CISC 7610 Lecture 2Review of relational databases

Topics:Relational database management systems

Example data modeling problemSchema normalization

SQL queries

Relational database management systems

A relational database management system (RDBMS)

● Uses relational data structures

● Has a declarative data manipulation language at least as powerful as the relational algebra

● Not required, but typically also– Supports ACID transactions

– Uses SQL as the data manipulation language

Uses relational data structures

● Relation: table with rows and columns

● Attribute: column

● Tuple: row

● Key: combination of attributes that uniquely identifies each row

● Integrity rules: Constraints imposed upon the database

Uses relational data structures

Artist Album Track Num Released Track Dur

David Bowie

Space Oddity

1 1969 Space Oddity

5:15

David Bowie

… Ziggy Stardust ...

10 1972 Suffragette city

3:25

David Bowie

Best of Bowie

1 2002 Space Oddity

5:15

David Bowie

Best of Bowie

8 2002 Suffragette city

3:25

Queen Hot space 11 1982 Under pressure

4:02

Uses relational data structures

Artist Album Track Num Released Track Dur

David Bowie

Space Oddity

1 1969 Space Oddity

5:15

David Bowie

… Ziggy Stardust ...

10 1972 Suffragette city

3:25

David Bowie

Best of Bowie

1 2002 Space Oddity

5:15

David Bowie

Best of Bowie

8 2002 Suffragette city

3:25

Queen Hot space 11 1982 Under pressure

4:02

Relation

Tuple

Attribute Key

Has a declarative data manipulation language

● Declarative: says what, not how to manipulate data– Other examples of declarative programming languages?

● Relational algebra– Selection: extract a subset of tuples

– Projection: extract a subset of attributes

– Cartesian product: extract all combinations of pairs of tuples from two relations

– Union: combine two sets of tuples

– Set difference: remove one set of tuples from another

Supports ACID transactions

● Transaction: A sequence of DB operations that represents a single real-world operation

● ACID properties – Guaranteed by RDBMSs– Atomicity: all operations happen or none

– Consistency: transaction moves DB from one state that meets integrity constraints to another

– Isolation: concurrent transactions have the same effect as serial

– Durability: once committed, transaction’s effects are permanent

● Example: bank account transfer

● Relaxed by NoSQL databases in various combinations

Structured query language (SQL)

● Data definition language– Define relational schemata (plural of “schema”)

– Create/alter/delete tables and their attributes

● Data manipulation language– Insert/delete/modify tuples in relations

– Query one or more tables

● Can implement relational algebra, but also takes some liberties with it

Example data modeling problem

Example data: Music collection

● Artists: Name

● Albums: Name, Release date

● Tracks: Name, Duration, Number

● Each album has one artist

● Tracks can appear on multiple albums (compilations)

Entity-relationship diagrams

EntityAttribute

Relationship

Cardinality

Entity2

Cardinality2

Do: Draw ER diagram for ex data

● Artists: Name

● Albums: Name, Release date

● Tracks: Name, Duration, Number

● Each album has one artist

● Tracks can appear on multiple albums (compilations)

Entity-relationship diagrams

Artist

NameContains

many

Album

Name

Release date

Album

Created

Name

Duration

Track number

many

many

one

Translating ER diagrams to schema

● Entities become tables

● Attributes become their attributes

● Many-to-many relationships become join tables– Can have additional attributes

● Other relationships become foreign keys– One-to-one, many-to-one, one-to-many

– Attributes added to table

Do: Translate ER diagram to schema for example data

Translating ER diagrams to schema

Artists

Id Name

Albums

Id Name Release ArtistId

AlbumsHaveTracks

AlbumId TrackId Number

Track

Id Name Duration

SQL CREATE statement

CREATE TABLE table_name

(

column_name1 data_type(size),

column_name2 data_type(size),

column_name3 data_type(size),

....

);

SQL INSERT statement

INSERT INTO table_name

(column1,column2,column3,...)

VALUES

(value1,value2,value3,...);

Do: Populate tables with ex data

Artists

Id Name

1 David Bowie

2 Queen

Albums

Id Name Release ArtistId1 Space oddity 1969 1

2 … Ziggy startdust ...

1972 1

3 Best of Bowie 2002 1

4 Hot space 1982 2

AlbumsHaveTracks

AlbumId TrackId Number

1 1 1

2 2 10

3 1 1

3 2 8

4 3 11

Track

Id Name Duration

1 Space oddity

5:15

2 Suffragette city

3:25

3 Under pressure

4:02

Schema normalization

Schema normalization:Unnormalized data

Artist Album Released Track Num Track Dur

David Bowie Space Oddity

1969 1 Space Oddity

5:15

David Bowie … Ziggy Stardust ...

1972 10 Suffragette city

3:25

David Bowie Best of Bowie

2002 1 Space Oddity

5:15

David Bowie Best of Bowie

2002 8 Suffragette city

3:25

Queen Hot space 1982 11 Under pressure

4:02

Schema normalization:Anomalies in unnormalized data

● The above example unnormalized schema can suffer from three types of “anomalies”–

Schema normalization:Anomalies in unnormalized data

● The above example unnormalized schema can suffer from three types of “anomalies”– Update anomaly: repeated data could be inconsistent

between rows

– Insertion anomaly: can’t add info on artist or album that doesn’t have a track

– Deletion anomaly: deleting the last track deletes an album or artist

Schema normalization:Normal forms

● Schema normalization factors logically independent data into independent relations

● And links them using foreign key relationships● Projection is the process of factoring an unnormalized

relation into separate normalized relations● Boyce-Codd normal form: there are only non-trivial

functional dependencies from superkeys (sets of attributes that uniquely identify entities) to other attributes

Schema normalization:Unnormalized data

Artist Album Released Track Num Track Dur

David Bowie Space Oddity

1969 1 Space Oddity

5:15

David Bowie … Ziggy Stardust ...

1972 10 Suffragette city

3:25

David Bowie Best of Bowie

2002 1 Space Oddity

5:15

David Bowie Best of Bowie

2002 8 Suffragette city

3:25

Queen Hot space 1982 11 Under pressure

4:02

Schema normalization:Normalized data

Artists

Id Name

1 David Bowie

2 Queen

Albums

Id Name Release ArtistId1 Space oddity 1969 1

2 … Ziggy startdust ...

1972 1

3 Best of Bowie 2002 1

4 Hot space 1982 2

AlbumsHaveTracks

AlbumId TrackId Number

1 1 1

2 2 10

3 1 1

3 2 8

4 3 11

Track

Id Name Duration

1 Space oddity

5:15

2 Suffragette city

3:25

3 Under pressure

4:02

Reminder: Main question of course

How can systems process and store multimedia data so that users can find what they are looking for in

the future?

SQL queries

Queries: find what they are looking for

● Search through the data

● Search through complex relationships

● Aggregate over the data for reporting

● And do all of this efficiently...

SQL SELECT, single table

SELECT attribute1, attribute2

FROM relation

WHERE attribute1 = 'condition'

ORDER BY attribute2;

Do: Write a select query to answer

What is the duration of “Suffragette City”?

SQL SELECT, multiple tables

SELECT r1.attribute1, r2.attribute1

FROM relation1 AS r1,

Relation2 AS r2

WHERE attribute1 = 'condition'

AND r1.attribute1 = r2.attribute2

ORDER BY r1.attribute1;

Do: Write a select query to answer

Find the AlbumIds of all of David Bowie's albums

Do: Write a select query to answer

Find the TrackIds of all of David Bowie's tracks

How would you write a select query to answer

● Find all songs containing David Bowie's vocals

● Find all songs at 120 beats per minute

● Find all songs sampled by other artists– These all require further modeling or analysis of the

audio...

How do we make databases that are

● Effective (correct, durable, coherent, ...)– Transactions

● Efficient– Concurrency

– Memory hierarchy

– Indexing

– Query optimization