2. The method as recited in claim 1 , wherein said utilizing machine learning to build said prediction of query runtime tree comprises: decomposing an historical database query of said historical query information into one or more historical query features.
2. The method as recited in claim 1 , wherein said utilizing machine learning to build said prediction of query runtime tree comprises: decomposing an historical database query of said historical query information into one or more historical query features. 3. The method as recited in claim 2 , wherein said decomposing an historical database query of said historical query information into one or more historical query features comprises: decomposing said historical database query into said historical query feature, said historical query feature selected from the group of historical query features consisting of: an optimizer cost, a total number of operators, a total operator cost, a total operator cardinality, a total number of tables, a measure of bushiness of a query tree, a measure of process parallelism, a total number of joins, a total join cardinality, a total number of group-bys and sorts, a total cardinality of group-bys and sorts, a total number of input/outputs, a total input/output cardinality, and an accuracy of table statistics.