Abstract: Beneficial query parameterization of one or more databases may be automatically determined. Data associated with a workload of the one or more databases may be automatically gathered and accessed. The gathered data may then be automatically analyzed to determine whether query parameterization would be beneficial to the one or more databases. Based on the analysis one or more query parameterization recommendations may be generated for presentation to a user of the one or more databases. A user response associated with implementing at least one of the one or more query parameterization recommendations may then be received. Upon implementing at least one of the one or more recommendations, the at least one implemented recommendation may be automatically verified whether an expected improvement of the one or more databases has been realized. When the expected improvement has not been realized, the one or more databases may be reverted to a previous state.

Abstract: Mechanisms for improving resource usage of a computing system by properly balancing the timing of performance degradation analysis of an executable action set of a query based on an impact on the workload of that query. The mechanism collects workload metrics for each of multiple queries and their associated executable action sets. Performance degradation occurs when the current executed action set is no longer appropriate for the corresponding query such that more resources are used than are necessary. For each query, the system determines whether to perform degradation analysis of the current executable action set in a manner that is workload aware. That is, queries that occupy more computing resources or that impact the overall workload more will tend to have degradation analysis performed more frequently than those queries that have less impact on the overall workload. This results in improved resource usage for the workload with low overhead.

Abstract: Mechanisms for improving resource usage of a computing system by properly balancing the timing of performance degradation analysis of an executable action set of a query based on an impact on the workload of that query. The mechanism collects workload metrics for each of multiple queries and their associated executable action sets. Performance degradation occurs when the current executed action set is no longer appropriate for the corresponding query such that more resources are used than are necessary. For each query, the system determines whether to perform degradation analysis of the current executable action set in a manner that is workload aware. That is, queries that occupy more computing resources or that impact the overall workload more will tend to have degradation analysis performed more frequently than those queries that have less impact on the overall workload. This results in improved resource usage for the workload with low overhead.

Abstract: Beneficial query parameterization of one or more databases may be automatically determined. Data associated with a workload of the one or more databases may be automatically gathered and accessed. The gathered data may then be automatically analyzed to determine whether query parameterization would be beneficial to the one or more databases. Based on the analysis one or more query parameterization recommendations may be generated for presentation to a user of the one or more databases. A user response associated with implementing at least one of the one or more query parameterization recommendations may then be received. Upon implementing at least one of the one or more recommendations, the at least one implemented recommendation may be automatically verified whether an expected improvement of the one or more databases has been realized. When the expected improvement has not been realized, the one or more databases may be reverted to a previous state.

Abstract: Automated validation of the creation of indices in an environment that include multiple and potential many databases, such as perhaps a cloud computing environment. A validation module validates index impact of a created index by using a validation data store that contains validation data originating from a database collection. Index impact may be estimated by evaluating validation data generated prior to and after the creation of the index to thereby determine whether the created index results in overall improved query performance on the database collection for those queries that target the newly indexed database entity. Such validation data need not even contain private data that was contained within the query itself, and might be, for instance, query performance data, or execution plans associated with the query, with private data redacted.