1. virtual techdays INDIA │ 9-11 February 2011 SQL 2008 Query Tuning Praveen Srivatsa │ Principal SME – StudyDesk91 │ Director, AsthraSoft Consulting │ Microsoft Regional Director, Bangalore │ MVP, ASP.NET

2. Query Architecture

3. Query optimizer and statistics SQL Server always used a statistic based query optimizer Statistical information is used to chose the optimal index for a particular query The most important information for the optimizer is: – Selectivity or density: How many different values exist in this column of the table? – Histogram over the values of a column: How often does one particular value exist?

4. Analyzing Factors Application Database Design Microsoft SQL Server Operating System Hardware

5. SQL Coding Issues Excessive Scanning Poor use of indexes/missing Indexes Out of Date Table Statistics Non-selective Triggers ‘Where’ clause not limiting record set enough Excessive Recompiles Long blocking durations (multi-connections)

6. Where Limit number of Columns Avoid <>, OR, and not Indexed fields Avoid calculated columns Avoid functions Avoid redundant criteria

7. Search Arguments Equality Predicate SELECT * FROMCUSTOMER WHERE CUSTOMER_NAME = 'CUSTOMER#000000047' Range Predicate SELECT * FROMCUSTOMER WHERE CUSTOMER_ID BETWEEN 100 AND 200 Conjunctions SELECT * FROMORDER_ITEM WHERE SHIP_DATE = '1998-07-02' AND SUPPLIER_ID = 350 ANDPRODUCT_ID > 80000 Search arguments useful in driving index seeks

8. Non-Search Arguments Not Predicates SELECT * FROMCUSTOMER WHERE CUSTOMER_ID <> 100 Like pattern on left of column SELECT * FROMCUSTOMER WHERE CUSTOMER_NAME LIKE ‘%47‘ Function on column SELECT * FROMORDER_ITEM WHERE YEAR(SHIP_DATE) = 1998 Non-search arguments may still be evaluated in an index In Where clause of Index Scan or Index Seek Operator

9. Understanding Joins Outer Table Inner Table Join Method

10. Nested Loop Algorithm – Get Row From Outer Table (1st input to Join operator in showplan) Get Matching Row From Inner Table (2nd input to Join operator in showplan) Output Composite Result Loop Through Inner Table When Inner Table Exhausted,Loop on Outer Table Outer Table Inner Table

11. Merge Join Algorithm Get next row from outer table – Get next row from inner table with same key – If found output and loop on inner table – If not found loop on outer table Join Sequence Match and Merge Outer Table Inner Table

12. Hash Join Algorithm Scan Smaller (Build) Table – Hash Build Key Values; Store in Hash Table – 1st input to Join operator in SHOWPLAN Scan Larger (Probe) table – Hash Probe Key Value; Look Up in Hash Table – If Found, Output Result – 2nd input to Join operator in SHOWPLAN Build Table Hash Table Probe Table Lookup in Hash Table Lookup in Hash Table Hash Join Key Key Join Hash

13. Troubleshooting Join Performance Is join predicate missing? Indexes on join column(s)? Join columns exact data type match? Examine Statistics Profile for estimates versus actual – Hash Join: EstimateRows for child operator producing Build input – Nested Loop: EstimateExecute for inner table (2nd input) – Merge Scan: EstimateRows for Sort Operator (if present) Examine STATISTCS IO – SET STATISTICS IO ON – If Hash Join, are workfiles present Indicate Build tables have spilled to tempdb Last Resort: Join Hints

14. Stored Procedures Execution Plan Usage Keep them small Recompiles – Causes – Avoiding Analysis

15. Intelligent Indexing Minimizing result sets to columns needed (Covering index approach) instead of select * Avoid indexing fields that change frequently Compound Indexes – Unique >>>>Non-Unique

16. How do you do Partitioning? Before a table is partitioned two things have to be created, a partition Function and Schema Partition Function – Each row of an index/table is assigned to a partition (numbered 1, 2, 3,...) by using a “Partition Function” SQL Server 2005 supports Range partitioning only on a single column User defines the key column, the number of partitions, and the partition boundary points Partition Scheme – Each partition for a partition function is mapped to a physical storage location (Filegroup) through a “Partition Scheme”