Abstract: Application service configuration of a timeframe for performing dataloss failover (failover that does not attempt full data replication to the secondary data store) from a primary data store to the secondary data store. A data-tier service, such as perhaps a database as a service (or DBaaS), could receive that configuration from the application service and automatically perform the dataloss failover as configured by the application service. This relieves the application service from having to manage the failover workflow while still allowing the application service to appropriately balance the timing of dataloss failover, which will depend on a very application-specific optimal balance between the negative effects of operational latency versus dataloss.

Abstract: Application service configuration of a timeframe for performing dataloss failover (failover that does not attempt full data replication to the secondary data store) from a primary data store to the secondary data store. A data-tier service, such as perhaps a database as a service (or DBaaS), could receive that configuration from the application service and automatically perform the any dataloss failover as configured by the application service. This relieves the application service from having to manage the failover workflow while still allowing the application service to appropriately balance the timing of dataloss failover, which will depend on a very application-specific optimal balance between the negative effects of operational latency versus dataloss.

Abstract: A Distributed Availability Group (DAG) spans two AGs, each spanning one or more replica nodes and functioning as primary or secondary AG. A primary AG is replicated to the secondary AG synchronously or asynchronously. A failover in the DAG results in the AGs swapping their roles. Multiple DAGs can be linked together as a chain, which provides many useful features including disaster recovery across geographical regions, massive read scale (numerous readable secondary nodes), online migration of databases (across different operating systems and computing environments). The systems using DAGs can replicate databases across multiple independent high availability (HA) failover clusters using complex replication topologies and allow for manual failover and failback. The systems allow chaining of multiple AGs to provision a treelike structure of replicas and numerous secondary replicas without impacting performance.

Abstract: A Distributed Availability Group (DAG) spans two AGs, each spanning one or more replica nodes and functioning as primary or secondary AG. A primary AG is replicated to the secondary AG synchronously or asynchronously. A failover in the DAG results in the AGs swapping their roles. Multiple DAGs can be linked together as a chain, which provides many useful features including disaster recovery across geographical regions, massive read scale (numerous readable secondary nodes), online migration of databases (across different operating systems and computing environments). The systems using DAGs can replicate databases across multiple independent high availability (HA) failover clusters using complex replication topologies and allow for manual failover and failback. The systems allow chaining of multiple AGs to provision a treelike structure of replicas and numerous secondary replicas without impacting performance.

Abstract: A Distributed Availability Group (DAG) spans two AGs, each spanning one or more replica nodes and functioning as primary or secondary AG. A primary AG is replicated to the secondary AG synchronously or asynchronously. A failover in the DAG results in the AGs swapping their roles. Multiple DAGs can be linked together as a chain, which provides many useful features including disaster recovery across geographical regions, massive read scale (numerous readable secondary nodes), online migration of databases (across different operating systems and computing environments). The systems using DAGs can replicate databases across multiple independent high availability (HA) failover clusters using complex replication topologies and allow for manual failover and failback. The systems allow chaining of multiple AGs to provision a treelike structure of replicas and numerous secondary replicas without impacting performance.

Abstract: A Distributed Availability Group (DAG) spans two AGs, each spanning one or more replica nodes and functioning as primary or secondary AG. A primary AG is replicated to the secondary AG synchronously or asynchronously. A failover in the DAG results in the AGs swapping their roles. Multiple DAGs can be linked together as a chain, which provides many useful features including disaster recovery across geographical regions, massive read scale (numerous readable secondary nodes), online migration of databases (across different operating systems and computing environments). The systems using DAGs can replicate databases across multiple independent high availability (HA) failover clusters using complex replication topologies and allow for manual failover and failback. The systems allow chaining of multiple AGs to provision a treelike structure of replicas and numerous secondary replicas without impacting performance.

Abstract: A Distributed Availability Group (DAG) spans two AGs, each spanning one or more replica nodes and functioning as primary or secondary AG. A primary AG is replicated to the secondary AG synchronously or asynchronously. A failover in the DAG results in the AGs swapping their roles. Multiple DAGs can be linked together as a chain, which provides many useful features including disaster recovery across geographical regions, massive read scale (numerous readable secondary nodes), online migration of databases (across different operating systems and computing environments). The systems using DAGs can replicate databases across multiple independent high availability (HA) failover clusters using complex replication topologies and allow for manual failover and failback. The systems allow chaining of multiple AGs to provision a treelike structure of replicas and numerous secondary replicas without impacting performance.

Abstract: The subject disclosure relates to load balancing systems and methods. In one embodiment, a reactive load balancer can receive feedback from a first database node, and allocate resources to the first database node based, at least, on the feedback. The feedback is dynamic and comprises information indicative of a load level at the first database node. In some embodiments, the feedback includes information indicative of a load level at a second, under loaded, database node. In other embodiments, load balancing is performed by an overloaded node polling a set of devices (e.g., cell phone, personal computer, PDA) at which resources may be available. Specifically, the method includes polling devices for resource availability at the devices, and receiving price information for resources provided by at least one device. The overloaded node utilizes the resource in response to providing payment of the price. Auction models or offer/counteroffer approaches can be employed.

Abstract: Architecture that facilitates the restoration of a cluster database in a scalable way using backups (e.g., SQL database backups) and a partition rebuild mechanism to achieve a high level of partition level data consistency, even when restore fails on individual machines and/or machine failure occurs. The architecture restores replicas of the partitions in consideration that the backups may be created at different points and at different times. Optimized parallelism is achieved in restoring each database machine using local backups, which eliminates cross-machine network traffic. Thus, fast recovery of the distributed database can be accomplished on the order of hours over thousands of machines and terabytes of data.