Narrow Results

DevOps is an emerging concept and methodology for bridging the gap in the process of software development. At present, applying DevOps to data analytical system (DAS) is increasingly embraced. But the characteristics of this system, such as data protection, always leads to a series of constrains. It results in more difficulty of conducting DevOps on DAS. Moreover, there are no DevOps solutions for reference. Therefore, exploring DevOps for DAS is valuable. In this paper, we illustrate DevOps demands of DAS from different perspectives, and constantly emphasize the importance of automation toolchain. Based on them, a process model for DAS DevOps (D²Ops) is proposed to clarify participants activities. In order to improve the efficiency, we attempt to integrate the automation toolchain. With the consideration of stability, six generic process components are designed to support this model. They can be the selection criteria for specific automation tools. We also present a reference facility based on these generic process components, and illustrate its implementation combining with a practical case. Furthermore, for a better D²Ops practice, the cross-cutting concerns are considered from the perspective of its data intensive trait.

Leading paradigms to develop, deploy, and operate applications such as continuous delivery, configuration management, and the merge of development and operations (DevOps) are the foundation for various techniques and tools to implement automated deployment. To make such applications available for users and customers, these approaches are typically used in conjunction with Cloud computing to automatically provision and manage underlying resources such as storage and virtual servers. A major class of these automation approaches follow the idea of converging toward a desired state of a resource (e.g. a middleware component deployed on a virtual machine). This is achieved by repeatedly executing idempotent scripts to reach the desired state. Because of major drawbacks of this approach, we discuss an alternative deployment automation approach based on compensation and fine-grained snapshots using container virtualization. We perform an evaluation comparing both approaches in terms of difficulties at design time and performance at runtime. Moreover, we discuss concepts, strategies, and implementations to effectively combine different deployment automation approaches.