Abstract: Computing resources can be assigned to sub-plans within a query plan to effect parallel execution of the query plan. For example, computing resources in a grid can be represented by nodes, and a shortest path technique can be applied to allocate machines to the sub-plans. Computing resources can be provisionally allocated as the query plan is divided into query plan segments containing one or more sub-plans. Based on provisional allocations to the segments, the computing resources can then be allocated to the sub-plans within respective segments. Multiprocessor computing resources can be supported. The techniques can account for data locality. Both pipelined and partitioned parallelism can be addressed. Described techniques can be particularly suited for efficient execution of bushy query plans in a grid environment. Parallel processing will reduce the overall response time of the query.

Abstract: Computing resources can be assigned to sub-plans within a query plan to effect parallel execution of the query plan. For example, computing resources in a grid can be represented by nodes, and a shortest path technique can be applied to allocate machines to the sub-plans. Computing resources can be provisionally allocated as the query plan is divided into query plan segments containing one or more sub-plans. Based on provisional allocations to the segments, the computing resources can then be allocated to the sub-plans within respective segments. Multiprocessor computing resources can be supported. The techniques can account for data locality. Both pipelined and partitioned parallelism can be addressed. Described techniques can be particularly suited for efficient execution of bushy query plans in a grid environment. Parallel processing will reduce the overall response time of the query.