Parallelism in relational data base systems: architectural issues and design approaches

  • Authors:

  • Hamid Pirahesh;C. Mohan;Josephine Cheng;T. S. Liu;Pat Selinger

  • Affiliations:

  • Data Base Technology Institute, IBM Almaden Research Center, San Jose, CA;Data Base Technology Institute, IBM Almaden Research Center, San Jose, CA;Data Base Technology Institute, IBM Santa Teresa Laboratory, San Jose, CA;Data Base Technology Institute, IBM Santa Teresa Laboratory, San Jose, CA;Data Base Technology Institute, IBM Almaden Research Center, San Jose, CA

  • Venue:
  • DPDS '90 Proceedings of the second international symposium on Databases in parallel and distributed systems
  • Year:
  • 1990

Quantified Score

Hi-index 0.00

Visualization

Abstract

With current systems, some important complex queries may take days to complete because of: (1) the volume of data to be processed, (2) limited aggregate resources. Introducing parallelism addresses the first problem. Cheaper, but powerful computing resources solve the second problem. According to a survey by Brodie,1 only 10% of computerized data is in data bases. This is an argument for both more variety and volume of data to be moved into data base systems. We conjecture that the primary reasons for this low percentage are that data base management systems (DBMSs) still need to provide far greater functionality and improved performance compared to a combination of application programs and file systems. This paper addresses the issues and solutions relating to intraquery parallelism in a relational DBMS supporting SQL. Instead of focussing only on a few algorithms for a subset of the problems, we provide a broad framework for the study of the numerous issues that need to be addressed in supporting parallelism efficiently and flexibly. We also discuss the impact that parallelization of complex queries has on short transactions which have stringent response time constraints. The pros and cons of the shared nothing, shared disks and shared everything architectures for parallelism are enumerated. The impact of parallelism on a number of components of an industrial-strength DBMS are pointed out. The different stages of query processing during which parallelism may be gainfully employed are identified. The interactions between parallelism and the traditional systems' pipelining technique are analyzed. Finally, the performance implications of parallelizing a specific complex query are studied. This gives us a range of sample points for different parameters of a parallel system architecture, namely, I/O and communication bandwidth as a function of aggregate MIPS.