1. Database Performance Tuning and Query Optimization
Chapter 11
Database Performance Tuning and Query Optimization
Database Systems: Design, Implementation, and Management, Seventh Edition, Rob and Coronel

2. In this chapter, you will learn:
Basic database performance-tuning concepts
How a DBMS processes SQL queries
About the importance of indexes in query processing
About the types of decisions the query optimizer has to make

3. In this chapter, you will learn (continued):
Some common practices used to write efficient SQL code
How to formulate queries and tune the DBMS for optimal performance

4. 11.1 Database Performance-Tuning Concepts
Goal of database performance is to execute queries as fast as possible
Database performance tuning
Set of activities and procedures designed to reduce response time of database system

5. Database Performance-Tuning Concepts (continued)

6. Database Performance-Tuning Concepts (continued)
All factors must be checked to ensure that each one operates at its optimum level and has sufficient resources to minimize occurrence of bottlenecks
Good database performance starts with good database design

7. Performance Tuning: Client and Server
Database performance-tuning activities can be divided into:
Client side
Objective is to generate SQL query that returns correct answer in least amount of time, using minimum amount of resources at server end
SQL performance tuning
Server side
DBMS environment must be properly configured to respond to clients’ requests in fastest way possible, while making optimum use of existing resources
DBMS performance tuning

8. DBMS Architecture All data in database are stored in data files
Automatically expand in predefined increments known as extends
Generally grouped in file groups of table spaces
Table space or file group is logical grouping of several data files that store data with similar characteristics

9. DBMS Architecture (continued)

10. DBMS Architecture (continued)
DBMS retrieve data from permanent storage and place it in RAM
Data cache or buffer cache is shared, reserved memory area that stores most recently accessed data blocks in RAM
SQL cache or procedure cache is shared, reserved memory area that stores most recently executed SQL statements or PL/SQL procedures, including triggers and functions

11. DBMS Architecture (continued)
An input/output request is low-level (read or write) data access operation to/from computer devices
Working with data in data cache is many times faster than working with data in data files because DBMS doesn’t have to wait for hard disk to retrieve data
Majority of performance-tuning activities focus on minimizing number of I/O operations

12. Database Statistics
Refers to number of measurements about database objects and available resources
Tables
Indexes
Number of processors used
Processor speed
Temporary space available
Make critical decisions about improving query processing efficiency
Can be gathered manually by DBA or automatically by DBMS

13. Database Statistics (continued)

14. 11.2 Query Processing DBMS processes queries in three phases
Parsing (剖析): The DBMS parses the SQL query and chooses the most efficient access/execution plan
Execution: The DBMS executes the SQL query, using the chosen execution plan
Fetching: The DBMS fetches the data and sends the result set back to the client

15. SQL Parsing Phase
Breaking down (parsing) query into smaller units and transforming original SQL query into slightly different version of original SQL code
Fully equivalent
Optimized query results are always the same as original query
More efficient
Optimized query will almost always execute faster than original query

16. SQL Parsing Phase (continued)

17. SQL Parsing Phase (continued)
Query optimizer analyzes SQL query and finds most efficient way to access data
Access plans are DBMS-specific and translate client’s SQL query into series of complex I/O operations required to read the data from the physical data files and generate result set

18. SQL Parsing Phase (continued)

19. SQL Execution Phase SQL Fetching Phase
All I/O operations indicated in access plan are executed
SQL Fetching Phase
Rows of resulting query result set are returned to client
DBMS may use temporary table space to store temporary data

20. 11.3 Indexes and Query Optimization
Crucial in speeding up data access
Facilitate searching, sorting, and using aggregate functions as well as join operations
Ordered set of values that contains index key and pointers
More efficient to use index to access table than to scan all rows in table sequentially
Data sparsity determines whether index is needed

21. Indexes and Query Optimization (continued)

22. 11.4 Optimizer Choices Rule-based optimizer Cost-based optimizer
Uses set of preset rules and points to determine best approach to execute query
Cost-based optimizer
Uses sophisticated algorithms based on statistics about objects being accessed to determine best approach to execute query

23. Example With the following database statistics:
SELECT P_CODE, P_DESCRIPT, P_PRICE, V_NAME, V_STATE
FROM PRODUCT P, VENDOR V
WHERE P.V_CODE=V.V_CODE
AND V.V_STATE=‘FL’;
With the following database statistics:
The PRODUCT table has 7000 rows
The VENDOR table has 300 rows
10 vendors come from Florida
1000 products come from vendors in Florida

24. Optimizer Choices (continued)

25. Example
Assume the PRODUCT table has the index PQOH_NDX in the P_QOH attribute
SELECT MIN(P_QOH) FROM PRODUCT
could be resolved by reading only the first entry in the PQOH_NDX index

26. Using Hints to Affect Optimizer Choices
Oracle 版本
SQL Server 2005 的語法請參考：

27. 11.5 SQL Performance Tuning
Evaluated from client perspective
Most current-generation relational DBMSs perform automatic query optimization at the server end
Most SQL performance optimization techniques are DBMS-specific and are rarely portable

28. Index Selectivity Indexes are likely used when: Index Selectivity
Indexed column appears by itself in search criteria of WHERE or HAVING clause
Indexed column appears by itself in GROUP BY or ORDER BY clause
MAX or MIN function is applied to indexed column
Data sparsity on indexed column is high
Index Selectivity
Measure of how likely an index will be used

29. Index Selectivity (continued)
General guidelines for creating and using indexes:
Create indexes for each attribute in WHERE, HAVING, ORDER BY, or GROUP BY clause
Do not use in small tables or tables with low sparsity
Declare primary and foreign keys so optimizer can use indexes in join operations
Declare indexes in join columns other than PK/FK

30. Conditional Expressions
Normally expressed within WHERE or HAVING clauses of SQL statement
Restricts output of query to only rows matching conditional criteria

31. Common Practices to write efficient Conditional Expressions
Use simple columns or literals as operands in a conditional expression – avoid functions
Numeric field comparisons are faster than character, date, and NULL comparisons
Equality comparisons are faster than inequality comparisons—the slowest is “LIKE” comparison
Transform conditional expressions to use literals
Write equality conditions first
For multiple AND (OR) conditions, write the condition most likely to be false (true) first
Avoid the use of NOT logical operator

32. 11.6 Query Formulation
Identify what columns and computations are required
Check simple expressions, aggregate functions, granularity of the raw data
Identify source tables
Determine how to join tables
Determine what selection criteria is needed
Check simple comparisons, single value to multiple values, nested comparisons, grouped data selection
Determine in what order to display output

33. 11.7 DBMS Performance Tuning
Includes global tasks such as managing DBMS processes in primary memory and structures in physical storage
Includes applying several practices examined in previous section
DBMS performance tuning at server end focuses on setting parameters used for:
Data cache: large enough
SQL cache: same query may be submitted by many users
Sort cache
Optimizer mode

34. DBMS Performance Tuning (continued)
Some general recommendations for creation of databases:
Use RAID (Redundant Array of Independent Disks) to provide balance between performance and fault tolerance
Minimize disk contention
At least the following table spaces: system table space, user data table space, index table space, temporary table space, rollback segment table space
Put high-usage tables in their own table spaces

35. DBMS Performance Tuning (continued)
Assign separate data files in separate storage volumes for indexes, system, and high-usage tables
Partition tables based on usage
Use denormalized tables where appropriate
Store computed and aggregate attributes in tables

36. DBMS Performance Tuning (continued)

37. 11.8 Query Optimization Example (跳過)
請參考 SQL Server 版本

38. Query Optimization Example
請參考 SQL Server 版本

39. Query Optimization Example
請參考 SQL Server 版本

40. Query Optimization Example
請參考 SQL Server 版本

41. Query Optimization Example
請參考 SQL Server 版本

42. Query Optimization Example
請參考 SQL Server 版本

43. Query Optimization Example
請參考 SQL Server 版本

44. Query Optimization Example
請參考 SQL Server 版本

45. Query Optimization Example
請參考 SQL Server 版本

46. Summary Database performance tuning SQL performance tuning
Refers to set of activities and procedures designed to ensure that end-user query is processed by DBMS in minimum amount of time
SQL performance tuning
Refers to activities on client side designed to generate SQL code that returns correct answer in least amount of time, using minimum amount of resources at server end

47. Summary (continued)
DBMS performance tuning refers to activities on server side oriented to ensure that DBMS is properly configured to respond to clients’ requests in fastest way possible while making optimum use of existing resources
DBMS architecture is represented by many processes and structures (in memory and in permanent storage) used to manage a database

48. Summary (continued)
Database statistics refers to a number of measurements gathered by the DBMS that describe snapshot of database objects’ characteristics
DBMS processes queries in three phases: Parsing, Execution and Fetching
Indexes are crucial in process that speeds up data access

49. Summary (continued)
During query optimization, DBMS must choose what indexes to use, how to perform join operations, what table to use first, and so on
Hints are used to change optimizer mode for current SQL statement
SQL performance tuning deals with writing queries that make good use of statistics

50. Summary (continued)
Query formulation deals with how to translate business questions into specific SQL code to generate required results
DBMS performance tuning includes tasks such as managing DBMS processes in primary memory and structures in physical storage