Cloud-Native Databases: A Survey
Haowen Dong, Chao Zhang, Guoliang Li, Huanchen Zhang
Abstract
Cloud databases have been widely accepted and deployed due to their unique advantages, such as high elasticity, high availability, and low cost. Many new techniques, such as compute-storage disaggregation and the log is the database, have been proposed recently to seek for higher elasticity and lower cost. To better harness the power of cloud databases, it is crucial to study and compare the pros and cons of their key techniques. In this paper, we offer a comprehensive survey of cloud-native databases. Particularly, we investigate and summarize the state-of-the-art cloud-native OLTP and OLAP databases, respectively. In the first part, we discuss three types of architectures of cloud-native OLTP database. Then we introduce their key techniques including data placement strategy, storage layer consistency, compute layer consistency, multi-layer recovery, and HTAP optimization. In the second part, we present two kinds of architectures of cloud-native OLAP databases. Then we take a deep dive into their key techniques regarding storage management, query processing, serverless computing, data protection, and machine learning in databases. Finally, we discuss the research challenges and opportunities.