This blog entry is participating in T-SQL Tuesday #017, hosted this month by Matt Velic.You are invited to visit his blog and join the party and read more blogs participating in this month’s theme: APPLY Knowledge.
I’ve been a fan of the APPLY operator since the beginning. That’s why it’s incorporated into the name of my blog. And I've blogged about its power about a dozen times.
I don’t know how anyone lived without it before SQL2005.
Practically everyone knows that you can use it to invoke table-valued functions. That is its most obvious usage and it’s about the only way you’ll see APPLY demonstrated in 97% of the books on SQL Server (if they even mention it at all).
But, as you can see by the twinkle in APPLY’s eyes at the left (even behind the cool shades), he’s got other things up his sleeve. (Okay, the goofy pointed hat and the goatee are a little much, but hey, if you want great artwork, you’re in the wrong place… Michael J. Swart or Kendra Little are the masters of illustration).
APPLY is capable of soooooo much more than just invoking TVF’s. It is incredibly versatile. It seems like it can do anything!
Let’s take a look at what you can do!
Call Table-Valued Functions!
Yawn. Okay, so this is the most common way people use APPLY. Here’s a quick demo… For each Vista credit card expiring in Jun2008, let’s get the Contact information for that card using a built-in function in AdventureWorks.
select f.FirstNameThat’s very convenient, but kind of boring in the grand scheme of things. So let’s move on.
,f.LastName
,f.JobTitle
,f.ContactType
,cc.CardNumber
from Sales.CreditCard cc
join Sales.ContactCreditCard ccc on cc.CreditCardID=ccc.CreditCardID
cross apply dbo.ufnGetContactInformation(ccc.ContactID) f
where cc.ExpYear=2008
and cc.ExpMonth=6
and cc.CardType='Vista'
/*
FirstName LastName JobTitle ContactType CardNumber
--------- -------- ---------------- ------------- --------------
Peggy Justice Owner Store Contact 11119759315644
John McClane Purchasing Agent Store Contact 11119490672347
Laura Cai NULL Consumer 11112813884091
Natalie Gonzales NULL Consumer 11114369564985
Jarrod Sara NULL Consumer 11116045498593
Katherine Bailey NULL Consumer 11119100149656
Stephanie Gray NULL Consumer 11112324154556
Shawna Sharma NULL Consumer 11116413893110
Mindy Rai NULL Consumer 11115163407997
Jackson Jai NULL Consumer 11112011871602
And so on... (74 rows total)
*/
Execute SubQueries!
Why even bother to create a function when you can just create a table on the fly via a correlated subquery with APPLY?
You are only limited by your imagination.
Here’s an example…
For each store with a main office in Wisconsin, let’s look at the top 3 products (and their dollar amounts) that they bought in terms of dollars.
select c.CustomerIDThat’s pretty slick, huh?
,s.Name
,f.ProductID
,ProductName=p.Name
,f.PurchaseAmt
from Sales.Customer c
join Sales.Store s on c.CustomerID=s.CustomerID
join Sales.CustomerAddress ca on c.CustomerID=ca.CustomerID
join Person.Address a on ca.AddressID=a.AddressID
join Person.StateProvince sp on a.StateProvinceID=sp.StateProvinceID
cross apply (select top 3 ProductID
,PurchaseAmt=sum(LineTotal)
from Sales.SalesOrderHeader soh
join Sales.SalesOrderDetail sod on soh.SalesOrderID=sod.SalesOrderID
where CustomerID=c.CustomerID
group by ProductID
order by sum(LineTotal) desc) f
join Production.Product p on f.ProductID=p.ProductID
where ca.AddressTypeID=3 --MainOffice
and sp.StateProvinceCode='WI'
/*
CustomerID Name ProductID ProductName PurchaseAmt
---------- --------------------------- --------- -------------------------- ------------
418 Good Bike Shop 795 Road-250 Black, 52 30367.350000
418 Good Bike Shop 794 Road-250 Black, 48 24136.807500
418 Good Bike Shop 792 Road-250 Red, 58 23508.517500
453 Unique Bikes 773 Mountain-100 Silver, 44 16319.952000
453 Unique Bikes 771 Mountain-100 Silver, 38 12239.964000
453 Unique Bikes 772 Mountain-100 Silver, 42 12239.964000
543 Friendly Neighborhood Bikes 782 Mountain-200 Black, 38 9638.958000
543 Friendly Neighborhood Bikes 868 Women's Mountain Shorts, M 671.904000
543 Friendly Neighborhood Bikes 869 Women's Mountain Shorts, L 335.952000
606 Little Bicycle Supply Shop 717 HL Road Frame - Red, 62 1717.800000
606 Little Bicycle Supply Shop 838 HL Road Frame - Black, 44 858.900000
606 Little Bicycle Supply Shop 738 LL Road Frame - Black, 52 809.328000
*/
Shred XML!
Using the .nodes() function, coupled with the .value() and .query() functions, we can use APPLY to do some cool tricks with XML.
For the first 10 JobCandidates, let’s pull information out of the Resume column, which is of type XML. We’ll get their Name and the schools (there might be more than one) that they attended, listing them in order of their graduation date.:
with xmlnamespacesAs Miley Cyrus would say: That’s really cool.
(
'http://schemas.microsoft.com/sqlserver/2004/07/adventure-works/Resume' as ns
)
select JobCandidateID
,Name
,Education=stuff(EduList,1,2,'')
from HumanResources.JobCandidate
cross apply
Resume.nodes('/ns:Resume') F_ResumeNode(ResumeNode)
cross apply
ResumeNode.nodes('(./ns:Name)') F_NameNode(NameNode)
cross apply
(select Name=NameNode.value('(./ns:Name.First[1])','nvarchar(50)')
+' '
+NameNode.value('(./ns:Name.Last[1])','nvarchar(50)')
) F_Name
cross apply
(select EduList=ResumeNode.query('for $p in (./ns:Education)
order by $p/ns:Edu.EndDate
return concat("; ",string($p/ns:Edu.School))'
).value('.','nvarchar(200)')
) F_Edu
where JobCandidateID<=10
/*
JobCandidateID Name Education
-------------- -------------------- ----------------------------------------------------
1 Shai Bassli Midwest State University
2 Max Benson Evergreen High School ; Everglades State College
3 Krishna Sunkammurali Western University
4 Stephen Jiang Louisiana Business College of New Orleans
5 Thierry D'Hers Université d'Aix-Marseille
6 Christian Kleinerman Lycée technique Émile Zola ; Université de Perpignan
7 Lionel Penuchot Université de Lyon
8 Peng Wu Western University
9 Shengda Yang Evergreen High School ; Soutern State College
10 Tai Yee Midwest State University
*/
Introduce New Columns!
This is probably the best use of APPLY because it makes code so much more clear.
Consider the following query, which groups the 2002 Sales by Month. That’s done by the DATEADD/DATEDIFF logic, but it has to be repeated in the GROUP BY and the SELECT and the ORDER BY:
select Mth=datename(monthI don’t know about you, but I hate all that repetition, and it looks a little busy. So APPLY to the rescue:
,dateadd(month
,datediff(month,'19000101',OrderDate)
,'19000101'))
,Total=sum(TotalDue)
from Sales.SalesOrderHeader
where OrderDate>='20020101'
and OrderDate<'20030101'
group by dateadd(month
,datediff(month,'19000101',OrderDate)
,'19000101')
order by dateadd(month
,datediff(month,'19000101',OrderDate)
,'19000101')
/*
Mth Total
--------- ------------
January 1605782.1915
February 3130823.0378
March 2643081.0798
April 1905833.9088
May 3758329.2949
June 2546121.9618
July 3781879.0708
August 5433609.3426
September 4242717.7166
October 2854206.7518
November 4427598.0006
December 3545522.738
*/
select Mth=datename(month,FirstDayOfMth)Now isn’t that much clearer as to what’s going on? And it costs nothing at all! The query plans of both of the queries above are exactly the same!
,Total=sum(TotalDue)
from Sales.SalesOrderHeader
cross apply
(
select FirstDayOfMth=dateadd(month
,datediff(month,'19000101',OrderDate)
,'19000101')
) F_Mth
where OrderDate>='20020101'
and OrderDate<'20030101'
group by FirstDayOfMth
order by FirstDayOfMth
/*
Mth Total
--------- ------------
January 1605782.1915
February 3130823.0378
March 2643081.0798
April 1905833.9088
May 3758329.2949
June 2546121.9618
July 3781879.0708
August 5433609.3426
September 4242717.7166
October 2854206.7518
November 4427598.0006
December 3545522.738
*/
Perform Complicated Calculations!
This is the part of APPLY that I really love. Let’s look at an example.
Let’s say that you have a table of comma-delimited lists of one or more integers:
create table #tYour job: Pull out only the rows that have the 4th number in the list less than 50 and sort the output by the 3rd number in the list.
(
ID int identity(1,1)
,ListOfNums varchar(50)
)
insert #t
values ('279,37,972,15,175')
,('17,72')
,('672,52,19,23')
,('153,798,266,52,29')
,('77,349,14')
select * from #t
/*
ID ListOfNums
-- -----------------
1 279,37,972,15,175
2 17,72
3 672,52,19,23
4 153,798,266,52,29
5 77,349,14
*/
Easy, right? Ha ha ha ha ha ha ha hee hee hee hee hee hee ho ho ho ho haw haw giggle chuckle guffaw!
Before the APPLY operator, SQL2000 folks would have to resort to something ludicrous like this in order to accomplish this task:
select IDBut now, through the magic of APPLY, you can have a much clearer query:
,ListOfNums
from #t
where substring(ListOfNums+',,,,',charindex(',',ListOfNums+',,,,',
charindex(',',ListOfNums+',,,,',charindex(',',ListOfNums+',,,,')+1)+1)+1,
(charindex(',',ListOfNums+',,,,',charindex(',',ListOfNums+',,,,',
charindex(',',ListOfNums+',,,,',charindex(',',ListOfNums+',,,,')+1)+1)+1)-
charindex(',',ListOfNums+',,,,',charindex(',',ListOfNums+',,,,',
charindex(',',ListOfNums+',,,,')+1)+1))-1)
< 50
order by substring(ListOfNums+',,,,',charindex(',',ListOfNums+',,,,',
charindex(',',ListOfNums+',,,,')+1)+1,(charindex(',',ListOfNums+',,,,',
charindex(',',ListOfNums+',,,,',charindex(',',ListOfNums+',,,,')+1)+1)-
charindex(',',ListOfNums+',,,,',charindex(',',ListOfNums+',,,,')+1))-1)
/*
ID ListOfNums
-- -------------
2 17,72
5 77,349,14
3 672,52,19,23
1 279,37,972,15
*/
select IDSee how I used APPLY to write a little program of a sort? First, I added commas to the end of the column to account for possibly missing numbers in the list. Then I calculated the position of the first comma in that string (p1). Then I calculated the position of the second comma (p2), and that can only be done by using the p1 position I calculated in the previous step. I continue on getting the position of the third and fourth comma. And now that I have those, I can pull out Num3 (from between the second and third comma) and Num4 (from between the third and fourth comma). And I can now use those values in my WHERE and ORDER BY clause.
,ListOfNums
from #t
cross apply (select WorkString=ListOfNums+',,,,') F_Str
cross apply (select p1=charindex(',',WorkString)) F_P1
cross apply (select p2=charindex(',',WorkString,p1+1)) F_P2
cross apply (select p3=charindex(',',WorkString,p2+1)) F_P3
cross apply (select p4=charindex(',',WorkString,p3+1)) F_P4
cross apply (select Num3=convert(int,substring(WorkString,p2+1,p3-p2-1))
,Num4=convert(int,substring(WorkString,p3+1,p4-p3-1))) F_Nums
where Num4<50
order by Num3
/*
ID ListOfNums
-- -------------
2 17,72
5 77,349,14
3 672,52,19,23
1 279,37,972,15
*/
And the best part? NO COST! The above two queries are exactly the same as far as the optimizer is concerned. All those CROSS APPLYs are glommed together into a Compute Scalar operator, essentially coming up with really complicated expressions like you see in the first query. Take a look at the query plan yourself and you’ll see.
Replace the UNPIVOT operator!
Throw the UNPIVOT operator out the window… The optimizer really translates it into an APPLY operator under the hood anyway… and you can have control over NULLs and differing datatypes.
Look at the following example, which is a function that accepts a CustomerID and spits out information on the customer in a vertical fashion (note that there can be multiple contacts for a customer… this just spits out the first one… thus the ROW_NUMBER() logic):
create function VerticalMainOfficeDataWatch it in action:
(
@CustomerID int
)
returns table
as
return
with BaseData as
(
select SeqNo=row_number() over (order by ContactType)
,Name,AddressLine1,AddressLine2,City,StateProvinceName
,PostalCode,CountryRegionName
,ContactType,ContactName,Phone,EmailAddress
,YearOpened,NumberEmployees,Specialty
,SquareFeet,Brands,AnnualSales
from AdventureWorks.Sales.vStoreWithDemographics
cross apply (select ContactName=isnull(Title+' ','')
+FirstName+' '
+isnull(MiddleName+' ','')
+LastName
+isnull(' '+Suffix,'')) F_Name
where CustomerID=@CustomerID
and AddressType='Main Office'
)
select Property,Value
from BaseData
cross apply
(values ('NAME AND ADDRESS:','')
,(' Name',Name)
,(' Address',AddressLine1)
,(' ',AddressLine2)
,(' City',City)
,(' State/Province',StateProvinceName)
,(' Postal Code',PostalCode)
,(' Country/Region',CountryRegionName)
,('','')
,('CONTACT:','')
,(' Type',ContactType)
,(' Name',ContactName)
,(' Phone',Phone)
,(' EmailAddress',EmailAddress)
,('','')
,('DEMOGRAPHIC INFO:','')
,(' Year Opened',str(YearOpened,4))
,(' Number of Employees',convert(varchar(10),NumberEmployees))
,(' Specialty',Specialty)
,(' Square Feet',convert(varchar(10),SquareFeet))
,(' Brands',Brands)
,(' Annual Sales','$'+convert(varchar(20),AnnualSales,1))) P(Property,Value)
where SeqNo=1
and Value is not null
select * from VerticalMainOfficeData(34)Can UNPIVOT do that? Not in a million years. It’s toast!
/*
Property Value
--------------------- -----------------------------
NAME AND ADDRESS:
Name Cycles Wholesaler & Mfg.
Address Science Park South, Birchwood
Stanford House
City Warrington
State/Province England
Postal Code WA3 7BH
Country/Region United Kingdom
CONTACT:
Type Owner
Name Ms. Barbara J. German
Phone 1 (11) 500 555-0181
EmailAddress barbara4@adventure-works.com
DEMOGRAPHIC INFO:
Year Opened 1999
Number of Employees 15
Specialty Touring
Square Feet 21000
Brands 4+
Annual Sales $800,000.00
*/
Make JOINs Extinct!
`
Okay, this is really kind of a joke, but really, think about it… What is a JOIN? For each row in the first table, I want to JOIN it somehow with a row or rows in the second table. That sounds like an APPLY type of thing, doesn’t it? Well it is!
Look at the following traditional JOIN query, which finds all the Accessories that are Yellow, Blue, White:
select SubCategoryName=s.NameYou can replace that JOIN with a CROSS APPLY:
,p.ProductID
,ProductName=p.Name
,p.Color
from Production.ProductSubCategory s
join Production.Product p on s.ProductSubcategoryID=p.ProductSubcategoryID
where s.ProductCategoryID=3 --Accessories
and p.Color in ('Yellow','Blue','White')
order by SubCategoryName
,p.ProductID
/*
SubCategoryName ProductID ProductName Color
--------------- --------- ------------------------------- ------
Jerseys 881 Short-Sleeve Classic Jersey, S Yellow
Jerseys 882 Short-Sleeve Classic Jersey, M Yellow
Jerseys 883 Short-Sleeve Classic Jersey, L Yellow
Jerseys 884 Short-Sleeve Classic Jersey, XL Yellow
Socks 709 Mountain Bike Socks, M White
Socks 710 Mountain Bike Socks, L White
Socks 874 Racing Socks, M White
Socks 875 Racing Socks, L White
Vests 864 Classic Vest, S Blue
Vests 865 Classic Vest, M Blue
Vests 866 Classic Vest, L Blue
*/
select SubCategoryName=s.NameThe query plans for both of those are exactly the same!
,p.ProductID
,ProductName=p.Name
,p.Color
from Production.ProductSubCategory s
cross apply (select *
from Production.Product
where ProductSubcategoryID=s.ProductSubcategoryID) p
where s.ProductCategoryID=3 --Accessories
and p.Color in ('Yellow','Blue','White')
order by SubCategoryName
,p.ProductID
/*
SubCategoryName ProductID ProductName Color
--------------- --------- ------------------------------- ------
Jerseys 881 Short-Sleeve Classic Jersey, S Yellow
Jerseys 882 Short-Sleeve Classic Jersey, M Yellow
Jerseys 883 Short-Sleeve Classic Jersey, L Yellow
Jerseys 884 Short-Sleeve Classic Jersey, XL Yellow
Socks 709 Mountain Bike Socks, M White
Socks 710 Mountain Bike Socks, L White
Socks 874 Racing Socks, M White
Socks 875 Racing Socks, L White
Vests 864 Classic Vest, S Blue
Vests 865 Classic Vest, M Blue
Vests 866 Classic Vest, L Blue
*/
And LEFT JOINs can be replaced by OUTER APPLYs!
Today UNPIVOTs and JOINs… Tomorrow the world! Bwu hu hu ha ha ha ha haaaaaaa (Diabolical laughter).
Do it ALL!
For my final example, I’ll do ALL of the above (except for the JOIN replacement, which was just kind of a joke/trick anyway).
In doing the examples above, my query cache got populated with the text and plans of the queries I executed. We will look in the cache for the CROSS APPLY(TOP 3) query that was in the Execute SubQueries! section above, shred its query plan, looking for the operators, figure out their percentage cost, and list them in descending order of that cost. For Scans and Seeks and Joins, we will show the table, column and/or index used. And it will be presented in a vertical manner.
Note that the challenge here is finding the cost of each operator… it is not stored in the plan. Each operator has a Total Subtree Cost, but that is the cost of the operator itself PLUS the Subtree Costs of each of its immediate children operators. So for each operator, I had to find its children, total up their Subtree Costs and subtract that from the Subtree Cost of the operator to get the Individual Cost of the operator. This is done in the CROSS APPLY of the OperatorCosts CTE.
Hopefully the comments are self-explanatory:
with xmlnamespacesIs that, like, waaaaay cool, or what?!
(
default 'http://schemas.microsoft.com/sqlserver/2004/07/showplan'
)
,OperatorData as
(
select ParentNodeID
,NodeID
,OperatorDesc
,ScanSchema,ScanTable,ScanIndex
,LoopSchema,LoopTable,LoopColumn
,HashSchema,HashTable,HashColumn
,SubTreeCost
from sys.dm_exec_query_stats qs
cross apply
--Get the Query Text
sys.dm_exec_sql_text(qs.sql_handle) qt
cross apply
--Get the Query Plan
sys.dm_exec_query_plan(qs.plan_handle) qp
cross apply
--Get the RelOp nodes from the Plan
qp.query_plan.nodes('//RelOp') F_RelNodes(RelNode)
cross apply
--Pull out the various attributes from the RelOp Node
--And also extract the ParentNodeID of the operator
(select ParentNodeID=RelNode.value('(../../@NodeId)','int')
,NodeID=RelNode.value('(./@NodeId)','int')
,LogicalOp=RelNode.value('(./@LogicalOp)','varchar(50)')
,PhysicalOp=RelNode.value('(./@PhysicalOp)','varchar(50)')
,SubTreeCost=RelNode.value('(./@EstimatedTotalSubtreeCost)','float')
) F_OpInfo
cross apply
--Make a nice description out of the Operator
(select OperatorDesc=case
when LogicalOp=PhysicalOp
then PhysicalOp
else PhysicalOp+' ('+LogicalOp+')'
end
) F_OpDesc
outer apply
--Get child nodes having to do with a Scan/Seek
--Note that OUTER APPLY is used since there may not
--be any child nodes of this type
RelNode.nodes('(./IndexScan[1]/Object[1])')
F_ScanNode(ScanNode)
outer apply
--And pull out their Table/Index information
(select ScanSchema=ScanNode.value('(./@Schema)','varchar(50)')
,ScanTable=ScanNode.value('(./@Table)','varchar(50)')
,ScanIndex=ScanNode.value('(./@Index)','varchar(100)')
) F_ScanInfo
outer apply
--Get child nodes having to do with Nested Loops
--Note that OUTER APPLY is used since there may not
--be any child nodes of this type
RelNode.nodes('(./NestedLoops[1]/OuterReferences[1]/ColumnReference[1])')
F_LoopNode(LoopNode)
outer apply
--And pull out their Table/Column information
(select LoopSchema=LoopNode.value('(./@Schema)','varchar(50)')
,LoopTable=LoopNode.value('(./@Table)','varchar(50)')
,LoopColumn=LoopNode.value('(./@Column)','varchar(50)')
) F_LoopInfo
outer apply
--Get child nodes having to do with Hash Joins
--Note that OUTER APPLY is used since there may not
--be any child nodes of this type
RelNode.nodes('(./Hash[1]/HashKeysBuild[1]/ColumnReference[1])')
F_HashNode(HashNode)
outer apply
--And pull out their Table/Column information
(select HashSchema=HashNode.value('(./@Schema)','varchar(50)')
,HashTable=HashNode.value('(./@Table)','varchar(50)')
,HashColumn=HashNode.value('(./@Column)','varchar(50)')
) F_HashInfo
where qt.text like '%select top 3 ProductID%'
and qt.text not like '%with xmlnamespaces%' --Exclude this query
)
,OperatorCosts as
(
--Calculate the Individual Costs by subtracting each Operator's
--SubTreeCost minus its immediate children's SubTreeCosts
select NodeID
,OperatorDesc
,ScanSchema,ScanTable,ScanIndex
,LoopSchema,LoopTable,LoopColumn
,HashSchema,HashTable,HashColumn
,OperatorCost=convert(numeric(16,8),SubTreeCost-ChildrenSubTreeCost)
from OperatorData o
cross apply
--Calculate the sum of the SubTreeCosts of the immediate children
(select ChildrenSubTreeCost=isnull(sum(SubTreeCost),0)
from OperatorData
where ParentNodeID=o.NodeID) F_ChildCost
)
,CostPercents as
(
--Calculate the CostPercent using a window function
select NodeID
,OperatorDesc
,ScanSchema,ScanTable,ScanIndex
,LoopSchema,LoopTable,LoopColumn
,HashSchema,HashTable,HashColumn
,CostPercent=convert(numeric(5,1),100*OperatorCost/sum(OperatorCost) over ())
from OperatorCosts
)
select Information
from CostPercents
cross apply
--UNPIVOT the information into a vertical presentation
(values ('NodeID '+convert(varchar(5),NodeID)
+' ('+convert(varchar(10),CostPercent)+'%):')
,(' '+OperatorDesc)
,(' Table: '+ScanSchema+'.'+ScanTable)
,(' Index: '+ScanIndex)
,(' Table: '+LoopSchema+'.'+LoopTable)
,(' Column: '+LoopColumn)
,(' Table: '+HashSchema+'.'+HashTable)
,(' Column: '+HashColumn)) P(Information)
where Information is not null --Eliminate NULL rows
order by CostPercent desc
/*
Information
----------------------------------------------------------------
NodeID 13 (19.6%):
Sort (TopN Sort)
NodeID 15 (19.6%):
Sort
NodeID 9 (18.2%):
Clustered Index Scan
Table: [Sales].[CustomerAddress]
Index: [PK_CustomerAddress_CustomerID_AddressID]
NodeID 11 (14.5%):
Clustered Index Seek
Table: [Sales].[Store]
Index: [PK_Store_CustomerID]
NodeID 20 (8.7%):
Clustered Index Seek
Table: [Sales].[SalesOrderDetail]
Index: [PK_SalesOrderDetail_SalesOrderID_SalesOrderDetailID]
NodeID 5 (5.8%):
Hash Match (Inner Join)
Table: [Person].[Address]
Column: AddressID
NodeID 12 (4.9%):
Clustered Index Seek
Table: [Sales].[Customer]
Index: [PK_Customer_CustomerID]
NodeID 18 (4.7%):
Index Seek
Table: [Sales].[SalesOrderHeader]
Index: [IX_SalesOrderHeader_CustomerID]
NodeID 30 (2.3%):
Clustered Index Seek
Table: [Production].[Product]
Index: [PK_Product_ProductID]
NodeID 7 (0.6%):
Index Seek
Table: [Person].[StateProvince]
Index: [AK_StateProvince_StateProvinceCode_CountryRegionCode]
NodeID 8 (0.6%):
Index Seek
Table: [Person].[Address]
Index: [IX_Address_StateProvinceID]
NodeID 6 (0.2%):
Nested Loops (Inner Join)
Table: [Person].[StateProvince]
Column: StateProvinceID
NodeID 3 (0.2%):
Nested Loops (Inner Join)
Table: [Sales].[CustomerAddress]
Column: CustomerID
NodeID 0 (0.0%):
Nested Loops (Inner Join)
Table: [Sales].[SalesOrderDetail]
Column: ProductID
NodeID 1 (0.0%):
Nested Loops (Inner Join)
Table: [Sales].[Customer]
Column: CustomerID
NodeID 2 (0.0%):
Nested Loops (Inner Join)
Table: [Sales].[CustomerAddress]
Column: CustomerID
NodeID 19 (0.0%):
Compute Scalar
NodeID 16 (0.0%):
Compute Scalar
NodeID 17 (0.0%):
Nested Loops (Inner Join)
Table: [Sales].[SalesOrderHeader]
Column: SalesOrderID
NodeID 14 (0.0%):
Stream Aggregate (Aggregate)
*/
I hope I’ve convinced you how powerful the APPLY operator can be. I couldn’t live without it.
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