Abstract: Methods for optimization in query plans are performed by computing systems via a query optimizer advisor. A query optimizer advisor (QO-Advisor) is configured to steer a query plan optimizer towards more efficient plan choices by providing rule hints to improve navigation of the search space for each query in formulation of its query plan. The QO-Advisor receives historical information of a distributed data processing system as an input, and then generates a set of rule hint pairs based on the historical information. The QO-Advisor provides the set of rule hint pairs to a query plan optimizer, which then optimizes a query plan of an incoming query through application of a rule hint pair in the set. This application is based at least on a characteristic of the incoming query matching a portion of the rule hint pair.

Abstract: Systems and methods for tuning hyperparameters for a machine learning model using a challenger champion model are described. A set of challenger configurations are generated based on a hyperparameter for tuning and a subset of the set of challenger configurations are scheduled for evaluation based on a loss function. A loss value derived from the loss function for the challenger configurations is compared to a loss value derived from the loss function for a champion configuration, and the champion configuration is replaced with the challenger configuration based on the comparison of the loss value derived from the loss function for the challenger configuration and the loss value derived from the loss function for the champion configuration. When the champion is replaced, a new set of challenger configurations is generated based on the new champion configuration.