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Using the Aggregation Design Wizard
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After you develop a measure group, you can use the Aggregation Design Wizard to design aggregations . A greater number of aggregations provides faster query response time, but uses more disk space and takes longer to process . So, when you use the Aggregation Design Wizard, you will be asked to restrict the number of aggregations that are designed by limiting the estimated amount of storage space the aggregations will use, by setting a limit for the performance gain the aggregations will provide, or by manually stopping the wizard while it is designing aggregations . The wizard, assuming that all queries are equally likely, will design the combination of aggregations that provide the best tradeoff between increased
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query performance and increased storage space given the restriction you have set . The wizard uses a sophisticated algorithm that considers the number of records in the fact table, the number of members in each attribute hierarchy, and the attribute relationships in a dimension . The wizard looks for aggregations that summarize many fact records into a few values and aggregations that can be used to calculate other aggregate values quickly . The wizard designs the aggregations that provide the greatest improvement in query performance compared to disk storage space first, so as you design more and more aggregations, the additional benefit from each additional aggregation decreases . The Aggregation Design Wizard will also compare the potential number of values in an aggregation with the number of fact records that will be processed into a measure group partition . The wizard will not create an aggregation if the number of values in the aggregation is more than one-third the number of fact records . Suppose the wizard is considering an aggregation design that contains the leaf level of the Subcategory attribute hierarchy (37 members), the leaf level of the Date attribute hierarchy (1,279 members), and the All level of all other attribute hierarchies . This aggregation will have 37 1,279 = 47,323 values . If less than 47,323 3 = 141,969 fact records are being processed into the measure group, then the wizard will not create this aggregation, as they are not likely to improve query performance greatly . The potential number of aggregations in a measure group can become very large . An aggregation contains either the All member or the members of the leaf level of every attribute hierarchy . Analysis Services will not create an aggregation that contains the leaf level of every attribute hierarchy in the cube . This aggregation would be redundant with the leaf level of the cube . Therefore, the number of potential aggregations in a measure group is equal to 2N 1, where N is the number of attribute hierarchies that have an All level . The simple measure group used in this chapter has 12 attribute hierarchies, so it has 4,095 potential aggregations . Because it can take the Aggregation Design Wizard a long time to consider every possible aggregation in a measure group, the wizard does not consider whether to use the All member or the leaf level members of every attribute hierarchy when it designs aggregations . Instead, the wizard uses the following criteria to create a pool of attributes that are eligible to be considered when it designs aggregations:
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The attribute is used as the granularity attribute to relate the dimension to the measure group . The attribute is a parent attribute in a parent-child hierarchy; that is, the value of the attribute s Usage property is Parent . The value of the attribute s AggregationUsage property is Unrestricted . The attribute is used as the top level of a user-defined hierarchy .
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