For beginning database professionals, execution plans can be intimidating. This 1-hour session takes you through the basics of reading graphical execution plans and drills into key operators, warnings, and properties. Learn how, why, and when an execution plan is created and saved to plan cache. Reading execution plans becomes much less intimidating as we walk through the flow and process of a graphical execution plan. After looking at the key operators, you will be able to analyze the operators of your execution plans, view the operator properties, and identify common performance-tuning opportunities. Finally, you will be able to capture the execution plan and save it for analysis and comparison after modifications.

1. Execution Plans for
Mere Mortals
A beginners look at execution plans.
Mike Lawell, Teammate, Linchpin People

2. 2
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3. 3
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4. Agenda
We will be taking a look at Graphical Execution Plans from a beginners
perspective. The ride will include the following:
• Execution Steps
• Execution Plan Basics
• Basic Operators
• Join Operators
• Other Operators
4

5. Execution
Step by Step

6. 6
On The Inside
Relational Engine
Optimizer
Command
Parser
Query
Executor
SNI*
User
Storage Engine
Buffer
Manager
Access
Methods
Transaction
Manager
SQL OS
Buffer Pool
Borrowed from Bradley Ball’s “SQL Internals, Recovery Models, & Backups” presentation
Plan Cache
Data
Cache
Data
*SQL Server Network Interface (SNI)

7. Relation Engine
1. Is the query syntactically correct?
SELECT [Name], [Number] FORM [Production].[Product]
Incorrect syntax near 'Production'.
SELCT [Name], [Number] FROM [Production].[Product]
Msg 156, Level 15, State 1, Line 3
Incorrect syntax near the keyword 'FROM'.
2. No Errors will be Output as Parse Tree to Algebrizer
Query Parsing
7

8. 8
Query Parsing
SELECT [Name], [Number] FORM [Production].[Product]
Incorrect syntax near 'Production'.

9. 9
Query Parsing
SELECT [Name], [Number] FROM [Production].[Product]

10. 10
Create Plan
Execute
Parallel/
Not Parallel
Store in Plan
Cache (*)
Aggregate
Binding
Resolve
Data Types
Name
Resolution
Plan Creation
Algebrizer Query Optimizer
Recompile?
Storage Engine
No Results
Returned
7
Cost >
Cost for
Parallelism
8
No
Find Plan
Does Plan
Exist?
5
Yes
Hash Checked
in Plan Cache
3 4
6
1 2
5

11. What is a Hash?
A hash is a number that is generated by reading the contents of a document or
message. Different messages should generate different hash values, but the
same message causes the algorithm to generate the same hash value.
SELECT HASHBYTES('SHA','Hellow World')
0x5F341D31F6B6A8B15BC4E6704830BF37F99511D1
SELECT HASHBYTES('SHA','Hello World')
0x0A4D55A8D778E5022FAB701977C5D840BBC486D0
SELECT HASHBYTES('SHA','Hello World')
0xC2F14A3E371A79A792E6ED78FB07A4B0C26618B4
https://technet.microsoft.com/en-us/library/cc837966(v=sql.100).aspx

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Cardinality Estimation
The optimization process depends on a cost estimation of each physical operator and the
estimated number of records (cardinality estimation) to be processed.
DBCC SHOW_STATISTICS ("[Sales].[SalesOrderDetailEnlarged]",
[PK_SalesOrderDetailEnlarged_SalesOrderID_SalesOrderDetailID]) WITH HISTOGRAM;
GO
The accuracy of the cardinality estimation depends upon the distribution of values in one or more
columns of a table (statistics).
RANGE_HI_KEY RANGE_ROWS EQ_ROWS DISTINCT_RANGE_ROWS AVG_RANGE_ROWS
43659 0 12 0 1
47355 16015 68 3695 4.334236
51739 24056 72 4383 5.488478
57046 20733 67 5306 3.907463
65174 27775 48 8127 3.41762
101719 131906 36 36544 3.609512
107533 22424 60 5813 3.857561

13. 14
Cardinality Estimation
There are multiple factors that can negatively impact the cardinality estimation
process:
1. Out of date Statistics
SELECT object_name(object_id) as TableName, name AS stats_name,
STATS_DATE(object_id, stats_id) AS statistics_update_date
FROM sys.stats
WHERE object_id = OBJECT_ID('[Sales].[SalesOrderDetailEnlarged]');
2. Cardinality Estimation Errors
https://www.sqlskills.com/blogs/joe/cardinality-estimation-model-version/

14. 15
Which cardinality version am I using?

15. Execution Plan
Basics
Getting Started

16. 17
Required Permissions
Server or database roles with permissions:
sysadmin, dbcreator or db_owner or showplan
To give showplan rights to the database:
GRANT SHOWPLAN TO [username];

17. How to Read
18

18. Basic Operators
Start Here

19. Basic Operators
DML Statements Access Methods
20
Other Operators

20. Join Operators
Making it Happen

21. 22
Nested Loop
Non-blocking
Description
For each row in the top (outer) input, scan the
bottom (inner) input, and output matching rows.
Where does it happen: Joins with
indexes on join columns.

22. Nested Loop
SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate]
FROM [Sales].[SalesOrderHeader] soh
JOIN [Sales].[SalesOrderDetail] sod
ON sod.[SalesOrderID] = soh.[SalesOrderID]
WHERE soh.[OrderDate] = '2013-08-30 00:00:00.000'
Non-blocking
23

23. Nested Loop
SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate]
FROM [Sales].[SalesOrderHeader] soh
JOIN [Sales].[SalesOrderDetail] sod
ON sod.[SalesOrderID] = soh.[SalesOrderID]
WHERE soh.[OrderDate] = '2013-08-30 00:00:00.000'
Outer Loop
Inner Loop
Non-blocking
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24. 25
Nested Loop

25. 26
Non-blocking
Nested Loop
SalesOrderID RevisionNumber OrderDate
55233 8 2013-08-30 00:00:00.000
55234 8 2013-08-30 00:00:00.000
55235 8 2013-08-30 00:00:00.000
55236 8 2013-08-30 00:00:00.000
55237 8 2013-08-30 00:00:00.000
55238 8 2013-08-30 00:00:00.000
55239 8 2013-08-30 00:00:00.000
55240 8 2013-08-30 00:00:00.000
55241 8 2013-08-30 00:00:00.000
55242 8 2013-08-30 00:00:00.000
55243 8 2013-08-30 00:00:00.000
55244 8 2013-08-30 00:00:00.000
55245 8 2013-08-30 00:00:00.000
55246 8 2013-08-30 00:00:00.000
55247 8 2013-08-30 00:00:00.000
55248 8 2013-08-30 00:00:00.000
55249 8 2013-08-30 00:00:00.000
55250 8 2013-08-30 00:00:00.000
55251 8 2013-08-30 00:00:00.000
55252 8 2013-08-30 00:00:00.000
55253 8 2013-08-30 00:00:00.000
SalesOrderID ProductID CarrierTrackingNumber OrderQty
55233 738 1590-499E-95 5
55233 792 1590-499E-95 5
55233 793 1590-499E-95 4
55233 794 1590-499E-95 4
55233 795 1590-499E-95 4
55233 796 1590-499E-95 5
55233 797 1590-499E-95 6
55233 798 1590-499E-95 2
55233 799 1590-499E-95 1
55233 800 1590-499E-95 1
55233 801 1590-499E-95 6
55233 835 1590-499E-95 6
55233 874 1590-499E-95 8
55233 875 1590-499E-95 8
55233 938 1590-499E-95 5
55233 939 1590-499E-95 5
55233 940 1590-499E-95 4
55233 973 1590-499E-95 5
55233 974 1590-499E-95 1
55233 975 1590-499E-95 1

26. 27
Description
Match rows from two suitably sorted input
tables exploiting their sort order.
Where does it happen: Joins with
indexes on join columns sorted in an
appropriate sort order.
Merge
Non-blocking

27. Merge
SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate]
FROM [Sales].[SalesOrderHeader] soh
JOIN [Sales].[SalesOrderDetail] sod
ON sod.[SalesOrderID] = soh.[SalesOrderID]
Non-blocking
28

28. 29
Merge
SalesOrderID RevisionNumber OrderDate
55233 8 2013-08-30 00:00:00.000
55234 8 2013-08-30 00:00:00.000
55235 8 2013-08-30 00:00:00.000
55236 8 2013-08-30 00:00:00.000
55237 8 2013-08-30 00:00:00.000
55238 8 2013-08-30 00:00:00.000
55239 8 2013-08-30 00:00:00.000
55240 8 2013-08-30 00:00:00.000
55241 8 2013-08-30 00:00:00.000
55242 8 2013-08-30 00:00:00.000
55243 8 2013-08-30 00:00:00.000
55244 8 2013-08-30 00:00:00.000
55245 8 2013-08-30 00:00:00.000
55246 8 2013-08-30 00:00:00.000
55247 8 2013-08-30 00:00:00.000
55248 8 2013-08-30 00:00:00.000
55249 8 2013-08-30 00:00:00.000
55250 8 2013-08-30 00:00:00.000
55251 8 2013-08-30 00:00:00.000
55252 8 2013-08-30 00:00:00.000
55253 8 2013-08-30 00:00:00.000
SalesOrderID ProductID CarrierTrackingNumber OrderQty
55233 738 1590-499E-95 5
55233 792 1590-499E-95 5
55233 793 1590-499E-95 4
55233 794 1590-499E-95 4
55233 795 1590-499E-95 4
55233 796 1590-499E-95 5
55233 797 1590-499E-95 6
55233 798 1590-499E-95 2
55233 799 1590-499E-95 1
55233 800 1590-499E-95 1
55233 801 1590-499E-95 6
55233 835 1590-499E-95 6
55233 874 1590-499E-95 8
55233 875 1590-499E-95 8
55233 938 1590-499E-95 5
55233 939 1590-499E-95 5
55233 940 1590-499E-95 4
55233 973 1590-499E-95 5
55233 974 1590-499E-95 1
55233 975 1590-499E-95 1
Non-blocking

29. 30
Hash Match
Description
Use each row from the top input to build a hash
table, and each row from the bottom input to
probe into the hash table, outputting all
matching rows.
Where does it happen: Missing Index,
Missing Where Clause, Where Clause that is
non-sargable.

30. Hash Match
Blocking
SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty]
FROM [Sales].[SalesOrderDetail] sod1
JOIN [Sales].[SalesOrderDetail] sod2
ON [sod1].[ProductID] = [sod2].[ProductID]
AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber]
AND [sod1].[OrderQty] > 5
31

31. Hash Match
Hash Table
Blocking
32
Column A Column B
98234750198709872 8749872
Hashed Value
8Ax94Dd343S

32. Hash Match
Hash Table 1
Blocking
33
8Ax94Dd343S
1) From the first input (table) the
column data is hashed and put in
buckets in a “hash” table (in
tempdb).
2) Then it reads rows from input 2,
hashes the join columns and
compares the hash and values to
output matches.
8Ax94Dd343S
8Bx40XdF43D
8CxXDVa91f If a Match…
“Send” row data to next
operator and go to the next row.

33. 34
Fixed Hash Match

34. Other Operators
Fun Stuff

35. 36
Parallelism
SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty]
FROM [Sales].[SalesOrderDetail] sod1
JOIN [Sales].[SalesOrderDetail] sod2
ON [sod1].[ProductID] = [sod2].[ProductID]
AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber]

36. 37
Parallelism
SET STATISTICS IO ON
SET STATISTICS TIME ON
SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty]
FROM [Sales].[SalesOrderDetail] sod1
JOIN [Sales].[SalesOrderDetail] sod2
ON [sod1].[ProductID] = [sod2].[ProductID]
AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber]
SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty]
FROM [Sales].[SalesOrderDetail] sod1
JOIN [Sales].[SalesOrderDetail] sod2
ON [sod1].[ProductID] = [sod2].[ProductID]
AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber]
OPTION(MAXDOP 1)

37. 38
Parallelism
Table 'SalesOrderDetail'. Scan count 18, logical reads 2490, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical
reads 0, lob read-ahead reads 0.
Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-
ahead reads 0.
Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob
read-ahead reads 0.
SQL Server Execution Times:
CPU time = 9593 ms, elapsed time = 2381 ms.
Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-
ahead reads 0.
Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob
read-ahead reads 0.
Table 'SalesOrderDetail'. Scan count 2, logical reads 2490, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads
0, lob read-ahead reads 0.
SQL Server Execution Times:
CPU time = 7941 ms, elapsed time = 8372 ms.

38. 39
Parallelism

39. 40
Hash Aggregate
SELECT [sod1].[CarrierTrackingNumber], sum([sod1].[UnitPrice]) UnitPriceTotal,
sum([sod1].[OrderQty]) as OrderQtyTotal
FROM [Sales].[SalesOrderDetail] sod1
JOIN [Sales].[SalesOrderDetail] sod2
ON [sod1].[ProductID] = [sod2].[ProductID]
AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber]
AND [sod1].[OrderQty] > 5
GROUP BY [sod1].[CarrierTrackingNumber]

40. 41
Stream Aggregate
SELECT [sod1].[CarrierTrackingNumber], sum([sod1].[UnitPrice])
UnitPriceTotal, sum([sod1].[OrderQty]) as OrderQtyTotal
FROM [Sales].[SalesOrderDetail] sod1
JOIN [Sales].[SalesOrderDetail] sod2
ON [sod1].[ProductID] = [sod2].[ProductID]
AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber]
AND [sod1].[OrderQty] > 5
GROUP BY [sod1].[CarrierTrackingNumber]

41. 42
Sort Spill
Operator used tempdb to spill data during execution with spill level 2
SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate]
FROM [Sales].[SalesOrderHeader] soh
JOIN [Sales].[SalesOrderDetailEnlarged] sod
ON sod.[SalesOrderID] = soh.[SalesOrderID]
WHERE [sod].[CarrierTrackingNumber] > '41D0-42A8-A5'
ORDER BY [soh].[RevisionNumber], [soh].[OrderDate]

42. 43
Sort Spill
SELECT TOP 1 [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate]
FROM [Sales].[SalesOrderHeader] soh
JOIN [Sales].[SalesOrderDetailEnlarged] sod
ON sod.[SalesOrderID] = soh.[SalesOrderID]
WHERE [sod].[CarrierTrackingNumber] > '41D0-42A8-A5'
ORDER BY [soh].[RevisionNumber], [soh].[OrderDate]

43. 44
Implicit Conversion
SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate]
FROM [Sales].[SalesOrderHeaderEnlarged] soh
JOIN [Sales].[SalesOrderDetailEnlarged] sod
ON sod.[SalesOrderID] = soh.[SalesOrderID]
WHERE CONVERT(varchar(10),[soh].[OrderDate],110) = '04-14-2014'

44. Parameter Sniffing
During the optimization phase the parameters supplied to the parameterized
query or stored procedure are used to determine how well an index will work
based upon the statistics.
It usually results in a better plan, but sometimes do to skewed distribution of
data is results in bad parameter sniffing.

45. 46
SQL Server 2016 Live Query
Live Query works with SQL Server 2014 + using SSMS 2016
SELECT pr.Name as ProductName, sod.OrderQty, sod.UnitPrice, soh.OrderDate
FROM [Sales].[SalesOrderDetailEnlarged] sod
JOIN [Sales].[SalesOrderHeaderEnlarged] soh
ON soh.SalesOrderID = sod.SalesOrderID
JOIN [Production].[Product] pr
ON pr.ProductID = sod.ProductID
WHERE soh.OrderDate between '2014-10-01' AND '2015-10-01'
OPTION(MAXDOP 1)

46. 48
Cool Tools/Products
SQL Server Query Plan Analysis by Joe Sack

47. 49
Additional Blogs to Checkout
When to Breakdown Complex Queries
Optimizing Your Query Plans with the SQL Server 2014 Cardinality Estimator

48. 50
Resources
http://www.red-gate.com/community/books/
Downloadable
FREE!

49. 51
Session Evaluations
ways to access
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50. 52
Mike Lawell
Mike Lawell
Principal Consultant
Twitter: @SQLDiver
Blog: SQLServerAssociates.com
LinkedIn: LinkedIn.com/in/MikeLawell