Snowflake Inc.

Amazon Technologies, Inc.

Jaeyun Noh

Thierry Cruanes

Subramanian Muralidhar

Damien Carru

Nicola Dan Onose

Ryan Michael Thomas Shelly

Brian Smith

Stefano Stefani

Sangil Song

Sudipta Sengupta

Saileshwar Krishnamurthy

Kamal Kant Gupta

Yongsik Yoon

Benoit Dageville

Jiaqi Yan

Yuri L Volobuev

Igor Demura

Murali Brahmadesam

Yujie Li

Brian Charles Forney

Shriram Sridharan

Nanda Kaushik

Weili Gu

Mohammad Saber Mirzaei

Sungheun Wi

Remote grouping operations may be used to perform queries for distinct values. A query may be received at a query engine that includes a request for distinct items from a data set stored in a data store. To perform the query, a request may be made to the data store to group items from the data set by value when returned from the data store to the query engine. The query engine may then evaluate the items grouped by value that are returned from the data store to determine distinct items as part of performing the query.

Remote grouping operations to perform queries for distinct values

Techniques described herein can allow users to share cached results of an original query with other users while protecting sensitive information. The techniques described herein can check whether the other users have access to the underlying data queried before allowing those users to see the stored query results. That is, the system may perform privilege checks on the shared users before giving them access to the stored query results but without having to re-run the original query.

Privilege based access checks for query results

Interleaved infrastructure can interleave fragment execution during query compilation. For example, compilation can be paused, and a plan fragment is executed. The results of the plan fragment can be fed back into the compiler, which can then restart the compilation and modify the compilation based on the results of the plan fragment execution. The interleaved workflow provides a flexible, robust infrastructure, which can enable and improve a variety of database features and query optimizations.

Interleaved execution infrastructure in database engines

Techniques for making machine learning inference calls for database query processing are described. In some embodiments, a method of making machine learning inference calls for database query processing may include generating a first batch of machine learning requests based at least on a query to be performed on data stored in a database service, wherein the query identifies a machine learning service, sending the first batch of machine learning requests to an input buffer of an asynchronous request handler, the asynchronous request handler to generate a second batch of machine learning requests based on the first batch of machine learning requests, and obtaining a plurality of machine learning responses from an output buffer of the asynchronous request handler, the machine learning responses generated by the machine learning service using a machine learning model in response to receiving the second batch of machine learning requests.

Machine learning inference calls for database query processing

Growing community of inventors

Sunnyvale, CA, United States of America

Jaeyun Noh