Multi-tenant cloud databases offer services more cheaply and conveniently, with advantages like paying on demand, scaling on demand, automatic deployment, high availability, self-maintenance, and shared resources. Now more and more enterprises and individuals begin to host their database services on database as a service (DaaS) platforms. These DaaS platforms provide services to multiple tenants in accordance with their service-level agreements (SLAs), while improving revenue for themselves. However, due to the dynamic, heterogeneous, and competitive characteristics of multiple tenants and their loads, it is a very challenging task for DaaS platform providers to adaptively plan and schedule resources according to dynamic loads while complying with multi-tenants’ SLAs. For common types of multi-tenant cloud databases, such as relational databases, this survey firstly analyzes the challenges faced by resource planning and scheduling of multi- tenant cloud databases in detail and then outlines related key scientific issues. Then, it provides a framework of related techniques and a summary of existing research in four areas: resource planning and scheduling technologies, resource forecasting technologies, resource elastic scaling technologies, and resource planning and scheduling tools for existing databases. Lastly, this survey provides suggestions for future research directions on resource planning and scheduling technologies for multi-tenant cloud databases.