Modern applications demand high performance and cost efficient database management systems (DBMSs). Their workloads may be diverse, ranging from online transaction processing to analytics and decision support. The cloud infrastructure enables disaggregation of monolithic DBMSs into components that facilitate software-hardware co-design. This is realized using pools of hardware resources, i.e., CPUs, GPUs, memory, FPGA, NVM, etc., connected using high-speed networks. This disaggregation trend is being adopted by cloud DBMSs because hardware re-provisioning can be achieved by simply invoking software APIs. Disaggregated DBMSs separate processing from storage, enabling each to scale elastically and independently. They may disaggregate compute usage based on functionality, e.g., compute needed for writes from compute needed for queries and compute needed for compaction. They may also use disaggregated memory, e.g., for intermediate results in a shuffle or for remote caching. The DBMS monitors the characteristics of a workload and dynamically assembles its components that are most efficient and cost effective for the workload. This paper is a summary of a panel session that discussed the capability, challenges, and opportunities of these emerging DBMSs and disaggregated hardware systems.

翻译：现代应用对数据库管理系统（DBMS）提出了高性能与高成本效益的需求。其工作负载可能多种多样，涵盖在线事务处理、分析及决策支持等场景。云基础设施使得将单体式DBMS解耦为多个组件成为可能，从而促进软硬件协同设计。这一目标通过使用硬件资源池（即CPU、GPU、内存、FPGA、NVM等）并借助高速网络互连来实现。由于硬件资源的重新调配仅需通过调用软件API即可完成，云数据库管理系统正逐步采用这种解耦趋势。解耦式DBMS将处理与存储分离，使两者能够独立弹性扩展。它们可根据功能对计算资源进行解耦，例如将写入所需计算、查询所需计算与数据压缩所需计算分离。此外，它们也可采用解耦式内存，例如用于混洗操作的中间结果或远程缓存。DBMS通过监控工作负载特征，动态组装对该工作负载最高效且最具成本效益的组件。本文是对一次专题研讨会的总结，该研讨会探讨了这些新兴DBMS与解耦式硬件系统的能力、挑战与机遇。