On Disk Allocation of Intermediate Query Results in Parallel Database Systems

For complex queries in parallel database systems, substantial amounts of data must be redistributed between operators executed on different processing nodes. Frequently, such intermediate results cannot be held in main memory and must be stored on disk. To limit the ensuing performance penalty, a data allocation must be found that supports parallel I/O to the greatest possible extent. In this paper, we propose declustering even self-contained units of temporary data processed in a single operation (such as individual buckets of parallel hash joins) across multiple disks. Using a suitable analytical model, we find that the improvement of parallel I/O outweighs the penalty of increased fragmentation

Performance Analysis of a Load Balancing Hash-Join Algorithm for a Shared Memory Multiprocessor

Within the last several years, there has been a growing interest in applying general multiproces-sor systems to relational database query process-ing. Efficient parallel algorithms have been designed for the join operation but usually have a failing in that their performance deteriorates greatly when the data is nonuniform. In this paper, we propose a new version of the hash-based join algorithm that balances the load between the processors, for any given bucket, in a shared everything environment. We develop an analytical model of the cost of the algorithm and implement the algorithm on a shared memory multiprocessor machine. We also perform a number of experiments comparing our model with our empirical results. 1