T-SQL Tuesday #16
This blog entry is participating in the T-SQL Tuesday #16 for the month of February, hosted by Jes Schultz Borland (Blog|Twitter).
Back in 2001 I was involved in a project migrating an e-commerce site to a new platform. We were mapping and moving data from an Oracle 8.1.5 to an Oracle 8.1.7 instance. The framework was the beloved Dynamo running on Sun Solaris. The Dynamo engineers decided to use sequencers on each entity (e.g. product, category, etc). I was trying to figure out the best reusable PL/SQL block to move the data creating the correct sequence until I was pointed out that Oracle 8.1.6 supported Analytical Functions. After reading and practicing I discovered the power of those functions.
For the purpose of this blog I will refer the Analytical Functions as Windowed Functions instead. They are very useful as it allows the user to crunch measures of subsets of data maintaining the “raw” detail level data. For example, it allows us to get the average price of a product category while also displaying the unit price of each product. It can also help us rank a product by its price and “window” it by category.
Examples
Basic Aggregation:
1 2 3 4 5 | -- Qty of products and average price SELECT COUNT(productKey) AS [Quantity] ,AVG(StandardCost) AS [AverageCost] FROM [dbo].[DimProduct] |
Distribution of Data:
1 2 3 4 5 6 | -- Tier by Product Cost (4 tiers) SELECT NTILE(4) OVER(ORDER BY [StandardCost]) AS [Tier] ,productKey ,StandardCost FROM [dbo].[DimProduct] |
Average per Category with raw data:
1 2 3 4 5 6 7 | -- Take the Average for each Product Subcategory and attach to the detail level data SELECT AVG(StandardCost) OVER(PARTITION BY [ProductSubcategoryKey]) AS [AverageCostSubPerCat] ,productKey ,[ProductSubcategoryKey] ,StandardCost FROM [dbo].[DimProduct] |
Ranking:
1 2 3 4 5 6 7 8 | -- Rank Products based on the minimum and maximum cost per Product Subcategory SELECT [ProductSubcategoryKey] ,RANK() OVER(PARTITION BY [ProductSubcategoryKey] ORDER BY StandardCost, [ProductKey]) AS sequence_min ,RANK() OVER(PARTITION BY [ProductSubcategoryKey] ORDER BY StandardCost DESC, [ProductKey] DESC) AS sequence_max ,[ProductKey] ,StandardCost FROM [dbo].[DimProduct] WHERE StandardCost IS NOT NULL |
Averages excluding certain ranks:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | -- Get the Average and Qty. of the products per category excluding the minimum and maximum cost per Product Subcategory (above) WITH CTE_sequence AS ( SELECT [ProductSubcategoryKey] ,RANK() OVER(PARTITION BY [ProductSubcategoryKey] ORDER BY StandardCost, [ProductKey]) AS sequence_min ,RANK() OVER(PARTITION BY [ProductSubcategoryKey] ORDER BY StandardCost DESC, [ProductKey] DESC) AS sequence_max ,[ProductKey] ,StandardCost FROM [dbo].[DimProduct] WHERE StandardCost IS NOT NULL ) SELECT [ProductSubcategoryKey] ,COUNT([ProductKey]) AS Quantity ,AVG(StandardCost) AS Average_Cost FROM CTE_sequence WHERE sequence_min > 0 AND sequence_max > 0 GROUP BY [ProductSubcategoryKey] |
There are more functions that can be used and the user even has the option to dice the data further by using HAVING and slicing with GROUP BY CUBE | ROLLUP which plots the data in “pivotable” format.
Tags: Analytical Functions, Analytics, Oracle, sql server, TSQL Tuesday, TSQL2sDay, Windowed Functions
