Database management systems are divided into transactional (OLTP) systems and analytical (OLAP) systems according to application scenarios. With the growing demand for real-time data analysis and the increasing popularity of mixed OLTP and OLAP tasks, the industry has begun to focus on database management systems that support hybrid transactional/analytical processing (HTAP). An HTAP database system not only needs to meet the requirements of high-performance transaction processing but also supports real-time analysis for data freshness. Therefore, it poses new challenges to the design and implementation of database systems. In recent years, some prototypes and products with diverse architectures and technologies have emerged in industry and academia. This study reviews the background and development status of HTAP databases and classifies current HTAP databases from the perspective of storage and computing. On this basis, this study summarizes the key technologies used in the storage and computing of HTAP systems from bottom to top. Under this framework, the design ideas, advantages and disadvantages, and applicable scenarios of various systems are introduced. In addition, according to the evaluation benchmarks and metrics of HTAP databases, this study also analyzes the relationship between the design of various HTAP databases and their performance as well as data freshness. Finally, this study combines cloud computing, artificial intelligence, and new hardware technologies to provide ideas for future research and development of HTAP databases.