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In the next phase, each block that has been identified as needing recovery is reconstructed from a combination of the original block on disk, the most recent past image available, and the.

The Calendar server consists of the server-side processes that give end users access to the Calendar data stored in nodes. It is launched from a set of files in the directory $ORACLE_HOME/ocal on UNIX, and in %ORACLE_HOME%\ocal on Windows. The Calendar server can be installed as a self-contained product. It does not require the rest of Oracle Collaboration Suite; it does not run within an Oracle Application Server middle tier; and it need not be dependent on an Oracle Application Server infrastructure instance. The Oracle Calendar Application System (OCAS) is a set of additional server-side facilities that run in an Oracle Application Server middle tier instance and provides, among other things, the Calendar web client. Users connect to it through the Web Cache and the Apache web listener. The Calendar Application System is installed as part of Oracle Collaboration Suite and is based on a set of files in the directory $ORACLE_HOME/ocas on UNIX, or %ORACLE_HOME%\ocas on Windows. The Calendar Application System makes use of the Oracle Application Server infrastructure for Single Sign-On and is fully integrated with the Oracle Collaboration Suite.

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contents of the redo log of the failed instance. The recovered block is written back to disk, immediately after which the recovery resources are released, so that the block becomes available again. When all blocks have been recovered, the system is fully available again. A block may be modified by the failed instance but not exist as a past image or a current block in any of the remaining instances. In this case, Oracle uses the redo logfilesof the failed instance to reconstruct the changes made to the block since the block was last written to disk. The buffer cache is flushed to disk by the database writer process when free buffers are required for new blocks. It is also flushed to disk whenever there is a database checkpoint, which occurs whenever there is a log file switch. Therefore, you only need to recover redo generated since the last logfileswitch. Consequently, the amount of redo and the amount of time required to apply the redo will be proportional to the size of the redo log file. You can reduce the amount of time required for recovery by adjusting the mean time to recovery using the FASTSTARTMTTRTARGET parameter, which allows you to specify the maximum amount of time, in seconds, in which you wish the database to perform crash recovery. The FAST_START_MTTR_ T R E adjusts the frequency of checkpoints to limit potential recovery time. This parameter was A GT introduced in Oracle 9.0.1 and replaces the FASTSTARTIOTARGET parameter, which specifies the target number of database blocks to be written in the event of a recovery, and the L G C E K ON _ O _HC P I T I T R A parameter, which specifies the frequency of checkpoints in terms of 512-byte redo log blocks. NE V L

Summary

This chapter provided an overview of various options for creating user interfaces with F#. The scope of this topic is frankly enormous, and it would be impossible to cover all the options for user-interface programming in F#. For example, you can find hundreds of third-party components built on ASP.NET, WinForms, or WPF. These help raise the level of abstraction when creating user interfaces. You can also find libraries that offer complete alternative programming models, such as DirectX, which is designed for high-performance 3D graphics. The next chapter will take a look at another important programming task how to access data.

This chapter described some of the internal algorithms that are used within a RAC database. While understanding the exact implementation of each algorithm is not essential, having an appreciation of them when designing new systems is highly beneficial. As you have seen, much of the performance of an Oracle RAC database depends on the application. Therefore, testing and benchmarking your application prior to going into production are vital to determine its effect on GCS and GES. By aiming to minimize the amount of interconnect traffic your application generates, you will maximize both performance and scalability.

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