Homework 10 Solutions

hw10.zip

Solution Files

You can find the solutions in the hw10.sql file.

To check your progress, you can run sqlite3 directly by running:

python3 sqlite_shell.py --init hw10.sql

You should also check your work using ok:

python3 ok

Visualizing SQL

The CS61A SQL Web Interpreter is a great tool for visualizing and debugging SQL statements!

To get started, visit code.cs61a.org and hit Start SQL interpreter on the launch screen.

Most tables used in assignments are already available for use, so let's try to execute a SELECT statement:

In addition to displaying a visual representation of the output table, the "Step-by-step" button lets us step through the SQL execution and visualize every transformation that takes place. For our example, clicking on the next arrow will produce the following visuals, demonstrating exactly how SQL is grouping our rows to form the final output!

Required Questions

SQL

Dog Data

In each question below, you will define a new table based on the following tables about dogs.

CREATE TABLE parents (parent TEXT, child TEXT);

INSERT INTO parents VALUES
  ('ace', 'bella'),
  ('ace', 'charlie'),
  ('daisy', 'hank'),
  ('finn', 'ace'),
  ('finn', 'daisy'),
  ('finn', 'ginger'),
  ('ellie', 'finn');

CREATE TABLE dogs (name TEXT, fur TEXT, height INTEGER);

INSERT INTO dogs VALUES
  ('ace',     'long',  26),
  ('bella',   'short', 52),
  ('charlie', 'long',  47),
  ('daisy',   'long',  46),
  ('ellie',   'short', 35),
  ('finn',    'curly', 32),
  ('ginger',  'short', 28),
  ('hank',    'curly', 31);

CREATE TABLE sizes (size TEXT, min INTEGER, max INTEGER);

INSERT INTO sizes VALUES
  ('toy',      24, 28),
  ('mini',     28, 35),
  ('medium',   35, 45),
  ('standard', 45, 60);

The parents table contains one row for each parent-child relationship; for example, the first row describes that the dog "ace" is the parent of the dog "bella". The dogs table contains the name, fur type, and height for each dog. The sizes table has one row for each classification of dog. A dog matches a particular classification if its height is greater than min and less than or equal to max.

Your queries should still perform correctly even if the values in these tables change. For example, if you are asked to list all dogs with a name that starts with h, you should write:

SELECT name FROM dogs WHERE name LIKE "h%";

The % sign matches any number of characters, and patterns in sqlite are case in-sensitive, so the pattern "h%" matches all strings that begin with h or H. This query would still be correct if a row was added with the name harry. Contrastingly, writing a query like SELECT "hank"; would not, and only works for the example input.

Q1: By Parent Height

Create a table by_parent_height that has a column of the names of all dogs that have a parent, ordered by the height of the parent dog from tallest parent to shortest parent.

-- All dogs with parents ordered by decreasing height of their parent
CREATE TABLE by_parent_height AS
  SELECT child FROM parents, dogs WHERE name = parent ORDER BY height desc;

For example, finn has a parent ellie with height 35, and so should appear before ginger who has a parent finn with height 32. The names of dogs with parents of the same height should appear together in any order. For example, bella and charlie should both appear at the end, but either one can come before the other.

For our example tables, the by_parent_height table should look like this:

+----------+
| chil     |
+----------+
| hank     |
| finn     |
| ace      |
| daisy    |
| ginger   |
| bella    |
| charlie  |
+----------+

Use Ok to test your code:

python3 ok -q by_parent_height

We need information from both the parents and the dogs table. This time, the only rows that make sense are the ones where a child is matched up with their parent. Finally, we order the result by descending height.

Q2: Size of Dogs

The Fédération Cynologique Internationale classifies a standard poodle as over 45 cm and up to 60 cm. The sizes table describes this and other such classifications, where a dog must be over the min and less than or equal to the max in height to qualify as size.

Create a size_of_dogs table with two columns, one for each dog's name and another for its size.

-- The size of each dog
CREATE TABLE size_of_dogs AS
  SELECT name, size FROM dogs, sizes
    WHERE height > min AND height <= max;

The size_of_dogs table should look like this:

+----------+----------+
| name     | size     |
+----------+----------+
| ace      | toy      |
| bella    | standard |
| charlie  | standard |
| daisy    | standard |
| ellie    | mini     |
| finn     | mini     |
| ginger   | toy      |
| hank     | mini     |
+----------+----------+

Use Ok to test your code:

python3 ok -q size_of_dogs

We know that at a minimum, we need information from both the dogs and sizes table. Finally, we filter and keep only the rows that make sense: a size that corresponds to the size of the dog we're currently considering.

Q3: Sentences

Siblings are pairs of dogs that have the same parent. Create a table that contains a row for each pair of siblings that have the same size classification, with a single column that contains a sentence describing the siblings by their size.

-- [Optional] Filling out this helper table is recommended
CREATE TABLE siblings AS
  SELECT a.child AS first, b.child AS second FROM parents AS a, parents AS b
    WHERE a.parent = b.parent AND a.child < b.child;
-- Sentences about siblings that are the same size
CREATE TABLE sentences AS
  SELECT "The two siblings, " || first || " and " || second || ", have the same size: " || a.size
    FROM siblings, size_of_dogs AS a, size_of_dogs AS b
    WHERE a.size = b.size AND a.name = first AND b.name = second;

Each sibling pair should appear only once in the output, and siblings should be listed in alphabetical order (e.g. "bella and charlie..." instead of "charlie and bella..."), as follows:

sqlite> SELECT * FROM sentences;
The two siblings, bella and charlie, have the same size: standard
The two siblings, ace and ginger, have the same size: toy

Hint: First, create a helper table containing the names of each pair of siblings. This will make comparing the sizes of siblings when constructing the main table easier. Make sure to not pair a child with themselves and do not include duplicate pairs.

Hint: If you join a table with itself, use AS within the FROM clause to give each table an alias.

Hint: In order to concatenate two strings into one, use the || operator, e.g. SELECT "hello" || "world"; will return helloworld.

Use Ok to test your code:

python3 ok -q sentences

Roughly speaking, there are two tasks we need to solve here:

Figure out which dogs are siblings

A sibling is someone you share a parent with. This will probably involve the parents table.

It might be tempting to join this with dogs, but there isn't any extra information provided by a dogs table that we need at this time. Furthermore, we still need information on sibling for a given dog, since the parents table just associates each dog to a parent.

The next step, therefore, is to match all children to all other children by joining the parents table to itself. The only rows here that make sense are the rows that represent sibling relationships since they share the same parent.

Remember that we want to avoid duplicates! If dog A and B are siblings, we don't want both A/B and B/A to appear in the final result. We also definitely don't want A/A to be a sibling pair. Enforcing ordering on the sibling names ensures that we don't have either issue.

Construct sentences based on sibling information

After determining the siblings, constructing the sentences just requires us to get the size of each sibling. We could join on the dogs and sizes tables as we did in an earlier problem, but there's no need to redo that work. Instead, we'll reuse our size_of_dogs table to figure out the size of each sibling in each pair.

Q4: Low Variance

We want to create a table that contains the height range (defined as the difference between maximum and minimum height) of all dogs that share a fur type. However, we'll only consider fur types where each dog with that fur type is within 30% of the average height of all dogs with that fur type; we call this the low variance criterion.

For example, if the average height for short-haired dogs is 10, then in order to be included in our output, all dogs with short hair must have a height of at most 13 and at least 7 (inclusive).

Hint: MIN, MAX, and AVG will be useful here.

Hint: You may want to first find the average height and make sure that:
* There are no heights smaller than 0.7 (i.e. 70%) of the average.
* There are no heights greater than 1.3 (i.e. 130%) of the average.

-- Height range for each fur type where all of the heights differ by no more than 30% from the average height
CREATE TABLE low_variance AS
  SELECT fur, MAX(height) - MIN(height) AS height_range FROM dogs GROUP BY fur
      HAVING MIN(height) >= .7 * AVG(height) AND MAX(height) <= 1.3 * AVG(height);

Your output should have two columns, in this order: the fur type and the height_range for the fur types that meet this criteria. For the example tables, it should look like this:

+------------+--------------+
| fur        | height_range |
+------------+--------------+
| Curly      | 1            |
+------------+--------------+

The average height of long-haired dogs is 39.7, so the low variance criterion requires the height of each long-haired dog to be between 27.8 and 51.6. However, ace is a long-haired dog with height 26, which is outside this range. For short-haired dogs, bella falls outside the valid range (check!). Thus, neither short nor long haired dogs are included in the output. There are two curly haired dogs: finn with height 32 and hank with height 31. This gives a height range of 1.

Use Ok to test your code:

python3 ok -q low_variance

Submit Assignment

Submit this assignment by uploading any files you've edited to the appropriate Pensieve assignment. Lab 00 has detailed instructions.

Make sure to submit hw10.sql to the autograder!

Exam Practice

The following are some SQL exam problems from previous semesters that you may find useful as additional exam practice.