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Preparing and Deploying Server Clusters Although clusters can be designed to handle failure, they are not fault tolerant with regard to user data. The cluster by itself doesn t guard against loss of a user s work. Typically, the recovery of lost work is handled by the application software, meaning the application software must be designed to recover the user s work or it must be designed in such a way that the user session state can be maintained in the event of failure. Clusters help to resolve the need for high availability, high reliability, and high scalability. High availability refers to the ability to provide user access to an application or a service a high percentage of scheduled times while attempting to reduce unscheduled outages. A cluster implementation is highly available if it meets the organization s scheduled uptime goals. Availability goals are achieved by reducing unplanned downtime and then working to improve total hours of operation for the related applications and services. High reliability refers to the ability to reduce the frequency of system failure while attempting to provide fault tolerance in case of failure. A cluster implementation is highly reliable if it minimizes the number of single points of failure and reduces the risk that failure of a single component or system will result in the outage of all applications and services offered. Reliability goals are achieved by using redundant, fault-tolerant hardware components, application software, and systems. High scalability refers to the ability to add resources and computers while attempting to improve performance. A cluster implementation is highly scalable if it can be scaled up and out. Individual systems can be scaled up by adding more resources such as CPUs, memory, and disks. The cluster implementation can be scaled out by adding more computers. Tip
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Design for availability A well-designed cluster implementation uses redundant systems and components so that the failure of an individual server doesn t affect the availability of the related applications and services. Although a well-designed solution can guard against application failure, system failure, and site failure, cluster technologies do have limitations.
Cluster technologies depend on compatible applications and services to operate properly. The software must respond appropriately when failure occurs. Cluster technology cannot protect against failures caused by viruses, software corruption, or human error. To protect against these types of problems, organizations need solid data protection and recovery plans.
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When you first create a cube, a default MDX script that defines calculations in the cube is also created. This script is identified by the Calculate command that appears first on the Calculation tab of the cube designer. You add to this script every time you create a new calculated member or a named set. This script tells the server to calculate the entire cube on the first pass. The MDX expressions and statements that you add to the MDX script then get processed in the order in which they appear in the Script Organizer. You can use the debugging features in Visual Studio to observe the interaction of calculations during execution of the MDX script. As you step through the script, you use the debugger s cube browser to slice and dice data or to review the calculated members at each step. You can also make changes to the script while debugging to correct problems that you find. In this procedure, you ll add a script assignment to hide measure values for a group of employees Abbas and employees managed by Abbas and then debug the MDX script. Override cube values with a script assignment 1. Click the Calculations tab, and then click the Script View button on the toolbar. Your screen looks like this:
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Clusters are organized in loosely coupled groups often referred to as farms or packs. A farm is a group of servers that run similar services but don t typically share data. They are called a farm because they handle whatever requests are passed out to them using identical copies of data that is stored locally. Because they use identical copies of data rather than sharing data, members of a farm operate autonomously and are also referred to as clones.
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