Rubrik, Inc.

International Business Machines Corporation

Seagate Technology Incorporated

Tarun Thakur

Prasenjit Sarkar

Ajaykrishna Raghavan

Pin Zhou

James Lee Hafner

David Darden Chambliss

Randy L Roberson

Clark Edward Lubbers

Kun Liu

Tyrone W A Grandison

Evimaria Terzi

Karan Gupta

Varun Bhagwan

Maohua Lu

Prashant Pandey

Neville Carvalho

William Cody

Bryan J Hickerson

Justus Joseph Pendleton

Christan E Grant

Embodiments disclosed herein provide systems, methods, and computer readable media for container based application reification. In a particular embodiment, an application reification system is provided including one or more computer readable storage media and a processing system operatively coupled with the one or more computer readable storage media. The application reification system further includes program instructions stored on the one or more computer readable storage media that, when read and executed by the processing system, direct the processing system to preserve a version of application data at a first time and a configuration of an application at the first time. At a second time subsequent to the first time, the program instructions direct the processing system to create a template for a container containing the application in the configuration and a pointer to the version of the application data in a secondary storage repository.

Container based application reification

Embodiments disclosed herein provide systems, methods, and computer readable media for data lineage based multi-data store recovery. In a particular embodiment, a method provides identifying first data in a first table of a plurality of tables stored in a plurality of data stores and restoring the first data to a first correct version of the first data in a prior version of the first table. The method further provides identifying a second table of the plurality of tables that descends from the first table and includes second descendent data that stems from the first data. The method also provides restoring the second descendent data to a second correct version of the second descendent data in a prior version of the second table.

Data lineage based multi-data store recovery

Embodiments disclosed herein provide systems, methods, and computer readable media for infinite versioning by automatic coalescing. In a particular embodiment, a method provides determining an age range for a plurality of data versions stored in a secondary data repository and identifying first data versions of the plurality of data versions that are within the age range. The method further provides determining a compaction ratio for the first data versions and compacting the first data versions based on the compaction ratio.

Infinite versioning by automatic coalescing

An example networked computing system for iterative node level recovery comprises a node cluster; a database; at least one processor configured by instructions to perform operations comprising at least: identifying a failed node among existing nodes in the node cluster; identifying and initiating a replacement node as a new node for the node cluster; accessing at the database a logical backup of the node cluster; retrieving logical backup data of the node cluster and identifying specific rows of backup data to be restored to the new node; restoring the specific data rows to the new node; identifying new data written by applications, to the existing nodes of the node cluster, during restoration of the new node; iteratively accessing supplementary back up data to identify supplementary data rows to be restored to the new node; and iteratively restoring the supplementary data rows to the new node until the new node is synchronized with the existing nodes in the node cluster.

Node level recovery for clustered databases

An example networked computing system comprises a node cluster; a database; at least one processor configured by instructions to perform operations in a method of node level recovery, the method comprising operations including at least: identifying a failed node among existing nodes in the node cluster; identifying and initiating a replacement node as a new node for the node cluster; accessing at the database a logical backup of the node cluster; retrieving logical backup data of the node cluster from the logical backup and applying a node level filter to identify rows of backup data associated with the failed node; and restoring the data rows identified by the node level filter to the new node.

Embodiments disclosed herein provide systems, methods, and machine-readable media for container-based application reification. In an example embodiment, an application reification system preserves a version of application data at a first time and a configuration of an application at the first time. At a second time, the application reification system creates a template for a container containing the application in the configuration and a pointer to the version of the application data in a secondary storage repository.

Embodiments disclosed herein provide systems, methods, and computer readable media for a converged mechanism for protecting data. In a particular embodiment, a method provides identifying a level of importance for a plurality of data items and tracking changes to the plurality of data items. The method further provides aggregating the changes based on the level of importance and logging the aggregated changes.

Converged mechanism for protecting data

Embodiments disclosed herein provide systems, methods, and computer readable media for recovering data across relational and non-relational databases. In a particular embodiment, a method includes identifying first data having a first version time in a first version store storing data versions from a relational database. Based on the first version time, the method provides identifying second data, associated with the first data, in a second version store storing data versions from a non-relational database. The method further provides recovering the first data to the relational database and the second data to the non-relational database.

Growing community of inventors

Fremont, CA, United States of America

Tarun Thakur