Why indexes matter in SQL

Introduction

Indexes help databases find information faster, just like an index in a book helps you find a topic quickly.

When you want to speed up searching for specific data in a large table.

When you often filter or sort data by certain columns.

When you want to improve performance of queries that join tables on specific columns.

When you have a large database and want to reduce the time it takes to get results.

When you want to avoid scanning the entire table for every query.

Syntax

SQL

CREATE INDEX index_name ON table_name (column_name);

An index is created on one or more columns of a table.

The index name should be unique within the database.

Examples

Creates an index on the 'name' column of the 'customers' table to speed up searches by customer name.

SQL

CREATE INDEX idx_customer_name ON customers (name);

Creates an index on the 'order_date' column of the 'orders' table to speed up queries filtering by date.

SQL

CREATE INDEX idx_order_date ON orders (order_date);

Creates a multi-column index on 'category' and 'price' in the 'products' table to speed up queries filtering by both.

SQL

CREATE INDEX idx_product_category ON products (category, price);

Sample Program

This example creates a table 'employees', inserts some data, creates an index on the 'department' column, and then shows the query plan for searching employees in the 'Sales' department. The index helps the database find these employees faster.

SQL

CREATE TABLE employees (
  id INT PRIMARY KEY,
  name VARCHAR(100),
  department VARCHAR(50)
);

INSERT INTO employees (id, name, department) VALUES
(1, 'Alice', 'Sales'),
(2, 'Bob', 'HR'),
(3, 'Charlie', 'Sales'),
(4, 'Diana', 'IT');

CREATE INDEX idx_department ON employees (department);

EXPLAIN QUERY PLAN SELECT * FROM employees WHERE department = 'Sales';

Important Notes

Indexes speed up data retrieval but can slow down data insertion and updates because the index must be updated too.

Not every column needs an index; use indexes on columns you search or sort by often.

Too many indexes can use extra storage and reduce performance on writes.

Summary

Indexes make searching data faster by creating a quick lookup structure.

Use indexes on columns you often filter or join on.

Indexes improve read speed but can slow down writes.

Practice

1. Why do indexes matter in a database?

easy

A. They increase the size of the database without any benefit.

B. They automatically fix data errors in tables.

C. They slow down all queries regardless of type.

D. They make searching data faster by creating a quick lookup structure.

Solution

1. Step 1: Understand what indexes do

   Indexes create a special structure that helps the database find data quickly, like a book's index.

2. Step 2: Identify the main benefit

   This structure speeds up searching and filtering operations, making queries faster.

3. Final Answer:

   They make searching data faster by creating a quick lookup structure. -> Option D

4. Quick Check:

   Indexes improve search speed = B [OK]

Hint: Indexes speed up searches by acting like a book's index [OK]

Common Mistakes:

• Thinking indexes slow down all queries
• Believing indexes fix data errors
• Assuming indexes only increase database size

2. Which SQL statement correctly creates an index on the column email in the users table?

easy

A. CREATE INDEX users ON idx_email(email);

B. MAKE INDEX idx_email ON users(email);

C. CREATE INDEX idx_email ON users(email);

D. INDEX CREATE idx_email ON users(email);

Solution

1. Step 1: Recall correct SQL syntax for creating an index

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

2. Step 2: Match the syntax with options

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

3. Final Answer:

   CREATE INDEX idx_email ON users(email); -> Option C

4. Quick Check:

   Correct CREATE INDEX syntax = C [OK]

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

Common Mistakes:

• Using wrong keywords like MAKE or INDEX CREATE
• Swapping table and index names
• Missing ON keyword

3. Given a table orders with 1 million rows and an index on customer_id, what is the expected result of this query?

SELECT * FROM orders WHERE customer_id = 123;

medium

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

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

C. The query will fail because indexes cannot be used in WHERE clauses.

D. The query will scan all 1 million rows, taking a long time.

Solution

1. Step 1: Understand index usage in WHERE clause

   An index on customer_id helps the database quickly locate rows matching that value.

2. Step 2: Predict query behavior

   The database uses the index to avoid scanning all rows, speeding up the query.

3. Final Answer:

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

4. Quick Check:

   Index speeds WHERE search = D [OK]

Hint: Indexes speed up WHERE filters on indexed columns [OK]

Common Mistakes:

• Thinking indexes cause full table scans
• Believing indexes prevent queries from running
• Assuming indexes hide data from results

4. You created an index on the username column, but your query is still slow:

SELECT * FROM users WHERE username LIKE '%admin%';

What is the most likely reason?

medium

A. The index was created on the wrong table.

B. Indexes cannot speed up LIKE queries with a leading wildcard '%'.

C. The query syntax is incorrect and causes errors.

D. Indexes always slow down SELECT queries.

Solution

1. Step 1: Analyze the LIKE pattern

   A LIKE pattern starting with '%' means the search can match anywhere in the string, preventing index use.

2. Step 2: Understand index limitations

   Indexes work best when the search starts from the beginning of the string, so leading wildcards disable index usage.

3. Final Answer:

   Indexes cannot speed up LIKE queries with a leading wildcard '%'. -> Option B

4. Quick Check:

   Leading '%' disables index use = A [OK]

Hint: Leading '%' in LIKE disables index use [OK]

Common Mistakes:

• Assuming all LIKE queries use indexes
• Thinking index on wrong table causes this
• Believing indexes always slow SELECT

5. You have a table sales with columns sale_id, product_id, and sale_date. You often run this query:

SELECT * FROM sales WHERE product_id = 101 AND sale_date >= '2024-01-01';

Which indexing strategy will likely improve query speed the most?

hard

A. Create a composite index on (product_id, sale_date).

B. Create separate indexes on product_id and sale_date.

C. Create a single index on sale_date only.

D. Do not create any index to keep inserts fast.

Solution

1. Step 1: Understand query filters

   The query filters by product_id and a range on sale_date.

2. Step 2: Choose best index type

   A composite index on both columns helps the database quickly find rows matching both conditions together.

3. Step 3: Compare with other options

   Separate indexes may help but are less efficient than a composite index for combined filters.

4. Final Answer:

   Create a composite index on (product_id, sale_date). -> Option A

5. Quick Check:

   Composite index matches multi-column filter = A [OK]

Hint: Use composite index for multi-column WHERE filters [OK]

Common Mistakes:

• Creating index on only one column
• Using separate indexes instead of composite
• Avoiding indexes to speed inserts without reason