Microsoft Technology Licensing, LLC

Varun Kunjbihari Tibrewal

Girish Mittur Venkataramanappa

Sandeep Lingam

Sameer Arun Verkhedkar

Steven John Lindell

Zhengguo Sun

Colin Neville

Hanumantha Rao Kodavalla

Panagiotis Antonopoulos

Peter Byrne

Raghavendra Thallam Kodandaramaih

Wei Ting Chen

Adrian-Leonard Radu

Wei Chen

A relational database system that implements persistent version storage may include an in-row module that is executable by one or more processors to implement an in-row scheme by storing in-row previous version information within a payload of a row within a database page. The relational database system may additionally include an off-row module that is executable by the one or more processors to implement an off-row scheme by storing off-row previous version information in an off-row page that is separate from the database page. The relational database system may additionally include a storage policy that defines when previous version information is stored in accordance with the in-row scheme and when the previous version information is stored in accordance with the off-row scheme. The relational database system may additionally include a cleanup module that cleans up older versions when they are deemed unnecessary by the system.

Persistent version storage for relational database management system

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. The systems automatically seed new database replicas to facilitate building a complex topology of DAGs.

Distributed availability groups of databases for data centers including seeding, synchronous replications, and failover

Distributed availability groups of databases for data centers for providing massive read scale

Distributed availability groups of databases for data centers including different commit policies

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. The systems automatically seed new database replicas that to facilitate building a complex topology of DAGs.

Distributed availability groups of databases for data centers

Distributed availability groups of databases for data centers including failover to regions in different time zones

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Varun Kunjbihari Tibrewal