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Part III Advanced Design
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row for every possible combination of products, employees, and dates . In reality, the products were introduced gradually over time, and each product was not sold by every employee every day, so the FactResellerSales table actually contains 60,855 rows . However, even though the fact table contains only 60,855 detail rows, the measure group can display up to 224,771,460 detail levels cells for each measure . Of course, most of these cells would be empty . When you think about the potential size of a measure group, you must also include the aggregated values . A measure group must appear to contain a summarized value for every possible combination of members from each attribute hierarchy in every dimension . Even though three of them are hidden, there are six total attribute hierarchies in the Product dimension: Category, Subcategory, Product, Size, Color, and Product ID that contain 5, 38, 505, 20, 11, and 607 members, respectively . (There are only four categories, but remember that by default each attribute hierarchy contains an All member .) The Employee dimension contains the Employees parent-child hierarchy, which has 290 members, and the Date dimension contains four attribute hierarchies: Calendar Year, Calendar Quarter, Month, and Date, which contain 5, 15, 43, and 1,280 members . The Reseller Sales measure group also contains two measures, Reseller Order Quantity and Reseller Sales Amount . So this simple measure group must appear to contain 5 38 505 20 11 607 290 5 15 43 1,280 2 = 30,677,787,381,120,000,000 values . Even with this small sample database, the measure group must appear to contain 504,112,848,264,235 times as many values as the fact table contains rows! This is a very clear example of data explosion, which was introduced in 2 . Data explosion is a major issue when you work with OLAP cubes, because it can cause an OLAP database to consume a large amount of disk space and can cause measure groups to take a very long time to process . The simplest way to avoid data explosion is to avoid storing aggregations altogether and instead calculate them on demand . But when you have a large data warehouse, this option quickly takes its toll on query performance, because requesting a single high-level summary value from a measure group could require retrieving and summarizing a very large number of detail values . The challenge of OLAP is to make queries as fast as possible while avoiding data explosion . Microsoft SQL Server 2008 Analysis Services provides several features that allow the database administrator to control and fine-tune the relationship between the physical size of a measure group and the time it takes to process the measure group and the speed of the queries . In fact, Analysis Services provides options that allow for both compact data files and responsive queries . Analysis Services provides for compact data files and responsive queries by creating some, but usually not all, of the possible aggregations in a measure group . Although you can easily create a measure group without aggregations none of the measure groups created previously in this book have any aggregations aggregations can make a tremendous difference in query time for large measure groups . You can use the Aggregation Design Wizard and the Usage-Based Optimization Wizard to design aggregations . These wizards allow you to make a tradeoff between creating more aggregations to improve query response time at the cost
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13 Designing Aggregations
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of using more storage space and increasing processing time . Regardless of how many aggregations you design, a measure group always appears to contain every possible aggregated value . When you request a summary value from a measure group, Analysis Services uses whatever aggregations are available to retrieve the value as quickly as possible . For example, suppose you request Reseller Sales Amount for CY 2010, but this aggregated value has not been precalculated and stored in the measure group . If the aggregated values for Reseller Sales Amount for the Calendar Quarter attribute exist in the measure group, Analysis Services can quickly retrieve the four quarters of CY 2010 and calculate the year total . Don t confuse an aggregated value with an aggregation . An aggregated value is a single summary value retrieved from a measure group . An aggregation is all the possible combinations of the members of one level from each attribute hierarchy in every dimension in a measure group . Consider the three aggregations that could be created just from the Color and Size attribute hierarchies in the Product dimension and a single measure . One aggregation would contain a single value equal to the summary value for all colors and all sizes . A second aggregation would contain 10 values equal to the 10 different colors summarized for all sizes . A third aggregation would contain 19 values equal to the 19 different sizes summarized for all colors . Of course, this is a simple example . The Product dimension contains other attribute hierarchies, and even the simple measure group used in this chapter contains other dimensions . Remember that an aggregation contains summarized values for the members of one level of every attribute hierarchy in every dimension in a measure group . Aggregations are physically stored in measure group partitions . A partition in Analysis Services is the physical location of the data stored in a measure group . Each measure group in a cube has one or more corresponding partitions . Because every cube has at least one measure group, every cube has at least one partition . Each time you create an additional measure group, another partition is created . Queries run faster against a partitioned cube because Analysis Services only needs to read data from the partitions that contain the answers to the queries . Queries can run even faster when the partition also stores aggregations, the precalculated totals for measures . Analysis Services often can retrieve the aggregated value faster than it can calculate the same value from the detail data at query time .
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