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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
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
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
Analyzing Across Multiple Dimensions → multidimensional requests: Show total sales across all products at increasing aggregation levels for a … counts, across many dimensions are vital to multidimensional analyses. Therefore, analytical tasks require convenient and efficient data aggregation.
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
GROUPING SETS Statement Equivalent GROUP BY Statement → GROUP BY GROUPING SETS(a, b, c) GROUP BY a UNION ALL GROUP BY b UNION ALL GROUP BY c GROUP BY GROUPING SETS(a, b, (b, c)) GROUP BY a UNION ALL GROUP BY b UNION ALL GROUP BY b, c GROUP BY GROUPING SETS((a, b, c)) GROUP BY a, b, c GROUP BY GROUPING SETS(a, (b), ()) GROUP BY a UNION ALL GROUP BY b UNION ALL GROUP BY () GROUP BY GROUPING SETS(a, ROLLUP(b, c)) GROUP BY a UNION ALL GROUP BY ROLLUP(b, c)
Concatenated Groupings → Concatenated groupings offer a concise way to generate useful combinations of groupings. Groupings specified with concatenated groupings yield the cross-product of groupings from each grouping set. The cross-product operation enables even a small number of concatenated groupings to generate a large number of final groups. The concatenated groupings are specified simply by listing multiple grouping
Hierarchy Handling in ROLLUP and CUBE → The ROLLUP and CUBE extensions work independently of any hierarchy metadata in your system. Their calculations are based entirely on the columns specified in the SELECT statement in which they appear. This approach enables CUBE and ROLLUP to be used whether or not hierarchy metadata is available. The simplest way to handle levels in hierarchical dimensions is by using the ROLLUP extension and indicating
Working with Hierarchical Cubes in SQL → This section illustrates examples of working with hierarchical cubes.
Overview of SQL for Aggregation in Data Warehouses → Aggregation is a fundamental part of data warehousing. To improve aggregation performance in your … aggregation, from the most detailed up to a grand total. CUBE is an extension similar to ROLLUP
ROLLUP Syntax → rounding. This query returns the following sets of rows: Regular aggregation rows that would be
Querying Hierarchical Cubes in SQL → the specific group each row belongs to, based on the aggregation level of the grouping-columns in … unneeded aggregation groups from query processing based on the outer query conditions. The outer … that each dimension's aggregation hierarchy is precomputed in combination with each of the other
GROUPING Function → GROUPING handles these problems. Using a single column as its argument, GROUPING returns 1 when it encounters a NULL value created by a ROLLUP or CUBE operation. That is, if the NULL indicates the row is a subtotal, GROUPING returns a 1. Any other type of value, including a stored NULL, returns a 0. GROUPING appears in the selection list portion of a SELECT statement. Its form is: SELECT … [GROUPING(dimension_column)…]
Channel Country → France US Total Internet 9,597 124,224 133,821 Direct Sales 61,202 638,201 699,403 Total 70,799 762,425 833,224 Consider that even a simple report such as this, with just nine values in its grid, generates four subtotals and a grand total. Half of the values needed for this report would not be calculated with a query that requested SUM(amount_sold) and did a GROUP BY(channel_desc, country_id). To