Microsoft Technology Licensing, LLC

Vinod Sridharan

Wolfram Schulte

John Thomas Erickson

Dharma Kiritkumar Shukla

Sudipta Sengupta

Karthik Raman

Momin Mahmoud Al-Ghosien

Shireesh Kumar Thota

Madhan Gajendran

Pankaj Sharma

Lars Kuhtz

Ankur Savailal Shah

Dandan He

Mikhail Mikhailovich Koltachev

Hari Sudan Sundar

Samer Boshra

Liang Li

Jayanta Mondal

Krishnan Sundaram

Oliver Drew Leonard Towers

Siddhesh Dilip Vethe

Adrian Ilcu Predescu

Brandon Chong

Aravind Ramachandran Krishna

Karan Vishwanath Popali

Orestis Kostakis

Balachandar Perumalswamy

Vivek Ravindran

Ashwini Singh

Xianzheng Dou

Erik C Mavrinac

Databases are often provided according to various organizational models (e.g., document-oriented storage, key/value stores, and relational database), and are accessed through various access models (e.g., SQL, XPath, and schemaless queries). As data is shared across sources and applications, the dependency of a data service upon a particular organizational and/or access models may become confining. Instead, data services may store data in a base representation format, such as an atom-record-sequence model. New data received in a native item format may be converted into the base representation format for storage, and converted into a requested format to fulfill data requests. Queries may be translated from a native query format into a base query format that is applicable to the base representation format of the data set, e.g., via translation into an query intermediate language (such as JavaScript) and compilation into opcodes that are executed by a virtual machine within the database engine.

Unified data organization for multi-model distributed databases

Deduplication is integrated with software building and chunk storing. A dedup module includes dedup software, a build graph interface, and a chunk store interface. A dedup graph includes a portion of the build graph, and a portion that represents build artifact file chunks. The dedup software queries whether chunks are present in the chunk store, submits a chunk for storage when the chunk is not already present, and avoids submitting the chunk when it is present. Queries may use hash comparisons, a hash tree dedup graph, chunk expiration dates, content addressable chunk store memory, inference of a child node's presence, recursion, and a local cache of node hashes and node expiration dates, for example. A change caused by the build impacts fewer dedup graph nodes than directory graph nodes, resulting in fewer storage operations to update the chunk storage with new or changed build artifacts.

Chunk storage deduplication using graph structures

Technologies that allow for a significant reduction in the time required to incrementally build large computer programs, and increase in the scale of build systems that perform builds. The time reduction is caused by reducing the time required for processing systems in a distributed build system to acquire files needed for the respective processing system to perform their respective part of the build. The scale increase comes from relying on local processing systems instead of centralized processing systems. This is done by establishing a tier of cache locations on which appropriate files for a build may be found by the appropriate processing system. A system may be established that allows for the processor systems to validate that the files have not been tampered with by using signatures, and were appropriately identified.

Ordered cache tiering for program build files

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Vinod Sridharan