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his chapter deals primarily with the day-to-day management of Oracle databases and covers several major features that help you manage your database: Server-generated alerts are automatically raised by the database to let you know when problems occur. The Automatic Workload Repository (AWR) is the new infrastructure that automatically collects and maintains numerous performance statistics for self-tuning purposes. The Automatic Database Diagnostic Monitor (ADDM) that you saw in 17 uses the AWR data for its analyses. The Active Session History (ASH) deals with recent session activity, and the database uses it to tune its own performance. The advisory framework provides a common framework for various database advisors that supply information about resource utilization and performance. You ve seen several of these advisors in other chapters, such as the ADDM, the SQL Access Advisor, and the SQL Tuning Advisor. I explain the common infrastructure of the advisory framework in this chapter. The DBMS_FILE_TRANSFER package lets you transfer operating system files directly through the database. The Oracle Scheduler provides a very powerful way to schedule complex database jobs. Automatic maintenance tasks take care of routine DBA tasks such as collecting optimizer statistics. The new diagnostic framework includes several tools, such as the Automatic Diagnostic Repository (ADR), the Support Workbench, incident packaging service (IPS), and the SQL Repair Advisor, that help you diagnose and repair database failures. In this chapter, I also discuss the management of redo logs as well as the creation of database links to connect to remote databases from your database. The preceding topics cover most of the Oracle DBA s daily management tasks, and familiarity with them is essential to performing typical data movement, space organization, performance tuning, and other database management tasks.

Oracle DBAs regularly collect several types of performance statistics in order to analyze database bottlenecks and other performance issues. In Oracle Database 11g, DBAs now have access to several new types of performance statistics. Besides database statistics at the system and session levels (such as wait statistics, segment usage statistics, and so on), there are also operating system statistics (such as CPU statistics, disk usage statistics, and memory usage statistics) and network statistics. Based on how the various performance statistics are collected and aggregated, you can divide these statistics into two groups: cumulative statistics and database metrics. While cumulative statistics show the accumulated values of key database statistics, metrics measure the rate of change in the cumulative performance statistics.

Cumulative statistics are the accumulated total value of particular statistics since the start of an Oracle instance. The total logons statistic, for example, is a cumulative statistic. Oracle collects several types of cumulative statistics, including statistics for segments and SQL statements, as well as session-wide and system-wide statistics. By comparing the delta values the change in the value of the cumulative statistics between a beginning and an ending period Oracle analyzes database performance during a specific interval of time. The Automatic Workload Repository stores important cumulative statistics. I discuss the AWR in the The Automatic Workload Repository section of this chapter.

Although MSMQ doesn t support filtering directly, an indirect form of filtering is possible using triggers. A trigger is a rule that can be attached to incoming queues. When a message arrives that satisfies a trigger condition, the trigger action is performed. Trigger conditions consist of a true-false predicate. Multiple predicates can be chained together, but only with AND operators. Conditions can be associated with various message properties or the message body. As an example, a trigger might have this condition: Message body contains "Toyota" When a trigger fires, its action is performed. Actions can invoke a COM object method or a standalone executable program. Triggers can only be set using an administrative tool and not programmatically. Actions can include a set of predefined parameters, indicating the value of a message property, a queue property, or other. Triggers add overhead to message queues. Every time a message arrives, MSMQ checks all the triggers attached to the queue. The overhead is proportional to the number of triggers defined. Since triggers can run essentially any program, there is the possibility that a trigger can take a fairly long time to execute. To improve performance, MSMQ supports two ways to run the triggers: sequentially or in parallel. In the first case, all the triggers are checked sequentially. In the second case, the triggers are run concurrently on separate threads.

Sample data represents a sample of the total amount of data available. The Active Session History feature automatically collects session sample data, which represents a sample of the current state of all active sessions. ASH collects the data in memory, where you can view it with the help of V$ views. The AWR helps save the ASH data permanently by collecting it as part of its regular snapshots. I discuss the ASH feature in detail in the Active Session History section of this chapter.

A good way to evaluate database performance is by comparing database performance statistics from two periods, where the first period reflects good performance. The statistics from the period when the database performed well are called baseline data. By comparing current performance with the base period s performance, you can check whether the database is doing better or worse.