Microsoft Technology Licensing, LLC

Other

Tomas Talius

Lev Novik

Yunxin Wu

Robin Dhananjay Dhamankar

Santeri Olavi Voutilainen

Ajay Kalhan

Alexander Evgenievich Nosov

Irena Hudis

Shaoyu Zhou

Bruno H M Denuit

Istvan Cseri

Michael Nelson

Jason Duane Clark

Hanumantha Rao Kodavalla

Douglas Brian Terry

Alexandre Olegovich Verbitski

Marc Todd Friedman

Andrew Eyring Kimball

Sandeep Lingam

Sameer Arun Verkhedkar

Vishrut Shah

Tony Petrossian

Joachim Hammer

Cihangir Biyikoglu

Daniel L Lemmond

Satyanarayana Narlapuram

Deepak Bansal

Nigel Robin Ellis

Rishabh Tewari

Michael Ray Clark

Conor J Cunningham

Pankaj Arora

Shankar Pal

Harish Kumar Chandrappa

Parag Sharma

Martin J Sleeman

Qun Guo

Vivek Jawahir Jhaveri

Oliver Nicholas Seeliger

Vishal Kathuria

Zhongwei Wu

Shize Xu

Hao Xia

Yixue Zhu

Shekhar Agarwal

Jeffrey A East

Nimish Aggarwal

Amit Dilip Ghosh

Muhunthan Sivapragasam

Anitha Adusumilli

Ling Bao

Clifford T Dibble

Sree Kuchibhotla

Hanh Huynh Huu

Christian Rober

Mareen A Philip

Alastair I Bell

Bryan MacFarlane

Valeri N Tolkov

Longzhang Fu

Dhruv Malik

Don Cao

Joemmanuel Ponce Galindo

Ann Tang

Jeremie E Sartor

Jin He

Mark Scurrell

Managing databases implemented in a cloud computing environment. A method includes detecting that a database implemented in the cloud computing environment is in a state of non-use. The method further includes as a result of detecting that a database implemented in the cloud computing environment is in a state of non-use, instantiating a workload in the cloud computing environment to deactivate the database. The workload is configured to store metadata for the database and database data remotely in cloud storage such that the database can be reactivated at a later time.

Dynamic deactivation of cold database in database service

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.

Application service-level configuration of dataloss failover

Techniques for allowing access to shared cloud resource using private network addresses are disclosed herein. In one embodiment, a connection packet representing a connection request to a shared cloud resource in the cloud computing system can be intercepted. In response, the connection packet can be encapsulated with data representing one or more of a VNET ID, a VNET source address, or a VNET destination address of a virtual network from which the connection packet is received. The encapsulated connection packet can then be forwarded to the shared cloud resource while retaining the data representing one or more of the VNET ID, the VNET source address, or the VNET destination address for access control at the shared cloud resource.

Accessing cloud resources using private network addresses

A maximum lag between data stores can be specified that corresponds to a recovery point objective defined in a service level agreement. Lag can be monitored during a data replication between a primary data store and a secondary data store located in geographically different regions. Activity on the primary data store including incoming data transactions can be throttled as a function of the lag and the maximum lag.

Recovery point objective enforcement

Architecture that provides the capability to automatically (e.g., dynamically) reorganize (repartition) an existing partition by dividing (splitting) or recombining (merging) logical databases. This reorganization can be performed to logical databases belonging to the same customer, and based on the partitioning of the tables in these databases. This can include not only splitting secondary replicas of a partition or merging secondary replicas of the partition, but also splitting off secondary replicas of the partition to create a new partition and merging two partitions into one partition. Moreover, these operations can occur while the logical databases are accepting workload (online).

Reorganization of data under continuous workload

Streaming database replication is provided by merging a stream of user transactions with a stream of copy transactions comprising copy data into a combined stream on a source. A target receives a single stream that includes copy transaction and concurrent user transactions in an order that enables conflicts between data being copied and user transactions to be handled correctly. Furthermore, locks applied to data subject to a copy transaction or user transaction can be released once the copy transaction or user transaction is added to the combined stream.

Data seeding optimization for database replication

A database associated with a primary database cluster may be replicated in a backup database cluster located in a different location in order to provide a highly-available fault tolerant database service. The databases are reconciled through a cluster management module distributed in each database cluster. The cluster management module uses a set of reconciliation data structures to track locally the reconciled states of each database in each database cluster, the operations made locally to the databases in each database cluster, and the author of the operations. The cluster management module in each database cluster engages in a stateless messaging protocol using the set of reconciliation data structures to determine whether or not the databases may be reconciled.

Reconciliation of geo-replicated database clusters

Methods, systems, and computer program products are provided for partitioning online databases. Online database operations, such as, for example, SPLIT, MERGE, and DROP, are used to alter the arrangement of partitions in a federated database. A SPLIT operation splits rows at one partition across a plurality of other partitions. A MERGE operation merges rows at a plurality of partitions in to one partition. A DROP operation shifts responsibility for rows of data from one partition to another partition and then drops the rows from the one partition.

Partitioning online databases

Architecture that eliminates the need for on-disk full backups of data retaining only changes that have occurred, in a separate table. Thus, the architecture provides for incremental recovery of incremental changes in a relational database (e.g., SQL). The architecture provides improved recovery time and recovery point objectives. By using the incremental capture of changed data (e.g., in an XML format), the capability is provided to capture schema changes, query the incremental change data and efficiently restore user data to an earlier point-in-time state. Changes (e.g., insert, update and delete operations) are tracked (e.g., continuously) by a set of triggers and the incrementally captured changed rows are inserted in a data capture table (a differential change 'delta' table) in a human-readable format (e.g., XML). Rollback is also provided.

Logical data backup and rollback using incremental capture in a distributed database

The present invention extends to methods, systems, and computer program products for partitioning online databases. Online database operations, such as, for example, SPLIT, MERGE, and DROP, are used to alter the arrangement of partitions in a federated database. A SPLIT operation splits rows at one partition across a plurality of other partitions. A MERGE operation merges rows at a plurality of partitions in to one partition. A DROP operation shifts responsibility for rows of data from one partition to another partition and then drops the rows from the one partition.

Work item rules for a work item tracking system are provided. Work item rules may be accessible, usable and subject to interpretation by multiple software entities. Further, work item rules may be configured to be subject to creation and change by users, for example, by exposure to users through a user interface. A work item rule may specify an identifier by which the abstraction can be identified and/or a name, and may specify, or be indicative of, a condition and an action to be taken if the condition is satisfied. In response to a first user action affecting a first work item rule, one or more work item rules corresponding to the first user and/or the first work item may be determined. The one or more work item rules then may be interpreted, and the user action responded to based on the interpretation.

Work item rules for a work item tracking system

A database is queried as of any wall-clock time within a retention period, via undo that uses database snapshots and a list of page level modifications. The snapshot is user-identified, automatically generated, or extracted from a backup. The list is maintained in a transaction log by persisting page content before a page is re-used, persisting deleted rows before they are moved, persisting compensation log record undo information, and/or logging a full page. To rewind an entire database, the undo scans the transaction log in reverse LSN order and undoes all page modifications. Undo reverses reallocated pages, table truncation, and/or table deletion, as well as page-level modifications of a schema, metadata values, and/or system tables. An as-of query is handled using as-of page(s) from a sparse page file. If the sparse page file does not already contain the responsive page(s), they are created and added to it.

Database point-in-time restore and as-of query

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Sammamish, WA, United States of America

Tomas Talius