US 12,130,788 B1
Assisted database anomaly mitigation
Vikramank Yogendra Singh, San Francisco, CA (US); Zhao Song, San Jose, CA (US); Balakrishnan Narayanaswamy, San Jose, CA (US); Maxym Kharchenko, Issaquah, WA (US); Jeremiah C Wilton, Seattle, WA (US); Vijay Gopal Joshi, Kenmore, WA (US); Joshua Tobey Oberwetter, Seattle, WA (US); and Kyle Henderson Hailey, Nevada City, CA (US)
Assigned to Amazon Technologies, Inc., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on Sep. 30, 2021, as Appl. No. 17/491,384.
Int. Cl. G06F 7/00 (2006.01); G06F 9/48 (2006.01); G06F 9/50 (2006.01); G06F 16/21 (2019.01); G06F 16/2455 (2019.01); G06N 20/00 (2019.01)
CPC G06F 16/217 (2019.01) [G06F 9/485 (2013.01); G06F 9/5038 (2013.01); G06F 16/24553 (2019.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
at least one processor; and
at least one memory comprising instructions that, in response to execution by the at least one processor, cause the system to at least:
collect a time series of first data indicative of database queries being processed by a database system;
collect second data indicative of conditions of the database system observed while processing the database queries;
identify an anomalous period of operation of the database system based, at least in part, on analysis of the time series of first data by a machine learning model; and
generate one or more recommendations for tuning performance of the database system based, at least in part, on evaluation of one or more hypotheses related to conditions observed during the anomalous period of operation.