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In a data warehouse/decision-support system, partitioning is not only a great administrative tool, but something that will speed up processing. For example, you may have a large table on which you need to perform an ad hoc query. You always do the ad hoc query by sales quarter, as each sales quarter contains hundreds of thousands of records and you have millions of online records. So, you want to query a relatively small slice of the entire data set, but it is not really feasible to index it based on the sales quarter. This index would point to hundreds of thousands of records, and doing the index range scan in this way would be terrible (refer to 11 Indexes for more details on this). A full table scan is called for to process many of your queries, but you end up having to scan millions of records, most of which won t apply to your query. Using an intelligent partitioning scheme, you can segregate the data by quarter such that when you query the data for any given quarter, you will full scan only that quarter s data. This is the best of all possible solutions. In addition, in a data warehouse/decision-support system environment, parallel query is used frequently. Here, operations such as parallel index range scans or parallel fast full index scans are not only meaningful, but also beneficial to us. We want to maximize our use of all available resources, and parallel query is a way to do it. So, in this environment, partitioning stands a very good chance of speeding up processing.

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By default, Oracle places the converted files in the flash recovery area, without changing the datafile names.

Lastly, hash partitioning is useful in high update contention environments, as mentioned in the Enhanced Statement Performance section when we talked about OLTP systems Instead of having a single hot segment, we can hash partition a segment into 16 pieces, each of which is now receiving modifications..

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Once you move the converted files over to the target system (or move the files over first and convert them later), use the Data Pump Import utility as follows to import the metadata into the target database: $ impdp system/password DUMPFILE=sales.dmp DIRECTORY=dpump_dir TRANSPORT_DATAFILES=/salesdb/sales_101.dbf, /salesdb/sales_201.dbf As you can see, you just plug in the tablespaces and use the Data Pump Import utility to integrate the datafiles and their metadata (found in the test.dmp file). As you ve seen in this chapter, the Data Pump Export and Import utilities are valuable assets to a DBA and help you perform numerous tasks. The transportable tablespaces feature is of great help, especially when you re dealing with very large tables. Instead of performing a laborious and long export and import job, all you need to do is copy data fields at the operating system level, and then export and import the metadata. Time and again, you ll find yourself relying on the wonderful set of tools that are part of the Data Pump technology. It isn t an exaggeration to say that in many databases, the Data Pump utilities will be among the most frequently used of all DBA tools.

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There are currently six methods by which you can partition tables in Oracle: Range partitioning: You may specify ranges of data that should be stored together For example, everything that has a timestamp within the month of Jan-2010 will be stored in partition 1, everything with a timestamp within Feb-2010 in partition 2, and so on This is probably the most commonly used partitioning mechanism in Oracle Hash partitioning: You saw this in the first example in this chapter A column (or columns) has a hash function applied to it, and the row will be placed into a partition according to the value of this hash List partitioning: You specify a discrete set of values, which determines the data that should be stored together.

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