Snowflake Inc.

Microsoft Technology Licensing, LLC

Nitish Jindal

Sangyong Hwang

Zixuan Geng

Thierry Cruanes

Jiaqi Yan

Craig Steven Freedman

Yongsik Yoon

Daniel Georg Schall

Chandrashekhar Kadiam

Devin Leo Rider

The subject technology receives a first query plan, the first query plan including a set of statements, the set of statements including at least one statement with at least a reference to a particular column in a first table. The subject technology identifies a first predicate from the at least one statement, the first predicate corresponding to a join operation. The subject technology determines, for the particular column, a set of expression properties. The subject technology, based on the set of expression properties, determines a first derived predicate, the first derived predicate corresponding to a first filter operation. The subject technology generates a new operation based on the first derived predicate. The subject technology generates a new query plan based at least in part on the join operation of first predicate, the first derived predicate, and the new operation of the first derived predicate.

Secure predicate derivation of queries using metadata

Techniques for join elimination are described herein. Join elimination can identify and eliminate unnecessary joins in a query plan node. For example, join elimination can involve a semantic query optimization technique, which removes reference to a table whose columns are only referenced in join predicates if the joins do not filter/expand rows in the result. Such joins can cause significant performance issues on larger datasets if not optimized.

Join elimination

The subject technology provides embodiments for enabling derivation of predicates not only from other predicates but also from metadata such as expression properties. In examples, predicates are derived, avoiding unwanted impact on cardinality estimation. In other examples, predicates are derived, avoiding artificial runtime errors and providing a way to avoid security issues with secure views.

Examples described herein generally relate to executing a received graph database query. The received graph database query can be converted into a recursive common table expression (CTE). Multiple alternative processes for executing the recursive CTE can be generated based on the recursive CTE. A cost associated with each of the multiple alternative processes can be determined. One of the multiple alternative processes can be converted into a multi-step sequence based on the associated cost. The multi-step sequence can be executed on a database to retrieve a set of results in response to the received graph database query.

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Bellevue, WA, United States of America

Nitish Jindal