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: Techniques are disclosed for accessing a dimensional data model when processing a query. In one embodiment, the query is processed by performing a two-stage zigzag join operation including a zigzag main join and a zigzag back join. The zigzag main join is configured to generate a set of row identifiers of fact records satisfying any local predicates in the query. The zigzag back join is configured to generate query results based on the set of row identifiers. The query results are returned responsive to the query.

Abstract: Techniques are disclosed for accessing a dimensional data model when processing a query. In one embodiment, the query is processed by performing a two-stage zigzag join operation including a zigzag main join and a zigzag back join. The zigzag main join is configured to generate a set of row identifiers of fact records satisfying any local predicates in the query. The zigzag back join is configured to generate query results based on the set of row identifiers. The query results are returned responsive to the query.