Join order and performance impact in SQL - Mini Project: Build & Apply

Join Order and Performance Impact

📖 Scenario: You work as a data analyst for an online bookstore. The database has two tables: books and sales. You want to find the titles of books sold along with the total quantity sold. You will explore how changing the order of joins can affect query performance.

🎯 Goal: Build SQL queries that join the books and sales tables in different orders and observe the impact on performance.

📋 What You'll Learn

Create the books table with columns book_id (integer), title (text), and author (text).

Create the sales table with columns sale_id (integer), book_id (integer), and quantity (integer).

Insert the specified sample data into both tables.

Write a SQL query joining books to sales to get book titles and total quantity sold.

Write a SQL query joining sales to books to get the same result.

Compare the two queries to understand join order impact.

💡 Why This Matters

🌍 Real World

Understanding join order helps optimize database queries in real business applications like sales reporting.

💼 Career

Database developers and analysts often tune queries by changing join order to improve speed and reduce resource use.

1

Create tables and insert data

Create the books table with columns book_id (integer), title (text), and author (text). Then create the sales table with columns sale_id (integer), book_id (integer), and quantity (integer). Insert these exact rows into books: (1, 'The Great Gatsby', 'F. Scott Fitzgerald'), (2, '1984', 'George Orwell'), (3, 'To Kill a Mockingbird', 'Harper Lee'). Insert these exact rows into sales: (101, 1, 3), (102, 2, 5), (103, 1, 2), (104, 3, 4).

SQL

-- Create tables and insert data as instructed
-- Your code here

Hint

Use CREATE TABLE statements for both tables. Then use INSERT INTO with multiple rows for data.

2

Set up aggregation for total quantity

Create a SQL snippet that defines an aggregation to calculate total quantity sold per book. Use SUM(quantity) AS total_quantity in your SELECT statement. This will be used in the next step.

SQL

CREATE TABLE books (
  book_id INTEGER,
  title TEXT,
  author TEXT
);

CREATE TABLE sales (
  sale_id INTEGER,
  book_id INTEGER,
  quantity INTEGER
);

INSERT INTO books (book_id, title, author) VALUES
  (1, 'The Great Gatsby', 'F. Scott Fitzgerald'),
  (2, '1984', 'George Orwell'),
  (3, 'To Kill a Mockingbird', 'Harper Lee');

INSERT INTO sales (sale_id, book_id, quantity) VALUES
  (101, 1, 3),
  (102, 2, 5),
  (103, 1, 2),
  (104, 3, 4);

-- Add SUM(quantity) AS total_quantity to use in next query
-- Your code here

Hint

Use SUM(quantity) AS total_quantity in your SELECT clause to prepare for aggregation.

3

Write query joining books to sales

Write a SQL query that joins books to sales using books.book_id = sales.book_id. Select books.title and the total quantity sold as total_quantity. Group the results by books.title. Use the aggregation SUM(quantity) AS total_quantity.

SQL

CREATE TABLE books (
  book_id INTEGER,
  title TEXT,
  author TEXT
);

CREATE TABLE sales (
  sale_id INTEGER,
  book_id INTEGER,
  quantity INTEGER
);

INSERT INTO books (book_id, title, author) VALUES
  (1, 'The Great Gatsby', 'F. Scott Fitzgerald'),
  (2, '1984', 'George Orwell'),
  (3, 'To Kill a Mockingbird', 'Harper Lee');

INSERT INTO sales (sale_id, book_id, quantity) VALUES
  (101, 1, 3),
  (102, 2, 5),
  (103, 1, 2),
  (104, 3, 4);

-- Write query joining books to sales
-- Your code here

Hint

Use JOIN with books.book_id = sales.book_id. Select books.title and SUM(quantity) AS total_quantity. Group by books.title.

4

Write query joining sales to books

Write a SQL query that joins sales to books using sales.book_id = books.book_id. Select books.title and the total quantity sold as total_quantity. Group the results by books.title. Use the aggregation SUM(quantity) AS total_quantity. This query reverses the join order from the previous step.

SQL

CREATE TABLE books (
  book_id INTEGER,
  title TEXT,
  author TEXT
);

CREATE TABLE sales (
  sale_id INTEGER,
  book_id INTEGER,
  quantity INTEGER
);

INSERT INTO books (book_id, title, author) VALUES
  (1, 'The Great Gatsby', 'F. Scott Fitzgerald'),
  (2, '1984', 'George Orwell'),
  (3, 'To Kill a Mockingbird', 'Harper Lee');

INSERT INTO sales (sale_id, book_id, quantity) VALUES
  (101, 1, 3),
  (102, 2, 5),
  (103, 1, 2),
  (104, 3, 4);

-- Write query joining sales to books
-- Your code here

Hint

Use JOIN with sales.book_id = books.book_id. Select books.title and SUM(quantity) AS total_quantity. Group by books.title.

Practice

1. Which statement best describes the impact of join order on SQL query results?

easy

A. Join order affects query speed but not the final result data.

B. Join order changes the final result data returned by the query.

C. Join order always causes syntax errors if incorrect.

D. Join order determines the number of columns in the result.

Solution

1. Step 1: Understand join order effect on data

   Join order does not change the rows or columns returned if the joins are correct and conditions are the same.

2. Step 2: Understand join order effect on performance

   Join order can affect how fast the database processes the query but not the actual data returned.

3. Final Answer:

   Join order affects query speed but not the final result data. -> Option A

4. Quick Check:

   Join order impacts speed, not data [OK]

Hint: Join order changes speed, not output data [OK]

Common Mistakes:

• Thinking join order changes the result rows
• Confusing join order with join type
• Assuming join order causes syntax errors

2. Which SQL join syntax is correct for joining two tables employees and departments on department_id?

easy

A. SELECT * FROM employees JOIN departments USING employees.department_id = departments.department_id;

B. SELECT * FROM employees JOIN departments WHERE employees.department_id = departments.department_id;

C. SELECT * FROM employees, departments ON employees.department_id = departments.department_id;

D. SELECT * FROM employees JOIN departments ON employees.department_id = departments.department_id;

Solution

1. Step 1: Identify correct JOIN syntax

   The correct syntax uses JOIN ... ON condition to specify join keys.

2. Step 2: Check each option

   SELECT * FROM employees JOIN departments ON employees.department_id = departments.department_id; uses JOIN ... ON correctly. USING with a full equality condition is invalid as USING expects column names only. JOIN ... WHERE is invalid syntax for explicit joins. Comma-separated tables with ON is invalid.

3. Final Answer:

   SELECT * FROM employees JOIN departments ON employees.department_id = departments.department_id; -> Option D

4. Quick Check:

   JOIN ... ON is correct syntax [OK]

Hint: Use JOIN ... ON for correct join syntax [OK]

Common Mistakes:

• Using WHERE instead of ON for join condition
• Mixing comma joins with ON clause
• Incorrect USING clause syntax

3. Given tables orders (1000 rows) and customers (10 rows), which join order is likely faster?
Query 1: SELECT * FROM orders JOIN customers ON orders.customer_id = customers.id;
Query 2: SELECT * FROM customers JOIN orders ON customers.id = orders.customer_id;

medium

A. Query 1 is faster because orders is first.

B. Query 2 is faster because customers is first and smaller.

C. Both queries have the same speed always.

D. Query 2 will cause an error due to join order.

Solution

1. Step 1: Analyze table sizes and join order

   Joining smaller tables first often helps performance because fewer rows are processed early.

2. Step 2: Compare queries

   Query 2 starts with the smaller customers table (10 rows), likely reducing intermediate data size and speeding up join.

3. Final Answer:

   Query 2 is faster because customers is first and smaller. -> Option B

4. Quick Check:

   Smaller table first improves speed [OK]

Hint: Join smaller tables first for better speed [OK]

Common Mistakes:

• Assuming join order never affects speed
• Thinking larger table first is always better
• Believing join order causes errors

4. Consider this SQL query:
SELECT * FROM A JOIN B ON A.id = B.a_id JOIN C ON B.id = C.b_id;
It runs very slowly. Which fix can improve performance by changing join order?

medium

A. Remove the join with table C.

B. Add WHERE A.id = B.a_id instead of ON clause.

C. Rewrite as SELECT * FROM C JOIN B ON B.id = C.b_id JOIN A ON A.id = B.a_id;

D. Use CROSS JOIN instead of JOIN.

Solution

1. Step 1: Understand join order impact on performance

   Changing join order to start with smaller or more selective tables can speed up query execution.

2. Step 2: Evaluate options

   Rewriting as SELECT * FROM C JOIN B ON B.id = C.b_id JOIN A ON A.id = B.a_id; changes join order to start with C, possibly smaller or more filtered, improving speed. Replacing ON with WHERE breaks join syntax. Removing a join loses data. CROSS JOIN explodes row count without filters.

3. Final Answer:

   Rewrite as SELECT * FROM C JOIN B ON B.id = C.b_id JOIN A ON A.id = B.a_id; -> Option C

4. Quick Check:

   Changing join order can improve speed [OK]

Hint: Reorder joins to start with smaller tables [OK]

Common Mistakes:

• Replacing ON with WHERE for joins
• Removing necessary joins
• Using CROSS JOIN without filtering

5. You have three tables: sales (1 million rows), products (1000 rows), and categories (50 rows). To optimize a query joining all three, which join order is best for performance?
Options:
A) sales JOIN products JOIN categories
B) products JOIN sales JOIN categories
C) categories JOIN products JOIN sales
D) sales JOIN categories JOIN products

hard

A. Join categories first, then products, then sales.

B. Join products first, then sales, then categories.

C. Join sales first, then products, then categories.

D. Join sales first, then categories, then products.

Solution

1. Step 1: Analyze table sizes and join order impact

   Joining smaller tables first reduces intermediate result size and speeds up query.

2. Step 2: Evaluate options based on table sizes

   Categories (50 rows) is smallest, then products (1000 rows), then sales (1 million rows). Joining in order categories -> products -> sales is best.

3. Final Answer:

   Join categories first, then products, then sales. -> Option A

4. Quick Check:

   Smallest to largest join order improves speed [OK]

Hint: Join tables from smallest to largest for best speed [OK]

Common Mistakes:

• Joining largest table first slows query
• Ignoring table size in join order
• Assuming join order doesn't affect performance