Why Composite indexes in MySQL? - Purpose & Use Cases

The Big Idea

What if your database could find combined info instantly, like flipping to the exact page in a book?

The Scenario

Imagine you have a huge phone book and you want to find people by both their last name and city. Without any special help, you have to flip through every page, checking each name and city one by one.

The Problem

Doing this by hand is slow and tiring. You might miss some entries or take a long time to find what you want. In databases, searching without the right tools means the computer works harder and slower, especially when many people ask at once.

The Solution

Composite indexes act like a smart guide that sorts the phone book first by last name, then by city. This way, the database quickly jumps to the right section, making searches much faster and more accurate.

Before vs After

✗ Before

SELECT * FROM contacts WHERE last_name = 'Smith' AND city = 'Boston'; -- no index, full scan

✓ After

CREATE INDEX idx_name_city ON contacts(last_name, city);
SELECT * FROM contacts WHERE last_name = 'Smith' AND city = 'Boston';

What It Enables

Composite indexes let databases find combined information quickly, making apps feel faster and more responsive.

Real Life Example

Online stores use composite indexes to quickly find products by category and price range, so you see what you want without waiting.

Key Takeaways

Searching multiple columns without indexes is slow and inefficient.

Composite indexes speed up queries that filter by several columns together.

They help databases handle complex searches smoothly and quickly.

Practice

1. What is the main purpose of a composite index in MySQL?

easy

A. To create a backup of the database automatically

B. To speed up queries that filter or sort by multiple columns together

C. To store data in multiple tables simultaneously

D. To encrypt data in multiple columns

Solution

1. Step 1: Understand what an index does

   An index helps MySQL find data faster by organizing it for quick lookup.

2. Step 2: Understand composite index purpose

   A composite index uses multiple columns together to speed up queries that filter or sort by those columns combined.

3. Final Answer:

   To speed up queries that filter or sort by multiple columns together -> Option B

4. Quick Check:

   Composite index = speed up multi-column queries [OK]

Hint: Composite indexes speed up multi-column filters and sorts [OK]

Common Mistakes:

• Thinking composite indexes store data
• Confusing indexes with backups
• Assuming encryption is related to indexes

2. Which of the following is the correct syntax to create a composite index on columns first_name and last_name in a table users?

easy

A. CREATE INDEX idx_name ON users (first_name, last_name);

B. CREATE INDEX idx_name ON users first_name, last_name;

C. CREATE INDEX idx_name (first_name, last_name) ON users;

D. CREATE INDEX idx_name ON users WHERE first_name, last_name;

Solution

1. Step 1: Recall correct CREATE INDEX syntax

   The syntax is: CREATE INDEX index_name ON table_name (column1, column2);

2. Step 2: Match syntax with options

   CREATE INDEX idx_name ON users (first_name, last_name); matches the correct syntax with parentheses around columns and ON keyword before table name.

3. Final Answer:

   CREATE INDEX idx_name ON users (first_name, last_name); -> Option A

4. Quick Check:

   CREATE INDEX ... ON table (columns) [OK]

Hint: Use parentheses around columns after ON table_name [OK]

Common Mistakes:

• Omitting parentheses around columns
• Placing columns before ON keyword
• Using WHERE instead of column list

3. Given a composite index on (city, age) in a table people, which query will use the index efficiently?

medium

A. SELECT * FROM people WHERE city = 'Paris' AND age = 30;

B. SELECT * FROM people WHERE age = 30;

C. SELECT * FROM people WHERE age BETWEEN 20 AND 40;

D. SELECT * FROM people WHERE city LIKE '%Paris%' ORDER BY age DESC;

Solution

1. Step 1: Understand composite index column order

   The index is on (city, age), so queries filtering city first then age use it best.

2. Step 2: Analyze queries for index usage

   SELECT * FROM people WHERE city LIKE '%Paris%' ORDER BY age DESC;: LIKE '%Paris%' prevents index use due to leading wildcard. SELECT * FROM people WHERE age = 30;: filters only age (second column), skips prefix. SELECT * FROM people WHERE age BETWEEN 20 AND 40;: range on age only, cannot use prefix. SELECT * FROM people WHERE city = 'Paris' AND age = 30;: city then age equality matches prefix perfectly.

3. Final Answer:

   SELECT * FROM people WHERE city = 'Paris' AND age = 30; -> Option A

4. Quick Check:

   Filter starts with first index column = SELECT * FROM people WHERE city = 'Paris' AND age = 30; [OK]

Hint: Filter starting with first index column uses composite index best [OK]

Common Mistakes:

• Assuming any column filter uses composite index fully
• Ignoring column order in composite index
• Thinking ORDER BY always uses composite index

4. You created a composite index on (department, salary) but your query SELECT * FROM employees WHERE salary > 50000; is slow. What is the likely problem?

medium

A. The table has no data

B. The index syntax was incorrect

C. Composite indexes cannot be used with numeric columns

D. The query does not filter on the first column of the composite index

Solution

1. Step 1: Recall composite index usage rules

   Composite indexes work best when queries filter starting from the first indexed column.

2. Step 2: Analyze the query and index

   The index is on (department, salary), but query filters only salary (second column), so index is not used efficiently, causing slowness.

3. Final Answer:

   The query does not filter on the first column of the composite index -> Option D

4. Quick Check:

   Filter must start with first index column [OK]

Hint: Filter must include first index column to use composite index [OK]

Common Mistakes:

• Assuming any column filter uses composite index
• Blaming syntax without checking query
• Thinking numeric columns can't be indexed

5. You have a table orders with columns customer_id, order_date, and status. You often run this query:
SELECT * FROM orders WHERE customer_id = 123 AND status = 'shipped' ORDER BY order_date DESC;
Which composite index will optimize this query best?

hard

A. CREATE INDEX idx_order_date_customer_status ON orders (order_date, customer_id, status);

B. CREATE INDEX idx_status_customer_date ON orders (status, customer_id, order_date);

C. CREATE INDEX idx_customer_status_date ON orders (customer_id, status, order_date);

D. CREATE INDEX idx_customer_order_date ON orders (customer_id, order_date);

Solution

1. Step 1: Identify query filter and order columns

   The query filters by customer_id and status, then orders by order_date descending.

2. Step 2: Choose index column order for best performance

   Index should start with columns used in WHERE filters in order: customer_id, then status, then order_date for ORDER BY.

3. Step 3: Match index options

   CREATE INDEX idx_customer_status_date ON orders (customer_id, status, order_date); matches this order exactly, so it optimizes filtering and ordering best.

4. Final Answer:

   CREATE INDEX idx_customer_status_date ON orders (customer_id, status, order_date); -> Option C

5. Quick Check:

   Index columns match WHERE then ORDER BY order [OK]

Hint: Index columns order: filter columns first, then order column [OK]

Common Mistakes:

• Putting ORDER BY column before filter columns
• Ignoring filter columns order
• Creating index missing important filter column