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Now create the Streams administrator user in the database, as follows: SQL> CREATE USER strmadmin IDENTIFIED BY strmadmin DEFAULT TABLESPACE streams_tbs TEMPORARY TABLESPACE temp QUOTA UNLIMITED ON streams_tbs; 3. Grant the CONNECT, RESOURCE, and DBA roles to the Streams administrator: SQL> GRANT DBA TO strmadmin; 4. Use the GRANT_ADMIN_PRIVILEGE procedure in the DBMS_STREAMS_AUTH package to grant necessary privileges to the Streams administrator: SQL> BEGIN DBMS_STREAMS_AUTH.GRANT_ADMIN_PRIVILEGE( GRANTEE => 'strmadmin', GRANT_PRIVILEGES => true); END; / 5. Create a database link between the source and target databases, as shown here: SQL> CREATE DATABASE LINK targetdb CONNECT TO strmadmin IDENTIFIED BY strmadmin USING 'target.db.world'; 6. Oracle Streams moves data between the source and destination databases using queues. You need to create a queue on both the source and destination databases. To do this, you must run the following procedure on both the source and the target databases. This will create both queues with their default names. EXEC DBMS_STREAMS_ADM.SET_UP_QUEUE(); 7. You need to enable supplemental logging for all the tables on the source databases for which you intend to capture changes. You set up supplemental logging in the following manner: SQL> ALTER TABLE emp ADD SUPPLEMENTAL LOG DATA (PRIMARY KEY, UNIQUE) COLUMNS; 8. Configure the capture process in the source database, using the ADD_TABLE_RULES procedure of the DBMS_STREAMS_ADM package, as shown here: SQL> BEGIN DBMS_STREAMS_ADM.ADD_TABLE_RULES( table_name => 'scott.emp', streams_type => 'capture', streams_name => 'capture_stream', queue_name => 'strmadmin.streams_queue', include_dml => true, include_ddl => true, inclusion_rule => true); END; /

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Now that you ve configured your Oracle Streams setup, you can test the setup by starting a capture process and using an apply process to replicate the emp table from the source database to the emp table in the target database. First, capture the changes using the following procedure: SQL> BEGIN DBMS_CAPTURE_ADM.START_CAPTURE( capture_name => 'capture_stream'); END; / To migrate the captured changes to the destination database, run the following procedure: SQL> BEGIN DBMS_APPLY_ADM.START_APPLY( apply_name => 'apply_stream'); END; / The Oracle Streams feature was discussed very briefly here. However, it s a very powerful feature useful for database replication, migration, and upgrades. The primary interface to Streams is this collection of Oracle-supplied PL/SQL packages. You saw how to use various Oracle-supplied PL/SQL packages to set up and manage the Streams feature, so you know exactly what s happening during the change capture and propagation phases. To help users configure, administer, and monitor their Streams environments, Oracle provides a Streams tool in the OEM Console. I recommend using the Streams feature with the help of the OEM Database Control interface, for convenience.

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lmost all Oracle DBAs are familiar with Oracle s export and import utilities, which help in loading data into and unloading data from databases. In both the Oracle Database 10g and Oracle Database 11g releases, you must use the more versatile and powerful Data Pump Export and Import utilities to export and import data. The old export and import utilities continue to be available under Oracle Database 11g, but Oracle would prefer you to use the Data Pump technology because it offers more sophisticated features. For example, you can now interrupt export/import jobs in the middle and then resume them. You can restart failed export and import jobs. You can remap object attributes to modify the objects. You can easily monitor your Data Pump jobs from a different session, and you can even modify job attributes on the fly during the course of a job. It s easy to move massive amounts of data quickly using parallelization techniques. Because Oracle provides you the APIs for the Data Pump technology, you can easily incorporate export/import jobs within PL/SQL programs. You can also use the powerful transportable tablespaces feature to transport large amounts of data quickly, even across disparate operating system platforms. This chapter provides in-depth coverage of the Data Pump technology, as well as transportable tablespaces.

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Events have been around for quite some time in software systems, although under different names. I ll summarize some of the most important developments. Distributed systems started using event-oriented models in the 1970s. The first systems used shared memory, starting with a centralized shared memory model in multiprocessing systems.1 The memory represented the rendezvous area for senders and receivers. Processors communicated by posting messages to the shared memory. Distributed shared memory models, with systems interconnected by network connections,2 provided better performance and resilience to failures. The Model-View-Controller design pattern3 developed in the Smalltalk world is probably the first paradigm to display a clear event-based architecture. Events were used to maintain synchronization between the data (model) of a system and observers (views) of the data. The relationship between the model and views was later generalized as the subject-observer design pattern,4 popularized in the 1990s. Researchers in the artificial intelligence (AI) community used the shared memory model to support the blackboard paradigm.5 AI processes collaborated in a multiprocessing system to interpret data. Some processes acted as producers of information and would post messages on the blackboard. Other processes would keep an eye on the blackboard contents, taking any messages that were related to their field of expertise. In the mid-1980s, the Actors system6 became one of the first multiprocessing systems to use events in the modern sense. Concurrent processes interacted using a pattern-oriented broadcasting

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