公开(公告)号：US12111805B2

公开(公告)日：2024-10-08

申请号：US18160969

申请日：2023-01-27

Applicant: TAMIRAS PER PTE. LTD., LLC

Inventor： Guy Shaked ,  Vladimir Braverman ,  Marina Sadetsky

IPC: G06F16/00 ,  G06F16/21 ,  G06F16/9535

CPC classification number: G06F16/217 ,  G06F16/213 ,  G06F16/9535

Abstract: A system is configured for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store.

公开(公告)号：US12111804B2

公开(公告)日：2024-10-08

申请号：US18510250

申请日：2023-11-15

Applicant: Oracle International Corporation

Inventor： Debajyoti Roy

IPC: G06F16/21 ,  G06F16/215 ,  G06F16/23 ,  G06N20/00

CPC classification number: G06F16/215 ,  G06F16/217 ,  G06F16/2386 ,  G06N20/00

Abstract: Techniques are disclosed for tuning external invocations utilizing weight-based parameter resampling. In one example, a computer system determines a plurality of samples, each sample being associated with a parameter value of a plurality of potential parameter values of a particular parameter. The computer system assigns weights to each of the parameter values, and then selects a first sample for processing via a first external invocation based on a weight of the parameter value of the first sample. The computer system then determines feedback data associated with a level of performance of the first external invocation. The computer system adjusts the weights of the parameter values of the particular parameter based on the feedback data. The computer system then selects a second sample of the plurality of samples to be processed via execution of a second external invocation based on the adjustment of weights of the parameter values.

公开(公告)号：US12099485B1

公开(公告)日：2024-09-24

申请号：US17568675

申请日：2022-01-04

Applicant: Ool LLC

Inventor： Gitanjali Swamy

IPC: G06F16/21 ,  G06F16/24 ,  G06F16/242 ,  G06F16/2458 ,  G06F16/248 ,  G06F16/28 ,  G06F16/332 ,  G06F16/35

CPC classification number: G06F16/217 ,  G06F16/2428 ,  G06F16/2465 ,  G06F16/248 ,  G06F16/285 ,  G06F16/3326 ,  G06F16/3328 ,  G06F16/355

Abstract: A decision support system and method, which receives user inputs comprising: at least one user criterion, and at least one user input tuning parameter representing user tradeoff preferences for producing an output; and selectively produces an output of tagged data from a clustered database in dependence on the at least one user criterion, the at least one user input tuning parameter, and a distance function; receives at least one reference-user input parameter representing the at least one reference-user's analysis of the tagged data and the corresponding user inputs, to adapt the distance function in accordance with the reference-user inputs as a feedback signal; and clusters the database in dependence on at least the distance function, wherein the reference-user acts to optimize the distance function based on the user inputs and the output, and on at least one reference-user inference.

公开(公告)号：US12056128B2

公开(公告)日：2024-08-06

申请号：US17820067

申请日：2022-08-16

Applicant: Palantir Technologies Inc.

Inventor： James Ding

IPC: G06F16/2455 ,  G06F16/21 ,  G06F16/22 ,  G06F16/242

CPC classification number: G06F16/24554 ,  G06F16/213 ,  G06F16/217 ,  G06F16/221 ,  G06F16/242 ,  G06F16/2423

Abstract: A database is configured to analyze user queries to dynamically partition the database according to a partition scheme. User queries can be rewritten based on the partition scheme so that, in response to queries, partitions including relevant data are read while partitions including irrelevant data can be skipped, reducing latency. Files can be named according to the partition scheme and stored on respective partitions so that low partition management can be implemented by underlying systems. Blocks within files can be sorted and statistics can be determined. The statistics can be used to find and read relevant blocks and skip irrelevant blocks.

公开(公告)号：US20240256255A1

公开(公告)日：2024-08-01

申请号：US18313067

申请日：2023-05-05

Applicant: Salesforce, Inc.

Inventor： Kaushal Mittal ,  Thomas Fanghaenel

IPC: G06F8/65 ,  G06F16/21

CPC classification number: G06F8/65 ,  G06F16/217

Abstract: Techniques for downgrading of database software. Code areas that may affect downgradability may be delineated with code markers. Changes to these sections can be made to prevent a new database version from being built unless a process is taken to indicate that any new features be designated as available, but not yet allowed for N software releases. This provides a downgrade window of N releases that will support functionality currently allowed by the database. In response to receiving a downgrade request to a target older database software version, that request can be permitted if all the database features are available or allowed in the target version. If at least one of the database features is not available in the target version, the downgrade requested is not permitted. If the request is permitted, the downgrade operation is commenced.

公开(公告)号：US11994963B2

公开(公告)日：2024-05-28

申请号：US17577729

申请日：2022-01-18

Applicant: Oracle International Corporation

Inventor： Nagarajan Muthukrishnan ,  Binoy Sukumaran ,  Garret F. Swart ,  Sumanta Chatterjee ,  Ravi Shankar Thammaiah

IPC: G06F11/20 ,  G06F9/455 ,  G06F16/21

CPC classification number: G06F11/2025 ,  G06F9/45558 ,  G06F11/2041 ,  G06F16/217 ,  G06F2009/4557 ,  G06F2009/45583 ,  G06F2201/815

Abstract: Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment that has a database cluster that hierarchically contains many pluggable databases in many container databases in many virtual machines, a computer identifies many alternative placements that respectively assign each pluggable database instance (PDB) to a respective container database management system (CDBMS). For each alternative placement, a respective placement score is calculated based on the PDBs and the CDBMSs. Based on the placement scores of the alternative placements, a particular placement is selected with a best placement score that indicates optimal resilience for accommodating adversity such as failover and overcrowding.

公开(公告)号：US11971869B2

公开(公告)日：2024-04-30

申请号：US17974152

申请日：2022-10-26

Applicant: Oracle International Corporation

Inventor： Jesse Kamp ,  Allison L. Holloway ,  Meichun Hsu ,  Hideaki Kimura ,  Boris Klots ,  Vasudha Krishnaswamy ,  Kartik Kulkarni ,  Teck Hua Lee ,  Yunrui Li ,  Aurosish Mishra ,  Ajit Mylavarapu ,  Sukhada Pendse ,  Garret F. Swart ,  Shasank K. Chavan ,  Tirthankar Lahiri ,  Juan R. Loaiza

IPC: G06F16/22 ,  G06F16/11 ,  G06F16/174 ,  G06F16/18 ,  G06F16/21 ,  G06F16/23 ,  G06F16/27

CPC classification number: G06F16/2255 ,  G06F16/128 ,  G06F16/1756 ,  G06F16/1865 ,  G06F16/217 ,  G06F16/2322 ,  G06F16/27

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

公开(公告)号：US20240111739A1

公开(公告)日：2024-04-04

申请号：US18534559

申请日：2023-12-08

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yiwen ZHU ,  Subramaniam Venkatraman KRISHNAN ,  Konstantinos KARANASOS ,  Carlo CURINO ,  Isha TARTE ,  Sudhir DARBHA

IPC: G06F16/21 ,  G06F11/30 ,  G06F11/34 ,  G06F16/17 ,  G06F16/182 ,  G06F16/188 ,  G06N20/00

CPC classification number: G06F16/217 ,  G06F11/3006 ,  G06F11/3433 ,  G06F16/1727 ,  G06F16/1734 ,  G06F16/182 ,  G06F16/1834 ,  G06F16/188 ,  G06N20/00

Abstract: An automated tuning service is used to automatically tune, or modify, the operational parameters of a large-scale cloud infrastructure. The tuning service performs automated and fully data/model-driven configuration based from learning various real-time performance of the cloud infrastructure. Such performance is identified through monitoring various telemetric data of the cloud infrastructure. The tuning service leverages a mix of domain knowledge and principled data-science to capture the essence of our cluster dynamic behavior in a collection of descriptive machine learning (ML) models. The ML models power automated optimization procedures for parameter tuning, and inform administrators in most tactical and strategical engineering/capacity decisions (such as hardware and datacenter design, software investments, etc.). Rich “observational” models (models collected without modifying the system) are combined with judicious use of “fighting” (testing in production), allowing the tuning service to automatically configure operational parameters of a large cloud infrastructure for a broad range of applications.

公开(公告)号：US20240103994A1

公开(公告)日：2024-03-28

申请号：US18530914

申请日：2023-12-06

Applicant: Microsoft Technology Licensing, LLC

Inventor： Zi YE ,  Justin Grant MOELLER ,  Ya LIN ,  Willis LANG

IPC: G06F11/34 ,  G06F11/30 ,  G06F16/21 ,  G06F16/27

CPC classification number: G06F11/3433 ,  G06F11/3006 ,  G06F11/3457 ,  G06F16/217 ,  G06F16/27

Abstract: Methods, systems, and computer program products are provided for creating a resource management testing environment. An initial population of databases is established in a database ring, having an in initial count of databases and different types of databases that are determined based on an initial database population model. The initial population model receives ring classification information for the database ring from a ring grouping model. A sequence of database population-change events is generated based on a model, to change the population of the databases over time in the ring. An orchestration framework performs testing of resource manager operations based on the model-defined initial population of databases and the model-defined populations of databases changed over time. Model-defined resource usage metrics for each database are utilized to test the resource manager operations. Resource usage metrics and database add/drop events of a production system are used to train the models.

公开(公告)号：US20240095223A1

公开(公告)日：2024-03-21

申请号：US18510250

申请日：2023-11-15

Applicant: Oracle International Corporation

Inventor： Debajyoti Roy

IPC: G06F16/215 ,  G06F16/21 ,  G06F16/23 ,  G06N20/00

CPC classification number: G06F16/215 ,  G06F16/217 ,  G06F16/2386 ,  G06N20/00

Abstract: Techniques are disclosed for tuning external invocations utilizing weight-based parameter resampling. In one example, a computer system determines a plurality of samples, each sample being associated with a parameter value of a plurality of potential parameter values of a particular parameter. The computer system assigns weights to each of the parameter values, and then selects a first sample for processing via a first external invocation based on a weight of the parameter value of the first sample. The computer system then determines feedback data associated with a level of performance of the first external invocation. The computer system adjusts the weights of the parameter values of the particular parameter based on the feedback data. The computer system then selects a second sample of the plurality of samples to be processed via execution of a second external invocation based on the adjustment of weights of the parameter values.