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MIS 385/MBA 664Systems Implementation with DBMS/Database Management

Dave Salisburysalisbury@udayton.edu (email)http://www.davesalisbury.com/ (web site)

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Physical Database Design

The purpose of the physical design process is to translate the logical description of the data into technical specifications for storing and retrieving data

Goal: create a design that will provide adequate performance and insure database integrity, security, and recoverability

Decisions made in this phase have a major impact on data accessibility, response times, security, and user friendliness.

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Physical Design Process

Inputs Normalized relations Volume estimates Attribute definitions Response time

expectations Data security needs Backup/recovery

needs Integrity expectations DBMS technology used

Decisions Attribute data types Physical record

descriptions (doesn’t always match logical design)

File organizations Indexes and database

architectures Query optimization

Leads to

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Determining volume and usage

Data volume statistics represent the size of the business calculated assuming business growth over a period of several years

Usage is estimated from the timing of events, transaction volumes, and reporting and query activity. Less precise than volume statistics

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Figure 6.1 - Composite usage map (Pine Valley Furniture Company)

Usage statistics for PVFC

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Figure 6.1 - Composite usage map (Pine Valley Furniture Company)

Data Volumes

Data volumes

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Figure 6.1 - Composite usage map (Pine Valley Furniture Company)

Access Frequencies

Access Frequencies (per hour)

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Figure 6.1 - Composite usage map (Pine Valley Furniture Company)

One usage analysis

Usage analysis:140 purchased parts accessed per hour 80 quotations accessed from these 140 purchased part accesses 70 suppliers accessed from these 80 quotation accesses

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Figure 6.1 - Composite usage map (Pine Valley Furniture Company)

Another usage analysis

Usage analysis:75 suppliers accessed per hour 40 quotations accessed from these 75 supplier accesses 40 purchased parts accessed from these 40 quotation accesses

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Physical Design Decisions

Specify the data type for each attribute from the logical data model minimize storage space and maximize integrity

Specify physical records by grouping attributes from the logical data model

Specify the file organization technique to use for physical storage of data records

Specify indexes to optimize data retrieval Specify query optimization strategies

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Designing Fields

Field: smallest unit of data in database

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

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Choosing Data Types

CHAR – fixed-length character VARCHAR2 – variable-length character

(memo) LONG – large number NUMBER – positive/negative number INTEGER – positive/negative numbers

up to 38 digits DATE – actual date – stores

c/y/m/d/h/m/s BLOB – binary large object (good for

graphics, sound clips, etc.)

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Data Format

Data type selection goals minimize storage represent all possible values

eliminate illegal values improve integrity support manipulation

Note: these have different relative importance

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Data coding

e.g., C(OAK), B(MAPLE) , etc Implement by creating a look-up table There is a trade-off in that you must

create and store a second table and you must access this table to look up the code value

Consider using when a field has a limited number of possible values, each of which occupies a relatively large amount of space, and the number of records is large and/or the number of record accesses is small

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Code saves space, but costs an additional lookup to obtain actual value.

Example code-look-up table at PVFC

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Data Format decisions (integrity)

Data integrity controls Default value Range control Null value control Referential

integrity

Missing data substitute an

estimate report missing

data sensitivity testing Triggers can be

used to perform these operations

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For example...

Suppose you were designing the age field in a student record at your university. What decisions would you make about: data type integrity (range, default, null) How might your decision vary by other characteristics about the student such as degree sought?

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Physical Records

Physical Record: A group of fields stored in adjacent memory locations and retrieved together as a unit

Page: The amount of data read or written in one I/O operation

Blocking Factor: The number of physical records per page

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Denormalization

Transforming normalized relations into unnormalized physical record specifications

Benefits: Can improve performance (speed) be 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 Many-to-many relationship with attributes Reference data (1:N relationship where 1-side has

data not used in any other relationship)

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Denormalization: two entities with one-to-one relationship

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Extra table access required

Null description possible

Denormalization: many-to-many relationship with nonkey attributes

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Extra table access required

Data duplication

Denormalization: reference data

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Consider the following normalized relations

STORE(Store_Id, Region, Manager_Id, Square_Feet)

EMPLOYEE(Emp_Id, Store_Id, Name, Address)

DEPARTMENT(Dept#, Store_ID, Manager_Id, Sales_Goal)

SCHEDULE(Dept#, Emp_Id, Date, hours)

What opportunities might exist for denormalization?

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Partitioning

Horizontal Partitioning: Distributing the rows of a table into several separate files

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 table into several separate files

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 Partitions often correspond with User

Schemas (user views)

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Partitioning

Advantages of Partitioning: Records used together are grouped together Each partition can be optimized for performance Security, recovery Partitions stored on different disks: contention Take advantage of parallel processing capability

Disadvantages of Partitioning: Slow retrievals across partitions Complexity

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Data Replication

Purposely storing the same data in multiple locations of the database

Improves performance by allowing multiple users to access the same data at the same time with minimum contention

Sacrifices data integrity due to data duplication

Best for data that is not updated often

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Designing Physical Files

Physical File: A named portion of secondary memory

allocated for the purpose of storing physical records

Constructs to link two pieces of data: Sequential storage. Pointers.

File Organization: How the files are arranged on the disk.

Access Method: How the data can be retrieved based on

the file organization.

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Records of the file are stored in sequence by the primary key field values.

If not sortedAverage time to find desired record = n/2.

1

2

n

If sorted – every insert or delete requires resort

Sequential file organization

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Sequential Retrieval

Consider a file of 10,000 records each occupying 1 page

Queries that require processing all records will require 10,000 accesses

e.g., Find all items of type 'E' Many disk accesses are wasted if few

records meet the condition However, very effective if most or all

records will be accessed (e.g., payroll)

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Indexed File Organizations

Index – a separate table that contains organization of records for quick retrieval – like an index in a book.

Primary keys are automatically indexed Oracle has a CREATE INDEX operation, and

MS ACCESS allows indexes to be created for most field types

Indexing approaches: B-tree index Bitmap index Hash Index Join Index

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Fig. 6-7b – B-tree index

uses a tree search

Average time to find desired record = depth of the tree

Leaves of the tree are all at same level

consistent access time

Tree Search

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Hashing

A technique for reducing disk accesses for direct access

Avoids an indexNumber of accesses per record can be close to one

The hash field is converted to a hash address by a hash function

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Hashed File Organization

Hashing Algorithm: Converts a primary key value into a record address

Division-remainder method is common hashing algorithm

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Hash algorithmUsually uses division-remainder to determine record position. Records with same position are grouped in lists.

Hashing

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Hashing

Hash address = remainder after dividing SSN by 10000 (in this case)

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Shortcomings of Hashing

Different hash fields may convert to the same hash address these are called Synonyms store the colliding record in an overflow area

Long synonym chains degrade performance

There can be only one hash field per record

The file can no longer be processed sequentially

More collisions between synonyms leads to reduced access speed

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Bitmap saves on space requirements

Rows - possible values of the attribute

Columns - table rows

Bit indicates whether the attribute of a row has the values

Bitmap index organization

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Join Index – speeds up join operations

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Comparing File Organizations

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Clustering Files

In some relational DBMSs, related records from different tables can be stored together in the same disk area

Useful for improving performance of join operations

Primary key records of the main table are stored adjacent to associated foreign key records of the dependent table

e.g. Oracle has a CREATE CLUSTER command

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Indexing

An index is a table file that is used to determine the location of rows in another file that satisfy some condition

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Querying with an Index

Read the index into memorySearch the index to find records meeting the condition

Access only those records containing required data

Disk accesses are substantially reduced when the query involves few records

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Maintaining an Index

Adding a record requires at least two disk accesses: Update the file Update the index

Trade-off: Faster queries Slower maintenance (additions, deletions,

and updates of records) Thus, more static databases benefit more

overall

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Rules for Using Indexes

Use on larger tables Index the primary key of each table Index search fields (fields frequently in WHERE clause)

Fields in SQL ORDER BY and GROUP BY commands

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

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Rules for Using Indexes

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

Null values will not be referenced from an index

Use indexes heavily for non-volatile databases; limit the use of indexes for volatile databases Why? Because modifications (e.g.

inserts, deletes) require updates to occur in index files

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Rules for Adding Derived Columns

Use when aggregate values are regularly retrieved.

Use when aggregate values are costly to calculate.

Permit updating only of source data.

Create triggers to cascade changes from source data.

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Another rule of thumb to increase performance

Consider contriving a shorter field or selecting another candidate key to substitute for a long, multi-field primary key (and all associated foreign keys)

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RAID

Redundant Arrays of Inexpensive Disks

Exploits economies of scale of disk manufacturing for the computer market

Can give greater security Increases fault tolerance of systemsNot a replacement for regular backup

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RAID

The operating system sees a set of physical drives as one logical drive

Data are distributed across physical drives

All levels, except 0, have data redundancy or error-correction features

Parity codes or redundant data are used for data recovery

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Mirroring

Write Identical copies of file are written to each drive in

array Read

Alternate pages are read simultaneously from each drive

Pages put together in memory Access time is reduced by approximately the number

of disks in the array Read error

Read required page from another drive Tradeoffs

Provides data security Reduces access time Uses more disk space

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Mirroring

Complete Data Set

Complete Data Set

No parity

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Striping

Three drive model Write

Half of file to first drive Half of file to second drive Parity bit to third drive

Read Portions from each drive are put together in memory

Read error Lost bits are reconstructed from third drive’s parity data

Tradeoffs Provides data security Uses less storage space than mirroring Not as fast as mirroring

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Striping

One-Half Data Set

One-Half Data Set

Parity Codes

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Here, pages 1-4 can be read/written simultaneously

RAID with four disks and striping

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Raid Types

Raid 0 Maximized parallelism No redundancy No error correction no fault-tolerance

Raid 1 Redundant data – fault

tolerant Most common form

Raid 2 No redundancy One record spans across

data disks Error correction in multiple

disks– reconstruct damaged data

Raid 3 Error correction in one disk Record spans multiple data

disks (more than RAID2) Not good for multi-user

environments, Raid 4

Error correction in one disk Multiple records per stripe Parallelism, but slow

updates due to error correction contention

Raid 5 Rotating parity array Error correction takes place

in same disks as data storage

Parallelism, better performance than Raid4

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Legacy

Systems

Current

Technology

Data

Warehouses

Database Architectures

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Query Optimization

Parallel Query Processing Override Automatic Query Optimization Data Block Size -- Performance tradeoffs:

Block contention Random vs. sequential row access speed Row size Overhead

Balancing I/O Across Disk Controllers

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Query Optimizer Factors

Type of Query Highly selective. All or most of the records of a file.

Unique fields Size of files Indexes Join Method

Nested-Loop Merge-Scan (Both files must be ordered or

indexed on the join columns.)

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Query Optimization

Wise use of indexesCompatible data typesSimple queriesAvoid query nestingTemporary tables for query groupsSelect only needed columnsNo sort without index