CSCI-UA:0060-02 - New York Universitysandhaus/CSCUA60/doc/class_7.pdf · CSCI-UA:0060-02 Database...

CSCI-UA.0060-002: Database Design and Web Implementation

CSCI-UA:0060-02Database Design &

Web Implementation

Professor Evan [email protected]@nytimes.com

Lecture #7: Normalization

Tuesday, February 19, 13

mailto:[email protected]





Administrivia

§Readings: • None for next class

§Homework:• How’s it going?• Questions?• Airline example to demonstrate what’s expected.

3



Airline Example

§Use cases• Allow customers to book a flight.• Allow customers to see the model of plane flown on a

flight.

4



Airline Example

§Use cases• Allow customers to book a flight.• Allow customers to see the model of plane flown on

a flight.

5



On Today’s Menu

§Normalization• 1st Normal Form• 2nd Normal Form• 3rd Normal Form

§Return To Spotify§ 2012 Election Results Example

6



Assumptions

§Normalization assumes that it is preferable to minimize the amount of data stored, this is a point of view not an ironclad law of the universe.

7



Normalization

8



De-normalized data

§De-normalized data is data that can be more efficiently represented

§Symptoms• Repeated data, • Multiple values per column• Mixture of multiple entities in a single record

9

Artist Artist Popularity

Album Track Release Year

Length Track Popularity

Green Day 0.71529 American Idiot American Idiot 2004 176.556 0.75Green Day 0.71529 American Idiot Boulevard Of Broken Dreams 2004 493.877 0.72Green Day 0.71529 Dookie Basket Case 1994 181.759 0.77Wizards from Kansas 0.00008 Reunion River Road 2011 217.551 0.00000Wizards from Kansas 0.00008 Reunion Orion 2011 268.952 0.00000Fun 0.68483 Some Nights We Are Young 2012 277.345 0.91729



Update Anomalies

§With de-normalized data its possible to update some instances of an entity without updating all instances of that entity.

10







Update Anomalies

§With de-normalized data its possible to update some instances of an entity without updating all instances of that entity.

11







Insert Anomalies

§With de-normalized data it may be necessary to insert empty values for components of a composite primary key.

12







Insert Anomalies

§With de-normalized data it may be necessary to insert empty values for components of a composite primary key.

13




Green Day 0.71529 American Idiot American Idiot 2004 176.556 0.75Green Day 0.71529 American Idiot Boulevard Of Broken Dreams 2004 493.877 0.72Green Day 0.71529 Dookie Basket Case 1994 181.759 0.77Green Day 0.71529 Uno [empty] 2012Wizards from Kansas 0.00008 Reunion River Road 2011 217.551 0.00000Wizards from Kansas 0.00008 Reunion Orion 2011 268.952 0.00000Fun 0.68483 Some Nights We Are Young 2012 277.345 0.91729



Delete Anomalies

§With de-normalized data is is possible to inadvertently delete important entities.

14







Delete Anomalies

§With de-normalized data is is possible to inadvertently delete important entities.

15




Green Day 0.71529 American Idiot American Idiot 2004 176.556 0.75Green Day 0.71529 American Idiot Boulevard Of Broken Dreams 2004 493.877 0.72Green Day 0.71529 Dookie Basket Case 1994 181.759 0.77Wizards from Kansas 0.00008 Reunion River Road 2011 217.551 0.00000Wizards from Kansas 0.00008 Reunion Orion 2011 268.952 0.00000



Functional Dependency

§ If knowing the value of a set of columns X uniquely determines the value of another set of columns Y then we can say that Y is functionally dependent on X, written Y → X.

16

Area Code

Phone Number

First Last

913 555-1234 Ronald Sandhaus212 226-2000 Evan Sandhaus718 226-2000 Tara Sandhaus913 844-1212 Francis Sandhaus



Functional Dependency

§ In this example: • [Area Code, Phone Number] → [First Last]

17

Area Code

Phone Number

First Last




Key - A NEW Definition Based On Functional Dependency

§The key fields functionally determines all of the other fields in a table.

18

Area Code

Phone Number

First Last




Primary Key - A NEW Definition Based On Functional Dependency

§A Primary Key contains no subset of fields that is also a key.

19

Area Code

Phone Number

Zip First Last

913 555-1234 66211 Ronald Sandhaus212 226-2000 66211 Evan Sandhaus718 226-2000 66211 Tara Sandhaus913 844-1212 66211 Francis Sandhaus



1st Normal Form

§A database is in 1st normal form if no column contains multiple values.

20

Album Id Album Album Countries

1 American Idiot US, UK, AU1 American Idiot US, UK, AU2 Dookie US, UK3 Reunion US3 Reunion US4 Some Nights US, NZ, FR



1st Normal Form

§A database is in 1st normal form if no column contains multiple values.

21

Album Id Album1 American Idiot1 American Idiot2 Dookie3 Reunion3 Reunion4 Some Nights

Album Id FK

Album Countries

1 US1 UK1 AU1 US1 UK1 AU2 US2 UK3 US3 US4 US4 NZ4 FR



Primary Key?

22


Album Release Year

Track Length Track Popularity

Green Day 0.71529 American Idiot2004 American Idiot 176.556 0.75Green Day 0.71529 American Idiot2004 Boulevard Of Broken Dreams493.877 0.72Green Day 0.71529 Dookie 1994 Basket Case 181.759 0.77Wizards from Kansas 0.00008 Reunion 2011 River Road 217.551 0.00000Wizards from Kansas 0.00008 Reunion 2011 Orion 268.952 0.00000Fun 0.68483 Some Nights 2012 We Are Young 277.345 0.91729



2nd Normal Form

§A database is in 2nd normal form if it contains no tables in which a column depends on just part of the primary key.

23


Album Release Year


Green Day 0.71529 American Idiot 2004 American Idiot 176.556 0.75Green Day 0.71529 American Idiot 2004 Boulevard Of Broken Dreams493.877 0.72Green Day 0.71529 Dookie 1994 Basket Case 181.759 0.77Wizards from Kansas 0.00008 Reunion 2011 River Road 217.551 0.00000Wizards from Kansas 0.00008 Reunion 2011 Orion 268.952 0.00000Fun 0.68483 Some Nights 2012 We Are Young 277.345 0.91729



Primary Key?

24


Album Release Year


Green Day 0.71529 American Idiot2004 American Idiot 176.556 0.75Green Day 0.71529 American Idiot2004 Boulevard Of Broken Dreams493.877 0.72Green Day 0.71529 Dookie 1994 Basket Case 181.759 0.77Wizards from Kansas 0.00008 Reunion 2011 River Road 217.551 0.00000Wizards from Kansas 0.00008 Reunion 2011 Orion 268.952 0.00000Fun 0.68483 Some Nights 2012 We Are Young 277.345 0.91729



2nd Normal Form

§Functional Dependencies• [Artist] → [Artist Popularity]• [Album] → [Release Year]• [Track] → [Length, Popularity]

25


Album Release Year


Green Day 0.71529 American Idiot 2004 American Idiot 176.556 0.75Green Day 0.71529 American Idiot 2004 Boulevard Of Broken Dreams493.877 0.72Green Day 0.71529 Dookie 1994 Basket Case 181.759 0.77Wizards from Kansas 0.00008 Reunion 2011 River Road 217.551 0.00000Wizards from Kansas 0.00008 Reunion 2011 Orion 268.952 0.00000Fun 0.68483 Some Nights 2012 We Are Young 277.345 0.91729



Second Normal Form

26


Green Day 0.71529Wizards from Kansas 0.00008Fun 0.68483

Album Artist FKAmerican Idiot Green DayDookie Green DayReunion Wizards From KansasSome Nights Fun

Track Album FK Release Year


American Idiot American Idiot 2004 176.56 0.75Boulevard Of Broken DreamsAmerican Idiot 2004 493.88 0.72Basket Case Dookie 1994 181.76 0.77River Road Reunion 2011 217.55 0.00000Orion Reunion 2011 268.95 0.00000We Are Young Some Nights 2012 277.35 0.91729



3rd Normal Form

§A database is in 3rd normal form if it contains no table in which a column depends on another non-key column

27






3rd Normal Form


28






3rd Normal Form

29


Green Day 0.71529Wizards from Kansas 0.00008Fun 0.68483

Album Release Year Artist FKAmerican Idiot 2004 Green DayDookie 1994 Green DayReunion 2011 Wizards From KansasSome Nights 2012 Fun

Track Album FK Length Track Popularity

American Idiot American Idiot 176.56 0.75Boulevard Of Broken DreamsAmerican Idiot 493.88 0.72Basket Case Dookie 181.76 0.77River Road Reunion 217.55 0.00000Orion Reunion 268.95 0.00000We Are Young Some Nights 277.35 0.91729



Election Normalization

30

ElectionDate State Postal

County Name Race Number

Office ID

Office Name Candidate Number

Party First Name

Last Name

Incumbent Vote Count

Nov 6, 2012 AZ Arizona 4021 S U.S. Senate 8790 Dem Richard Carmona 0 912776Nov 6, 2012 AZ Arizona 4021 S U.S. Senate 8789 GOP Jeff Flake 0 992323Nov 6, 2012 AZ Arizona 4021 S U.S. Senate 9005 Lib Marc Victor 0 88397Nov 6, 2012 CA California 8619 S U.S. Senate 19128 Dem Dianne Feinstein 1 6286938Nov 6, 2012 AZ Arizona 4021 S U.S. Senate 19137 GOP Elizabeth Emken 0 3929593Nov 6, 2012 MA Massachusetts 24012 S U.S. Senate 31822 Dem Elizabeth Warren 0 1678997Nov 6, 2012 AZ Arizona 4021 S U.S. Senate 31823 GOP Scott Brown 1 1450044

§A database is in 2nd normal form if it contains no tables in which a column depends on just part of the primary key.



Election Normalization

31

ElectionDate State Postal

County Name Race Number

Office ID

Office Name

Nov 6, 2012 AZ Arizona 4021 S U.S. SenateNov 6, 2012 CA California 8619 S U.S. SenateNov 6, 2012 MA Massachusetts 24012 S U.S. Senate


Candidate Number

Party First Name

Last Name

Incumbent Vote Count

8790 Dem Richard Carmona 0 9127768789 GOP Jeff Flake 0 9923239005 Lib Marc Victor 0 88397

19128 Dem Dianne Feinstein 1 628693819137 GOP Elizabeth Emken 0 392959331822 Dem Elizabeth Warren 0 167899731823 GOP Scott Brown 1 1450044



Relating - Implementation

32



Relating Implementation

33



Relating - Implementation

34



Use Case

§Spotify• Find all of the albums for an artist• Find all of the track names, lengths, and popularity

on an album.• Display an album’s title, popularity, release year and

cover art.• Display the last n tracks a user has listened to• Allow users to create playlists of tracks

35


CSCI-UA:0060-02 - New York Universitysandhaus/CSCUA60/doc/class_7.pdf · CSCI-UA:0060-02 Database...

Documents

Transcript of CSCI-UA:0060-02 - New York Universitysandhaus/CSCUA60/doc/class_7.pdf · CSCI-UA:0060-02 Database...