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AGGREGATION → Within a model, the AGGREGATION function allows you to create a model that represents a custom … aggregate. Such an aggmap can be used for dynamic aggregation with the AGGREGATE function.
Note: Because the AGGREGATION function is intended only for dynamic aggregation, a model that contains… → A list of one or more dimension values to include in the custom aggregation. The specified values … a variable. Examples Example 7-9 Using the AGGREGATION Function to Create a Custom Aggregate The … specification of the model using the AGGREGATION function. DEFINE mytime_custagg MODEL MODEL JOINLINES … ('DIMENSION time' 'My_Time
SQL for Aggregation → Aggregation is a fundamental part of data warehousing. To improve aggregation performance in your … aggregation, from the most detailed up to a grand total Calculate all possible combinations of aggregations … multidimensional requests: Show total sales across all products at increasing aggregation levels for a
Considerations when Using Aggregation → This section discusses the following topics. Hierarchy Handling in ROLLUP and CUBE Column Capacity in ROLLUP and CUBE HAVING Clause Used with GROUP BY Extensions ORDER BY Clause Used with GROUP BY Extensions Using Other Aggregate Functions with ROLLUP and CUBE
aggregation → aggregation is synonymous with summarization, and aggregate data is synonymous with summary data.
Aggregation Level Bit Vector GROUPING_ID → a, b 0 0 0 a 0 1 1 b 1 0 2 Grand Total 1 1 3 GROUPING_ID clearly distinguishes groupings created by grouping set specification, and it is very useful during refresh and rewrite of materialized views.
Aggregation Operators → Analytic workspaces provide an extensive list of aggregation methods, including weighted, hierarchical, and weighted hierarchical methods.
Aggregation Improvements → In Oracle11 g, the following changes have been made to enhance aggregation: Aggregation by
20 SQL for Aggregation in Data Warehouses → This chapter discusses aggregation of SQL, a basic aspect of data warehousing. It contains these … topics: Overview of SQL for Aggregation in Data Warehouses ROLLUP Extension to GROUP BY CUBE Extension … Considerations when Using Aggregation Computation Using the WITH Clause Working with Hierarchical Cubes in SQL
ORDER BY Clause Used with GROUP BY Extensions → -aggregate columns. This requirement means that queries using ORDER BY along with aggregation
Using Other Aggregate Functions with ROLLUP and CUBE → most common type of aggregation, these extensions can also be used with all other functions available
ROLLUP Syntax → rounding. This query returns the following sets of rows: Regular aggregation rows that would be
GROUP_ID Function → While the extensions to GROUP BY offer power and flexibility, they also allow complex result sets that can include duplicate groupings. The GROUP_ID function lets you distinguish among duplicate groupings. If there are multiple sets of rows calculated for a given level, GROUP_ID assigns the value of 0 to all the rows in the first set. All other sets of duplicate rows for a particular grouping are
GROUPING SETS Syntax → GROUPING SETS syntax lets you define multiple groupings in the same query. GROUP BY computes all the groupings specified and combines them with UNION ALL. For example, consider the following statement: GROUP BY GROUPING sets (channel_desc, calendar_month_desc, country_id ) This statement is equivalent to: GROUP BY channel_desc UNION ALL GROUP BY calendar_month_desc UNION ALL GROUP BY country_id Table
Composite Columns → groupings. For example, in CUBE or ROLLUP, composite columns would mean skipping aggregation across certain … Columns You do not have full control over what aggregation levels you want with CUBE and ROLLUP. For
Working with Hierarchical Cubes in SQL → This section illustrates examples of working with hierarchical cubes.
Optimized Performance → Not only multidimensional issues, but all types of processing can benefit from enhanced aggregation … details to higher levels will benefit from optimized aggregation performance. These extensions … provide aggregation features and bring many benefits, including: Simplified programming requiring less SQL … and network traffic
When to Use CUBE → Consider Using CUBE in any situation requiring cross-tabular reports. The data needed for cross-tabular reports can be generated with a single SELECT using CUBE. Like ROLLUP, CUBE can be helpful in generating summary tables. Note that population of summary tables is even faster if the CUBE query executes in parallel. CUBE is typically most suitable in queries that use columns from multiple dimensions
Calculating Subtotals Without CUBE → Just as for ROLLUP, multiple SELECT statements combined with UNION ALL statements could provide the same information gathered through CUBE. However, this might require many SELECT statements. For an n-dimensional cube, 2 to the n SELECT statements are needed. In the three-dimension example, this would mean issuing SELECT statements linked with UNION ALL. So many SELECT statements yield inefficient
Column Capacity in ROLLUP and CUBE → CUBE, ROLLUP, and GROUPING SETS do not restrict the GROUP BY clause column capacity. The GROUP BY clause, with or without the extensions, can work with up to 255 columns. However, the combinatorial explosion of CUBE makes it unwise to specify a large number of columns with the CUBE extension. Consider that a 20-column list for CUBE would create 2 to the 20 combinations in the result set. A very large