Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Pravin Mittal

Hanumantha Rao Kodavalla

Andrew Whitaker

Dhruv Goel

Stefano Stefani

Alexandre Olegovich Verbitski

Srikumar Rangarajan

Sandeep Bhatia

Ming-Chuan Wu

Kanishka Chaturvedi

Fabricio Voznika

Lishi Jiang

Maruthi Manohar Reddy Devarenti

Abhijeet More

VamsiKrishna Chaitanya Manchem

Rajesh Iyer

Hong Yang

Nilanjan Basu

Abhilash Reddy Koppula

Nitin Ahuja

Pushap Goyal

A non-relational database may be emulated using a relational database with a distributed data store. A request to access the non-relational database may be received at a frontend for a relational database engine that emulates an interface for a non-relational database engine. The request may be translated into the format for the relational database engine and performed by the relational database engine. The relational database engine may access data for the non-relational database at storage nodes that store the data in a relational table column of non-relational data type.

Emulating a non-relational database on a relational database with a distributed data store

A database testing system may generate, based on a randomization process, test data for a database. The system may generate a query that, when processed by the database, produces a first result that comprises the row. The system may modify the query based on a randomization process and in view of the semantic properties of the query language associated with the queries. The system may predict the outcome of the modifies query based on the semantic properties and on the generated test data. The system may verify that the modified query, when processed by the database, produces results consistent with the prediction.

Semantic-aware fuzz-based functional testing

Aspects of the subject matter described herein relate to optimized reverse key indexes. In aspects, a dispersion function disperses index values such that they are distributed across multiple pages of an index. The dispersion function utilizes a dispersion factor that indicates to what extent the index values are dispersed. Because the index values are dispersed, contention regarding inserts may be reduced or eliminated and other advantages realized.

Optimized reverse key indexes

Methods, systems, and computer-readable media are disclosed for partitioning contended synchronization objects. A particular method determines a contention-free value of a performance metric associated with a synchronization object of a data structure. A contended value of the performance metric is measured, and the synchronization object is partitioned when the contended value of the performance metric exceeds a multiple of the contention-free value of the performance metric.

Partitioning of contended synchronization objects

Described is a technology by which a database management system more efficiently processes read requests than write requests for a read-mostly database table, which is a database table that is generally read far more often than written. A lock partitioning mechanism may be implemented to provide a read lock for each CPU of a plurality of CPUs, in which only one read lock is acquired to process a read request. A database table state changing mechanism may be implemented that more efficiently processes read requests relative to write requests by maintaining the database table in a read-only state until a write request is processed. When a write request is received, the database table is changed to a read-write state to process the write request. The database table is then reset back to a read-only state, such as by running a background thread.

Read mostly database tables

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Redmond, WA, United States of America

Pravin Mittal