Automated Database Tuning using Dynamic SGA Parameters

Hitesh Kumar Sharma

ISBN 10: 365975112X ISBN 13: 9783659751127
Verlag: LAP LAMBERT Academic Publishing Mai 2017, 2017
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This item is printed on demand - Print on Demand Titel. Neuware -The knowledge of the system, the data stored, the workload and the inter-dependency between them is a major requirement for tuning a Database Management System (DBMS). Due to complexity of the DBMSs and the diversity of their workload, there is a need for automatic tuning of DBMS. Self-managing (or autonomic) databases are intended to reduce the total cost of ownership by automatically adapting to evolving workloads and environments. To reach this goal, commercial DBMSs have recently been equipped with self-management functions, which support the database administrator (DBA) in identifying the appropriate indexes or in sizing the memory areas. However, existing techniques suffer from several problems: First, they are often implemented as off-line tools that have to be explicitly triggered by a DBA. Second, they strictly focus on automating one particular administration task, without considering possible side-effects on other components. This book defines the automated manner to make the system self tune in variable workload.Books on Demand GmbH, Überseering 33, 22297 Hamburg 184 pp. Englisch. Bestandsnummer des Verkäufers 9783659751127

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The knowledge of the system, the data stored, the workload and the inter-dependency between them is a major requirement for tuning a Database Management System (DBMS). Due to complexity of the DBMSs and the diversity of their workload, there is a need for automatic tuning of DBMS. Self-managing (or autonomic) databases are intended to reduce the total cost of ownership by automatically adapting to evolving workloads and environments. To reach this goal, commercial DBMSs have recently been equipped with self-management functions, which support the database administrator (DBA) in identifying the appropriate indexes or in sizing the memory areas. However, existing techniques suffer from several problems: First, they are often implemented as off-line tools that have to be explicitly triggered by a DBA. Second, they strictly focus on automating one particular administration task, without considering possible side-effects on other components. This book defines the automated manner to make the system self tune in variable workload.

Reseña del editor: The knowledge of the system, the data stored, the workload and the inter-dependency between them is a major requirement for tuning a Database Management System (DBMS). Due to complexity of the DBMSs and the diversity of their workload, there is a need for automatic tuning of DBMS. Self-managing (or autonomic) databases are intended to reduce the total cost of ownership by automatically adapting to evolving workloads and environments. To reach this goal, commercial DBMSs have recently been equipped with self-management functions, which support the database administrator (DBA) in identifying the appropriate indexes or in sizing the memory areas. However, existing techniques suffer from several problems: First, they are often implemented as off-line tools that have to be explicitly triggered by a DBA. Second, they strictly focus on automating one particular administration task, without considering possible side-effects on other components. This book defines the automated manner to make the system self tune in variable workload.

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Titel: Automated Database Tuning using Dynamic SGA ...
Verlag: LAP LAMBERT Academic Publishing Mai 2017
Erscheinungsdatum: 2017
Einband: Taschenbuch
Zustand: Neu

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ISBN 10: 365975112X ISBN 13: 9783659751127
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Taschenbuch. Zustand: Neu. Automated Database Tuning using Dynamic SGA Parameters | A Practical Approach | Hitesh Kumar Sharma | Taschenbuch | 184 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783659751127 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Artikel-Nr. 109242914

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