Translating SQL Into Relational Algebra: Optimization, Semantics, and Equivalence of SQL Queries
IEEE Transactions on Software Engineering
Database machines and database management
Database machines and database management
Optimization of nested SQL queries revisited
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Quantitative analysis of computer systems
Quantitative analysis of computer systems
Optimizing SQL queries for parallel execution
ACM SIGMOD Record
Parallel database systems: the future of high performance database systems
Communications of the ACM
Database tuning: a principled approach
Database tuning: a principled approach
Multi-join on parallel processors
DPDS '90 Proceedings of the second international symposium on Databases in parallel and distributed systems
On optimizing an SQL-like nested query
ACM Transactions on Database Systems (TODS)
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Performance study on optimal processor assignment in parallel relational databases
ICS '97 Proceedings of the 11th international conference on Supercomputing
Performance evaluation of processor allocation algorithms for parallel query execution
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
ParNum '99 Proceedings of the 4th International ACPC Conference Including Special Tracks on Parallel Numerics and Parallel Computing in Image Processing, Video Processing, and Multimedia: Parallel Computation
Information Sciences—Informatics and Computer Science: An International Journal
Performance analysis of "Groupby-After-Join" query processing in parallel database systems
Information Sciences—Informatics and Computer Science: An International Journal
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A high-performance parallel system for processing databases is presented, which adopts a distributed memory architecture and has been successfully implemented on a transputer platform. In addition to developing and implementing a variety of rules and schemes for parallelizing database queries, general analytic models for distributed memory database processing have been formulated, which have been successfully validated against measurements. Experimental data also indicate that the system is able to attain near linear speedup for the join operation. We also demonstrate that a linear speedup is unattainable for distributed memory database systems, and an upper bound governing the maximum transaction rate is derived. The present system is also reconfigurable and adopts a novel processor allocation scheme based on equalizing the elapsed time among different cooperating processors, which is able to readjust the processing resources assigned to particular database tasks in accordance with changing demands on the system.