Parallel algorithms for the execution of relational database operations

Abstract

This paper presents and analyzes algorithms for parallel processing of relational database operations in a general multiprocessor framework. To analyze alternative algorithms, we introduce an analysis methodology which incorporates I/O, CPU, and message costs and which can be adjusted to fit different multiprocessor architectures. Algorithms are presented and analyzed for sorting, projection, and join operations. While some of these algorithms have been presented and analyzed previously, we have generalized each in order to handle the case where the number of pages is significantly larger than the number of processors. In addition, we present and analyze algorithms for the parallel execution of update and aggregate operations.

Optimizing Reduction Computations In a Distributed Environment

“…Shatdal and Naughton [31] examine two algorithms for relational group-by queries on distributed-memory systems. Bitton et al [5] describe two sorting based algorithms, similar to the two algorithms developed by Shatdal and Naughton, for a shared disk cache architecture. Chang et.…”

Parallel algorithms for computing temporal aggregates

“…Bitton et al proposed two parallel algorithms for processing (conventional) aggregate functions [1]. The Subqueries with a Parallel Merge algorithm computes partial aggregates on each partition and combines the partial results in a parallel merge stage to obtain a final result.…”

“…The coordinator concatenates the results and sends the final result to the client. 1 A total of p 2 local partitions are created by p worker nodes. …”

Parallel sorting on a shared-nothing architecture using probabilistic splitting

“…Beck et al BBW88] describes a similar sort. Other earlier algorithms that also used a nal sequential merge phase include Bitton et al BBDW83], and Valduriez and Gardarin VG84].…”