Snowflake Inc.

Boyung Lee

Jiaqi Yan

Allison Waingold Lee

Yongsik Yoon

Nicola Dan Onose

Xuelai Cui

Thierry Cruanes

William H Waddington

Sangyong Hwang

Shrainik Jain

Li Yan

Prasanna Rajaperumal

Abdul Q Munir

Yi Pan

Prashant Gaharwar

Vineet Garg

Joshua Klahr

Jayanta Das

Hossein Ahmandi

Kevin Ye Jie Li

Andrew Xue

Wenye Li

The subject technology receives a query, the query including a statement for performing the query. The subject technology performs a first lookup operation on a multi-phase cache based on the query. The subject technology performs, in response to a first cache miss of the multi-phase cache, parsing of the statement from the query. The subject technology performs, based on the parsing, a compilation process on the query to generate a compiled query plan, the compilation process determining an optimization and a generalization for the query. The subject technology determines that the compiled query plan is cacheable. The subject technology registers, in response to the compiled query plan being cacheable, a dummy entry in the multi-phase cache.

Multi-phase query plan caching

The subject technology receives a query, the query including a set of statements for performing the query. The subject technology populates a compilation context based at least in part the query. The subject technology invokes a compiler to perform a compilation process based on the compilation context. The subject technology performs a lookup operation on a stored plan cache for an exact match based on information from the compilation context. The subject technology, in response to determining an exact match, determines whether the particular query plan requires re-compilation based on a data dependent optimization. The subject technology determines whether a plan cache entry corresponding to the particular query plan includes a data property constraint. The subject technology determines whether the data property constraint still holds based on a set of data properties.

Query plan cache in database systems

Queries executed against a materialized view can execute up to orders of magnitude faster than equivalent queries on a source (or base) table. However, although a query can reference a materialized view directly, a user (e.g., query author) may not know about a relevant materialized view. Moreover, if a source table has multiple materialized views generated, the user may not know which materialized view to reference in the query. Thus, embodiments of the present disclosure provide techniques for automatically rewriting queries directed to a source table to utilize existing materialized views.

Transient materialized view rewrite

A method includes parsing a query to determine a plurality of data processing operations associated with the query and including an AS OF JOIN operation between first time series data in a first table and second time series data in a second table. A query plan of the query is generated. The query plan includes a plurality of nodes corresponding to the plurality of data processing operations. At least one of the plurality of nodes corresponding to the AS OF JOIN operation is modified to generate a modified query plan of the query. The modifying is based on applying a UNION operation on at least a first portion of column data in the first table and the second table to obtain a combined table. Execution of the query by at least one of a plurality of computing nodes is scheduled based on the modified query plan.

Inexact timestamp range matching join for time series data (AS OF JOIN)

Caching query plans in database systems

The subject technology receives a query, the query including a set of statements for performing the query. The subject technology populates a compilation context based at least in part the query. The subject technology provides the compilation context to a compiler. The subject technology invokes the compiler to perform a compilation process based on the compilation context, the compilation process comprising performing a lookup operation on a stored plan cache for an exact match based on information from the compilation context, the stored plan cache including a set of stored query plans, and determining whether the exact match of a particular query plan is found in the stored plan cache to avoid compiling the query using the compilation context.

Query plan caching for networked database systems

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Kirkland, WA, United States of America

Boyung Lee