Automatic Storage Structure Selection for hybrid Workload
This work addresses the challenge for database users in adapting to changing query sets in hybrid workloads, though it appears incremental as it builds on existing storage engine and data model concepts.
The paper tackles the problem of dynamically selecting optimal storage structures for databases under hybrid workloads, proposing an automatic system that uses machine learning to model costs and a column-oriented layout generation algorithm, resulting in significant performance improvements over default structures.
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.