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One simple new feature added to Oracle Database 11g is the ability to reuse an existing Data Pump dump file in an Oracle directory. Even though the feature seems trivial, it is yet another granular feature that can provide easier manageability to both DBAs and developers. This particular feature is a great enhancement in the world of ASM since Data Pump directories can reside inside ASM. Unless you have access to the asmcmd utility, you will have to use SQL to delete the files in ASM. The asmcmd utility is not available to developers. The REUSE_DUMPFILE option takes a parameter of [Y/N]. The following example reuses the dump file named encrypted.dmp: expdp dumpfile=oratmp:encrypted.dmp tables=rodba.docs \ userid="'/ as sysdba'" encryption_mode=password \ encryption_password=oracle123 ENCRYPTION_ALGORITHM=aes256 \ reuse_dumpfiles=Y If the dump file exists, it is overwritten with this new export. If the dump file does not exist, then Data Pump creates a new file having the name specified.

Although adapting an API to existing patterns is desirable, it should never be done at the cost of verbosity or complexity. The less you need to understand the API s details to use it, the better.

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Oracle Database 11g provides the REMAP_DATA parameter, which allows you to transform data during the export or import operation. To perform a data remap operation, you must create a stored package function and supply the package.function name to the REMAP_DATA parameter as a command-line option. In the following example, the SALARY column data will be transformed to illustrate the data remapping feature. The salary of every employee can be remapped to $35,000 before storing the data on disk. A simple package with a single function is provided to remap the salary column. The edba_remap function must be passed into the REMAP_DATA parameter of Data Pump. create or replace package edba_remap is function remap_sal (p_sal number) return number; end; / create or replace package body edba_remap is function remap_sal (p_sal number) return number as v_sal number := 35000; begin return v_sal; end; end; / SQL> Package created. SQL> Package body created. We now have a package function to remap our SALARY column to the value $35,000. Let s export the EMP table and remap the salary information. The REMAP_DATA parameter accepts the schema name.table_name.column_name value followed by a colon followed by a package. function_name value. In the following example, the SALARY column of the EMP table for the RODBA schema is being remapped to $35,000 during the export: expdp dumpfile=oratmp:emp.dmp tables=rodba.emp \ userid=rodba/oracle123 reuse_dumpfiles=y \ remap_data=rodba.emp.sal:edba_remap.remap_sal Once the export is complete and the salary information is remapped, let s import the data into another schema and confirm that the salary information did actually get changed:

o far, the chapters in this book have concentrated mostly on source or binary compatibility. Suggestions usually advise exposing fewer classes, methods, and fields and thereby help their evolution. This is indeed desirable, because if we want clueless assembly of applications built on top of our libraries, we need the applications to link and not throw UnsatisfiedLinkErrors. However, API design doesn t end when applications link. At that point the real quest is just beginning! Then you need the linked pieces of an application to continue working together. Also, they shouldn t only work once. Instead, they should continue when modular parts of the application are replaced by their newer versions. Only then are you aligned with the clueless application assembly approach. And you can only achieve this if you know and understand the implications of functional compatibility.

Extending Django s template system with a custom template tag can be a bit tricky at first, so let s start simply. You ll write a tag that fetches the latest five entries and puts them into a template variable named latest_entries. To start, you ll need to create a place for this tag s code to live. In the coltrane application directory, add a new directory called templatetags. In that, create two empty files: __init__. py (remember, this is necessary to tell Python that a directory contains loadable Python code) and coltrane_tags.py, which will be the file where your library of custom template tags lives. Next, inside coltrane_tags.py, add a couple of import statements at the top: from django import template from coltrane.models import Entry

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