Microsoft Technology Licensing, LLC

Ankur Jauhari

Min Shao

Nidhi Verma

Patrick Simek

Randy David Thomson

Kalyan K Kona

Sundaravadivelan Paranthaman

Shobana Krishnamoorthy

Bradley S Smith

Zohar Raz

Yogesh Kumar

Lei Sun

Rahul Nigam

Girish Mittur Venkataramanappa

Todd F Pfleiger

Balinder Malhi

Le Chang

Wei-qiang Guo

Roberta Cannerozzi

Divyachapan Sridharan Padur

Kumar Gaurav Khanna

Corey Roussel

Zhengguo Sun

Sean Lamont Grant Livingston

Hari Sudan Sundar

Mustafaraj M Dhrolia

Matthew Snider

Sudharsan Ganesan

Prashant Gaurav

Per Mikael Horal

Gaia Carini

Nikola Puzovic

Dylan Thomas Nunley

Adam Thomas Bishop

Muthukumaran Arumugam

Kiran Kumar Kolli

Fuyuan Bie

Brad Smith

Tarun Bhatia

Manoj Singiresu

Ana Trajkovic

Brikesh Kumar

Guilherme Rodrigues Nogueira De Souza

A database management system for managing a database includes each document being stored as a number of replicas for accessibility and data preservation. The system includes: a processor; a network interface; and a memory comprising programming instructions for execution by the processor to implement a database management service, the service configured to maintain a primary replica of a document, a number of secondary replicas of the document, and another log-only replica storing a log of changes to the document rather than contents of the document. The service makes head reads to the primary replica as needed when a read request to the number of secondary replicas does not result in a quorum.

Document database system using replicas of a stored document

A data processing system implements techniques for provisioning and operating databases including receiving a request to provision a new database that includes multiple replicas. The data processing system provisions the new database including the plurality of replicas. The plurality of replicas includes a primary replica, one or more secondary replicas, and one or more delta replicas. The primary replica is configured to handle read and write operations. The one or more secondary replicas include a copy of data and transactional log information of the primary replica and are unable to handle write operations. The one or more delta replicas are a copy of the transactional log information of the primary replica and are unable to handle read operations or write operations.

Techniques for stateful persisted service resource reduction

Reliable feature deployment is provided. Features are evaluated for their readiness to be graduated and features that are ready to be graduated are added to a graduation list. When features are added to the graduation list, evaluation of the features is discontinued, thereby reducing the performance and runtime costs associated with deploying features. Furthermore, engineers can consult the graduation list to identify features that are ready for graduation, thereby reducing the risk of premature flight graduation. Data associated with features is analyzed to map the features to corresponding flights. In this way, when an engineer identifies a feature for graduation, the engineer is able to identify the corresponding flight (or flights) from the mapping. Even when a feature is selected for graduation, the present systems provide additional safeguards to ensure that corresponding flights are not improperly or prematurely graduated, thereby preventing a subpar customer experience.

Reliable feature graduation

A computing system comprises a controller configured to detect a reassignment input to reassign all computing resources from a source server farm to a target server farm, a resource capacity reassignment component configured to incrementally reassign all of the computing resources from the source server farm to the target server farm, in a plurality of fixed increments, and a database move component configured to, after reassigning each fixed increment of the computing resources to the target server farm, move a set of databases, corresponding to a previously reassigned fixed increment of computing resources, from the source server farm to the target server farm, for servicing at the target server farm.

Rolling capacity upgrade control

The techniques disclosed herein enable systems to deploy upgrade events in a controlled manner to different resource units that provide a service utilizing predefined rollout policies. To deploy an upgrade event, a system determines a risk factor for the upgrade event and presents predefined rollout policies to a feature group for selection based on the risk factor. Upon selection of a rollout policy, the system can deploy the upgrade event according to parameters defined by the selected rollout policy. The system is further configured to analyze telemetry data received from the resource units to determine an updated risk factor and determine whether the updated risk factor crosses a risk factor threshold. If the updated risk factor crosses the threshold, the system can identify another rollout policy to replace the selected policy. In addition, the system can receive an override workflow request to expedite upgrade event deployment.

Reliable deployment of upgrade events for a service based on predefined rollout policies

Issues may arise when customers that are highly engaged with application services do not receive reliable feature updates on a predictable schedule. For instance, releasing and updating features may burden cloud infrastructure and/or may not perform properly. Not only are unpredictable feature updates associated with inconsistent customer messaging and potential service disruptions, such unpredictable updates may cause a random end user experience and customer dissatisfaction with the product. The methods and systems address these issues by dynamically ranking tenants and distributing updates and releases to tenants with lower feature usage before distributing to tenants with higher feature usage. The ranking of the tenants may dynamically change based on current usage. In this way, higher ranked tenants receive access to new and updated features after additional regression testing. This quality-driven, phased release of features enables load level management of computing resources as well as a predictable feature experience for end users.

Predictable distribution of program instructions

A request input indicative of a request to deploy computing resources in a target server farm is received, and a fixed increment of the computing resources to be removed from a source server farm and deployed in the target server farm is identified. The fixed increment of computing resources is removed from the source server farm, and deployed to the target server farm. Based on deploying the fixed increment of computing resources to the target server farm, it is determined whether the target server farm has deployed adequate resource capacity to service a fixed increment of databases. If so, the fixed increment of databases is moved from the source server farm to the target server farm, and, if not, additional computing resources is reassigned from the source server farm to the target server farm.

In one embodiment, a network file management systemmay use a persistent metadata storeand an access control listto facilitate a batch rollout. The network file management systemmay maintain a content database. A network file management systemmay store a metadata set for the content databasein a separate persistent metadata store

Large scale network system upgrade

In order to upgrade a source farm to a target farm, the target farm is created with an initial increment of resource capacity that is a fraction of a final resource capacity of the target farm. A corresponding increment of the databases attached to the source farm are then switched from the source farm to the target farm. The resource capacity at the source farm, that was used to service the moved databases, is then removed from the source farm and added to the target farm, and another increment of the databases are switched from the source farm to the target farm. This continues until a full capacity of resources is deployed at the target farm, and the databases are fully moved from the source farm to the target farm.

Technologies are described for updating a live system with static changes. According to some examples, changes such as installing and/or removing features may be applied to a cloud based service in live environment without disrupting the service. New software may be patched and installed on an upgraded machine (i.e., server). New features, diagnostics, and language packs may be added. Existing features and certificates may be removed. To accomplish these, new software to be installed and existing software to be removed may be detected. Removal and additions may be performed without preparation and activation of replacement farms.

Updating live system with static changes

Disclosed herein are systems, methods, and software to enhance the upgrade process with respect to software service deployments. In at least one implementation, a user interface to an administrative portal for administering an initial deployment of a software service is presented and a notification that an upgrade is available is surfaced therein. In response to a selection of the notification in the user interface, upgrade controls are surfaced in the user interface for controlling a pace of the upgrade with respect to service components of the initial deployment. The upgrade is then applied incrementally to the service components based least in part on the pace of the upgrade specified via the upgrade controls.

Dynamic pacing for service upgrades

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Bothell, WA, United States of America

Ankur Jauhari