Abstract: Tuning a production database system through the use of a remote mimic. In response to receipt of a query tuning request against a database system, information about that system is obtained and a mimic of the system is set up in a remote system environment. The mimic aims to imitate the database system in all relevant ways with respect to the tuning request. A tuning analysis is then performed on this mimic system such that there is substantially no impact to operations of the original database system. Tuning results are then applied to the original database system. The entire process takes place with little or no human intervention.

Abstract: Tuning a production database system through the use of a remote mimic. In response to receipt of a query tuning request against a database system, information about that system is obtained and a mimic of the system is set up in a remote system environment. The mimic aims to imitate the database system in all relevant ways with respect to the tuning request. A tuning analysis is then performed on this mimic system such that there is substantially no impact to operations of the original database system. Tuning results are then applied to the original database system. The entire process takes place with little or no human intervention.

Abstract: Tuning a production database system through the use of a remote mimic. In response to receipt of a query tuning request against a database system, information about that system is obtained and a mimic of the system is set up in a remote system environment. The mimic aims to imitate the database system in all relevant ways with respect to the tuning request. A tuning analysis is then performed on this mimic system such that there is substantially no impact to operations of the original database system. Tuning results are then applied to the original database system. The entire process takes place with little or no human intervention.

Abstract: Tuning a production database system through the use of a remote mimic. In response to receipt of a query tuning request against a database system, information about that system is obtained and a mimic of the system is set up in a remote system environment. The mimic aims to imitate the database system in all relevant ways with respect to the tuning request. A tuning analysis is then performed on this mimic system such that there is substantially no impact to operations of the original database system. Tuning results are then applied to the original database system. The entire process takes place with little or no human intervention.

Abstract: An adaptive query execution plan enhancement is provided by: selecting a sample of literal sets from an execution history of a query statement; determining a plurality of access paths by applying each literal set in the sample to the query statement; for each given access path of the plurality of access paths, determining a total execution cost by applying each literal set in the sample to the given access path; and selecting a preferred access path from the plurality of access paths based on the total execution costs for each given access path. A plurality of preferred access paths for a plurality of query statements in an application workload is collected and may be presented as a query execution plan enhancement recommendation to users.

Abstract: An adaptive query execution plan enhancement is provided by: selecting a sample of literal sets from an execution history of a query statement; determining a plurality of access paths by applying each literal set in the sample to the query statement; for each given access path of the plurality of access paths, determining a total execution cost by applying each literal set in the sample to the given access path; and selecting a preferred access path from the plurality of access paths based on the total execution costs for each given access path. A plurality of preferred access paths for a plurality of query statements in an application workload is collected and may be presented as a query execution plan enhancement recommendation to users.

Abstract: The present invention provides a method and an apparatus for aggregating database runtime information and analyzing application performance. According to one aspect of the present invention, there is provided a method for aggregating database runtime information, comprising: aggregating said database runtime information based on queries; and aggregating, based on objects, said database runtime information that is aggregated based on queries.

Abstract: The application relates to a tool and a method for customizing hint. According to the invention, provided is at least one hint service adapter configured to collect relevant information of different database platforms, validate customized hint on real database platforms and provide feedback, and deploy the customized hint on database platform. Also provided is hint defining means configured to customize hint independently of database platforms, and providing customized hint to the at least one hint service adapter.