Abstract: Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional shared-nothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper reviews the techniques used by such systems, and surveys current commercial and research systems. 1.

Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional shared-nothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper reviews the techniques used by such systems, and surveys current commercial and research systems.

This paper introduces Clustera, an integrated computation and data management system. In contrast to traditional clustermanagement systems that target specific types of workloads, Clustera is designed for extensibility, enabling the system to be easily extended to handle a wide variety of job types ranging from computationally-intensive, long-running jobs with minimal I/O requirements to complex SQL queries over massive relational tables. Another unique feature of Clustera is the way in which the system architecture exploits modern software building blocks including application servers and relational database systems in order to realize important performance, scalability, portability and usability benefits. Finally, experimental evaluation suggests that Clustera has good scale-up properties for SQL processing, that Clustera delivers performance comparable to Hadoop for MapReduce processing and that Clustera can support higher job throughput rates than previously published results for the Condor and CondorJ2 batch computing systems. 1.

Abstract: Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional shared-nothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper reviews the techniques used by such systems, and surveys current commercial and research systems. 1.