1.
Uncovering SQL Server Query Problems with
Execution Plans
Tony Davis
tony.davis@red-gate.com
@tonytheeditor
#SQLintheCityUK

2.
About Me
Books
SQL Server transaction log
SQL Server Source Control
Speaker
#SQLintheCityUK

3.
SELECT ProductID ,
ProductNumber ,
dbo.LineItemTotal(ProductID)
AS SumTotal
FROM Production.Product p
GO
SELECT bth.ProductID ,
bth.TransactionDate ,
bth.Quantity ,
bth.ActualCost
FROM dbo.bigTransactionHistory bth
WHERE bth.TransactionDate >= '20100701'
AND bth.TransactionDate
<= CURRENT_TIMESTAMP
ORDER BY bth.TransactionDate DESC;
GO
Why Bother Learning Execution Plans?
WITH Totals
AS ( SELECT DATEADD(m, u.theMonth, 0) AS TheMonth ,
SUM(CASE WHEN u.theCol = 'DateJoined'
THEN u.Registrations
ELSE 0
END) AS PeopleJoined ,
SUM(CASE WHEN u.theCol = 'DateLeft'
THEN u.Registrations
ELSE 0
END) AS PeopleLeft
FROM ( SELECT DATEDIFF(MONTH, 0, DateJoined)
AS DateJoined ,
DATEDIFF(MONTH, 0, DateLeft)
AS DateLeft ,
COUNT(*) AS Registrations
FROM dbo.Registrations2
GROUP BY DATEDIFF(MONTH, 0, DateJoined) ,
DATEDIFF(MONTH, 0, DateLeft)
) AS d UNPIVOT ( theMonth FOR theCol
IN ( d.DateJoined,
d.DateLeft ) ) AS u
GROUP BY u.theMonth
HAVING SUM(CASE WHEN u.theCol = 'DateJoined'
THEN u.Registrations
ELSE 0
END) > 0
)
SELECT TheMonth ,
PeopleJoined ,
PeopleLeft ,
SUM(PeopleJoined - PeopleLeft) OVER ( ORDER BY TheMonth
ROWS UNBOUNDED PRECEDING ) AS CurrentSubscribers
FROM Totals;

4.
From Query to Execution plan
SELECT bth.ProductID ,
bth.TransactionDate ,
bth.Quantity ,
bth.ActualCost
FROM dbo.bigTransactionHistory bth
WHERE bth.TransactionDate >= '20100701'
AND bth.TransactionDate <=
CURRENT_TIMESTAMP
ORDER BY bth.TransactionDate DESC;
GO
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5.
Plan
1
Plan
2
Plan
3
Plan
4
Plan
57
Plan
n?
Algebrizer
(Query Binding)
Syntax Checking
(Query Parsing)
Query
Optimization
Plan
Query
Execution
Engine
Plan
Cache
Metadata
Table/index structures
SELECT bth.ProductID ,
bth.TransactionDate ,
bth.Quantity ,
bth.ActualCost
FROM dbo.bigTransactionHistory bth
WHERE bth.TransactionDate >= '20100701'
AND bth.TransactionDate <= CURRENT_TIMESTAMP
ORDER BY bth.TransactionDate DESC;
GO
Index/Column Statistics
Volume/distribution of data
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6.
Getting the Execution Plan
.NET Code Profiler
- e.g. ANTS Performance Profiler
Get plans for previously executed
queries from the plan cache
- e.g. SQL Trace or Extended Events
- Using sys.dm_exec_cached_plans
Plans in cache contain no runtime
information

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7.
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8.
Getting the Execution Plan
.NET Code Profiler
- e.g. ANTS Performance Profiler
Get plans for previously executed
queries from the plan cache
- e.g. SQL Trace or Extended Events
- Using sys.dm_exec_cached_plans
Plans in cache contain no runtime
information
 SET
During testing, request the plan for
a query
• “estimated” plan – no runtime
• “actual” plan - with runtime
There is only 1 plan!

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9.
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10.
Execution Plans for Developers
• Don’t examine plan for every query
• Gather stats, focus on critical, frequent, resource-
intensive queries
• Sometimes the code logic is just wrong
– rip it up and start again!
• Sometimes the basic logic is fine
– But some subtler problem causes poor execution performance
– This is where execution plans can help!
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11.
• With SQL Server:
• We submit SQL describing the data set we
want
• The Optimizer decides how to execute it
• Some developers bring imperative approach line-
by-line control to SQL Server…
Problem: Row-by-row strategy
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31.
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32.
Solution: set-based approach
– Take the list to the supermarket ;)
– Define single statement (if possible) to return required
data set
• Make as few ‘passes’ through the base tables as possible
• Aggregate early
• Reduce the working set to as few rows as possible
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33.
WITH Totals
AS ( SELECT DATEADD(m, u.theMonth, 0) AS TheMonth ,
SUM(CASE WHEN u.theCol = 'DateJoined'
THEN u.Registrations
ELSE 0
END) AS PeopleJoined ,
SUM(CASE WHEN u.theCol = 'DateLeft'
THEN u.Registrations
ELSE 0
END) AS PeopleLeft
FROM ( SELECT DATEDIFF(MONTH, 0, DateJoined)
AS DateJoined ,
DATEDIFF(MONTH, 0, DateLeft)
AS DateLeft ,
COUNT(*) AS Registrations
FROM dbo.Registrations2
GROUP BY DATEDIFF(MONTH, 0, DateJoined) ,
DATEDIFF(MONTH, 0, DateLeft)
) AS d UNPIVOT ( theMonth FOR theCol
IN ( d.DateJoined,
d.DateLeft ) ) AS u
GROUP BY u.theMonth
HAVING SUM(CASE WHEN u.theCol = 'DateJoined'
THEN u.Registrations
ELSE 0
END) > 0
)
SELECT TheMonth ,
PeopleJoined ,
PeopleLeft ,
SUM(PeopleJoined - PeopleLeft) OVER ( ORDER BY TheMonth
ROWS UNBOUNDED PRECEDING ) AS CurrentSubscribers
FROM Totals;
Classic Running Total Problem
A Registrations table containing a list of
subscribers, with the dates that the subscribers
joined and left
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34.
WITH Totals
AS ( SELECT DATEADD(m, u.theMonth, 0) AS TheMonth ,
SUM(CASE WHEN u.theCol = 'DateJoined'
THEN u.Registrations
ELSE 0
END) AS PeopleJoined ,
SUM(CASE WHEN u.theCol = 'DateLeft'
THEN u.Registrations
ELSE 0
END) AS PeopleLeft
FROM ( SELECT DATEDIFF(MONTH, 0, DateJoined)
AS DateJoined ,
DATEDIFF(MONTH, 0, DateLeft)
AS DateLeft ,
COUNT(*) AS Registrations
FROM dbo.Registrations2
GROUP BY DATEDIFF(MONTH, 0, DateJoined) ,
DATEDIFF(MONTH, 0, DateLeft)
) AS d UNPIVOT ( theMonth FOR theCol
IN ( d.DateJoined,
d.DateLeft ) ) AS u
GROUP BY u.theMonth
HAVING SUM(CASE WHEN u.theCol = 'DateJoined'
THEN u.Registrations
ELSE 0
END) > 0
)
SELECT TheMonth ,
PeopleJoined ,
PeopleLeft ,
SUM(PeopleJoined - PeopleLeft) OVER ( ORDER BY TheMonth
ROWS UNBOUNDED PRECEDING ) AS CurrentSubscribers
FROM Totals;
A single ‘pass’
of the base table
Early
Aggregation
10000001690
Execution Plan
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35.
Problem: Excessive logical reads
• “Read 1000 pages to return 100 rows”
– 1 page = 1 logical read
– Goal: return data with few logical reads as possible
– Sometimes we force SQL Server to read more pages than necessary (excessive IO)
Inefficient data access paths (indexes)
Inefficient SQL forcing SQL Server to do
excessive work

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36.
Name
(Index Key)
A - Z
+ LocationID
Name
(Index Key)
A - E
+ LocationID
Name
(Index Key)
F - I
+ LocationID
Name
(Index Key)
J - M
+ LocationID
Name
(Index Key)
N - R
+ LocationID
Name
(Index Key)
S - V
+ LocationID
Name
(Index Key)
W - Z
+ LocationID
Root Node
Intermediate Level
Leaf Level
Name
(Index Key)
A - M
+ LocationID
Name
(Index Key)
N - Z
+ LocationID
WHERE
l.Name ='Paint';Non-clustered Index
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37.
Name
(Index Key)
A - Z
+ LocationID
Name
(Index Key)
A - E
+ LocationID
Name
(Index Key)
F - I
+ LocationID
Name
(Index Key)
J - M
+ LocationID
Name
(Index Key)
N - R
+ LocationID
Name
(Index Key)
S - V
+ LocationID
Name
(Index Key)
W - Z
+ LocationID
Root Node
Intermediate Level
Leaf Level
Name
(Index Key)
A - M
+ LocationID
Name
(Index Key)
N - Z
+ LocationID
WHERE
l.Name ='Paint';Non-clustered Index
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38.
LocationID = 6;
(ProductID,
LocationID)
(Clustering Key)
1 to 12000
(ProductID,
LocationID)
1-6000
(ProductID,
LocationID)
6001-12000
Data rows
for
ProductID
1-2000
Data rows
for
ProductID
2001-4000
Data rows
for
ProductID
4001-6000
Data rows
for
ProductID
6001-8000
Data rows
for
ProductID
80001-10000
Data rows
for
ProductID
10001-12000
Root Node
Intermediate Level
Leaf Level(502,6) (4325,6) (10324,6)(7865,6)
Clustered Index
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39.
LocationID = 6;
(ProductID,
LocationID)
(Clustering Key)
1 to 12000
(ProductID,
LocationID)
1-6000
(ProductID,
LocationID)
6001-12000
Data rows
for
ProductID
1-2000
Data rows
for
ProductID
2001-4000
Data rows
for
ProductID
4001-6000
Data rows
for
ProductID
6001-8000
Data rows
for
ProductID
80001-10000
Data rows
for
ProductID
10001-12000
Root Node
Intermediate Level
Leaf Level
Clustered Index
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40.
LocationID = 6;
(ProductID,
LocationID)
(Clustering Key)
1 to 12000
(ProductID,
LocationID)
1-6000
(ProductID,
LocationID)
6001-12000
Data rows
for
ProductID
1-2000
Data rows
for
ProductID
2001-4000
Data rows
for
ProductID
4001-6000
Data rows
for
ProductID
6001-8000
Data rows
for
ProductID
80001-10000
Data rows
for
ProductID
10001-12000
Root Node
Intermediate Level
Leaf Level
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41.
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42.
ProductID
(Index Key)
A - Z
+ SalesOrderID
ProductID
(Index Key)
1 - 150
+ SalesOrderID
ProductID
(Index Key)
151 - 300
+ SalesOrderID
ProductID
(Index Key)
301 - 450
+ SalesOrderID
ProductID
(Index Key)
451 - 600
+ SalesOrderID
ProductID
(Index Key)
601 - 750
+ SalesOrderID
ProductID
(Index Key)
751 - 900
+ SalesOrderID
Root Node
Intermediate Level
Leaf Level
ProductID
(Index Key)
A - M
+ SalesOrderID
ProductID
(Index Key)
N - Z
+ SalesOrderID
Lookup
CarrierTrackingNumber
in clustered index
for each row
Key Lookup
SELECT ProductID ,
SalesOrderID ,
CarrierTrackingNumber
FROM Sales.SalesOrderDetail
WHERE ProductID = 709;
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43.
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44.
#SQLintheCityUK
• For small tables, scanning an index is very
efficient operation
• BUT…for critical and frequently-executed
queries:
– Provide covering non-clustered indexes that the
queries can seek
– Minimize logical reads (IO) required to gather the
data
Solution: optimize indexes for workloadSolution: optimize indexes for workload

45.
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–Worst Strategy: No indexes.
Query performance will be terrible
–Second worst: Index on every column; same
column participating in numerous indexes
Data modification performance will be awful
–Don’t do “SELECT *”
Makes effective indexing VERY hard!
Solution: optimize indexes for workloadSolution: optimize indexes for workload

46.
#SQLintheCityUK
• Don’t index query by query
• Goal: small set of indexes to help most important and frequent queries
• Analyze the workload as a whole
– Profiler and Database Engine Tuning Advisor
• Good start point…
• Not necessarily good end point
– Refine recommendations using Missing Index (and other) DMVs
• What is the write : read ratio (>1?) for the index?
• How many plans currently associated with index?
• How often are those plans used?
• How many reads might the missing index have helped? Don’t create if this number is small…
– See Performance Tuning with SQL Server Dynamic Management Views (free eBook)
• http://bit.ly/1jVG3IW
Solution: optimize indexes for workload

47.
“The explicit data type conversion
has rendered the predicate non-SARGable!”
Problem: Non-SARGable Predicates
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48.
Beware of SARG-ability
• Non-SARGable means that the optimizer can’t
use the expression in a seek operation
• Cause: use of function directly on column in
WHERE or JOIN
– Various incarnations
– All lead to excessive IO (scans when seeks should be viable)
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49.
SARG-able predicate
…WHERE FirstName = 'bob';  SARGable
FirstName LastName
Aaron Bertrand
Aaron Darrenovsky
Amber Smith
Apple Paltrow
Bob Duffy
Bob Carolgees
Chris Christofferson
…etc...
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50.
Non SARGable predicate
…WHERE REVERSE(FirstName) = 'bob';
FirstName LastName
Aaron Bertrand
Aaron Darrenovsky
Amber Smith
Apple Paltrow
Bob Duffy
Bob Carolgees
Chris Christofferson
…etc...
REVERSE ('Aaron') = 'bob' ?
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51.
Non SARG-able predicate
…WHERE REVERSE(FirstName) = 'bob';
FirstName LastName
Aaron Bertrand
Aaron Darrenovsky
Amber Smith
Apple Paltrow
Bob Duffy
Bob Carolgees
Chris Christofferson
…etc...
REVERSE ('Aaron') = 'bob' ?
#SQLintheCityUK

52.
Non SARGable predicate
…WHERE REVERSE(FirstName) = 'bob';
FirstName LastName
Aaron Bertrand
Aaron Darrenovsky
Amber Smith
Apple Paltrow
Bob Duffy
Bob Carolgees
Chris Christofferson
…etc...
REVERSE ('Amber') = 'bob' ?
 Non-SARGable
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53.
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54.
Other common explicit conversions
-- Using functions like LTRIM, RTRIM etc.
-- often happens where people don't trust
consistency of data inputs
SELECT bp.ProductID ,
bp.Name ,
bp.ProductNumber
FROM dbo.bigProduct bp
WHERE LTRIM(bp.ProductNumber) LIKE 'AR%';
GO
SELECT bp.ProductID ,
bp.Name ,
bp.ProductNumber
FROM dbo.bigProduct bp
WHERE bp.ProductNumber LIKE N'%1000';
GO
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55.
Other causes of Non-SARG
• Implicit Data type conversions
– Variable data type doesn’t match column type
– SQL Server uses CONVERT_IMPLICIT
– Problem if column is lower precedence type
• Data type precedence order: http://bit.ly/1ENOwjZ
• Misuse of User Defined Functions (UDFs)
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56.
• Rewrite the query to avoid direct use of
function on column
• Use helper functions
• Avoid data type mismatches
• Be careful when using UDFs!
– Convert scalar UDFs to inline TVFs
– http://bit.ly/1oN9ysK
Solutions to Non-SARGability
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57.
• One query size fits all
• Leads to wildly inaccurate estimations and
inappropriate execution plans
Problem: Overly Generic SQL
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58.
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59.
• Write SQL and stored procedures for a specific, defined
purpose
• Various ‘workarounds’ though none without drawbacks
– Recompile on each execution
– Dynamic SQL
– See Gail Shaw’s “How to Confuse the Query Optimizer”
(http://bit.ly/1Mb8Rnp)
Solutions to Generic SQL
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60.
Other issues that cause
poor estimations
• Stale statistics
• Problems with parameter sniffing
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61.
Very wild estimation!
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62.
Conclusions
• Plans tell us exactly how SQL Server executed your
query
– Focus on critical and frequently-executed queries
– Use plans to:
• spot common mistakes in the code
• Uncover problems with inefficient indexing
– Work with rather than against SQL Server
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63.
Thank you to…
• Grant Fritchey - @GFritchey
• Hugo Kornelis - @Hugo_Kornelis
• Gail Shaw - @SQLintheWild
• Rodney Landrum - @SQLBeat
• Phil Factor - @Phil_Factor
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