Microsoft Technology Licensing, LLC

Karthik Saligrama Ramachandra

Carlo Aldo Curino

Alekh Jindal

Avrilia Floratou

Kameswara Venkatesh Emani

Surabhi Gupta

Sanket Jayant Purandare

Jayant Ramaswamy Haritsa

Harish Doraiswamy

The present disclosure relates to methods and systems for using the computer graphics pipeline to execute database query operations on a graphics processing unit (GPU). The methods and systems use the graphics pipeline to transform relational data into images of the relational data. The methods and systems use the graphics pipeline to perform relational database operations on the images in response to a query and output a query result for the database operations.

GPU-based database operators using the graphics pipeline

Systems, methods, and devices are described for performing scalable data processing operations. A queue that includes a translatable portion comprising indications of data processing operations translatable to data queries and a non-translatable portion comprising indications of non-translatable data processing operations is maintained. A determination that a first data processing operation of a first code block statement is translatable to a database query is made. An indication of the first data processing operation is included in the translatable portion of the queue. Responsive to a determination that a second data processing operation of a second code block statement is undeferrable, the translatable portion of the queue is compiled into a database query. An execution of the database query to be executed by a database engine to generate a query result is caused. A result dataset corresponding to the query result is transmitted to an application configured to analyze the result dataset.

System and method for scalable data processing operations

Methods and systems are provided for converting a loop (e.g., a cursor loop) to a declarative Structured Query Language (SQL) query that invokes a custom aggregate function. The loop includes a select query and a loop body that includes a program fragment that can be evaluated over a result set of the select query one row at a time. The system verifies that the loop body does not modify a persistent state of the database. A custom aggregate function that expresses the loop body is automatically constructed according to a contract. An aggregate class comprising aggregation methods of the contract are used to construct the aggregate function based on results of static analysis. The select query is automatically rewritten to form a declarative SQL query that invokes the custom aggregate function. The declarative SQL query may be executed by a database management system (DBMS) SQL server.

Optimizing cursor loops in relational database systems using custom aggregates

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Bengaluru, India

Karthik Saligrama Ramachandra