CS 564 Database Management Systems: Design and Implementation Lecture 5: SQL Chapters 3 and 5 in Cow...

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CS 564Database Management Systems: Design and Implementation

Lecture 5: SQL

Chapters 3 and 5 in Cow Book

Slide ACKs: AnHai Doan, Jeff Naughton, and Jignesh Patel

Arun Kumar

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Introducing SQL

Structured English QUEry Language (SEQUEL);

TL;DR name is SQL Invented at - you guessed it - IBM!

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What is SQL?

SQL is “the” querying language for relational data Simple English-based syntax, but precise, formal

semantics (compiled down to relational algebra) Key advantages:

Physical Data Independence (“how” data is

stored on machine independent of “what”, i.e., SQL

queries)

Logical Data Independence (notion of views in

SQL enables simpler queries on same schema)

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Major SQL Components

Data Definition Language (DDL)

Data Manipulation Language (DML)

Embedded and Dynamic SQL

Cursors and Triggers

Security

Transaction Management

Remote Database Access

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Create Table

Drop Table, Alter Table

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CREATE TABLE

MovieID Name ReleaseDate Director

20 Inception 07/13/2010 Christopher Nolan

… … …

Movies

CREATE TABLE Movies (

MovieID INTEGER,

Name CHAR(30),

ReleaseDate DATE,

Director CHAR(20),

PRIMARY KEY (MovieID))

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Integrity Constraint (IC)

A logical condition (invariant) that must hold true

on any instance of a given database schema

A legal relation instance satisfied all ICs

Overuse/abuse of ICs is a danger!

Part of schema; cannot infer from data exactly!

Two main types:

Key Constraint

Referential Integrity Constraint

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Key Constraints in SQL

Key vs. Superkey

Primary key vs. Candidate key vs. Alternate key

MovieID Name ReleaseDate Director IMDB_URLMovies

CREATE TABLE Movies (MovieID INTEGER,Name CHAR(30),ReleaseDate DATE,Director CHAR(20),IMDB_URL CHAR(20),PRIMARY KEY (MovieID),UNIQUE (IMDB_URL))

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Referential Integrity Constraints

CREATE TABLE Ratings( RatingID INTEGER, Numstars REAL, Timestamp DATE, UserID INTEGER, MovieID INTEGER, PRIMARY KEY (RatingID), FOREIGN KEY (UserID) REFERENCES Users(UserID), FOREIGN KEY (MovieID) REFERENCES Movies(MovieID))

RatingID NumStars Timestamp UserID MovieID

1 3.5 08/27/15 79 20

… … … … …

Ratings

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Referential Integrity Constraint (RIC)

A Foreign Key value should not be NULL!

Student

Name AgeSID

Department

Name AddressDID

Major

SID Name Age MajorDID

79 Alice 19 CS

13 Bob 21 NULL

48 John NULL ST

… … … …

Students Foreign Key?DID Name Addre

ss

CS Computer Sciences

1210 …

ST Statistics Blah

… … …

Department

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What if a Department tuple is

deleted?!

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Enforcing RIC

We have 3 options:

Refuse to allow the deletion! Delete all tuples in Students that reference the

deleted DID in Department

Set the corresponding DID in Students to some

default value, or in the worst case, NULL

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Enforcing RIC in SQL

Refuse to allow the deletion!

CREATE TABLE Student( SID INTEGER, Name CHAR(30), Age INTEGER, DID INTEGER, PRIMARY KEY (SID), FOREIGN KEY (DID) REFERENCES Department(DID)

ON DELETE NO ACTION)

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Delete all tuples in Students that reference the

deleted DID in Department


ON DELETE CASCADE)

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Set the corresponding DID in Students to some

default value, or in the worst case, NULL


ON DELETE SET DEFAULT)

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Participation Constraint in SQL

Student

Name AgeSID

Department

Name AddressDID

Major

CREATE TABLE Student( SID INTEGER, Name CHAR(30), Age INTEGER, DID INTEGER NOT NULL, PRIMARY KEY (SID), FOREIGN KEY (DID) REFERENCES Department(DID)

ON DELETE NO ACTION)

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Create Table

Drop Table, Alter Table

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DROP TABLE



… … …

Movies

DROP TABLE Movies;

Entire relation (both instance and schema) will be removed

forever from the database! Be careful!

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ALTER TABLE


… … … …

Movies

ALTER TABLE Movies

ADD COLUMN IMDB_URL CHAR(20);

IMDB_URL

ALTER TABLE Movies

ADD CONSTRAINT UNIQUE(IMDB_URL);

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Embedded and Dynamic SQL

Cursors and Triggers

Security



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Insert, Delete, Update

Basic SQL queries

Set and bag operations in SQL

Nested queries

Aggregates in SQL

Null Values in SQL

Usage in Integrity Constraints in DDL

Views

22

INSERT


MovieID INTEGER,

Name CHAR(30),

ReleaseDate DATE,

Director CHAR(20),




MovieID Name ReleaseDate DirectorMovies

INSERT INTO Movies

VALUES ( 20,

“Inception”,

“07/13/2010”,

“Christopher Nolan”

)

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INSERT




On an INSERT, the RDBMS always checks all Integrity

Constraints; if any IC fails, insertion of the tuple fails!

INSERT INTO Movies VALUES

(20, “Her”, “12/18/2013”, “Spike Jonze”);

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DELETE



16 Avatar 2009 Jim Cameron


DELETE FROM Movies

WHERE Name = “Avatar”;

All tuples that match the WHERE constraint/predicate will be

removed forever from the instance! Be careful!

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UPDATE





UPDATE Movies

SET Director = “James Cameron”

WHERE Director = “Jim Cameron”;

All tuples that match the WHERE constraint/predicate in the

instance will be updated! Be careful!

James Cameron

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Basic SQL queries


Nested queries

Aggregates in SQL

Null Values in SQL


Views

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Basic Form of an SQL Query

SELECT [DISTINCT] target-listFROM relation-list[WHERE condition]

List of attributes to projectOptional

List of relations (possibly with “aliases”)

Selection/join condition (optional)

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What does it mean logically?

SELECT [DISTINCT] target-listFROM relation-list[WHERE condition]

1. Cross-product of relations in relation-list

2. If condition given, apply it to filter out tuples

3. Remove attributes not present in target-list

4. If DISTINCT given, deduplicate tuples in result

The above is only a logical interpretation. It is NOT a “plan” an RDBMS would use in general to run an SQL query!

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Recall the Netflix Example

RatingID Stars Timestamp UserID MovieID

1 3.5 08/27/15 79 20

… … … … …

UserID Name Age JoinDate

79 Alice 23 01/10/13

80 Bob 41 05/10/13

… … … …

MovieID Name Year Director

20 Inception 2010 Christopher Nolan


… … … …

Ratings

Users

Movies

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Example SQL Query

SELECT M.NameFROM Movies MWHERE M.Year = 2013

Movies (M)MovieID Name Year Director



53 Gravity 2013 Alfonso Cuaron

74 Blue Jasmine 2013 Woody Allen

Name (Year = 2013 (M))Example: Get the names of movies released in 2013

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Example SQL Query

SELECT M.Name FROM Movies M WHERE M.Year = 2013






Name

Gravity

Blue Jasmine

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Example SQL Query

SELECT * FROM Movies M WHERE M.Year = 2013









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Example SQL Query

SELECT M.NameFROM Movies MWHERE M.Year <> 2013






Name (Year ≠ 2013 (M))Example: Get the names of movies from years other than 2013

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Example SQL Query

SELECT M.YearFROM Movies M






Year(M)Example: For which years do we have movie data?

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Example SQL Query

SELECT M.Year FROM Movies M






Year

2010

2009

2013

2013

SQL allows repetitions of tuples in a relation!

Called “bag semantics” vs. RA’s set semantics

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DISTINCT in SQL

SELECT DISTINCT M.Year FROM Movies M






Year

2010

2009

2013

DISTINCT needed to achieve set

semantics of RA’s Project in SQL

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Aliases in SQL







Why bother with the alias? Not needed here!

SELECT Name FROM Movies WHERE Year = 2013

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Aliases in SQL – Useful for Joins!

Movies (M)MovieID Name Year DirectorID

SELECT M.Name FROM Movies M, Directors DWHERE D.Name = “Jim Cameron”

AND M.DirectorID = D.DIDAliases help disambiguate attributes with the same name from multiple relations (or even a self-join!)

Example: Get names of movies directed by “Jim Cameron”

Directors (D)DID Name Age

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More SQL Examples

Example: Get names of movies released in 2013 by Woody

Allen or some other director 50 years or older

SELECT M.Name FROM Movies M, Directors DWHERE (D.Name = “Woody Allen” OR

D.Age >= 50) AND M.Year = 2013AND M.DirectorID = D.DID



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SQL vs. TRC

Example: Get names of movies released in 2013 by Woody Allen or some other director 50 years or older

SELECT M.Name FROM Movies M, Directors DWHERE (D.Name = “Woody Allen” OR D.Age >= 50) AND M.Year = 2013 AND M.DirectorID = D.DID

{t | m M d D ((d.Name = “Woody Allen” d.Age 50)

m.Year = 2013 m.DirectorID = d.DID t.Name = m.Name))}



You can even “LIKE” in SQL!

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LIKE in SQL

SELECT DISTINCT M.NameFROM Movies MWHERE M.Director LIKE “Blue%”






Example: Get the directors of movies that start with “Blue”

“%” matches any number of characters; “_” matches one

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ORDER BY in SQL







Name

Gravity

Blue Jasmine

ORDER BY M.NameName

Blue Jasmine

Gravity

Useful for data readability

Ordering defined by domain semantics

Can specify DESC; multiple attributes

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LIMIT in SQL

SELECT M.Name FROM Movies M WHERE M.Year >= 2010






ORDER BY M.YearYear

Inception

Gravity

Blue Jasmine

Also useful for data readability

Prevents “flooding” of screen with data

Be wary of using it without ORDER BY!

LIMIT 2

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Basic SQL queries


Nested queries

Aggregates in SQL

Null Values in SQL


Views

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UNION in SQL

Get the IDs of users that have rated a movie directed by “Ang Lee” or a movie that released in 2013

RID Stars UID MID

MID Name Year Director

Ratings (R)

Movies (M)

SELECT R.UID FROM Ratings R, Movies M WHERE R.MID = M.MID AND

M.Director = “Ang Lee”UNIONSELECT R.UID FROM Ratings R, Movies M WHERE R.MID = M.MID AND M.Year = 2013

Union-compatible!

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Semantics of UNION in SQL



42 Life of Pi 2012 Ang Lee


RID Stars UID MID

1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42R M


M.Director = “Ang Lee”UNIONSELECT R.UID FROM Ratings R, Movies M WHERE R.MID = M.MID AND M.Year = 2013

UID

79

UID

79

123

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Semantics of UNION in SQL

UNIONUID

79

123

UID

79

UID

79

123

UNION implicitly deduplicates tuples (unlike SELECT)!

Q. How to retain duplicates with UNION?

UNION ALLUID

79

123

UID

79

UID

79

79

123

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INTERSECT in SQL





RID Stars UID MID

1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42R M


M.Director = “Ang Lee”INTERSECTSELECT R.UID FROM Ratings R, Movies M WHERE R.MID = M.MID AND M.Year = 2013

UID

79

UID

79

123 UID

79

INTERSECT

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EXCEPT (Set Difference) in SQL





RID Stars UID MID

1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42R M


M.Director = “Ang Lee”EXCEPTSELECT R.UID FROM Ratings R, Movies M WHERE R.MID = M.MID AND M.Year = 2013

UID

79

UID

79

123 UID

123

EXCEPT

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The Contentious Bag Semantics!

UID

79

79

79

123

123

80

UID

79

123

123

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UNION ALL UID

79

79

79

79

123

123

123

123

80

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Add the number of repetitions

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UID

79

79

79

123

123

80

UID

79

123

123

92

EXCEPT ALL UID

79

79

80Subtract the number

of repetitions

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UID

79

79

79

123

123

80

UID

79

123

123

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INTERSECT ALL UID

79

123

123Minimum of the

number of repetitions

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Basic SQL queries


Nested queries

Aggregates in SQL

Null Values in SQL


Views

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Nested Queries

SELECT M.NameFROM Movies MWHERE M.MID IN (

A powerful feature of SQL!

A query within a query!

Get the names of movies that have a 5-star rating

RID Stars UID MID


Ratings (R)

Movies (M)

SELECT R.MIDFROM Ratings RWHERE R.Stars = 5 )

QUERCEPTION

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Nested Queries

SELECT M.NameFROM Movies MWHERE M.MID NOT IN (

Get the names of movies that do not have a 5-star rating

RID Stars UID MID


Ratings (R)

Movies (M)

SELECT R.MIDFROM Ratings RWHERE R.Stars = 5

)

Be careful!

Not the same as

IN with R.Stars <> 5

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Nested Queries with Correlation

SELECT M.NameFROM Movies MWHERE EXISTS (

Returns TRUE if the

nested query’s result

is NOT empty

Get the names of movies that have a 5-star rating

RID Stars UID MID


Ratings (R)

Movies (M)

SELECT *FROM Ratings RWHERE R.Stars = 5

AND R.MID = M.MID )

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SELECT M.NameFROM Movies MWHERE NOT EXISTS (

Get the names of movies that do not have a 5-star rating

RID Stars UID MID


Ratings (R)

Movies (M)

SELECT *FROM Ratings RWHERE R.Stars = 5

AND R.MID = M.MID )

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SELECT M.NameFROM Movies MWHERE UNIQUE (

Get the names of movies with at most one 5-star rating

RID Stars UID MID


Ratings (R)

Movies (M)

SELECT R.StarsFROM Ratings RWHERE R.Stars = 5

AND R.MID = M.MID )

Returns TRUE if the

nested query’s result

has NO duplicates

Empty set

yields TRUE!

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SELECT M.NameFROM Movies MWHERE NOT UNIQUE (

Get the names of movies that have multiple 5-star ratings

RID Stars UID MID


Ratings (R)

Movies (M)

SELECT R.StarsFROM Ratings RWHERE R.Stars = 5

AND R.MID = M.MID )

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Set Comparison Operators in SQL

We saw IN, NOT IN, EXISTS, NOT EXISTS,

UNIQUE, NOT UNIQUE

Also available: op ANY, op ALL

op is an arithmetic/equality comparator(=, <>, >, >=, <, <=)

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SELECT M1.NameFROM Movies M1WHERE M1.Year > ANY (

Get the names of movies that released after some movie directed by Jim Cameron

MID Name Year DirectorMovies (M)

SELECT M2.YearFROM Movies M2WHERE M2.Director =

“Jim Cameron” )

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SELECT M1.NameFROM Movies M1WHERE M1.Year > ALL (

Get the names of movies that released after Jim Cameron stopped making movies

MID Name Year DirectorMovies (M)

SELECT M2.YearFROM Movies M2WHERE M2.Director =

“Jim Cameron” )

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Rewriting INTERSECT using IN

Get the IDs of users that have rated a movie directed by “Ang Lee” and a movie that released in 2013

RID Stars UID MID


Ratings (R)

Movies (M)


M.Director = “Ang Lee”INTERSECTSELECT R.UID FROM Ratings R, Movies M WHERE R.MID = M.MID AND M.Year = 2013

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Rewriting INTERSECT using IN

Get the IDs of users that have rated a movie directed by “Ang Lee” and a movie that released in 2013

RID Stars UID MID


Ratings (R)

Movies (M)

SELECT R2.UID FROM Ratings R2, Movies M2 WHERE R2.MID = M2.MID

AND M2.Year = 2013 )

SELECT R1.UID FROM Ratings R1, Movies M1 WHERE M1.Director = “Ang Lee”

AND R1.MID = M1.MID AND R1.UID IN (Similarly,EXCEPT can be rewritten using NOT IN

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Division in SQL

RID Stars UID MID


Ratings (R)

Movies (M)

SELECT R1.UID FROM Ratings R1WHERE NOT EXISTS (

(SELECT M.MID FROM Movies M)EXCEPT(SELECT R2.MID FROM Ratings R2WHERE R2.UID = R1.UID)

)

List the IDs of users that have rated ALL movies

UID,MID (R) / MID (M)

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Division without using EXCEPT

RID Stars UID MID


Ratings (R)

Movies (M)

SELECT R1.UID FROM Ratings R1WHERE NOT EXISTS (

(SELECT M.MID FROM Movies MWHERE NOT EXISTS (

(SELECT R2.MID FROM Ratings R2WHERE R2.MID = M.MID

AND R2.UID = R1.UID ))))

List the IDs of users that have rated ALL movies

UID,MID (R) / MID (M)

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Basic SQL queries


Nested queries

Aggregates in SQL

Null Values in SQL


Views

71

5 Native Aggregate Functions in SQL

COUNT ([DISTINCT] attribute)

AVG ([DISTINCT] attribute)

SUM ([DISTINCT] attribute)

MAX (attribute)

MIN (attribute)

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Aggregate Functions in SQL

SELECT COUNT(*)FROM Movies MWHERE M.Year > 2010





91 Interstellar 2014 Christopher Nolan

How many movies came out after 2010?

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SELECT COUNT(*) FROM Movies M WHERE M.Year > 2010






How many movies came out after 2010?

COUNT(*)

2

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SELECT COUNT(DISTINCT M.Director)FROM Movies M






For how many directors do we have?

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What is the average age of the users?

UserID Name Age JoinDate

79 Alice 23 01/10/13

80 Bob 41 05/10/13

123 Carol 19 08/09/14

420 Dan 20 03/01/15

Users (U)

SELECT AVG(U.Age) FROM Users U

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RatingID Stars UserID MovieID

1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42

Ratings (R)

SELECT MAX(R.Stars) AS MaxStars,MIN(R.Stars) AS MinStars

FROM Ratings RWHERE R.MovieID = 42

What are the highest and lowest ratings for MovieID 42?

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1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42

Ratings (R)

SELECT MAX(R.Stars) AS MaxStars,MIN(R.Stars) AS MinStarsFROM Ratings R WHERE R.MovieID = 42

MaxStars MinStars

4.5 3.5

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1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42

Ratings (R)

SELECT R.MovieID, MAX(R.Stars)FROM Ratings R

Which MovieID(s) have the highest rating?

Other attributes NOT allowed in the target-list as such!

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1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42

Ratings (R)

SELECT DISTINCT R.MovieIDFROM Ratings RWHERE R.Stars = (SELECT MAX(R2.Stars)

FROM Ratings R2)

Which MovieID(s) have the highest rating?

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Group By Aggregate in SQL

(R)


1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42

Ratings (R)

SELECT R.UserID, COUNT(DISTINCT R.MovieID)

FROM Ratings RGROUP BY R.UserID

How many movies has each user rated?

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(R)

SELECT [DISTINCT] target-listFROM relation-list[WHERE condition]GROUP BY grouping-listHAVING group-condition

X

Condition on each groupin aggregate

target-list must be in this form:

X’, Agg(Y)

Subset of X

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(R)


1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42

Ratings (R)

What is the average rating for each movie?

SELECT R.MovieID, AVG(R.Stars) AS AvgRating

FROM Ratings RGROUP BY R.MovieID

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1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42

Ratings (R)

SELECT R.MovieID, AVG(R.Stars)AS AvgRating

FROM Ratings R GROUP BY R.MovieID

MovieID AvgRating

20 4.0

42 4.0

53 2.5

One tuple in output per unique value of R.MovieID (aka “group”)

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1 3.5 79 42

2 4.0 80 20

8 3.5 79 71

10 2.5 79 50

Ratings (R)

How many times has each user given each rating?

SELECT R.UserID, R.Stars, COUNT(*) AS RateCount

FROM Ratings RGROUP BY R.UserID, R.Stars

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1 3.5 79 42

2 4.0 80 20

8 3.5 79 71

10 2.5 79 50

R


FROM Ratings RGROUP BY R.UserID, R.Stars

UserID Stars RateCount

79 2.5 1

79 3.5 2

80 4.0 1

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RatingID Stars UserID MovieID1 3.5 79 42

2 4.0 80 20

8 3.5 79 71

10 2.5 79 50

Ratings (R)

How many times has each user given each rating above 3?


FROM Ratings RWHERE R.Stars > 3GROUP BY R.UserID, R.Stars

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1 3.5 79 42

2 4.0 80 20

8 3.5 79 71

10 2.5 79 50

R


FROM Ratings R WHERE R.Stars > 3GROUP BY R.UserID, R.Stars

UserID Stars RateCount

79 3.5 2

80 4.0 1

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Group By Aggregate with HAVING


1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42

Ratings (R)

Get the average rating by users with at least 2 ratings

SELECT R.UserID, AVG(R.Stars) AS AvgStars

FROM Ratings RGROUP BY R.UserIDHAVING COUNT(*) >= 2

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Group By Aggregate with HAVING


1 3.5 79 42

2 4.0 80 20

3 2.5 79 53

4 4.5 123 42

Ratings (R)

SELECT R.UserID, AVG(R.Stars) AS AvgRating

FROM Ratings R GROUP BY R.UserIDHAVING COUNT(*) > 2

UserID AvgRating

79 3.0For each “group”, apply the group-condition in HAVING

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Surprise Review Question!

RatingID Stars UserID MovieIDRatings (R)

Get the number of ratings for each movie directed by

“Christopher Nolan” wherein the average rating is over 4

SELECT R.MovieID, COUNT(R.Stars) AS NumHighRatings

FROM Ratings R, Movies MWHERE M.Director = “Christopher Nolan”

AND R.MovieID = M.MovieIDGROUP BY R.MovieIDHAVING AVG(R.Stars) > 4

Movies (M) MovieID Name Year Director

91



Basic SQL queries


Nested queries

Aggregates in SQL

Null Values in SQL


Views

92

NULL Values in SQL

Recall that NULL is useful to represent:

- Unknown entries (data collection/entry issue)

- Inapplicable attributes (bad schema design!)

A “headache” for SQL and RDBMS developers!

93

NULL Values in SQL


2 4.0 80 20

8 NULL 79 71

10 2.5 79 50

Ratings (R)

Get the movies and ratings with over 3 stars

SELECT R.MovieID, R.StarsFROM Ratings RWHERE R.Stars > 3

SQL will “fail” a tuple it is NOT *sure* will satisfiy condition!

94

NULL Values in SQL


2 4.0 80 20

8 NULL 79 71

10 2.5 79 50

Ratings (R)


MovieID Stars

42 3.5

20 4.0

95

Wait, what about complex predicates?

96

NULL Values in SQL


2 4.0 80 20

8 NULL 79 71

10 2.5 79 50

Ratings (R)

Get the movies and ratings over 3 stars or by UserID 79


OR UserID = 79

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Binary Logic (the usual)

AND TRUE FALSE

TRUE TRUE FALSE

FALSE FALSE FALSE

OR TRUE FALSE

TRUE TRUE TRUE

FALSE TRUE FALSE

NOT TRUE FALSE

FALSE TRUE

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This great edifice of scientific history

collapses in the face of NULL!

99

Three-Valued (Ternary) Logic in SQL

OR TRUE FALSE UNKOWN

TRUE TRUE TRUE TRUE

FALSE TRUE FALSE UNKOWN

UNKOWN TRUE UNKOWN UNKOWN

Along with the regular TRUE and FALSE, a new value

“UNKNOWN” in logic to handle predicates on NULL

100

Three-Valued (Ternary) Logic in SQL

AND TRUE FALSE UNKOWN

TRUE TRUE FALSE UNKOWN

FALSE FALSE FALSE FALSE

UNKOWN UNKOWN FALSE UNKOWN

NOT TRUE FALSE UNKOWN

FALSE TRUE UNKOWN

101

NULL Values in SQL


22 2.0 80 21

8 NULL 79 71

10 2.5 79 50

Ratings (R)

Get the movies rated over 3 stars or by UserID 79


OR UserID = 79

OR T F U

T T T T

F T F U

U T U U

SQL will only “pass” tuples that evaluate to “TRUE”!

102

NULL Values in SQL


22 2.0 80 21

8 NULL 79 71

10 2.5 79 50

Ratings (R)


OR UserID = 79MovieID Stars

42 3.5

79 NULL

79 2.5

UNKOWN OR TRUE = TRUE

103

NULL Values in SQL


21 3.5 81 20

8 NULL 79 71

11 NULL 99 50

Ratings (R)

SELECT DISTINCT R.StarsFROM Ratings R

Stars

3.5

NULL

So, “UNKNOWN = UNKNOWN” for duplicate elimination

(a quirk of SQL!)

104

Surprise Review Question!


2 4.0 80 20

8 NULL 79 71

10 2.5 79 50

What is the output of this query?

SELECT R.RatingID FROM Ratings RWHERE R.Stars > 3 OR R.Stars <= 3

RatingID1

2

10

105

“The Law of Excluded Middle” Fails!

A fundamental law in binary logic:

Given a predicate , we always have

R.Stars > 3 OR R.Stars <= 3

The above law does NOT hold in three-valued logic!

Given a predicate , can be TRUE or UNKOWN!

OR T F U

T T T T

F T F U

U T U U

NOT T F U

F T U

106

Are there other uses for NULL?

107

“Outer Joins” in SQL


Example: Get all movies by name and its average ratings


SELECT M.Name, AVG(R.Stars)FROM Ratings R, Movies MWHERE R.MovieID = M.MovieIDGROUP BY M.Name

Q. But what if some movie has no ratings in R?

108






Movies (M)


1 3.5 79 20

2 4.0 80 20

3 2.5 79 16

4 4.5 80 16

Ratings (R)

109







1 3.5 79 20

2 4.0 80 20

3 2.5 79 16

4 4.5 80 16

SELECT M.Name, AVG(R.Stars)FROM Ratings R, Movies MWHERE R.MovieID = M.MovieIDGROUP BY M.Name

Name AVG(Stars)

Inception 3.75

Avatar 3.5

110



Get all movies by name and its average ratings


SELECT M.Name, AVG(R.Stars)FROM Ratings R

NATURAL RIGHT OUTER JOIN Movies M

GROUP BY M.Name

Outer Joins in SQL ensure tuples from one (or both) relation(s)

are present in the output even if they do not have matches!

111


SELECT M.Name, R.StarsFROM Ratings R NATURAL RIGHT OUTER JOIN Movies M

Name Stars

Inception 3.5

Inception 4.0

Avatar 2.5

Avatar 4.5

Gravity NULL






1 3.5 79 20

2 4.0 80 20

3 2.5 79 16

4 4.5 80 16

Outer Joins introduce

NULL values!

This is okay – we

have already made

peace with NULL!

112


SELECT M.Name, AVG(R.Stars)FROM Ratings R NATURAL RIGHT OUTER JOIN Movies M

Name AVG(Stars)

Inception 3.75

Avatar 3.5

Gravity NULLGROUP BY M.Name






1 3.5 79 20

2 4.0 80 20

3 2.5 79 16

4 4.5 80 16

Aggregation of anattribute with NULL

leads to NULL!

Name Stars

Inception 3.5

Inception 4.0

Avatar 2.5

Avatar 4.5

Gravity NULL

SELECT M.Name, R.StarsFROM Ratings R NATURAL RIGHT OUTER JOIN Movies M

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Basic SQL queries


Nested queries

Aggregates in SQL

Null Values in SQL


Views

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Recall Integrity Constraints in DDL

Key Constraints


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CREATE TABLE



MovieID INTEGER,

Name CHAR(30),

ReleaseDate DATE,

Director CHAR(20),


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Key Constraints in SQL

Key vs. Superkey

Primary key vs. Candidate key vs. Alternate key

MovieID Name ReleaseDate Director IMDB_URLMovies

CREATE TABLE Movies (MovieID INTEGER,Name CHAR(30),ReleaseDate DATE,Director CHAR(20),IMDB_URL CHAR(20),PRIMARY KEY (MovieID),UNIQUE (IMDB_URL))

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CREATE TABLE Ratings( RatingID INTEGER, Stars REAL, UserID INTEGER, MovieID INTEGER, PRIMARY KEY (RatingID), FOREIGN KEY (UserID) REFERENCES Users(UserID), FOREIGN KEY (MovieID) REFERENCES Movies(MovieID))

RatingID Stars UserID MovieIDRatings

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DML-based Integrity Constraints

CHECK (Stars >= 0 AND Stars <= 5)



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DML-based Integrity Constraints

CONSTRAINT NoSpectreRatings CHECK (“Spectre” <> (

SELECT M.MovieName FROM Movies M WHERE M.MovieID = Ratings.MovieID))



120

ASSERTIONS in SQL

Nicer way to enforce constraints that straddle relations


Ensure that the number of movies is no more than users

CREATE ASSERTION NoMoreMoviesCHECK (

(SELECT COUNT(*) FROM Movies) <= (SELECT COUNT(*) FROM Users))

UserID Name Age JoinDateUsers (U)

121



Basic SQL queries


Nested queries

Aggregates in SQL

Null Values in SQL


Views

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What is a View?

A “virtual relation” – exists only as a query, not physically

Existence depends upon the real parent relations

Useful to avoid writing some (sub) queries repeatedly

Also useful to “split up” complex SQL queries



Get the average ratings, year, and director for each movie

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Creation of Views with DDL+DML



Get the average ratings, year, and director for each movie

CREATE VIEW MovieAvg AS SELECT M.MovieID, M.Year, M.Director

AVG(R.RatingID) AS AvgStarsFROM Movies M, Ratings RWHERE M.MovieID = R.MovieIDGROUP BY R.MovieID

MovieAvg MovieID Year Director AvgStars

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Querying Views with DML

Get the overall average ratings of movies by each directorSELECT M.Director, AVG(M.AvgStars)FROM MoviesAvg MGROUP BY M.Director


SELECT M.Year, AVG(M.AvgStars)FROM MoviesAvg MGROUP BY M.Year

Get the overall average ratings of movies by each year

The RDBMS will implicitly replace MoviesAvg in the

FROM clause with the DML that creates MoviesAvg!

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Embedded SQL and Cursors

Triggers

Security



126

Embedded SQL

Issue SQL commands from within a “host” programming

language, e.g., a C++/Java program

- ODBC/JDBC with “connect” statement

- “Host variables” in the C++/Java program

SQL relations are multi-sets of tuples; no apriori bound

on number of records!

- Dynamic linked lists in host languages, e.g., C

- A SQL-supported mechanism called “Cursor”

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Cursor

An SQL construct to obtain the results of an SQL query

one tuple-at-a-time in a host PL (“a pointer that moves”) Syntax to “open”, “fetch”, and “move” a cursor’s pointer ORDER BY is often needed to be sure of the tuples’

orderEXEC SQL DECLARE MyCursor CURSOR FOR

SELECT U.Name FROM Users UWHERE U.Age >= 18ORDER BY U.Name

Can also use Cursors to

check complex

conditions in host PL;

also update the relation!

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Embedded SQL and Cursors

Triggers

Security



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What is a Trigger?

An SQL construct to “monitor” an DBMS instance –

behaves like a “daemon process” that is always running It enables us to specify some “actions” every time some

pre-defined “change” occurs, e.g., an INSERT/DELETE

CREATE TRIGGER triggername

EVENT

CONDITION

ACTION

What “triggers” this statement

A boolean-valued SQL statement

A procedure in PL/SQL (a

procedural extension to SQL)

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Triggers: Example

CREATE TRIGGER RateCounterInit BEFORE INSERT ON UsersDECLARE

rcount INTEGER;BEGIN

rcount := 0;END

CREATE TRIGGER RateCounterIncr AFTER INSERT ON UsersWHEN (new.Stars < 1) FOR EACH ROWBEGIN

rcount := rcount + 1;END

A PL/SQL procedure

A PL/SQL procedure

Act only if condition satisfied

Define “when” and “what” the change is

BEFORE/AFTER for “when”

INSERT/UPDATE/DELETE for “what”

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Triggers: Options

“When” of the event:

BEFORE

AFTER

INSTEAD OF (applicable only to views)

“What” of the event:

INSERT

DELETE

UPDATE (can also do UPDATE OF a single attribute)

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Triggers: Options

Action can be performed on two different basis:

FOR EACH ROW

FOR EACH STATEMENT

Values that are changed can be referred to using:

“new.attribute” – the new value before the event

“old.attribute” – the old value after the event

Set of changed records as a “temporary relation too!

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Triggers: Example

Referencing old and new values of an attribute

CREATE TRIGGER RatingGetsReducedAFTER UPDATE OF Stars ON RatingsREFERENCING

OLD ROW AS oldrNEW ROW AS newr

FOR EACH ROWWHEN (newr.Stars < oldr.Stars) INSERT INTO ReducedRatingsVALUES (newr.UID, newr.MID,

newr.Stars - oldr.Stars)

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Triggers: Example

Set of changed records as a “temporary relation”:

CREATE TRIGGER LowNolanRatingsAFTER INSERT ON RatingsREFERENCING NEW TABLE NewRatingsFOR EACH STATEMENT

INSERT INTO LowNolanRatingsSELECT N.UserID, N.MovieID, N.StarsFROM NewRatings NWHERE N.Stars <= 3 AND

N.MovieID IN (SELECT M.MovieIDFROM Movies M

WHERE M.DIRECTOR = “Christopher Nolan”)

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Triggers and Views: Example



CREATE VIEW MovieAvg AS SELECT M.MovieID, M.Year, M.Director

AVG(R.RatingID) AS AvgStarsFROM Movies M, Ratings RWHERE M.MovieID = R.MovieIDGROUP BY R.MovieID


Triggers help propagate changes when views are “modified”

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Triggers and Views: Example


Triggers help propagate changes when views are “edited”RatingID Stars UserID MovieIDRatings (R)


CREATE TRIGGER MovieAvgAddINSTEAD OF INSERT ON MovieAvgREFERENCING NEW ROW AS newrFOR EACH ROW

INSERT INTO MOVIES (MovieID, Year, Director)VALUES (newr.MovieID, newr.Year,

newr.Director)

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Triggers vs. Constraints

Both help maintain data consistency/integrity

Constraints are “declarative”; triggers are “operational”

Having many interrelated triggers can cause weird /

unexpected behaviors; constraints are easier to

understand/reason about

Triggers are more powerful, expressive; several use-cases:

- For complex app actions (e.g., enforce credit limits)

- Helps auto-complete “forms” in some apps!

- Generate logs for specific auditing/security reasons

- and many others …

CS 564 Database Management Systems: Design and Implementation Lecture 5: SQL Chapters 3 and 5 in Cow...

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Transcript of CS 564 Database Management Systems: Design and Implementation Lecture 5: SQL Chapters 3 and 5 in Cow...