1. Ashwani Roy Understanding Graphical Execution Plans Level 200

2. Query Processing lifecycle by Database Engine Elements in a Execution Plans Important Execution Plan Operators Agenda

3. Query Parsing Query Optimization Query Execution What Happens when a Query is submitted

4. Query Plan

5. Logical and Physical Operators Parallelism Physical Operators Cursor Operators Language Elements Operators in an Execution Plan

6. Columns in a PlanRowsEstimateIO ExecutesEstimateCPU StmtIdAvgRowSize NodeIdTotalSubtreeCost ParentOutputList PhysicalOpWarnings LogicalOpType ArgumentParallel DefinedValuesEstimateExecutions EstimateRows

7. If a Cached Plan exists then SQL Server will use this cached plan DEMO 01 Cached Query Plans

8. Important Operators in Execution Plans Select (Result) Sort Clustered Index Seek Clustered Index Scan Non-clustered Index Scan Non-clustered Index Seek Table ScanRID LookupKey LookupHash Match Nested Loops Merge JoinTopCompute Scalar Constant Scan FilterLazy SpoolSpoolEager SpoolStream Aggregate Distribute Streams Repartition Streams Gather Streams BitmapSplit

9. Index Seek Reads B-tree entries to determine the data page The Argument column contains the name of the nonclustered index being used Prefered for highly selective queries

10. Index Seek

11. Index Scan Horizontal traversal of the leaf level of the index from the first page to the last Retrieves all rows from the nonclustered index The Argument column contains the name of the nonclustered index being used

12. Clustered Index Scan The clustered index scan’s logical and physical operator scans the clustered index The Argument column contains the name of the clustered index If the table does not have Clustered Index the same Query will produce Table Scan

13. DEMO 02

14. Clustered Index Seek Cluster index seek Uses the seeking ability of indexes to retrieve rows The Argument column contains the name of the clustered index being used Seek() predicate contains the columns used for seeking

15. Bookmark Lookups Uses a bookmark to look up a row in a clustered index or table The Argument column contains the bookmark label Can be removed by covering columns May have a performance improvement

16. KEY LOOKUP A Key Lookup is a bookmark lookup on a table with a clustered index. Means that the optimizer cannot retrieve the rows in a single operation, and has to use a clustered key (or a row ID) to return the corresponding rows from a clustered index (or from the table itself). Performance can be improved by making Non-Clustered Index or Covering Index

17. DEMO 03

18. RID Lookup A type of bookmark lookup Occurs on a heap table (a table that doesn't have a clustered index) Uses a row identifier to find the rows to return.

19. DEMO 04

20. Nested Loop The top input to the nested loop is the outer table The bottom input to the nested loop is the inner table For each outer row, searches for matching rows are in the inner input table Effective if the outer input is very small and the inner input is preindexed and very large Optimizer sometimes sorts the outer input to improve locality of the searches on the index over the inner input Best when search exploits an index (indexes on join columns are used) Low memory requirement

21. Hash Join The top input is build input, the smaller of the two inputs The bottom input is probe input The hash join first scans or computes the whole build input Requires at least one equality clause in the join predicate Good for ad-hoc queries

22. Merge Join Both inputs should be sorted on the merge column keys An index on a correct set of columns is useful A many-to-many merge join uses a temporary table to store rows Very fast if the data that you want can be obtained presorted from existing B-tree indexes

23. WHICH JOIN IS GOOD NONE AND ALL A Merge Join is an efficient way to join two tables,# when the join columns are pre sorted if the join columns are not pre sorted, the query optimizer has the option of a) sorting the join columns first, then performing a Merge Join, or b) performing a less efficient Hash Join. The query optimizer considers all the options and generally chooses the execution plan that uses the least resources.

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25. Stream Aggregation The argument column of the plan output shows the list of columns of the GROUP BY or DISTINCT clause The list of aggregate expressions will appear in the Defined Values column of the plan output Best for smaller sets or sets already sorted Input is sorted and output is ordered

26. Hash Aggregation Used with large sets Aggregations are evaluated while building the hash Input can be in random order; output is always in random order

27. DEMO 06

28. Rewinds and Rewinds

29. Questions THANK YOU