Salesforce, Inc.

Colm McHugh

Yi Xia

Prateek Swamy

Bradley Glasbergen

Rama Krishna Korlapati

Anup Ghatage

Yen-Li Laih

Techniques are disclosed relating to database query optimizers. In some embodiments, a system receives, from a query optimizer, a plurality of query plans for a database maintained by the database system. The system retrieves a set of database statistics for the database and generates, via a data synthesizer, a plurality of synthetic datasets, where generating a given synthetic dataset is performed based on a given query plan of the plurality of query plans and the set of database statistics, and includes generating a plurality of synthetic data tuples. The system executes the plurality of query plans on the plurality of synthetic datasets and updates the query optimizer based on results of executing the plurality of query plans on the plurality of synthetic datasets. The disclosed data synthesis may advantageously improve query performance due to more efficient query plans being selected for execution of requested queries.

Synthetic data generation for query plans

Techniques are disclosed relating to query planning and execution. A computer system can receive a database statement that comprises a LIKE predicate that defines a set of pattern parameters. The computer system may generate first and second query paths for a query plan associated with the database statement. The first query path utilizes an index associated with a database table specified by the database statement while the second query path does not utilize the index. The computer system executes the database statement in accordance with the query plan and values that are provided for the set of pattern parameters. As a part of executing the database statement, the computer system may evaluate those values to determine whether they are prefix constants and execute the first query path instead of the second query path if all the values are prefix constants.

Database like peeking

Techniques are disclosed relating to managing database queries. In some embodiments, a server system receives a query from a computer system and determines a set of aspects for the query, including at least a number of columns specified in the query and a computational cost of executing the query. The system generates a query vector based on the set of aspects determined for the query. The system then compares the query vector with a plurality of clusters, ones of the plurality of clusters comprising two or more previously generates query vectors generated based on aspects of queries previously received by the server system. Based on the comparing, specifically a distance between the query vector and the plurality of clusters of previously generated query vectors, the system classifies the query. Based on a classification of the query determined during the classifying, the system manages the query.

Anomalous query identification using query clustering

Techniques are disclosed relating to implementing multiple index scans. A computer system may store a database table comprising fields and indexes corresponding to those fields. The computer system may receive a request to access records based on a Boolean expression that affects a selection of records from the database table and that comprises clauses, at least two of which are joined by an AND operation. The computer system may access the requested records. The accessing may include, for a given one of the at least two clauses, performing an index scan on an index that corresponds to the given clause to identify records that satisfy that clause. The computer system may update a hash table based on the identified records and then identify the requested records based on the hash table. The Boolean expression may include clauses joined by an OR operation that are processed using multiple index scans.

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Dublin, Ireland

Colm McHugh