按需云原生容器存储设计及其在kubernetes上的hdfs实践

Jian Lin, Lin Huang, Tao Zhou, Dongming Xie, Bo Yu
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引用次数: 0

摘要

近年来,云原生大数据服务开始流行。确定了这些服务的两个支柱:计算和存储的分离体系结构,以及特定于应用程序的控制器机制。就云上大数据的存储而言,当前的实践侧重于管理单个本地存储集群或构建独立的PaaS存储服务。本文主要研究云原生容器化存储。提出了一种按需供应的设计方案,扩展了主流存储架构,支持多租户存储集群的动态供应。它的HDFS-on-Kubernetes实例被实现。通过全局端点供应和动态卷供应机制,该供应程序支持以自动化的方式创建和管理具有全栈资源的多个按需存储集群。它保证了主机网络和本地存储的本机性能,并通过实验和生产应用得到了验证。由于其高级抽象和单点配置机制,它也易于使用。该设计和提供程序已经服务于工业场景中的实际业务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An On-Demand Cloud-Native Containerized Storage Design and its Practice of HDFS-on-Kubernetes
Cloud-native big data services become popular in recent years. Two pillars of these services are identified: the separation architecture of compute and storage, and the application-specific controller mechanism. In terms of storage for big data on the cloud, current practices focus on managing a single on-premise storage cluster or building independent PaaS storage services. This paper focuses on the cloud-native containerized storage. An on-demand provisioning design is proposed, which extends the mainstream storage architecture and supports the provisioning of storage clusters for multi-tenancy in a dynamic manner. Its instance of HDFS-on-Kubernetes is implemented. With the mechanisms of global endpoint provisioning and dynamic volume provisioning, this provisioner enables the creation and management of multiple on-demand storage clusters with full-stack resources in an automated way. It guarantees the native performance of host network and local storage, which has been validated through experiments and production applications. It is also easy to use because of its high-level abstraction and single-point configuration mechanism. The design as well as the provisioner has served real business in industrial scenarios.
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