2009-09-15 SLIDE 1IS 257 – Fall 2009 Physical Database Design University of California, Berkeley...
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IS 257 – Fall 2009 2009-09-15 SLIDE 1
Physical Database Design
University of California, Berkeley
School of Information
I 257: Database Management
IS 257 – Fall 2009 2009-09-15 SLIDE 2
Lecture Outline
• Review– Normalization
• Physical Database Design
• Access Methods
IS 257 – Fall 2009 2009-09-15 SLIDE 3
Lecture Outline
• Review– Normalization
• Physical Database Design
• Access Methods
IS 257 – Fall 2009 2009-09-15 SLIDE 4
Database Design Process
ConceptualModel
LogicalModel
External Model
Conceptual requirements
Conceptual requirements
Conceptual requirements
Conceptual requirements
Application 1
Application 1
Application 2 Application 3 Application 4
Application 2
Application 3
Application 4
External Model
External Model
External Model
Internal Model
IS 257 – Fall 2009 2009-09-15 SLIDE 5
Normalization
• Normalization theory is based on the observation that relations with certain properties are more effective in inserting, updating and deleting data than other sets of relations containing the same data
• Normalization is a multi-step process beginning with an “unnormalized” relation– Hospital example from Atre, S. Data Base:
Structured Techniques for Design, Performance, and Management.
IS 257 – Fall 2009 2009-09-15 SLIDE 6
Normal Forms
• First Normal Form (1NF)
• Second Normal Form (2NF)
• Third Normal Form (3NF)
• Boyce-Codd Normal Form (BCNF)
• Fourth Normal Form (4NF)
• Fifth Normal Form (5NF)
IS 257 – Fall 2009 2009-09-15 SLIDE 7
Normalization
Boyce-Codd and
Higher
Functional dependencyof nonkey attributes on the primary key - Atomic values only
Full Functional dependencyof nonkey attributes on the primary key
No transitive dependency between nonkey attributes
All determinants are candidate keys - Single multivalued dependency
IS 257 – Fall 2009 2009-09-15 SLIDE 8
Unnormalized Relations
• First step in normalization is to convert the data into a two-dimensional table
• In unnormalized relations data can repeat within a column
IS 257 – Fall 2009 2009-09-15 SLIDE 9
Unnormalized RelationPatient # Surgeon # Surg. date Patient Name Patient Addr Surgeon Surgery Postop drugDrug side effects
1111145 311
Jan 1, 1995; June 12, 1995 John White
15 New St. New York, NY
Beth Little Michael Diamond
Gallstones removal; Kidney stones removal
Penicillin, none-
rash none
1234243 467
Apr 5, 1994 May 10, 1995 Mary Jones
10 Main St. Rye, NY
Charles Field Patricia Gold
Eye Cataract removal Thrombosis removal
Tetracycline none
Fever none
2345 189Jan 8, 1996 Charles Brown
Dogwood Lane Harrison, NY
David Rosen
Open Heart Surgery
Cephalosporin none
4876 145Nov 5, 1995 Hal Kane
55 Boston Post Road, Chester, CN Beth Little
Cholecystectomy Demicillin none
5123 145May 10, 1995 Paul Kosher
Blind Brook Mamaroneck, NY Beth Little
Gallstones Removal none none
6845 243
Apr 5, 1994 Dec 15, 1984 Ann Hood
Hilton Road Larchmont, NY
Charles Field
Eye Cornea Replacement Eye cataract removal
Tetracycline Fever
IS 257 – Fall 2009 2009-09-15 SLIDE 10
First Normal Form
• To move to First Normal Form a relation must contain only atomic values at each row and column.– No repeating groups– A column or set of columns is called a
Candidate Key when its values can uniquely identify the row in the relation.
IS 257 – Fall 2009 2009-09-15 SLIDE 11
First Normal Form
Patient # Surgeon #Surgery DatePatient NamePatient AddrSurgeon Name Surgery Drug adminSide Effects
1111 145 01-Jan-95 John White
15 New St. New York, NY Beth Little
Gallstones removal Penicillin rash
1111 311 12-Jun-95 John White
15 New St. New York, NY
Michael Diamond
Kidney stones removal none none
1234 243 05-Apr-94 Mary Jones10 Main St. Rye, NY Charles Field
Eye Cataract removal
Tetracycline Fever
1234 467 10-May-95 Mary Jones10 Main St. Rye, NY Patricia Gold
Thrombosis removal none none
2345 189 08-Jan-96Charles Brown
Dogwood Lane Harrison, NY David Rosen
Open Heart Surgery
Cephalosporin none
4876 145 05-Nov-95 Hal Kane
55 Boston Post Road, Chester, CN Beth Little
Cholecystectomy Demicillin none
5123 145 10-May-95 Paul Kosher
Blind Brook Mamaroneck, NY Beth Little
Gallstones Removal none none
6845 243 05-Apr-94 Ann Hood
Hilton Road Larchmont, NY Charles Field
Eye Cornea Replacement
Tetracycline Fever
6845 243 15-Dec-84 Ann Hood
Hilton Road Larchmont, NY Charles Field
Eye cataract removal none none
IS 257 – Fall 2009 2009-09-15 SLIDE 12
Second Normal Form
• A relation is said to be in Second Normal Form when every nonkey attribute is fully functionally dependent on the primary key.– That is, every nonkey attribute needs the full
primary key for unique identification
IS 257 – Fall 2009 2009-09-15 SLIDE 13
Second Normal Form
Patient # Patient Name Patient Address
1111 John White15 New St. New York, NY
1234 Mary Jones10 Main St. Rye, NY
2345Charles Brown
Dogwood Lane Harrison, NY
4876 Hal Kane55 Boston Post Road, Chester,
5123 Paul KosherBlind Brook Mamaroneck, NY
6845 Ann HoodHilton Road Larchmont, NY
IS 257 – Fall 2009 2009-09-15 SLIDE 14
Second Normal Form
Surgeon # Surgeon Name
145 Beth Little
189 David Rosen
243 Charles Field
311 Michael Diamond
467 Patricia Gold
IS 257 – Fall 2009 2009-09-15 SLIDE 15
Second Normal Form
Patient # Surgeon # Surgery Date Surgery Drug Admin Side Effects
1111 145 01-Jan-95Gallstones removal Penicillin rash
1111 311 12-Jun-95
Kidney stones removal none none
1234 243 05-Apr-94Eye Cataract removal Tetracycline Fever
1234 467 10-May-95Thrombosis removal none none
2345 189 08-Jan-96Open Heart Surgery
Cephalosporin none
4876 145 05-Nov-95Cholecystectomy Demicillin none
5123 145 10-May-95Gallstones Removal none none
6845 243 15-Dec-84Eye cataract removal none none
6845 243 05-Apr-94Eye Cornea Replacement Tetracycline Fever
IS 257 – Fall 2009 2009-09-15 SLIDE 16
Third Normal Form
• A relation is said to be in Third Normal Form if there is no transitive functional dependency between nonkey attributes– When one nonkey attribute can be determined with
one or more nonkey attributes there is said to be a transitive functional dependency.
• The side effect column in the Surgery table is determined by the drug administered – Side effect is transitively functionally dependent on
drug so Surgery is not 3NF
IS 257 – Fall 2009 2009-09-15 SLIDE 17
Third Normal Form
Patient # Surgeon # Surgery Date Surgery Drug Admin
1111 145 01-Jan-95 Gallstones removal Penicillin
1111 311 12-Jun-95Kidney stones removal none
1234 243 05-Apr-94 Eye Cataract removal Tetracycline
1234 467 10-May-95 Thrombosis removal none
2345 189 08-Jan-96 Open Heart Surgery Cephalosporin
4876 145 05-Nov-95 Cholecystectomy Demicillin
5123 145 10-May-95 Gallstones Removal none
6845 243 15-Dec-84 Eye cataract removal none
6845 243 05-Apr-94Eye Cornea Replacement Tetracycline
IS 257 – Fall 2009 2009-09-15 SLIDE 18
Third Normal Form
Drug Admin Side Effects
Cephalosporin none
Demicillin none
none none
Penicillin rash
Tetracycline Fever
IS 257 – Fall 2009 2009-09-15 SLIDE 19
Boyce-Codd Normal Form
• Most 3NF relations are also BCNF relations.
• A 3NF relation is NOT in BCNF if:– Candidate keys in the relation are composite
keys (they are not single attributes)– There is more than one candidate key in the
relation, and– The keys are not disjoint, that is, some
attributes in the keys are common
IS 257 – Fall 2009 2009-09-15 SLIDE 20
BCNF Relations
Patient # Patient Name
1111 John White
1234 Mary Jones
2345Charles Brown
4876 Hal Kane
5123 Paul Kosher
6845 Ann Hood
Patient # Patient Address
111115 New St. New York, NY
123410 Main St. Rye, NY
2345Dogwood Lane Harrison, NY
487655 Boston Post Road, Chester,
5123Blind Brook Mamaroneck, NY
6845Hilton Road Larchmont, NY
IS 257 – Fall 2009 2009-09-15 SLIDE 21
Fourth Normal Form
• Any relation is in Fourth Normal Form if it is BCNF and any multivalued dependencies are trivial
• Eliminate non-trivial multivalued dependencies by projecting into simpler tables
IS 257 – Fall 2009 2009-09-15 SLIDE 22
Fifth Normal Form
• A relation is in 5NF if every join dependency in the relation is implied by the keys of the relation
• Implies that relations that have been decomposed in previous NF can be recombined via natural joins to recreate the original relation.
IS 257 – Fall 2009 2009-09-15 SLIDE 23
Normalization
• Normalization is performed to reduce or eliminate Insertion, Deletion or Update anomalies.
• However, a completely normalized database may not be the most efficient or effective implementation.
• “Denormalization” is sometimes used to improve efficiency.
IS 257 – Fall 2009 2009-09-15 SLIDE 24
Denormalization
• Usually driven by the need to improve query speed
• Query speed is improved at the expense of more complex or problematic DML (Data manipulation language) for updates, deletions and insertions.
IS 257 – Fall 2009 2009-09-15 SLIDE 25
Downward Denormalization
Customer
ID
Address
Name
Telephone
Order
Order No
Date Taken
Date Dispatched
Date Invoiced
Cust ID
Before:Customer
ID
Address
Name
Telephone
Order
Order No
Date Taken
Date Dispatched
Date Invoiced
Cust ID
Cust Name
After:
IS 257 – Fall 2009 2009-09-15 SLIDE 26
Upward DenormalizationOrder
Order No
Date Taken
Date Dispatched
Date Invoiced
Cust ID
Cust Name
Order Price
Order Item
Order No
Item No
Item Price
Num Ordered
Order
Order No
Date Taken
Date Dispatched
Date Invoiced
Cust ID
Cust Name
Order Item
Order No
Item No
Item Price
Num Ordered
IS 257 – Fall 2009 2009-09-15 SLIDE 27
Using RDBMS to help normalize
• Example database: Cookie
• Database of books, libraries, publisher and holding information for a shared (union) catalog
IS 257 – Fall 2009 2009-09-15 SLIDE 28
Cookie relationships
IS 257 – Fall 2009 2009-09-15 SLIDE 29
Cookie BIBFILE relation
ACCNO AUTHOR TITLE LOC PUBID DATE PRICE PAGINATIONILL HEIGHTA003 AMERICAN LIBRARY ASSOCIATIONALA BULLETIN CHICAGO 04 $3.00 63 V. ILL. 26T082 ANDERSON, THEODORETHE TEACHING OF MODERN LANGUAGESPARIS 53 1955 $10.95 294 P. 22C024 AXT, RICHARD G. COLLEGE SELF STUDY : LECTURES ON INSTITUBOULDER, CO. 51 1960 $7.00 X, 300 P. GRAPHS 28B006 BALDERSTON, FREDERICK E.MANAGING TODAYS UNIVERSITYSAN FRANCISCO 27 1975 $6.00 XVI, 307 P. 24B007 BARZUN, JACQUES TEACHER IN AMERICAGARDEN CITY 18 1954 $7.00 280 P. 18B005 BARZUN, JACQUES THE AMERICAN UNIVERSITY : HOW IT RUNS, WNEW YORK 24 1970 $5.00 XII, 319 P. 20B008 BARZUN, JACQUES THE HOUSE OF INTELLECTNEW YORK 24 1961 $8.00 VIII, 271 P. 21B010 BELL, DANIEL THE COMING OF POST-INDUSTRIAL SOCIETY :NEW YORK 09 1976 $10.00 XXVII, 507 P. 21B009 BENSON, CHARLES S. IMPLEMENTING THE LEARNING SOCIETYSAN FRANCISCO 27 1974 $9.00 XVII, 147 P. 24B012 BERG, IVAR EDUCATION AND JOBS : THE GREAT TRAININGBOSTON 10 1971 $12.00 XX, 200 P. 21B011 BERSI, ROBERT M. RESTRUCTURING THE BACCALAUREATEWASHINGTON, D.C.03 1973 $11.00 IV, 160P. 23B014 BEVERIDGE, WILLIAM I.THE ART OF SCIENTIFIC INVESTIGATIONNEW YORK 58 1957 $14.00 XIV, 239 P. 18B013 BIRD, CAROLINE THE CASE AGAINST COLLEGENEW YORK 08 1975 $13.00 XII, 308 P. 18B016 BISSELL, CLAUDE T. THE STRENGTH OF THE UNIVERSITYTORONTO 57 1968 $14.00 VII, 251 P. 21B017 BLAIR, GLENN MYERS EDUCATIONAL PSYCHOLOGYNEW YORK 30 1962 $11.00 678 P. 24F047 BLAKE, ELIAS, JR. THE FUTURE OF THE BLACK COLLEGESCAMBRIDGE, MA.02 1971 $14.25 VIII, PP. 539 23B116 BOLAND, R.J. CRITICAL ISSUES IN INFORMATION SYSTEMS RCHICHESTER, ENG.63 1987 $30.95 XV, 394 P. ILL. 24S102 BROWN, SANBORN C., ED.SCIENTIFIC MANPOWERCAMBRIDGE, MASS.29 1971 $4.00 X, 180 P. 26B118 BUCKLAND, MICHAEL K.LIBRARY SERVICES IN THEORY AND CONTEXTELMSFORD, NY 70 1983 $12.00 XII, 201 P. ILL. 23B018 BUDIG, GENE A. ACADEMIC QUICKSAND : SOME TRENDS AND ISSLINCOLN, NEBRASKA37 1973 $13.00 74 P. 23C031 CALIFORNIA. DEPT. OF JUSTICELAW IN THE SCHOOLMONTCLAIR, N.J. 35 1974 $0.50 IV, 87 P. 21C032 CAMPBELL, MARGARET A.WHY WOULD A GIRL GO INTO MEDICINE?OLD WESTBURY, N.Y.48 1973 $1.50 V, 114 P. 24C034 CARNEGIE COMMISSION ON HIGHERA DIGEST OF REPORTS OF THE CARNEGIE COMMNEW YORK 30 1974 $3.50 399 P. 24
IS 257 – Fall 2009 2009-09-15 SLIDE 30
How to Normalize?
• Currently no way to have multiple authors for a given book, and there is duplicate data spread over the BIBFILE table
• Can we use the DBMS to help us normalize?
• Access example…
IS 257 – Fall 2009 2009-09-15 SLIDE 31
DBMS Assisted Normalization
• SELECT DISTINCT items to be factored out into a new table
• Add new ID (autonumber) to table create primary key
• SELECT DISTINCT ID from new table and original table ID to new linking table
• Remove column(s) factored out from original table
IS 257 – Fall 2009 2009-09-15 SLIDE 32
Lecture Outline
• Review– Normalization
– Using Relational DBs in normalization
• Physical Database Design
• Access Methods
IS 257 – Fall 2009 2009-09-15 SLIDE 33
Database Design Process
ConceptualModel
LogicalModel
External Model
Conceptual requirements
Conceptual requirements
Conceptual requirements
Conceptual requirements
Application 1
Application 1
Application 2 Application 3 Application 4
Application 2
Application 3
Application 4
External Model
External Model
External Model
Internal Model
PhysicalDesign
IS 257 – Fall 2009 2009-09-15 SLIDE 34
Physical Database Design
• Many physical database design decisions are implicit in the technology adopted– Also, organizations may have standards or an
“information architecture” that specifies operating systems, DBMS, and data access languages -- thus constraining the range of possible physical implementations.
• We will be concerned with some of the possible physical implementation issues
IS 257 – Fall 2009 2009-09-15 SLIDE 35
Physical Database Design
• The primary goal of physical database design is data processing efficiency
• We will concentrate on choices often available to optimize performance of database services
• Physical Database Design requires information gathered during earlier stages of the design process
IS 257 – Fall 2009 2009-09-15 SLIDE 36
Physical Design Information
• Information needed for physical file and database design includes:– Normalized relations plus size estimates for them– Definitions of each attribute– Descriptions of where and when data are used
• entered, retrieved, deleted, updated, and how often
– Expectations and requirements for response time, and data security, backup, recovery, retention and integrity
– Descriptions of the technologies used to implement the database
IS 257 – Fall 2009 2009-09-15 SLIDE 37
Physical Design Decisions
• There are several critical decisions that will affect the integrity and performance of the system– Storage Format– Physical record composition– Data arrangement– Indexes– Query optimization and performance tuning
IS 257 – Fall 2009 2009-09-15 SLIDE 38
Storage Format
• Choosing the storage format of each field (attribute). The DBMS provides some set of data types that can be used for the physical storage of fields in the database
• Data Type (format) is chosen to minimize storage space and maximize data integrity
IS 257 – Fall 2009 2009-09-15 SLIDE 39
Objectives of data type selection
• Minimize storage space• Represent all possible values• Improve data integrity• Support all data manipulations• The correct data type should, in minimal
space, represent every possible value (but eliminate illegal values) for the associated attribute and can support the required data manipulations (e.g. numerical or string operations)
IS 257 – Fall 2009 2009-09-15 SLIDE 40
Access Data Types
• Numeric (1, 2, 4, 8 bytes, fixed or float)• Text (255 max)• Memo (64000 max)• Date/Time (8 bytes)• Currency (8 bytes, 15 digits + 4 digits decimal)• Autonumber (4 bytes)• Yes/No (1 bit)• OLE (limited only by disk space)• Hyperlinks (up to 64000 chars)
IS 257 – Fall 2009 2009-09-15 SLIDE 41
Access Numeric types
• Byte – Stores numbers from 0 to 255 (no fractions). 1 byte
• Integer– Stores numbers from –32,768 to 32,767 (no fractions) 2 bytes
• Long Integer (Default) – Stores numbers from –2,147,483,648 to 2,147,483,647 (no fractions). 4
bytes
• Single– Stores numbers from -3.402823E38 to –1.401298E–45 for negative
values and from 1.401298E–45 to 3.402823E38 for positive values.4 bytes
• Double– Stores numbers from –1.79769313486231E308 to –
4.94065645841247E–324 for negative values and from 1.79769313486231E308 to 4.94065645841247E–324 for positive values. 15 8 bytes
• Replication ID– Globally unique identifier (GUID) N/A 16 bytes
IS 257 – Fall 2009 2009-09-15 SLIDE 42
Controlling Data Integrity
• Default values
• Range control
• Null value control
• Referential integrity (next time)
• Handling missing data
IS 257 – Fall 2009 2009-09-15 SLIDE 43
Designing Physical Records
• A physical record is a group of fields stored in adjacent memory locations and retrieved together as a unit
• Fixed Length and variable fields
IS 257 – Fall 2009 2009-09-15 SLIDE 44
Designing Physical/Internal Model
• Overview
• terminology
• Access methods
IS 257 – Fall 2009 2009-09-15 SLIDE 45
Physical Design
• Internal Model/Physical Model
OperatingSystem
Access Methods
DataBase
User request
DBMSInternal ModelAccess Methods
External Model
Interface 1
Interface 3
Interface 2
IS 257 – Fall 2009 2009-09-15 SLIDE 46
Physical Design
• Interface 1: User request to the DBMS. The user presents a query, the DBMS determines which physical DBs are needed to resolve the query
• Interface 2: The DBMS uses an internal model access method to access the data stored in a logical database.
• Interface 3: The internal model access methods and OS access methods access the physical records of the database.
IS 257 – Fall 2009 2009-09-15 SLIDE 47
Physical File Design
• A Physical file is a portion of secondary storage (disk space) allocated for the purpose of storing physical records
• Pointers - a field of data that can be used to locate a related field or record of data
• Access Methods - An operating system algorithm for storing and locating data in secondary storage
• Pages - The amount of data read or written in one disk input or output operation
IS 257 – Fall 2009 2009-09-15 SLIDE 48
Internal Model Access Methods
• Many types of access methods:– Physical Sequential– Indexed Sequential– Indexed Random– Inverted– Direct– Hashed
• Differences in – Access Efficiency– Storage Efficiency
IS 257 – Fall 2009 2009-09-15 SLIDE 49
Physical Sequential
• Key values of the physical records are in logical sequence
• Main use is for “dump” and “restore”
• Access method may be used for storage as well as retrieval
• Storage Efficiency is near 100%
• Access Efficiency is poor (unless fixed size physical records)
IS 257 – Fall 2009 2009-09-15 SLIDE 50
Indexed Sequential
• Key values of the physical records are in logical sequence
• Access method may be used for storage and retrieval
• Index of key values is maintained with entries for the highest key values per block(s)
• Access Efficiency depends on the levels of index, storage allocated for index, number of database records, and amount of overflow
• Storage Efficiency depends on size of index and volatility of database
IS 257 – Fall 2009 2009-09-15 SLIDE 51
Index Sequential
Data File
Block 1
Block 2
Block 3
AddressBlockNumber
1
2
3
…
ActualValue
Dumpling
Harty
Texaci
...
AdamsBecker
Dumpling
GettaHarty
MobileSunociTexaci
IS 257 – Fall 2009 2009-09-15 SLIDE 52
Indexed Sequential: Two Levels
Address
7
8
9
…
Key Value
385
678
805
001003
.
.150
705710
.
.785
251..
385
455480
.
.536
605610
.
.678
791..
805
Address
1
2
Key Value
150
385
Address
3
4
Key Value
536
678
Address
5
6
Key Value
785
805
IS 257 – Fall 2009 2009-09-15 SLIDE 53
Indexed Random
• Key values of the physical records are not necessarily in logical sequence
• Index may be stored and accessed with Indexed Sequential Access Method
• Index has an entry for every data base record. These are in ascending order. The index keys are in logical sequence. Database records are not necessarily in ascending sequence.
• Access method may be used for storage and retrieval
IS 257 – Fall 2009 2009-09-15 SLIDE 54
Indexed Random
AddressBlockNumber
2
1
3
2
1
ActualValue
Adams
Becker
Dumpling
Getta
Harty
BeckerHarty
AdamsGetta
Dumpling
IS 257 – Fall 2009 2009-09-15 SLIDE 55
Btree
F | | P | | Z |
R | | S | | Z |H | | L | | P |B | | D | | F |
Devils
AcesBoilersCars
MinorsPanthers
Seminoles
Flyers
HawkeyesHoosiers
IS 257 – Fall 2009 2009-09-15 SLIDE 56
Inverted
• Key values of the physical records are not necessarily in logical sequence
• Access Method is better used for retrieval
• An index for every field to be inverted may be built
• Access efficiency depends on number of database records, levels of index, and storage allocated for index
IS 257 – Fall 2009 2009-09-15 SLIDE 57
Inverted
AddressBlockNumber
1
2
3
…
ActualValue
CH 145
CS 201
CS 623
PH 345
CH 145101, 103,104
CS 201102
CS 623
105, 106
Adams
Becker
Dumpling
Getta
Harty
Mobile
Studentname
CourseNumber
CH145
cs201
ch145
ch145
cs623
cs623
IS 257 – Fall 2009 2009-09-15 SLIDE 58
Direct
• Key values of the physical records are not necessarily in logical sequence
• There is a one-to-one correspondence between a record key and the physical address of the record
• May be used for storage and retrieval• Access efficiency always 1• Storage efficiency depends on density of
keys• No duplicate keys permitted
IS 257 – Fall 2009 2009-09-15 SLIDE 59
Hashing
• Key values of the physical records are not necessarily in logical sequence
• Many key values may share the same physical address (block)
• May be used for storage and retrieval• Access efficiency depends on distribution of
keys, algorithm for key transformation and space allocated
• Storage efficiency depends on distibution of keys and algorithm used for key transformation
IS 257 – Fall 2009 2009-09-15 SLIDE 60
Comparative Access Methods
IndexedNo wasted space for databut extra space for index
Moderately Fast
Moderately FastVery fast with multiple indexesOK if dynamic OK if dynamic
Easy but requiresMaintenance ofindexes
FactorStorage spaceSequential retrieval on primary keyRandom Retr.Multiple Key Retr.Deleting records
Adding records
Updating records
SequentialNo wasted space
Very fast
ImpracticalPossible but needsa full scancan create wasted spacerequires rewriting fileusually requires rewriting file
Hashedmore space needed foraddition and deletion ofrecords after initial load
Impractical
Very fast
Not possiblevery easy
very easy
very easy
IS 257 – Fall 2009 2009-09-15 SLIDE 61
Database Creation in Access
• Simplest to use a design view– wizards are available, but less flexible
• Need to watch the default values
• Helps to know what the primary key is, or if one is to be created automatically– Automatic creation is more complex in other
RDBMS and ORDBMS
• Need to make decision about the physical storage of the data
IS 257 – Fall 2009 2009-09-15 SLIDE 62
Database Creation in Access
• Some Simple Examples
IS 257 – Fall 2009 2009-09-15 SLIDE 63
Next Time
• Indexes and when to index
• Integrity Constraints
• Referential Integrity