USING MACHINE LEARNING TO ESTIMATE QUERY RESOURCE CONSUMPTION IN MPPDB

摘要

Methods and apparatus are provided for using machine learning to estimate query resource consumption in a massively parallel processing database (MPPDB). In various embodiments, the machine learning may jointly perform query resource consumption estimation for a query and resource extreme events detection together, utilize an adaptive kernel that is configured to learn most optimal similarity relation metric for data from each system settings, and utilize multi-level stacking technology configured to leverage outputs of diverse base classifier models. Advantages and benefits of the disclosed embodiments include providing faster and more reliable system performance and avoiding resource issues such as out of memory (OOM) occurrences.