Query optimization techniques in MySQL - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Index Usage in Query Optimization

Which of the following statements best explains how indexes improve query performance in MySQL?

A Indexes create a quick lookup structure that helps MySQL find rows without scanning the whole table.

B Indexes store a sorted copy of the entire table data, allowing faster full table scans.

C Indexes duplicate all columns of a table to speed up data retrieval.

D Indexes compress the data to reduce disk space, which indirectly speeds up queries.

💡 Hint

Think about how you find a word quickly in a dictionary without reading every page.

Indexes act like a quick lookup guide, allowing MySQL to find rows matching conditions without scanning every row.

❓ query_result

intermediate

Effect of WHERE Clause on Query Execution

Given the table employees(id, name, department, salary) with an index on department, what will be the output count of this query?

SELECT COUNT(*) FROM employees WHERE department = 'Sales';

Assume there are 1000 rows total, and 200 belong to 'Sales'.

MySQL

SELECT COUNT(*) FROM employees WHERE department = 'Sales';

A 1000

B 0

C Syntax error

D 200

💡 Hint

The WHERE clause filters rows matching the condition.

The query counts only rows where department is 'Sales', which is 200 rows.

📝 Syntax

advanced

Identifying the Correct Query to Use EXPLAIN

Which of the following queries correctly uses EXPLAIN to analyze the query plan for selecting all columns from orders where status is 'pending'?

A SELECT EXPLAIN * FROM orders WHERE status = 'pending';

B EXPLAIN SELECT * FROM orders WHERE status = 'pending';

C EXPLAIN WHERE status = 'pending' SELECT * FROM orders;

D SELECT * FROM orders EXPLAIN WHERE status = 'pending';

💡 Hint

EXPLAIN is placed before the SELECT statement.

EXPLAIN must come before the SELECT statement to show the query plan.

❓ optimization

advanced

Choosing the Best Query for Performance

Which query is generally faster when retrieving the top 5 highest salaries from employees table with an index on salary?

A SELECT * FROM employees ORDER BY salary ASC LIMIT 5;

B SELECT * FROM employees WHERE salary IN (SELECT DISTINCT salary FROM employees ORDER BY salary DESC LIMIT 5);

C SELECT * FROM employees ORDER BY salary DESC LIMIT 5;

D SELECT * FROM employees WHERE salary >= ALL (SELECT salary FROM employees);

💡 Hint

Think about how indexes help with sorting and limiting results.

Ordering by salary descending with LIMIT 5 uses the index efficiently to get top salaries quickly.

🔧 Debug

expert

Diagnosing a Slow Query with JOINs

You have two tables: customers(id, name) and orders(id, customer_id, amount). The query below runs very slowly:

SELECT customers.name, orders.amount FROM customers JOIN orders ON customers.id = orders.customer_id WHERE orders.amount > 1000;

What is the most likely cause of the slow performance?

A Missing index on <code>orders.customer_id</code> causing full table scan on orders.

B Using JOIN instead of UNION causes slowness.

C The WHERE clause should be on customers table, not orders.

D Selecting columns instead of using SELECT * causes slow query.

💡 Hint

Indexes on join columns help speed up matching rows.

Without an index on orders.customer_id, MySQL must scan the entire orders table for each customer, slowing the query.

Practice

1. What is the main purpose of using indexes in MySQL query optimization?

easy

A. To encrypt the data for security

B. To speed up data retrieval by reducing the search area

C. To store backup copies of data

D. To increase the size of the database

Solution

1. Step 1: Understand what indexes do

   Indexes help the database find rows faster by creating a quick lookup structure.

2. Step 2: Relate indexes to query speed

   By reducing the number of rows scanned, indexes speed up data retrieval significantly.

3. Final Answer:

   To speed up data retrieval by reducing the search area -> Option B

4. Quick Check:

   Indexes improve search speed = D [OK]

Hint: Indexes speed up searches by narrowing data to scan [OK]

Common Mistakes:

• Thinking indexes increase database size only
• Confusing indexes with backups
• Believing indexes encrypt data

2. Which of the following is the correct syntax to check the query execution plan in MySQL?

easy

A. EXPLAIN SELECT * FROM users;

B. PLAN SELECT * FROM users;

C. SHOW PLAN SELECT * FROM users;

D. DESCRIBE PLAN SELECT * FROM users;

Solution

1. Step 1: Recall the command to view query plans

   MySQL uses the EXPLAIN keyword before a SELECT query to show the execution plan.

2. Step 2: Identify the correct syntax

   Only EXPLAIN SELECT * FROM users; is valid syntax to check the plan.

3. Final Answer:

   EXPLAIN SELECT * FROM users; -> Option A

4. Quick Check:

   EXPLAIN shows query plan = C [OK]

Hint: Use EXPLAIN before SELECT to see query plan [OK]

Common Mistakes:

• Using SHOW PLAN which is invalid in MySQL
• Forgetting EXPLAIN keyword
• Confusing DESCRIBE with EXPLAIN

3. Given the table employees(id INT PRIMARY KEY, name VARCHAR(50), department_id INT) with an index on department_id, what will be the effect of this query?
SELECT * FROM employees WHERE department_id = 5;

medium

A. The query will scan the entire table without using the index

B. The query will return no rows because indexes filter data

C. The query will fail due to missing index

D. The query will use the index to quickly find rows with department_id = 5

Solution

1. Step 1: Understand index usage on department_id

   Since there is an index on department_id, MySQL can use it to locate matching rows efficiently.

2. Step 2: Analyze query behavior

   The WHERE clause filters by department_id = 5, so the index helps avoid scanning all rows.

3. Final Answer:

   The query will use the index to quickly find rows with department_id = 5 -> Option D

4. Quick Check:

   Index on filter column speeds query = B [OK]

Hint: Indexed columns in WHERE speed up searches [OK]

Common Mistakes:

• Assuming index is ignored automatically
• Thinking query fails without index
• Believing indexes filter out rows

4. You wrote this query:
SELECT * FROM orders WHERE customer_id = 10 AND status = 'shipped';
You notice it runs slowly. Which fix will most likely improve performance?

medium

A. Add ORDER BY customer_id

B. Remove the WHERE clause

C. Add an index on (customer_id, status)

D. Use SELECT COUNT(*) instead of SELECT *

Solution

1. Step 1: Identify slow query cause

   The query filters on two columns but may lack a composite index to speed filtering.

2. Step 2: Apply composite index for optimization

   Adding an index on (customer_id, status) helps MySQL quickly find matching rows using both filters.

3. Final Answer:

   Add an index on (customer_id, status) -> Option C

4. Quick Check:

   Composite index speeds multi-column filters = A [OK]

Hint: Use composite index for multiple WHERE columns [OK]

Common Mistakes:

• Removing WHERE clause loses filtering
• ORDER BY does not speed filtering
• Changing SELECT * to COUNT(*) changes result, not speed

5. You have a large table sales(date DATE, product_id INT, amount DECIMAL) without indexes. You want to find total sales for product 101 in 2023 quickly. Which optimization technique is best?

hard

A. Create an index on (product_id, date) and use a query filtering both

B. Add a full table scan hint to force scanning all rows

C. Use SELECT * without WHERE to get all data

D. Drop the table and recreate it

Solution

1. Step 1: Identify query needs

   The query filters by product_id and date range, so indexing these columns helps speed filtering.

2. Step 2: Apply composite index for efficient filtering

   Creating an index on (product_id, date) allows MySQL to quickly find relevant rows without scanning the whole table.

3. Final Answer:

   Create an index on (product_id, date) and use a query filtering both -> Option A

4. Quick Check:

   Composite index on filter columns = A [OK]

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

Common Mistakes:

• Forcing full table scan slows query
• Selecting all rows wastes time
• Dropping table loses data