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Note SLAs also specify the cause of possible service interruptions and the expectations regarding the resumption of normal service. If the disruption of service is not due to a database failure, obviously other factors come into play, such as the network and the servers. You should, however, list the potential reasons for a database failure and the time it will take to recover from each of those failures. The total time taken for any recovery, of course, will include the time taken to restore the lost or damaged files and the time to recover the database. 16 covers the recovery process.

The type of backup and recovery strategy you want to adopt depends very much on the level of uptime specified in your SLA. The uptime level reflects how quickly you must recover from a failure. If the SLA states that you can take a whole day to restore and recover your database, then you may not need to do a nightly online backup. You can get by with a once-a-week cold backup (if you re allowed the downtime for it). If your SLA specifies a 99.999 percent uptime, you may want to invest in Oracle Real Application Clusters (RAC), for example. Usually, you ll find that uptime and cost are directly proportional to each other. What happens if you find out you can t make your main production server function for a very long time Maybe you should have a standby database in place to take over for the main database in such a case.

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Note In releases before Oracle 11g, you will have to use SELECT T_SEQ.NEXTVAL INTO :NEW.PK FROM DUAL;

In general, range partitioning is useful when you have data that is logically segregated by some value(s). Time-based data immediately comes to the forefront as a classic example partitioning by Sales Quarter, Fiscal Year, or Month. Range partitioning is able to take advantage of partition elimination in many cases, including the use of exact equality and ranges (less than, greater than, between, and so on). Hash partitioning is suitable for data that has no natural ranges by which you can partition. For example, if you had to load a table full of census-related data, there might not be an attribute by which it would make sense to range partition by. However, you would still like to take advantage of the administrative, performance, and availability enhancements offered by partitioning. Here, you would simply pick a unique or almost unique set of columns to hash on. This would achieve an even distribution of data across as many partitions as you d like. Hash partitioned objects can take advantage of partition elimination when exact equality or IN ( value, value, ... ) is used, but not when ranges of data are used. List partitioning is suitable for data that has a column with a discrete set of values, and partitioning by the column makes sense based on the way your application uses it (e.g., it easily permits partition elimination in queries). Classic examples would be a state or region code or, in fact, many code type attributes in general.

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