Database Systems Design, Implementation, and Management

Database Systems: Design,
Implementation, and
Management
Eighth Edition
Chapter 11
Database Performance Tuning and
Query Optimization
Database Systems, 8th Edition 2
Objectives
• 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
– Some common practices used to write efficient SQL
code
– How to formulate queries and tune the DBMS for
optimal performance
– Performance tuning in SQL Server 2005

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
• All factors must operate at optimum level with
minimal bottlenecks
• Good database performance starts with
good database design
Performance Tuning: Client and Server
• Client side
– Generate SQL query that returns correct answer
in least amount of time
• Using minimum amount of resources at server

– SQL performance tuning
• Server side
– DBMS environment configured to respond to
clients’ requests as fast as possible
• Optimum use of existing resources
– DBMS performance tuning
DBMS Architecture
• All data in database are stored in data files
• Data files
– Automatically expand in predefined increments
known as extends
– Grouped in file groups or table spaces
• Table space or file group:
– Logical grouping of several data files that store
data with similar characteristics
Basic DBMS architecture

DBMS Architecture (continued)
• Data cache or buffer cache: shared, reserved
memory area
– Stores most recently accessed data blocks in RAM
• SQL cache or procedure cache: stores most
recently executed SQL statements
– Also PL/SQL procedures, including triggers and
functions
• DBMS retrieves data from permanent storage and
places it in RAM
DBMS Architecture (continued)
• Input/output request: low-level data access
operation to/from computer devices, such as
memory, hard disks, videos, and printers
• Data cache is faster than data in data files
– DBMS does not wait for hard disk to retrieve data
• Majority of performance-tuning activities focus on
minimizing I/O operations
• Typical DBMS processes:
– Listener, User, Scheduler, Lock manager, Optimizer

Database Statistics
• 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
– UPDATE STATISTICS table_name [index_name]
– Auto-Update and Auto-Create Statistics option
• 資料庫屬性-> 自動更新統計資料
• 資料庫屬性-> 自動建立統計資料
Ch08: dbcc show_statistics (customer,
PK__CUSTOMER__24927208 )
Ch08: dbcc show_statistics (customer, CUS_UI1)

補充SQL Server 2005
11.2 Query Processing
• DBMS processes queries in three phases
– Parsing
• DBMS parses the query and chooses the most
efficient access/execution plan
– Execution
• DBMS executes the query using chosen
execution plan
– Fetching
• DBMS fetches the data and sends the result back
to the client
Query Processing
SQL Parsing Phase
• Break down query into smaller units

• Transform 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
SQL Parsing Phase (continued)
• Query optimizer analyzes SQL query and finds most
efficient way to access data
– Validated for syntax compliance
– Validated against data dictionary
• Tables, column names are correct
• User has proper access rights
– Analyzed and decomposed into more atomic components
– Optimized through transforming into a fully equivalent but
more efficient SQL query
– Prepared for execution by determining the execution or
access plan

SQL Parsing Phase (continued)
• Access plans are DBMS-specific
– 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
• DBMS checks if access plan already exists for
query in SQL cache
• DBMS reuses the access plan to save time
• If not, optimizer evaluates various plans
– Chosen plan placed in SQL cache
SQL Execution and Fetching Phase
• All I/O operations indicated in access plan are
executed
– Locks acquired
– Data retrieved and placed in data cache
– Transaction management commands processed

• Rows of resulting query result set are returned to
client
• DBMS may use temporary table space to store
temporary data
– The server may send only the first 100 rows of 9000 rows
Query Processing Bottlenecks
• Delay introduced in the processing of an I/O
operation that slows the system
– CPU
– RAM
– Hard disk
– Network
– Application code
SQL 敘述
輸入完成
後先不要
執行查
詢, 請按
下工具列
的顯示估

計執行計
劃鈕
：
11.3 Indexes and Query Optimization
• Indexes
– 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
Indexes and Query Optimization
• Data sparsity: number of different values a column
could possibly have
• Indexes implemented using: ( 課本p. 453)
– Hash indexes
– B-tree indexes: most common index type. Used in tables in

which column values repeat a small number of times. The
leaves contain pointers to records It is self-balanced.
– Bitmap indexes: 0/1
• DBMSs determine best type of index to use
– Ex: CUST_LNAME with B-tree and REGION_CODE with
Bitmap indexes
Database Systems, 8th Edition 24B-tree and bitmap index
representation
25
Index Representation for the
CUSTOMER table
SELECT CUS_NAME
FROM CUSTOMER
WHERE CUS_STATE=‘FL’
Requires only 5 accesses to STATE_INDEX,
5 accesses to CUSTOMER
11.4 Optimizer Choices
• Rule-based optimizer
– Preset rules and points

– Rules assign a fixed cost to each operation
• Cost-based optimizer
– Algorithms based on statistics about objects
being accessed
– Adds up processing cost, I/O costs, resource
costs to derive total cost
Example
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
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
Using Hints to Affect Optimizer Choices
• Optimizer might not choose best plan
• Makes decisions based on existing statistics
– Statistics may be old
– Might choose less efficient decisions
• Optimizer hints: special instructions for the
optimizer embedded in the SQL command text
Oracle 版本

MS SQL Server 的語法請參考：
http://msdn.microsoft.com/en-us/library/ms187713.aspx
SQL Server Query Hints Example
select o.customerid,companyname
from orders as o inner MERGE join customers as c
on o.customerid = c.customerid
from orders as o inner HASH join customers as c
from orders as o inner LOOP join customers as c
select city, count(*)
from customers
group by city
OPTION (HASH GROUP)
Database Systems: Design, Implementation, and Management
Eighth EditionObjectives11.1 Database Performance-Tuning
ConceptsPowerPoint PresentationPerformance Tuning: Client
and ServerDBMS ArchitectureSlide 7DBMS Architecture
(continued)Slide 9Database StatisticsSlide 11Slide 1211.2
Query ProcessingSlide 14SQL Parsing PhaseSQL Parsing Phase
(continued)Slide 17Slide 18SQL Execution and Fetching
PhaseQuery Processing BottlenecksSlide 2111.3 Indexes and
Query OptimizationIndexes and Query OptimizationSlide
24Slide 2511.4 Optimizer ChoicesExampleSlide 28Slide
29Using Hints to Affect Optimizer ChoicesSlide 31Slide 32

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