1. Parameter Sniffing in SQL Server Stored Procedures
Miloš Radivojević
Parameter Sniffing in SQL Server Stored Procedures

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3. Miloš Radivojević Data Platform MVP
Principal Database Consultant, bwin GVC Vienna, Austria
Co-Founder: SQL Pass Austria
Conference Speaker, Book Author
Contact:
Twitter: @MilosSQL

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5. Introduction

6. Demo – Sample Table
10 M rows
Indexes: student_id and exam_date

7. Demo- Requirements Requirements:
Two input parameters: Student Id and Order Date
Both parameters are optional
The result set should contain up to 10 rows sorted by Exam Note descending
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

8. Demo- Application ALTER PROCEDURE dbo.getExams @student_id INT = NULL,
@exam_date DATETIME = NULL AS
BEGIN
SELECT
TOP (10) student_id, exam_number, exam_date, exam_note
FROM
dbo.StudentExams
WHERE
(student_id IS NULL)
AND
(exam_date IS NULL)
ORDER BY
exam_note DESC
END
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

9. Solution You have to use SSMS!
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem
You have to use SSMS!

10. Agenda
What is Parameter Sniffing? When and Why It is a Problem? Symptoms of Parameter Sniffing Problem Solutions for The Parameter Sniffing Problem

11. Execution Plan The Query Optimizer considerations:
Table size
Data distribution & Statistics
Indexes
Selectivity of columns in the filter clauses
Values of submitted parameters for the first invocation
Submitted parameter values often decide about cardinality
By default it reuses an exec. plan

12. What is Parameter Sniffing?
During stored procedure compilation, the values from parameters are evaluated (sniffed) and used to create an execution plan
Future executions will re-use this plan
This is a behaviour, not a bug
It is good for invocations with similar parameters
It can significantly, sometimes dramatically degrade the performance for non-common parameters

13. When PS is a Problem? When several execution plans are possible
Stored procedures prone to parameter sniffing
with parameters participating in range operators
with optional parameters
Not only limited to stored procedures
Static parameterized queries
Dynamic queries executed with sp_executesql

14. Example
ALTER PROCEDURE INT = DATETIME = NULL AS BEGIN SELECT TOP (10) student_id, exam_number, exam_date, exam_note FROM dbo.StudentExams WHERE (student_id IS NULL) AND (exam_date IS NULL) ORDER BY exam_note DESC END

15. Example Execution time: 202 ms Logical reads: 17.580
EXEC dbo.getExams 2001, NULL;
Execution time: 202 ms
Logical reads:

16. Example Execution time: 7.335 ms Logical reads: 40.017.504
EXEC dbo.getExams NULL, ' ';
Execution time: ms
Logical reads:

17. Example Execution time: 180 ms Logical reads: 29.742
EXEC dbo.getExams NULL, ' ';
Execution time: 180 ms
Logical reads:

18. Example Execution time: 6.445 ms Logical reads: 40.647.499
EXEC dbo.getExams 2001, NULL;
Execution time: ms
Logical reads:

19. Results
Execution time in milliseconds
Logical Reads

20. Symptoms of Parameter Sniffing
What?
Execution of a stored procedure within an application is suddenly slow
Significantly increasing of I/O reads
When and How?
Yesterday it was OK, problems started one hour ago…
For some parameter combinations it works well
The same procedure in another application or in the SSMS works perfect!

21. SSMS Mistery
It works instantly in the SSMS, but it takes 5 seconds in the application
A new execution plan is created for the stored procedure invocation within SSMS!
Factors that affect plan-reuse
ANSI_NULLS ANSI_PADDING
ANSI_WARNINGS ARITHABORT
QUOTED_IDENTIFIER CONCAT_NULL_YIELDS_NULL
DATEFORMAT
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

22. SSMS Mistery
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

23. PS Challenge – What To Do?
SQL Server Optimizer:
Considers input parameters
Reuses execution plans
We can suggest to the Optimizer:
Do not consider input parameters!
Do not “blindly” reuse an execution plan!
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

24. Solutions – Disable Parameter Sniffing Effect
SQL Server uses average distribution statistics to choose an execution plan
Neutrlaize parameter values – an average solution
It does not work always!
How to implement?
Using the OPTIMIZE FOR UKNOWN query hint
Wrap parameters in local variables
Using TF 4136
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

25. Solutions – Disable Parameter Sniffing Effect
SQL Server uses average distribution statistics to choose an execution plan
Neutralize parameter values – an average solution
It does not work always!
How to implement?
Using the OPTIMIZE FOR UKNOWN query hint
Wrap parameters in local variables
Using TF 4136
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

26. Solutions – Disable Parameter Sniffing
ALTER PROCEDURE INT = DATETIME = NULL AS BEGIN SELECT TOP (10) student_id, exam_number, exam_date, exam_note FROM dbo.StudentExams WHERE (student_id IS NULL) AND (exam_date IS NULL) ORDER BY exam_note DESC OPTION (OPTIMIZE FOR UNKNOWN) END

27. Solutions – Disable Parameter Sniffing
ALTER PROCEDURE INT = DATETIME = NULL AS BEGIN DATETIME SELECT TOP (10) student_id, exam_number, exam_date, exam_note FROM dbo.StudentExams WHERE (student_id IS NULL) AND (exam_date IS NULL) ORDER BY exam_note DESC END

28. Example Execution time: 420 ms Logical reads: 681.550
EXEC dbo.getExams 2001, NULL;
Execution time: 420 ms
Logical reads:

29. Example Execution time: 505 ms Logical reads: 681.550
EXEC dbo.getExams NULL, ' ';
Execution time: 505 ms
Logical reads:

30. Results
Execution time in milliseconds

31. Solutions – Choose Favorite Combination(s)
Goal: To work perfect for the most common or important combination(s)
SQL Server generates an optimal execution plan for it and reuses it
Need to contact business people
How to implement?
Using the OPTIMIZE FOR query hint
Query Decomposition (IF)
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

32. Solutions – Choose Favorite Combinations
ALTER PROCEDURE INT = DATETIME = NULL AS BEGIN SELECT TOP (10) student_id, exam_number, exam_date, exam_note FROM dbo.StudentExams WHERE (student_id IS NULL) AND (exam_date IS NULL) ORDER BY exam_note DESC OPTION (OPTIMIZE FOR = 1)) END

33. Results
Execution time in milliseconds

34. Solutions – Disable Execution Plan Re-use
Goal: Always get the optimal execution plan
Pros:
Optimal plan for each execution
The plan can be better than the best plan in the initial solution!!!
Cons:
Compiled by each execution
How to implement?
Define SP with the option WITH RECOMPILE (not recommended!)
OPTION (RECOMPILE) at the statement level
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

35. Solutions – Disable Execution Plan Re-use
ALTER PROCEDURE INT = DATETIME = NULL AS BEGIN SELECT TOP (10) student_id, exam_number, exam_date, exam_note FROM dbo.StudentExams WHERE (student_id IS NULL) AND (exam_date IS NULL) ORDER BY exam_note DESC OPTION (RECOMPILE) END

36. Example Execution time: 36 ms Logical reads: 79
EXEC dbo.getExams 2001, NULL;
Execution time: 36 ms
Logical reads: 79

37. Example Execution time: 37 ms Logical reads: 7.156
EXEC dbo.getExams NULL, ' ';
Execution time: 37 ms
Logical reads: 7.156

38. Results
Execution time in milliseconds

39. Solutions – Static or Dynamic Query Decomposition
Goal: Always get the optimal execution plan and avoid recompilation
Pros:
Optimal plan for each execution
Reuse
The plan can be better than the best plan in the initial solution!!!
Cons:
Maintenance problems, SQL Injection...
How to implement?
Static SQL (Decission Tree Implementation)
Dynamic SQL
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

40. Solutions – Decision Tree
ALTER PROCEDURE dbo.getExams
@student_id INT = NULL,
@exam_date DATETIME = NULL AS
BEGIN
IS NOT NULL
SELECT TOP (10) student_id, exam_number, exam_date, exam_note
FROM dbo.StudentExams
WHERE student_id
ORDER BY exam_note DESC
ELSE
IS NOT NULL
WHERE exam_date
END

41. Example Execution time: 36 ms Logical reads: 79
EXEC dbo.getExams 2001, NULL;
Execution time: 36 ms
Logical reads: 79

42. Example Execution time: 43 ms Logical reads: 7.036
EXEC dbo.getExams NULL, ' ';
Execution time: 43 ms
Logical reads: 7.036

43. Results
Execution time in milliseconds

44. Solutions – Decision Tree Dynamic
ALTER PROCEDURE INT = DATETIME = NULL AS BEGIN nvarchar(600) = N'SELECT TOP (10) student_id, exam_number, exam_date, exam_note FROM dbo.StudentExams WHERE 1 = 1 ' IS NOT NULL AND student_id ' IS NOT NULL AND exam_date ' ORDER BY exam_note DESC ' EXEC = END

45. Example Execution time: 36 ms Logical reads: 79
EXEC dbo.getExams 2001, NULL;
Execution time: 36 ms
Logical reads: 79

46. Example Execution time: 37 ms Logical reads: 7.156
EXEC dbo.getExams NULL, ' ';
Execution time: 37 ms
Logical reads: 7.156

47. Results
Execution time in milliseconds

48. Solutions – The Combined Solution
Goal: Always get the optimal execution plan and reuse it for most common parameters
Pros:
Optimal plan for most important executions
Reuse
The plan can be better than the best plan in the initial solution!!!
How to implement?
Static SQL Decission Tree Implementation combined with OPTION (RECOMPILE)
A typical example is a web search page with optional search criteria
The result set can be very large, but also empty
It is challenge to optimize the implementation for all search requests
Too many search criteria sometimes – application design problem

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