explores OpenShift Operators, which extend Kubernetes functionality by embedding domain-specific operational intelligence into software controllers. A custom MongoDB Operator was developed using the Operator SDK and the Go (Golang) programming language. Go’s concurrency model and native support for Kubernetes APIs make it an ideal choice for implementing the Operator’s reconciliation loop, which continuously aligns the cluster’s actual state with the desired configuration defined in Custom Resources (CRs). The thesis concludes with a prototype implementation demonstrating the end-to-end automation of MongoDB provisioning and deployment within an OpenShift environment. The results show that integrating Kubernetes, OpenShift Operators, and Jenkins CI/CD pipelines significantly improves the efficiency and reliability of database lifecycle management, effectively automate MongoDB management, reduce operational overhead, and serve as a scalable foundation for autonomous cloud-native database systems in large-scale enterprise platforms. The research also identifies key challenges—such as managing complex configurations and ensuring security compliance—and proposes directions for further enhancement toward building a robust, enterprise-ready automation framework.