Snowflake Inc.

Amazon Technologies, Inc.

Lishi Jiang

Benoit Dageville

Thierry Cruanes

William H Waddington

Khaled Yagoub

Max Heimel

Alexander Miller

Moritz Eyssen

Wumengjian Zhu

Adrian Hamza

Stefano Stefani

Sandeep Bhatia

Ming-Chuan Wu

Andrew Whitaker

Kanishka Chaturvedi

Pravin Mittal

Maruthi Manohar Reddy Devarenti

Abhijeet More

Dhruv Goel

VamsiKrishna Chaitanya Manchem

Rajesh Iyer

Hong Yang

Nilanjan Basu

Abhilash Reddy Koppula

Nitin Ahuja

Pushap Goyal

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

The subject technology generates, by a compute service manager, a schema hash value for a new schema version associated with a new schema version value, the schema hash value based on determining a sum of hash values of a set of attributes of value columns, the set of attributes comprises a column identifier, and a logical type of a column. The subject technology stores a mapping of the schema hash value to the new schema version value for a table in a metadata database. The subject technology stores a new schema entry based on the schema hash value, the new schema version value, and a new column for the table in the metadata database, the metadata database storing multiple entries for different schema versions, each entry including a particular schema hash value for mapping to a corresponding schema version from the different schema versions.

Schema evolution for key columnar data into row-organized sequences

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

The subject technology receives a statement to perform an operation to add a new column into a table. The subject technology generates a schema hash value for a new schema version associated with a new schema version value. The subject technology stores a mapping of the schema hash value to the new schema version value for the table in a metadata database. The subject technology stores a new schema entry based on the schema hash value, the new schema version value, and the new column for the table in the metadata database. The subject technology performs an operation to add the new column to the table.

Schema evolution for the serialization of non-primary key columnar data into row-organized byte sequences

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Bellevue, WA, United States of America

Lishi Jiang