Dynamic query allocation to virtual warehouses

Patent metadata

Field	Value
Publication number	US-12353422-B2
Application number	US-202318212302-A
Country	US
Kind code	B2
Filing date	Jun 21, 2023
Priority date	Jul 13, 2021
Publication date	Jul 8, 2025
Grant date	Jul 8, 2025

Abstract

Methods, systems, and apparatuses for managing and selecting virtual warehouses for execution of queries on one or more data warehouses are described herein. A request to execute a query may be received. An execution plan, for the query, may be identified. A processing complexity for the query may be predicted based on the query and the execution plan. A plurality of virtual warehouses may be identified. An operating status and processing capabilities of the plurality of virtual warehouses may be determined. A subset of the plurality of virtual warehouses may be selected based on the processing complexity, the operating status of the plurality of virtual warehouses, and the processing capabilities of the plurality of virtual warehouses. The query may be executed on one of the subset of the plurality of virtual warehouses.

First claim

What is claimed is:

1. A computing device comprising:
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the computing device to:
receive, from a user device, a request to execute a query on at least one of a plurality of data warehouses;
identify an execution plan for the query by determining one or more sub-queries to be executed with respect to one or more of the plurality of data warehouses;
predict, based on the query and the execution plan, a processing complexity of the query;
identify a plurality of virtual warehouses, wherein each of the plurality of virtual warehouses comprises a respective set of computing resources configured to:
execute one or more queries with respect to at least a portion of the plurality of data warehouses;
collect results from the one or more queries; and
provide, to the user device, access to the collected results;

based on the processing complexity of the query and processing capabilities of the plurality of virtual warehouses, instantiate a new virtual warehouse different from the plurality of virtual warehouses; and
cause the new virtual warehouse to execute the query.

2. The computing device of claim 1 , wherein the instructions, when executed by the one or more processors, cause the computing device to predict the processing complexity of the query by causing the computing device to:
provide, as input to a trained machine learning model, the execution plan, wherein the trained machine learning model is trained based on a history of queries executed by the plurality of data warehouses; and
receive, from the trained machine learning model and based on the input, a prediction of the processing complexity of the query.

3. The computing device of claim 1 , wherein the instructions, when executed by the one or more processors, cause the computing device to instantiate the new virtual warehouse further based on an operating status of the plurality of virtual warehouses.

4. The computing device of claim 1 , wherein the instructions, when executed by the one or more processors, cause the computing device to cause the new virtual warehouse to execute the query by causing the computing device to:
modify a quantity of computing resources available to one or more servers that provide the new virtual warehouse.

5. The computing device of claim 1 , wherein the instructions, when executed by the one or more processors, cause the computing device to instantiate the new virtual warehouse further based on a historical operating status trend of at least a portion of the plurality of virtual warehouses.

6. The computing device of claim 1 , wherein the instructions, when executed by the one or more processors, cause the computing device to predict the processing complexity of the query by causing the computing device to:
determine a configuration of at least one table of the one or more of the plurality of data warehouses, wherein the predicted processing complexity is based on the configuration.

7. The computing device of claim 1 , wherein the instructions, when executed by the one or more processors, cause the computing device to:
send, based on the processing complexity of the query satisfying a threshold, a notification to the user device; and
receive, from the user device, a modification to the query, wherein the instructions, when executed by the one or more processors, cause the computing device to cause the new virtual warehouse to execute the query based on the modification.

8. The computing device of claim 1 , wherein the instructions, when executed by the one or more processors, cause the computing device to cause the new virtual warehouse to execute the query by causing the computing device to:
determine a first cost associated with execution of the query by the new virtual warehouse;
determine a time period such that, during the time period, execution of the query by the new virtual warehouse is associated with a second cost lower than the first cost; and
cause the new virtual warehouse to execute the query during the time period.

9. A method comprising:
receiving, from a user device, a request to execute a query on at least one of a plurality of data warehouses;
identifying an execution plan for the query by determining one or more sub-queries to be executed with respect to one or more of the plurality of data warehouses;
predicting, based on the query and the execution plan, a processing complexity of the query;
identifying a plurality of virtual warehouses, wherein each of the plurality of virtual warehouses comprises a respective set of computing resources configured to:
execute one or more queries with respect to at least a portion of the plurality of data warehouses;
collect results from the one or more queries; and
provide, to the user device, access to the collected results;

based on the processing complexity of the query and processing capabilities of the plurality of virtual warehouses, instantiating a new virtual warehouse different from the plurality of virtual warehouses; and
causing the new virtual warehouse to execute the query.

10. The method of claim 9 , wherein predicting the processing complexity of the query comprises:
providing, as input to a trained machine learning model, the execution plan, wherein the trained machine learning model is trained based on a history of queries executed by the plurality of data warehouses; and
receiving, from the trained machine learning model and based on the input, a prediction of the processing complexity of the query.

11. The method of claim 9 , wherein instantiating the new virtual warehouse is further based on an operating status of the plurality of virtual warehouses.

12. The method of claim 9 , wherein causing the new virtual warehouse to execute the query comprises modifying a quantity of computing resources available to one or more servers that provide the new virtual warehouse.

13. The method of claim 9 , wherein instantiating the new virtual warehouse is further based on a historical operating status trend of at least a portion of the plurality of virtual warehouses.

14. The method of claim 9 , wherein predicting the processing complexity of the query comprises:
determining a configuration of at least one table of the one or more of the plurality of data warehouses, wherein the predicted processing complexity is based on the configuration.

15. The method of claim 9 , further comprising:
sending, based on the processing complexity of the query satisfying a threshold, a notification to the user device; and
receiving, from the user device, a modification to the query, wherein the causing the new virtual warehouse to execute the query is based on the modification.

16. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors of a computing device, cause the computing device to:
receive, from a user device, a request to execute a query on at least one of a plurality of data warehouses;
identify an execution plan for the query by determining one or more sub-queries to be executed with respect to one or more of the plurality of data warehouses;
predict, based on the query and the execution plan, a processing complexity of the query;
identify a plurality of virtual warehouses, wherein each of the plurality of virtual warehouses comprises a respective set of computing resources configured to:
execute one or more queries with respect to at least a portion of the plurality of data warehouses;
collect results from the one or more querie

Assignees

• Capital One Services Llc

Inventors

• Kandukuri Praveen
• Salim Syed
• Hardatt Karamchandradatt
• Gurram Nagender
• Bharathan Ganesh
• Batra Yudhish

Classifications

• G06F16/256

  in federated or virtual databases · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F9/5005

  to service a request · CPC title

• G06F16/24575 Primary

  using context · CPC title

• G06F16/283 Primary

  Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP · CPC title