GeMDA: A multidimensional data partitioning technique for multiprocessor database systems
Several studies have repeatedly demonstrated that both the performance and scalability of a shared nothing parallel database system depend on the physical layout of data across the processing nodes of the system. Today, data is allocated in these systems using horizontal partitioning strategies. This approach has a number of drawbacks. If a query involves the partitioning attribute, then typically only a small number of the processing nodes can be used to speedup the execution of this query. On the other hand, if the predicate of a selection query includes an attribute other than the partitioning attribute, then the entire data space must be searched. Again, this results in waste of computing resources. In recent years, several multidimensional data declustering techniques have been proposed to address these problems. However, these schemes are too restrictive (e.g., FX, ECC, etc.), or optimized for a certain type of queries (e.g., DM, HCAM, etc.). In this paper, we introduce a new technique which is flexible, and performs well for general queries, We prove its optimality properties, and present experimental results showing that our scheme outperforms DM and HCAM by a significant margin.
parallel database system data allocation data fragmentation query processing system utilization