The virtualization, high availability, high scheduling elasticity, and other characteristics of cloud infrastructure provide cloud databases with many advantages, such as the out-of-the-box feature, high reliability and availability, and pay-as-you-go model. Cloud databases can be divided into two categories according to the architecture design: cloud-hosted databases and cloud-native databases. Cloud-hosted databases, deploying the database system in the virtual machine environment on the cloud, offer the advantages of low cost, easy operation and maintenance, and high reliability. Besides, cloud-native databases take full advantage of the characteristic elastic scaling of the cloud infrastructure. The disaggregated compute and storage architecture is adopted to achieve the independent scaling of computing and storage resources and further increase the cost-performance ratio of the databases. However, the disaggregated compute and storage architecture poses new challenges to the design of database systems. This survey is an in-depth analysis of the architecture and technology of the cloud-native database system. Specifically, the architectures of cloud-native online transaction processing (OLTP) and online analytical processing (OLAP) databases are classified and analyzed, respectively, according to the difference in the resource disaggregation mode, and the advantages and limitations of each architecture are compared. Then, on the basis of the disaggregated compute and storage architectures, this study explores the key technologies of cloud-native databases in depth by functional modules. The technologies under discussion include those of cloud-native OLTP (data organization, replica consistency, main/standby synchronization, failure recovery, and mixed workload processing) and those of cloud-native OLAP (storage management, query processing, serverless-aware compute, data protection, and machine learning optimization). At last, the study summarizes the technical challenges for existing cloud-native databases and suggests the directions for future research.