Microsoft Technology Licensing, LLC

Jayanta Mondal

Dharma Kiritkumar Shukla

Thomas Moscibroda

Oliver Drew Leonard Towers

Siddhesh Dilip Vethe

Manish Sharma

John Robert Pao

Karthik Raman

Momin Mahmoud Al-Ghosien

Sudipto Das

Shireesh Kumar Thota

Pankaj Sharma

Madhan Gajendran

Ankur Savailal Shah

Hari Sudan Sundar

Mikhail Mikhailovich Koltachev

Samer Boshra

Liang Li

Adrian Ilcu Predescu

Krishnan Sundaram

Vinod Sridharan

Brandon Chong

Aravind Ramachandran Krishna

Karan Vishwanath Popali

Orestis Kostakis

Balachandar Perumalswamy

Vivek Ravindran

Ashwini Singh

Adrian I Predescu

Oliver D Towers

Shireesh K Thota

Liang Chen

Various examples of improving an in-memory graph query engine using a persisted storage component are provided. The method includes updating data stored in an in-memory graph query engine and, based on updating the data, converting the data to a plain text form that may be more efficiently stored in the persistent storage component. The method further includes updates to additional in-memory graph query engines from the persistent storage component such that in-memory data stored in the graph query engines is synchronized.

Memory graph query engine with persisted storage

In-memory graph query engine with persisted storage

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

A partitioning advisor for online transaction processing (OLTP) workloads generates a workload dependency graph based on a schema defining a structure of a relational database and a workload associated with an OLTP application that accesses the relational database. Based on the workload dependency graph, the partitioning advisor generates one or more partitioning strategy recommendations for sharding the relational database. The partitioning advisor may also render a visualization based on the workload dependency graph, enabling a user to see the impact each recommended partitioning strategy is predicted to have.

Partitioning advisor for online transaction processing workloads

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Jayanta Mondal