Autonomous Index Optimization in XML Databases

Abstract

Defining suitable indexes is a major task when optimizing a database. Usually, a human database administrator defines a set of indexes in the design phase of the database. This can be done manually or with the help of so called index wizard tools analyzing predefined database operations. Even having an optimal initial set of indexes when setting up a database, there is no guarantee that these indexes will suit future demands. Rather, it is realistic that the typical usage of the database will change after a while because new queries appear, for instance. In consequence, the existing indexes are suboptimal. The typical way to handle this problem is that a database administrator maintains the database permanently. In XML database management systems (XDBMS) this problem becomes even worse: Because XML queries cover both content and structure the number of possible queries and indexes is significantly higher. Additionally, for XML data without schema information, queries and indexes cannot be defined in advance, because the structure and the content of the data is not restricted. Both facts tend to result in higher maintenance costs for XML indexes compared to relational indexes. In this paper we show by performance measurements that an adaptive XDBMS that analyzes its workload periodically and creates/drops XML indexes automatically guarantees a high performance over the total life time of a database. Although we present our index system called KeyX the idea and the results are transferable to other XML indexing approaches.