| Massive database sizes and growing demands for decision support and data mining result in long-running queries in extensible object relational database systems, particularly in non-traditional application domains such as geo-scientific computing, image processing and data warehousing analysis. Full support of parallelism in query evaluation is desired for acceptable performance. However, queries are frequently processed sub-optimally due to (1) only coarse or inaccurate estimates of the query characteristics and database statistics available prior to query evaluation; (2) changes in system configuration and resource availability during query evaluation. In a distributed environment, dynamically reconfiguring query execution plans (QEPs), which adapts QEPs to the environment as well as the query characteristics, is a promising means to significantly improve query evaluation performance. By introducing the concepts of windows and abstract data type orderings, we have developed a novel approach for classifying operators in a distributed query processing environment. Based on such a classification, we have designed triggered run-time optimization mechanisms to re-optimize suboptimal query plan configurations on-the-fly. In addition, we have implemented an algorithm to coordinate the steps in a reconfiguration and introduce alternatives for execution context check-pointing and restoring. Experimental results are reported and discussed. |
