US 12,066,993 B2
Constraint-based index tuning in database management systems utilizing reinforcement learning
Wentao Wu, Bellevue, WA (US); Chi Wang, Redmond, WA (US); Tarique Ashraf Siddiqui, Redmond, WA (US); Vivek Ravindranath Narasayya, Redmond, WA (US); and Surajit Chaudhuri, Kirkland, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Jun. 3, 2022, as Appl. No. 17/832,274.
Claims priority of provisional application 63/325,538, filed on Mar. 30, 2022.
Prior Publication US 2023/0315702 A1, Oct. 5, 2023
Int. Cl. G06F 16/21 (2019.01); G06F 16/22 (2019.01); G06F 16/2453 (2019.01)
CPC G06F 16/217 (2019.01) [G06F 16/2246 (2019.01); G06F 16/2453 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for performing index tuning within a relational database system, comprising:
receiving, at an index tuner, a workload comprising a set of queries corresponding to one or more databases;
generating, by the index tuner, a set of candidate indexes based on the set of queries, wherein the set of candidate indexes comprises multiple candidate index combinations;
determining, by the index tuner, an index configuration from the set of candidate indexes by:
identifying a plurality of index configurations from the set of candidate indexes utilizing one or more reinforcement learning models;
determining workload computing costs for a subset of the plurality of index configurations based on a query optimizer and a predetermined threshold tuning constraint; and
selecting the index configuration from the plurality of index configurations based on the workload computing costs, wherein the index configuration comprises a subset of candidate indexes from the set of candidate indexes; and
providing the subset of candidate indexes in response to receiving the workload.