Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The method disclosed includes scanning data items stored in the first plurality of nodes of a first cluster. While scanning, creating a partition group index indexing the data items into a plurality of partition groups. Each partition group corresponds to a node of the first plurality of nodes and comprises a subset of data items stored in the node. Storing the index. Instantiating a second cluster, comprising generating per node data, for each node of a second plurality of nodes, based on mappings between the partition groups and the first plurality of nodes. Identifying the data items included in the partition groups according to the partition group index and loading the data items included in the partition groups onto the second plurality of nodes.

Abstract: The method disclosed is for instantiating a second cluster based on a first cluster. For at least one node of a second plurality of nodes, generating per node data based on mappings between a plurality of partition groups and a first plurality of nodes, the first plurality of nodes corresponding to the first cluster. The method further discloses identifying data items included in the plurality of partition groups based on the mappings between the plurality of partition groups and the first plurality of nodes. The method further discloses each partition group corresponding to a node of the first plurality of nodes and comprising a subset of data items stored in the node. The method further discloses loading the data items included in the plurality of partition groups onto the second plurality of nodes, the second plurality of nodes corresponding to the second cluster.

Abstract: A method of restoring version data stored across two or more cloud environments is provided. An example method includes accessing, in a second cloud environment, first metadata describing a first data version, the first data version including first data items and first metadata, wherein at least the first data items are stored in a first cloud environment and the first metadata is stored in a third cloud environment. In response to an instruction received in the second cloud environment, the first data items are restored to the second cloud environment using the first metadata.

Abstract: An example networked computing system comprises a node cluster; a database; at least one processor configured by instructions to perform operations in a method of node level recovery, the method comprising operations including at least: identifying a failed node among existing nodes in the node cluster; identifying and initiating a replacement node as a new node for the node cluster; accessing at the database a logical backup of the node cluster; retrieving logical backup data of the node cluster from the logical backup and applying a node level filter to identify rows of backup data associated with the failed node; and restoring the data rows identified by the node level filter to the new node.

Abstract: 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.

Abstract: 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.

Abstract: In some examples, a data management and storage (DMS) cluster comprises peer DMS nodes and a distributed data store implemented across the peer DMS nodes. The peer DMS nodes are configured to collectively implement operations in a method of remote linking of snappables for data transfer between a first node cluster and a second node cluster among the peer DMS nodes.

Abstract: A streaming distributed decentralized database task system can perform multiple tasks on clusters of nodes without overloading the clusters' computational resources, such as disk, memory, processors, and network bandwidth. A cluster master can manage a job and add items to node queues. A node manager accepts or rejects queue items based on streaming task limits that are applied at the node level.

Abstract: A method for multi-layer quality of service (QoS) management in a distributed computing environment includes receiving a workload and identifying a workload QoS class for the workload. The workload is scheduled to be run compute nodes. The workload QoS class is translated to a plurality of different resource layer QoS classes, and the plurality of QoS classes are communicated to a workload execution manager of the compute nodes. The workload execution manager extends the plurality of different resource layer QoS classes to respective resource layers involved in the running of the workload.

Abstract: A system for multi-layer quality of service (QoS) management in a distributed computing environment includes a management node hosting a workload scheduler configured to receive a workload and identify a workload QoS class for the workload. The workload scheduler schedules running of the workload on compute nodes, translate the workload QoS class to a plurality of different resource layer QoS classes, and communicates the plurality of QoS classes to a workload execution manager of the compute nodes. The workload execution manager is configured to extend the plurality of different resource layer QoS classes to respective resource layers involved in the running of the workload.

Abstract: A streaming distributed decentralized database task system can perform multiple tasks of parallel jobs on clusters of nodes without overloading the clusters' computational resources, such as disk, memory, processors, and network bandwidth. A cluster master can manage a job and add items to node queues. A node manager accepts or rejects queue items based on streaming task limits that are applied at the node level.

Abstract: The method disclosed includes scanning data items stored in the first plurality of nodes of a first cluster. While scanning, creating a partition group index indexing the data items into a plurality of partition groups. Each partition group corresponds to a node of the first plurality of nodes and comprises a subset of data items stored in the node. Storing the index. Instantiating a second cluster, comprising generating per node data, for each node of a second plurality of nodes, based on mappings between the partition groups and the first plurality of nodes. Identifying the data items included in the partition groups according to the partition group index and loading the data items included in the partition groups onto the second plurality of nodes.

Abstract: The method disclosed is for instantiating a second cluster based on a first cluster. For at least one node of a second plurality of nodes, generating per node data based on mappings between a plurality of partition groups and a first plurality of nodes, the first plurality of nodes corresponding to the first cluster. The method further discloses identifying data items included in the plurality of partition groups based on the mappings between the plurality of partition groups and the first plurality of nodes. The method further discloses each partition group corresponding to a node of the first plurality of nodes and comprising a subset of data items stored in the node. The method further discloses loading the data items included in the plurality of partition groups onto the second plurality of nodes, the second plurality of nodes corresponding to the second cluster.

Abstract: An example networked computing system comprises a node cluster; a database; at least one processor configured by instructions to perform operations in a method of node level recovery, the method comprising operations including at least: identifying a failed node among existing nodes in the node cluster; identifying and initiating a replacement node as a new node for the node cluster; accessing at the database a logical backup of the node cluster; retrieving logical backup data of the node cluster from the logical backup and applying a node level filter to identify rows of backup data associated with the failed node; and restoring the data rows identified by the node level filter to the new node.

Abstract: An example networked computing system for filtered node level recovery comprises a node cluster; a database; and at least one processor to perform operations comprising at least: identifying a failed node among existing nodes in the node cluster; identifying and initiating a replacement node as a new node for the node cluster; accessing at the database a logical backup of the node cluster; retrieving logical backup data of the node cluster and applying a node-level filter to identify specific rows of backup data to be restored to the new node; restoring the specific data rows to the new node; identifying new data written by applications, to the existing nodes of the node cluster, during restoration of the new node; accessing supplementary back up data and applying the node-level filter to identify supplementary data rows to be restored to the new node; and restoring the supplementary data rows to the new node.

Abstract: An example networked computing system for iterative node level recovery comprises a node cluster; a database; at least one processor configured by instructions to perform operations comprising at least: identifying a failed node among existing nodes in the node cluster; identifying and initiating a replacement node as a new node for the node cluster; accessing at the database a logical backup of the node cluster; retrieving logical backup data of the node cluster and identifying specific rows of backup data to be restored to the new node; restoring the specific data rows to the new node; identifying new data written by applications, to the existing nodes of the node cluster, during restoration of the new node; iteratively accessing supplementary back up data to identify supplementary data rows to be restored to the new node; and iteratively restoring the supplementary data rows to the new node until the new node is synchronized with the existing nodes in the node cluster.

Abstract: 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.