DOUBLE vs. DECIMAL: Choosing the Right Data Type for Numbers in MariaDB

The DOUBLE data type is used to store double-precision floating-point numbers in MariaDB. These are numbers with a decimal point that can represent a very wide range of values, typically around 1.797 x 10^308 to -2.225 x 10^-308.

Here are some key points about DOUBLE:

• Storage: It uses more storage space than the single-precision FLOAT data type but offers greater precision (around 15 decimal places).
• Accuracy: Due to the nature of floating-point arithmetic, calculations involving DOUBLE might not always be exact. There can be slight rounding errors in some cases.
• Signed or Unsigned: You can define DOUBLE as SIGNED (allowing negative values) or UNSIGNED (for positive values only). There's also a special UNSIGNED ZEROFILL option that pads the number with leading zeros for a specific display format.

Using DOUBLE in Queries

When you write MariaDB queries, you can declare columns in your tables to be of the DOUBLE data type. This is appropriate for storing values like:

• Monetary amounts (though DECIMAL might be better for perfect precision)
• Scientific measurements
• Engineering calculations

Here's an example of creating a table with a DOUBLE column:

CREATE TABLE products (
  id INT PRIMARY KEY,
  name VARCHAR(255),
  price DOUBLE
);

In this example, the price column can store double-precision floating-point numbers.

Things to Consider

• If you need exact decimal precision, consider using the DECIMAL data type instead. DECIMAL allows you to specify the total number of digits and the number of decimal places to ensure accurate storage.
• Be aware of potential rounding errors when working with floating-point numbers.

CREATE TABLE products (
  id INT PRIMARY KEY AUTO_INCREMENT,  -- Auto-increment for unique IDs
  name VARCHAR(255) NOT NULL,
  price DOUBLE NOT NULL
);

This code creates a table named products with three columns:

• id: An integer (INT) that automatically increments (AUTO_INCREMENT) to provide unique IDs for each product.
• name: A variable-length character string (VARCHAR(255)) to store the product name, with a maximum length of 255 characters.
• price: A double-precision floating-point number (DOUBLE) to store the product price. The NOT NULL constraint ensures price data is always entered.

Inserting Data with DOUBLE Values:

INSERT INTO products (name, price)
VALUES ('T-Shirt', 19.99), ('Coffee Mug', 8.50);

This code inserts two rows of data into the products table. The first row stores "T-Shirt" as the name and 19.99 (a DOUBLE value) as the price. The second row stores "Coffee Mug" and 8.50 (another DOUBLE value).

Selecting and Working with DOUBLE Data:

SELECT id, name, price FROM products;

SELECT name, price * 1.05 AS discounted_price FROM products;

The first query selects all columns (id, name, and price) from the products table.

The second query selects the name and calculates a discounted price by multiplying the price by 1.05 (assuming a 5% discount) and aliasing the result as discounted_price. Since 1.05 is a fixed value, the calculation should be accurate within the limits of DOUBLE precision.

• Use Case: If you require exact decimal precision, use the DECIMAL data type. It allows you to define the total number of digits and the number of decimal places to ensure perfect storage of values like currency amounts.
• Example:

CREATE TABLE orders (
  id INT PRIMARY KEY AUTO_INCREMENT,
  customer_id INT,
  total_amount DECIMAL(10,2) NOT NULL  -- 10 total digits, 2 decimal places
);

This example stores the total_amount with up to 2 decimal places (e.g., 123.45).

Casting or Converting Data Types:

• Use Case: You might have data stored in another data type (like INTEGER) that you want to use in calculations requiring floating-point precision. In such cases, you can cast the data type within the query itself.

SELECT product_id, name, price * quantity AS total_price
FROM products, orders
WHERE products.id = orders.product_id;

-- Assuming 'quantity' in 'orders' table is an INTEGER

Here, the price (DOUBLE) is multiplied by quantity (assumed to be INTEGER). The result is still a DOUBLE due to implicit type conversion.

Storing Integer Components Separately:

• Use Case: For very specific use cases, you might consider storing the integer and decimal parts of a value separately in two integer columns. This can be useful for calculations where exact precision is crucial, especially for large numbers.

This approach is less common and requires additional processing logic.