Microsoft Technology Licensing, LLC

Pedro Celis

Dexter Paul Bradshaw

Anil Kumar Nori

Chengliang Zhang

Georgiy Reynya

Sadashivan Krishnamurthy

Alexandre Olegovich Verbitski

Sameet Harishanker Agarwal

David G Campbell

Hanumantha Rao Kodavalla

Jose A Blakeley

Praveen Seshadri

F Soner Terek

J Patrick Thompson

Soner Terek

Quentin J Clark

Lubor Kollar

Peter M Spiro

Dale Woodford

Arthur T Whitten

Shailesh Vaishnavi

Crash recovery of a computing system in the context in which the computing system includes a volatile memory which has contents lost due to the crash, an intermediate non-volatile memory that (together with the volatile memory) contributes to the active memory of the computing system, and non-volatile storage. The recovery occurs by identifying pages that were in the volatile memory at the time of the crash and that are in the intermediate non-volatile memory and recovers those pages into the volatile memory. In embodiments in which access speeds for the intermediate non-volatile memory may be much faster than for the storage, and in which much of the pages to be recovered are recovered from the intermediate non-volatile memory, the recovery time may be reduced.

Crash recovery using non-volatile memory

Crash recovery of a computing system in the context in which the computing system includes a volatile memory which has contents lost due to the crash, an intermediate non-volatile memory that (together with the volatile memory) contributes to the active memory of the computing system, and non-volatile storage. The recovery occurs by identifying pages that were in the volatile memory at the time of the crash. For each of these pages, the recovery determines whether to recover the page into the volatile memory from either the intermediate non-volatile memory or the storage, and then performs the recovery. In embodiments in which access speeds for the intermediate non-volatile memory may be much faster than for the storage, and in which much of the pages to be recovered are recovered from the intermediate non-volatile memory, the recovery time may be reduced.

A buffer manager that manages blocks of memory amongst multiple levels of buffer pools. For instance, there may be a first level buffer pool for blocks in first level memory, and a second level buffer pool for blocks in second level memory. The first level buffer pool evicts blocks to the second level buffer pool if the blocks are not used above a first threshold level. The second level buffer pool evicts blocks to a yet lower level if they have not used above a second threshold level. The first level memory may be dynamic random access memory, whereas the second level memory may be storage class memory, such as a solid state disk. By using such a storage class memory, the working block set of the buffer manager may be increased without resorting to lower efficiency random block access from yet lower level memory such as disk.

Multi-level buffer pool extensions

Aspects of the subject matter described herein relate to a buffer pool for a database system. In aspects, secondary memory such as solid state storage is used to extend the buffer pool of a database system. Thresholds such as hot, warm, and cold for classifying pages based on access history of the pages may be determined via a sampling algorithm. When a database system needs to free space in a buffer pool in main memory, a page may be evicted to the buffer pool in secondary memory or other storage based on how the page is classified and conditions of the secondary memory or other storage.

Buffer pool extension for database server

Data management between a common data store and multiple applications of multiple disparate application frameworks. A data storage component is provided that facilitates the storage of data, which data includes structured, semi-structured, and unstructured data. A common data platform interfaces to the data storage component to provide data services accessible by a plurality of disparate application frameworks, which data services allow a corresponding application of the different frameworks to access the data.

Platform for data services across disparate application frameworks

Flexible and efficient partitioning of a table is accomplished by hashing the key for an entry, and determining a partition for the entry by generating a sequence of pseudo-random numbers using the hashed key as the seed. As many pseudo-random numbers are generated as there are partitions available. The partition in which the entry belongs corresponds to the largest number in the sequence generated with the hashed key and the pseudo-random number generator. In this way, repartitioning of a partitioned table with a first number of partitions to a second number of partitions can be done with minimal movement and upon repartitioning, the new partition is the same partition that that would have resulted had a new partitioning been performed with the second number of partitions.

Associative hash partitioning using pseudo-random number generator

Various embodiments of the present invention are directed to a data store comprising Items, Elements, and Relationships. An Item is a unit of data storable in a data store and further comprises said Element and said Relationship. An Element is an instance of a type comprising one or more fields. A Relationship is a link between at least two Items. The data store further comprising a Core Schema to define a set of Core Items by which a hardware/software interface system understands and directly processes said set of Core Items in a predetermined and predictable way. The Core Items are derived (directly or indirectly) from a common single Base Item which, in turn, is a foundational Item in a Base Schema.

Systems and methods for data modeling in an item-based storage platform

Various embodiments of the present invention are directed to a storage platform comprising a database engine, a data store, an application programming interface. The data store is implemented on the database engine for storing data therein, and the data store implements a data model that supports the organization, searching, sharing, synchronization, and security of data stored in the data store. Specific types of data are described in schemas. The application programming interface enables application programs to access services and capabilities of the storage platform and to access the data described in the schemas. The storage platform supports interoperability with existing file systems, enables users and systems to synchronize data stored in different instances of the data store, and provides the ability for application programs to be notified about and to track changes made to the data in the data store.

Growing community of inventors

Redmond, WA, United States of America

Pedro Celis