Snowflake Inc.

Oracle International Corporation

Snowflake Computing Inc.

Snowflake Lnc.

Thierry Cruanes

Benoit Dageville

Marcin Zukowski

Allison Waingold Lee

Jiaqi Yan

Subramanian Muralidhar

Prasanna Rajaperumal

Istvan Cseri

Florian Andreas Funke

Peter Povinec

Philipp Thomas Unterbrunner

Igor Demura

William H Waddington

Torsten Walther Grabs

Damien Carru

Nileema Shingte

Saurin Shah

Ismail Oukid

Stefan Richter

Abdul Q Munir

Jeffrey Rosen

Martin Hentschel

Varun Ganesh

Libo Wang

Bhaskar Ghosh

David Schultz

Artin Avanes

Justin Langseth

Christian Kleinerman

Matthew J Glickman

Khalid Zaman Bijon

Ashish Motivala

Robert Muglia

Guangyan Hu

Daniel Freundel

Zheng Mi

Nicola Dan Onose

Mohamed Zait

Unmesh Jagtap

Ryan Michael Thomas Shelly

Bowei Chen

Monica J Holboke

Jaeyun Noh

Tyler Arthur Akidau

Simon Holm Jensen

Yongsik Yoon

Sangyong Hwang

Vikas Jain

Jeremy Yujui Chen

Brian Smith

Rushan Chen

Zixi Zhang

Daniel N Meredith

Tyler Jones

Dinesh Chandrakant Kulkarni

Ganeshan Ramachandran Iyer

Hong Su

Li Yan

Boyung Lee

Mahmud Allahverdiyev

Xuelai Cui

Wei Ey Li

Pui Kei Johnston Chu

Isaac Kunen

Karl Dias

Uri Shaft

Rafi Ahmed

Polita Paulus

Khaled Yagoub

Shrikanth Shankar

Ari Wolfe Mozes

Sunil Parrokkaren Chakkappen

Max Heimel

Alexander Miller

Peter Clayton Belknap

Moritz Eyssen

Patrick A Amor

William A Pugh

Yujie Li

Marianne Shaw

Lishi Jiang

Yiu Woon Lau

Xu Xu

Andrew Witkowski

Eric Robinson

Nithin Mahesh

Ashish Thusoo

Alan Paul Wood

Sankar Subramanian

Selcuk Aya

Harmeek Singh Bedi

Raja Suresh Krishna Balakrishnan

Anupam Singh

Adiel Yoaz

Yali Zhu

Brian Charles Forney

Hailing Yu

Nathan K Folkert

A data platform for managing an application as a first-class database object. The data platform includes at least one processor and a memory storing instructions that cause the at least one processor to perform operations including detecting a data request from a browser for a data object located on the data platform, executing a stored procedure, the stored procedure containing instructions that cause the at least one processor to perform additional operations including instantiating a User Defined Function (UDF) server, an application engine, and the application within a security context of the data platform based on a security policy determined by an owner of the data object. The data platform then communicates with the browser using the application engine as a proxy server.

First class database object server application

A data platform creates an application in a data-provider account, where the application includes one or more application programming interfaces (APIs) corresponding to one or more underlying code blocks. The data platform shares homomorphically encrypted provider data with the application in the data-provider account. The data platform installs, in a data-consumer account, an application instance of the application. The data platform shares homomorphically encrypted consumer data with the application instance in the data-consumer account. The data platform invokes one or more of the APIs of the application instance to execute respective associated underlying code blocks, which are not visible to the data-consumer account, and which operate on the shared homomorphically encrypted provider data and the shared homomorphically encrypted consumer data. The data platform saves homomorphically encrypted output of the one or more respective associated underlying code blocks locally within the data-consumer account.

Data clean rooms using defined access with homomorphic encryption

Techniques for continuous ingestion of files using custom file formats are described. A custom file format may include formats not natively supported by a data system. Unstructured files (e.g., images) may also be considered custom file formats. A custom file format may be set using a user defined table function and scanner options.

Continuous ingestion of custom file formats

A database system facilitates secure data sharing by implementing projection constraints within a query processing framework. Upon receiving a query directed to a shared dataset, the system, utilizing hardware processors, identifies a subset of data within the dataset that is subject to a projection constraint policy. The applicability of the projection constraint is determined based on the context of the query, which is derived from a data sharing agreement. The system processes the query by selectively restricting the projection of data values from constrained columns, while allowing specific operations to be performed on the data. The output generated in response to the query is compliant with the projection constraint policy, providing derived data based on the allowed operations without revealing the actual data values. This ensures the confidentiality of sensitive information while enabling collaborative data analysis and sharing among various users of the database system.

Projection constraint policies in a database system

Example resource management systems and methods are described. In one implementation, a system includes a memory and a processing device operatively coupled to the memory. The processing device is to receive a query referencing database data stored in a storage platform, determine a task associated with processing the received query, and create an execution node comprising cache resources and processing resources. Furthermore, a size of the cache resources of the execution node is determined upon creation of the execution node, based at least in part on the task, and processing resources of the execution node are determined upon creation of the execution node, based at least in part on the task. The execution node is included within a plurality of execution nodes to process the task associated with processing the received query.

Resource management systems and methods

A system and method for pruning data based on metadata. The method may include receiving a query that includes a plurality of predicates and identifying one or more applicable files including database data satisfying at least one of the plurality of predicates. The identifying the one or more applicable files including reading metadata stored in a metadata store that is separate from the database data. The method further includes pruning inapplicable files including database data that does not satisfy at least one of the plurality of predicates to create a reduced set of files and reading the reduced set of files to execute the query.

Data pruning based on metadata

Example resource management systems and methods are described. In one implementation, a resource manager is configured to manage data processing tasks associated with multiple data elements. An execution platform is coupled to the resource manager and includes multiple execution nodes configured to store data retrieved from multiple remote storage devices. Each execution node includes a cache and a processor, where the cache and processor are independent of the remote storage devices. A metadata manager is configured to access metadata associated with at least a portion of the multiple data elements.

Systems, methods, and devices for sharing materialized views in multiple tenant database systems. A method includes defining a materialized view over a source table that is associated with a first account of a multiple tenant database. The method includes defining cross-account access rights to the materialized view to a second account such that that second account can read the materialized view without copying the materialized view. The method includes modifying the source table for the materialized view. The method includes identifying whether the materialized view is stale with respect to the source table by merging the materialized view and the source table.

Sharing materialized views in multiple tenant database systems

A method implementing a fault-tolerant data warehouse including allocating a plurality of processing units to a data warehouse, the processing units located in different availability zones, an availability zone comprising one or more data centers. The method further includes, as a result of monitoring a number of queries running at an input degree of parallelism on the plurality of processing units of the data warehouse, determining that the number of queries is serviceable by one fewer processing units. The method further includes routing a query from a first processing unit to a second processing unit within the data warehouse, the query having a common session identifier with another query previously provided to the second processing unit, the second processing unit determined to be caching a data segment associated with a cloud storage resource, usable by the query, and removing the first processing unit from the data warehouse.

Multi-cluster warehouse

A method for a first set of processors and a second set of processors comprises, the first set of processors processing a set of queries, as a result of a change in utilization of the first set of processors, processing the set of queries using the second set of processors. The change in processors is independent of a change in storage resources, the storage resources shared by the first set of processors and the second set of processors.

Resource provisioning systems and methods

A method and apparatus for managing a set of storage resources for a set of queries is described. In an exemplary embodiment, a method provisions processing resources of an execution platform and provisions storage resources of a storage platform. The execution platform uses the storage platform, which is shared with the execution platform, to process the set of queries. The method changes a number of the storage resources provisioned for the storage platform based on a storage capacity utilization by the set of queries of the storage resources. The method changes the number of the storage resources independently to a change of the processing resources in the execution platform. The method processes the set of queries using the changed number of the storage resources provisioned for the storage platform.

Storage resource provisioning systems and methods

In an embodiment, an application is created on a data-provider platform. The application includes one or more application programming interfaces (APIs) corresponding to one or more underlying code blocks. Provider data is shared with the application on the data-provider platform. An application instance of the application is installed in a trusted execution environment (TEE). The application instance includes one or more APIs corresponding to the one or more APIs in the application on the data-provider platform. Consumer data is shared with the application instance from a data-consumer platform. One or more of the APIs of the application instance are invoked to execute, on the TEE, respective associated underlying code blocks that are not visible on the TEE. The output of the one or more respective associated underlying code blocks is saved to the data-consumer platform.

Data clean rooms using defined access in trusted execution environment

The subject technology receives, at a first execution node, a first transaction, the first transaction to be executed on linearizable storage. The subject technology determines whether the first execution node corresponds to a rank indicating a leader worker. The subject technology, in response to the first execution node corresponding to the rank indicating the leader worker, performs, by the first execution node, an initialization process for executing the first transaction. The subject technology broadcasts a first read timestamp associated with the first transaction to a set of execution nodes, the set of execution nodes being different than the first execution node. The subject technology executes, by the first execution node, at least a first operation from the first transaction.

Distributed execution of transactional queries

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

System and method for efficiently querying external tables are described herein. In an embodiment, a database platform receives a query that is directed at least in part to external data in an external table stored on a data storage platform that is external to the database platform. The external table includes a plurality of partitions. The database platform identifies, from external-table metadata, a subset of the plurality of partitions of the external table as including data that potentially satisfies the query. The external-table metadata is stored by the database platform. The database platform identifies data that satisfies the query by scanning the identified subset of the partitions, and responds to the query at least in part with the identified data that satisfies the query.

Systems and methods for efficiently querying external tables

Growing community of inventors

San Mateo, CA, United States of America

Thierry Cruanes