Index selection strategy in MySQL - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Effect of Index on Query Result

Consider a table employees with columns id, name, and department_id. An index exists on department_id. What will be the output of the following query?

SELECT department_id, COUNT(*) AS total FROM employees GROUP BY department_id;

Assume the table has 3 departments with 5, 7, and 8 employees respectively.

MySQL

SELECT department_id, COUNT(*) AS total FROM employees GROUP BY department_id;

A [{"department_id": 1, "total": 5}, {"department_id": 2, "total": 7}, {"department_id": 3, "total": 8}]

B [{"department_id": 1, "total": 7}, {"department_id": 2, "total": 5}, {"department_id": 3, "total": 8}]

C [{"department_id": 1, "total": 8}, {"department_id": 2, "total": 7}, {"department_id": 3, "total": 5}]

D [{"department_id": 1, "total": 20}]

💡 Hint

Grouping counts employees per department. The index helps speed but does not change counts.

The query groups employees by department_id and counts them. The index on department_id speeds up grouping but does not affect the counts. The counts match the given numbers per department.

🧠 Conceptual

intermediate

Choosing Index for Query Optimization

You have a table orders with columns order_id, customer_id, order_date, and status. Which index would best optimize this query?

SELECT * FROM orders WHERE customer_id = 123 AND status = 'shipped';

A An index on (customer_id, status)

B No index needed

C An index on (status, order_date)

D An index on (order_date)

💡 Hint

Think about which columns appear in the WHERE clause together.

The query filters by customer_id and status. An index on both columns in that order helps the database quickly find matching rows.

📝 Syntax

advanced

Syntax for Creating a Composite Index

Which of the following is the correct syntax to create a composite index on columns last_name and first_name in MySQL?

A CREATE INDEX idx_name ON employees (last_name) AND (first_name);

B CREATE INDEX idx_name ON employees last_name, first_name;

C CREATE INDEX idx_name (last_name, first_name) ON employees;

D CREATE INDEX idx_name ON employees (last_name, first_name);

💡 Hint

Remember the order of clauses in the CREATE INDEX statement.

The correct syntax is CREATE INDEX index_name ON table_name (column1, column2);. Option D follows this exactly.

❓ optimization

advanced

Index Selection for Range Queries

Given a table products with columns product_id, category_id, and price, which index will best optimize this query?

SELECT * FROM products WHERE category_id = 10 AND price BETWEEN 100 AND 200;

A An index on (price, category_id)

B An index on (category_id) only

C An index on (category_id, price)

D An index on (price) only

💡 Hint

Consider the order of columns in the index for equality and range conditions.

For queries with equality on the first column and range on the second, the index should have the equality column first. So (category_id, price) is best.

🔧 Debug

expert

Why Does This Index Not Improve Query Performance?

A developer created an index on email column of the users table to speed up this query:

SELECT * FROM users WHERE email LIKE '[e-mail]';

Why does the index not improve performance?

A Because the index was created on the wrong column

B Because the LIKE pattern starts with a wildcard, the index cannot be used efficiently

C Because the query is missing an ORDER BY clause

D Because the email column is not unique

💡 Hint

Think about how indexes work with LIKE patterns.

Indexes can be used efficiently only if the LIKE pattern does not start with a wildcard. Starting with '%' prevents the database from using the index to speed up the search.

Practice

1. What is the main purpose of using an index in a MySQL database?

easy

A. To increase the size of the database

B. To speed up data retrieval by creating shortcuts

C. To backup the database automatically

D. To store data in a compressed format

Solution

1. Step 1: Understand what an index does

   An index creates a shortcut to quickly find data without scanning the whole table.

2. Step 2: Compare options with index purpose

   Only To speed up data retrieval by creating shortcuts describes speeding up data retrieval, which is the main use of indexes.

3. Final Answer:

   To speed up data retrieval by creating shortcuts -> Option B

4. Quick Check:

   Index = Speed up search [OK]

Hint: Indexes speed up searches by creating shortcuts [OK]

Common Mistakes:

• Thinking indexes compress data
• Confusing indexes with backups
• Believing indexes increase database size

2. Which of the following is the correct syntax to create an index on the column email in a table users?

easy

A. CREATE INDEX email_index ON users (email);

B. CREATE INDEX ON users email_index (email);

C. CREATE INDEX users.email_index (email);

D. INDEX CREATE email_index ON users (email);

Solution

1. Step 1: Recall correct CREATE INDEX syntax

   The correct syntax is: CREATE INDEX index_name ON table_name (column_name);

2. Step 2: Match syntax with options

   CREATE INDEX email_index ON users (email); matches the correct syntax exactly, others have wrong order or keywords.

3. Final Answer:

   CREATE INDEX email_index ON users (email); -> Option A

4. Quick Check:

   CREATE INDEX index_name ON table (column) [OK]

Hint: Remember: CREATE INDEX name ON table (column) [OK]

Common Mistakes:

• Swapping index name and table name
• Omitting ON keyword
• Wrong keyword order

3. Given a table orders with an index on customer_id, what will happen when you run this query?

SELECT * FROM orders WHERE customer_id = 123;

medium

A. The query will fail with a syntax error

B. The query will scan the entire table ignoring the index

C. The query will use the index to quickly find matching rows

D. The query will update the customer_id values

Solution

1. Step 1: Understand index usage in WHERE clause

   When a column in WHERE has an index, MySQL uses it to find rows faster.

2. Step 2: Analyze query behavior

   The query filters by customer_id, which has an index, so it uses the index to speed up search.

3. Final Answer:

   The query will use the index to quickly find matching rows -> Option C

4. Quick Check:

   Indexed column in WHERE = index used [OK]

Hint: WHERE on indexed column uses index automatically [OK]

Common Mistakes:

• Assuming full table scan always happens
• Confusing SELECT with UPDATE
• Expecting syntax errors in correct queries

4. You created an index on last_name but your query SELECT * FROM employees WHERE first_name = 'John'; is slow. What is the likely problem?

medium

A. The index on last_name is not used because the query filters on first_name

B. The index on last_name causes a syntax error

C. The query is missing a JOIN clause

D. The index automatically updates first_name values

Solution

1. Step 1: Identify which column is indexed

   The index is on last_name, but the query filters on first_name.

2. Step 2: Understand index usage

   Indexes only help if the filtered column is indexed; here first_name is not indexed, so index is unused.

3. Final Answer:

   The index on last_name is not used because the query filters on first_name -> Option A

4. Quick Check:

   Index column ≠ filtered column = no index use [OK]

Hint: Index helps only if filtered column matches indexed column [OK]

Common Mistakes:

• Expecting index on different column to help
• Thinking index causes syntax errors
• Confusing filtering with updating

5. You have a table products with columns category, price, and stock. You often run this query:

SELECT * FROM products WHERE category = 'Books' AND price < 20;

Which index strategy is best to speed up this query?

hard

A. Create an index only on price

B. Create separate indexes on category and stock

C. Create a composite index on (stock, price)

D. Create a composite index on (category, price)

Solution

1. Step 1: Analyze query filter columns

   The query filters on category and price together.

2. Step 2: Choose index covering both columns

   A composite index on (category, price) helps MySQL filter efficiently on both conditions.

3. Step 3: Evaluate other options

   Separate indexes or indexes on stock do not help this query's WHERE clause.

4. Final Answer:

   Create a composite index on (category, price) -> Option D

5. Quick Check:

   Composite index on filtered columns = best speed [OK]

Hint: Composite index on all filtered columns speeds queries best [OK]

Common Mistakes:

• Indexing unrelated columns
• Using separate indexes instead of composite
• Ignoring query filter columns