MDB-KCP: persistence framework of in-memory database with CRIU-based container checkpoint in Kubernetes

Jeongmin Lee, Hyeongbin Kang, Hyeon-jin Yu, Ji-Hyun Na, Jungbin Kim, Jae-hyuck Shin, Seo-Young Noh
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Abstract

As the demand for container technology and platforms increases due to the efficiency of IT resources, various workloads are being containerized. Although there are efforts to integrate various workloads into Kubernetes, the most widely used container platform today, the nature of containers makes it challenging to support persistence for memory-centric workloads like in-memory databases. In this paper, we discuss the drawbacks of one of the persistence support methods used for in-memory databases in a Kubernetes environment, namely, the data snapshot. To address these issues, we propose a compromise solution of using container checkpoints. Through this approach, we can perform checkpointing without incurring additional memory usage due to CoW, which is a problem in fork-based data snapshots during snapshot creation. Additionally, container checkpointing induces up to 7.1 times less downtime compared to the main process-based data snapshot. Furthermore, during database recovery, it is possible to achieve up to 11.3 times faster recovery compared to the data snapshot method.
MDB-KCP:在 Kubernetes 中使用基于 CRIU 的容器检查点的内存数据库持久化框架
随着对容器技术和平台的需求因 IT 资源效率的提高而增加,各种工作负载正在被容器化。尽管人们正在努力将各种工作负载集成到 Kubernetes(目前使用最广泛的容器平台)中,但容器的特性使得为内存数据库等以内存为中心的工作负载提供持久性支持具有挑战性。在本文中,我们将讨论 Kubernetes 环境中用于内存数据库的一种持久性支持方法(即数据快照)的缺点。为了解决这些问题,我们提出了使用容器检查点的折中解决方案。通过这种方法,我们可以执行检查点,而不会因 CoW(快照创建过程中基于 fork 的数据快照存在的问题)导致额外的内存使用。此外,与基于主进程的数据快照相比,容器检查点导致的停机时间最多可减少 7.1 倍。此外,在数据库恢复期间,与数据快照方法相比,恢复速度最多可提高 11.3 倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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