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# Physical Join Operators - PowerPoint PPT Presentation

Physical Join Operators. 2010. Ami Levin. Session Goals. SQL Server implements three different physical operators to perform joins. In this session we will see how each of these three operators work, its advantages and challenges.

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## PowerPoint Slideshow about ' Physical Join Operators' - emmy

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Presentation Transcript

### Physical Join Operators

2010

Ami Levin

• SQL Server implements three different physical operators to perform joins. In this session we will see how each of these three operators work, its advantages and challenges.

• We will try to understand the logic behind the optimizer’s decisions on which operator to use for various joins using (semi) real life examples and see examples about how to avoid common pitfalls.

Equi-Inner-Join

SELECTX,Y,Z…

FROM[Table1] INNER JOIN [Table2]

ON [Table1].[C1] = [Table2].[C1]

AND [Table1].[C2] = [Table2].[C2]

WHERE…

Next Time

• Outer Joins

• Non Equi-Joins

• Logical Processing Order

• NULL Value Issues

• Join Parallelism

• Partitioned Joins

Fetch next row from blue input

Start

No More Rows?

Quit

True

False

Find matching rows in red input

• “Outer Loop” = The Number of Iterations

• At Least One Small Input Preferable

• “Inner Operation” = Work for Each Iteration

• Index/Table Scan

• Index Seek with Lookup

• Covering Index Seek

• Joins Parents and Childs

• Most Common Relationship is One-to-Many

• Parent ISIndexed Primary Key or Unique

• Indexing Foreign Keys Enables Efficient Use of Nested Loops

DEMO

Start

Merge

No More Rows?

Quit

True

False

Fetch next row from red input

Rows Match?

True

False

• Input Must be Pre-Sorted

• By All Join Expression(s)

• Pre-Sorted in Plan, not necessarily in DB…

• Immediate& Sorted Match Outputs

• FASTFIRSTROW Hint

• Very Efficient and Simple Operator

DEMO

Fetch next row from red input

Start

No more rows?

No more rows?

True

True

False

Quit

False

Hash- Match

Apply “hash” function

Apply “hash” function

Place row in “hash” bucket

Probe bucket for matching rows

• Hash Function Selection

• CPU, Memory and potential I/O Overhead

• No Sorting Whatsoever

• Probing Costs Not Revealed

• May Indicate Sub-Optimal Indexing

DEMO

• Books On Line

• Microsoft White Papers

• “SQL Server 2008 Internals”

• Kalen Delaney, Kimberly L.Tripp and more…

• Craig Freedman’s MSDN Blog

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Implementing Common Business Calculations in DAX

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