Rubrik, Inc.

Vijay Karthik

Abdullah Reza

Abhay Mitra

Vivek Sanjay Jain

Kushaagra Goyal

Arohi Kumar

Avishek Ganguli

Christopher L Wong

Bristy Sikder

Siddharth Bidasaria

Akhilesh Krishnan

Anshul Mohan Gupta

Shubham Tagra

Kevin Mu

Stephen Charles O'Hara-Smith

Akshay Agrawal

Aman Bansal

Samyak Jain

Dhawal Upadhyay

Satwant Rana

Christopher Wong

Sandeep Majji

Vaibhav Gosain

Nitin Rathor

A storage system may store one or more snapshots of a computing system to support backup and restoration of data stored at the computing system. The storage system may identify an expiration of a first snapshot indicating a first set of physical storage locations to which first data of the computing system was stored as part of a first backup procedure. The storage system may identify a first subset physical storage locations of the first set as storing a first portion of the first data that is superseded by second data associated with a second snapshot. Based on identifying the first subset, the storage system may delete the first portion of the first data from the first subset of physical storage locations and retain a second portion of the first data at a second subset of physical storage locations.

Backup data consolidation

A method for recovering files from a filesystem stored across sparse files in a cloud environment is described. According to the method, a data management system may receive a request to read the files. The data management system may identify one or more target address ranges corresponding to the files indicated via the request. The data management system may read index information for the sparse files in the cloud environment. The index information may indicate respective address ranges for data blocks within the sparse files. The data management system may identify one or more data blocks within one or more sparse files as corresponding to address ranges that overlap with the one or more target address ranges based on the index information. The data management system may transmit, to the cloud environment, one or more read requests for the identified one or more data blocks.

Multi-phase file recovery from cloud environments

Methods, systems, and devices for data management are described. Data management clusters may be configured to store metadata associated with providing data protection for a set of computing objects. A data management cluster may be triggered to export respective metadata to a cloud environment, where the respective metadata may be associated with providing data protection for one or more of the computing objects. Based on the export operation being triggered, the respective metadata may be downloaded from the cloud environment and written to a database that is configured to store a version of the metadata associated with providing the data protection for the set of computing objects. An interface for managing the operation of the data management clusters may be provided, where a content of the interface may reflect the version of the metadata stored at the database.

Incremental synchronization of metadata

A computer-implemented method at a data management system comprises: receiving, at a storage appliance from a server hosting a virtual machine, a write made to the virtual machine; computing, at the storage appliance, a fingerprint of the transmitted write; comparing, at the storage appliance, the computed fingerprint to malware fingerprints in a malware catalog; repeating the computing and comparing; and disabling the virtual machine if a number of matches from the comparing breaches a predetermined threshold over a predetermined amount of time.

Malware protection for virtual machines

Methods, systems, and devices for data management are described. A data enrichment service supported by a data management system (DMS) may receive, from a first application in a destination computing environment of the DMS, a set of enrichment definitions for metadata synchronization between the first application and a second application in a source computing environment of the DMS. A change data capture (CDC) service supported by the DMS may generate a set of data records that correspond to metadata changes associated with the second application. The data enrichment service may transform the set of data records by using data enrichment components to modify the set of data records according to the set of enrichment definitions provided by the first application. The data enrichment components may be dynamically partitioned into groups that execute in parallel. The second application may push the enriched data records to the first application in real-time.

Techniques for source-side metadata enrichment

Techniques for data management are described. A monotonically increasing time source that indicates an elapsed time since a reference event may be activated. Multiple snapshots of a computing object may be generated in accordance with a schedule for backing up the computing object, where the schedule may include a retention duration for retaining snapshots. Based on generating the snapshots, timestamps for the snapshots may be stored, where the timestamps may indicate respective values of the monotonically increasing time source. As part of an expiration job, a reference value of the monotonically increasing time source may be identified based on the retention duration and a current value indicated by the monotonically increasing time source. Also, a snapshot of the snapshots may be expired based on a timestamp of the snapshot corresponding to a value of the monotonically increasing time source that is less than the reference value.

Time variation protection for snapshots

A method of consolidating snapshots includes receiving a request to consolidate a first snapshot with a second snapshot into a third snapshot, the first and second snapshots stored in separate backup files, each backup file organized as a directory where data parts of the first and second snapshots can be hard linked to locations outside of the backup file, comparing the data parts of the first and second snapshots to determine if any second snapshot data part fully overlaps with any first snapshot data part, responsive to determining that a second snapshot data part fully overlaps with a first snapshot data part, hard linking the determined second snapshot data part into the third snapshot, and storing the third snapshot in the backup file.

Consolidating snapshots using partitioned patch files

In some examples, a data management system processes snapshots of a distributed file system, the distributed file system having files, each file comprising multiple data chunks. The data management system performs operations including storing file-to-chunk mapping in file system metadata; creating, for each chunk, a chunk generation ID by associating each chunk with a file system generation ID; in a next-generation snapshot of the distributed file system, incrementing, for all chunks in the next-generation snapshot, the respective chunk generation IDs; and taking a snapshot of the file system metadata and storing an updated file-to-chunk mapping associated with the next-generation snapshot.

Snapshot and restoration of distributed file system

In some examples, a method comprises: receiving a request to read data within a specified range from a backup file storing at least one base snapshot and at least one incremental snapshot; looking up the specified range in range filters from the backup file, the range filters corresponding to snapshots stored in the backup file and each range filter comprising bits indicating whether data exists at respective ranges within the snapshot corresponding to the respective range filter; and in response to the looking up, reading the requested data from the looked-up range in the backup file.

Snapshot range filters

In some examples, a data management and storage (DMS) platform, comprises peer DMS nodes in a node cluster, a distributed data store comprising local and cloud storage, and at least one processor configured to perform operations in a method of creating a local consolidated patch file from a patch file chain stored in the cloud storage. The operations include, in a first dry-run phase, creating a logical patch file image of data blocks in one or more cloud patch files stored in the cloud storage; in a second data-transfer phase, downloading at least some of the data blocks from the cloud patch files identified by the logical patch file image, the second data-transfer phase comprising a coalescing operation to construct a set of coalesced reads of the data blocks; and creating and storing, in the local storage, the local consolidated patch file using the downloaded data blocks.

Smart coalescing in data management systems

Methods, systems, and devices for data management are described. A first application in a destination computing environment of a data management system (DMS) may determine that a bulk-push criterion is satisfied for a second application in a source computing environment of the DMS. The first application may transmit, to an asynchronous metadata service, a request indicating the second application for which the bulk-push criterion is satisfied. The request may be configured to cause the asynchronous metadata service to query a database in the source computing environment, identify a latest version of one or more rows that include metadata associated with the second application, and generate data records indicating the latest version of the one or more rows that include the metadata associated with the second application. The first application may receive the data records via an asynchronous data stream between the first application and the second application.

Techniques for asynchronously pushing metadata in bulk

In some examples, a data management and storage (DMS) platform comprises peer DMS nodes in a node cluster, a distributed data store comprising local and cloud storage, and at least one processor configured to perform operations in a method of creating a local consolidated patch file from a patch file chain stored in the cloud storage. Example operations comprise, in a first dry-run phase, creating a patch file image of data blocks in one or more cloud patch files stored in the cloud storage; in a second data-transfer phase, downloading at least some of the data blocks from the cloud patch files identified by the patch file image; and creating and storing, in the local storage, the local consolidated patch file using the downloaded data blocks.

Two-phase snapshot recovery

Methods, systems, and devices for data management are described. A cloud data backup and recovery system may include a destination system and a source system. The destination system may be operable to provide backup and recovery services for the source system. The destination system may communicate a set of push/pull notifications with the source system. Each push/pull notifications may indicate an event is being performed in the source system. The destination system may determine an event is being performed in the source system based on transmitting a push notification or receiving a pull notification, where the push or pull notification may include metadata associated with the event. The destination system may retrieve, from the source system, data that is modified by the event based on the metadata. The destination system may synchronize a database of the destination system based on the data retrieved from the source system.

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Santa Clara, CA, United States of America

Vijay Karthik