💎🏭SQL Reloaded: The WINDOW Clause in SQL Server 2022 (Part I: Simple Aggregations)

Between the many new features introduced in SQL Server 2022, Microsoft brings the WINDOW clause for defining the partitioning and ordering of a dataset which uses window functions. But before unveiling the change, let's take a walk down the memory lane.

In the early ages of SQL Server, many of descriptive statistics techniques were resuming in using the averages over a dataset that contained more or less complex logic. For example, to get the total and average of sales per Month, the query based on AdventureWorks database would look something like:

-- aggregated sales orders - detailed (SQL Server 2000+)
SELECT SOL.ProductID
, Year(SOH.OrderDate) [Year]
, Month(SOH.OrderDate) [Month]
, SUM(SOL.OrderQty) TotalQty
, AVG(SOL.OrderQty) AverageQty
FROM Sales.SalesOrderDetail SOL
     JOIN Sales.SalesOrderHeader SOH
	   ON SOL.SalesOrderID = SOH.SalesOrderID
WHERE SOL.ProductId IN (745)
  AND Year(SOH.OrderDate) = 2012
  AND Month(SOH.OrderDate) BETWEEN 1 AND 3
GROUP BY SOL.ProductID
, Year(SOH.OrderDate) 
, Month(SOH.OrderDate)

When possible, the base logic was encapsulated within a view, hiding query's complexity from the users, allowing also reuse and better maintenance, just to mention a few of the benefits of this approach:

-- encapsulating the logic into a view (SQL Server 2000+)
CREATE OR ALTER VIEW Sales.vSalesOrders
AS
-- sales orders details
SELECT SOL.SalesOrderID
, SOL.ProductID
, Cast(SOH.OrderDate as Date) OrderDate
, Year(SOH.OrderDate) [Year]
, Month(SOH.OrderDate) [Month]
, Cast(SOL.OrderQty as decimal(18,2)) OrderQty
-- many more columns 
FROM Sales.SalesOrderDetail SOL
     JOIN Sales.SalesOrderHeader SOH
	   ON SOL.SalesOrderID = SOH.SalesOrderID
/* -- some constraints can be brought directly into the view
WHERE SOL.ProductID IN (745)
  AND Year(SOH.OrderDate) = 2012
  AND Month(SOH.OrderDate) BETWEEN 1 AND 3
*/

The above query now becomes:

 

-- aggregated sales orders - simplified (SQL Server 2000+)
SELECT SOL.ProductID
, SOL.[Year]
, SOL.[Month]
, SUM(SOL.OrderQty) TotalQty
, AVG(SOL.OrderQty) AverageQty
FROM Sales.vSalesOrders SOL
WHERE SOL.ProductID IN (745)
  AND SOL.[Year] = 2012
  AND SOL.[Month] BETWEEN 1 AND 3
GROUP BY SOL.ProductID
, SOL.[Year]
, SOL.[Month]

In many cases, performing this kind of aggregations was all the users wanted. However, quite often it's useful to look and the individual sales and consider them as part of the broader picture. For example, it would be interesting to know, what's the percentage of an individual value from the total (see OrderQtyPct) or any similar aggregate information. In SQL Server 2000 addressing such requirements would involve a join between the same view - one query with the detailed records, while the other contains the aggregated data:

-- sales orders from the totals (SQL Server 2000+)
SELECT SOL.*
, ASO.TotalQty
, Cast(CASE WHEN ASO.TotalQty <> 0 THEN 100*SOL.OrderQty/ASO.TotalQty ELSE 0 END as decimal(18,2)) OrderQtyPct
, ASO.AverageQty
FROM Sales.vSalesOrders SOL
     JOIN ( -- aggregated sales orders
		SELECT SOL.ProductID
		, SOL.[Year]
		, SOL.[Month]
		, SUM(SOL.OrderQty) TotalQty
		, AVG(SOL.OrderQty) AverageQty
		FROM Sales.vSalesOrders SOL
		WHERE SOL.ProductID IN (745)
		  AND SOL.[Year] = 2012
		  AND SOL.[Month] BETWEEN 1 AND 3
		GROUP BY SOL.ProductId
		, SOL.[Year]
		, SOL.[Month]
	) ASO
	ON SOL.ProductID = ASO.ProductID 
   AND SOL.[Year] = ASO.[Year]
   AND SOL.[Month] = ASO.[Month]
ORDER BY SOL.ProductID
, SOL.OrderDate

Now, just imagine that you can't create a view and having to duplicate the logic each time the view is used! Aggregating the data at different levels would require similar joins to the same view or piece of logic. 

Fortunately, SQL Server 2005 came with two long-awaited features, common table expressions (CTEs) and window functions, which made a big difference. First, CTEs allowed defining inline views that can be referenced multiple times. Secondly, the window functions allowed aggregations within a partition.

 

-- sales orders with CTE SQL Server 2005+ query 
WITH CTE
AS (--sales orders in scope
SELECT SOL.SalesOrderID 
, SOL.ProductID
, Cast(SOH.OrderDate as Date) OrderDate
, Year(SOH.OrderDate) [Year]
, Month(SOH.OrderDate) [Month]
, SOL.OrderQty
FROM Sales.SalesOrderDetail SOL
     JOIN Sales.SalesOrderHeader SOH
	   ON SOL.SalesOrderID = SOH.SalesOrderID
WHERE SOL.ProductID IN (745)
  AND Year(SOH.OrderDate) = 2012
  AND Month(SOH.OrderDate) BETWEEN 1 AND 3
)
SELECT SOL.SalesOrderID 
, SOL.ProductID
, SOL.OrderDate
, SOL.[Year]
, SOL.[Month]
, SOL.OrderQty
, SUM(SOL.OrderQty) OVER(PARTITION BY SOL.[Year], SOL.[Month]) TotalQty
, AVG(SOL.OrderQty)  OVER(PARTITION BY SOL.[Year], SOL.[Month]) AverageQty
FROM CTE SOL
WHERE SOL.ProductID IN (745)
  AND SOL.[Year] = 2012
  AND SOL.[Month] BETWEEN 1 AND 3

This kind of structuring the queries allows to separate the base logic for better maintainability, readability and fast prototyping. Once the logic from CTE becomes stable, it can be moved within a view, following to replace the CTE reference with view's name in the final query. On the other side, the window functions allow writing more flexible and complex code, even if the statements can become occasionally complex (see OrderQtyPct):

 

-- aggregated sales orders (SQL Server 2005+)
SELECT SOL.SalesOrderID 
, SOL.ProductID
, SOL.OrderDate
, SOL.[Year]
, SOL.[Month]
, SOL.OrderQty
, SUM(SOL.OrderQty) OVER(PARTITION BY SOL.[Year], SOL.[Month]) TotalQty
, AVG(SOL.OrderQty)  OVER(PARTITION BY SOL.[Year], SOL.[Month]) AverageQty
, Cast(CASE WHEN SUM(SOL.OrderQty) OVER(PARTITION BY SOL.[Year], SOL.[Month]) <> 0 THEN 100*SOL.OrderQty/SUM(SOL.OrderQty) OVER(PARTITION BY SOL.[Year], SOL.[Month]) ELSE 0 END as decimal(18,2)) OrderQtyPct
FROM Sales.vSalesOrders SOL
WHERE SOL.ProductID IN (745)
  AND SOL.[Year] = 2012
  AND SOL.[Month] BETWEEN 1 AND 3

Now, SQL Server 2022 allows to further simplify the logic by allowing to define with a name the window at the end of the statement and reference the name in several window functions (see last line of the query):

-- Aggregations per month (SQL Server 2022+)
SELECT SOL.SalesOrderID 
, SOL.ProductID
, SOL.OrderDate
, SOL.[Year]
, SOL.[Month]
, SOL.OrderQty
, SUM(SOL.OrderQty) OVER SalesByMonth AS TotalQty
, AVG(SOL.OrderQty) OVER SalesByMonth AS AverageQty
, Cast(CASE WHEN SUM(SOL.OrderQty) OVER SalesByMonth <> 0 THEN 100*SOL.OrderQty/SUM(SOL.OrderQty) OVER SalesByMonth ELSE 0 END as decimal(18,2)) OrderQtyPct
FROM Sales.vSalesOrders SOL
WHERE SOL.ProductID IN (745)
  AND SOL.[Year] = 2012
  AND SOL.[Month] BETWEEN 1 AND 3
WINDOW SalesByMonth AS (PARTITION BY SOL.[Year], SOL.[Month])

Moreover, several windows can be defined. For example, aggregating the data also per year:

-- Aggregations per month and year (SQL Server 2022+) 
SELECT SOL.SalesOrderID 
, SOL.ProductID
, SOL.OrderDate
, SOL.[Year]
, SOL.[Month]
, SOL.OrderQty
, SUM(SOL.OrderQty) OVER SalesByMonth AS TotalQtyByMonth
, AVG(SOL.OrderQty) OVER SalesByMonth AS AverageQtyByMonth
, Cast(CASE WHEN SUM(SOL.OrderQty) OVER SalesByMonth <> 0 THEN 100*SOL.OrderQty/SUM(SOL.OrderQty) OVER SalesByMonth ELSE 0 END as decimal(18,2)) OrderQtyPctByMonth
, SUM(SOL.OrderQty) OVER SalesByYear AS TotalQtyByYear
, AVG(SOL.OrderQty) OVER SalesByYear AS AverageQtyByYear
, Cast(CASE WHEN SUM(SOL.OrderQty) OVER SalesByYear <> 0 THEN 100*SOL.OrderQty/SUM(SOL.OrderQty) OVER SalesByYear ELSE 0 END as decimal(18,2)) OrderQtyPctByYear
FROM Sales.vSalesOrders SOL
WHERE SOL.ProductID IN (745)
  AND SOL.[Year] = 2012
  AND SOL.[Month] BETWEEN 1 AND 3
WINDOW SalesByMonth AS (PARTITION BY SOL.[Year], SOL.[Month])
, SalesByYear AS  (PARTITION BY SOL.[Year])

Isn't that cool? It will take probably some time to get used in writing and reading this kind of queries, though using descriptive names for the windows should facilitate the process! 

As a side note, even if there's no product function like in Excel, there's a mathematical trick to transform a product into a sum of elements by applying the Exp (exponential) and Log (logarithm) functions (see example).

Notes:
The queries work also in a SQL databases in Microsoft Fabric. Just replace the Sales with SalesLT schema (see post, respectively GitHub repository with the changed code).

Happy coding!

💎🏭SQL Reloaded: Query Patterns in SQL Server (Part IV: Window Functions)

For a long time aggregate functions were the only tool for statistical purposes available for raw SQL scripting. Their limitations become more evident with the introduction of window functions, which allow to apply the aggregates over a defined partition. Besides aggregate window functions, the use of ranking, respectively value window functions, opened the door to a new set of techniques. Here are a few examples based on the tables defined in a previous post.

Ranking windows functions allow ranking a record within the whole dataset or a partition by providing a sorting key:

-- Ranking window functions (RANK, DENSE_RANK, ROW_NUMBER)
SELECT A.CourseId 
, C.CourseName 
, A.StudentId 
, S.StudentName 
, A.StartDate 
, A.EndDate 
, RANK() OVER (ORDER BY A.Mark) [Rank over whole set]
, DENSE_RANK() OVER (ORDER BY A.Mark) [Dense Rank over whole set]
, ROW_NUMBER() OVER (ORDER BY A.Mark) [Row Number over whole set]
--, RANK() OVER (PARTITION BY A.CourseId ORDER BY A.StartDate) [Rank over Course]
--, RANK() OVER (PARTITION BY A.StudentId ORDER BY A.StartDate) [Rank over Student]
--, DENSE_RANK() OVER (PARTITION BY A.CourseId ORDER BY A.StartDate) [Dense Rank over Course]
--, DENSE_RANK() OVER (PARTITION BY A.StudentId ORDER BY A.StartDate) [Dense Rank over Student]
--, ROW_NUMBER() OVER (PARTITION BY A.CourseId ORDER BY A.StartDate) [Dense Rank over Course]
--, ROW_NUMBER() OVER (PARTITION BY A.StudentId ORDER BY A.StartDate) [Dense Rank over Student]
FROM dbo.T_Allocations A
     JOIN dbo.T_Courses C
       ON A.CourseId = C.CourseId 
     JOIN dbo.T_Students S
       ON A.StudentId = S.StudentId 
ORDER BY C.CourseName 
, S.StudentName 

Being able to rank records allows to easier select the last, respectively the first records from a dataset or a given partition:

-- first course 
SELECT *
, A.StartDate 
FROM dbo.T_Students S
     LEFT JOIN (
		SELECT A.CourseId
		, A.StudentId 
		, A.StartDate 
		, RANK() OVER (PARTITION BY A.StudentId ORDER BY A.StartDate) Ranking 
		FROM dbo.T_Allocations A
    ) A
   ON S.StudentId = A.StudentId 
  AND A.Ranking = 1 
ORDER BY S.StudentName

The aggregate window functions function similarly as their simple counterparts, except that they are valid over a partition. If the partition is left out, the functions apply over the whole data set.

-- aggregate window functions 
SELECT A.CourseId 
, C.CourseName 
, A.StudentId 
, S.StudentName 
, A.StartDate 
, A.EndDate 
, SUM(A.Mark) OVER (PARTITION BY A.StudentId) [Sum]
, MAX(A.Mark) OVER (PARTITION BY A.StudentId) [Max]
, MIN(A.Mark) OVER (PARTITION BY A.StudentId) [Min]
, AVG(A.Mark) OVER (PARTITION BY A.StudentId) [Avg]
, COUNT(A.Mark) OVER (PARTITION BY A.StudentId) [Count]
FROM dbo.T_Allocations A
     JOIN dbo.T_Courses C
       ON A.CourseId = C.CourseId 
     JOIN dbo.T_Students S
       ON A.StudentId = S.StudentId 
ORDER BY C.CourseName 
, S.StudentName 

When the ordering attributes are specified, running averages, respectively sums derive:

-- Running averages/sums and ranking via Sum and ORDER BY
SELECT A.StudentId 
, A.CourseId
, A.StartDate
, A.EndDate
, A.Mark 
, AVG(Cast(Mark as decimal(4,2))) OVER (PARTITION BY A.StudentId ORDER BY A.StartDate, A.AllocationId) AvgGrade
, SUM(Mark) OVER (PARTITION BY A.StudentId ORDER BY A.StartDate, A.AllocationId) SumGrade
, RANK() OVER (PARTITION BY A.StudentId ORDER BY A.StartDate, A.AllocationId) Ranking
FROM dbo.T_Allocations A
ORDER BY A.StudentId
, A.StartDate

Running averages were introduced only with SQL Server 2008. Previously, one could still calculate them using common table expressions and a ranking function:

 

-- Running averages/sums and ranking via common table expressions 
WITH CTE AS (
	SELECT A.StudentId 
	, A.CourseId
	, A.StartDate
	, A.EndDate
	, A.Mark 
	, RANK() OVER (PARTITION BY A.StudentId ORDER BY A.StartDate, A.AllocationId) Ranking
	FROM dbo.T_Allocations A
), 
DAT AS (
	SELECT A.StudentId 
	, A.CourseId
	, A.StartDate
	, A.EndDate
	, A.Mark 
	, A.Ranking
	, A.Mark SumMark
	FROM CTE A
	WHERE A.Ranking = 1
	UNION ALL
	SELECT CTE.StudentId 
	, CTE.CourseId
	, CTE.StartDate
	, CTE.EndDate
	, CTE.Mark 
	, CTE.Ranking
	, CTE.Mark + DAT.Mark SumMark 
	FROM CTE
		 JOIN DAT
		   ON CTE.StudentID = DAT.StudentId 
		  AND CTE.Ranking-1 = DAT.Ranking 
) 
SELECT DAT.StudentId 
, DAT.CourseId
, DAT.StartDate
, DAT.EndDate
, DAT.Mark 
, cast(DAT.SumMark/DAT.Ranking as decimal(4,2)) AvgMark
, DAT.SumMark
, DAT.Ranking
FROM DAT
ORDER BY DAT.StudentId
, DAT.StartDate
   

Notes:

The queries work also in SQL databases in Microsoft Fabric.

Happy coding!

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💎🏭SQL Reloaded: Query Patterns in SQL Server (Part III: Aggregate Functions)

As their names denote, aggregate functions are used to summarize information across the whole data set or based on several attributes. The following queries exemplify the use of aggregates based on the tables created in a previous post:

-- aggreates over the whole table 
SELECT COUNT(*) NoRecords 
, MIN(StartDate) StartDate
, MAX(EndDate) EndDate
, DateDiff(d, MIN(StartDate), MAX(EndDate)) [DiffDays]
FROM dbo.T_Allocations A

-- aggregates based on several several attributes 
 SELECT A.CourseId
 , A.StudentId 
 , COUNT(*) NoRecords 
 , MIN(StartDate) StartDate
 , MAX(EndDate) EndDate
 , DateDiff(d, MIN(StartDate), MAX(EndDate)) [DiffDays]
 FROM dbo.T_Allocations A
GROUP BY A.CourseId
 , A.StudentId 

Correlated subqueries can be of help either inside of a SELECT or an CROSS, respectively OUTER APPLY: 

-- correlated subquery within a SELECT 
 SELECT *
 , (SELECT COUNT(*) NoRecords 
     FROM dbo.T_Allocations A
     WHERE C.CourseId = A.CourseId
     GROUP BY A.CourseId) NoRecords 
 FROM dbo.T_Courses C


 -- CROSS APPLY (correlated subquery)
 SELECT *
 , A.NoRecords
 , A.StartDate
 FROM dbo.T_Courses C
      CROSS APPLY (
         SELECT A.CourseId
         , COUNT(*) NoRecords 
         , MIN(StartDate) StartDate
         FROM dbo.T_Allocations A
         WHERE C.CourseId = A.CourseId
         GROUP BY A.CourseId
     ) A
 ORDER BY C.CourseName

-- OUTER APPLY (correlated subquery)
 SELECT *
 , A.NoRecords
 , A.StartDate
 FROM dbo.T_Courses C
      OUTER APPLY (
         SELECT A.CourseId
         , COUNT(*) NoRecords 
         , MIN(StartDate) StartDate
         FROM dbo.T_Allocations A
         WHERE C.CourseId = A.CourseId
         GROUP BY A.CourseId
     ) A
 ORDER BY C.CourseName

Aggregates are useful in a series of techniques like the listing of duplicates:

-- identifying duplicates via an aggregate 
SELECT A.*
FROM dbo.T_Allocations A
     JOIN (
  SELECT A.CourseId
  , A.StudentId 
  , COUNT(*) NoRecords 
  FROM dbo.T_Allocations A
  GROUP BY A.CourseId
  , A.StudentId 
  HAVING COUNT(*)>1
  ) DUP
   ON A.CourseId = DUP.CourseId 
  AND A.StudentId = DUP.Studentid
ORDER BY A.CourseId
, A.StudentId 

A similar technique can be used to remove the duplicates from the base table:

-- removing the duplicates except the last record  
 DELETE dbo.T_Allocations 
 FROM (
 SELECT A.StudentId 
 , A.CourseId
 , A.StartDate
 , A.EndDate
 , Max(A.AllocationId) AllocationId
 FROM dbo.T_Allocations A
 GROUP BY A.StudentId 
 , A.CourseId
 , A.StartDate
 , A.EndDate
 HAVING count(*)>1
  ) A
WHERE dbo.T_Allocations.CourseId = A.CourseId
  AND dbo.T_Allocations.StudentId = A.StudentId
  AND dbo.T_Allocations.StartDate = A.StartDate
  AND dbo.T_Allocations.EndDate = A.EndDate
  AND dbo.T_Allocations.AllocationId <> A.AllocationId

When the number of values is fixed and unique within a grouping, one can use aggregate functions to display the values within a matrix:

-- Matrix display via aggregates 
SELECT S.StudentId 
, S.StudentName 
, CASE WHEN A.Course1 = 1 THEN 'x' ELSE '-' END Course1
, CASE WHEN A.Course2 = 1 THEN 'x' ELSE '-' END Course2
, CASE WHEN A.Course3 = 1 THEN 'x' ELSE '-' END Course3
, CASE WHEN A.Course4 = 1 THEN 'x' ELSE '-' END Course4
, CASE WHEN A.Course5 = 1 THEN 'x' ELSE '-' END Course5
FROM dbo.T_Students S
     LEFT JOIN (
  SELECT A.StudentId 
  , Max(CASE WHEN A.CourseId = 1 THEN 1 ELSE 0 END) Course1
  , Max(CASE WHEN A.CourseId = 2 THEN 1 ELSE 0 END) Course2
  , Max(CASE WHEN A.CourseId = 3 THEN 1 ELSE 0 END) Course3
  , Max(CASE WHEN A.CourseId = 4 THEN 1 ELSE 0 END) Course4
  , Max(CASE WHEN A.CourseId = 5 THEN 1 ELSE 0 END) Course5
  FROM dbo.T_Allocations A
  GROUP BY A.StudentId
    ) A
   ON S.StudentId = A.StudentId 
ORDER BY S.StudentName 

Notes:

The queries work also in SQL databases in Microsoft Fabric.

Happy coding!

Previous Post <<||>> Next Post

💠SQL Server: Window Functions [new feature]

Introduction

     In the past, in the absence or in parallel with other techniques, aggregate functions proved to be quite useful in order to solve several types of problems that involve the retrieval of first/last record or the display of details together with averages and other aggregates. Typically their use involves two or more joins between a dataset and an aggregation based on the same dataset or a subset of it. An aggregation can involve one or more columns that make the object of analysis. Sometimes it might be needed multiple such aggregations based on different sets of columns. Each such aggregation involves at least a join. Such queries can become quite complex, though they were a price to pay in order to solve such problems.

Partitions

     The introduction of analytic functions in Oracle and of window functions, a similar concept, in SQL Server, allowed the approach of such problems from a different simplified perspective. Central to this feature it’s the partition (of a dataset), its meaning being same as of mathematical partition of a set, defined as a division of a set into non-overlapping and non-empty parts that cover the whole initial set. The introduction of partitions it’s not necessarily something new, as the columns used in a GROUP BY clause determines (implicitly) a partition in a dataset. The difference in analytic/window functions is that the partition is defined explicitly inline together with a ranking or average function evaluated within a partition. If the concept of partition is difficult to grasp, let’s look at the result-set based on two Products (the examples are based on AdventureWorks database):
 

-- Price Details for 2 Products 
SELECT A.ProductID  
, A.StartDate 
, A.EndDate 
, A.StandardCost  
FROM [Production].[ProductCostHistory] A 
WHERE A.ProductID IN (707, 708) 
ORDER BY A.ProductID 
, A.StartDate 

   In this case a partition is “created” based on the first Product (ProductId = 707), while a second partition is based on the second Product (ProductId = 708). As a parenthesis, another partitioning could be created based on ProductId and StartDate; considering that the two attributes are a key in the table, this will partition the dataset in partitions of 1 record (each partition will have exactly one record).

Details and Averages

     In order to exemplify the use of simple versus window aggregate functions, let’s consider a problem in which is needed to display Standard Price details together with the Average Standard Price for each ProductId. When a GROUP BY clause is applied in order to retrieve the Average Standard Cost, the query is written under the form: 

-- Average Price for 2 Products 
SELECT A.ProductID  
, AVG(A.StandardCost) AverageStandardCost 
FROM [Production].[ProductCostHistory] A 
WHERE A.ProductID IN (707, 708) 
GROUPBY A.ProductID  
ORDERBY A.ProductID 

 

    In order to retrieve the details, the query can be written with the help of a FULL JOIN as follows:

-- Price Details with Average Price for 2 Products - using JOINs 
SELECT A.ProductID  
, A.StartDate 
, A.EndDate 
, A.StandardCost 
, B.AverageStandardCost 
, A.StandardCost - B.AverageStandardCost DiffStandardCost 
FROM [Production].[ProductCostHistory] A    
  JOIN ( -- average price        
    SELECT A.ProductID         
    , AVG(A.StandardCost) AverageStandardCost         
    FROM [Production].[ProductCostHistory] A        
    WHERE A.ProductID IN (707, 708)        
    GROUP BY A.ProductID      
) B  
    ON A.ProductID = B.ProductID 
WHERE A.ProductID IN (707, 708) 
ORDERBY A.ProductID 
, A.StartDate 

    

    As pointed above the partition is defined by ProductId. The same query written with window functions becomes:

-- Price Details with Average Price for 2 Products - using AVG window function 
SELECT A.ProductID  
, A.StartDate 
, A.EndDate 
, A.StandardCost 
, AVG(A.StandardCost) OVER(PARTITION BY A.ProductID) AverageStandardCost 
, A.StandardCost - AVG(A.StandardCost) OVER(PARTITION BY A.ProductID) DiffStandardCost 
FROM [Production].[ProductCostHistory] A 
WHERE A.ProductID IN (707, 708) 
ORDER BY A.ProductID 
, A.StartDate 

    As can be seen, in the second example, the AVG function is defined using the OVER clause with PartitionId as partition. Even more, the function is used in a formula to calculate the Difference Standard Cost. More complex formulas can be written making use of multiple window functions.  

The Last Record

     Let’s consider the problem of retrieving the nth record. Because with aggregate functions is easier to retrieve the first or last record, let’s consider that is needed to retrieve the last Standard Price for each ProductId. The aggregate function helps to retrieve the greatest Start Date, which farther helps to retrieve the record containing the Last Standard Price.

-- Last Price Details for 2 Products - using JOINs 
SELECT A.ProductID  
, A.StartDate 
, A.EndDate 
, A.StandardCost 
FROM [Production].[ProductCostHistory] A  
    JOIN ( -- average price          
    SELECT A.ProductID          
    , Max(A.StartDate) LastStartDate          
    FROM [Production].[ProductCostHistory] A          
    WHERE A.ProductID IN (707, 708)          
    GROUP BY A.ProductID      
) B      
   ON A.ProductID = B.ProductID  
  AND A.StartDate = B.LastStartDate 
WHERE A.ProductID IN (707, 708) 
ORDERBY A.ProductID 
,A.StartDate 

  

With window functions the query can be rewritten as follows:

-- Last Price Details for 2 Products - using AVG window function 
SELECT * 
FROM (-- ordered prices      
    SELECT A.ProductID      
    , A.StartDate      
    , A.EndDate      
    , A.StandardCost      
    , RANK() OVER(PARTITION BY A.ProductID ORDER BY A.StartDate DESC) Ranking      
    FROM [Production].[ProductCostHistory] A     
    WHERE A.ProductID IN (707, 708) 
  ) A 
WHERE Ranking = 1 
ORDER BY A.ProductID 
, A.StartDate 

  

   As can be seen, in order to retrieve the Last Standard Price, was considered the RANK function, the results being ordered descending by StartDate. Thus, the Last Standard Price will be always positioned on the first record. Because window functions can’t be used in WHERE clauses, it’s needed to encapsulate the initial logic in a subquery. Similarly could be retrieved the First Standard Price, this time ordering ascending the StartDate. The last query can be easily modified to retrieve the nth records (this can prove to be more difficult with simple average functions), the first/last nth records.

Conclusion

    Without going too deep into details, I shown above two representative scenarios in which solutions based on average functions could be simplified by using window functions. In theory the window functions provide greater flexibility but they have their own trade offs too. In the next posts I will attempt to further detail their use, especially in the context of Statistics.