Reducing database system query transaction delay

Abstract

A processing system including at least one processor may obtain a first set of performance records of a database system, train a machine learning model in accordance with the first set of performance records, where the machine learning model that is trained in accordance with the first set of performance records is configured to predict a latency of a query transaction for a designated time period, present a user interface with a plurality of settings of the database system that are user-adjustable, where the plurality of settings is associated with at least a portion of the first set of performance records, calculate a first predicted latency of a query transaction at the designated time period via the machine learning model in accordance with a set of values of the plurality of settings, and present the first predicted latency via the user interface.

What is claimed is: 
     
         1 . A method comprising:
 obtaining, by a processing system including at least one processor, a first set of performance records of a database system;   training, by the processing system, a machine learning model in accordance with the first set of performance records, where the machine learning model that is trained in accordance with the first set of performance records comprises a plurality of independent variables representing a plurality of performance metrics associated with the first set of performance records of the database system and at least one dependent variable comprising a query sub-operation delay at a server node of the database system for a designated time period, wherein the plurality of performance metrics includes a plurality of configuration settings for at least one resource quota pool of the database system, wherein the designated time period comprises a predefined time block within a time unit comprising one of: a day, a week, a month, or a year, wherein the predefined time block is a recurrent time block within the time unit, and wherein the predefined time block comprises less than an entirety of the time unit;   obtaining, by the processing system via a user interface, at least one input selecting the designated time period;   selecting, by the processing system, a set of values of the plurality of configuration settings for the at least one resource quota pool of the database system for the designated time period at the server node in accordance with the machine learning model, wherein the selecting comprises applying candidate sets of values of the plurality of configuration settings to the machine learning model and selecting the set of values of the plurality of configuration settings that provides a least query sub-operation delay at the server node of the database system for the at least one resource quota pool for the designated time period; and   presenting, by the processing system, the set of values of the plurality of configuration settings via the user interface.   
     
     
         2 . The method of  claim 1 , wherein the training the machine learning model in accordance with the first set of performance records utilizes time stamps and the plurality of performance metrics associated with the first set of performance records as inputs, wherein for each performance record of the first set of performance records, the plurality of performance metrics includes: configuration setting values for a plurality resource quota pools, observed values associated with usage of the database system, and a delay measurement, wherein the plurality of resource quota pools includes the at least one resource quota pool. 
     
     
         3 . The method of  claim 1 , wherein the selecting comprises:
 identifying a selected number of candidate performance metrics of the plurality of performance metrics with a greatest effect on the query sub-operation delay according to the machine learning model;   identifying performance metrics of the candidate performance metrics that are associated with adjustable configuration settings of the database system, wherein the plurality of configuration settings for which the set of values is selected comprises the adjustable configuration settings that are identified; and   identifying the set of values of the plurality of configuration settings that minimizes the query sub-operation delay according to the machine learning model.   
     
     
         4 . The method of  claim 3 , wherein for the identifying the set of values of the plurality of configuration settings of the adjustable configuration settings that minimizes the query sub-operation delay, performance metrics of the candidate performance metrics that are not associated with the adjustable configuration settings are assumed to be average values based upon the first set of performance records. 
     
     
         5 . The method of  claim 3 , wherein the adjustable configuration settings comprise user-adjustable configuration settings. 
     
     
         6 . The method of  claim 1 , further comprising:
 obtaining an input to implement the set of values of the plurality of configuration settings for the designated time period at the server node; and   sending an instruction to the server node to implement the set of values of the plurality of configuration settings for the designated time period.   
     
     
         7 . The method of  claim 1 , further comprising:
 obtaining a second set of performance records of the database system;   detecting that a deviation of the second set of performance records from the first set of performance records exceeds a threshold deviation; and   retraining the machine learning model in accordance with the second set of performance records.   
     
     
         8 . The method of  claim 1 , wherein the at least one input is further selecting a given resource quota pool of the at least one resource quota pool, wherein the machine learning model that is trained in accordance with the first set of performance records is configured to predict the query sub-operation delay at the server node of the database system for the given resource quota pool for the designated time period. 
     
     
         9 . The method of  claim 8 , wherein the training the machine learning model in accordance with the first set of performance records utilizes time stamps and the plurality of performance metrics associated with the first set of performance records as inputs, wherein for each performance record of the first set of performance records, the plurality of performance metrics includes: a resource quota pool identifier, configuration setting values for a plurality resource quota pools, observed values associated with usage of the database system, and a delay measurement, wherein the plurality of resource quota pools includes the at least one resource quota pool. 
     
     
         10 . The method of  claim 8 , wherein the selecting comprises selecting the set of values of the plurality of configuration settings for at least the given resource quota pool for the designated time period at the server node in accordance with the machine learning model. 
     
     
         11 . The method of  claim 10 , further comprising:
 obtaining an input to implement the set of values of the plurality of configuration settings for at least the given resource quota pool for the designated time period at the server node; and   sending an instruction to the server node to implement the set of values of the plurality of configuration settings for at least the given resource quota pool for the designated time period.   
     
     
         12 . The method of  claim 8 , wherein the given resource quota pool is associated with a designated type of query sub-operation. 
     
     
         13 . The method of  claim 1 , wherein the at least one input is further selecting a particular query sub-operation type, wherein the machine learning model that is trained in accordance with the first set of performance records is configured to predict the query sub-operation delay at the server node of the database system for the particular query sub-operation type for the designated time period. 
     
     
         14 . The method of  claim 13 , wherein the training the machine learning model in accordance with the first set of performance records utilizes time stamps and the plurality of performance metrics associated with the first set of performance records as inputs, wherein for each performance record of the first set of performance records, the plurality of performance metrics includes: configuration setting values for a resource quota pool in which a query sub-operation executes, observed values associated with usage of the database system, and a delay measurement. 
     
     
         15 . The method of  claim 13 , wherein the selecting comprises selecting the set of values of the plurality of configuration settings for the particular query sub-operation type for the designated time period at the server node in accordance with the machine learning model. 
     
     
         16 . The method of  claim 15 , further comprising:
 obtaining an input to implement the set of values of the plurality of configuration settings for the particular query sub-operation type for the designated time period at the server node; and   sending an instruction to the server node to implement the set of values of the plurality of configuration settings for the particular query sub-operation type for the designated time period.   
     
     
         17 . The method of  claim 16 , further comprising:
 establishing a new resource quota pool for the particular query sub-operation type, wherein the instruction to the server node comprises an instruction to the server node to activate the new resource quota pool and to implement the set of values of the plurality of configuration settings for the particular query sub-operation type for the designated time period via the new resource quota pool.   
     
     
         18 . A method comprising:
 obtaining, by a processing system including at least one processor, a first set of performance records of a database system;   training, by the processing system, a machine learning model in accordance with the first set of performance records, wherein the machine learning model that is trained in accordance with the first set of performance records comprises a plurality of independent variables representing a plurality of performance metrics associated with the first set of performance records of the database system and at least one dependent variable comprising a query transaction latency for a designated time period, wherein the plurality of performance metrics includes a plurality of settings of the database system that are user-adjustable, wherein the designated time period comprises a predefined time block within a time unit comprising one of: a day, a week, a month, or a year, wherein the predefined time block is a recurrent time block within the time unit, and wherein the predefined time block comprises less than an entirety of the time unit;   presenting, by the processing system, a user interface with the plurality of settings of the database system that are user-adjustable;   calculating, by the processing system, a first predicted latency of a query transaction at the designated time period via the machine learning model in accordance with a set of values of the plurality of settings selected via the user interface; and   presenting, by the processing system, the first predicted latency via the user interface.   
     
     
         19 . The method of  claim 18 , further comprising:
 obtaining at least a first input via the user interface to adjust at least one of the plurality of settings to a selected value; and   adjusting the at least one of the plurality of settings to the selected value in accordance with the at least the first input;   wherein the calculating the first predicated latency of the query transaction at the designated time period via the machine learning model is in accordance with the set of values of the plurality of settings that includes the at least one of the plurality of settings that is adjusted to the selected value.   
     
     
         20 . The method of  claim 18 , wherein the plurality of settings is associated with at least a portion of the plurality of independent variables, wherein the at least the portion of the plurality of independent variables comprises a selected number of independent variables of the machine learning model with the highest coefficient absolute values and that are associated with the plurality of settings that are user-adjustable.