Abstract: In one example, a method includes performing various operations at a backup server. These operations may include receiving, from a node, a request for restoration of a cumulative backup that includes a plurality of partial backups that have been merged together, where the plurality of partial backups includes data from a group of one or more nodes, and the group includes the node. The method further includes checking the cumulative backup to determine whether or not the cumulative backup has been finalized, accessing the cumulative backup when it has been determined that the cumulative backup has been finalized, and restoring the cumulative backup to a target node.

Abstract: One embodiment is related to a method for generating a searchable metadata table based on a backup data stream from a client to a backup system, comprising: generating a plurality of intermediate tables; and generating a searchable metadata table based on the plurality of intermediate tables, wherein the searchable metadata table may comprise, for each file or directory in a row, an inode number, a metadata hash value, an acl hash value, a name, a full path, a size, and a time of last modification.

Abstract: A method and system for implementing agentless virtual disk metadata indexing. Specifically, the disclosed method and system entails the parsing of data within a virtual machine and/or virtual disk snapshot to extract filesystem metadata information during data backup operations. Extraction of the filesystem metadata information is performed based on an understanding of a filesystem layout for a format in which the snapshot is defined, rather than based on the mounting and traversing of the snapshot. Furthermore, the extracted filesystem metadata information is encoded in a schema that may facilitate indexing of the information for use in a metadata searching and/or retrieval service.

Abstract: One embodiment is related to a method for performing garbage collection at a backup system, comprising: determining an object that is present in a preceding backup but is no longer present in a present backup when or after the present backup is made; associating an indicator indicative of the preceding backup with the object in response to the determination that the object is no longer present in the present backup; determining whether the object is to be deleted from the backup system based on the indicator and a policy during garbage collection; and deleting the object in response to determining that the object is to be deleted.

Abstract: Embodiments for a method of implementing dynamic subgraphs in a graph database by defining vertices of a subgraph descriptively in such a way that the original intent of the content is preserved while the subgraph itself is changing. A database process preserves a “view” of the database as a stored query which will define a subgraph members while the subgraph members are modified, and as opposed to a method in which edges are induced from their vertices to be included in the subgraph, by defining the content of a subgraph descriptively using a database view, vertices which are new and should be contained in the subgraph will automatically be included.

Abstract: Combining a Merkle tree with a graph database by defining a Merkle tree with each node having a hashed value of the metadata for the node and any children of that node, associating non-hashed data with the hashed data for each node, wherein the non-hashed data has an up-pointer from a child node to any of its immediate parent node, and defining bi-directional edges between the nodes of the Merkle tree having a graph database structure to create a reference for the up-pointers associated with each Merkle tree node. The bi-directional edges with up-pointers along with the Merkle tree hash scheme allows efficient traversal of the tree where the hash values indicate non-changed nodes to prevent traversing the database, and further allows efficient path definitions by allowing database processes to walk up edges.

Abstract: In one example, a method includes performing various operations at a backup server. These operations may include receiving, from a node, a request for restoration of a cumulative backup that includes a plurality of partial backups that have been merged together, where the plurality of partial backups includes data from a group of one or more nodes, and the group includes the node. The method further includes checking the cumulative backup to determine whether or not the cumulative backup has been finalized, accessing the cumulative backup when it has been determined that the cumulative backup has been finalized, and restoring the cumulative backup to a target node.

Abstract: In one example, a method for method for backing up data begins when a backup notice is received from a node. In response to the notice, a slot is reserved in an accounting system for storage of a cumulative backup. One or more partial backups are then received and merged with any data present in the slot so as to create or modify a cumulative backup in the slot, where the cumulative backup includes the one or more partial backups and any other data in the slot. Upon receipt of an indication that the cumulative backup is final, the cumulative backup is recorded, and access to the cumulative backup enabled.

Abstract: Views of files in an archival data storage system are generated by a backup view generator. A storage application generates and stores archival data in an archive system, the archival data corresponding to client data stored on a server or in memory associated with one or more client nodes. The storage application also generates backup files of the archival data which may be stored in a local memory. A set of metadata attributes are associated with each of the backup files. The backup views are generated by comparing metadata values in a view definition file to the sets of attributes associated with the backup files. Generated backup views can be exported for processing, including searching the backup views or displaying the backup views in a user interface.

Abstract: Techniques for deduplicating a backup stream with ACL data embedded therein are described. According to one embodiment, a storage system receives a backup stream from a client, where the backup stream includes multiple data streams to be stored in the storage system. Each data stream represents a file or a directory of one or more files of a file system associated with the client. The storage system is to scan the backup stream to recognize an access control list (ACL) marker associated with at least one of the data streams, where the ACL marker identifies ACL data representing an ACL of the at least one data stream. The storage system is to chunk using a predetermined chunking algorithm the ACL data into a plurality of ACL data chunks to be stored in the storage system in a deduplicated manner.

Abstract: This disclosure is describes a system and method for organizing and storing backup data by inode number. Data objects on a file system may be streamed to a backup client that identifies the inode numbers for each streamed data object before streaming the objects to storage. The inode numbers are parsed to create one or more inode directories that can be browsed during a recovery process. In this fashion, the file system can be quickly backed up without requiring the backup client to determine the file system's directory hierarchy.

Abstract: A priority restore agent in a data storage system generates a priority restore data set for a client computer system or device by identifying a set of active data sets and/or a set of key data sets within client system data generated by the client computer system. The priority restore agent looks at or processes file system attributes for the client system data and compares these attributes with predefined restore parameters. The restore parameters may indicate that any file that has been accessed, modified, or created within a particular period of time be included in the priority restore data set. The key data sets may be identified in a set of automated restore rules. A data protection application within the data storage system can restore data in the priority restore data set onto the client computer system after a disaster or system crash.

Abstract: New full backups are generated by combining an incremental backup with a previous full backup. A previous full backup is stored in a backup server in a hash file system format. A file server generates an incremental backup of a data set on the file server by identifying and dumping files/directories of the data set that are new/modified into a tar file that is sent to an accelerator. The accelerator parses the incremental backup tar file and converts it to a hash file system format that includes metadata, hash values, and new/modified data atomics. The accelerator merges the incremental backup into the previous full backup to generate a new full backup by altering metadata and hash values of a copy of the previous full backup such that the resulting metadata and hash values describe and point to new/modified directories and files as well as unmodified directories and files.

Abstract: A priority restore agent in a data storage system generates a priority restore data set for a client computer system or device by identifying a set of active data sets and/or a set of key data sets within client system data generated by the client computer system. The priority restore agent looks at or processes file system attributes for the client system data and compares these attributes with predefined restore parameters. The restore parameters may indicate that any file that has been accessed, modified, or created within a particular period of time be included in the priority restore data set. The key data sets may be identified in a set of automated restore rules. A data protection application within the data storage system can restore data in the priority restore data set onto the client computer system after a disaster or system crash.