Abstract: Analytical database has been widely deployed in modern corporations which are posing increasing demand for the speed of data analysis.In the era of big data,analytical database is faced with a number of new challenges.Firstly,the complexity of data keeps increasing,therefore,more efforts have to be put into system configuration,such as index creation.Secondly,without prior knowledge about the patterns of workload,system administrators have to build and rebuild indexes repeatedly,in order to meet the time constraints.Apparently,traditional approaches to index construction and maintenance can not work well in the new environment.Database cracking provides an alternative to solve the problem.Using database cracking,a DBA does not need to fine-tune the system configuration.Instead,the database can automatically adjust itself to fit the workload during query execution.In recent years,a series of database cracking algorithms have been proposed,while none of them is optimal in all situations.The paper proposed a cache conscious cost model for database cracking.Based on the model,we created a new adaptive index,which can combine the advantages of several previous cracking approaches.Extensive experiments were conducted to demonstrate the effectiveness of our method.

Key words: Adaptive merging,Database cracking,Adaptive indexing,Hybrid algorithm