Why table design affects performance in MySQL - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select all columns from the employees table.

MySQL

SELECT [1] FROM employees;

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

A*

BALL

Ccolumns

Deverything

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ALL' instead of '*'

Writing 'columns' or 'everything' which are not valid SQL keywords

The asterisk (*) selects all columns from the table.

2fill in blank

medium

Complete the code to create an index on the 'email' column to improve search speed.

MySQL

CREATE INDEX idx_email ON users([1]);

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

Ausername

Bpassword

Cemail

Did

Attempts:

3 left

💡 Hint

Common Mistakes

Indexing columns that are rarely searched

Indexing large text columns unnecessarily

Creating an index on the 'email' column helps speed up queries filtering by email.

3fill in blank

hard

Fix the error in the query to select employees with salary greater than 50000.

MySQL

SELECT * FROM employees WHERE salary [1] 50000;

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

A=

B>

C<

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' which selects only salaries equal to 50000

Using '<' which selects salaries less than 50000

The '>' operator selects salaries greater than 50000.

4fill in blank

hard

Fill both blanks to create a table with an auto-incrementing primary key.

MySQL

CREATE TABLE products (id [1] [2] PRIMARY KEY, name VARCHAR(100));

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

AINT

BPRIMARY KEY

CAUTO_INCREMENT

DVARCHAR

Attempts:

3 left

💡 Hint

Common Mistakes

Using VARCHAR for the id column

Forgetting AUTO_INCREMENT for unique IDs

The 'id' column should be an INT and AUTO_INCREMENT to generate unique IDs automatically.

5fill in blank

hard

Fill all three blanks to select the average salary grouped by department.

MySQL

SELECT [1], AVG([2]) FROM employees GROUP BY [3];

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

Adepartment

Bsalary

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by name instead of department

Averaging the wrong column

We select the department, calculate the average salary, and group results by department.

Practice

1. Why does using proper data types in a MySQL table improve performance?

easy

A. It reduces the amount of storage space and speeds up data retrieval.

B. It makes the table look nicer in the database.

C. It allows more users to connect at the same time.

D. It automatically creates backups of the data.

Solution

1. Step 1: Understand data types and storage

   Choosing the right data type means the database uses only the needed space for each value.

2. Step 2: Impact on speed

   Smaller data sizes mean faster reading and writing, improving performance.

3. Final Answer:

   It reduces the amount of storage space and speeds up data retrieval. -> Option A

4. Quick Check:

   Proper data types = less space + faster queries [OK]

Hint: Smaller data types mean faster access and less storage [OK]

Common Mistakes:

• Thinking data types only affect appearance
• Believing data types control user connections
• Confusing data types with backup features

2. Which of the following is the correct way to define a primary key in a MySQL table?

easy

A. CREATE TABLE users (id INT PRIMARY KEY);

B. CREATE TABLE users (id INT PRIMARY, KEY);

C. CREATE TABLE users (id INT, PRIMARY(id));

D. CREATE TABLE users (id INT, KEY PRIMARY);

Solution

1. Step 1: Review MySQL primary key syntax

   The correct syntax places PRIMARY KEY immediately after the column type.

2. Step 2: Check each option

   CREATE TABLE users (id INT PRIMARY KEY); uses 'id INT PRIMARY KEY' which is valid syntax.

3. Final Answer:

   CREATE TABLE users (id INT PRIMARY KEY); -> Option A

4. Quick Check:

   Primary key syntax = 'column type PRIMARY KEY' [OK]

Hint: Primary key goes right after column type [OK]

Common Mistakes:

• Separating PRIMARY and KEY keywords incorrectly
• Placing KEY before PRIMARY
• Using comma incorrectly between keywords

3. Given the table products(id INT PRIMARY KEY, name VARCHAR(50), price DECIMAL(5,2)), what will be the result of this query?
SELECT * FROM products WHERE price < 10.00;

medium

A. No rows will be returned because price is stored as text.

B. Syntax error because of the decimal comparison.

C. All products with price less than 10.00 will be listed.

D. Only products with price exactly 10.00 will be shown.

Solution

1. Step 1: Understand the table schema

   The price column is DECIMAL(5,2), a numeric type suitable for decimal comparisons.

2. Step 2: Analyze the query condition

   The query selects rows where price is less than 10.00, which is valid and will return matching rows.

3. Final Answer:

   All products with price less than 10.00 will be listed. -> Option C

4. Quick Check:

   DECIMAL supports numeric comparisons correctly [OK]

Hint: DECIMAL columns compare numbers, not text [OK]

Common Mistakes:

• Assuming DECIMAL is text type
• Thinking decimal comparisons cause syntax errors
• Confusing less than with equal to

4. A table has no indexes and is very slow when searching by a column. What is the best fix?

medium

A. Use SELECT * instead of SELECT column.

B. Change the column data type to TEXT.

C. Delete half of the rows to reduce size.

D. Add an index on the column used in the search.

Solution

1. Step 1: Identify the cause of slow search

   Without indexes, MySQL must scan all rows, causing slow searches.

2. Step 2: Apply indexing to improve speed

   Adding an index on the searched column lets MySQL find rows faster.

3. Final Answer:

   Add an index on the column used in the search. -> Option D

4. Quick Check:

   Indexes speed up searches [OK]

Hint: Index columns you search often for faster queries [OK]

Common Mistakes:

• Changing data type without indexing
• Deleting data instead of optimizing
• Using SELECT * slows queries, but not main cause

5. You have a table with many duplicate rows causing slow queries and large storage. What table design change will best improve performance?

hard

A. Increase the size of all columns to store more data.

B. Add a UNIQUE constraint to prevent duplicates and clean existing data.

C. Remove all indexes to speed up inserts.

D. Change all VARCHAR columns to TEXT for flexibility.

Solution

1. Step 1: Identify the problem with duplicates

   Duplicates cause extra storage and slow queries because more rows must be scanned.

2. Step 2: Use UNIQUE constraint to fix duplicates

   Adding UNIQUE prevents duplicates and cleaning data reduces size and speeds queries.

3. Final Answer:

   Add a UNIQUE constraint to prevent duplicates and clean existing data. -> Option B

4. Quick Check:

   Unique constraints remove duplicates and improve performance [OK]

Hint: Use UNIQUE keys to avoid duplicates and speed queries [OK]

Common Mistakes:

• Thinking bigger columns improve speed
• Removing indexes slows queries
• Using TEXT instead of VARCHAR wastes space