Abstract: Embodiments described herein are directed to generating and returning materialized views for queries (or subexpressions thereof) having a particular relationship with each other. For instance, machine learning-based techniques may be utilized to identify query subexpressions that have at least one of a semantically equivalent relationship or a containment relationship with each other. Responsive to identifying such relationship(s), a materialized view may be generated for the identified subexpressions. When a query is subsequently received, machine learning-based techniques may be utilized to determine whether a subexpression of the query possesses at least one of a semantically equivalent relationship or a containment relationship with another subexpression for which a materialized view has been generated. Responsive to determining that such a subexpression of the query possesses one or more of such relationships, the materialized view generated for the other subexpression is returned.

Abstract: Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.

Abstract: Embodiments described herein are directed to generating and returning materialized views for queries (or subexpressions thereof) having a particular relationship with each other. For instance, machine learning-based techniques may be utilized to identify query subexpressions that have at least one of a semantically equivalent relationship or a containment relationship with each other. Responsive to identifying such relationship(s), a materialized view may be generated for the identified subexpressions. When a query is subsequently received, machine learning-based techniques may be utilized to determine whether a subexpression of the query possesses at least one of a semantically equivalent relationship or a containment relationship with another subexpression for which a materialized view has been generated. Responsive to determining that such a subexpression of the query possesses one or more of such relationships, the materialized view generated for the other subexpression is returned.

Abstract: Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.

Abstract: Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.

Abstract: System, methods, apparatuses, and computer program products are disclosed for auto-scaling of a deployment based on resource utilization data for a workload executing on the deployment. A resource availability is determined based on the resource utilization data and a current resource allocation of the deployment. A severity of resource throttling of the workload may be determined based on the resource utilization data, and a scaling factor is determined based at least on the severity of resource throttling. In response to at least the resource availability satisfying a predetermined condition with a predetermined threshold, the deployment is scaled based on the scaling factor.

Abstract: Embodiments described herein are directed to generating and returning materialized views for queries (or subexpressions thereof) having a particular relationship with each other. For instance, machine learning-based techniques may be utilized to identify query subexpressions that have at least one of a semantically equivalent relationship or a containment relationship with each other. Responsive to identifying such relationship(s), a materialized view may be generated for the identified subexpressions. When a query is subsequently received, machine learning-based techniques may be utilized to determine whether a subexpression of the query possesses at least one of a semantically equivalent relationship or a containment relationship with another subexpression for which a materialized view has been generated. Responsive to determining that such a subexpression of the query possesses one or more of such relationships, the materialized view generated for the other subexpression is returned.