Teradata Us, Inc.

Matthew Edward Antoun

Douglas Paul Brown

Louis Martin Burger

Frank Roderic Vandervort

Christopher James Antoun

Artur Borycki

Chrisopher James Antoun

In some examples, a system receives an input graph representation of one or more query plans for one or more database queries, and generates, by an embedding machine learning model based on the input graph representation, a feature vector that provides a distributed representation of the one or more query plans. The system determines, using the feature vector, one or more user behaviors and/or workload characteristics of one or more workloads in one or more database systems.

Query graph embedding

A database system receives a query. The database system retrieves an old query execution plan (QEP), OldPlan, for the query. The database system submits the query to an optimizer. The optimizer returns a new QEP, NewPlan, for the query. The database system submits the OldPlan and the NewPlan to a machine learning classifier (ML classifier). The ML classifier predicts that executing the NewPlan will result in a performance regression as compared to executing the OldPlan. The database system executes the OldPlan instead of the NewPlan.

Reducing query optimizer plan regressions with machine learning classification

Growing community of inventors

Santa Clara, CA, United States of America

Matthew Edward Antoun