JOIN performance considerations in MySQL - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to join two tables on the common column.

MySQL

SELECT * FROM orders JOIN customers ON orders.customer_id [1] customers.id;

Drag options to blanks, or click blank then click option'

A=

B!=

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' instead of '=' causes no matching rows.

Using '<' or '>' does not correctly join related rows.

The JOIN condition must use '=' to match rows where the customer IDs are equal.

2fill in blank

medium

Complete the code to select only orders with matching customers using INNER JOIN.

MySQL

SELECT orders.id, customers.name FROM orders [1] JOIN customers ON orders.customer_id = customers.id;

Drag options to blanks, or click blank then click option'

AFULL

BLEFT

CRIGHT

DINNER

Attempts:

3 left

💡 Hint

Common Mistakes

Using LEFT JOIN returns unmatched rows from orders.

Using FULL JOIN is not supported in MySQL.

INNER JOIN returns only rows with matching keys in both tables.

3fill in blank

hard

Fix the error in the JOIN condition to improve performance by using indexed columns.

MySQL

SELECT * FROM products JOIN categories ON products.[1] = categories.id;

Drag options to blanks, or click blank then click option'

Adescription

Bname

Ccategory_id

Dprice

Attempts:

3 left

💡 Hint

Common Mistakes

Joining on non-indexed columns causes slow queries.

Using columns unrelated to the join condition.

Joining on the foreign key 'category_id' which is indexed improves performance.

4fill in blank

hard

Fill both blanks to write a query that uses an index-friendly JOIN and filters results efficiently.

MySQL

SELECT o.id, c.name FROM orders o [1] JOIN customers c ON o.customer_id [2] c.id WHERE c.status = 'active';

Drag options to blanks, or click blank then click option'

AINNER

BLEFT

C=

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using LEFT JOIN returns unmatched rows, slowing query.

Using '!=' in join condition returns incorrect results.

INNER JOIN with '=' on indexed keys returns matching active customers efficiently.

5fill in blank

hard

Fill all three blanks to write a query that joins three tables efficiently with proper conditions.

MySQL

SELECT p.name, c.name, s.quantity FROM products p [1] JOIN categories c ON p.category_id [2] c.id [3] stock s ON p.id = s.product_id WHERE s.quantity > 0;

Drag options to blanks, or click blank then click option'

AINNER

B=

CINNER JOIN

DLEFT

Attempts:

3 left

💡 Hint

Common Mistakes

Using LEFT JOIN may include unwanted rows.

Using wrong join conditions causes slow queries.

Use INNER JOINs with '=' conditions to join tables on indexed keys and filter stock with quantity > 0.

Practice

1. Which factor most improves the performance of a JOIN operation in MySQL?

easy

A. Having indexes on the columns used in the JOIN condition

B. Using SELECT * to retrieve all columns

C. Joining tables without any WHERE clause

D. Using subqueries instead of JOINs

Solution

1. Step 1: Understand JOIN performance basics

   JOINs work faster when MySQL can quickly find matching rows using indexes on the join columns.

2. Step 2: Evaluate options for performance impact

   Using SELECT * or no WHERE clause does not improve speed; subqueries often perform worse than JOINs.

3. Final Answer:

   Having indexes on the columns used in the JOIN condition -> Option A

4. Quick Check:

   Indexes speed up JOINs = B [OK]

Hint: Indexes on join columns speed up JOINs [OK]

Common Mistakes:

• Thinking SELECT * is faster
• Ignoring the need for indexes
• Assuming subqueries are always faster

2. Which of the following is the correct syntax to perform an INNER JOIN between tables orders and customers on customer_id?

easy

A. SELECT * FROM orders JOIN customers ON orders.customer_id = customers.customer_id;

B. SELECT * FROM orders INNER JOIN customers WHERE orders.customer_id = customers.customer_id;

C. SELECT * FROM orders JOIN customers USING (customer_id);

D. SELECT * FROM orders, customers ON orders.customer_id = customers.customer_id;

Solution

1. Step 1: Review INNER JOIN syntax

   The correct INNER JOIN syntax uses JOIN ... ON condition, like in SELECT * FROM orders JOIN customers ON orders.customer_id = customers.customer_id;.

2. Step 2: Check other options for syntax errors

   SELECT * FROM orders INNER JOIN customers WHERE orders.customer_id = customers.customer_id; uses WHERE instead of ON, which is invalid for JOIN syntax. SELECT * FROM orders JOIN customers USING (customer_id); requires parentheses around the column name. SELECT * FROM orders, customers ON orders.customer_id = customers.customer_id; mixes comma join with ON, which is invalid.

3. Final Answer:

   SELECT * FROM orders JOIN customers ON orders.customer_id = customers.customer_id; -> Option A

4. Quick Check:

   JOIN ... ON is correct syntax = A [OK]

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

Common Mistakes:

• Using WHERE instead of ON for JOIN condition
• Mixing comma joins with ON clause
• Incorrect use of USING without parentheses

3. Given the tables:
employees(id, name, dept_id)
departments(id, dept_name)
and the query:

SELECT e.name, d.dept_name FROM employees e JOIN departments d ON e.dept_id = d.id WHERE d.dept_name = 'Sales';

What will be the result if there are 3 employees in 'Sales' department?

medium

A. Empty result because of missing GROUP BY

B. All employees regardless of department

C. Only department names without employee names

D. 3 rows with employee names and 'Sales' as dept_name

Solution

1. Step 1: Analyze the JOIN and WHERE clause

   The JOIN matches employees to departments by dept_id. The WHERE filters to only 'Sales' department.

2. Step 2: Determine output rows

   Since 3 employees belong to 'Sales', the query returns 3 rows with their names and 'Sales' as dept_name.

3. Final Answer:

   3 rows with employee names and 'Sales' as dept_name -> Option D

4. Quick Check:

   JOIN + WHERE filters = 3 matching rows [OK]

Hint: WHERE filters rows after JOIN, so only matching rows show [OK]

Common Mistakes:

• Assuming all employees are returned
• Expecting aggregation without GROUP BY
• Thinking only department names are returned

4. You wrote this query but it runs very slowly:

SELECT * FROM orders o JOIN customers c ON o.customer_id = c.id WHERE c.country = 'USA';

What is the most likely cause of poor performance?

medium

A. JOIN condition uses wrong columns

B. Using SELECT * instead of specific columns

C. Missing index on customers.country column

D. WHERE clause should be in the JOIN condition

Solution

1. Step 1: Identify performance bottleneck

   The WHERE clause filters by c.country = 'USA'. Without an index on country, MySQL scans all customers.

2. Step 2: Evaluate other options

   SELECT * does not cause slow JOINs. JOIN condition is correct. WHERE clause placement is valid.

3. Final Answer:

   Missing index on customers.country column -> Option C

4. Quick Check:

   Index missing on filter column = C [OK]

Hint: Index columns used in WHERE filters for faster JOINs [OK]

Common Mistakes:

• Blaming SELECT * for slow performance
• Misplacing WHERE clause inside JOIN
• Assuming JOIN condition is incorrect

5. You have two large tables: products (1 million rows) and sales (10 million rows). You want to find total sales per product. Which approach improves JOIN performance the most?

hard

A. Use a subquery to get sales totals, then join with products without indexes

B. Create indexes on sales.product_id and products.id, then use INNER JOIN with GROUP BY

C. Join without indexes and filter results in application code

D. Use CROSS JOIN and filter with WHERE clause

Solution

1. Step 1: Understand data size and join keys

   Large tables require indexes on join keys to avoid full scans and speed up matching.

2. Step 2: Evaluate join strategies

   INNER JOIN with proper indexes and GROUP BY efficiently aggregates sales per product. Subqueries without indexes or CROSS JOIN cause slow performance.

3. Final Answer:

   Create indexes on sales.product_id and products.id, then use INNER JOIN with GROUP BY -> Option B

4. Quick Check:

   Indexes + JOIN + GROUP BY = best performance [OK]

Hint: Index join keys and aggregate in SQL, not app [OK]

Common Mistakes:

• Skipping indexes on join columns
• Using CROSS JOIN causing huge intermediate results
• Filtering in application instead of SQL