MIS480 3

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• Week 6

• Physical Database Design and Performance • Chapter 5

Objectives

• Define terms

• Describe the physical database design process

• Choose storage formats for attributes

• Select appropriate file organizations

• Describe three types of file organization

• Describe indexes and their appropriate use

• Translate a database model into efficient structures

• Know when and how to use denormalization

Physical Database Design

• Purpose–translate the logical design of data into the technical specifications for storing and retrieving data.

• Goal–create a design for storing data that will provide adequate performance and ensure database integrity, security, and recoverability.

Physical Database Design

4 Logical Physical

Physical Design Process

Normalized relations

Volume estimates

Attribute definitions

Response time expectations

Data security needs

Backup/recovery needs

Integrity expectations

DBMS technology used

Inputs

Attribute data types

Physical record descriptions (doesn’t

always match logical design)

File organizations

Indexes and database architectures

Query optimization

Leads to

Decisions

Figure 5-1 Composite usage map

(Pine Valley Furniture Company)

Figure 5-1 Composite usage map

(Pine Valley Furniture Company) (cont.)

Data volumes

Figure 5-1 Composite usage map

(Pine Valley Furniture Company) (cont.)

Access Frequencies (per

hour)

Figure 5-1 Composite usage map

(Pine Valley Furniture Company) (cont.)

Usage analysis: 14,000 purchased parts accessed per

hour 

8000 quotations accessed from these 140

purchased part accesses 

7000 suppliers accessed from these 8000

quotation accesses

Figure 5-1 Composite usage map

(Pine Valley Furniture Company) (cont.)

Usage analysis: 7500 suppliers accessed per hour 

4000 quotations accessed from these

7500 supplier accesses 

4000 purchased parts accessed from

these 4000 quotation accesses

Designing Fields

•Field: smallest unit of application data recognized by system software

•Field design • Choosing data type • Coding, compression, encryption • Controlling data integrity

Choosing Data Types

Figure 5-2 Example of a code look-up table (Pine Valley Furniture Company)

Code saves space, but costs an

additional lookup to obtain actual

value

Field Data Integrity

• Default value –assumed value if no explicit value

• Range control –allowable value limitations (constraints or validation rules)

• Null value control –allowing or prohibiting empty fields

• Referential integrity –range control (and null value allowances) for foreign-key to primary-key match- ups

Handling Missing Data

• Substitute an estimate of the missing value (e.g., using a formula)

• Construct a report listing missing values.

• In programs, ignore missing data unless the value is significant (sensitivity testing)

Denormalization

• Transforming normalized relations into non-normalized physical record specifications

• Benefits: • Can improve performance (speed) by reducing number of table lookups (i.e. reduce number of

necessary join queries)

• Costs (due to data duplication) • Wasted storage space

• Data integrity/consistency threats

• Common denormalization opportunities • One-to-one relationship (Fig. 5-3)

• Many-to-many relationship with non-key attributes (associative entity) (Fig. 5-4)

• Reference data (1:N relationship where 1-side has data not used in any other relationship) (Fig. 5-5)

Figure 5-3 A possible denormalization situation: two entities with one-to-one relationship

Figure 5-4 A possible denormalization situation: a many-to-many relationship with nonkey attributes

Extra table access

required

Null description possible

Figure 5-5 A possible

denormalization situation:

reference data

Extra table access

required

Data duplication

Denormalize with caution

• Denormalization can • Increase chance of errors and inconsistencies

• Reintroduce anomalies

• Force reprogramming when business rules change

• Perhaps other methods could be used to improve performance of joins • Organization of tables in the database (file organization and clustering)

• Proper query design and optimization

Partitioning

• Horizontal Partitioning: Distributing the rows of a logical relation into several separate tables

• Useful for situations where different users need access to different rows

• Three types: Key Range Partitioning, Hash Partitioning, or Composite Partitioning

• Vertical Partitioning: Distributing the columns of a logical relation into several separate physical tables

• Useful for situations where different users need access to different columns

• The primary key must be repeated in each file

• Combinations of Horizontal and Vertical 21

Partitioning pros and cons

• Advantages of Partitioning: • Efficiency: Records used together are grouped together • Local optimization: Each partition can be optimized for performance • Security: data not relevant to users are segregated • Recovery and uptime: smaller files take less time to back up • Load balancing: Partitions stored on different disks, reduces contention

• Disadvantages of Partitioning: • Inconsistent access speed: Slow retrievals across partitions • Complexity: Non-transparent partitioning • Extra space or update time: Duplicate data; access from multiple partitions

Designing Physical database Files

• Physical File: • A named portion of secondary memory allocated

for the purpose of storing physical records

• Tablespace–named logical storage unit in which data from multiple tables/views/objects can be stored

• Tablespace components • Segment – a table, index, or partition

• Extent –contiguous section of disk space

• Data block – smallest unit of storage 23

File Organizations

• Technique for physically arranging records of a file on secondary storage

• Factors for selecting file organization: • Fast data retrieval and throughput • Efficient storage space utilization • Protection from failure and data loss • Minimizing need for reorganization • Accommodating growth • Security from unauthorized use

Indexed File Organizations

• Storage of records sequentially or nonsequentially with an index that allows software to locate individual records

• Index: a table or other data structure used to determine in a file the location of records that satisfy some condition

• Primary keys are automatically indexed

• Other fields or combinations of fields can also be indexed; these are called secondary keys (or nonunique keys)

Rules for Using Indexes

1. Use on larger tables

2. Index the primary key of each table

3. Index search fields (fields frequently in WHERE clause)

4. Fields in SQL ORDER BY and GROUP BY commands

5. When there are >100 values but not when there are <30 values

Rules for Using Indexes

6. Avoid use of indexes for fields with long values; perhaps compress values first

7. If key to index is used to determine location of record, use surrogate (like sequence number) to allow even spread in storage area

8. DBMS may have limit on number of indexes per table and number of bytes per indexed field(s)

9. Be careful of indexing attributes with null values; many DBMSs will not recognize null values in an index search