Snowflake Inc.

Oracle International Corporation

Snowflake Computing Inc.

Snowflake Lnc.

Benoit Dageville

Thierry Cruanes

Marcin Zukowski

Subramanian Muralidhar

Istvan Cseri

Allison Waingold Lee

Pui Kei Johnston Chu

Torsten Walther Grabs

Prasanna V Krishnan

Martin Hentschel

Justin Langseth

Matthew J Glickman

Christian Kleinerman

Ashish Motivala

Peter Povinec

Philipp Thomas Unterbrunner

Damien Carru

Karl Dias

Shreyas Narendra Desai

Jiaqi Yan

Mohamed Zait

Khaled Yagoub

Johan Harjono

Kunal Prafulla Nabar

Nileema Shingte

Steven James Pelley

Mark Charles Ramacher

Saurin Shah

Eric Robinson

Gary C Ngai

Florian Andreas Funke

Unmesh Jagtap

William H Waddington

Simon Holm Jensen

Di Wu

Mohamed Ziauddin

Bowen Zhang

Peter Clayton Belknap

Ismail Oukid

Prasanna Rajaperumal

Stefan Richter

Yair Sarig

Marcus Fallen

Igor Demura

Yi Fang

Bhaskar Ghosh

Vishnu Dutt Paladugu

Yizhi Zhu

Jonathan D Klein

Nithin Mahesh

Ganeshan Ramachandran Iyer

Dinesh Das

Varun Ganesh

Spyridon Triantafyllis

Haowei Yu

German Alberto Gil Echeverri

Jiaxing Liang

Graham Wood

Leng Leng Tan

Uri Shaft

Tyler Arthur Akidau

Alex Tsukerman

Sahaj Saini

Abdul Q Munir

Leonidas Galanis

Chieh-Sheng Wang

Tyler Jones

Dinesh Chandrakant Kulkarni

Zheng Mi

Venkateshwaran Venkataramani

Jun Luo

Hong Su

Tirthankar Lahiri

Wei Ey Li

Abhinav Gupta

David Schultz

Polita Paulus

Ashish Thusoo

Srikanth Bellamkonda

Ari Wolfe Mozes

Gianfranco Putzolu

Jae Young Yoon

Sunil Parrokkaren Chakkappen

Vadim Antonov

John Mark Beresniewicz

Amit Ganesh

Carol L Colrain

Hakan Jakobsson

Shrikanth Shankar

Sanjay Kaluskar

Vasudha Krishnaswamy

Sujatha Muthulingam

Ekrem Soylemez

Shilpa Lawande

James G Pan

Kiran Badrinarain Goyal

Andre Kruglikov

Chao Liang

Stephen John Vivian

Replication and failover of data is disclosed. A method includes replicating the data stored in a primary deployment such that the data is further stored in a secondary deployment. The method includes executing one or more transactions on the data at the primary deployment to cause a change to the data to be stored in the primary deployment. The method includes propagating the one or more transactions on the data to the secondary deployment to perform a failover to the secondary deployment while the change to the data is absent from the secondary deployment.

Data replication and data failover in data storage systems

As described herein, a N-Gram index may be created and the search may be conducted using the index, which will lead to faster search results. The N-Gram index may also include partial N-Gram components to capture more relevant data. Moreover, as described herein, the search may also take into account recent log data that has not yet been indexed. Techniques for building an index store using log data and efficiently searching the index store and log data to process search requests are described herein.

Metadata search via N-gram index

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

An anti-abuse system is provided for a data-platform. An anti-abuse scanner of the data-platform detects a creation of an application package by a provider of content to the data platform where the application package includes a set of files for deployment on the data platform. The anti-abuse scanner performs a review o the set of files to detect malicious content where the review is based on a set of analysis rules and generates a deployment decision for the application package based on a result of the review.

Application trust framework

Systems, methods, and devices for batch ingestion of data into a table of a database. A method includes determining a notification indicating a presence of a user file received from a client account to be ingested into a database. The method includes identifying data in the user file and identifying a target table of the database to receive the data in the user file. The method includes generating an ingest task indicating the data and the target table. The method includes assigning the ingest task to an execution node of an execution platform, wherein the execution platform comprises a plurality of execution nodes operating independent of a plurality of shared storage devices collectively storing database data. The method includes registering metadata concerning the target table in a metadata store after the data has been fully committed to the target table by the execution node.

Batch data ingestion in database systems

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

Sharing data in a data exchange across multiple cloud computing platforms and/or cloud computing platform regions is described. An example computer-implemented method can include creating a listing in a data exchange, the listing including a data set hosted by a first cloud computing entity. The data set can be shared with a second cloud computing entity. The method further includes receiving a request associated with a customer account of the second cloud computing entity to access the data set of the listing hosted by the first cloud computing entity and replicating at least a subset of the data set of the listing from the first cloud computing entity to a provider account at the second cloud computing entity to be accessible by the customer account at the second cloud computing entity.

Creating a global data sharing listing

Techniques for database replication include a method performed by at least one hardware processor of a resource management node. The method includes detecting a request that a database instance of a database stored at a first network deployment is available at a second network deployment. Responsive to the request, the second network deployment is provisioned with a plurality of data objects. A first data object of the plurality of data objects includes a database shell. The database shell is reconfigured as the database instance of the database based on pushing data of the database into the database shell. Access to the database instance at the second network deployment is authorized based on at least a second data object of the plurality of data objects.

Database replication using a resource management node

Systems, methods, and devices for retrying a query. A method includes receiving a query directed to database data and assigning execution of the query to one or more execution nodes of an execution platform, the one or more execution nodes configured to execute the query on a first version of a database platform. The method includes determining that execution of the query was unsuccessful. The method includes assigning a first retry execution of the query to the one or more execution nodes of the execution platform and determining whether a regression or an intermittent fault caused the execution of the query to be unsuccessful based at least in part on whether the first retry execution of the query was successful or unsuccessful.

Identifying software regressions based on query retry attempts in a database environment

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

Systems, methods, and devices for tracking a series of changes to database data are disclosed. A method includes executing a transaction to modify data in a micro-partition of a table of a database by generating a new micro-partition that embodies the transaction. The method includes associating transaction data with the new micro-partition, wherein the transaction data comprises a timestamp when the transaction was fully executed, and further includes associating modification data with the new micro-partition that comprises an indication of one or more rows of the table that were modified by the transaction. The method includes joining the transaction data with the modification data to generate joined data and querying the joined data to determine a listing of intermediate modifications made to the table between a first timestamp and a second timestamp.

Tracking intermediate changes in database data

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Foster City, CA, United States of America

Benoit Dageville