公开(公告)号：US11657069B1

公开(公告)日：2023-05-23

申请号：US17105214

申请日：2020-11-25

Applicant: Amazon Technologies, Inc.

Inventor： Balakrishnan Narayanaswamy ,  Gokul Soundararajan ,  Jiayuan Chen ,  Yannis Papakonstantinou ,  Vuk Ercegovac ,  George Constantin Caragea ,  Sriram Krishnamurthy ,  Nikolaos Koulouris

IPC: G06F16/28 ,  G06F16/24 ,  G06F16/2458 ,  G06N20/00 ,  G06F16/2453 ,  G06F8/41

CPC classification number: G06F16/283 ,  G06F8/447 ,  G06F16/2465 ,  G06F16/2471 ,  G06F16/24535 ,  G06F16/24542 ,  G06N20/00

Abstract: A database system may use a machine learning model creation system to create a machine learning model from data stored in the database system responsive to a request from a client. The database system may obtain an executable version of the machine learning model, based on an uncompiled hardware agnostic version of the machine learning model, according to the hardware configuration of one or more computing resources selected by the database system to perform requests to the database system that invoke the machine learning model to generate predictions.

公开(公告)号：US11537616B1

公开(公告)日：2022-12-27

申请号：US16915928

申请日：2020-06-29

Applicant: Amazon Technologies, Inc.

Inventor： Chunbin Lin ,  Naresh Chainani ,  Gaurav Saxena ,  George Constantin Caragea ,  Mohammad Rezaur Rahman

IPC: G06F7/00 ,  G06F16/2453 ,  G06N5/04 ,  G06N20/00

Abstract: Performance measures are predicted for queries to prioritize query performance at a database system. A trained machine learning model for the database system may be applied to a query to determine a predicted performance measure for the query. The predicted performance measure may be compared with other predicted performance measures for other waiting queries to determine a priority for executing the query.

公开(公告)号：US10922316B2

公开(公告)日：2021-02-16

申请号：US16007697

申请日：2018-06-13

Applicant: Amazon Technologies, Inc.

Inventor： Gaurav Saxena ,  George Constantin Caragea ,  Naresh Kishin Chainani ,  Martin Grund

IPC: G06F16/00 ,  G06F16/2453 ,  G06F16/28

Abstract: Database queries may be performed using resources based on a determined size of the database query. Database query size may be dynamically determined for a database query when the query is received. The database query may be assigned to resources used for database queries of the determined size. In some embodiments, timeouts may be applied to reassign database queries to different resources if the performance of the database query exceeds a timeout threshold.

公开(公告)号：US11762860B1

公开(公告)日：2023-09-19

申请号：US17118288

申请日：2020-12-10

Applicant: Amazon Technologies, Inc.

Inventor： Mohammad Rezaur Rahman ,  George Constantin Caragea ,  Raj Narayan Sett ,  Gaurav Saxena ,  Naresh Chainani ,  Chunbin Lin

IPC: G06F16/2455 ,  G06F16/2453 ,  G06N20/00 ,  G06F16/23 ,  G06F18/214 ,  G06F11/34

CPC classification number: G06F16/24568 ,  G06F11/3409 ,  G06F16/2308 ,  G06F16/24539 ,  G06F16/24542 ,  G06F18/214 ,  G06N20/00

Abstract: Database systems may dynamically management concurrency levels for performing queries. A query may be received at a database system and a memory usage for the query may be predicted. A determination may be made as to whether available memory is enough to satisfy the predicted memory usage for the query. If the available memory is enough to satisfy the predicted memory usage for the query, then an increase in a concurrency level for performing queries at the database system may be made. The query may be allowed to execute concurrently with other queries according to the increased concurrency level.

公开(公告)号：US11636124B1

公开(公告)日：2023-04-25

申请号：US17105201

申请日：2020-11-25

Applicant: Amazon Technologies, Inc.

Inventor： Balakrishnan Narayanaswamy ,  Gokul Soundararajan ,  Jiayuan Chen ,  Yannis Papakonstantinou ,  Vuk Ercegovac ,  George Constantin Caragea ,  Sriram Krishnamurthy ,  Nikolaos Koulouris

IPC: G06F16/00 ,  G06F16/2458 ,  G06F16/2453 ,  G06K9/62 ,  G06N20/00 ,  G06F16/28

Abstract: A database system may include a machine learning model which may be used to perform various data analytics for data stored in the database system. In response to a request to invoke the machine learning model to generate a prediction from data stored in the database system, the database system may perform one or more optimization operations, as part of a query plan, to prepare the data to make it suitable for use by the machine learning model.

公开(公告)号：US10776368B1

公开(公告)日：2020-09-15

申请号：US15650704

申请日：2017-07-14

Applicant: Amazon Technologies, Inc.

Inventor： George Constantin Caragea ,  Andrew Edward Caldwell ,  Anurag Windlass Gupta ,  Michail Petropoulos

IPC: G06F17/00 ,  G06F16/2458 ,  G06F16/2453

Abstract: Cardinality values can be derived from an approximate quantile summary. An approximate quantile summary can be generated for a column of a database table at data ingestion, data update, upon request, in response to a query, and in various other scenarios. When a query is received that includes a predicate directed to the column of the approximate quantile summary, a cardinality value may be derived from the boundary values of one or more quantiles that include the predicate. The cardinality value may then be used to select a query plan. The query may be performed according to the selected query plan.

公开(公告)号：US20190384845A1

公开(公告)日：2019-12-19

申请号：US16007697

申请日：2018-06-13

Applicant: Amazon Technologies, Inc.

Inventor： Gaurav Saxena ,  George Constantin Caragea ,  Naresh Kishin Chainani ,  Martin Grund

IPC: G06F17/30

Abstract: Database queries may be performed using resources based on a determined size of the database query. Database query size may be dynamically determined for a database query when the query is received. The database query may be assigned to resources used for database queries of the determined size. In some embodiments, timeouts may be applied to reassign database queries to different resources if the performance of the database query exceeds a timeout threshold.