Abstract: As one of the most popular technologies, database systems have been developed for more than 50 years, and are mature enough to support many real scenarios. Although many researches still focus on the traditional database optimization tasks, the performance improvement is little. Actually, with the advent of big data, we have met the new gap obstructing the further performance improvement of database systems. The database systems face challenges in two aspects. Firstly, the increase of data volume requires the database system to process tasks more quickly. Secondly, the rapid change of query workload and its diversity make database systems impossible to adjust the system knobs to the optimal configuration in real time. Fortunately, machine learning may be the dawn bringing an unprecedented opportunity for the traditional database systems to lead us to the new optimization direction. In this paper, we introduce how to combine machine learning into the further development of database management systems. We focus on the current research work of machine learning for database systems, mainly including the machine learning for storage management and query optimization, as well as automatic database management systems. This area has also opened various challenges and problems to be solved. Thus, based on the analysis of existing technologies, the future challenges, which may be encountered in machine learning for database systems, are pointed out.