Multi-weighted tree based query optimization method for parallel relational database systems

A multi-weighted tree based query optimization method for parallel relational databases is proposed. The method consists of a multi-weighted tree based parallel query plan model, a cost model for parallel query plans and a query optimizer. The parallel query plan model models three types of parallelism of query execution, processor and memory allocation to operations, memory allocation to buffers in pipelines and data redistribution among processors. The cost model takes the waiting time of operations in pipelining execution into consideration and is computable in a bottom-up fashion. The query optimizer addresses the query optimization problem in the context of Select-Project-Join queries. Heuristics for determining the processor allocation to operations and the memory allocation to operations and buffers in pipelines are derived and used in the query optimizer. In addition, the query optimizer considers multiple join algorithms, and can make an optimal choice of join algorithm for each join operation in a query.