Application backup and management

Patent metadata

Field	Value
Publication number	US-11663085-B2
Application number	US-201816018000-A
Country	US
Kind code	B2
Filing date	Jun 25, 2018
Priority date	Jun 25, 2018
Publication date	May 30, 2023
Grant date	May 30, 2023

Abstract

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.

First claim

The invention claimed is:

1. A method for snapshot generation at a data management and storage (DMS) cluster, comprising:
identifying a plurality of machines within a compute infrastructure that are configured to execute an application;
receiving a request to generate a snapshot of the application;
generating data fetch jobs for the plurality of machines in response to the request, wherein the data fetch jobs are associated with the application;
determining whether the data fetch jobs are ready for execution;
delaying execution of one or more data fetch jobs based at least in part on the determining;
allocating additional resources for the one or more data fetch jobs in response to the execution of the one or more data fetch jobs being delayed; and
generating the snapshot of the application based at least in part on parallel execution of the data fetch jobs that are ready for execution at the plurality of machines, wherein:
executing the data fetch jobs comprises freezing the plurality of machines, capturing images of the plurality of machines, transferring the captured images to the DMS cluster, and releasing the plurality of machines; and
the snapshot of the application is generated based at least in part on combining the captured images at the DMS cluster, wherein combining the captured images comprises combining a full snapshot of a first machine and an incremental snapshot of a second machine different from the first machine.

2. The method of claim 1 , further comprising:
adding the data fetch jobs to a job queue accessible to peer DMS nodes in the DMS cluster; and
retrieving, by the peer DMS nodes, the data fetch jobs from the job queue for execution at the plurality of machines.

3. The method of claim 2 , further comprising:
associating one or more service level agreements with the plurality of machines, wherein the data fetch jobs are added to the job queue according to the one or more service level agreements.

4. The method of claim 2 , further comprising:
associating a start time with the data fetch jobs, wherein:
the data fetch jobs are added to the job queue according to the start time; and
determining whether the data fetch jobs are ready for execution includes determining whether the peer DMS nodes are ready to execute the data fetch jobs at the start time.

5. The method of claim 1 , wherein generating the snapshot of the application is based on combining an incremental snapshot of a machine with a full snapshot of the machine.

6. The method of claim 1 , further comprising:
allocating the data fetch jobs to peer DMS nodes in the DMS cluster based on processing and data storage resources of the peer DMS nodes.

7. The method of claim 1 , further comprising:
determining that a data fetch job failed to successfully execute at a first machine;
generating additional data fetch jobs for the first machine associated with the failed data fetch job and a second machine associated with a successful data fetch job;
executing the additional data fetch jobs to generate a full snapshot of the first machine and an incremental snapshot of the second machine;
generating the snapshot of the application by combining the full snapshot of the first machine with the incremental snapshot of the second machine; and
storing the snapshot of the application in a distributed data store.

8. The method of claim 1 , further comprising:
storing the snapshot of the application in a distributed data store in response to determining that the data fetch jobs were successfully executed at the plurality of machines.

9. The method of claim 1 , wherein the plurality of machines comprises:
a database server configured to execute the application;
a file server configured to execute the application; and
a web server configured to execute the application.

10. The method of claim 1 , wherein determining whether the data fetch jobs are ready for execution comprises determining whether one or more job engines are ready to execute the data fetch jobs.

11. The method of claim 1 , wherein determining whether the data fetch jobs are ready for execution is based on using a synchronizer to determine whether the data fetch jobs have been retrieved from a job queue.

12. A data management and storage (DMS) cluster, comprising:
at least one processor; and
memory coupled to the at least one processor, the memory storing instructions that are executable by the at least one processor to cause the DMS cluster to:
identify a plurality of machines within a compute infrastructure that are configured to execute an application;
receive a request to generate a snapshot of the application;
generate data fetch jobs for the plurality of machines in response to the request, wherein the data fetch jobs are associated with the application;
determine whether the data fetch jobs are ready for execution;
delay execution of one or more data fetch jobs based at least in part on the determining;
allocate additional resources for the one or more data fetch jobs in response to the execution of the one or more data fetch jobs being delayed; and
generate the snapshot of the application based at least in part on parallel execution of the data fetch jobs that are ready for execution at the plurality of machines, wherein:
the instructions to execute the data fetch jobs are executable by the at least one processor to cause the DMS cluster to freeze the plurality of machines, capture images of the plurality of machines, transfer the captured images to the DMS cluster, and release the plurality of machines; and
the instructions to generate the snapshot are executable by the at least one processor to cause the DMS cluster to generate the snapshot of the application based at least in part on combining the captured images at the DMS cluster, wherein the instructions to combine the captured images at the DMS cluster are executable by the at least one processor to cause the DMS to combine a full snapshot of a first machine and an incremental snapshot of a second machine different from the first machine.

13. The DMS cluster of claim 12 , wherein the instructions are further executable by the at least one processor to cause the DMS cluster to:
add the data fetch jobs to a job queue accessible to peer DMS nodes in the DMS cluster; and
retrieve, by the peer DMS nodes, the data fetch jobs from the job queue for execution at the plurality of machines.

14. The DMS cluster of claim 13 , wherein the instructions are further executable by the at least one processor to cause the DMS cluster to:
associate a start time with the data fetch jobs, wherein:
the data fetch jobs are added to the job queue according to the start time; and
the instructions to determine whether the data fetch jobs are ready for execution are executable by the at least one processor to cause the DMS cluster to determine whether the peer DMS nodes are ready to execute the data fetch jobs at the start time.

15. The DMS cluster of claim 12 , wherein the instructions to generate the snapshot of the application are executable by the at least one processor to cause the DMS cluster to combine an incremental snapshot of a machine with a full snapshot of the machine.

16. The DMS cluster of claim 12 , wherein the instructions are further executable by the at least one processor to cause the DMS cluster to:
allocate the data fetch jobs to peer DMS nodes in the DMS cluster based on processing and data storage resources of the peer DMS nodes.

17. The DMS cluster of claim 12 , wherein the instructions are further executable by the at least

Assignees

• Rubrik Inc

Inventors

• Wang Zhicong
• Meadowcroft Benjamin
• Palit Biswaroop
• Chakraborty Atanu
• Vohra Hardik
• Mitra Abhay
• Goyal Saurabh
• Srivastava Sanjari
• Agarwal Swapnil
• Shah Rahil
• Malpani Mudit
• Singh Janmejay
• Bhave Ajay Arvind
• Pandey Prateek

Classifications

• G06F11/1461 Primary

  Backup scheduling policy · CPC title

• G06F9/4881

  Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues · CPC title

• G06F2201/815

  Virtual · CPC title

• G06F11/2041

  with more than one idle spare processing component · CPC title

• G06F2201/84

  Using snapshots, i.e. a logical point-in-time copy of the data · CPC title