Abstract: In some examples, a system identifies sub-portions of a database query, assigns identifiers to the identified sub-portions, and adds the identifiers to a data structure. The system generates a fingerprint representing the database query based on applying a fingerprint function on the data structure including the identifiers.

Abstract: A process includes accessing a query that is associated with joining a fact table with a plurality of dimension tables. The fact table includes a primary key that includes a set of columns, which are covered by the dimension tables. The process includes determining a query plan for processing the query. The query plan has an associated join order. Determining the query plan includes determining a plurality of sets of the plurality of dimension tables, where each set includes dimension tables, which cover the set of columns of the primary key. Determining the query plan includes evaluating costs that are associated with joining subsets of the plurality of sets with the fact table. Based on the costs, a given subset of the plurality of subsets is selected. Determining the query plan includes constraining the join order based on the given subset so that the dimension tables of the given subset are joined before the fact table.

Abstract: In some examples, a system identifies sub-portions of a database query, assigns identifiers to the identified sub-portions, and adds the identifiers to a data structure. The system generates a fingerprint representing the database query based on applying a fingerprint function on the data structure including the identifiers.

Abstract: In some examples, a system identifies sub-portions of a database query, assigns identifiers to the identified sub-portions, and adds the identifiers to a data structure. The system generates a fingerprint representing the database query based on applying a fingerprint function on the data structure including the identifiers.

Abstract: A process includes accessing a query that is associated with joining a fact table with a plurality of dimension tables. The fact table includes a primary key that includes a set of columns, which are covered by the dimension tables. The process includes determining a query plan for processing the query. The query plan has an associated join order. Determining the query plan includes determining a plurality of sets of the plurality of dimension tables, where each set includes dimension tables, which cover the set of columns of the primary key. Determining the query plan includes evaluating costs that are associated with joining subsets of the plurality of sets with the fact table. Based on the costs, a given subset of the plurality of subsets is selected. Determining the query plan includes constraining the join order based on the given subset so that the dimension tables of the given subset are joined before the fact table.

Abstract: A technique includes identifying a plurality of substitute candidates for a given multiple join of tables. Each substitute candidate includes a candidate multiple join of tables, and the number of tables of each candidate multiple join is less than the number of tables of the given multiple join. A given substitute candidate is selected based on a cardinality of the candidate multiple join of the given substitute candidate; a join order for the given multiple join is determined based on the given substitute candidate; and the query is processed based on the determined join order.

Abstract: An input is accessed representing a tuning parameter for a first query statement and a tolerance level. The tolerance level represents a degree of acceptable discrepancy between the first query statement and another query statement. A first fingerprint is generated for the first query statement based on a content of the first query statement and the tolerance level; and the first fingerprint and the tuning parameter are stored. The first fingerprint is used as an index for an optimizer to associate the tuning parameter with a second query statement that corresponds to the first fingerprint.

Abstract: A relational database system for performing a join operation between a first table and a second table may include a query analyzer and an optimization engine. The query analyzer may compare predicates of a join operation query for an American National Standards Institute (ANSI) compliant database. The optimization engine, based upon comparison of the predicates, is to bypass a scan of the second table as part of outputting semantically correct results for the join operation.

Abstract: A technique includes identifying a plurality of substitute candidates for a given multiple join of tables. Each substitute candidate includes a candidate multiple join of tables, and the number of tables of each candidate multiple join is less than the number of tables of the given multiple join. A given substitute candidate is selected based on a cardinality of the candidate multiple join of the given substitute candidate; a join order for the given multiple join is determined based on the given substitute candidate; and the query is processed based on the determined join order.

Abstract: An input is accessed representing a tuning parameter for a first query statement and a tolerance level. The tolerance level represents a degree of acceptable discrepancy between the first query statement and another query statement. A first fingerprint is generated for the first query statement based on a content of the first query statement and the tolerance level; and the first fingerprint and the tuning parameter are stored. The first fingerprint is used as an index for an optimizer to associate the tuning parameter with a second query statement that corresponds to the first fingerprint.

Abstract: Various aspects of the subject technology relate to methods, systems, and machine-readable media for dynamic handling of skew to deliver consistent runtime performance for prepared queries. The method includes detecting skewed data in a table, the skewed data comprising a skewed value and a skewed column The method also includes formulating a first predicate between the skewed column and the skewed value. The method also includes formulating a second predicate between a dynamic parameter and the skewed value. The method also includes deriving a first query based on the first predicate. The method also includes deriving a second query based on the first query. The method also includes generating a query plan based on the first query, the second query, and the second predicate.