LEVERAGING QUERY EXECUTIONS TO IMPROVE INDEX RECOMMENDATIONS

Systems and techniques for leveraging query executions to improve index recommendations are described herein. In an example, a machine learning model is adapted to receive a first query plan and a second query plan for performing a query with a database, where the first query plan is different from...

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Bibliographic Details
Main Authors Das, Sudipto, Chaudhuri, Surajit, Ding, Bailu, Marcus, Ryan, Narasayya, Vivek R, Swaminathan, Adith, Ma, Lin
Format Patent
LanguageEnglish
Published 27.08.2020
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Systems and techniques for leveraging query executions to improve index recommendations are described herein. In an example, a machine learning model is adapted to receive a first query plan and a second query plan for performing a query with a database, where the first query plan is different from the second query plan. The machine learning model may be further adapted to determine execution cost efficiency between the first query plan and the second query plan. The machine learning model is trained using relative execution cost comparisons between a set of pairs of query plans for the database. The machine learning model is further adapted to output a ranking of the first query plan and second query plan, where the first query plan and second query plan are ranked based on execution cost efficiency.
Bibliography:Application Number: US201916282116