Abstract: Systems, methods, and computer-executable instructions for reorganizing a physical layout of data of a database a database. A workload is selected from previously executed database operations. A total resource consumption of the previously executed database operations and of the workload is determined. The total resource consumption of the workload is more than a predetermined threshold of the total resource consumption of the previously executed database operations. Optimization operations for the database are determined using the workload. A cloned database of the database is created. The optimization operations are executed on the cloned database. A database operation is received for the database. The database operation is executed on the database and the cloned database. The performance of the cloned database is verified as being improved compared to the performance of the database based on the executing of the database operation on the database and the cloned database.

Abstract: Systems, methods, and computer-executable instructions for reorganizing a physical layout of data of a database a database. A workload is selected from previously executed database operations. A total resource consumption of the previously executed database operations and of the workload is determined. The total resource consumption of the workload is more than a predetermined threshold of the total resource consumption of the previously executed database operations. Optimization operations for the database are determined using the workload. A cloned database of the database is created. The optimization operations are executed on the cloned database. A database operation is received for the database. The database operation is executed on the database and the cloned database. The performance of the cloned database is verified as being improved compared to the performance of the databased based on the executing of the database operation on the database and the cloned database.

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: 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: A system that automatically formulates recommendations or suggestions for creating indexes on database entities that will improve the overall query performance of a database and/or collection of databases for those queries that target a database entity for which index creation is recommended. A gathering module gathers at least a portion of historical data automatically generated by the database or database collection. An index recommendation module uses the gathered historical data to generate recommended indexing tasks on the basis of estimated greatest impact on overall query performance. An index creation module then initiates an indexing task of the generated set of one or more recommended indexing tasks to thereby create at least one corresponding index on at least one corresponding database entity to thereby improve overall query performance on the database or database collection.

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.