Abstract: Described herein is a system and method of connectivity migration of an executing virtual application and/or guest operating system. State associated with a first instance of an application and/or a guest operating system executing on a first virtual machine is captured. Information regarding connectivity state associated with a plurality of running connections between the first virtual machine and client device(s) is also captured (e.g., layers 2, 3 and 4). The captured state information can be provided to a second virtual machine which utilizes the captured station information to establish state for a second instance of the application, a second instance of the guest operating system, and/or connectivity of the plurality of running connections between the second virtual machine and client device(s). The state of the second instance of the application can be synchronized with the state of the second instance of the guest operating system.

Abstract: Described herein is a system and method of connectivity migration of an executing virtual application and/or guest operating system. State associated with a first instance of an application and/or a guest operating system executing on a first virtual machine is captured. Information regarding connectivity state associated with a plurality of running connections between the first virtual machine and client device(s) is also captured (e.g., layers 2, 3 and 4). The captured state information can be provided to a second virtual machine which utilizes the captured station information to establish state for a second instance of the application, a second instance of the guest operating system, and/or connectivity of the plurality of running connections between the second virtual machine and client device(s). The state of the second instance of the application can be synchronized with the state of the second instance of the guest operating system.

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: 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: 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: Described herein is a system and method of connectivity migration of an executing virtual application and/or guest operating system. State associated with a first instance of an application and/or a guest operating system executing on a first virtual machine is captured. Information regarding connectivity state associated with a plurality of running connections between the first virtual machine and client device(s) is also captured (e.g., layers 2, 3 and 4). The captured state information can be provided to a second virtual machine which utilizes the captured station information to establish state for a second instance of the application, a second instance of the guest operating system, and/or connectivity of the plurality of running connections between the second virtual machine and client device(s). The state of the second instance of the application can be synchronized with the state of the second instance of the guest operating system.

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: 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 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: A system and method for creation, modification and/or use of plan guide(s) is provided. Plan guide(s) can be employed to optimize the performance of query(ies) when it is not possible and/or desirable to change the text of the query directly. Plan guides can be useful, for example, when a small subset of queries in a database application deployed from a third-party vendor is not performing as expected. Plan guides influence optimization of queries by attaching query hints to them. In accordance with an aspect of the subject invention, a query plan execution system is provided. The system includes an execution component, a query optimizer, and, a plan cache. Plan guides and other associated metadata are stored in a plan guide metadata store.