Rubrik, Inc.

Prasenjit Sarkar

Pin Zhou

Tarun Thakur

Ajaykrishna Raghavan

Maohua Lu

Rohit Shekhar

Hyo Jun Kim

Venkatesh Kempapura Sharma

Mudit Malpani

Rohit Agrawal

Udbhav Prasad

Deepti Kochar

Neville Carvalho

Hyojun Kim

Rajath Subramanyam

Srilekha Gudipati

Kaustubh Raval

Methods, systems, and devices for data management are described. The method may include obtaining first subsamples of a data table, where the first subsamples include information from a first quantity of columns of the data table. The method may also include processing the first subsamples of the data table to identify whether the information included in the first subsamples includes a target type of information. The method may include obtaining a second subsample of the data table, where the second subsample includes information from a subset of columns of the first quantity of columns. The method may include processing the second subsample of the data table to identify whether information included in the subset comprises the target type of information and identifying, based at least in part on processing the one or more first subsamples and the second subsample, one or more locations of the target type of information within the data table.

Detection of target data in databases

Described herein is a system that restores a database by processing a portion of the database. The system restores the database to a previous state at a particular time by reverting data entries that have changed since the time to their initial values before the change. Data entries that have changed after the restore time are identified. For the data entries that have changed after the restore time, their initial values before the change are determined from various sources. The system determines a database version that is created most recently before the restore time. The system additionally identifies changes to the database between the restore time and when the database version is created. The initial values can be determined from either the database version or the changes made to the database between the restore time and when the database version is created.

Partial database restoration

In some examples, a method of performing a backup of a group of relational databases comprises identifying database files to be fetched in the group of relational databases; grouping the identified database files into batches; based on configuration parameters of the identified database files, identifying, among the batches, a sub-set of batches of database files that are eligible to be fetched in parallel for the backup; configuring a single fetch call to a call stack to fetch the sub-set of eligible batches; and determining a push or pull model for the configured single fetch call based at least in part on feedback from a most resource-constrained element in the call stack.

Server group fetch in database backup

In some examples, a cluster comprises peer nodes and a distributed data store implemented across the peer nodes, a method of remote linking of data objects for data transfer between a first node cluster and a second node cluster among the peer nodes, the method comprising: creating a data object group including multiple remote data objects, wherein a plurality of remote data objects in the data object group represent a same first virtual machine and are registrable on at least the first and second node clusters of the peer DMS nodes; creating or identifying remote links to a plurality of the remote data objects in the data object group; designating a member of the data object group as an active member of the group; and assigning a task to the active member to be completed using remote links.

Remote snappable linking

In some examples, performing a backup of a group of relational databases comprises identifying the group of relational databases; identifying I/O traffic flowing into or out of the group of relational databases; temporarily freezing the I/O traffic flowing into or out of the group of relational databases; identifying a set of batch backup operations to be performed during the temporary I/O freeze; identifying relational dependencies between at least some databases in the group of relational databases; based on the identified relational dependencies, identifying, among the set of batch backup operations, a first sub-set of backup operations eligible to be performed in parallel, and a second sub-set of backup operations not eligible to be performed in parallel; conducting the backup, wherein conducting the backup comprises performing the identified first sub-set of backup operations in parallel and performing the identified second sub-set of backup operations sequentially; and unfreezing the I/O traffic.

Scalable group backup in relational databases

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 sub-cluster recovery in a data storage environment having a plurality of storage nodes. In a particular embodiment, the method provides scanning data items in the plurality of nodes. While scanning, the method further provides indexing the data items into an index of a plurality of partition groups. Each partition group includes data items owned by a particular one of the plurality of storage nodes. The method then provides storing the index.

Sub-cluster recovery using a partition group index

Techniques for data management are described. A request to mount a version of a database based on data stored at a computing system may be received. Based on receiving the request, a container at the computing that is configured to support operating the version of the database may be configured. Based on creating the container, a database request for the version of the database may be received at the container. In response to the database request, the container may access a set of data stored at the computing system, the set of data being associated with the version of the database. The container may output a response to the database request based on accessing the set of data.

Database live mounts using containers

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

The technology disclosed herein enables the restoration of a database version across cloud environments. In a particular embodiment, a method provides receiving, in a second cloud environment from a first cloud environment, first metadata describing a first data version stored in the first cloud environment. The first data version includes first data items and the first metadata. After receiving the first metadata, the method provides receiving, in the second cloud environment, an instruction to restore the first data items to the second cloud environment. In response to the instruction, the method provides restoring the first data items to the second cloud environment using the first metadata.

Database restoration across cloud environments

In some examples, a duster comprises peer nodes and a distributed data store implemented across the peer nodes, a method of remote linking of data objects for data transfer between a first node cluster and a second node cluster among the peer nodes; the method comprising: creating a data object group including multiple remote data objects, wherein a plurality of remote data objects in the data object group represent a same first virtual machine and are registrable on at least the first and second node clusters of the peer DMS nodes; creating or identifying remote links to a plurality of the remote data objects in the data object group; designating a member of the data object group as an active member of the group; and assigning a task to the active member to be completed using remote links.

In some examples, a method of performing a backup of a group of relational databases comprises identifying the group of relational databases; identifying I/O traffic flowing into or out of the group of relational databases; temporarily freezing the I/O traffic flowing into or out of the group of relational databases; identifying a set of batch backup operations to be performed during the temporary I/O freeze; identifying relational dependencies between at least some databases in the group of relational databases; based on the identified relational dependencies, identifying, among the set of batch backup operations, a first sub-set of backup operations eligible to be performed in parallel, and a second sub-set of backup operations not eligible to be performed in parallel; conducting the backup, wherein conducting the backup comprises performing the identified first sub-set of backup operations in parallel and performing the identified second sub-set of backup operations sequentially; and unfreezing the I/O traffic.

Growing community of inventors

Los Gatos, CA, United States of America

Prasenjit Sarkar