Decimal and floating-point types in MySQL - Time & Space Complexity

Time Complexity: Decimal and floating-point types

O(n)

Understanding Time Complexity

When working with decimal and floating-point types in MySQL, it is important to understand how operations on these types scale as data grows.

We want to know how the time to process these numbers changes when we handle more rows or bigger numbers.

Scenario Under Consideration

Analyze the time complexity of the following SQL query using decimal and floating-point columns.

SELECT AVG(price) AS avg_price
FROM products
WHERE price > 100.00;
    

This query calculates the average price of products where the price is greater than 100.00, using decimal or floating-point values.

Identify Repeating Operations

Look for repeated steps in the query execution.

• Primary operation: Scanning each row in the products table to check the price condition and include it in the average calculation.
• How many times: Once for every row in the table.

How Execution Grows With Input

As the number of rows increases, the database must check more prices and add more values to the average.

Input Size (n)	Approx. Operations
10	10 checks and additions
100	100 checks and additions
1000	1000 checks and additions

Pattern observation: The work grows directly with the number of rows; doubling rows doubles the work.

Final Time Complexity

Time Complexity: O(n)

This means the time to compute the average grows in a straight line as the number of rows increases.

Common Mistake

[X] Wrong: "Calculations on decimal or floating-point types take longer time per row, so the overall time grows faster than the number of rows."

[OK] Correct: Each calculation takes a small, fixed amount of time, so the total time still grows linearly with the number of rows, not faster.

Interview Connect

Understanding how numeric data types affect query time helps you explain performance clearly and shows you know how databases handle data at scale.

Self-Check

What if we added an index on the price column? How would the time complexity change?

Practice

1. Which MySQL data type should you use to store exact monetary values without rounding errors?

easy

A. VARCHAR

B. FLOAT

C. DECIMAL

D. DOUBLE

Solution

1. Step 1: Understand the purpose of DECIMAL

   DECIMAL stores exact decimal numbers with fixed digits, ideal for money.

2. Step 2: Compare with FLOAT and DOUBLE

   FLOAT and DOUBLE store approximate values, which can cause rounding errors.

3. Final Answer:

   DECIMAL -> Option C

4. Quick Check:

   Exact decimal storage = DECIMAL [OK]

Hint: Use DECIMAL for exact money values, not FLOAT or DOUBLE [OK]

Common Mistakes:

• Choosing FLOAT or DOUBLE for money causing rounding errors
• Using VARCHAR which stores text, not numbers

2. Which of the following is the correct syntax to define a DECIMAL column with 8 digits total and 2 digits after the decimal point in MySQL?

easy

A. DECIMAL(8,2)

B. DECIMAL(2,8)

C. DECIMAL(10,2)

D. DECIMAL(8)

Solution

1. Step 1: Recall DECIMAL syntax

   DECIMAL(M,D) means M total digits, D digits after decimal point.

2. Step 2: Match given digits

   We want 8 total digits and 2 after decimal, so DECIMAL(8,2) is correct.

3. Final Answer:

   DECIMAL(8,2) -> Option A

4. Quick Check:

   DECIMAL(total, decimals) = DECIMAL(8,2) [OK]

Hint: DECIMAL(M,D): M=total digits, D=digits after decimal [OK]

Common Mistakes:

• Swapping M and D values
• Using DECIMAL(8) which lacks decimal precision

3. Given the table prices with a column cost FLOAT(7,2), what will be the result of inserting 12345.6789 and then selecting cost?

medium

A. 12345.679

B. 12345.68

C. 12345.67

D. 12345.000

Solution

1. Step 1: Understand FLOAT(7,2) meaning

   FLOAT(7,2) means 7 digits total, 2 after decimal, but FLOAT is approximate.

2. Step 2: Insert and round value

   12345.6789 rounds to 12345.68 when stored with 2 decimals.

3. Final Answer:

   12345.68 -> Option B

4. Quick Check:

   FLOAT rounds to 2 decimals = 12345.68 [OK]

Hint: FLOAT rounds approximate values to specified decimals [OK]

Common Mistakes:

• Expecting exact storage like DECIMAL
• Ignoring rounding behavior of FLOAT

4. You defined a column as FLOAT(5,2) but notice values like 1234.567 are stored as 1234.56. What is the likely cause and how to fix it?

medium

A. MySQL truncates decimals by default; enable rounding mode

B. FLOAT(5,2) syntax is invalid; use FLOAT without parameters

C. The value exceeds FLOAT range; use DOUBLE instead

D. FLOAT stores approximate values; use DECIMAL(7,3) for exact decimals

Solution

1. Step 1: Recognize FLOAT behavior

   FLOAT stores approximate values and rounds to specified decimals, causing truncation.

2. Step 2: Choose correct type for exact decimals

   DECIMAL stores exact decimals and preserves precision, so use DECIMAL(7,3).

3. Final Answer:

   FLOAT stores approximate values; use DECIMAL(7,3) for exact decimals -> Option D

4. Quick Check:

   Approximate FLOAT -> switch to DECIMAL for precision [OK]

Hint: Use DECIMAL for exact decimals, FLOAT is approximate [OK]

Common Mistakes:

• Thinking FLOAT(5,2) is invalid syntax
• Assuming DOUBLE fixes decimal truncation
• Expecting MySQL to round automatically without changing type

5. You need to store scientific measurements with very large or very small values and some decimal precision. Which MySQL data type is best and why?

hard

A. FLOAT or DOUBLE, because they store approximate floating-point numbers with wide range

B. DECIMAL, because it stores exact fixed-point numbers

C. VARCHAR, to store numbers as text for precision

D. INT, because it stores whole numbers efficiently

Solution

1. Step 1: Understand scientific measurement needs

   Scientific data often requires wide range and floating precision, not exact fixed decimals.

2. Step 2: Choose FLOAT or DOUBLE for wide range

   FLOAT and DOUBLE support very large/small values with approximate precision, ideal for science.

3. Final Answer:

   FLOAT or DOUBLE, because they store approximate floating-point numbers with wide range -> Option A

4. Quick Check:

   Wide range + approximate decimals = FLOAT/DOUBLE [OK]

Hint: Use FLOAT/DOUBLE for scientific data, DECIMAL for money [OK]

Common Mistakes:

• Choosing DECIMAL which limits range
• Using VARCHAR which wastes space and loses numeric features
• Using INT which cannot store decimals