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Execution plans for mere mortals

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The document provides an overview of execution plans in SQL Server and what aspects to examine. It discusses the key stages in the execution process including parsing, optimization, and execution. Specific operators like nested loops, hash matches, and parallelism are also covered. The document is intended to help beginners understand execution plans and what aspects provide useful information for troubleshooting query performance issues.

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Execution Plans for Mere Mortals A beginners overview of execution plans and what to look for.
Who Am I? Mike Lawell Mike Lawell Principal Consultant Twitter: @SQLDiver Blog: SQLServerAssociates.com LinkedIn: LinkedIn.com/in/MikeLawell
EXECUTION STEPS
On The Inside Relational Engine Optimizer Command Parser Query Executor SNI User Storage Engine Buffer Manager Access Methods Transaction Manager SQL OS Buffer Pool Borrowed from Bradley Ball’s “SQL Internals, Recovery Models, & Backups” presentation Plan Cache Data Cache Data
Query Parsing Relation Engine 1. Is the query syntactically correct? SELECT [Nmae], [Number] FROM [Production].[Product] Msg 207, Level 16, State 1, Line 3 Invalid column name 'Nmae'. SELCT [Name], [Number] FROM [Production].[Product] Msg 156, Level 15, State 1, Line 3 Incorrect syntax near the keyword 'FROM'. 2. No Errors Output as Parse Tree to Algebrizer
Query Parsing SELECT [Nmae], [Number] FROM [Production].[Product] Msg 207, Level 16, State 1, Line 3 Invalid column name 'Nmae'.
Query Parsing SELECT [Name], [Number] FROM [Production].[Product]
Algebrizer 1. Resolve the Names of the objects (tables, columns, etc.) 2. Resolve the data types for the objects being accessed. 3. Finds aggregate operations (min, max, group by) and performs an operation called aggregate binding. If the names of the objects, columns aren’t found an error is output and the process halts. If it is successful the algebrizer outputs the query processor tree to the query optimizer. The query hash is generated and passed with the query processor tree.
Query Optimizer 1. The query optimizer checks the hash against the plan cache. If it exists use it and the optimization step is skipped and plan is passed to query execution. 2. The query optimizer uses the query processor tree and the statistics to determine an estimated execution plan. 3. The optimizer goes through a process of using different types of joins and indexes and assigns a cost to each step by CPU and I/O and determines a cost for each possible estimated plan it generates. 4. Once an acceptable estimated plan is found it is stored in the plan cache then sent to the query execution.
Query Execution Storage Engine 1. The storage engine determines if a recompile was triggered causing a change in the estimated execution plan. 2. SQL Server determines based upon cost whether it exceeds the threshold for parallelism changing the estimated plan for parallelism. 3. Statistics that have changed causing a change in the estimated plan. SQL Server returns the results.
Cardinality Estimation The optimization process depends on a cost estimation of each physical operator and the estimated number of records (cardinality estimation) to be processed. DBCC SHOW_STATISTICS ("[Sales].[SalesOrderDetailEnlarged]", [_WA_Sys_00000003_3D7E1B63]); The accuracy of the cardinality estimation depends upon the distribution of values in one or more columns of a table (statistics). Name Updated Rows Rows Sampled Steps Average key length String Index Filter Expression Unfiltered Rows _WA_Sys_00000003_3D7E1B63 Nov 26 2014 11:26AM 1000 1000 196 24 YES NULL 1000 RANGE_HI_KEY RANGE_ROWS EQ_ROWS DISTINCT_RANGE_ROWS AVG_RANGE_ROWS 002EE045-E30 0 1 0 1 0169388E-8EC 6 1 6 1 031622B3-B68 3 1 3 1 0424F840-4C2 3 1 3 1 04C4FEAC-7C1 3 1 3 1
Swag Question Name _WA_Sys_00000003_3D7E1B63
Cardinality Estimation There are multiple factors that can negatively impact the cardinality estimation process: 1. Out of date Statistics SELECT object_name(object_id) as TableName, name AS stats_name, STATS_DATE(object_id, stats_id) AS statistics_update_date FROM sys.stats WHERE object_id = OBJECT_ID('[Sales].[SalesOrderDetailEnlarged]'); 2. Cardinality Estimation Errors https://www.sqlskills.com/blogs/joe/cardinality-estimation-model-version/
Swag Question Why 70 vs 120?
EXECUTION PLAN BASICS
Required Permissions Server or database roles with permissions: sysadmin, dbcreator or db_owner or showplan To give showplan rights to the database: GRANT SHOWPLAN TO [username];
How to Read
BASIC OPERATORS
Basic Operators
JOIN OPERATORS
Nested LoopNon-blocking Description For each row in the top (outer) input, scan the bottom (inner) input, and output matching rows. Where does it happen: Joins with indexes on join columns.
Nested Loop SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[Ord FROM [Sales].[SalesOrderHeader] soh JOIN [Sales].[SalesOrderDetail] sod ON sod.[SalesOrderID] = soh.[SalesOrderID] WHERE soh.[OrderDate] = '2013-08-30 00:00:00.000' Non-blocking Outer Inner
Nested Loop SalesOrderID RevisionNumber OrderDate 55233 8 2013-08-30 00:00:00.000 55234 8 2013-08-30 00:00:00.000 55235 8 2013-08-30 00:00:00.000 55236 8 2013-08-30 00:00:00.000 55237 8 2013-08-30 00:00:00.000 55238 8 2013-08-30 00:00:00.000 55239 8 2013-08-30 00:00:00.000 55240 8 2013-08-30 00:00:00.000 55241 8 2013-08-30 00:00:00.000 55242 8 2013-08-30 00:00:00.000 55243 8 2013-08-30 00:00:00.000 55244 8 2013-08-30 00:00:00.000 55245 8 2013-08-30 00:00:00.000 55246 8 2013-08-30 00:00:00.000 55247 8 2013-08-30 00:00:00.000 55248 8 2013-08-30 00:00:00.000 55249 8 2013-08-30 00:00:00.000 55250 8 2013-08-30 00:00:00.000 55251 8 2013-08-30 00:00:00.000 55252 8 2013-08-30 00:00:00.000 55253 8 2013-08-30 00:00:00.000 SalesOrderID ProductID CarrierTrackingNumber OrderQt 55233 738 1590-499E-95 5 55233 792 1590-499E-95 5 55233 793 1590-499E-95 4 55233 794 1590-499E-95 4 55233 795 1590-499E-95 4 55233 796 1590-499E-95 5 55233 797 1590-499E-95 6 55233 798 1590-499E-95 2 55233 799 1590-499E-95 1 55233 800 1590-499E-95 1 55233 801 1590-499E-95 6 55233 835 1590-499E-95 6 55233 874 1590-499E-95 8 55233 875 1590-499E-95 8 55233 938 1590-499E-95 5 55233 939 1590-499E-95 5 55233 940 1590-499E-95 4 55233 973 1590-499E-95 5 55233 974 1590-499E-95 1 55233 975 1590-499E-95 1 Non-blocking
Description Match rows from two suitably sorted input tables exploiting their sort order. Where does it happen: Joins with indexes on join columns sorted in an appropriate sort order. MergeNon-blocking
MergeNon-blocking SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate] FROM [Sales].[SalesOrderHeader] soh JOIN [Sales].[SalesOrderDetail] sod ON sod.[SalesOrderID] = soh.[SalesOrderID]
MergeNon-blocking SalesOrderID RevisionNumber OrderDate 55233 8 2013-08-30 00:00:00.000 55234 8 2013-08-30 00:00:00.000 55235 8 2013-08-30 00:00:00.000 55236 8 2013-08-30 00:00:00.000 55237 8 2013-08-30 00:00:00.000 55238 8 2013-08-30 00:00:00.000 55239 8 2013-08-30 00:00:00.000 55240 8 2013-08-30 00:00:00.000 55241 8 2013-08-30 00:00:00.000 55242 8 2013-08-30 00:00:00.000 55243 8 2013-08-30 00:00:00.000 55244 8 2013-08-30 00:00:00.000 55245 8 2013-08-30 00:00:00.000 55246 8 2013-08-30 00:00:00.000 55247 8 2013-08-30 00:00:00.000 55248 8 2013-08-30 00:00:00.000 55249 8 2013-08-30 00:00:00.000 55250 8 2013-08-30 00:00:00.000 55251 8 2013-08-30 00:00:00.000 55252 8 2013-08-30 00:00:00.000 55253 8 2013-08-30 00:00:00.000 SalesOrderID ProductID CarrierTrackingNumber OrderQt 55233 738 1590-499E-95 5 55233 792 1590-499E-95 5 55233 793 1590-499E-95 4 55233 794 1590-499E-95 4 55233 795 1590-499E-95 4 55233 796 1590-499E-95 5 55233 797 1590-499E-95 6 55233 798 1590-499E-95 2 55233 799 1590-499E-95 1 55233 800 1590-499E-95 1 55233 801 1590-499E-95 6 55233 835 1590-499E-95 6 55233 874 1590-499E-95 8 55233 875 1590-499E-95 8 55233 938 1590-499E-95 5 55233 939 1590-499E-95 5 55233 940 1590-499E-95 4 55233 973 1590-499E-95 5 55233 974 1590-499E-95 1 55233 975 1590-499E-95 1
Hash Match Description Use each row from the top input to build a hash table, and each row from the bottom input to probe into the hash table, outputting all matching rows. Where does it happen: Missing Index, Missing Where Clause, Where Clause that is non-sargable.
Hash MatchBlocking SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty] FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] AND [sod1].[OrderQty] > 5
Hash MatchBlocking ProductID CarrierTrackingNumber Hash 707 4BC2-4C6D-83 -767544381 711 2AA4-4D12-9F -767515863 715 C811-4D40-80 -767499885 715 8FCD-4D55-8F -767496256 714 CC2F-4D47-8C -767488607 712 BE3B-4D30-A7 -767488106 707 9EBF-4C7E-82 -767471833 716 F78B-4D68-8E -767470049 712 8EBE-4D2C-AB -767459569 716 7DE5-4D74-A7 -767455808 715 46EF-4D44-A9 -767449380 711 9FC2-4D08-A7 -767447057 ProductID CarrierTrackingNumbe 736 2554-4F68-AB -767556491 717 9526-4D73-83 -767556433 715 CC2F-4D47-8C -767554143 715 2960-4D59-96 -767553412 722 9708-4E22-A4 -767551834 722 6F5C-4E3C-B5 -767549829 717 7E0A-4D6E-9E -767545458 707 4BC2-4C6D-83 -767544381 716 F108-4D76-8A -767543168 711 B900-4D0D-AF -767541067 711 67A0-4D10-87 -767539667 707 8441-4C6F-89 -767535798 Hash Table 1 Hash Table 2
Fixed Hash Match
OTHER OPERATORS
Parallelism SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty] FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber]
Parallelism SET STATISTICS IO ON SET STATISTICS TIME ON SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty] FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty] FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] OPTION(MAXDOP 1)
Parallelism Table 'SalesOrderDetail'. Scan count 18, logical reads 2490, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. SQL Server Execution Times: CPU time = 9593 ms, elapsed time = 2381 ms. Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'SalesOrderDetail'. Scan count 2, logical reads 2490, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read- ahead reads 0. SQL Server Execution Times: CPU time = 7941 ms, elapsed time = 8372 ms.
Parallelism
Hash Aggregate SELECT [sod1].[CarrierTrackingNumber], sum([sod1].[UnitPrice]) UnitPriceTotal, sum([sod1].[OrderQty]) as OrderQtyTotal FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] AND [sod1].[OrderQty] > 5 GROUP BY [sod1].[CarrierTrackingNumber]
Stream Aggregate SELECT [sod1].[CarrierTrackingNumber], sum([sod1].[UnitPrice]) UnitPriceTotal, sum([sod1].[OrderQty]) as OrderQtyTotal FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] AND [sod1].[OrderQty] > 5 GROUP BY [sod1].[CarrierTrackingNumber]
Sort (spill) Operator used tempdb to spill data during execution with spill level 1 SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate] FROM [Sales].[SalesOrderHeader] soh JOIN [Sales].[SalesOrderDetailEnlarged] sod ON sod.[SalesOrderID] = soh.[SalesOrderID] WHERE [sod].[CarrierTrackingNumber] > '41D0-42A8-A5' ORDER BY [soh].[RevisionNumber], [soh].[OrderDate]
Sort Spill SELECT TOP 1 [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate] FROM [Sales].[SalesOrderHeader] soh JOIN [Sales].[SalesOrderDetailEnlarged] sod ON sod.[SalesOrderID] = soh.[SalesOrderID] WHERE [sod].[CarrierTrackingNumber] > '41D0-42A8-A5' ORDER BY [soh].[RevisionNumber], [soh].[OrderDate]
Implicit Conversion SELECT [sod].[ProductID], [sod].[CarrierTrackingNumber], [sod].[UnitPrice], [sod].[OrderQty], [rowguid] FROM [Sales].[SalesOrderDetail] sod WHERE LEFT([sod].[rowguid],4) = N'FE10'
Properties
Cool Tools/Products SQL Server Query Plan Analysis by Joe Sack
Resources http://www.red-gate.com/community/books/ Downloadable FREE!
Thank You! Mike Lawell Principal Consultant Twitter: @SQLDiver Blog: SQLServerAssociates.com LinkedIn: LinkedIn.com/in/MikeLawell Email: mike@sqlserverassociates.com
Who Am I? Mike Lawell Mike Lawell Principal Consultant Twitter: @SQLDiver Blog: SQLServerAssociates.com LinkedIn: LinkedIn.com/in/MikeLawell

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Execution plans for mere mortals

  • 1. Execution Plans for Mere Mortals A beginners overview of execution plans and what to look for.
  • 2. Who Am I? Mike Lawell Mike Lawell Principal Consultant Twitter: @SQLDiver Blog: SQLServerAssociates.com LinkedIn: LinkedIn.com/in/MikeLawell
  • 4. On The Inside Relational Engine Optimizer Command Parser Query Executor SNI User Storage Engine Buffer Manager Access Methods Transaction Manager SQL OS Buffer Pool Borrowed from Bradley Ball’s “SQL Internals, Recovery Models, & Backups” presentation Plan Cache Data Cache Data
  • 5. Query Parsing Relation Engine 1. Is the query syntactically correct? SELECT [Nmae], [Number] FROM [Production].[Product] Msg 207, Level 16, State 1, Line 3 Invalid column name 'Nmae'. SELCT [Name], [Number] FROM [Production].[Product] Msg 156, Level 15, State 1, Line 3 Incorrect syntax near the keyword 'FROM'. 2. No Errors Output as Parse Tree to Algebrizer
  • 6. Query Parsing SELECT [Nmae], [Number] FROM [Production].[Product] Msg 207, Level 16, State 1, Line 3 Invalid column name 'Nmae'.
  • 7. Query Parsing SELECT [Name], [Number] FROM [Production].[Product]
  • 8. Algebrizer 1. Resolve the Names of the objects (tables, columns, etc.) 2. Resolve the data types for the objects being accessed. 3. Finds aggregate operations (min, max, group by) and performs an operation called aggregate binding. If the names of the objects, columns aren’t found an error is output and the process halts. If it is successful the algebrizer outputs the query processor tree to the query optimizer. The query hash is generated and passed with the query processor tree.
  • 9. Query Optimizer 1. The query optimizer checks the hash against the plan cache. If it exists use it and the optimization step is skipped and plan is passed to query execution. 2. The query optimizer uses the query processor tree and the statistics to determine an estimated execution plan. 3. The optimizer goes through a process of using different types of joins and indexes and assigns a cost to each step by CPU and I/O and determines a cost for each possible estimated plan it generates. 4. Once an acceptable estimated plan is found it is stored in the plan cache then sent to the query execution.
  • 10. Query Execution Storage Engine 1. The storage engine determines if a recompile was triggered causing a change in the estimated execution plan. 2. SQL Server determines based upon cost whether it exceeds the threshold for parallelism changing the estimated plan for parallelism. 3. Statistics that have changed causing a change in the estimated plan. SQL Server returns the results.
  • 11. Cardinality Estimation The optimization process depends on a cost estimation of each physical operator and the estimated number of records (cardinality estimation) to be processed. DBCC SHOW_STATISTICS ("[Sales].[SalesOrderDetailEnlarged]", [_WA_Sys_00000003_3D7E1B63]); The accuracy of the cardinality estimation depends upon the distribution of values in one or more columns of a table (statistics). Name Updated Rows Rows Sampled Steps Average key length String Index Filter Expression Unfiltered Rows _WA_Sys_00000003_3D7E1B63 Nov 26 2014 11:26AM 1000 1000 196 24 YES NULL 1000 RANGE_HI_KEY RANGE_ROWS EQ_ROWS DISTINCT_RANGE_ROWS AVG_RANGE_ROWS 002EE045-E30 0 1 0 1 0169388E-8EC 6 1 6 1 031622B3-B68 3 1 3 1 0424F840-4C2 3 1 3 1 04C4FEAC-7C1 3 1 3 1
  • 13. Cardinality Estimation There are multiple factors that can negatively impact the cardinality estimation process: 1. Out of date Statistics SELECT object_name(object_id) as TableName, name AS stats_name, STATS_DATE(object_id, stats_id) AS statistics_update_date FROM sys.stats WHERE object_id = OBJECT_ID('[Sales].[SalesOrderDetailEnlarged]'); 2. Cardinality Estimation Errors https://www.sqlskills.com/blogs/joe/cardinality-estimation-model-version/
  • 16. Required Permissions Server or database roles with permissions: sysadmin, dbcreator or db_owner or showplan To give showplan rights to the database: GRANT SHOWPLAN TO [username];
  • 21. Nested LoopNon-blocking Description For each row in the top (outer) input, scan the bottom (inner) input, and output matching rows. Where does it happen: Joins with indexes on join columns.
  • 22. Nested Loop SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[Ord FROM [Sales].[SalesOrderHeader] soh JOIN [Sales].[SalesOrderDetail] sod ON sod.[SalesOrderID] = soh.[SalesOrderID] WHERE soh.[OrderDate] = '2013-08-30 00:00:00.000' Non-blocking Outer Inner
  • 23. Nested Loop SalesOrderID RevisionNumber OrderDate 55233 8 2013-08-30 00:00:00.000 55234 8 2013-08-30 00:00:00.000 55235 8 2013-08-30 00:00:00.000 55236 8 2013-08-30 00:00:00.000 55237 8 2013-08-30 00:00:00.000 55238 8 2013-08-30 00:00:00.000 55239 8 2013-08-30 00:00:00.000 55240 8 2013-08-30 00:00:00.000 55241 8 2013-08-30 00:00:00.000 55242 8 2013-08-30 00:00:00.000 55243 8 2013-08-30 00:00:00.000 55244 8 2013-08-30 00:00:00.000 55245 8 2013-08-30 00:00:00.000 55246 8 2013-08-30 00:00:00.000 55247 8 2013-08-30 00:00:00.000 55248 8 2013-08-30 00:00:00.000 55249 8 2013-08-30 00:00:00.000 55250 8 2013-08-30 00:00:00.000 55251 8 2013-08-30 00:00:00.000 55252 8 2013-08-30 00:00:00.000 55253 8 2013-08-30 00:00:00.000 SalesOrderID ProductID CarrierTrackingNumber OrderQt 55233 738 1590-499E-95 5 55233 792 1590-499E-95 5 55233 793 1590-499E-95 4 55233 794 1590-499E-95 4 55233 795 1590-499E-95 4 55233 796 1590-499E-95 5 55233 797 1590-499E-95 6 55233 798 1590-499E-95 2 55233 799 1590-499E-95 1 55233 800 1590-499E-95 1 55233 801 1590-499E-95 6 55233 835 1590-499E-95 6 55233 874 1590-499E-95 8 55233 875 1590-499E-95 8 55233 938 1590-499E-95 5 55233 939 1590-499E-95 5 55233 940 1590-499E-95 4 55233 973 1590-499E-95 5 55233 974 1590-499E-95 1 55233 975 1590-499E-95 1 Non-blocking
  • 24. Description Match rows from two suitably sorted input tables exploiting their sort order. Where does it happen: Joins with indexes on join columns sorted in an appropriate sort order. MergeNon-blocking
  • 25. MergeNon-blocking SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate] FROM [Sales].[SalesOrderHeader] soh JOIN [Sales].[SalesOrderDetail] sod ON sod.[SalesOrderID] = soh.[SalesOrderID]
  • 26. MergeNon-blocking SalesOrderID RevisionNumber OrderDate 55233 8 2013-08-30 00:00:00.000 55234 8 2013-08-30 00:00:00.000 55235 8 2013-08-30 00:00:00.000 55236 8 2013-08-30 00:00:00.000 55237 8 2013-08-30 00:00:00.000 55238 8 2013-08-30 00:00:00.000 55239 8 2013-08-30 00:00:00.000 55240 8 2013-08-30 00:00:00.000 55241 8 2013-08-30 00:00:00.000 55242 8 2013-08-30 00:00:00.000 55243 8 2013-08-30 00:00:00.000 55244 8 2013-08-30 00:00:00.000 55245 8 2013-08-30 00:00:00.000 55246 8 2013-08-30 00:00:00.000 55247 8 2013-08-30 00:00:00.000 55248 8 2013-08-30 00:00:00.000 55249 8 2013-08-30 00:00:00.000 55250 8 2013-08-30 00:00:00.000 55251 8 2013-08-30 00:00:00.000 55252 8 2013-08-30 00:00:00.000 55253 8 2013-08-30 00:00:00.000 SalesOrderID ProductID CarrierTrackingNumber OrderQt 55233 738 1590-499E-95 5 55233 792 1590-499E-95 5 55233 793 1590-499E-95 4 55233 794 1590-499E-95 4 55233 795 1590-499E-95 4 55233 796 1590-499E-95 5 55233 797 1590-499E-95 6 55233 798 1590-499E-95 2 55233 799 1590-499E-95 1 55233 800 1590-499E-95 1 55233 801 1590-499E-95 6 55233 835 1590-499E-95 6 55233 874 1590-499E-95 8 55233 875 1590-499E-95 8 55233 938 1590-499E-95 5 55233 939 1590-499E-95 5 55233 940 1590-499E-95 4 55233 973 1590-499E-95 5 55233 974 1590-499E-95 1 55233 975 1590-499E-95 1
  • 27. Hash Match Description Use each row from the top input to build a hash table, and each row from the bottom input to probe into the hash table, outputting all matching rows. Where does it happen: Missing Index, Missing Where Clause, Where Clause that is non-sargable.
  • 28. Hash MatchBlocking SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty] FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] AND [sod1].[OrderQty] > 5
  • 29. Hash MatchBlocking ProductID CarrierTrackingNumber Hash 707 4BC2-4C6D-83 -767544381 711 2AA4-4D12-9F -767515863 715 C811-4D40-80 -767499885 715 8FCD-4D55-8F -767496256 714 CC2F-4D47-8C -767488607 712 BE3B-4D30-A7 -767488106 707 9EBF-4C7E-82 -767471833 716 F78B-4D68-8E -767470049 712 8EBE-4D2C-AB -767459569 716 7DE5-4D74-A7 -767455808 715 46EF-4D44-A9 -767449380 711 9FC2-4D08-A7 -767447057 ProductID CarrierTrackingNumbe 736 2554-4F68-AB -767556491 717 9526-4D73-83 -767556433 715 CC2F-4D47-8C -767554143 715 2960-4D59-96 -767553412 722 9708-4E22-A4 -767551834 722 6F5C-4E3C-B5 -767549829 717 7E0A-4D6E-9E -767545458 707 4BC2-4C6D-83 -767544381 716 F108-4D76-8A -767543168 711 B900-4D0D-AF -767541067 711 67A0-4D10-87 -767539667 707 8441-4C6F-89 -767535798 Hash Table 1 Hash Table 2
  • 32. Parallelism SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty] FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber]
  • 33. Parallelism SET STATISTICS IO ON SET STATISTICS TIME ON SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty] FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] SELECT [sod1].[ProductID], [sod1].[CarrierTrackingNumber], [sod1].[UnitPrice], [sod1].[OrderQty] FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] OPTION(MAXDOP 1)
  • 34. Parallelism Table 'SalesOrderDetail'. Scan count 18, logical reads 2490, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. SQL Server Execution Times: CPU time = 9593 ms, elapsed time = 2381 ms. Table 'Workfile'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'SalesOrderDetail'. Scan count 2, logical reads 2490, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read- ahead reads 0. SQL Server Execution Times: CPU time = 7941 ms, elapsed time = 8372 ms.
  • 36. Hash Aggregate SELECT [sod1].[CarrierTrackingNumber], sum([sod1].[UnitPrice]) UnitPriceTotal, sum([sod1].[OrderQty]) as OrderQtyTotal FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] AND [sod1].[OrderQty] > 5 GROUP BY [sod1].[CarrierTrackingNumber]
  • 37. Stream Aggregate SELECT [sod1].[CarrierTrackingNumber], sum([sod1].[UnitPrice]) UnitPriceTotal, sum([sod1].[OrderQty]) as OrderQtyTotal FROM [Sales].[SalesOrderDetail] sod1 JOIN [Sales].[SalesOrderDetail] sod2 ON [sod1].[ProductID] = [sod2].[ProductID] AND [sod1].[CarrierTrackingNumber] = [sod2].[CarrierTrackingNumber] AND [sod1].[OrderQty] > 5 GROUP BY [sod1].[CarrierTrackingNumber]
  • 38. Sort (spill) Operator used tempdb to spill data during execution with spill level 1 SELECT [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate] FROM [Sales].[SalesOrderHeader] soh JOIN [Sales].[SalesOrderDetailEnlarged] sod ON sod.[SalesOrderID] = soh.[SalesOrderID] WHERE [sod].[CarrierTrackingNumber] > '41D0-42A8-A5' ORDER BY [soh].[RevisionNumber], [soh].[OrderDate]
  • 39. Sort Spill SELECT TOP 1 [sod].[CarrierTrackingNumber], [sod].[OrderQty], [soh].[RevisionNumber], [soh].[OrderDate] FROM [Sales].[SalesOrderHeader] soh JOIN [Sales].[SalesOrderDetailEnlarged] sod ON sod.[SalesOrderID] = soh.[SalesOrderID] WHERE [sod].[CarrierTrackingNumber] > '41D0-42A8-A5' ORDER BY [soh].[RevisionNumber], [soh].[OrderDate]
  • 40. Implicit Conversion SELECT [sod].[ProductID], [sod].[CarrierTrackingNumber], [sod].[UnitPrice], [sod].[OrderQty], [rowguid] FROM [Sales].[SalesOrderDetail] sod WHERE LEFT([sod].[rowguid],4) = N'FE10'
  • 42. Cool Tools/Products SQL Server Query Plan Analysis by Joe Sack
  • 44. Thank You! Mike Lawell Principal Consultant Twitter: @SQLDiver Blog: SQLServerAssociates.com LinkedIn: LinkedIn.com/in/MikeLawell Email: mike@sqlserverassociates.com
  • 45. Who Am I? Mike Lawell Mike Lawell Principal Consultant Twitter: @SQLDiver Blog: SQLServerAssociates.com LinkedIn: LinkedIn.com/in/MikeLawell

Editor's Notes

  • #9: Object Names, Data types, Aggregate Binding
  • #10: Is it in plan cache? Estimated plan(s) Costs per plan? Sent to Storage Engine for execution.
  • #11: Is did recompile get triggered? Over cost threshold for parallelism?
  • #14: Cardinality Estimation Model Version 120 (new model) 70 old model.
  • #15: Cardinality Estimation Model Version 120 (new model) 70 old model.
  • #18: Physical order of process is SELECT pulls data from the next operator left to right top to bottom. Easier to understand from right to left top to bottom.
  • #29: Seeing a hash match is a good indication of an opportunity for tuning by adding an index, improving the join or where clause. If that isn’t possible, the hash match is the most efficient option you have.
  • #30: Table results are inserted into a temporary table and columns hashed for each table then matched from the temporary table. It is a blocking process as it must populate the hash tables completely before it can match the hash values for output.
  • #38: Average is a count and sum
  • #39: Disabled statistics, loaded table with