  In the use of database systems, the design of the storage engine and data
model directly affects the performance of the database when performing queries.
Therefore, the users of the database need to select the storage engine and
design data model according to the workload encountered. However, in a hybrid
workload, the query set of the database is dynamically changing, and the design
of its optimal storage structure is also changing. Motivated by this, we
propose an automatic storage structure selection system based on learning cost,
which is used to dynamically select the optimal storage structure of the
database under hybrid workloads. In the system, we introduce a machine learning
method to build a cost model for the storage engine, and a column-oriented data
layout generation algorithm. Experimental results show that the proposed system
can choose the optimal combination of storage engine and data model according
to the current workload, which greatly improves the performance of the default
storage structure. And the system is designed to be compatible with different
storage engines for easy use in practical applications.
