	US 12,189,627 B2
	Query optimization using reinforcement learning
Thomas A. Beavin, Milpitas, CA (US); Shuanglin Guo, Cupertino (CN); Brandon Jabr, Los Altos, CA (US); and Terence P. Purcell, Springfield, IL (US)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Sep. 13, 2022, as Appl. No. 17/931,588.
Claims priority of provisional application 63/366,070, filed on Jun. 9, 2022.
Prior Publication US 2023/0401207 A1, Dec. 14, 2023
Int. Cl. G06F 16/2453 (2019.01)

CPC G06F 16/24542 (2019.01)

15 Claims

1. A computer-implemented method to improve query performance in a database management system (DBMS), the method comprising:

receiving, by a query optimizer of the DMBS, a query for execution;

creating, by the query optimizer, an initial access path for the query based on a current state of a database of the DBMS;

executing the query based on the initial access path;

observing, by a query agent of the DBMS, the execution of the query, wherein the query agent analyzes the current state and based on the current state, determines a rewarding action from a set of actions to determine a change to the initial access path for the query;

based on a determination by the query agent that the change to the initial access path would improve the execution of the query, modifying the current state,

wherein the determination by the query agent that the change to the initial access path would improve the execution of the query is based on the query agent performing the query according to a second access path that is different from the initial access path.