Why table design affects performance in MySQL - Performance Analysis

Time Complexity: Why table design affects performance

O(n) without index, O(log n) with index

Understanding Time Complexity

Good table design helps databases work faster. We want to see how design choices change the work needed when data grows.

How does the way we build tables affect the time to run queries?

Scenario Under Consideration

Analyze the time complexity of this simple query on two different table designs.

-- Query to find a user by email
SELECT * FROM users WHERE email = '[e-mail]';

-- Table design 1: email is not indexed
-- Table design 2: email has an index
    

This query looks for a user by email. The difference is if the email column has an index or not.

Identify Repeating Operations

Look at what repeats when searching for the email.

• Primary operation: Checking rows to find matching email.
• How many times: Without index, it checks every row one by one. With index, it uses a fast lookup.

How Execution Grows With Input

As the number of rows grows, the work changes differently depending on design.

Input Size (n)	Approx. Operations Without Index	Approx. Operations With Index
10	10 checks	About 4 checks
100	100 checks	About 7 checks
1000	1000 checks	About 10 checks

Pattern observation: Without index, checks grow directly with rows. With index, checks grow slowly even if rows grow a lot.

Final Time Complexity

Time Complexity: O(n) without index, O(log n) with index

This means searching without an index takes longer as the table grows, but with an index it stays much faster.

Common Mistake

[X] Wrong: "Adding more columns to a table always slows down queries equally."

[OK] Correct: Only columns used in search or joins affect query speed much. Extra columns stored don't slow down searches if not used.

Interview Connect

Understanding how table design affects query speed shows you know how databases handle data. This skill helps you write faster queries and design better systems.

Self-Check

"What if we added a composite index on email and username? How would the time complexity change when searching by email only?"

Practice

1. Why does using proper data types in a MySQL table improve performance?

easy

A. It reduces the amount of storage space and speeds up data retrieval.

B. It makes the table look nicer in the database.

C. It allows more users to connect at the same time.

D. It automatically creates backups of the data.

Solution

1. Step 1: Understand data types and storage

   Choosing the right data type means the database uses only the needed space for each value.

2. Step 2: Impact on speed

   Smaller data sizes mean faster reading and writing, improving performance.

3. Final Answer:

   It reduces the amount of storage space and speeds up data retrieval. -> Option A

4. Quick Check:

   Proper data types = less space + faster queries [OK]

Hint: Smaller data types mean faster access and less storage [OK]

Common Mistakes:

• Thinking data types only affect appearance
• Believing data types control user connections
• Confusing data types with backup features

2. Which of the following is the correct way to define a primary key in a MySQL table?

easy

A. CREATE TABLE users (id INT PRIMARY KEY);

B. CREATE TABLE users (id INT PRIMARY, KEY);

C. CREATE TABLE users (id INT, PRIMARY(id));

D. CREATE TABLE users (id INT, KEY PRIMARY);

Solution

1. Step 1: Review MySQL primary key syntax

   The correct syntax places PRIMARY KEY immediately after the column type.

2. Step 2: Check each option

   CREATE TABLE users (id INT PRIMARY KEY); uses 'id INT PRIMARY KEY' which is valid syntax.

3. Final Answer:

   CREATE TABLE users (id INT PRIMARY KEY); -> Option A

4. Quick Check:

   Primary key syntax = 'column type PRIMARY KEY' [OK]

Hint: Primary key goes right after column type [OK]

Common Mistakes:

• Separating PRIMARY and KEY keywords incorrectly
• Placing KEY before PRIMARY
• Using comma incorrectly between keywords

3. Given the table products(id INT PRIMARY KEY, name VARCHAR(50), price DECIMAL(5,2)), what will be the result of this query?
SELECT * FROM products WHERE price < 10.00;

medium

A. No rows will be returned because price is stored as text.

B. Syntax error because of the decimal comparison.

C. All products with price less than 10.00 will be listed.

D. Only products with price exactly 10.00 will be shown.

Solution

1. Step 1: Understand the table schema

   The price column is DECIMAL(5,2), a numeric type suitable for decimal comparisons.

2. Step 2: Analyze the query condition

   The query selects rows where price is less than 10.00, which is valid and will return matching rows.

3. Final Answer:

   All products with price less than 10.00 will be listed. -> Option C

4. Quick Check:

   DECIMAL supports numeric comparisons correctly [OK]

Hint: DECIMAL columns compare numbers, not text [OK]

Common Mistakes:

• Assuming DECIMAL is text type
• Thinking decimal comparisons cause syntax errors
• Confusing less than with equal to

4. A table has no indexes and is very slow when searching by a column. What is the best fix?

medium

A. Use SELECT * instead of SELECT column.

B. Change the column data type to TEXT.

C. Delete half of the rows to reduce size.

D. Add an index on the column used in the search.

Solution

1. Step 1: Identify the cause of slow search

   Without indexes, MySQL must scan all rows, causing slow searches.

2. Step 2: Apply indexing to improve speed

   Adding an index on the searched column lets MySQL find rows faster.

3. Final Answer:

   Add an index on the column used in the search. -> Option D

4. Quick Check:

   Indexes speed up searches [OK]

Hint: Index columns you search often for faster queries [OK]

Common Mistakes:

• Changing data type without indexing
• Deleting data instead of optimizing
• Using SELECT * slows queries, but not main cause

5. You have a table with many duplicate rows causing slow queries and large storage. What table design change will best improve performance?

hard

A. Increase the size of all columns to store more data.

B. Add a UNIQUE constraint to prevent duplicates and clean existing data.

C. Remove all indexes to speed up inserts.

D. Change all VARCHAR columns to TEXT for flexibility.

Solution

1. Step 1: Identify the problem with duplicates

   Duplicates cause extra storage and slow queries because more rows must be scanned.

2. Step 2: Use UNIQUE constraint to fix duplicates

   Adding UNIQUE prevents duplicates and cleaning data reduces size and speeds queries.

3. Final Answer:

   Add a UNIQUE constraint to prevent duplicates and clean existing data. -> Option B

4. Quick Check:

   Unique constraints remove duplicates and improve performance [OK]

Hint: Use UNIQUE keys to avoid duplicates and speed queries [OK]

Common Mistakes:

• Thinking bigger columns improve speed
• Removing indexes slows queries
• Using TEXT instead of VARCHAR wastes space