CSE-4/562 Spring 2021 - Intro


CSE-4/562 Spring 2021

Feb 2, 2021

Garcia-Molina/Ullman/Widom: Ch. 1, 2.1-2.2


Why Are Databases Awesome?

They're Everywhere


Rank Organization Sales (B$) FY Market cap (B$) Headquarters
1 United States Microsoft 118.2 2019 946.5 Redmond, WA, US
2 United States Oracle 39.6 2019 186.3 Austin, TX, US
3 Germany SAP 29.1 2019 134.9 Walldorf, Germany
4 United States ADP 14.5 2019 52.0 New York City, NY, US
5 United States Adobe 9.5 2019 132 San Jose, CA, US
6 United States Salesforce 13.3 2019 120.9 San Francisco, CA, US
7 United States VMware 9.0 2019 77.2 Palo Alto, CA, US
8 United States Intuit 6.4 2019 66.8 Palo Alto, CA, US
9 United States SS&C Technologies 6.3 2019 16.0 Windsor, Connecticut
10 United States NetApp 6.2 2019 11.0 Sunnyvale, CA, US

8 of 10 Forbes Top Software Companies
Have a Focus on Data Management Systems

(Source wikipedia.org)

Interesting Problems

What is "Databases"?

How do we ask and answer questions about data?

How do we manipulate and persist data?



Data Modeling

Cost-Based Optimization


Join Algorithms

Index Data Structures


The Memory Hierarchy

Data Consistency

Which Tools To Use

And When?

Template for 90% of this class

What is the best, correct technique for task X, when Y is true?

  1. How do you define Correct and Best?
  2. What correct alternatives are available?
  3. How do you find the best available alternative

General Course Information


Algorithms / Data Structures
$O(\cdot)$ Analysis, Sort Algos, Trees, Hash Tables
How to use a database
CSE 4/560 (or equivalent)
Java, C++, C# or similar is usually good enough
(but you need actual programming experience)
(Rapid-start tutorial on Thursday)


Oliver Kennedy


Darshana Balakrishnan

Syllabus/Course Info



(same link)

CMS (Forum, Assignments)




$150 $50
No Index
ToC Summary

Course Structure

Concepts (50% of Grade)
  • Homework (20%; ~12 Assignments, Drop any 4); Groups of up to 4.
  • TBD: Comprehensive Final 20%
  • +10% to whichever you do better on
Practicum (50% of Grade)
  • Rebuild the guts of Spark's Catalyst Engine
  • Solo Project
  • 4 Checkpoints (+ 5 free points for Checkpoint 0)


Lectures will be on YouTube, streaming T/R from 12:30-2 and available for later replay.

You don't need to attend, but you are paying me to be here.

I'll be monitoring the chat. Don't ever hesitate to ask a question or comment (No need to raise your hand first).

Please keep things civil and be respectful of your fellow classmates, TA, and anyone else around.

I've torn the guts out of Catalyst. What remains:

  • SQL Parser
  • Logical Plans: Relational Algebra Trees
  • Expression: Primitive-valued expression trees + evaluation logic

Your mission: Replace the missing bits (sort of).

  • Analysis: Tidying up SQL's corner cases
  • Execution: Answering queries fast (single-node)
  • Optimization: Eliminating redundancy
    and picking the best algorithms.

We give you...

Gutted Catalyst

Data (CSV Files)

Schema Information (CREATE TABLE)

Questions (SQL Queries)

You give us...


(really really fast)

Real World Challenge

You get graded on your code's...

~1/3 credit for getting the right answer.
~2/3 credit for getting it reasonably fast.
+leaderboards for getting it really fast.

Checkpoint 0: "Hello World"

5/50 pts

  • Submit a simple Scala program
  • Make sure that the submission workflow works for you.

Checkpoint 1: "Get it Working"

10/50 pts

  • Interpret Relational Algebra (Spark's LogicalPlan)
  • Load CSV Files
  • Run Basic Select, Project, Join Queries
  • Nested Queries

Checkpoint 2: "Big Data"

12/50 pts

  • Order By
  • Limit
  • Too much data for memory

Checkpoint 3: "Aggregates"

8/50 pts

  • Aggregation

Checkpoint 4: "Precomputation"

15/50 pts

  • You get a few minutes to pre-compute
  • Load data
  • Cache views
  • Build indexes

Ways to Fail

  • Start your project at the last minute
  • Don’t go to office hours
  • Don’t ask questions (on Piazza or in class)
  • Wait until the deadline to submit for the first time
  • Cheat

Academic Integrity

Cheating is submitting any work that you did not perform by yourself as if you did.

References (when cited)
Wikipedia, Wikibooks (or similar): OK
Public Code
Stack Exchange (or similar): Not OK
Discussing concepts/ideas with classmates
“A hash index has O(1) lookups”: OK (except during exams 😇 )
Sharing code or answers with anyone
“Just have a look at how I implemented it”: NOT OK
For-hire code: NOT OK



Zero Tolerance
If I catch you submitting someone else’s code (including pay-for-code services), you will fail the class.
Group Responsibility
If your teammate cheats on a group assignment, the entire group will be penalized.
Share Code, Share Blame
If someone else submits your code or other course materials as their own, you will be penalized as well.


What does a Data Management System Do?

Analysis: Answering user-provided questions about data
What kind of tools can we give end-users?
  • Declarative Languages
  • Organizational Datastructures (e.g., Indexes)
Manipulation: Safely persisting and sharing data updates
What kind of tools can we give end-users?
  • Consistency Primitives
  • Data Validation Primitives

So let's talk structure...

Basic building blocks like Int, Float, Char, String
Several ‘fields’ of different types. (N-Tuple = N fields)
A Tuple has a ‘schema’ defining each field
A collection of unique records, all of the same type
An unordered collection of records, all of the same type
An ordered collection of records, all of the same type

Your data is currently an Unordered Set
of Tuples with 100 attributes each.

Tomorrow, you’ll be repeatedly asked for 1 specific attribute
from 5 specific tuples identified by the first attribute

Can you do better?


Better Idea: Rewrite data into a 99-Tuple of Maps keyed on the 1st attribute

This representation is equivalent and better for your needs.

Declarative specifications make it easier to find equivalences.

Declarative Languages

  • Don't need to think about algorithms.
  • Independent of the data representation.


  • Developed by IBM (for System R) in the 1970s.
  • Standard used by many vendors.
    • SQL-86 (original standard)
    • SQL-89 (minor revisions; integrity constraints)
    • SQL-92 (major revision; basis for modern SQL)
    • SQL-99 (XML, window queries, generated default values)
    • SQL 2003 (major revisions to XML support)
    • SQL 2008 (minor extensions)
    • SQL 2011 (minor extensions; temporal databases)

A Basic SQL Query

            SELECT  [DISTINCT] targetlist
            FROM    relationlist
            WHERE   condition
  1. Compute the $2^n$ combinations of tuples in all relations appearing in relationlist
  2. Discard tuples that fail the condition
  3. Delete attributes not in targetlist
  4. If DISTINCT is specified, eliminate duplicate rows

This is the least efficient strategy to compute a query! A good optimizer will find more efficient strategies to compute the same answer.

Example Data


Wildcards (*, tablename.*) are special targets that select all attributes.

'08/27/2015'180683348711'POINT (-73.84421521958048 40.723091773924274)'30'OnCurb''Alive''Fair''Acer rubrum''red maple''None''None''NoDamage''TreesCount Staff''None''No''No''No''No''No''No''No''No''No''108-005 70 AVENUE''11375''Forest Hills'4064'Queens'292816'QN17''Forest Hills'4073900'New York'40.72309177-73.844215221027431.14821202756.768749
'09/03/2015'200540315986'POINT (-73.81867945834878 40.79411066708779)'210'OnCurb''Alive''Fair''Quercus palustris''pin oak''None''None''Damage''TreesCount Staff''Stones''Yes''No''No''No''No''No''No''No''No''147-074 7 AVENUE''11357''Whitestone'4074'Queens'192711'QN49''Whitestone'4097300'New York'40.79411067-73.818679461034455.70109228644.837379
'09/05/2015'204026218365'POINT (-73.93660770459083 40.717580740099116)'30'OnCurb''Alive''Good''Gleditsia triacanthos var. inermis''honeylocust''1or2''None''Damage''Volunteer''None''No''No''No''No''No''No''No''No''No''390 MORGAN AVENUE''11211''Brooklyn'3013'Brooklyn'345018'BK90''East Williamsburg'3044900'New York'40.71758074-73.93660771001822.83131200716.891267
'09/05/2015'204337217969'POINT (-73.93445615919741 40.713537494833226)'100'OnCurb''Alive''Good''Gleditsia triacanthos var. inermis''honeylocust''None''None''Damage''Volunteer''Stones''Yes''No''No''No''No''No''No''No''No''1027 GRAND STREET''11211''Brooklyn'3013'Brooklyn'345318'BK90''East Williamsburg'3044900'New York'40.71353749-73.934456161002420.35833199244.253136
'08/30/2015'189565223043'POINT (-73.97597938483258 40.66677775537875)'210'OnCurb''Alive''Good''Tilia americana''American linden''None''None''Damage''Volunteer''Stones''Yes''No''No''No''No''No''No''No''No''603 6 STREET''11215''Brooklyn'3063'Brooklyn'394421'BK37''Park Slope-Gowanus'3016500'New York'40.66677776-73.97597938990913.775046182202.425999
... and 683783 more

            SELECT tree_id, spc_common, boroname
            FROM Trees
            WHERE boroname = 'Brooklyn'

In English, what does this query compute?

What is the ID, Commmon Name and Borough of Trees in Brooklyn?

189565'American linden''Brooklyn'
192755'London planetree''Brooklyn'
189465'London planetree''Brooklyn'
... and 177287 more

      SELECT latitude, longitude 
      FROM Trees, SpeciesInfo
      WHERE Trees.spc_common = SpeciesInfo.name
        AND SpeciesInfo.has_unpleasant_smell = 'Yes';

In English, what does this query compute?

What are the coordinates of Trees with bad smells?

... and more

      SELECT Trees.latitude, Trees.longitude 
      FROM Trees, SpeciesInfo
      WHERE Trees.spc_common = SpeciesInfo.name
        AND SpeciesInfo.has_unpleasant_smell = 'Yes';

... is the same as ...

      SELECT T.latitude, T.longitude 
      FROM Trees T, SpeciesInfo S
      WHERE T.spc_common = S.name
        AND S.has_unpleasant_smell = 'Yes';

... is (usually) the same as ...

      SELECT latitude, longitude 
      FROM Trees, SpeciesInfo
      WHERE spc_common = name
        AND has_unpleasant_smell = 'Yes';


            SELECT tree_id, 
                   stump_diam / 2 AS stump_radius,
                   stump_area = 3.14 * stump_diam * stump_diam / 4
            FROM Trees;

Arithmetic expressions can appear in targets or conditions. Use ‘=’ or ‘AS’ to assign names to these attributes. (The behavior of unnamed attributes is unspecified)


  SELECT tree_id, spc_common FROM Trees WHERE spc_common LIKE '%maple'
180683'red maple'
204325'sycamore maple'
205044'Amur maple'
184031'red maple'
208974'red maple'

SQL uses single quotes for ‘string literals’

LIKE is used for String Matches

%’ matches 0 or more characters


    SELECT tree_id FROM Trees WHERE spc_common = 'red maple'
    SELECT tree_id FROM Trees WHERE spc_common = 'sycamore maple'

Computes the set-union of any two union-compatible sets of tuples

Adding ALL preserves duplicates across the inputs (bag-union).

Aggregate Queries

    SELECT [DISTINCT] targetlist
    FROM relationlist
    WHERE condition
    GROUP BY groupinglist
    HAVING groupcondition

The targetlist now contains (a) Grouped attributes, and (b) Aggregate expressions.

Targets of type (a) must be a subset of the grouping-list

(intuitively each answer tuple corresponds to a single group, and each group must have a single value for each attribute)

groupcondition is applied after aggregation and may contain aggregate expressions.

Aggregate Queries

    SELECT spc_common, count(*) FROM Trees GROUP BY spc_common
''Schubert' chokecherry' 4888
'American beech' 273
'American elm' 7975
'American hophornbeam' 1081
'American hornbeam' 1517
... and more

Next time...

Scala for Java programmers.