Lecture 03-1: SQL Joins

DATA 503: Fundamentals of Data Engineering

Lucas P. Cordova, Ph.D.
lpcordova@willamette.edu

Willamette University

January 26, 2026

Joining Tables

Blockbuster Bend is the final video rental store. Today we connect data across the store’s database.

Blockbuster Bend Database

Load the Database

Run these commands to load the data into PostgreSQL:

createdb blockbuster
psql -U postgres -d blockbuster -f blockbuster-schema.sql
psql -U postgres -d blockbuster -f blockbuster-data.sql
CREATE DATABASE
CREATE TABLE
...
INSERT 0 1
...

ERD Overview

The ERD shows how tables connect through primary and foreign keys.

Crows-Foot Notation

Crows-Foot Notation

Key Tables for Joins

The Blockbuster Bend database contains several interconnected table groups:

  • film - Movie titles and details
  • language - Available languages
  • category - Film genres (Action, Comedy, etc.)
  • film_category - Links films to categories
  • actor - Actor names
  • film_actor - Links actors to films
  • customer - Customer information
  • staff - Employee records
  • inventory - Physical copies of films
  • rental - Rental transactions
  • payment - Payment records
  • store - Store locations
  • address - Street addresses
  • city - City names
  • country - Country names

Why Joins Matter

Business Questions Require Multiple Tables

Most real questions span multiple tables:

  • Which films were rented last month and by whom?
  • Which customers have never rented a film?
  • Which categories generate the most revenue at each store?
  • Which actors appear in Action films?

Joins let us answer these questions by connecting tables.

The Problem with Separate Tables

Consider these two tables from our database:

film_id title language_id
1 ACADEMY DINOSAUR 1
2 ACE GOLDFINGER 1
3 ADAPTATION HOLES 2
language_id name
1 English
2 Italian
3 Japanese
4 Mandarin
5 French
6 German

How do we see film titles with their language names in a single result?

Keys Enable Joins

Primary Key: Uniquely identifies each row in a table (e.g., language_id in language)

Foreign Key: References a primary key in another table (e.g., language_id in film)

Inner Joins

Inner Join Concept

An inner join returns only rows where the join condition is satisfied in both tables.

film language Result

Only matching rows are returned

  • Films with a valid language_id
  • Languages that have films assigned
  • Unmatched rows are excluded

Inner Join with Sample Data

Let’s trace through an inner join step by step:

film_id title language_id
1 ACADEMY DINOSAUR 1
2 ACE GOLDFINGER 1
3 ADAPTATION HOLES 2
4 AFFAIR PREJUDICE 6
5 AFRICAN EGG 4
language_id name
1 English
2 Italian
3 Japanese
4 Mandarin
5 French
6 German

The database compares each film’s language_id to the language table:

  • ACADEMY DINOSAUR (language_id=1) matches English
  • ACE GOLDFINGER (language_id=1) matches English
  • ADAPTATION HOLES (language_id=2) matches Italian
  • AFFAIR PREJUDICE (language_id=6) matches German
  • AFRICAN EGG (language_id=4) matches Mandarin
title name
ACADEMY DINOSAUR English
ACE GOLDFINGER English
ADAPTATION HOLES Italian
AFFAIR PREJUDICE German
AFRICAN EGG Mandarin

Note: Japanese and French have no films, so they do not appear.

SQL Query Structure

SQL Query Structure

Basic Inner Join Syntax

SELECT
    f.title,
    l.name AS language
FROM film AS f
JOIN language AS l
    ON f.language_id = l.language_id
ORDER BY f.title
LIMIT 5;
title                | language
---------------------+----------
ACADEMY DINOSAUR     | English
ACE GOLDFINGER       | English
ADAPTATION HOLES     | Italian
AFFAIR PREJUDICE     | German
AFRICAN EGG          | Mandarin
  • JOIN is shorthand for INNER JOIN
  • AS f and AS l create table aliases
  • ON specifies the join condition
  • We can reference columns from both tables

Table Aliases Keep Joins Readable

Without aliases, queries become verbose and harder to read:

SELECT
    f.title,
    f.release_year,
    l.name AS language
FROM film AS f
JOIN language AS l
    ON f.language_id = l.language_id
WHERE f.rating = 'PG'
ORDER BY f.title
LIMIT 3;
SELECT
    film.title,
    film.release_year,
    language.name AS language
FROM film
JOIN language
    ON film.language_id = language.language_id
WHERE film.rating = 'PG'
ORDER BY film.title
LIMIT 3;
title              | release_year | language
-------------------+--------------+----------
ACADEMY DINOSAUR   | 2012         | English
AGENT TRUMAN       | 2010         | English
ALASKA PHANTOM     | 2016         | English

Multi-Table Joins: Film to Category

Films connect to categories through the film_category bridge table:

SELECT
    f.title,
    c.name AS category
FROM film AS f
JOIN film_category AS fc
    ON f.film_id = fc.film_id
JOIN category AS c
    ON fc.category_id = c.category_id
WHERE c.name = 'Action'
ORDER BY f.title
LIMIT 5;
| title            | category |
| ---------------- | -------- |
| ACE GOLDFINGER   | Action   |
| ADAPTATION HOLES | Action   |
| AIRPLANE SIERRA  | Action   |
| ALASKA PHANTOM   | Action   |
| ANGELS LIFE      | Action   |
  • First join connects film to film_category
  • Second join connects film_category to category
  • The bridge table handles the many-to-many relationship

Film to Actor Join

The film_actor bridge table connects films and actors:

SELECT
    f.title,
    a.first_name,
    a.last_name
FROM film AS f
JOIN film_actor AS fa
    ON f.film_id = fa.film_id
JOIN actor AS a
    ON fa.actor_id = a.actor_id
WHERE f.title = 'ACADEMY DINOSAUR'
ORDER BY a.last_name, a.first_name;
| title            | first_name | last_name |
| ---------------- | ---------- | --------- |
| ACADEMY DINOSAUR | JOHNNY     | CAGE      |
| ACADEMY DINOSAUR | ROCK       | DUKAKIS   |
| ACADEMY DINOSAUR | CHRISTIAN  | GABLE     |
| ACADEMY DINOSAUR | PENELOPE   | GUINESS   |
| ACADEMY DINOSAUR | MARY       | KEITEL    |
| ACADEMY DINOSAUR | OPRAH      | KILMER    |
| ACADEMY DINOSAUR | WARREN     | NOLTE     |
| ACADEMY DINOSAUR | SANDRA     | PECK      |
| ACADEMY DINOSAUR | MENA       | TEMPLE    |
| ACADEMY DINOSAUR | LUCILLE    | TRACY     |

Practice: Customer Location Join

Write a query that returns:

  • customer_id
  • first_name
  • last_name
  • city

Join customer to address to city. Order by last_name, then first_name. Limit to 5 rows.

SELECT
    c.customer_id,
    c.first_name,
    c.last_name,
    ci.city
FROM customer AS c
JOIN address AS a
    ON c.address_id = a.address_id
JOIN city AS ci
    ON a.city_id = ci.city_id
ORDER BY c.last_name, c.first_name
LIMIT 5;
| customer_id | first_name | last_name | city                    |
| ----------- | ---------- | --------- | ----------------------- |
| 505         | RAFAEL     | ABNEY     | Talavera                |
| 504         | NATHANIEL  | ADAM      | Joliet                  |
| 36          | KATHLEEN   | ADAMS     | Arak                    |
| 96          | DIANA      | ALEXANDER | Augusta-Richmond County |
| 470         | GORDON     | ALLARD    | Hodeida                 |

Filtering: ON vs WHERE

Use ON for join conditions and WHERE for row filters:

SELECT
    f.title,
    f.rating,
    c.name AS category
FROM film AS f
JOIN film_category AS fc
    ON f.film_id = fc.film_id
JOIN category AS c
    ON fc.category_id = c.category_id
WHERE f.rating = 'PG'
    AND c.name = 'Comedy'
ORDER BY f.title
LIMIT 5;
| title            | rating | category |
| ---------------- | ------ | -------- |
| ALI FOREVER      | PG     | Comedy   |
| BLACKOUT PRIVATE | PG     | Comedy   |
| CAROL TEXAS      | PG     | Comedy   |
| CHARADE DUFFEL   | PG     | Comedy   |
| DISCIPLE MOTHER  | PG     | Comedy   |
  • ON defines how tables relate
  • WHERE filters the joined result
  • Putting filters in ON can produce unexpected results with outer joins

Outer Joins

When Inner Joins Are Not Enough

Inner joins exclude rows without matches. Sometimes we need to see unmatched rows:

  • Which languages have no films?
  • Which customers have never rented?
  • Which inventory items have never been rented?

Outer joins preserve unmatched rows.

Left Join Concept

A left join keeps all rows from the left table, even without matches.

All A B LEFT JOIN

All left table rows returned

  • Matching rows show data from both tables
  • Non-matching rows show NULL for right table columns

Left Join with Film and Inventory Data

Let’s say that we wish to list all films that we do not have a copy of in our inventory. In other words, we want to find all films that are not in the inventory table.

film (left table)

film_id title
1 ACADEMY DINOSAUR
2 ACE GOLDFINGER
3 ADAPTATION HOLES

… (1000 rows)

inventory (right table)

inventory_id film_id
1 1
2 2
3 3
4 4

… (4581 rows)

SELECT
  f.film_id,
  f.title,
  i.inventory_id
FROM film f
LEFT JOIN inventory i 
  ON i.film_id = f.film_id
WHERE i.inventory_id IS NULL
ORDER BY f.title;
film_id title inventory_id
14 ALICE FANTASIA NULL
33 APOLLO TEEN NULL
36 ARGONAUTS TOWN NULL
38 ARK RIDGEMONT NULL

… (42 rows)

  • LEFT JOIN keeps all films
  • Films without a matching inventory get NULL values
  • WHERE i.inventory_id IS NULL filters to only unmatched rows

Right Join Concept

A right join keeps all rows from the right table, even without matches.

A All B RIGHT JOIN

All right table rows returned

  • Equivalent to a left join with tables swapped
  • Less common in practice

Full Outer Join Concept

A full outer join keeps all rows from both tables.

All A All B FULL OUTER JOIN

All rows from both tables

  • Unmatched left rows show NULL for right columns
  • Unmatched right rows show NULL for left columns
  • Useful for finding all mismatches

What Would Left Join and Right Join Look Like for these Tables?

Table A

id value_a
1 Apple
2 Banana
3 Cherry

Table B

id value_b
2 Two
3 Three
4 Four

Full Outer Join Example

Table A

id value_a
1 Apple
2 Banana
3 Cherry

Table B

id value_b
2 Two
3 Three
4 Four
a.id value_a b.id value_b
1 Apple NULL NULL
2 Banana 2 Two
3 Cherry 3 Three
NULL NULL 4 Four 
-- Rows only in A
WHERE b.id IS NULL

-- Rows only in B
WHERE a.id IS NULL

-- Rows only in one table (not both)
WHERE a.id IS NULL OR b.id IS NULL

Outer Join Comparison Summary

Join Type Left Table Right Table Use Case
INNER JOIN Only matched Only matched Standard queries
LEFT JOIN All rows Only matched Find unmatched in right
RIGHT JOIN Only matched All rows Find unmatched in left
FULL OUTER JOIN All rows All rows Find all unmatched

Practice: Unrented Inventory

Find inventory items that have never been rented.

Return:

  • inventory_id
  • film_id
  • title
  • store_id

Order by store_id, then inventory_id. Limit to 5 rows.

SELECT
    i.inventory_id,
    i.film_id,
    f.title,
    i.store_id
FROM inventory AS i
LEFT JOIN rental AS r
    ON i.inventory_id = r.inventory_id
JOIN film AS f
    ON i.film_id = f.film_id
WHERE r.rental_id IS NULL
ORDER BY i.store_id, i.inventory_id
LIMIT 5;
inventory_id | film_id | title             | store_id
-------------+---------+-------------------+----------
1            | 1       | ACADEMY DINOSAUR  | 1
2            | 1       | ACADEMY DINOSAUR  | 1
...

Cross Joins

Cross Join Concept

A cross join (Cartesian product) returns every combination of rows from both tables.

3 rows 4 rows Result: 3 x 4 = 12 rows

No join condition

  • Every row in A pairs with every row in B
  • Result size = rows(A) x rows(B)
  • Can produce very large results

Cross Join with Sample Data

store

store_id
1
2

category (partial)

category_id name
1 Action
5 Comedy
7 Drama
store_id name
1 Action
1 Comedy
1 Drama
2 Action
2 Comedy
2 Drama

Every store paired with every category (2 x 3 = 6 rows).

Cross Join Use Case: Store-Category Grid

Generate a planning grid for all store-category combinations:

SELECT
    s.store_id,
    c.name AS category,
    0 AS planned_inventory
FROM store AS s
CROSS JOIN category AS c
WHERE s.store_id IN (1, 2)
ORDER BY s.store_id, c.name
LIMIT 10;
store_id | category   | planned_inventory
---------+------------+------------------
1        | Action     | 0
1        | Animation  | 0
1        | Children   | 0
1        | Classics   | 0
1        | Comedy     | 0
1        | Documentary| 0
1        | Drama      | 0
1        | Family     | 0
1        | Foreign    | 0
1        | Games      | 0
  • Inventory planning templates
  • Report scaffolding
  • Generating test data
  • Date/category combinations for analysis

Cross Join Caution

Cross joins can create enormous result sets:

Table A Rows Table B Rows Result Rows
100 100 10,000
1,000 1,000 1,000,000
10,000 10,000 100,000,000

Always use WHERE or LIMIT when exploring cross joins.

Self Joins

Self Join Concept

A self join joins a table to itself. This is useful when:

  • Comparing rows within the same table
  • Finding hierarchical relationships
  • Detecting duplicates or related records

Finding Customers with Same Last Name

SELECT
    c1.customer_id AS customer_1,
    c1.first_name AS first_1,
    c1.last_name,
    c2.customer_id AS customer_2,
    c2.first_name AS first_2
FROM customer AS c1
JOIN customer AS c2
    ON c1.last_name = c2.last_name
    AND c1.customer_id < c2.customer_id
ORDER BY c1.last_name, c1.customer_id
LIMIT 5;
customer_1 | first_1   | last_name | customer_2 | first_2
-----------+-----------+-----------+------------+---------
318        | BRIAN     | WYMAN     | 412        | JOHN
...
  • Table aliased as both c1 and c2
  • c1.customer_id < c2.customer_id prevents duplicate pairs
  • Without this condition, we would get (A,B) and (B,A)

Self Join for Hierarchical Data

Self joins work well for parent-child relationships:

-- If staff had a reports_to column:
SELECT
    e.first_name AS employee,
    m.first_name AS manager
FROM staff AS e
JOIN staff AS m
    ON e.reports_to = m.staff_id;
  • Organization charts
  • Category hierarchies
  • Reply threads in forums

Multi-Table Join Patterns

The Rental Transaction Chain

Tracking a rental requires joining multiple tables:

Complete Rental Query

SELECT
    c.first_name || ' ' || c.last_name AS customer,
    f.title,
    r.rental_date::date AS rented,
    r.return_date::date AS returned,
    p.amount
FROM customer AS c
JOIN rental AS r
    ON c.customer_id = r.customer_id
JOIN inventory AS i
    ON r.inventory_id = i.inventory_id
JOIN film AS f
    ON i.film_id = f.film_id
JOIN payment AS p
    ON r.rental_id = p.rental_id
ORDER BY r.rental_date DESC
LIMIT 5;
customer          | title              | rented     | returned   | amount
------------------+--------------------+------------+------------+-------
AUSTIN CINTRON    | SOMETHING DUCK     | 2022-07-27 | 2022-08-02 | 4.99
AUSTIN CINTRON    | TITANS JERK        | 2022-07-27 | 2022-08-01 | 4.99
AUSTIN CINTRON    | SUNRISE LEAGUE     | 2022-07-27 | 2022-07-28 | 2.99
...
  1. customer to rental via customer_id
  2. rental to inventory via inventory_id
  3. inventory to film via film_id
  4. rental to payment via rental_id

Actor Filmography Query

SELECT
    a.first_name,
    a.last_name,
    f.title,
    f.release_year,
    c.name AS category
FROM actor AS a
JOIN film_actor AS fa
    ON a.actor_id = fa.actor_id
JOIN film AS f
    ON fa.film_id = f.film_id
JOIN film_category AS fc
    ON f.film_id = fc.film_id
JOIN category AS c
    ON fc.category_id = c.category_id
WHERE a.last_name = 'GUINESS'
ORDER BY f.release_year, f.title;
first_name | last_name | title              | release_year | category
-----------+-----------+--------------------+--------------+----------
PENELOPE   | GUINESS   | ACADEMY DINOSAUR   | 2012         | Documentary
PENELOPE   | GUINESS   | ANACONDA CONFESSIONS| 2020        | Animation
...

Five tables joined through their foreign key relationships.

Join on Multiple Columns

Sometimes joins need multiple columns to match correctly:

SELECT
    p.payment_id,
    p.customer_id,
    p.rental_id,
    p.amount,
    r.rental_date::date
FROM payment AS p
JOIN rental AS r
    ON p.rental_id = r.rental_id
    AND p.customer_id = r.customer_id
WHERE p.customer_id = 1
ORDER BY r.rental_date
LIMIT 5;
payment_id | customer_id | rental_id | amount | rental_date
-----------+-------------+-----------+--------+------------
17503      | 1           | 76        | 2.99   | 2022-05-25
17504      | 1           | 573       | 0.99   | 2022-05-28
17505      | 1           | 1185      | 5.99   | 2022-06-15
...
  • Composite keys
  • Data validation
  • Ensuring correct matches in denormalized data

Common Pitfalls

Ambiguous Column Names

When two tables have the same column name:

SELECT
    customer_id,  -- Ambiguous!
    first_name,
    last_name
FROM customer
JOIN rental
    ON customer.customer_id = rental.customer_id;
ERROR: column reference "customer_id" is ambiguous
SELECT
    c.customer_id,  -- Qualified with alias
    c.first_name,
    c.last_name
FROM customer AS c
JOIN rental AS r
    ON c.customer_id = r.customer_id;

Missing Join Conditions

Forgetting the ON clause creates a cross join:

-- This creates a cross join!
SELECT f.title, c.name
FROM film AS f, category AS c
LIMIT 5;

Every film paired with every category (1000 x 16 = 16,000 rows).

Always use explicit JOIN ... ON syntax:

SELECT f.title, c.name
FROM film AS f
JOIN film_category AS fc ON f.film_id = fc.film_id
JOIN category AS c ON fc.category_id = c.category_id
LIMIT 5;

Cartesian Explosion

Adding more tables can multiply result sizes:

Prevention:

  • Check join conditions carefully
  • Use COUNT(*) before SELECT *
  • Add LIMIT during development

Join Verification Checklist

Before running a complex join:

  1. Are all join conditions specified?
  2. Are column references qualified with aliases?
  3. Is this an inner or outer join?
  4. Could any join create a Cartesian product?
  5. Have I tested with LIMIT first?

Practice Problems

Practice 1: Store Revenue by Category

For each store, find total revenue by film category.

Return:

  • store_id
  • category
  • total_revenue

Order by store_id, then total_revenue descending.

SELECT
    i.store_id,
    c.name AS category,
    SUM(p.amount) AS total_revenue
FROM payment AS p
JOIN rental AS r ON p.rental_id = r.rental_id
JOIN inventory AS i ON r.inventory_id = i.inventory_id
JOIN film AS f ON i.film_id = f.film_id
JOIN film_category AS fc ON f.film_id = fc.film_id
JOIN category AS c ON fc.category_id = c.category_id
GROUP BY i.store_id, c.name
ORDER BY i.store_id, total_revenue DESC;
store_id | category    | total_revenue
---------+-------------+--------------
1        | Sports      | 4892.19
1        | Sci-Fi      | 4756.98
1        | Animation   | 4656.30
...

Practice 2: Actors Without Films

Find any actors who have no films in the database.

Return:

  • actor_id
  • first_name
  • last_name

Order by last_name, first_name.

SELECT
    a.actor_id,
    a.first_name,
    a.last_name
FROM actor AS a
LEFT JOIN film_actor AS fa
    ON a.actor_id = fa.actor_id
WHERE fa.film_id IS NULL
ORDER BY a.last_name, a.first_name;
actor_id | first_name | last_name
---------+------------+----------
(0 rows - all actors have films in this database)

Practice 3: Customer Rental History

Create a rental history for customer MARY SMITH (customer_id = 1).

Return:

  • rental_date
  • title
  • category
  • amount

Order by rental_date descending. Limit to 10 rows.

SELECT
    r.rental_date::date,
    f.title,
    c.name AS category,
    p.amount
FROM customer AS cu
JOIN rental AS r ON cu.customer_id = r.customer_id
JOIN payment AS p ON r.rental_id = p.rental_id
JOIN inventory AS i ON r.inventory_id = i.inventory_id
JOIN film AS f ON i.film_id = f.film_id
JOIN film_category AS fc ON f.film_id = fc.film_id
JOIN category AS c ON fc.category_id = c.category_id
WHERE cu.customer_id = 1
ORDER BY r.rental_date DESC
LIMIT 10;

Key Takeaways

Join Type Summary

Join Type Returns NULL Handling
INNER JOIN Only matching rows No NULLs from join
LEFT JOIN All left + matched right NULLs for unmatched right
RIGHT JOIN All right + matched left NULLs for unmatched left
FULL OUTER JOIN All rows from both NULLs for unmatched on both sides
CROSS JOIN All combinations No join condition
SELF JOIN Table joined to itself Depends on join type used

Best Practices

  1. Always use table aliases for readability
  2. Qualify all column references to avoid ambiguity
  3. Use explicit JOIN syntax instead of comma-separated tables
  4. Put join conditions in ON, filters in WHERE
  5. Test with LIMIT before running full queries
  6. Verify row counts to catch Cartesian products

Exit Ticket

Write a query that answers:

Which films were rented in 2022 by customers from store 1?

Return the customer name, film title, and rental date.

Be ready to share your join path and key columns.

References

References

  1. Forta, B. (2024). SQL in 10 Minutes a Day (6th ed.). Addison-Wesley.
  2. PostgreSQL Documentation. SELECT - Joins. https://www.postgresql.org/docs/current/queries-table-expressions.html
  3. Silberschatz, A., Korth, H., & Sudarshan, S. (2019). Database System Concepts (7th ed.). McGraw-Hill.