Due by 11:59pm on Wednesday, April 26.

Starter Files

Download lab13.zip. Inside the archive, you will find starter files for the questions in this lab, along with a copy of the Ok autograder.

Topics

Consult this section if you want a refresher on the material for this lab, or if you're having trouble running SQL or SQLite on your computer. It's okay to skip directly to the questions and refer back here should you get stuck

SQL Basics

Creating Tables

You can create SQL tables either from scratch or from existing tables.

The following statement creates a table by specifying column names and values without referencing another table. Each SELECT clause specifies the values for one row, and UNION is used to join rows together. The AS clauses give a name to each column; it need not be repeated in subsequent rows after the first.

CREATE TABLE [table_name] AS
  SELECT [val1] AS [column1], [val2] AS [column2], ... UNION
  SELECT [val3]             , [val4]             , ... UNION
  SELECT [val5]             , [val6]             , ...;

Let's say we want to make the following table called big_game which records the scores for the Big Game each year. This table has three columns: berkeley, stanford, and year.

We could do so with the following CREATE TABLE statement:

CREATE TABLE big_game AS
  SELECT 30 AS berkeley, 7 AS stanford, 2002 AS year UNION
  SELECT 28,             16,            2003         UNION
  SELECT 17,             38,            2014;

Selecting From Tables

More commonly, we will create new tables by selecting specific columns that we want from existing tables by using a SELECT statement as follows:

SELECT [columns] FROM [tables] WHERE [condition] ORDER BY [columns] LIMIT [limit];

Let's break down this statement:

• SELECT [columns] tells SQL that we want to include the given columns in our output table; [columns] is a comma-separated list of column names, and * can be used to select all columns
• FROM [table] tells SQL that the columns we want to select are from the given table; see the joins section to see how to select from multiple tables
• WHERE [condition] filters the output table by only including rows whose values satisfy the given [condition], a boolean expression
• ORDER BY [columns] orders the rows in the output table by the given comma-separated list of columns
• LIMIT [limit] limits the number of rows in the output table by the integer [limit]

Note: We capitalize SQL keywords purely because of style convention. It makes queries much easier to read, though they will still work if you don't capitalize keywords.

Here are some examples:

Select all of Berkeley's scores from the big_game table, but only include scores from years past 2002:

sqlite> SELECT berkeley FROM big_game WHERE year > 2002;
28
17

Select the scores for both schools in years that Berkeley won:

sqlite> SELECT berkeley, stanford FROM big_game WHERE berkeley > stanford;
30|7
28|16

Select the years that Stanford scored more than 15 points:

sqlite> SELECT year FROM big_game WHERE stanford > 15;
2003
2014

SQL operators

Expressions in the SELECT, WHERE, and ORDER BY clauses can contain one or more of the following operators:

• comparison operators: =, >, <, <=, >=, <> or != ("not equal")
• boolean operators: AND, OR
• arithmetic operators: +, -, *, /
• concatenation operator: ||

Here are some examples:

Output the ratio of Berkeley's score to Stanford's score each year:

sqlite> select berkeley * 1.0 / stanford from big_game;
0.447368421052632
1.75
4.28571428571429

Output the sum of scores in years where both teams scored over 10 points:

sqlite> select berkeley + stanford from big_game where berkeley > 10 and stanford > 10;
55
44

Output a table with a single column and single row containing the value "hello world":

sqlite> SELECT "hello" || " " || "world";
hello world

Joins

To select data from multiple tables, we can use joins. There are many types of joins, but the only one we'll worry about is the inner join. To perform an inner join on two on more tables, simply list them all out in the FROM clause of a SELECT statement:

SELECT [columns] FROM [table1], [table2], ... WHERE [condition] ORDER BY [columns] LIMIT [limit];

We can select from multiple different tables or from the same table multiple times.

Let's say we have the following table that contains the names of head football coaches at Cal since 2002:

CREATE TABLE coaches AS
  SELECT "Jeff Tedford" AS name, 2002 as start, 2012 as end UNION
  SELECT "Sonny Dykes"         , 2013         , 2016        UNION
  SELECT "Justin Wilcox"       , 2017         , null;

When we join two or more tables, the default output is a cartesian product. For example, if we joined big_game with coaches, we'd get the following:

If we want to match up each game with the coach that season, we'd have to compare columns from the two tables in the WHERE clause:

sqlite> SELECT * FROM big_game, coaches WHERE year >= start AND year <= end;
17|38|2014|Sonny Dykes|2013|2016
28|16|2003|Jeff Tedford|2002|2012
30|7|2002|Jeff Tedford|2002|2012

The following query outputs the coach and year for each Big Game win recorded in big_game:

sqlite> SELECT name, year FROM big_game, coaches
...>        WHERE berkeley > stanford AND year >= start AND year <= end;
Jeff Tedford|2003
Jeff Tedford|2002

In the queries above, none of the column names are ambiguous. For example, it is clear that the name column comes from the coaches table because there isn't a column in the big_game table with that name. However, if a column name exists in more than one of the tables being joined, or if we join a table with itself, we must disambiguate the column names using aliases.

For examples, let's find out what the score difference is for each team between a game in big_game and any previous games. Since each row in this table represents one game, in order to compare two games we must join big_game with itself:

sqlite> SELECT b.Berkeley - a.Berkeley, b.Stanford - a.Stanford, a.Year, b.Year
...>        FROM big_game AS a, big_game AS b WHERE a.Year < b.Year;
-11|22|2003|2014
-13|21|2002|2014
-2|9|2002|2003

In the query above, we give the alias a to the first big_game table and the alias b to the second big_game table. We can then reference columns from each table using dot notation with the aliases, e.g. a.Berkeley, a.Stanford, and a.Year to select from the first table.

SQL Aggregation

Previously, we have been dealing with queries that process one row at a time. When we join, we make pairwise combinations of all of the rows. When we use WHERE, we filter out certain rows based on the condition. Alternatively, applying an aggregate function such as MAX(column) combines the values in multiple rows.

By default, we combine the values of the entire table. For example, if we wanted to count the number of flights from our flights table, we could use:

sqlite> SELECT COUNT(*) from FLIGHTS;
13

What if we wanted to group together the values in similar rows and perform the aggregation operations within those groups? We use a GROUP BY clause.

Here's another example. For each unique departure, collect all the rows having the same departure airport into a group. Then, select the price column and apply the MIN aggregation to recover the price of the cheapest departure from that group. The end result is a table of departure airports and the cheapest departing flight.

sqlite> SELECT departure, MIN(price) FROM flights GROUP BY departure;
AUH|932
LAS|50
LAX|89
SEA|32
SFO|40
SLC|42

Just like how we can filter out rows with WHERE, we can also filter out groups with HAVING. Typically, a HAVING clause should use an aggregation function. Suppose we want to see all airports with at least two departures:

sqlite> SELECT departure FROM flights GROUP BY departure HAVING COUNT(*) >= 2;
LAX
SFO
SLC

Note that the COUNT(*) aggregate just counts the number of rows in each group. Say we want to count the number of distinct airports instead. Then, we could use the following query:

sqlite> SELECT COUNT(DISTINCT departure) FROM flights;
6

This enumerates all the different departure airports available in our flights table (in this case: SFO, LAX, AUH, SLC, SEA, and LAS).

    $ sqlite3
    SQLite version 3.32.3

$ sudo apt install sqlite3
$ sqlite3 --version
3.32.3 2020-06-18 14:16:19

python3 sqlite_shell.py

Survey Data

In previous semesters, we asked 61A students to complete a brief online survey, which involved relatively random but fun questions. In this lab, we will interact with the results of the survey by using SQL queries to see if we can find interesting things in the data.

First, take a look at data.sql and examine the table defined in it. Note its structure. You will be working with the two tables in this file.

The first is the table students, which is the main results of the survey. Each column represents a different question from the survey, except for the first column, which is the time of when the result was submitted. This time is a unique identifier for each of the rows in the table. The last several columns all correspond to the last question on the survey (more details below.)

The second table is numbers, which is the results from the survey in which students could select more than one option from the numbers listed, which ranged from 0 to 10 and included 2021, 2022, 9000, and 9001. Each row has a time (which is again a unique identifier) and has the value 'True' if the student selected the column or 'False' if the student did not. The column names in this table are the following strings, referring to each possible number: '0', '1', '2', '4', '5', '6', '7', '8', '9', '10', '2021', '2022', '9000', '9001'

Since the survey was anonymous, we used the timestamp that a survey was submitted as a unique identifier. A time in students matches up with a time in numbers. For example, a row in students whose time value is "11/17/2021 10:52:40" matches up with the row in numbers whose time value is "11/17/2021 10:52:40". These entries come from the same Google form submission and thus belong to the same student.

Required Questions

Q1: What Would SQL print?

Note: There is no submission for this question

First, load the tables into sqlite3.

$ python3 sqlite_shell.py --init lab13.sql

Before we start, inspect the schema of the tables that we've created for you:

sqlite> .schema

This tells you the name of each of our tables and their attributes.

Let's also take a look at some of the entries in our table. There are a lot of entries though, so let's just output the first 20:

sqlite> SELECT * FROM students LIMIT 20;

If you're curious about some of the answers students put into the Google form, open up data.sql in your favorite text editor and take a look!

For each of the SQL queries below, think about what the query is looking for, then try running the query yourself and see!

sqlite> SELECT * FROM students LIMIT 30; -- This is a comment. * is shorthand for all columns!
______
selects first 30 records from students;
sqlite> SELECT color FROM students WHERE number = 7;
______
selects the color from students who said their favorite number was 7;
sqlite> SELECT song, pet FROM students WHERE color = "blue" AND date = "12/25";
______
selects the song and pet from students who said their favorite color was blue and picked December 25th;

Remember to end each statement with a ;! To exit out of SQLite, type .exit or .quit or hit Ctrl-C.

Q2: Go Bears! (And Dogs?)

Now that we have learned how to select columns from a SQL table, let's filter the results to see some more interesting results!

It turns out that 61A students have a lot of school spirit: the most popular favorite color was 'blue'. You would think that this school spirit would carry over to the pet answer, and everyone would want a pet bear! Unfortunately, this was not the case, and the majority of students opted to have a pet 'dog' instead. That is the more sensible choice, I suppose...

Write a SQL query to create a table that contains both the column color and the column pet, using the keyword WHERE to restrict the answers to the most popular results of color being 'blue' and pet being 'dog'.

You should get the following output:

sqlite> SELECT * FROM bluedog;
blue|dog
blue|dog
blue|dog
blue|dog
blue|dog
blue|dog
blue|dog
blue|dog
blue|dog
blue|dog

CREATE TABLE bluedog AS
  SELECT "REPLACE THIS LINE WITH YOUR SOLUTION";

This isn't a very exciting table, though. Each of these rows represents a different student, but all this table can really tell us is how many students both like the color blue and want a dog as a pet, because we didn't select for any other identifying characteristics. Let's create another table, bluedog_songs, that looks just like bluedog but also tells us how each student answered the song question.

You should get the following output:

sqlite> SELECT * FROM bluedog_songs;
blue|dog|Glimpse of Us
blue|dog|The Other Side of Paradise
blue|dog|Leave the Door Open
blue|dog|Bohemian Rhapsody
blue|dog|Dancing Queen
blue|dog|Palette
blue|dog|Bohemian Rhapsody
blue|dog|good 4 u
blue|dog|Clair de Lune
blue|dog|Dancing Queen

CREATE TABLE bluedog_songs AS
  SELECT "REPLACE THIS LINE WITH YOUR SOLUTION";

This distribution of songs actually largely represents the distribution of song choices that the total group of students made, so perhaps all we've learned here is that there isn't a correlation between a student's favorite color and desired pet, and what song they could spend the rest of their life listening to. Even demonstrating that there is no correlation still reveals facts about our data though!

Use Ok to test your code:

python3 ok -q bluedog
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Q3: The Smallest Unique Positive Integer

Who successfully managed to guess the smallest unique positive integer value? Let's find out!

Write an SQL query to create a table with the columns time and smallest which contains the timestamp for each submission that made a unique guess for the smallest unique positive integer - that is, only one person put that number for their guess of the smallest unique integer. Also include their guess in the output.

Hint: Think about what attribute you need to GROUP BY. Which groups do we want to keep after this? We can filter this out using a HAVING clause. If you need a refresher on aggregation, see the topics section.

The submission with the timestamp corresponding to the minimum value of this table is the timestamp of the submission with the smallest unique positive integer!

CREATE TABLE smallest_int_having AS
  SELECT "REPLACE THIS LINE WITH YOUR SOLUTION";

Use Ok to test your code:

python3 ok -q smallest-int-having
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Q4: Matchmaker, Matchmaker

Did you take 61A with the hope of finding a new group of friends? Well you're in luck! With all this data in hand, it's easy for us to find your perfect match. If two students want the same pet and have the same taste in music, they are clearly meant to be friends! In order to provide some more information for the potential pair to converse about, let's include the favorite colors of the two individuals as well!

In order to match up students, you will have to do a join on the students table with itself. When you do a join, SQLite will match every single row with every single other row, so make sure you do not match anyone with themselves, or match any given pair twice!

Important Note: When pairing the first and second person, make sure that the first person responded first (i.e. they have an earlier time). This is to ensure your output matches our tests.

Hint: When joining table names where column names are the same, use dot notation to distinguish which columns are from which table: [table_name].[column name]. This sometimes may get verbose, so it’s stylistically better to give tables an alias using the AS keyword. The syntax for this is as follows:

SELECT <[alias1].[column name1], [alias2].[columnname2]...>
    FROM <[table_name1] AS [alias1],[table_name2] AS [alias2]...> ...

The query in the football example from earlier uses this syntax.

Write a SQL query to create a table that has 4 columns:

• The shared preferred pet of the pair
• The shared favorite song of the pair
• The favorite color of the first person
• The favorite color of the second person

CREATE TABLE matchmaker AS
  SELECT "REPLACE THIS LINE WITH YOUR SOLUTION";

Use Ok to test your code:

python3 ok -q matchmaker
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Q5: Sevens

Let's take a look at data from both of our tables, students and numbers, to find out if students that really like the number 7 also chose '7' for the obedience question. Specifically, we want to look at the students that fulfill the below conditions:

Conditions:

• reported that their favorite number (column number in students) was 7
• have 'True' in column '7' in numbers, meaning they checked the number 7 during the survey

In order to examine rows from both the students and the numbers table, we will need to perform a join.

How would you specify the WHERE clause to make the SELECT statement only consider rows in the joined table whose values all correspond to the same student? If you find that your output is massive and overwhelming, then you are probably missing the necessary condition in your WHERE clause to ensure this.

Note: The columns in the numbers table are strings with the associated number, so you must put quotes around the column name to refer to it. For example if you alias the table as a, to get the column to see if a student checked 9001, you must write a.'9001'.

Write a SQL query to create a table with just the column seven from students, filtering first for students who said their favorite number (column number) was 7 in the students table and who checked the box for seven (column '7') in the numbers table.

The first 10 lines of this table should look like this:

sqlite> SELECT * FROM sevens LIMIT 10;
7
I find this question condescending
7
seven
7
7
7
7

CREATE TABLE sevens AS
  SELECT "REPLACE THIS LINE WITH YOUR SOLUTION";

Use Ok to test your code:

python3 ok -q sevens
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Submit

Make sure to submit this assignment by uploading any files you've edited to the appropriate Gradescope assignment. For a refresher on how to do this, refer to Lab 00.

Optional Questions

These questions are optional, but you must complete them in order to be checked off before the end of the lab period. They are also useful practice!

Cyber Monday

Q6: Price Check

After you are full from your Thanksgiving dinner, you realize that you still need to buy gifts for all your loved ones over the holidays. However, you also want to spend as little money as possible (you're not cheap, just looking for a great sale!).

Let's start off by surveying our options. Using the products table, write a query that creates a table average_prices that lists categories and the average price of items in the category (using MSRP as the price).

You should get the following output:

sqlite> SELECT category, ROUND(average_price) FROM average_prices;
computer|109.0
games|350.0
phone|90.0

CREATE TABLE average_prices AS
  SELECT "REPLACE THIS LINE WITH YOUR SOLUTION";

Use Ok to test your code:

python3 ok -q cyber-monday-part1
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Q7: The Price is Right

Now, you want to figure out which stores sell each item in products for the lowest price. Write a SQL query that uses the inventory table to create a table lowest_prices that lists items, the stores that sells that item for the lowest price, and the price that the store sells that item for.

You should expect the following output:

sqlite> SELECT * FROM lowest_prices;
Hallmart|GameStation|298.98
Targive|QBox|390.98
Targive|iBook|110.99
RestBuy|kBook|94.99
Hallmart|qPhone|85.99
Hallmart|rPhone|69.99
RestBuy|uPhone|89.99
RestBuy|wBook|114.29

CREATE TABLE lowest_prices AS
  SELECT "REPLACE THIS LINE WITH YOUR SOLUTION";

Use Ok to test your code:

python3 ok -q cyber-monday-part2
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Q8: Bang for your Buck

You want to make a shopping list by choosing the item that is the best deal possible for every category. For example, for the "phone" category, the uPhone is the best deal because the MSRP price of a uPhone divided by its ratings yields the lowest cost. That means that uPhones cost the lowest money per rating point out of all of the phones. Note that the item with the lowest MSRP price may not necessarily be the best deal.

Write a query to create a table shopping_list that lists the items that you want to buy from each category.

After you've figured out which item you want to buy for each category, add another column that lists the store that sells that item for the lowest price.

You should expect the following output:

sqlite> SELECT * FROM shopping_list;
GameStation|Hallmart
uPhone|RestBuy
wBook|RestBuy

CREATE TABLE shopping_list AS
  SELECT "REPLACE THIS LINE WITH YOUR SOLUTION";

Hint: You should use the lowest_prices table you created in the previous question.

Hint 2: One approach to this problem is to create another table, then create shopping_list by selecting from that table.

Use Ok to test your code:

python3 ok -q cyber-monday-part3
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Q9: Driving the Cyber Highways

Using the Mbs (megabits) column from the stores table, write a query to calculate the total amount of bandwidth needed to get everything in your shopping list.

CREATE TABLE total_bandwidth AS
  SELECT "REPLACE THIS LINE WITH YOUR SOLUTION";

Hint: You should use the shopping_list table you created in the previous question.

Use Ok to test your code:

python3 ok -q cyber-monday-part4
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