US 12,437,522 B2
Adapting learned cardinality estimators to data and workload drifts
Yao Lu, Redmond, WA (US); Srikanth Kandula, Redmond, WA (US); and Beibin Li, Redmond, WA (US)
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed by MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed on Dec. 31, 2021, as Appl. No. 17/566,996.
Prior Publication US 2023/0215150 A1, Jul. 6, 2023
Int. Cl. G06V 10/776 (2022.01); G06N 3/045 (2023.01); G06V 10/774 (2022.01)
CPC G06V 10/776 (2022.01) [G06N 3/045 (2023.01); G06V 10/7747 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method of updating a trained cardinality estimation model implement in a computing system, the method comprising:
receiving a cardinality estimation model with training predicates and cardinality labels;
detecting a drift in underlying data or predicates of the cardinality estimation model;
determining a type of the detected drift;
based on the type of the detected drift, synthesizing new test queries that mimic test queries for the detected drift;
selecting a portion of the new or synthesized test queries to annotate with cardinality labels so as to reduce annotation cost; and
updating the cardinality estimation model with newer predicates and cardinality labels.