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.

R-tree is a popular index which supports efficient queries on multi-dimensional data. The performance of R-tree mostly depends on how the tree structure is built if new data instances are inserted, which has been studied for years. Existing works can be categorized into two groups. One is the bulk-loading approaches that insert data instances in batch, but they cannot support real-time insertion. Hence, our focus is on the other one that inserts each data instance individually, and thus fresh data can be instantly queried. However, existing methods do not consider the workload information, which leads to limited potential optimization opportunity. Therefore, it is important to study workload-aware R-tree construction for efficient multi-dimensional data access. There are several challenges. First, how to represent the query workload is a challenge. Second, given a workload, it is challenging to accurately measure the benefit of a data insertion choice. Third, both range queries and kNN queries should be considered in the workload. To address these challenges, we propose a novel framework that leverages a learning-based method to solve the workload-aware R-tree construction problem. First, by extracting the query workload features, we learn a distribution for the workload using the space partition. Second, considering the distribution, we design a cost model to describe the benefits (i.e., query execution time) of different insertion choices and select the best one. Third, we convert the kNN queries to range search ones, so as to support the workload including both types of queries. Experimental results show that on OpenStreetMap real datasets, compared with baselines, we improve the query efficiency by 1.17x.

As database vendors are increasingly moving towards the cloud data service, i.e., databases as a service (DBaaS), cloud databases have become prevalent. Compared with the early cloud-hosted databases, the new generation of cloud databases, also known as cloud-native databases, seek for higher elasticity and lower cost by developing new techniques, e.g., compute-storage disaggregation and the log is the database. To better harness the power of these cloud databases, it is important to study and compare the pros and cons of their key techniques. In this tutorial, we offer a comprehensive survey of cloud-native databases. Based on various system architectures, we introduce a taxonomy for the state-of-the-art cloud-native OLTP databases and OLAP databases, respectively. We then take a deep dive into their key techniques regarding storage management, transaction processing, analytical processing, data replication, serverless computing, database recovery, and security. Finally, we discuss the research challenges and opportunities.