CSE 232A Graduate Database Systemscseweb.ucsd.edu/classes/fa19/cse232-a/slides/Topic0... ·...

CSE 232A Graduate Database Systems

Fall 2019

Arun Kumar

2009: Bachelors in CSE from IIT Madras

2009—16: MS and PhD in CS from UW-Madison

PhD thesis area: Data systems for ML workloads

2016-: Asst. Prof. at UC San Diego CSE 2019-: + Asst. Prof. at UC San Diego HDSI

Summers: 110F!

Winters: —40F!

Ahh! :)

About Myself

What is this course about? Why take it?

1. IBM’s Watson beats humans in Jeapordy!

How did Watson achieve that?

Watson devoured LOTS of data!

2. “Structured” data with Google search results

How does Google know that?

Google also devours LOTS of data!

3. Amazon’s “spot-on” recommendations

How does Amazon know that?

You guessed it! LOTS and LOTS of data!

Analysis

And innumerable “traditional” applications

Large-scale data management systems are the cornerstone of many digital applications, both modern and traditional

The Age of “Big Data”/“Data Science”

Data data everywhere, All the wallets did shrink! Data data everywhere, Nor any moment to think?

CSE 232A will get you thinking about the fundamentals of database systems

1. How are large structured datasets stored and organized?

2. How are “queries” handled? 3. How to make the system faster? 4. Deeper and more recent issues

What this course is NOT about

❖ NOT a course on basics of relational algebra or SQL ❖ Take CSE 132A instead (pre-requisite for 232A!)

❖ NOT a course on how to use an RDBMS for database-backed applications (triggers, physical tuning, etc.) ❖ Take CSE 132B instead

❖ NOT a course on distributed systems or transactions ❖ Take CSE 223B instead

http://cseweb.ucsd.edu/classes/fa19/cse232-a

And now for the (boring) logistics …

Prerequisites

❖ CSE 132A (or equivalent) is essential ❖ CSE 120 is also helpful ❖ For all other cases, email the instructor with

proper justification. A waiver can be considered.

Course Administrivia

❖ Lectures: MWF 3:00-3:50pm, Ledden Auditorium Attending ALL lectures is mandatory! ❖ Instructor: Arun Kumar; arunkk@eng.ucsd.edu Office hours: Wed 4-5pm, 3218 CSE (EBU3b) ❖ TAs: Nikos Koulouris, Kaiqi Yao, Aman Achpal, and

Allen Ordookhanians ❖ Piazza: TBD

Grading

❖ Midterm Exam 1: 20% Date: Friday, October 25; in-class ❖ Midterm Exam 2: 20% Date: Monday, November 25; in-class ❖ Final Exam: 60% (cumulative)

Date: Friday, December 13, 3-6pm; Room TBD

❖ (Optional/Extra Credit) 6 Paper Reviews: 3%

Course Outline

1. How are large structured datasets stored? Storage and file layout

2. How are “queries” handled? Indexing, sorting, relational operator implementations, and query processing

3. How to make the system faster? Query optimization and parallelism

4. Recent issues and trends Data systems for ML, Data integration and cleaning, semi-structured data, ML for RDBMSs

The primary focus will be the relational data model and Relational Database Management Systems (RDBMS)

Relational model in a nutshell

Basically, Relation:Table :: Pilot:Driver (okay, a bit more)

The model formalizes “operations” to manipulate relations

RatingID Rating Date UserID MovieID1 3.5 08/27/15 23294 202 4.0 07/20/15 4232 2933 2.5 08/02/15 54551 846… … … … …

Relational model in a nutshell

Invented by E. F. Codd in 1970s at IBM Research in CA

Relational DBMS in a nutshell

A software system to implement the relational model, i.e., enable users to manage data stored as relations

First RDBMSs: System R (IBM) and Ingres (Berkeley) in 1970s

A rare photo of the original System R manual

Mike Stonebraker won the Turing Award in 2015!

RDBMS software is now a USD 40+ billions/year industry! Numerous open source RDBMSs also popular

People still start companies about what are basically RDBMSs!

Course Textbook

Prescribed: “Database Management Systems” 3rd Edition Raghu Ramakrishnan and Johannes Gehrke

Optional: “Database Systems: The Complete Book” by Garcia-Molina, Widom, and Ullman “Big Data Integration” by Dong and Srivastava

Aka The “Cow Book” Which cow are you?

Tentative Course Schedule

Week Topic

0-1 Introduction; Data Storage (Disks; Files; New Hardware)

2-3 Indexing (B+ Tree; Hash Indexes; Learned Indexes); Sorting

3-4 Relational Operator Implementations; Query Processing

4 Midterm Exam 1 on Friday, 10/25

5-6 Query Optimization; Materialized Views

6-7 Parallel RDBMSs; Dataflow Systems; Cloud RDBMSs

7-8 Data Systems for ML Workloads

9 Midterm Exam 2 on Monday, 11/25

9-10 Data Integration and Cleaning; Semi-Structured Data

10 More ML for RDBMSs; Recap

11 Final Exam on Friday, 12/13

General Dos and Do NOTs

Do: ❖ Raise your hand before asking questions during lectures ❖ Discuss papers in the reading list with peers and others ❖ Participate in class discussions; and on Piazza, if you like ❖ Use “CSE232A:” as subject prefix for all emails to me/TAs Do NOT: ❖ Plagiarize or share content for your paper reviews ❖ Harass, cut off, or be disrespectful to others in class ❖ Use email as primary communication mechanism for

doubts/questions instead of Office Hours ❖ Record or quote the instructor’s anecdotes out of class! ☺

Questions?

Example Relational DB: Netflix!

RatingID NumStars Timestamp UserID MovieID1 3.5 08/27/15 79 20… … … … …

UserID Name Age JoinDate79 Alice 23 01/10/13… … …

MovieID Name ReleaseDate Director20 Inception 07/13/2010 Christopher Nolan… … …

Ratings

Movies

Recap: Relational Model

Relational Model: Basic Terms

RatingID NumStars Timestamp UserID MovieID1 3.5 08/27/15 79 202 4.0 07/20/15 4232 2933 2.5 08/02/15 54551 846… … … … …

What is a Relation?

A glorified table!

What are Attributes?

These things

What are Domains?

The mathematical “domains” for the attributes

Integers Real …

What is Arity?Ratings

Number of attributes

What are Tuples?What is Cardinality?

These thingsNumber of tuples

Ratings

Referring to “tuples”: Two notations

1. Without using attribute names (positional/sequence) 2. Using attribute names (named/set)

Ratings (R)

t[1] = 3.5t.NumStars = 3.5

What is Schema?

The relation name, and the name and logical descriptions of the attributes (including domains)

Aka “metadata”

Ratings

What is an Instance?

A given relation populated with a set of tuples (loose analogy: schema:instance::type:value in PL)

Instance 1

RatingID NumStars Timestamp UserID MovieID3292 1.5 06/27/14 794 10

294122 4.0 07/10/14 232 32974423 0.5 03/08/14 8451 846

… … … … …

Instance 2

Ratings

RatingID NumStars Timestamp UserID MovieID1 3.5 08/27/15 79 20… … … … …

What is a Relational Database?

UserID Name Age JoinDate79 Alice 23 01/10/13… … …

MovieID Name ReleaseDate Director20 Inception 07/13/2010 Christopher Nolan… … …

A collection of relations; similarly, schema vs. instance

Ratings

Movies

“Write Operations” on a Relation

❖ Insert Add tuples to a relation ❖ Delete Remove tuples from a relation (typically based on

“predicate” matches, e.g., “NumStars <= 4.5” ❖ Modify Logically, deletes + inserts, but typically implemented

as in-place updates to a relation instance

“Read Operations” on a Relation

❖ “Select” Select all tuples from Ratings with “UserID == 19” ❖ “Project” Select only Director attribute from Movies ❖ “Aggregate” Select Average of all NumStars in Ratings

And a few more formal “algebraic” operations …

Recap: Relational Algebra

Basic Relational Operations

Select Project Rename Cross Product (aka Cartesian Product) Set Operations: Union Set Difference

Select

RatingID NumStars Timestamp UserID MovieID1 3.5 08/27/15 79 202 4.0 07/20/15 80 203 2.5 08/02/14 79 164 4.5 03/05/14 80 16

Ratings (R)

Example: Get all ratings with 4.0 or more stars

“Selection condition/predicate”Select

“Operator”

Select

RatingID NumStars Timestamp UserID MovieID2 4.0 07/20/15 80 204 4.5 03/05/14 80 16

Schema preservedSubset of tuples

(satisfying selection condition)

Complex Selection Conditions

Project

Ratings (R)

Example: Get all MovieIDs

“Projection list”

Project

MovieID2016

R’ Schema reduced (to projection list)

Tuple values “deduplicated” (slightly different semantics in SQL)

Composition of Relational Ops

Example: Get UserID and NumStars of ratings with less than 3 stars

UserID NumStars79 2.5

R’RelOp(Relation) gives a Relation

Rename

Ratings (R)

Example: Rename Timestamp to RateDate

⇢C(2�>RateDate)(R)

⇢RatingID,NumStars,RateDate,UserID,MovieID(R)

Rename

Instance preserved Schema modified

RatingID NumStars RateDate UserID MovieID1 3.5 08/27/15 79 202 4.0 07/20/15 80 203 2.5 08/02/14 79 164 4.5 03/05/14 80 16

⇢C(2�>RateDate)(R)

Cross Product

UserID Name Age JoinDate79 Alice 23 01/10/1380 Bob 41 05/10/13

MovieID Name ReleaseYear Director20 Inception 2010 Christopher Nolan16 Avatar 2009 Jim Cameron

Users (U)

Movies (M)

❖ Cartesian product (construct all pairs of tuples across tables) ❖ Schema of output “concatenates” the input schemas ❖ Be careful with attribute name conflicts! Use Rename op

Cross Product

Users (U)

Movies (M)

UserID U.Name Age JoinDate MovieID M.Name ReleaseYear

Director

79 Alice 23 01/10/13 20 Inception 2010 Christopher Nolan

79 Alice 23 01/10/13 16 Avatar 2009 Jim Cameron80 Bob 41 05/10/13 20 Inception 2010 Christopher

Nolan80 Bob 41 05/10/13 16 Avatar 2009 Jim Cameron

⇢C(1�>U .Name(U)⇥ ⇢C(1�>M .Name(M)

RatingID NumStars Timestamp UserID MovieID1 3.5 08/27/15 79 202 4.0 07/20/15 80 203 2.5 08/02/14 79 16

R2Union of sets of tuples (instances)

Inputs must have identical schema: “Union-compatible”

RatingID NumStars Timestamp UserID MovieID1 3.5 08/27/15 79 202 4.0 07/20/15 80 203 2.5 08/02/14 79 164 4.5 03/05/14 80 165 5.0 06/09/13 135 20

Set Difference

Set difference of sets of tuples (instances) Inputs must be “Union-compatible”

Set Difference

RatingID NumStars Timestamp UserID MovieID1 3.5 08/27/15 79 202 4.0 07/20/15 80 20

R’ = R1 – R2

Derived and Other Relational Ops

Set Operation: Intersection Join Group By Aggregate

Intersection

Set intersection of sets of tuples (instances) Inputs must be “Union-compatible”

Intersection

❖ Equivalent Select on Cross Product, but “bypasses” full X

❖ Perhaps the most intensively studied Rel Op! ❖ Several “types” of Joins:

Natural Join and Equi-Join Condition Join (aka Theta Join) Semi-Join, Inner Join, Outer Join, Anti-Join, etc.

R ./JoinCondition M

�JoinCondition(R⇥M)

Natural Join

Movies (M)

Ratings (R)

R ./ M

Natural Join

RatingID

NumStars

Timestamp

UserID

MovieID

Name ReleaseYear

Director

1 3.5 08/27/15 79 20 Inception 2010 Christopher Nolan2 4.0 07/20/15 80 20 Inception 2010 Christopher Nolan3 2.5 08/02/14 79 16 Avatar 2009 Jim Cameron4 4.5 03/05/14 80 16 Avatar 2009 Jim Cameron

❖ “Join attributes”: attributes that determine matching tuples Have same name in both inputs! “MovieID” in R and M

❖ Implicit equality condition on join attributes If > 1 pair, implicit logical “and” of all equality terms ❖ Output schema concatenates input schemas But join attributes appear only once in output (Project)

R ./ M

Equi-Join

❖ Generalization of the Natural Join

❖ Attribute names of “join attributes” need not be the same ❖ EqualityCondition is a general boolean expression (logical

“and”, and/or “or”) of terms with equality predicates only ❖ Join attributes from both R and M in output (no Project)

Perhaps the most important and common type of Join! Lots of R&D on efficient implementations!

R ./EqualityCondition M

Equi-Join: Example

J K10 x20 y30 x

R1(J,K)P Qx 4y 9x 8

R2(P,Q) T(J,K,P,Q)J K P Q10 x x 420 y y 930 x x 810 x x 830 x x 4

T(J,K, P,Q) = R1 ./K=P R2

(Primary) Key-Foreign Key Join

❖ A special kind of equi-join ❖ One of the join attributes is the (Primary) Key of an input

relation; the other is a Foreign Key in the other relation

RatingID NumStars Timestamp UID MovieID

UserID Name Age JoinDate

Ratings

Also a common and important (sub) type of join with even more specialized efficient implementations!

Ratings ./UID=UserID Users

Condition Join (aka “Theta” Join)

❖ Generalization of the Equi-Join

R ./JoinCondition M

Condition Join: Example

J K10 x20 y30 x

R1(J,K)P Qx 4y 9x 12

R2(P,Q) T(J,K,P,Q)J K P Q10 x x 420 y x 420 y y 930 x x 430 x y 930 x x 12

Perhaps the most difficult type of Join to implement efficiently!

T(J,K, P,Q) = R1 ./J/2>Q R2

Join Expressions

Can compose many joins into a single complex expression

RatingID NumStars Timestamp UserID MovieID

UserID UName Age JoinDate

MovieID Name ReleaseYear Director

Ratings

UsersMovies

Q. What do we get as the output?

UserID

JoinDate

RatingID

NumStars

Timestamp

MovieID

Name ReleaseYEar

Director

AllStuff

Taxonomy of Joins

All kinds of joins

Inner joins

Outer joins Semi joins Anti joins

Theta joinsEqui joins

Natural joins

Key-Foreign Key Joins“Snowflake” joins

“Star” joins

Group By Aggregate

❖ NOT a part of relational algebra, but “Extended RA”! ❖ Useful for “analytics” queries that aggregate numerical data

RatingID NumStars Timestamp UserID MovieIDRatings

What is the average rating for each movie? How many movies has each user rated?

❖ Standard 5 numerical aggregations supported in SQL:

Count, Sum, Average, Maximum, and Minimum Extra: Median, Mode, Variance, Standard Deviation, etc.

Group By Aggregate

“Grouping Attributes” (Subset of R’s attributes)

A numerical attribute in R“Aggregate Function”

(SUM, COUNT, AVG, MAX, MIN)

❖ Output schema will have X and an extra numerical attribute (result of the aggregate function)

❖ Can list multiple aggregate functions in the same operation

�X,Agg(Y )(R)

Group By Aggregate

MovieID AVG(NumStars)20 3.7516 3.5

What is the average rating for each movie?

�MovieID,AV G(NumStars)(R)

Group By Aggregate

AVG(Age)25.75

What is the average age of the users?

123 Carol 19 08/09/14420 Dan 20 03/01/15

Users (U)

The set of Grouping Attributes can be empty too!

�AVG(Age)(U)

Recap: SQL

Basic Form of an SQL Query

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

List of attributes to projectOptional

List of relations (possibly with “aliases”)

Selection/join condition (optional)

What does it mean logically?

SELECT [DISTINCT] target-list FROM 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!

Example SQL Query

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

Movies (M)MovieID Name Year Director

20 Inception 2010 Christopher Nolan16 Avatar 2009 Jim Cameron53 Gravity 2013 Alfonso Cuaron74 Blue Jasmine 2013 Woody Allen

Example: Get the names of movies released in 2013

⇡Name(�Y ear=2013(M))

Example SQL Query

NameGravity

Blue Jasmine

Example SQL Query

SELECT * FROM Movies M WHERE M.Year = 2013

MovieID Name Year Director53 Gravity 2013 Alfonso Cuaron74 Blue Jasmine 2013 Woody Allen

Example SQL Query

SELECT M.Name FROM Movies M WHERE M.Year <> 2013

Example: Get the names of movies from years other than 2013

⇡Name(�Y ear 6=2013(M))

Example SQL Query

SELECT M.Year FROM Movies M

Example: For which years do we have movie data?⇡Y ear(M)

Example SQL Query

SELECT M.Year FROM Movies M

Year2010200920132013

SQL allows repetitions of tuples in a relation! Not the same semantics as RA’s Project

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

DISTINCT in SQL

SELECT DISTINCT M.Year FROM Movies M

Year201020092013

DISTINCT needed to achieve set semantics of RA’s Project in SQL

Aliases in SQL

Why bother with the alias? Not needed here!

SELECT Name FROM Movies WHERE Year = 2013

Aliases in SQL – Useful for Joins!

Movies (M)MovieID Name Year DirectorID

SELECT M.Name FROM Movies M, Directors D WHERE D.Name = “Jim Cameron” AND M.DirectorID = D.DID

Aliases 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

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 D WHERE (D.Name = “Woody Allen” OR D.Age >= 50) AND M.Year = 2013 AND M.DirectorID = D.DID

Movies (M)MovieID Name Year DirectorID

Directors (D)DID Name Age

LIKE in SQL

SELECT DISTINCT M.Director FROM Movies M WHERE M.Name LIKE “Blue%”

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

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

ORDER BY in SQL

NameGravity

Blue Jasmine

ORDER BY M.NameName

Blue JasmineGravity

Useful for data readability Ordering defined by domain semantics Can specify DESC; multiple attributes

LIMIT in SQL

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

MovieID Name Year Director20 Inception 2010 Christopher Nolan16 Avatar 2009 Jim Cameron53 Gravity 2013 Alfonso Cuaron74 Blue Jasmine 2013 Woody Allen

ORDER BY M.Year

YearInceptionGravity

Blue Jasmine

Also useful for data readability Prevents “flooding” of screen with data Be wary of using it without ORDER BY!

LIMIT 2

Aggregate Functions in SQL

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

20 Inception 2010 Christopher Nolan16 Avatar 2009 Jim Cameron53 Gravity 2013 Alfonso Cuaron91 Interstellar 2014 Christopher Nolan

How many movies came out after 2010?

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

How many movies came out after 2010?

COUNT(*)2

5 Native Aggregate Functions in SQL

❖ COUNT ([DISTINCT] attribute) ❖ AVG ([DISTINCT] attribute) ❖ SUM ([DISTINCT] attribute) ❖ MAX (attribute) ❖ MIN (attribute)

SELECT COUNT(DISTINCT M.Director) FROM Movies M

How many directors do we have?

RatingID Stars UserID MovieID1 3.5 79 422 4.0 80 203 2.5 79 534 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!

Ratings (R)

SELECT DISTINCT R.MovieID FROM Ratings R WHERE R.Stars = (SELECT MAX(R2.Stars) FROM Ratings R2)

Which MovieID(s) have the highest rating?

Group By Aggregate in SQL

SELECT [DISTINCT] target-list FROM relation-list [WHERE condition] GROUP BY grouping-list HAVING group-condition

Condition on each group in aggregate

target-list must be in this form: X’, Agg(Y)

Subset of X

�X,Agg(Y )(R)

Ratings (R)

What is the average rating for each movie?

SELECT R.MovieID, AVG(R.Stars) AS AvgRating FROM Ratings R GROUP BY R.MovieID

�MovieID,AVG(Stars)(R)

Ratings (R)

SELECT R.MovieID, AVG(R.Stars) AS AvgRating FROM Ratings R GROUP BY R.MovieID

MovieID AvgRating20 4.042 4.053 2.5

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

Q1. Which of the following is not a basic operation in relational algebra?

�[⇢./

Review Questions

Q2. What type of join is the following NOT an example of?

RID NumStars RateDate UID MIDUID UName Age JoinDateMID MName Year Director

R ./ U

Inner join

Outer join

Natural join

Theta join

Review Questions

Q3. How many movies did “Jim Cameron” direct?

�COUNT(⇤)(�Director=“Jim Cameron”(M))

�ReleaseYear ,COUNT(⇤)(�Director=“Jim Cameron”(M))

�COUNT(⇤)(�Director=“Jim Cameron”(M ./ R))

�Director=“Jim Cameron”(�COUNT(⇤)(M))A

Review Questions

Q4. Which of the following attributes is a primary key?

M.Year

Review Questions

Q5. Which of the following SQL features do not have a counterpart operation in extended relational algebra?

SELECT DISTINCT

GROUP BY

Review Questions

Q6. What is the cardinality of this query’s output?

RID NumStars RateDate UID MID1 3.5 08/27/15 79 202 4.0 07/20/15 80 203 2.5 08/02/14 79 164 4.5 03/05/14 80 165 5.0 06/09/13 135 20

SELECT COUNT(DISTINCT MID) FROM R

Review Questions

Q7. Get the year and director of all movies from the last decade that have the term “Avengers” in their title.

ASELECT Year, Director from M WHERE Year >= 2008 AND MName LIKE “%Avengers%”

MID MName Year DirectorM

BSELECT Year, Director from M WHERE Year >= 2008 AND MName LIKE “%Avengers”

CSELECT Year, Director from M WHERE Year >= 2008 AND MName LIKE “Avengers%”

DSELECT Year, Director from M WHERE Year >= 2008 AND MName = “Avengers”

Review Questions

RatingID Stars UserID MovieIDRatings (R)

Q8. Write an SQL query to get the number of 5-star ratings for each movie directed by “Christopher Nolan”

SELECT R.MovieID, COUNT(R.Stars) AS NumHighRatings FROM Ratings R, Movies M WHERE M.Director = “Christopher Nolan” AND R.MovieID = M.MovieID

AND R.Stars = 5 GROUP BY R.MovieID

Movies (M) MovieID Name Year Director

Review Questions

Advanced SQL Operations (Optional)

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” UNION SELECT R.UID FROM Ratings R, Movies M WHERE R.MID = M.MID AND M.Year = 2013

Union-compatible!

Semantics of UNION in SQL

MID Name Year Director20 Inception 2010 Christopher Nolan42 Life of Pi 2012 Ang Lee53 Gravity 2013 Alfonso Cuaron

RID Stars UID MID1 3.5 79 422 4.0 80 203 2.5 79 534 4.5 123 42 R M

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

UID79123

Semantics of UNION in SQL

UNIONUID79123

UID79123

UNION implicitly deduplicates tuples (unlike SELECT)!

Q. How to retain duplicates with UNION?

UNION ALLUID79123

UID7979123

INTERSECT in SQL

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

UID79123

INTERSECT

EXCEPT (Set Difference) in SQL

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

UID79123 UID

EXCEPT

The Contentious Bag Semantics!

UID79797912312380

UID7912312392

UNION ALL UID797979791231231231238092

Add the number of repetitions

UID79797912312380

UID7912312392

EXCEPT ALL UID797980Subtract the number

of repetitions

UID79797912312380

UID7912312392

INTERSECT ALL UID79123123Minimum of the

number of repetitions

CSE 232A Graduate Database Systemscseweb.ucsd.edu/classes/fa19/cse232-a/slides/Topic0... ·...

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Topic0 Administration

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