Query optimization techniques in MySQL - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of query optimization in databases?

The main goal is to make queries run faster and use fewer resources by finding the best way to access and process data.

beginner

How does using indexes help in query optimization?

Indexes allow the database to find rows faster without scanning the whole table, similar to using a book's index to find a topic quickly.

intermediate

What is the purpose of the EXPLAIN statement in MySQL?

EXPLAIN shows how MySQL plans to run a query, helping you understand if indexes are used and where the query might be slow.

beginner

Why should you avoid SELECT * in queries for optimization?

Selecting only needed columns reduces data transfer and processing time, making queries faster and less resource-heavy.

intermediate

What is query caching and how does it improve performance?

Query caching stores the result of a query so if the same query runs again, the database can return the stored result quickly without re-running it.

Which of the following helps MySQL find data faster?

A Using SELECT *

B Running queries without WHERE

C Using indexes

D Using ORDER BY on large tables

Indexes speed up data lookup by avoiding full table scans.

What does the EXPLAIN command show in MySQL?

A The query execution plan

B The number of rows in a table

C The database size

D The list of indexes

EXPLAIN shows how MySQL will execute a query, helping identify optimization opportunities.

Why is it better to avoid SELECT * in queries?

A It causes data loss

B It returns all columns, which can slow down the query

C It disables indexing

D It makes the query syntax incorrect

Selecting only needed columns reduces data processing and speeds up queries.

What is a benefit of query caching?

A Faster results for repeated queries

B Automatically creates indexes

C Deletes old data

D Improves network speed

Query caching stores results to quickly return them for repeated queries.

Which technique can help optimize a slow JOIN query?

A Using ORDER BY on all columns

B Using SELECT *

C Removing WHERE clauses

D Adding indexes on join columns

Indexes on join columns speed up matching rows between tables.

Explain how indexes improve query performance and when you should use them.

Think about how a book index helps find pages quickly.

Describe how the EXPLAIN statement helps in optimizing MySQL queries.

It's like a map of how the database will run your 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