This document discusses SQL Server execution plans. It begins with brief introductions of the author and what an execution plan is. It then explains how execution plans are created by walking through the relational engine process. It distinguishes between estimated and actual execution plans, and describes how plans can be viewed in text, XML, or graphical formats. The remainder of the document focuses on how to read and understand execution plans by examining different operator types, data flow arrows, tooltips and other properties. It provides examples of various logical and physical operators like scans, seeks, lookups and joins.

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2. Brief introduction into
SQL Server Execution Plans
Marek Maśko

3. About the Author
Marek Maśko
• Principal Database Analyst at Sabre
• Working with SQL Server since 2010
• SQL DBA, Dev & Architect
• MCP since 2012
• Contact Information:
Blog: blog.sqlterritory.com
Email: marek@sqlterritory.com
LinkedIn: https://pl.linkedin.com/in/marekmasko
Twitter: @MarekMasko

4. What is an Execution Plan?
Execution plans can tell you how SQL Server may execute a query, or how it
did execute a query.
An execution plan, simply put, is the result of the query optimizer's attempt
to calculate the most efficient way to implement the request represented
by the T-SQL query you submitted.

5. How it is created?
Relational Engine
T-SQL
Statement Parser Algebrizer
Query
Optimizer
Storage Engine
Query
Parsing
Parse
Tree
Normalization
and Binding
Query
Processor
Tree
Query
Optimization
Execution
Plan

6. Estimated and Actual Execution Plans
Estimated execution plan
• Output from the Optimizer
• Query doesn’t have to be executed
• Tells you what SQL Server would most likely do
• Stored in the plan cache
Actual execution plan
• Output from the actual query execution
• Tells you exactly what SQL Server did

7. Text Plan

8. XML Plan

9. Graphical Plan

10. DEMO
Let’s take a look...

11. HOW TO READ AN EXECUTION PLAN?

12. Operators
• Represented on Execution Plan as icons
• Represents various actions and decisions
• Each operator has a logical and a physical component

13. Data Flow Arrows
• Represents data passed between operators
• The direction of the arrow emphasizes further the direction of data flow.
• Thickness of the arrow reflects the amount of data passed

14. ToolTips
• Each Operator and Arrow has a ToolTip pop-up
• ToolTips present distinct set of data for each operator
• Physical Operator
• Logical Operator
• I/O Cost
• CPU Cost
• Rowcount
• Ordered

15. Properties
• Provides similar information to those from ToolTip
• Additional info regarding parallel query execution
• Some optimization data from Query Optimizer

16. Right to Left?
• Resultset 1 and 2 are joined using nested loops join, creating resultset 3
• Resultset 3 and 4 are joined using hash match join, creating resultset 5
• Resultset 5 and 6 are joined using nested loops join, creating resultset for the Select clause
1.
2.
3.
4.
5.
6.

17. DEMO
Let’s see more details...

18. Execution Plan Operators
Logical and Physical Operators
• Called iterators
• Appear as blue icons
• Represents query execution or
DML operations
Parallelism Physical Operators
• Appear as blue icons
• Represents parallelism operations
• Subset of logical and physical operators
Cursor Operators
• Appear as yellow icons
• Represents Transact-SQL Cursor
operations
Language Elements
• Appear as green icons
• Represents Transact-SQL language
elements (ASSIGN, DECLARE, IF, WHILE...)

19. LOGICAL AND PHYSICAL OPERATORS

20. Data Access
Scan and Seek Operators
• Table Scan
• Clustered Index Scan
• Clustered Index Seek
• Index Scan
• Index Seek
Lookup Operators
• Key Lookup
• RID Lookup
Join Operators
• Nested Loops Join
• Merge Join
• Hash Join

21. Scan and Seek Operators
Table Scan
• All rows in a heap table are searched
Clustered Index Scan
• All rows in a clustered index are searched
• Results may be ordered
Clustered Index Seek
• Looks up each row via clustered index key
• Similar to looking up name in a telephone book
• Results may be ordered

22. Scan and Seek Operators
Index Scan
• All rows in a non-clustered index are searched
Index Seek
• Looks up each row via index key
• Similar to looking up name in a telephone book
• Results may be ordered

23. Lookups
• Retrieves row from the base table after a non-clustered index seek
• Expensive for large row counts
• Can be avoided with a covering index
Key Lookup
• Lookup on a Clustered Index
• Uses the clustered index key to locate each row
RID Lookup
• Lookup on a Heap
• Uses the rowID to locate each row

24. Nested Loops Join
Get row from outer table
Get matching row from inner table
Output composite result
Loop through inner table
When inner table is exhausted, loop on outer table

25. Merge Join
Join
Sequence
Get next row from outer table
Get next row from inner table with the same key
If found, output and loop on inner table
If not found, loop on outer table

26. Hash Match Join
Build
Table
Probe
Table
Hash Join Key
Lookup in
Hash Table
1. Scan smaller (build) table
• Hash build key values; store in hash table
2. Scan larger (probe) table
• Hash probe key value; lookup in hash table; If found, output result

27. DEMO
Let’s take a look once again...

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