Understanding SQL Server Join Operators
Explore the implementation of three different physical operators in SQL Server for joining tables. Learn about their advantages, challenges, and how to optimize query performance. Dive into topics like outer joins, non-equi joins, and logical processing order. Gain insights into foreign key joins and nested loops to enhance efficiency.
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Presentation Transcript
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. 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 SELECT X,Y,Z FROM WHERE [Table1] INNER JOIN [Table2] ON [Table1].[C1] = [Table2].[C1] AND [Table1].[C2] = [Table2].[C2]
NEXT TIME Outer Joins Non Equi-Joins Logical Processing Order NULL Value Issues Join Parallelism Partitioned Joins
NESTED LOOPS Fetch next row from blue input Start No More Rows? Quit True False Find matching rows in red input
CONSIDERATIONS 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
FOREIGN KEY JOINS Joins Parents and Childs Most Common Relationship is One-to-Many Parent IS Indexed Primary Key or Unique Indexing Foreign Keys Enables Efficient Use of Nested Loops
Fetch next row from blue input Start M E R G E No More Rows? Quit True False Fetch next row from red input Rows Match? True False
CONSIDERATIONS 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
H A S H - M A T C H Fetch next row from blue input Fetch next row from red input Start True No more rows? No more rows? True False False Quit Apply hash function Apply hash function Place row in hash bucket Probe bucket for matching rows
CONSIDERATIONS Hash Function Selection CPU, Memory and potential I/O Overhead No Sorting Whatsoever Probing Costs Not Revealed May Indicate Sub-Optimal Indexing
CONCLUSION Nested Loops Merge Hash Small outer input Inner input indexed Pre-sorted inputs Sorting required Very large inputs Not well indexed Good choice when Low *Unless requires sorting Low *Unless requires sorting CPU consumption Low High Memory usage Low High Low * Hidden cost of probes Logical reads High Low Output matches Fast Fast Slow
FOR MORE INFORMATION Books On Line Microsoft White Papers SQL Server 2008 Internals Kalen Delaney, Kimberly L.Tripp and more Craig Freedman s MSDN Blog http://blogs.msdn.com/craigfr/about.aspx
COMING UP P/X001 Designing High Scale OLTP systems Thomas Kejser P/L001 TSQL Techniques Why and how to tune a routine Dave Ballantyne P/L002 Implementing Common Business Calculations in DAX Chris Webb P/L005 Consolidating data collection with SQLDIAG and analysing it all with SQLNexus Christian Bolton P/T007 Introduction to SQL Modelling Services Robert Hogg #SQLBITS
