Systems and methods for determining performance metrics of remote relational databases

Patent metadata

Field	Value
Publication number	US-10922206-B2
Application number	US-201916409051-A
Country	US
Kind code	B2
Filing date	May 10, 2019
Priority date	May 10, 2019
Publication date	Feb 16, 2021
Grant date	Feb 16, 2021

Abstract

Exemplary embodiments provide systems and methods for determining performance metrics or statistics relating to relational databases that are accessed remotely. Such embodiments may automatically discover the presence or identity of such remotely-stored databases using serverless code, query each database for performance information, convert the performance information into performance metrics, and store the performance metrics as time-series data in a time-series database. The performance metrics may be used to generate notifications, provide input to a machine learning process, adjust settings of the relational databases or an associated service, or provide a visualization of the performance of the databases, among other possibilities.

First claim

What is claimed is:

1. A method comprising:
automatically discovering one or more relational databases stored on one or more remote servers accessible through a network connection, wherein the automatically discovering is performed by auto-discovery code;
receiving an application programming interface (API) call at a gateway, the gateway interfacing with a time series collector to collect time series performance metrics relating to the one or more relational databases;
extracting, from the one or more relational databases via the network connection, performance data relating to a performance of the one or more relational databases, wherein extracting the performance data is performed by auto-scaling triggered code configured not to incur a charge when the auto-scaling triggered code is not running;
converting the performance data into performance metrics, the performance metrics represented as time series data configured to be stored in a time series database accessible to the time series collector; and
responding to the API call with the performance metrics.

2. The method of claim 1 , further comprising:
adding a new relational database to the one or more remote servers; and
automatically discovering the new relational database without receiving further input from a user.

3. The method of claim 1 , wherein the performance metrics relate to one or more of: a number or rate of queries to the relational databases, a number of connections to the relational databases, a size of the relational databases, a latency of queries to the relational databases, a replication status of the relational databases, an input/output status of the relational databases, or a status of a sequential scan of the relational databases.

4. The method of claim 1 , further comprising retrieving data metrics relating to data stored in the relational databases, and adding the data metrics to the time series database.

5. The method of claim 1 , further comprising:
detecting that a selected performance metric has exceeded a predetermined threshold; and
generating a notification to a database administrator identifying the selected performance metric.

6. The method of claim 1 , further comprising:
performing a machine learning process on the performance metrics; and
using a result of the machine learning process to change a setting of the relational databases to improve one or more of the performance metrics.

7. The method of claim 1 , wherein the automatically discovering comprises executing one or more scripts and submitting a query to the one or more remote servers.

8. The method of claim 1 , wherein the automatically discovering comprises generating an auto-discovery request, the transmitting the auto-discovery request to the one or more remote servers, and receiving a response.

9. A non-transitory computer-readable medium storing instructions configured to cause one or more processors to:
programmatically identify one or more relational databases stored on a cloud storage service;
receive a method invocation at a gateway, the gateway interfacing with an aggregator to collect temporally-indexed statistics relating to the one or more relational databases;
retrieve, from the cloud storage service, information pertaining to an operation of the one or more relational databases, wherein the retrieving of the information is performed by auto-scaling triggered code configured not to incur a charge with the cloud storage service when the code is not running;
convert the information into the statistics, the statistics configured to be stored in a temporally-indexed database accessible to the aggregator; and
respond to the method invocation with the statistics.

10. The medium of claim 9 , wherein the statistics relate to one or more of: a number or rate of queries to the relational databases, a number of connections to the relational databases, a size of the relational databases, a latency of queries to the relational databases, a replication status of the relational databases, an input/output status of the relational databases, or a status of a sequential scan of the relational databases.

11. The medium of claim 9 , further storing instructions for retrieving data statistics relating to data stored in the relational databases, and adding the data statistics to the temporally-indexed database.

12. The medium of claim 9 , further storing instructions for:
generating a visualization of the statistics; and
outputting the visualization to a user of the cloud storage service.

13. The medium of claim 9 , further storing instructions for:
applying an artificial intelligence to the statistics; and
recommending, with the artificial intelligence, a change to the cloud computing service to modify an operation of the cloud storage service with respect to the one or more relational databases.

14. The medium of claim 9 , further storing instructions configured to adding a new relational database to the cloud storage service.

15. The medium of claim 14 , further storing instructions configured to programmatically identify the new relational database after it is added to the cloud storage service.

16. An apparatus comprising:
a network interface configured to submit a query at a third-party computing service, the third-party computing service configured to operate one or more relational databases on behalf of another entity;
a memory storing respective identifiers of the one or more relational databases; and
a processor circuit configured execute instructions, wherein the third-party computing service is Amazon Web Service (AWS), and the instructions are embodied as an AWS Lambda that is configured to not incur a charge with the third-party computing service when the instructions are not executing, the instructions configured to cause the processor circuit to:
generate the query of the one or more relational databases based on the identifiers;
process a request from a gateway, the gateway interfacing with a collector to collect chronologically-ordered parameters relating to the one or more relational databases;
retrieve, from the third-party computing service, a response to the query;
use the response to the query to compute the parameters, the parameters configured to be stored in a chronologically-ordered database accessible to the collector; and
submit the parameters to the collector.

17. The apparatus of claim 16 , wherein the processor circuit is further configured to automatically register a new relational database incorporated into the third-party computing service.

18. The apparatus of claim 16 , wherein the parameters relate to one or more of: a number or rate of queries to the relational databases, a number of connections to the relational databases, a size of the relational databases, a latency of queries to the relational databases, a replication status of the relational databases, an input/output status of the relational databases, or a status of a sequential scan of the relational databases.

19. The apparatus of claim 16 , wherein the processor is further configured to access data parameters relating to entries in the relational databases, and to add the data parameters to the chronologically-ordered database.

20. The apparatus of claim 16 , wherein the processor is further configured to:
applying a model to the parameters; and
use the model to alter the third-party computing service or the relational databases to improve execution of database queries.

Assignees

• Capital One Services Llc

Inventors

• Anand Kumar
• Shaik Naseema
• Pandey Amit

Classifications

• G06N3/0985

  Hyperparameter optimisation; Meta-learning; Learning-to-learn · CPC title

• G06N3/092

  Reinforcement learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06F2201/80

  Database-specific techniques · CPC title

• G06F11/3447

  Performance evaluation by modeling · CPC title