An Evaluation of Generic Bulk Loading Techniques

Bulk loading refers to the process of creating an index from scratch for a given data set. This problem is well understood for B-trees, but so far, non-traditional index structures received modest attention. We are particularly interested in fast generic bulk loading techniques whose implementations only employ a small interface that is satisfied by a broad class of index structures. Generic techniques are very attractive to extensible database systems since different user-implemented index structures implementing that small interface can be bulk-loaded without any modification of the generic code. The main contribution of the paper is the proposal of two new generic and conceptually simple bulk loading algorithms. These algorithms recursively partition the input by using a main-memory index of the same type as the target index to be build. In contrast to previous generic bulk loading algorithms, the implementation of our new algorithms turns out to be much easier. Another advantage is that our new algorithms possess fewer parameters whose settings have to be taken into consideration. An experimental performance comparison is presented where different bulk loading algorithms are investigated in a system-like scenario. Our experiments are unique in the sense that we examine the same code for different index structures (R-tree and Slim-tree). The results consistently indicate that our new algorithms outperform asymptotically worst-case optimal competitors. Moreover, the search quality of the target index will be better when our new bulk loading algorithms are used.