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Value: The value of the TRANSFORM parameter can be Y (yes) or N (no). You ve already seen that the default value for the first three transform names is Y. This means that, by default, Data Pump imports an object s segment attributes and storage features. Alternatively, you can set the value for these parameters to N. If you assign a value of N, you specify not to import the original segment attributes and/or the storage attributes. The PCTSPACE transform name takes a number as its value. Object type: The object type specifies which types of objects should be transformed. You can choose from tables, indexes, tablespaces, types, clusters, constraints, and so on, depending on the type of transform you re employing for the TRANSFORM parameter. If you don t specify an object type when using the SEGMENT_ATTRIBUTES and STORAGE transforms, the transforms are applied to all tables and indexes that are part of the import. Here s an example of using the TRANSFORM parameter: $ impdp hr/hr TABLES=hr.employees \ DIRECTORY=dpump_dir1 DUMPFILE=hr_emp.dmp \ TRANSFORM=SEGMENT_ATTRIBUTES:N:table In this example, the SEGMENT_ATTRIBUTES transform is applied with the value of N. The object type is table. This specification of the TRANSFORM parameter means that the import job will not import the existing storage attributes for any table.

Note Partitioning is an extra cost option to the Enterprise Edition of the Oracle database. It is not available in the

Oracle Database 11g is a leading example of a relational database management system (RDBMS), although Oracle prefers to call its database an object-relational database management system (ORDBMS). (As you ll see toward the end of this chapter, you derive the object-relational model by combining object-oriented design with the traditional relational model.) Relational databases have become the pervasive model of organizing data in the last three decades, and they have revolutionized how companies manage their data. Relational database management systems use relationships among data to answer complex queries.

Note For full details on partitioning syntax, I refer you to either the Oracle SQL Reference Guide or to the

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Note Thanks to the many RDBMS wizards that walk users through the database creation process step by step, even novices can set up a database; the very ease with which you can create a database sometimes contributes to poorly designed databases. My own general rule of thumb is that if database design isn t your forte, find a person who is good at database design to help you. Putting some effort into good design up front will pay rich dividends later on.

In this chapter, we will investigate why you might consider using partitioning. The reasons range from increased availability of data to reduced administrative (DBA) burdens and, in certain situations, increased performance. Once you have a good understanding of the reasons for using partitioning, we ll look at how you may partition tables and their corresponding indexes. The goal of this discussion is not to teach you the details of administering partitions, but rather to present a practical guide to implementing your applications with partitions. We will also discuss the important fact that partitioning of tables and indexes is not a guaranteed fast = true setting for the database. It has been my experience that many developers and DBAs believe that increased performance is an automatic side effect of partitioning an object. Partitioning is just a tool, and one of three things will happen when you partition an index or table: the application using these partitioned tables might run slower, might run faster, or might not be affected one way or the other. I put forth that if you just apply partitioning without understanding how it works and how your application can make use of it, then the odds are you will negatively impact performance by just turning it on. Lastly, we ll investigate a very common use of partitions in today s world: supporting a large online audit trail in OLTP and other operational systems. We ll discuss how to incorporate partitioning and segment space compression to efficiently store online a large audit trail and provide the ability to archive old records out of this audit trail with minimal work.

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