Rubrik, Inc.

Zhicong Wang

Mudit Malpani

Benjamin Travis Meadowcroft

Hardik Vohra

Biswaroop Palit

Prateek Pandey

Saurabh Goyal

Janmejay Singh

Ajay Arvind Bhave

Atanu Chakraborty

Sanjari Srivastava

Swapnil Agarwal

Rahil Shah

Abhay Mitra

Pranava Kumar Adduri

Abhinav Adduri

Looi Chow Lee

Karthikeyan Attaiyempatty Srinivasan

Andrew Kwangyum Park, Jr

A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Application migration between environments

A data management and storage (DMS) cluster of peer DMS nodes manages data of an application distributed across a set of machines of a compute infrastructure. A DMS node associates a set of machines with the application, and generates data fetch jobs for the set of machines for execution by multiple peer DMS nodes. The DMS node determining whether each of the data fetch jobs for the set of machines is ready for execution by the peer DMS nodes. In response to determining that each of the data fetch jobs is ready for execution, the peer DMS nodes execute the data fetch jobs to generate snapshots of the set of machines. The snapshots may be full or incremental snapshots, and collectively form a snapshot of the application.

Application backup and management

A data management and storage (DMS) cluster of peer DMS nodes manages data of a compute infrastructure by generating snapshots of partitions of a fileset of the compute infrastructure and providing a passthrough for storing the snapshots in a data storage separate from the DMS cluster, such as a cloud computing system. In one approach, the DMS nodes determine partitions of a fileset using fileset metadata, generates snapshots of the partitions, and stores the snapshots in the data storage. Each DMS node may include a local storage which is used to facilitate creation of a snapshot of a partition. The snapshot may be removed from the local storage after being sent to the data storage. Rather than storing the snapshot, the DMS cluster stores fileset metadata that is referenced to retrieve the snapshot from the data storage. The snapshot is deployed to retrieve a file stored in the partition.

Fileset passthrough using data management and storage node

An example method of managing and storing data includes pulling and saving a first snapshot of a first state of a target machine running on a cloud service and creating a first virtual machine (VM) package associated with the first snapshot. The first VM package sufficient to instantiate a VM emulating the target machine with the first state on an on-premise destination VM platform. The method includes pulling and saving a second snapshot of a second state of the target machine, wherein the second state occurs after the first state. A second VM package is created and associated with the second snapshot, the second VM package sufficient to instantiate the VM emulating the target machine with the second state on the destination VM platform. The second VM package is created by updating the first VM package according to differences between the first snapshot and the second snapshot.

Incremental replication of data backup copies

In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

Automated review of source code for style issues

A data management and storage (DMS) cluster of peer DMS nodes manages data backup and disaster recovery using a service level agreement. The backup data can be replicated from a primary compute infrastructure to the secondary compute infrastructure. For example, primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. If the primary compute infrastructure is lost, the secondary compute infrastructure is initialized to replicate the primary compute infrastructure. Applications that were running in the primary compute infrastructure can failover to run in the secondary compute infrastructure. The replicated backup data is used to facilitate the migration. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines.

Data backup and disaster recovery between environments

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San Francisco, CA, United States of America

Zhicong Wang